Data on Vin Patel

Questions and answers

What is Data Visualization? #

Be honest: How often have you been swamped by the massive amount of data your business generates? #

And this applies to both on-premise data as well as the data on the cloud. The need for data analytic is only increasing in the age of big data.

The Powerful Advantages of IoT-based Predictive Maintenance

Vin Patel — Mon, 19 Dec 2022 11:18:37 +0000

Key takeaways

Manufacturers have always looked out to practice new approaches for enhanced maintenance and protection for all of their machinery and equipment.
It is proven that the implementation of predictive analytic in manufacturing sites has reduced the number of unexpected equipment failures by approximately 60%.
Manufacturing sites and processing plants will continue to boost their efficiency by utilizing predictive maintenance in the future to meet all competitive and challenging requirements.

Manufacturers have always looked out to practice new approaches for enhanced maintenance and protection for all of their machinery and equipment. Among these latest methods, IoT-based predictive analysis and maintenance have shown remarkable results recently. Since its beginning, predictive maintenance has transformed ancient and traditional maintenance methods into extremely advanced and efficient ways. In contrast to conventional maintenance schedules, predictive maintenance schedules specific tasks only when needed rather than conducting routine maintenance on all equipment. IoT performs predictive maintenance through sensors, gateways, and management systems to predict equipment failures and prevent them from occurring. Predictive maintenance has become a central part of business strategy for many organizations. Among the industries benefiting from predictive maintenance are oil and gas, the food and beverage sector, manufacturing firms, and IT services. This article describes how IoT-based predictive analytic can be used to predict maintenance within a manufacturing environment. Manufacturing companies involve heavy machinery and equipment, which can become a million-dollar loss in case of any breakdown.

Before you read further, it is necessary to have a clear concept of what predictive analytic and predictive maintenance denote. Predictive analytic uses historical data to identify likely future outcomes based on statistical modelling and machine learning techniques. In manufacturing sites, IoT collects machine information (such as temperature, voltage, current, and vibration) and tirelessly transmits the collected data to a cloud-based service platform in real-time. There is adequate bandwidth to handle large amounts of data when using wired and wireless connectivity solutions. This data allows us to analyze and reach predictive solutions. On the other hand, IoT-based predictive maintenance or PdM identifies patterns and predicts equipment failures by monitoring its condition during normal operation. It uses five basic components to carry out these resolutions. These components include predictive analytic, predictive maintenance software, sensors, data communication, and central data storage. Predictive analytic and machine learning will notify management of potential challenges before they arise, making the entire process faultless. Reducing production interruptions brings money savings and improved reliability in delivering products on time.

Benefits of IoT-based Predictive Maintenance #

It is seen that manufacturers who have adopted predictive maintenance have witnessed a wide range of benefits. These benefits include:

Reduced Maintenance and Material Cost #

The data utilized in predictive maintenance allows you to perform optimal inspection and maintenance of machinery without any occurrence of breakdown, which could have cost a tremendous amount of money and time. Studies, therefore, confirmed that the cost of maintenance operations is reduced by 20-50% with the application of predictive maintenance.

Reduced Machine Failure #

IoT-based predictive maintenance provides data that allows you to monitor the working conditions of any machinery customarily. This regular observation avails for identifying components that require repair or replacement, thus preventing the degradation of the entire workflow. It is proven that the implementation of predictive analytic in manufacturing sites has reduced the number of unexpected equipment failures by approximately 60%. Predictive maintenance has, in turn, resulted in the incremented service life of machines.

Reduced Repair Time #

Maintenance done using predictive methods reduces the time required for repairs and replacements—the unplanned time utilized due to the breakdown of machines results in the overall loss of companies. Regular analysis of the condition of the machines is necessary to identify the specific problems of each. Each repair can be planned by maintenance personnel in this way. An IoT-based predictive maintenance program identifies damage taking place on equipment along with other machinery. Predictive maintenance works on a formula for estimating the mean time between failures. This meantime allows experts to determine the exact number of hours required to perform repair or replacement without further allowing the machinery to promote high maintenance costs.

Enhanced Operator’s Safety #

With predictive maintenance, the risk of destructive machinery failure reduces, and with it, the rate of injury or death of a person operating such machinery goes down.

Increased Production #

IoT-based predictive maintenance has helped manufacturers gain a rise in production with the help of a process parameter monitoring method. It improves the operating efficiency and productivity of manufacturing and processing plants. A reduction of machine downtime results in increased output and a higher overall equipment effectiveness score.

Increased Life of Parts #

An early prediction of machinery or parts failure is directly proportional to the increased service life of industrial plants and machinery. It is crucial to maintain equipment and other plant systems by following a condition-based predictive maintenance program. A predictive maintenance program increases machine service life by five years after installation. Besides extending the life of plant equipment, this method reduces the severity of the damage, preventing defects propagation. One benefit of predictive maintenance is that it can automatically estimate the mean time between failures (MTBF).

It determines when replacing machinery is more cost-effective than continuing to absorb costly maintenance costs. The MTBF of any parts or machinery reduces every time a major repair or reconditioning occurs, and predictive maintenance plays a crucial role in it.

Competitive Advantage #

As a result of the increased sense of predictability and confidence in predictive maintenance, company branding can be improved while customer satisfaction increases.

Conclusion #

In a nutshell, predictive maintenance lifts manufacturing businesses to maintain a balance in keeping costs down and increasing asset availability. It improves the overall operation of manufacturing sites, and with techniques based on data collection, these methods further reduce the annual operating costs. The increasing use of predictive maintenance is confirmed to provide continuous resolutions and action measures necessary for addressing the systems before they fail, break, or run erroneously. A PdM-based business operation is a next evolution in how humans use machines for production. Manufacturing sites and processing plants will continue to boost their efficiency by utilizing predictive maintenance in the future to meet all competitive and challenging requirements.

Related reading

Why Should Organizations Invest in Real-Time Data

Vin Patel — Fri, 02 Dec 2022 15:46:26 +0000

Key takeaways

As oil is essential for keeping the ball rolling across major industries, data has emerged as an invaluable tool for organizations seeking to leverage digitalization.
Trend monitoring helps organizations compare data fetched from different periods to identify a pattern or trend.
Investing in real-time data monitoring solutions has become necessary for organizations looking to better understand their digital environments.

Saying, “Data is the new oil”, is indeed true. As oil is essential for keeping the ball rolling across major industries, data has emerged as an invaluable tool for organizations seeking to leverage digitalization.

But the real challenge is extracting value from the massive amount of data organizations generate daily. As of 2021, roughly 2.5 quintillion bytes of data are generated every day – data that can be stored in 2,500,000 Apple iPhone Pro Max with 1TB Capacity.

However, data alone cannot provide the value organizations aspire to gain. With analytic and monitoring tools, data which was once considered to be of no value can be worth the gold. The worth is even more when we analyze and monitor it in a real-time context. Real-time data monitoring enables organizations to act immediately on inputs and insights to accelerate growth and build trust among all entities involved.

What is Real-Time Data Monitoring and Analytic? #

Real-time data monitoring helps organizations to analyze data for the purpose of addition, deletion, and modification of data that exists in enterprise systems. Plus, it empowers organizations to review every process and function associated with data in real time. With these insights, organizations can make proactive decisions and equip themselves with the right tools and resources to change strategies and maximize profitability.

Embracing real-time data monitoring enables organizations to monitor better and manage the use of data across different enterprise systems. When organizations adopt real-time data monitoring and analytic solutions, they get unparalleled and valuable insights into the data extracted from different sources like running applications, networks, web servers, databases, etc.

Today, companies – big and small – use real-time data monitoring. However, corporations with hefty budgets are using these solutions due to their expensive nature.

Using real-time data monitoring provides numerous benefits:

It can help improve customer satisfaction by enabling companies to process requests or complaints and act upon them.
It can improve employee satisfaction by offering more thorough feedback about an employee’s performance.
It can help ensure compliance with industry regulations by identifying gaps before they become bigger issues.

What is Trend Monitoring? #

Organizations need to understand the past to be better prepared for the future. This is because consistent past events, also called trends, can help them predict what is likely to happen next – information beneficial that can help them stay ahead of the competition.

That’s why organizations are extremely keen on adopting trend monitoring – a process or method that uses past data to forecast current and future events. In other words, trend monitoring allows organizations to predict the future based on what has already happened in the past.

Trend monitoring helps organizations compare data fetched from different periods to identify a pattern or trend. As a result, organizations can become future-proof while adapting to what works and what doesn’t work for them.

With the rapid change in consumer behavior, the business landscape, and organizations’ willingness to manage data, trend monitoring is going to help organizations aspiring to get a competitive advantage.

How Can Real-Time Data Monitoring Benefit Your Business? #

If performed timely and accurately, real-time data monitoring can help organizations succeed in achieving their different objectives. There are multiple benefits that organizations can witness by implementing real-time data monitoring solutions.

Better Customer Service #

Real-time data monitoring provides organizations with insights into how customers perceive and experience their business. Organizations can use these insights to make real-time changes to improve the customer experience. This could include providing personalized offers to customers if they are having trouble looking for something on your website.

Higher Revenue #

Insights into customers’ purchasing behavior are important for any organization as they can change their strategies accordingly if they find any loopholes. Real-time data monitoring helps organizations identify these loopholes and gaps so that they better understand purchasing behavior and make required changes to increase conversion rates.

Enhanced Security #

Security has been a major concern for any organization, especially in the digital world where attackers are ready to permeate. With increasing caber-attacks and data breaches, companies need to be more prepared to secure their digital assets.

Real-time data monitoring can help organizations mitigate and prevent these threats by monitoring the network around the clock. This means that if something wrong happens, companies will know about it in real-time and can respond to it before more damage is done.

Furthermore, real-time data monitoring allows organizations to better protect their employees’ data by giving them insights into what applications they are using and which networks they are connected to. Based on these insights, organizations can ensure the safety of their digital assets, employees’ data, and enterprise systems by building an unbreakable perimeter around these tools. This helps organizations to effectively manage their network and ensure that only valid users have access to private information.

Importance of Real-Time Data Monitoring #

Real-time data monitoring enables organizations to have deep insights into different business activities. These insights can help organizations to make better business decisions for higher productivity and more savings.

Organizations must invest in real-time data monitoring because it provides a window to the business activities’ inner workings, allowing them to review and understand what’s happening at any moment. In addition, they will be in a better position to identify problems before they become too challenging to manage and see emerging patterns or trends, using which organizations can adjust their strategies. Investing in real-time data monitoring solutions has become necessary for organizations looking to better understand their digital environments.

Related reading

Questions and answers

What is Real-Time Data Monitoring and Analytic? #

What is Trend Monitoring? #

How Can Real-Time Data Monitoring Benefit Your Business? #

How to Improve Your Reach with IG Stories

Vin Patel — Thu, 01 Dec 2022 11:34:53 +0000

Key takeaways

Social media, and consequently digital marketing has come a long way since the inception of Instagram in 2010.
Stories on Instagram last only 24 hours but that’s not always enough time for everyone to watch them.
Consistency pays off in the social media sphere, so get started and then slowly refine as you grow.

Improving Your Reach with Instagram Stories | Strategies and Tactics #

Social media, and consequently digital marketing has come a long way since the inception of Instagram in 2010.

Maintaining steady headlines in the news, while remaining a consistent player in the digital space, Instagram is here to stay. Instagram Stories, a relatively newer feature, began in 2016. With regular updates and improvements, Stories is leagues ahead of its competitors like Snapchat and Twitter Moments.

Over 500 million people use Instagram Stories every day out of which more than 200 million business accounts are viewed each day. As a growing business, that is a huge audience to potentially tap into.

58% of Instagram users have become more interested in products or the brand after seeing them on Stories. That’s over half of the 500 million Instagram users.

But as a business, using IG Stories is more than just posting frequently. In a noisy space, it becomes imperative to use content strategies and tactics to set yourself apart.

Let’s look at some of the best ways you can organically increase your brands presence and improve your reach using Instagram Stories.

Use a Business Account #

Before you start working on any of these, check if your brands account is a public business account. Just a professional or public creator account will not do - it has to be a business account.

With a business account, you have access to Instagram Insights which has several useful metrics such as:

Reach and impressions
Content interactions
Follower analytic
Audience demographic information

Along with this, you will also have the ability to boost posts and promote your content. You can choose to add your business information in the public business information section which has a contact button and quick reply options (You can even include your business’s physical address if you have one).

Once a user clicks on your brands account, they have several ways to get in touch with you easily. And don’t sleep on the replies - answer all queries as quickly as possible.

Know Your Audience #

Blindly creating content and advertising your brand will not get you very far.

It’s important to know who your current audience is and if they match up with your target audience goals. Do you have a niche product? Is there a specific demographic that you want to market to? What kind of content is your current audience actively responding to?

You can easily answer these questions with a little market research and analysis.

By using Polls, Ask Me A Question, Emoji Slider, and many other interactive features, your audience will tell you who they are and what they want.

45% of users prefer keeping up with new trends and brands through Stories.

Once you figure out the age, gender, and other relevant demographics of your audience, you can start catering to their needs and increasing your reach.

Don’t forget to use the data you gain from these while designing creatives and using language that is appealing to your audience.

Timing and Consistency #

In the words of Anna Wintour, “It’s always about timing. If it’s too soon, no one understands. If it’s too late, everyone’s forgotten.” Timing is everything.

You may have the most creative designs and use all the features but if you don’t post on time it could be for naught. To gain maximum views you have to post when users engage with Instagram the most. If there’s a national ‘something’ day that is relevant to your brand make sure you’re ready to post when everyone’s waking up. If there’s a sale coming up soon you should post the Stories a bit earlier so that more people can get in on it before it goes live.

The best way to gauge when your audience is active is by using Instagram Insights on your business account. From which days of the week your viewers open up Instagram to what hours in the day they interact - it’s all valuable information that is at your disposal.

Apart from this, posting Stories consistently is key. The most sought-after ad space in the world is the audiences mind. Maintaining a presence there is the only way to gain loyal viewers and customers. By posting regularly and interacting with them, your brand will be in the best place to grow organically.

Collaborate With Influences #

As a brand, collaborating with celebrities and influences to promote your product/service to their audience is a fantastic way to reach new users and potential customers. Getting in touch with influences who are already in the same category as you is guaranteed to bring attention to your brand.

But if you’re a small business or in an especially niche area, finding an influence whose followers can most benefit from you or who is in a similar category is the way to go. No matter what your brand or business is, there will be an influence in that space. You just have to find them.

Once you have a deal with them and they’ve started promoting it on their Stories, get in there quickly and report them. A lot of small businesses shy away from Influence marketing because they don’t have the budget for it — but you can still collaborate with micro-influences using a barter system deal with your product or service.

Use Creative Story Templates #

In marketing, the best way to catch the customers attention is through design and style. Designing your content on par with your brands theme is important if you want to stand apart from others. A font or color change might completely shift the way viewers perceive your brand.

Not convinced? Ever heard of sensation transference, and the role it plays in color branding?

There are several Instagram template apes for Android and iOS devices. Our top recommendations would be Unfold InShot, and good old Canvas. You can also find free and stunning Story designs on freepik.com. The font, text size, text color, background color, and other parts of the template can be customized to your needs.

Template apes are a great way to design attractive Stories that are trending at that moment. Using a fixed template or theme for ad campaigns can boost your brands awareness. One-third of the most viewed stories are posted by businesses. When so many users are paying close attention to your Stories, it’s important to make them appealing.

Add Links to Websites #

A recent update enables anyone to add links in Stories, regardless of follower count. Adding a link increases your reach, giving users the quickest way to get to your desired page. Be it a particular product, a survey, or a service, one click on the link in your Stories gets them to see what you want.

Use Story Highlights #

Adding your Stories into organized Highlights on your brands profile is a great way to keep viewers coming back to your page. Stories on Instagram last only 24 hours but that’s not always enough time for everyone to watch them. Creating different highlights for different campaigns, events, and even products makes your information available at all times and easy to access.

You can go one step further and create a custom icon for each Highlight. Keeping your brands entire profile clean, creative, and concise paints a positive picture in your audiences minds.

Make sure you organize and label your Highlights clearly — this helps new users navigate content on your page quickly and easily. Having a Highlight with more than 15 stories is generally discouraged, split them up into multiples instead.

Conduct Contests and Giveaways #

One of the fastest ways to increase brand awareness is through contests and giveaways. This not only checks a lot of boxes for the sales part of your business but is also a great way to give back to the community.

Holding contests and giveaways open up your audience range in unexpected ways. Everyone loves freebies and the feeling of winning something. This motivates them to share your content and tell their friends and family about them - classic word-of-mouth marketing that works in your favor. When done regularly, contests and giveaways are perfect for improving your reach and gaining a loyal customer base.

Use More Videos, Boomerangs, and Host Live Sessions #

Videos are much more interactive than photos. It holds the viewers attention for longer and delays the dreaded swipe-left. You can get more engagement through videos, especially if you add stickers, links, and filters. Another pro - adding links and hashtags on videos as opposed to photos will keep them there for longer until the video ends. Videos go up to 15 seconds, while photos are only 7 seconds.

This means users will be looking at your content for more than double the time, increasing the chances of them interacting with it.

Boomerangs are a series of quick photos that plays and replays in reverse. It’s fun to watch and make - a unique and staple Instagram feature since it was introduced. Using short videos along with Boomerangs is also a fun way to keep your audience interested and engaged.

Going live is a fantastic way to meet and interact with your audience. Giving them a behind-the-scenes look, conducting Q&As, and bringing on guest speakers will boost your reach and bring in more viewers to your account.

Use Hashtags and The Location Sticker #

Instagram’s audience grows every day because everyone wants to get in on the fun and creative ways available on the app to express themselves. Loaded with features built for discovery and engagement, Stories have the potential to keep your target audience hooked.

Using unique or trending hashtags is a terrific way for your followers to identify you and for new viewers to find you. If you’re not sure exactly what hashtags to use, Instagram will suggest popular tags once you start typing while using the hashtag symbol or in the hashtag sticker. Keeping track of relevant hashtags is a huge part of many brands’ digital marketing strategies.

The location sticker is another excellent way for your target audience to find you. Try to add your location whenever you are organizing events and activities outside. It puts your brand on the Instagram map - anyone looking for something in that location will be able to find you easily.

Go a Step Further by Using AR Filters and Adding Audio #

AR (Augmented Reality) filters are a newer feature. It’s taken the Stories game to the next level. Apart from tacky face smoothening filters, many filters keep your audience hooked. You can create a special filter just for your brand using AR Spark. It is a powerful AR creation software that anyone can learn to use with some practice. If your filter is fun and catchy, it will spread like wildfire and bring you more viewers than any other feature on Stories.

Another way to keep your audience engaged and enthusiastic is through music. A lot of small businesses don’t pay enough attention to this feature. Using audio or music may seem tedious at first but with some effort, you can level up your Stories game. It adds flair and intrigue to your Stories that encourage more interactions and shares.

Curate User-generated Content #

UGC (or User-generated content) is content created by the users for your business. It’s one of the ways your audience represents you while also promoting your brand. We know that any PR is good PR - but we want it to be positive PR that will push your brand in the direction aligning with the goals you’ve set.

Add Call-To-Actions (CTAs) in your Stories and encourage your audience to tag your account whenever they post about you on their accounts. This way you can report them for additional content for your Stories. Doing this will portray your brand as one that cares about its audience and makes an effort to stay in touch with them.

For example, if you have a clothing brand, urge your customers to post a picture of themselves wearing your clothes, using your hashtags, and tagging you. Many potential customers look for pictures of the clothing they want to purchase being worn by regular people. The biggest pros of curating UGC are the additional content you will get and the extension of an audience through the users - making this a foolproof way to increase brand loyalty and your reach.

3, 2, 1 — Post! #

We’ve shared a lot of great ways to make the most out of the Instagram Story tool. Instagram is a dynamic and evolving platform, and stories might just be the tool that delivers exponential business growth and audience reach.

And while Strategies and Tactics can carry your content marketing plan, don’t let the pursuit of perfection paralyze you. Consistency pays off in the social media sphere, so get started and then slowly refine as you grow.

Related reading

The Need for Digitization in an Aging Population

Vin Patel — Thu, 01 Dec 2022 04:59:30 +0000

Key takeaways

Growing older in a digital age might change the paradigm of being vulnerable into something worthwhile, as the one billion older persons over 60 who exist today will quadruple to two billion in the next thirty years.
With home care technology, families can provide their loved ones with the essential medical care they need at a price that is more inexpensive than other healthcare options, including assisted living, nursing homes, and patient care.
Artificial intelligence (AI) and other applications of new and developing technology can improve well-being, allow individuals to age more healthfully, and increase access to better healthcare.

One of the significant worldwide trends of the twenty-first century, along with the rise of technology, is the megatrend of aging, which is defined as long lives to the age of 100 as a matter of course, and more older adults than young in all cultures as they modernize. Growing older in a digital age might change the paradigm of being vulnerable into something worthwhile, as the one billion older persons over 60 who exist today will quadruple to two billion in the next thirty years.

People have a longer lifespan on a global average. The population of those 65 and older was 727 million in 2020, and by 2050, that number is expected to have doubled to nearly 1.5 billion, representing a 16.3% rise.

AI and Big data: The Game-changers #

Big data and new technology are changing every industry under the sun. The advantages of this change in the healthcare industry are apparent and will significantly impact how the aging process develops in the future.

Big data offers improved ways to guarantee that the appropriate therapy is delivered and helps healthcare practitioners understand sickness and illness more clearly than ever before. Since wearable technology and smart homes may frequently take the role of human caregivers, people can maintain their independence longer.

The healthcare environment is already altering as a result of big data. Our capacity to gather data is improving, and with it comes to our ability to use it in more valuable and dependable ways and produce findings that stand on their own.

The Pittsburgh Health Data Alliance compiles data from numerous sources, such as medical records, wearable, genetic data, and even social media, to build a thorough portrait of a person for customized healthcare. Thousands of people are compared, theoretically overcoming the problem of inconsistent, erroneous representations of patient health. This illustration shows how data give medical practitioners insight into patients’ health. However, it also represents a development in research that examines the health of samples of the entire community.

How technology can serve the aging world #

In many ways, digital technologies promote healthy aging. People can use gadgets and applications (sensors, monitors, wristwatches, and mobiles) for ongoing health monitoring and feedback to improve disease prevention and health and well-being at all ages.

Digital technologies are giving healthcare workers, managers, and policymakers the tools they need to offer better care. They can operate more resilient health systems and increase survival rates by arming patients with accurate health information. New age gadgets are enhancing the quality of health datasets to enable more precise analysis, diagnosis, and forecasting of health conditions, especially using AI, big data, or virtual reality (VR) simulations. Through computing at the edge, we can aggregate data from gadgets and sensors and combines imaging, diagnosis, and data analytic.

ICTs, or information and communication technologies, can promote healthy aging and help individuals live longer, more fulfilling lives. Digital technologies are now a crucial enabler in treatment and prevention methods due to the growth of digital devices for personal and professional use. For instance, wearable and fitness equipment can offer continuous monitoring and prevention when patients go about their everyday activities. Enhancing ICT/accessibility ensures that everyone can utilize information and communication equally and fairly, regardless of age, gender, or ability (ICTs). Incorporating ICT accessibility also offers alternatives to using technology, ensuring that age-related impairments like reduced vision and hearing loss do not impede access to digital goods and services or the comprehension of information and communication, both of which could be crucial in an emergency or crisis.

Digital technology has been essential in preventing loneliness and isolation among elderly individuals cut off from family and friends, particularly during the COVID-19 pandemic. It helps compensate for issues and difficulties commonly associated with aging, including diminished eyesight or hearing. Thus, it prevents older people’s social isolation. Digital technology has aided in the fight against loneliness and isolation among elderly individuals cut off from family and friends, particularly during the COVID-19 pandemic.

Smart Home to the Rescue #

The global “smart” home care market is anticipated to grow from $8.7 billion in 2019 to $96.2 billion in 2030, according to the P&S Intelligence analysis. By 2050, there will be 1.5 billion older people, a projected doubling of the current number. The WHO also predicts that by 2050, there will be 2 billion individuals over the age of 60, a shift in the population that will significantly influence the rising emphasis on value-based care and home care technology.

With home care technology, families can provide their loved ones with the essential medical care they need at a price that is more inexpensive than other healthcare options, including assisted living, nursing homes, and patient care.

Home care technology can frequently take the role of conventional healthcare. Since people of all generations cannot access healthcare professionals in a traditional, in-person setting, telemedicine has grown in popularity over the past year. Telemedicine technologies enable the remote transmission of information, frequently via virtual mediums, between patients and doctors to address these previously recognized problems, including wait times and excessive healthcare expenditures. Patients can reduce travel time, transportation expenses, and clinic visits by scheduling appointments over the phone, through video calls, or by an app.

In order to do this, modern sensors and improved interoperability will be included in the fundamental design of our houses and applications to enable health-related monitoring. Continuous data touch points and non-intrusive health checks would be part of this throughout the day (i.e., in your bed, while in the shower, and opening the fridge). Each user will develop and control such metrics based on specific concerns and data preferences, which should ease any worries about 24/7 monitoring and related privacy issues.

Sensors will be set up to recognize different indicators, conditions, and symptoms of illness around your home. So that every household member has access to the care they require, these gadgets can automatically alert medical specialists when something is wrong. Data and vitals can now be continuously and discretely communicated with a doctor by patients who are recovering from treatment or disease.

The health and happiness of older persons and social isolation are strongly correlated by research. Different technologies have been found to have an impact on social isolation levels. Internet and communication technologies (ICTs), video conferencing, digital gaming, mobility tools, and social networking sites (SNS) are some. Most studies on the subject show that technologies, especially those that improve communication, increase connectivity levels and decrease depressive, lonely, and isolating experiences. However, the usefulness of ICTs and SNS has yielded mixed results.

Virtual reality has supplanted face-to-face communication in the post-COVID era, profoundly impacting social connectivity. For instance, seniors have access to virtual field trips like going to a museum with their old acquaintances and interacting with each other in a three-dimensional setting. Seniors can learn about healthcare and socialize socially while still in a safe environment, thanks to various readily available, reasonably cheap technologies. For instance, television alone may promote new health-related knowledge but decrease social engagement. In contrast, emails, video calling, or testing may enable it by reducing adult distance.

The maintenance of connections among older persons benefits greatly from long-distance phone or email communication. For instance, compared to people who solely communicate in person, many older folks who are geographically separated from their grandkids have the frequent phone and email exchanges and report feeling emotionally satisfied.

Future of Digitization For The Elderly #

Our planet is aging quickly. By 2050, there will be 1.5 billion individuals worldwide who are 65 years of age or older, up from 703 million in 2019. According to the most recent forecasts from UN DESA, the number of adults 80 and older will triple in the following 30 years. One in six persons worldwide will be over 65 by 2050, up from one in eleven in 2019. This is why the UN has designated 2020–2030 as the Decade of Healthy Aging.

The race is on to scale up the essential digital technologies so that people can age more healthily, have more purposeful lives, and assist society in adjusting to a rise in older and senior people. Artificial intelligence (AI) and other applications of new and developing technology can improve well-being, allow individuals to age more healthfully, and increase access to better healthcare.

Related reading

IIoT Technology In Supply Chain Operations

Vin Patel — Tue, 29 Nov 2022 14:49:16 +0000

Key takeaways

The Industrial Internet of Things or IIoT connects a variety of supply chain linkages to ensure that everyone involved in product creation and delivery has consistency, transparency, speed, and scalability.
IIoT sensors enable a consolidated picture of all data flowing from these distinct containers, which is very useful for factories that manage many products, components, and raw materials.
These trends include improvements to IIoT platform functionality and ease of use, simplified development of IIoT applications through access to APIs and established communications protocols, and lower costs of sensors and data storage.

The Industrial Internet of Things or IIoT connects a variety of supply chain linkages to ensure that everyone involved in product creation and delivery has consistency, transparency, speed, and scalability. However, even though there are now even more layers in the supply chain across all industries due to globalization and the Internet bringing us all closer together, organizations still face severe issues and hurdles in meeting these expectations. For example, shortages of medicines, crucial health products, and other items and temporary trade restrictions exposed their flaws. These trends and the trade war between the United States and China have caused global supply chain disruptions.

In this blog article, we will look at how IIoT functions in the supply chain and why it can help businesses significantly reduce supply chain barriers while enhancing product quality.

What is IIoT? #

The use of smart sensors and actuators to improve manufacturing and industrial processes is known as the industrial Internet of things (IIoT). IIoT, also known as the industrial Internet or Industry 4.0, takes advantage of the data machines have produced in industrial settings for years using the power of smart machines and real-time analytic. Smart machines are better than people at gathering and processing data in real-time. Still, they are also better at transmitting crucial information that can be utilized to make business choices faster and more correctly, according to the IIoT’s guiding principle. IoT focuses on home appliance management, which improves user comfort while conserving resources like power. The Industrial Internet of Things (IIoT) focuses on vital systems, including health care, aircraft, manufacturing machinery automation, linking machines and people, and data analytic.

How Is It Different from IoT? #

IoT focuses on home appliance management, which improves user comfort while conserving resources like power. On the other hand, the Industrial Internet of Things (IIoT) focuses on vital systems, including health care, aircraft, manufacturing machinery automation, linking machines and people, and data analytic. The Industrial Internet of Things, or IIoT, is a framework in which several equipment or devices are linked and synchronized using software tools. Industrial IoT devices span from large industrial robots to small environmental sensors.

How Can IIoT Help Supply Chain Operations? #

The supply chain has constantly been developing over the years, with the introduction of advanced technology such as sensors and artificial intelligence (AI) to keep the supply chain compact. Reduced inventory meant using equipment and raw materials efficiently and cost-effectively. However, when the COVID-19 pandemic struck, it created global supply chain disruptions as we had never seen before. Smart sensors and artificial intelligence (AI) can help build a more resilient system and repair many of the supply chain flaws exposed by the pandemic.

The worldwide IIoT market is expected to reach $1.11 trillion by 2028, according to Grand View Research, which implies the manufacturing industry should be able to analyze billions of actions daily in real-time and assure consistent and accurate data processing and association among machines and sensors. IIoT offers manufacturers options that save time, money, and resources, from predictive maintenance and better machine optime to offering end-to-end insight into the supply chain. Manufacturers may get visibility inside, outdoors, and on the road with an IoT strategy.

Enhanced Asset Tracking & Inventory Control #

Sensor utilization is reaching new heights thanks to the IIoT, especially regarding asset monitoring and determining where assets are in the supply chain at any given time. If a container has an asset tracker, a client may know if it is still on a ship or has arrived at a port. However, knowing the location of a single asset is insufficient when a product requires, say, 20 components before it can be manufactured. IIoT sensors enable a consolidated picture of all data flowing from these distinct containers, which is very useful for factories that manage many products, components, and raw materials. As asset trackers and sensors become more affordable, they are more widely used. Data management becomes increasingly critical as the number of sensors grows.

Globalization #

Physical supply chains are now under strain since they must quickly reach diverse parts of the world to meet varying needs. Businesses are disorganized and lack the adequate procedures that bind the chain together due to these complications, generating delays. In addition, different nations operate differently in terms of customs and the law, making it more challenging to get commodities. From the moment a product is produced until it leaves the final provider, the IIoT may make information about it more accessible. These stages include device verification and authentication to guarantee quick and secure transmission.

Predictive Maintenance #

Predictive maintenance is a proactive response to the common problems caused by equipment downtime. This is in contrast to the traditional reactive strategy, which may be much more damaging to firms. Businesses that use predictive maintenance approaches don’t waste time on routine maintenance that isn’t always necessary or well-timed. When problems are little and insignificant, they are easier to fix. Repetitive and needless expenditures are cut, and attention is focused just where and when it is required. There are further advantages to leveraging the Internet of Things to provide predictive maintenance in this fashion. This data-driven approach creates a clear audit trail, allowing you to understand what has occurred and needs to occur. This might be very important if you need to file a claim with your insurance or warranty company.

Warehouse Automation #

Automating inventory transportation into, within, and out of warehouses to consumers with little human intervention is known as warehouse automation. A corporation can reduce labor-intensive tasks such as repetitive physical labor and manual data input and analysis as part of an automation initiative. Warehouse automation enables robots or robots to collaborate with warehouse workers. People can take on more intricate, low-level jobs that benefit the firm since they are connected to the Internet and configured to conduct various duties.

Bottom Line #

Smart sensors and artificial intelligence (AI) can help build a more resilient system and repair several supply chain flaws exposed by the pandemic. The Industrial Internet of Things (IIoT) has already significantly influenced the manufacturing industry and the supply chain as a whole, enabling new capabilities and more intelligent, predictive, and proactive workflows that lead to increased efficiency. Companies are also leveraging significant technological advances that have significantly reduced the barrier to adopting IIoT solutions. These trends include improvements to IIoT platform functionality and ease of use, simplified development of IIoT applications through access to APIs and established communications protocols, and lower costs of sensors and data storage.

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Why Do You Need a Database Management System?

Vin Patel — Tue, 29 Nov 2022 14:13:43 +0000

Key takeaways

Everything runs on data today, and the vast amount of data produced daily can be hard to handle manually by any organization.
**A DBMS manages the metadata better:**Improved metadata management is among the most important benefits of DBMS used.
So, it would be a smart and logical decision for you to integrate a database management system for your company.

Everything runs on data today, and the vast amount of data produced daily can be hard to handle manually by any organization. What is the solution to this?: A database management system.

Philosopher Aristotle once said that *quantity, quality, position, place, time,*and actionare some of the best attributes to describe anything. In modern days, a database is a collection of data that has been set up so that it can be retrieved quickly and easily. It can be anything from a spreadsheet or word processing document to a piece of code with millions of lines.

Although Aristotle did not have any electronic means of database management back in the day, he was ready to group all the data and determine the inter-relationships to reach conclusions.

You do not need to do that manually if you have a Database Management System (DBMS) to do that for you automatically. A DBMS may streamline your data operations and boost the commercial value of your company’s data assets while relieving users throughout the company of tedious and time-consuming data processing duties.

What is a Database Management System? #

A database management system is a piece of software that is used to build and maintain one or more databases. It makes it simple to build databases, update tables, retrieve data, and improve data. Data is accessed, changed, and locked in a DBMS to avoid conflicts.

In the 1960s, the DBMS was first made available to the general public. The DBMS allowed authorized users to insert, delete, create, control, define, and revise data sets in addition to storing and extracting data sets. Data evolved over time to become specialized and more complicated. A database management system that could handle these changing requirements was therefore necessary.

The tools to maintain the database schema, which determines the structure of the database itself, are now provided by a database management system. Because the database developer will usually offer a distinct front-end for the client, the database management system is frequently only visible to the database developer. By the strictest definition, this front-end might be regarded as a standalone database management system.

If you have made it this far, you already understand how a database management system can be an absolute boon for your organization. Now, let us learn a few advantages of a DBMS that will make you want to integrate a management system right now!

Why do you need a Database Management System? #

Imagine you got an intern to work with you, and every time you need to assign work to him, you need to ask for his email address again and again. It would be beneficial if you had a central database system that could store all your employees’ email addresses and contact information.

The total amount of time that people looking for this information squander throughout the whole firm may surprise you. However, a central database that is open to anyone can quickly respond to all of the queries.

**A DBMS manages the metadata better:**Improved metadata management is among the most important benefits of DBMS used.

What is metadata, then? #

Data about data is known as metadata. Pretty perplexing, huh? Metadata contains details about the table’s name, column titles, and data kinds. For data to be accessible and usable, good metadata management is crucial.

When everything is properly set up, users may immediately find the data they want without having to go through several files. A DBMS makes it simpler to keep track of data changes and offers an effective solution to handle metadata.

Inventories can be tracked and managed efficiently: Effective inventory management occasionally resembles a balancing act. It’s simple to have too much, risking wastage by leaving it on the shelf, or too little, disappointing consumers and tarnishing your image. Manual inventory tracking makes it simple to undercount, enter incorrect data, and lose spreadsheets and notes.

A good database management system comes to your rescue here as well. You may minimize missed sales and eliminate such risks by employing an inventory monitoring database, particularly in conjunction with electronic data exchange and barcode scanning, while maximizing your chances for growth.

**A DBMS keeps all the data safe:**With all the data around, it is crucial for the company to keep its data secure. Not securing the data is equivalent to not locking your house before going on a long trip in a theft-prone neighborhood. A DBMS can enhance data security by offering a number of features that make it harder for unauthorized people to access the data.

A database management system may be used to regulate access, encrypt data, and authenticate users.

Only authorized users will be able to access the data, thanks to user authentication. The data is shielded from access by unauthorized users, thanks to encryption. Administrators can specify which users have access to what data using access control. With the use of this information, potential security vulnerabilities may be found and fixed.

**A good management system leads to improved decision-making:**Any business has to be able to make choices quickly and accurately. By giving you a speedy means to assess your data, a DBMS may aid with this. The technology allows you to produce reports and graphs displaying the evolution of your data over time. Making judgments concerning the course of your business based on this knowledge is possible.

Conclusion #

Now you know why all the big companies integrate an excellent database management system in their organization. As you can see, a DBMS may offer many benefits to any firm. DBMS can help increase the efficiency of your team by handling large databases. It provides security and saves both your time and money. So, it would be a smart and logical decision for you to integrate a database management system for your company.

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Questions and answers

What is a Database Management System? #

A database management system is a piece of software that is used to build and maintain one or more databases. It makes it simple to build databases, update tables, retrieve data, and improve data.

Why do you need a Database Management System? #

Data Warehouse vs. Transactional Databases: Key Differences

Vin Patel — Tue, 29 Nov 2022 13:58:53 +0000

Key takeaways

The use of big data in firms of all sizes and sectors is constantly expanding.
Information can be stored effectively (thus the creation of normalization) in the third standard form or 3NF.
A data warehouse architect with dimensional modelling expertise is crucial to building and developing your data warehouse.

The use of big data in firms of all sizes and sectors is constantly expanding. The average business is now in charge of 163 terabytes (163,000 gigabytes) of data, according to a 2016 IDG (International Data Group) survey. However, it is challenging to transform raw data into cutting-edge insights. Therefore, enterprises must understand the technique of corporate data management.

Data storage systems include databases and data warehouses. But they accomplish pretty different things. Therefore, we will describe their functions and essential distinctions in this article so you can decide which one is most appropriate for your data structure scenario.

Database Architecture #

A database architecture represents a Database Management System or DBMS design. It aids in the database management system’s creation, growth, use, and maintenance. The database system may be divided into separate parts that can be independently adjusted, changed, replaced, and altered thanks to DBMS design. Database architecture involves deciding how these databases will work and fit into existing systems and establishing standards for the security and programming components of these databases. Understanding a database’s aspects is also beneficial in the long run. A DBMS’s design is determined by its architecture. The architecture can be single-tier or mufti-tier, including 1-tier, 2-tier, 3-tier, and n-tier architectures, among others.

Data Warehouse - Defined #

A particular form of data management system called a “data warehouse” is intended to facilitate and assist business intelligence (BI) operations, particularly analytic. Data warehouses frequently have a lot of historical data and are only meant for queries and analysis. A data warehouse often uses a variety of sources, including transaction programmed and application log files.

Large volumes of data from many sources are centralized and consolidated in a data warehouse. As a result, organizations may gain valuable business insights from their data by using their analytical skills to enhance decision-making. In addition, it creates a historical record over time that data scientists and business analysts may use to their advantage.

A Brief Introduction to Transactional Databases #

A transactional database’s core table structures are created for quick and effective data inserts and updates (it’s all about getting data into the database), which is the main distinction between a data warehouse and a transactional database. On the other hand, the primary table structures for a data warehouse are created for quick data reads, enabling users to create straightforward but effective queries to get data for analytical reasons.

Comparison between 3NF vs Dimensional Modelling #

The issue of too complicated schema in the presenting area is dealt with via dimensional modelling. The information in a dimensional model is identical to that in a normalized model. These dimensionally structured tables are known as star schema if the presentation is based on a relational database. The data is stored in cubes if the presentation is based on an OLAP or multidimensional database. Both relational and multidimensional databases can benefit from dimensional modelling. Both share a recognized conceptual design with similar dimensions, but their physical implementations are different. Let us look at some significant differences between Third Normal Form (3NF) and Dimensional Modelling.

Third Normal Form (3NF) #

A relational database design technique called third standard form (3NF) employs normalization principles to eliminate data duplication, prevent anomalies, guarantee referential integrity, and streamline data administration.

Advantages of 3NF #

Even if they are not DWH or Data Integration specialists, most IT workers are familiar with normalization concepts. Consequently, it is considerably simpler to gain access to qualified resources. 3NF is organized and is acceptable for loading in almost real-time. It is pretty simple to comprehend and somewhat simple to expand. Information can be stored effectively (thus the creation of normalization) in the third standard form or 3NF. A form of data redundancy (identical data present more than once) is eliminated from the equation at each normalization step. Since the data is only present in one location after eliminating redundancy, changing the data is simple.

Disadvantages of 3NF #

Along with the advantages of 3NF, there are also a few downsides. It takes a long development time (mainly due to business changes and corresponding rework). 3NF is challenging to model out for a business. A Presentation Layer is necessary (not conductive to BI tools and OLAP). It has coupled fundamental distribution limits parallelism by introducing dependencies. For example, the ‘Subject Area Database’ is modelled by the perspectives of the day. When handling incoming to the Data Warehouse, duplicate data sources need a priority/trust layer, which wipes off the audit trail.

The information is modelled according to how the organization wants or believes the data should be structured (the “ideal” of our business") rather than according to reality. This often results in highly sophisticated and customized ETL (Extract, Transform, Load) logic to map the data to this model (which is unique and difficult to maintain). Before development can begin, it also necessitates a significant upfront modelling activity, and modifications along the way are exceedingly challenging to adopt because they always necessitate reworking associated ETL.

Dimensional Modelling #

Data Dimensional Modelling (DDM) is a method for effectively storing data in a Data Warehouse using Dimensions and Facts. As a result, the database is optimized for quicker data retrieval. To provide reports that enhance performance, dimensional models organize the data according to a predetermined framework.

Advantages of Dimensional Modelling #

Using dimensional modelling has the advantage of making it simpler to store and retrieve data after it has been placed in a data warehouse. As a result, many OLAP systems employ the dimensional model as their data model.

The benefits of dimensional modelling include:

Perfect for mufti-dimensional analysis.
It has subject-specific organization, so business users will find it simple to grasp.
Dimensional modelling is used to lessen the repetition of data (data landing). It is appropriate for historical archiving.
Higher levels and aggregate awareness are simple to link in aggregation points.

Disadvantages of Dimensional Modelling #

Due to renormalization, updates and deletions are expensive in dimensional modelling. It also employs specified pathways that are firmly established in ETL and uses dimensions’ throughout time (Type 1, 2 and 3). If one wants to construct a simple application, architecture needs several types of tables with various flavors (helper, bridge, junk, mini etc.). Grain problems are challenging to handle in dimensional modelling. It is also impractical to load in real-time. Transactional problems occur before dimension data. It is rigid modelling of fundamental components, including history, structure, and key distribution. These are some of the critical disadvantages of dimensional modelling.

Conclusion #

A data warehouse is essential to every organization’s contemporary analytic platform. Because it is set up for integrated reporting across all of your transactional systems and data sources, it differs from conventional transactional database solutions. A distinct database modelling method known as “dimensional modelling” is used to create a data warehouse. A data warehouse architect with dimensional modelling expertise is crucial to building and developing your data warehouse.

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Cloud Security Solutions: 5 Common Causes of Caber-Risk

Vin Patel — Tue, 29 Nov 2022 12:58:31 +0000

Key takeaways

The use of the cloud has risen dramatically in recent years, particularly during the pandemic.
Companies tend to neglect this function, so you should set limits on devices that can obtain company data.
However, as these types of services become more widely accepted, it is critical that security is not compromised in the name of convenience.

The use of the cloud has risen dramatically in recent years, particularly during the pandemic. Businesses now use cloud computing for a variety of purposes, including analytic, infrastructure as a service (IaaS), and data storage. However, as cloud adoption grows, attackers are focusing on exploiting the common cloud danger.

Since the cloud isn’t going away, businesses should make sure they’re doing their share to keep a secure cloud environment to protect themselves, their customers, and their cyberattack network as the threat grows.

With that in mind, here are five of the most prevalent cloud security threats:

Misconfigured Settings #

Misconfigured settings are a common source of cloud data breaches, with 68 percent of businesses citing this as their top cloud security concern.

Access to data may not be as controlled as it should be because cloud services are supposed to make things faster and easier. This could lead to a lot of unauthorized access.

Because you use the so-called “shared responsibility model” when working with a cloud provider, some people may believe that the cloud provider is responsible for your entire security. However, in most cases, the configuration is determined by the organization.

This indicates that your IT department should review your settings and permissions and ensure that basic security controls are in place. Restriction of access, suspension of mufti-object verification (MFA), and the use of any logging and monitoring tools available can all help you track and control what’s going on.

It’s also a good idea to verify your cloud testing frequently to ensure that no suspicious or unexpected activity is occurring due to improperly configured settings.

Inadequate Data Quality and Management #

It can be challenging to track all the data when it is stored in the cloud, which is why it is critical to ensure that the data is well documented.

With this knowledge, you may choose the appropriate level of protection and restrict access to the most critical data. Cloud services can make data exchange more accessible. Nevertheless, if not handled appropriately, this could pose a security risk. Managers can set up data sharing access, so it’s a good idea to think about what data these features should keep and what data they shouldn’t. Companies tend to neglect this function, so you should set limits on devices that can obtain company data.

Finally, cloud users should make sure that their data is kept as safe as possible while on the move. The cloud makes it tricky to monitor or prevent traffic, lowering data transfer visibility. As a result, it is critical to ensure that it is securely fastened. Client-on-client is the best option because it encrypts data on your end before sending it to a cloud server.

Inadequate Employee Training #

It’s critical to train employees on modern cloud operations and security protocols. Some hackers target cloud-based services in their phishing emails to obtain sensitive information, such as providing malicious URLs that appear to be from Google Drive or OneDrive and requiring information verification to access data. Employees must be able to recognize various types of danger as well as grasp some of the most significant threats to the company, such as shadow IT.

The use of anonymous software and devices on a company network can generate a slew of issues for businesses, as complete visibility is nearly impossible to achieve, especially when there are a lot of remote workers.

Around 80% of employees agree to use cloud-based software-as-a-service (SaaS) apes at work without IT authorization. Employees must be trained to reduce the risks of data loss and security breaches caused by unsafe equipment and software.

Inadequate Security Policies #

In any situation, safety should be addressed; the cloud is no exception.

The written policy aids in the documentation of users’ rules and regulations in order to establish how they should properly use cloud apes.

The following should be explained in the cloud security strategy:

Who is eligible to use the cloud?
What information should be kept in the cloud?

What are the best practices and methods for properly using the cloud?

Choosing the Wrong Service Provider #

#

There are numerous cloud service providers available. Choosing one that prioritizes security, on the other hand, will be extremely beneficial to you and your company.

Testing to verify if a cloud vendor meets known security requirements, as well as other critical features and capabilities like authentication, data encryption, disaster recovery, and technical support, is a smart place to start.

Conclusion #

Working from the cloud can be quite beneficial for both large and small groups. However, as these types of services become more widely accepted, it is critical that security is not compromised in the name of convenience.

Strong cybersecurity necessarily requires the proper use of technologies and processes, allowing your company to reap all of the benefits of cloud computing while reducing the danger of cyberattacks and safeguarding your company and its employees.

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EDR Software: A Enhancement Towards Cybersecurity

Vin Patel — Tue, 29 Nov 2022 12:31:47 +0000

Key takeaways

Hackers are becoming more adept at stealing data and conducting cyberattacks as a result of technological improvement.
However, the majority of providers often offer the following features: By gathering and combining data, EDR systems increase endpoint visibility.
The organization often finds out about a breach through a third party, such as law enforcement or one of its own clients or suppliers.

Today, cyberattacks are not a new thought. Hackers are becoming more adept at stealing data and conducting cyberattacks as a result of technological improvement. The potential of cybersecurity is likewise growing daily to protect critical data. One such security solution is Endpoint Detection and Reaction (EDR), which combines real-time continuous endpoint data monitoring and collecting with rules-based automated response and analysis capabilities.

Let us see how these EDR software works and the top EDR tools.

What is Endpoint Detection and Response (EDR)? #

It is also known as Endpoint Threat Detection and Response (ETDR). EDR, sometimes known as ETDR, is a security tool that monitors endpoint activity, uses data analytic and context-based information to identify suspect behavioral patterns, prevents attacks, and aids security analysts in cleaning up and restoring compromised systems.

Because they are the most efficient method of detecting intrusions, EDR technologies are a key part of a contemporary endpoint security strategy. They keep an eye on the environment of the target to spot assaults and gather telemetry information to aid in quick triage and investigative procedures.

The Role of EDR Tools #

The endpoints, which might be a staff workstation, laptop, server, cloud system, mobile, or IoT device, are tracked by the EDR tools. These technologies can notify security teams of suspicious behavior and facilitate quick endpoint attack investigation and containment.

EDR systems usually gather endpoint data, such as process execution, endpoint communication, and user loins; analyze data to find abnormalities and hostile behavior; and record data about harmful conduct. This gives security teams the information they need to look into and handle issues.

They also make it possible to do manual and automatic tasks to neutralize threats on the endpoint, such as deleting and reimaging the device or isolating it from the network.

How do These EDR Tools Work? #

Security teams now have the insight they need to find problems that might otherwise go undetected thanks to EDR security solutions, which keep track of all endpoint and workload activity and events.

Advanced threat detection, investigation, and response capabilities, such as incident data search, investigation alert triage, validation of suspicious behavior, threat hunting, and detection and containment of malicious activity, should all be included in an EDR solution.

By installing software agents on each endpoint, these EDR systems first gather endpoint data. The information is subsequently transmitted to an administrative hub, which is often a cloud-based EDR platform. Machine learning is used in the solution to correlate and evaluate the data. Threat intelligence feeds, which provide the context in the form of actual cyberattack examples, are a feature of several EDR platforms. To identify attacks, the system analyzes network and endpoint activity with these samples.

The technology then detects suspicious activities and sends notifications to necessary people and security experts. Additionally, it starts automated replies in response to preset triggers. The last benefit of EDR systems is that they keep data to support proactive threat hunting and future investigations.

EDR Security Capabilities #

EDR features are frequently different across suppliers. However, the majority of providers often offer the following features:

Integration #

By gathering and combining data, EDR systems increase endpoint visibility. Endpoint protection should be combined with additional security solutions because it does not completely protect against all threats.

Insights #

Simple EDR tools just offer data gathering and aggregation. Advanced EDR systems automate threat identification and warning procedures using machine learning and artificial intelligence (AI) technologies. By tying suspicious activity to the MITRE ATT&CK framework, certain technologies can identify trends.

Response #

EDR technologies offer reaction options to aid operators in resolving and looking into problems. Advanced technologies can additionally collect artifacts from questionable endpoints, evaluate live system memory, and integrate historical and real-time situational data.

Forensics #

EDR technologies provide forensics capabilities to assist in identifying risks and bringing to light related activity that could otherwise go unnoticed. Before a breach happens, it can assist in establishing timetables and locating impacted systems.

Automation #

Advanced EDR solutions can do remediation automatically.

Top EDR Tools to Help You #

Here is a list of a few EDR tools that can help you:

Sophos Intercept X #

The most complete endpoint protection solution available is Sophos Intercept X. Intercept X, which was created to thwart the broadest array of threats, has demonstrated the ability to halt even the most sophisticated ransomware and malware by combining many cutting-edge tactics.

Sophos Intercept X has not given any information about this service. It provides the current pricing plan for contacting the company.

Crowd Strike Falcon #

Crowd Strike Falcon is well-liked by both users and experts. Through three rounds of MITRE ATT&CK testing, Falcon has produced strong security scores, but when advanced features were taken into account, the product’s overall Detection score rose to the top of the list.

The price is above average; however, as all EDR systems reduce breach and cleanup costs by millions of dollars, pricing is relative, and well-implemented sophisticated features pay for themselves.

Cygnet 360 AutoXDR #

No matter the size, expertise, or resources of the security team, or the need for a mufti-product security stack, Cygnet enables any organization to put its cybersecurity on autopilot, streamlining and automating all of its security operations while offering improved levels of visibility and protection.

Bottom Line: The Importance of EDR #

All companies need to be aware by now that, no matter how sophisticated your defenses are, enemies will ultimately find a way through them with enough motivation, time, and resources.

Prevention alone cannot be relied upon. Your organization’s existing endpoint security solution may keep you in the dark when prevention fails. Attackers use this opportunity to loiter and move around inside your network.

Most attacks are quiet, which might let your attackers move about in your area if you don’t navigate them appropriately. Additionally, it can allow them to return whenever they want to your setting. The organization often finds out about a breach through a third party, such as law enforcement or one of its own clients or suppliers.

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The Future of Wealth Management? Teach or Hybrid?

Vin Patel — Tue, 22 Nov 2022 18:54:34 +0000

Key takeaways

But such seemingly simple goals can face some tirelessly complex hurdles.
With urbanized goals like sustaining a lifestyle, education planning for kids, healthcare costs of ageing parents, or even buying a new home, today’s consumer needs more appropriate real-time advice than ever before.
The paradigm shift in the wealth management landscape is occurring rapidly, and these digitalization trends bear testament to the massive change that the wealth management space is facing.

With futurists looking to re-invent the asset management value chain and consumers demanding more client-centricity, can tomorrow’s wealth management solution be led solely by teach? Digitalization is promising a massive upgrade to long-term financial planning. What can this mean for you as an investor?

An investor usually has two broad objectives: to grow their capital and, second, to protect what they already have. But such seemingly simple goals can face some tirelessly complex hurdles. From trying to understand asset classes and keeping track of portfolio performance to changing investment buckets based on market trends, an average investor often has a lot to contend with. Combine this with increased uncertainty and the rising cost of risk, and you have a consumer who is frayed at the edges and is demanding a lot more in the way of client-centricity.

As per a report published by Deloitte in 2017, we are about to witness the largest wealth transfer in history over the next 40 years, with almost USD 50 trillion changing hands from one generation to another. This makes it even more essential for institutions to gear up to address re-wired Millenials’ demands. With increased globalization, it’s vital to recognize that digital transformation is no longer a choice but an imperative for sustainability. This is something wealth managers have now realized.

Risking disruption to re-invent their value-chain, wealth management is now following in the footsteps of consumer-centric organizations like Amazon, Apple and Netflix, to name a few; to empower a digitally-savvy investor with tools and methodologies that up until now were the purview of a select few.

The Emergence of Wealth Teach #

Just as fintech transformed financial services, Healthtech promises a revolution in the investment management industry. It encompasses a range of different channels - from B2C rob-advisors to micro-investing platforms that allow users to regularly save small sums of money to even B2B providers that offer digital brokerages and wealth advisory. Healthtech is attempting to create a fullyoperational wealth management bank ‘in your pocket’.

With cutting-edge technology, firms like Wealthfront, Addepar and InvestCloud are helping investors benefit from Artificial Intelligence (AI) and deep-data-driven analytic to receive real-time personal financial management at a fraction of the cost.

No Longer A One-Size-Fits-All #

Thanks to Machine Learning, advisory firms are infusing data and analytic to serve clients holistically by generating relevant, timely, and actionable insights for clients and advisors alike.

Embedded at the heart of Healthtech is a behavior-based segmentation based on risk appetite, interests, and financial goals. This brings a more intuitive experience to the investor, going beyond just recommending portfolio allocation based on risk profile or asset under management (AUM). Instead, more customized offerings are made available based on client personas and interests. With this outside-in approach, technology is bridging the chasm between business objectives and customer expectations.

Taking Rob-Advisory To The Next Level #

But as consumer sentiment evolves, the new generation of wealth customer is looking beyond just growing their capital. With urbanized goals like sustaining a lifestyle, education planning for kids, healthcare costs of ageing parents, or even buying a new home, today’s consumer needs more appropriate real-time advice than ever before. This is perhaps where traditional wealth management has sometimes fallen short of its objectives. With analyst, stock picks undermining market indexes more often than not, and underdog plays making headline-worthy moves, advisors often find themselves dragging their feet when it comes to generating timely advice and meeting client goals.

For Healthtech innovators, this is a core focus area - bringing granular level needs-based advisory, real-time, to the mass market. By integrating economic trends and even social media analytic, whose singular stand in recent times has moved markets beyond measure, this type of advice becomes more relevant than ever.

Combining this with click-of-the-button UI, Healthtech brings in user-friendly omnichannel support to simplify investing and at the same time, give control back to the investor. For instance, an investor can individualize their financial planning through goal-based bucket creation to set aside a long-term retirement corpus while having an aggressive short-term bucket for saving up for a down payment on a house. If there are changes in market sentiments in the short term, the investor can shift money from one bucket to another. This is perhaps the most significant takeback from this value chain: more control, better individualization and better expectation management.

Taking it a step further, a personalized dashboard gives you 24/7 access to your portfolio with the ability to compare performances across different asset classes ranging from commodities, mutual funds, energy, real estate, and even art. But where does this leave the traditional human element of financial planning advisory?

The Convergence of Teach And The Human Element #

Wealth management has been a long-standing stalwart of a personal advisory model. But algorithms are slowly taking some of the more fundamental functions of human advisors. However, that doesn’t entirely render the human element obsolete. On the other hand, it augments the offerings while still retaining the traditional model’s personal and emotional component through a more hybrid approach.

Human advisory’s significance is invaluable for clients with material assets to invest in, or even when it comes to tax planning and estate management. This is why the digitalization of the Wealth Management space is so unique. The pairing of the human relationship with automation to create an omnichannel service makes financial planning a lot more approachable for the masses.

Wealth Management 2.0 #

The paradigm shift in the wealth management landscape is occurring rapidly, and these digitalization trends bear testament to the massive change that the wealth management space is facing. With the ramification of UI, the enormous shift in demographics now controlling wealth and the new and digitally re-wired customer at the helm, Healthtech seeks to bring far-reaching changes to a system that up until was the purview of a select few. We, for one, are excited at the potential of this change.

Welcome to Wealth Management 2.0.

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Cloud Computing in Healthcare Industry

Vin Patel — Tue, 22 Nov 2022 18:39:18 +0000

Key takeaways

The vast impact of digital transformation in the healthcare industry makes it’s future firm.
The cloud’s stored data delivers remote accessibility, which is considered one of the most important benefits.
It also enables stakeholders over the entire health IT space, vendors, providers, insurers, and patients to embrace the cloud’s advantages securely, flexibly, and cost-effectively.

The vast impact of digital transformation in the healthcare industry makes it’s future firm. What’s interesting in cloud computing with healthcare? Cloud computing has changed the traditional way of dealing with data. Big data analytic with cloud computing help enterprises manage patient data, make the best healthcare decisions, lessen operational costs, better collaboration, and so on.

Healthcare providers worldwide have understood the real power of cloud-based healthcare solutions. The necessary applications run in the cloud to make the hospital operations run seamlessly. It retrieves frameworks from unstructured data to help doctors diagnose without a glitch and allow patients to view their reports & prescriptions remotely. The data is stored and secured in the cloud. Cloud delivers hassle-free functional, operational, and economic advantages for everyone, including patients, staff, healthcare professionals, and healthcare companies.

Let’s analyze the healthcare industry’s transformation in the areas such as patient care, data security, diagnosis, and more with cloud computing solutions.

Follow Healthcare Policies in Security #

Cloud-based healthcare solutions are more efficient and secure when compared with physical records. The authoritative healthcare providers can only access the cloud’s stored data whenever needed to avoid data leakage and unnecessary situations.

Famous cloud platforms such as AWS, Azure, and GCP follow healthcare standards and their business partners contingent on the U. S. Health Insurance Portability and Accountability Act of 1996 (HIPAA). They use the protected cloud provider environment to access, process, maintain and store health reports. The process ensures that the patient data is protected and not used without necessity.

The Revolution of the Healthcare Industry with Cloud Computing #

A healthcare firm acquires two simultaneous benefits when shifting to the cloud. Both the patients and the healthcare workers get the benefits. It is proven that the cloud computing process has reduced operational costs and made healthcare professionals deliver top-notch personalized care. At the same time, the cloud elevates patient engagement with their health strategies. It allows patients to access their information and ensures enhanced patient results. Healthcare information and its remote accessibility liberate providers and patients in many ways and eliminate location barriers limiting access to healthcare.

A few of the benefits of cloud computing in the healthcare industry are as follows:

Enhanced Scalability #

Apart from being compelling and authentic, cloud-based healthcare solutions are also robust and scalable. As per the needs, cloud solutions give the flexibility to scale up/down infrastructure to fit in new data.

Moving to the cloud might be the right solution for unexpected situations; the best example is the current pandemic/COVID-19. A large number of hospitals use AWS load balancing and auto-scaling services for their trusted security and efficiency.

Build Collaboration #

Regardless of the source or storage, the collaboration aims at setting up data integrations all over the healthcare system. Subsequently, patient data is available anytime for sharing and getting knowledge to prepare the healthcare planning and delivery through interoperability elevated by cloud adoption.

Cloud computing enables healthcare providers to access patient data merged from multiple resources, distribute it to the primary stakeholders, and provide prescriptions on time and treatment guidelines. It also reduces the distance between the professionals, enabling them to review cases and provide their suggestions irrespective of the topological restrictions.

Various segments, such as the healthcare industry- pharmaceuticals, insurance, and payments, get together with the cloud’s collaboration mechanism, which allows patient data to be accessible from anywhere, anytime.

Improved Accessibility #

The cloud’s stored data delivers remote accessibility, which is considered one of the most important benefits. Cloud computing and healthcare is a good pair that has the power to increase and enhance many healthcare-related operations such as post-hospitalization care plans, telemedicine, and virtual medication attachment. It also improves access to healthcare services.

Telemedicine apes add the attributes of convenience to healthcare delivery and upgrade the patients with a seamless experience. Cloud-based telehealth applications and processes enable the easy sharing of healthcare information, enhance accessibility, and offer healthcare coverage to patients during the recovery phase.

Artificial Intelligence and Machine Learning #

Artificial Intelligence and Machine Learning influence the healthcare industry to a greater extent. With healthcare professionals’ busy schedules, now due to the global pandemic, complexities, and increased information, the healthcare industry adheres to AI and ML techniques. AI ML capabilities could be an essential solution to support clinical decisions and, ultimately, a quicker time-to-treatment.

As cloud platforms use AI and ML in their services, cloud computing can support the transformation of artificial intelligence into general healthcare functions and help professionals manage vast amounts of data.

Better Decision Making #

Healthcare enterprises offer machine learning approaches based on cloud solutions for accurate and knowledgeable diagnoses for clinical and medical data analytic. Cloud-based solutions deal with large volumes of structured and unstructured data in healthcare repositories. The predictive models of the cloud solutions make way for better decision-making.

Security is a Significant Concern #

One of the most critical challenges the healthcare industry faces is security. How secure is it to have all the applications and patient information in a third-party environment? Healthcare information requires confidentiality. The massive data in the hospital domain may be attacked by hackers or malicious actors, resulting in security and data breaches. The cloud network guarantees security as it has particular security tools that can inform you about malicious inputs.

Cloud servers help protect healthcare reports. If any machine fails, medical institutions may lose all their information and applications in terms of on-premises solutions. Cloud-based security solutions save users from these types of potential risks.

Scalability and Flexibility #

Healthcare organizations function in a high-powered environment. Cloud-based technologies used in healthcare areas, such as electronic medical records, mobile applications, patient portals, IoT devices, big data analytic yields better scalability and flexibility. It delivers problem-free applications, which enhances the decision-making process.

Healthcare providers must measure the data storage capacity and network requirements based on the service necessities or demands.

Cloud technology can scale up/down these storage requirements as per the needs of healthcare professionals.

As cloud migration can completely eradicate your conventional approaches to data handling, a well-organized strategy for the migration process is required. Cloud migration strategy not only removes risks but also reduces the chances of downtime, prevents data leaks, enhances data handling, and strengthens security standards.

Summary #

Cloud computing has occupied a significant portion of the healthcare sector. The blend of cloud computing with Artificial Intelligence, Machine Learning, Big Data Analytic, IoT in healthcare makes seamless business outcomes. It improves resource availability, and enhances interoperability while reducing costs. With multiple inherited benefits to overcome healthcare challenges, cloud computing is the best option to opt for. It also enables stakeholders over the entire health IT space, vendors, providers, insurers, and patients to embrace the cloud’s advantages securely, flexibly, and cost-effectively.

Related reading

Questions and answers

What’s interesting in cloud computing with healthcare? #

Cloud computing has changed the traditional way of dealing with data. Big data analytic with cloud computing help enterprises manage patient data, make the best healthcare decisions, lessen operational costs, better collaboration, and so on.

How secure is it to have all the applications and patient information in a third-party environment? #

Healthcare information requires confidentiality. The massive data in the hospital domain may be attacked by hackers or malicious actors, resulting in security and data breaches.

An Indispensable Idea to Building an AI-Powered Organization

Vin Patel — Tue, 22 Nov 2022 17:17:11 +0000

Key takeaways

***An insight-driven organization - It’s been ten years since this phrase found its way into active business vocabulary, and yet most companies have fallen short of truly being one.
Successful implementers of AI spend almost half of the analytic budget on activities that drove adoption.
This culture shift enables the speed of innovation as the rest of the organization adopts the test-and-learn approach that successfully drove the pilot AI initiatives.

An insight-driven organization - It’s been ten years since this phrase found its way into active business vocabulary, and yet most companies have fallen short of truly being one. Why? ‘Cultural barrier’ might be to blame.

Helping leaders make more informed, effective and intelligent business decisions is the core idea behind analytic. Yet, entrenching data, evaluation, and evidence-based reasoning into the organizations decision-making processes has fallen short of mainstream application in a wide variety of cases. In a report published by Deloitte on Becoming a Data-Driven Organization, surveyors found that “fewer than four in 10 (37 percent) place their companies in the top two categories of the IDO (Insight Driven Organization) Maturity Scale, and of those, only 10 percent fall into the highest category. The remaining 63 percent were aware of analytic but lacked infrastructure, found themselves still working in silos, or were expanding ad hoc analytic capabilities beyond silos”.

Reasons for this vast discrepancy ranged from lack of leadership direction to even a lack of trust in a machines interpretation of happenings. This would result in falling back onto traditional working mechanics and failure to expand on AI’s value proposition. In each of these cases, the organizations culture has played a significant role in accepting and successfully implementing deep data analytic."

As most executives eventually realize, embedding AI-based decision-making into an organization involves re-wiring the business at a fundamental level. While the initial work might seem substantial, the rewards are to be leveraged well into the future.

Here are some relatively simple ways to help employees become active participants in charting a path towards truly being an Insight Driven Organization (IDO).

Taking the Leap of Faith and Setting the Tone #

Analytic is already mainstream. However, most businesses still rely on structured data that fits into rows and columns, missing out on critical insights that are to be gained from product images, customer interaction recordings and social media messaging. Herein lies the argument for adopting technologically advanced AI-driven software that can sift, consolidate and show appropriate recommendations from troves of data. To make the shift, it’s essential organizations give due consideration to these four avenues-

Data Science is a Team Effort – Traditional working mindsets are often the most significant resistance to the adoption of AI, making it imperative that welcoming data-driven decision-making starts right from the top.
Cultivate the Talent- It’s essential to nurture a diverse team that is curious, numerally proficient, and capable of walking the median line between analytic and business requirements. This progressively creates a shift in the right direction.
Go experiential to Data-Driven– Augmenting human decision making with data-driven insights means trusting the algorithms suggestions. It also entails building the autonomy to act on those insights without higher-level authority. If every proposal generated by the systems has to be approved, then the purpose gets defeated.
Start with a Minimum Viable Product– While a business system with all bells and whistles may seem ideal, striving for one from the get-go is a fundamental mistake. Instead, look towards speeding up development and achieving gains within weeks with minimum viable products that can iteratively be scaled up.

Demystify Artificial Intelligence #

As the first line of defense towards building a data-powered organization is taken down, the attention now needs to swing towards setting up the required mindsets for success.

Explain the why’s - People often have qualms about adopting newer technology in the workplace, mainly because the need is never understood. Why break something that is working? But by demystifying data analytic and explaining the competitive edge the company is seeking to gain, executives can rapidly help in creating a unanimous acceptance of AI-driven insights
Bolster morale and highlight common ground – From the fear of obsolescence to strict reliance on the human rationale to build customer-driven relationships, an organization usually has a lot to contend with when taking basic calls suggested by algorithms. In such cases, making a good story, investing in building the correct information around new systems, and even encouraging adoption through scorecards and perks, can significantly help break down mental barriers.
Bridging the gap between technical and business – Much like translating software for languages, a new class of experts are now coming to the fore. Known as ‘Analytic Translators’, they play a crucial role in identifying roadblocks and streamlining business requirements while ensuring adoption goes smoothly.
Equal Budgeting for integration and adoption – It’s easy to sidestep this critical element. Companies are often found to allocate most of the budget towards developing the systems, forgetting that workflow redesign, communications, and training are equally vital. Successful implementers of AI spend almost half of the analytic budget on activities that drove adoption.

Expanding Acceptance with Organization Learning (OL) #

Once the basic machinations are in place, it’s time to develop acceptance. Facilitating AI implementation with Organization Learning has been shown to increase adoption exponentially. A top-down approach to educating teams works best and helps align priorities across the board.

Aim to target leadership with broader, higher-level educative content. OL should make executives understand the impact on workers’ roles, barriers to adoption, and talent development. Furthermore, offering guidance on instilling the underlying cultural changes required goes a long way toward a smooth implementation.

For those at the core of the business, the ‘Insight-Driven Decision-Makers’, OL should help bring them up to speed with sessions that incorporate real business scenarios. This focus enables staff to derive maximum advantage from AI’s insight capabilities and take effective business calls on product features and launches.

Once a model is securely up and running, an oft-forgotten faction is the analytic and translators (those who act as the intermediaries between IT and Business). Here, the organization needs to up their game by continually focusing on sharpening the hard and soft skills of those directly involved in creating reports and summaries. This will ensure that the algorithms being used are established on the most cutting-edge innovations in data science and that analytical reasoning is aptly applied to solve business problems.

Culture as a Catalyst #

Most AI transformations take up to 18-36 months to complete, with larger projects taking a few years. Herein lies the biggest challenges for most organizations – loss of momentum in the light of growing cost and negligible results. Address these challenges by targeting smaller gains in development and enforce adoption by

Incorporating learning and pushing aside misgivings – A key focus area for leadership, who need to walk the talk, steadfast commitment to AI needs to get highlighted even considering failed experiments. By highlighting what was learned in the pilots and reiterating the need for more significant data, organizations can rapidly scale up implementation and adoption efforts
Making business accountable and not the analytic team – Analytic teams are simply owners of AI products. It is the business factions that need to own products and see them to successful completion. A scorecard that effectively captures performance metrics is usually an excellent way to align analytic and business goals.
Retaining an eye on With vs Without - Comparing the results of decisions made with and without AI can encourage employees to embrace the generated reports. For instance, if on further investigation, executives learn that non-AI-supported forecasts are typically accurate, say about 50% of the times, that immediately bolsters the case for relying on something far more tangible than just intuition.

Creating a Virtuous Circle #

As is evident, successfully establishing a data-driven culture is the key driver that helps companies scale from carrying out analytic projects in silos to a fully insight-driven organization. Given the research, the willingness to act on analytically derived insights, make decisions, change processes, and adapt behaviors is best achieved when the organization in its entirety decides to take the leap of faith and encourage a unanimous move to the future of data-driven business.

Such actions create a favorable cycle of events. Interdisciplinary teams bring together the diverse skills and perspectives needed to build new and practical tools. With time, this leads to workers absorbing more collaborative and synergistic practices. Their vision expands as they work more closely with colleagues in other functions and geographies. Workforces move from trying to solve discrete problems in their departments to completely reimagining business and operating models. This culture shift enables the speed of innovation as the rest of the organization adopts the test-and-learn approach that successfully drove the pilot AI initiatives.

Related reading

8 Mistakes You Must Avoid in Your Content Marketing

Vin Patel — Tue, 22 Nov 2022 17:05:52 +0000

Key takeaways

Content being the king of all marketing activities worldwide, content marketing has gained popularity over time as one of the best investments a brand can make.
Here are some content promotion strategies you can follow: - Share your posts on social media in different formats.
Rushing in implementing your strategy or being a part of the rat race makes a brand prone to these mistakes.

Content being the king of all marketing activities worldwide, content marketing has gained popularity over time as one of the best investments a brand can make. It’s great to generate leads and build thought leadership in the industry- both of which make for a great brand. Seth Odin calls content marketing ’the only marketing left,’ given its authenticity and adaptability with new age media and the online generation.

But, as more people realize the importance of content marketing, the competition is rising, and the market is being saturated. As a result, user attention is diminishing. Your marketing efforts should be exceptionally calculated and in line with your business goals and vision.

This also means that you can’t afford mistakes. But, given the race for content marketing and the excessive amount of money companies keep aside for it, these mistakes are bound to happen. But if you’re in it to win it, then there’s a way to avoid those mistakes: by learning from them.

Let’s explore some common content marketing mistakes and how you can work your way around it for maximized results.

Content Table #

Not having a strong content strategy
Not understanding your audience
Not creating goal-oriented content
Not experimenting with content formats
Not promoting content effectively
Not keeping up with trends
Not paying attention to CTAs
Not tracking performance

Not Having A Strong Content Strategy #

More than 60% of businesses don’t have a documented content marketing strategy according to the Content Marketing Institute. This means that a majority of the businesses use content marketing with the ‘hope’ of making it work.

Content marketing is more than content creation and distribution. It uses a much strategic approach to understand the audience, align the audience’s needs and brand needs, and then establish a plan of action keeping in mind the analytic that needs to be tracked.

If you don’t have a documented strategy in place, your team will always be confused, and you would be investing a lot of time, effort, and money on things that may or may not yield the desired result. This is because you don’t know what you’re doing, how you will track it, and if it’s necessary to do it. It’s like shooting arrows blindly. Are you willing to take that risk? Probably not.

Consider following these steps to draft a content marketing strategy for your brand backed by an effective action plan:

Define your goals and objectives.
Understand your audience and their buying habits.
Do competitor research, understand where you stand in the market.
Based on research, decide on the channels you want to create content on and where your audience is present the most.
Decide what type of content you’re going to create.
Divide the work among your team. Content marketing is a team effort, and transparency always wins.
Start creating content after establishing an editorial calendar for efficiency.
Promote content.
Analyze your efforts by tracking metrics.

And there you have it, an elaborate content marketing strategy that defines your plan of action every step of the way. Documenting this process will help prevent gaps, give a better idea to the team, ensure efficiency in execution, and provide results. You can use team collaboration tools which immensely help in this area by coordinating everything in one place and avoiding any kind of confusion.

You can use this infographic by BlueHost as a checklist while creating your strategy and tweak it as per your brand goals and objectives.

You should focus on preparing a strategy because it lays out clear fundamentals useful in the long run to track progress and introduce changes for better results. Moreover, following a strategy can lead to the achievement of the larger goal because small wins lead to the big ones, and makes the process easier.

Pro tip: If you’re an individual or agency providing content marketing services to a client, you may want to use a proposal template to display an outline of your strategy for better conversions.

Not Understanding Your Audience #

Original diagram for Techfastly by Vin Patel.

While content is the glue that holds a content marketing strategy together, you cannot just brainstorm content ideas or create content on the first 5 things you see online. Even though it may reside with your industry and niche, it may not cater to the audience- and that’s the biggest mistake you can make.

If you want to generate leads, build a relationship with your customers, and nurture them, you need to understand them first. The more you know your audience, the better your content marketing results will be.

But how can you understand your audience? Consider starting by doing these things: Ask yourself some questions Before you understand them, understand who you want to serve first. Try defining their demographics, buying capacities, habits, daily activities, and things that would be ideal for ‘your’ customer.

Carry a market research Once you’ve understood who you want to target, try to see where you can find these people. What platforms are they present on, how do they communicate with each other, what language they use, what type of content they consume, how much they are willing to spend every month, etc. This will help give you a clearer idea about your audience and set a course for realistic expectations.

Look over to your competitors Your competitors, the ones who have a similar target audience as yours, are probably ahead of the game. They’ve been doing all of this and assessing their moves. You can see how their strategies have worked out and how the audience is responding to them. This will help you identify gaps that you can fill through your plan.

Carry a survey Once you’ve defined your audience, assessed your competitors, and gathered a fair amount of information about your audience, it’s good to fact-check. There’s nothing better than directly surveying your audience through a questionnaire, asking them essential questions that will help you create and execute your content marketing strategy better. Keep the survey crisp, and to the point; if it’s never-ending, no one is going to fill it.

If you’ve missed out on these points, then you’ve been making this mistake. But it’s never too late to reform it. Because the last thing you want is wasting your resources or watching your efforts yield minimum to no results.

After you’ve done your target audience research, it’s ideal to create a buyer persona- a semi-fictional representation of your ideal customer, which will help guide all your marketing strategies, especially your content such that it’s targeted directly at helping your audience.

Better be safe than sorry!

Not Creating Goal-oriented Content #

Every piece of content you create as a part of your content marketing strategy has a goal. This can be for SEO, for lead generation, to get contact details, to nurture your customers, and so on. But if you don’t know why you’re creating a particular piece of content, then you’re just wasting your resources.

Being specific on your goals and creating content around it ensures that you’re walking in the right direction, and your content will yield the desired results.

There are numerous stages of a buyer’s journey, and creating content, especially around it, is so important because it caters to a specific goal.

Here are the different stages of a buyer’s journey and the type of content you can create at each stage: Awareness stage Your prospect is aware of the problem they are facing at this stage, but don’t know if it is important enough to seek a solution. Here, they need to be targeted with content that can help them troubleshoot the problem and understand it better. The main goal here is to educate the customers, only then will they be able to understand properly.

The content for the awareness stage of the buyer’s journey can be:

Blog posts
Social media posts
White papers
eBooks
Webinar
How-to videos

ConsiderationAt this stage, they’ve gathered a brief idea about the problem they’re facing and contemplate if you’re the right solution for their problem. Here, they need to be targeted with content that portrays you as the best solution for their problem and a perfect fit for their requirements. This content needs to be backed by data and social proof that displays your expertise.

This stage is crucial because it’s only here that they decide if they should go forward with you or move on and choose someone else. It determines whether they will turn from a prospect to a paying customer, so the approach needs to be convincing.

The content for the consideration stage of the buyer’s journey can be:

Comparison videos
Case studies
Free Samples
Buying guides

Decision stageThey have decided that they want to be your customer at this stage, so they think what it takes to be your customer. Here, you need to present content that tells them they have made the right decision and can fully invest their faith in you and leave their problems at your disposal.

This stage is important because they’re on the edge of being converted, and the way you respond now also sets the course for future collaborations, and if they will continue purchasing from you.

The content for the decision and beyond stages of the buyer’s journey can be:

Free trials
Testimonials
Coupon and discount
Upgrades

Original diagram for Techfastly by Vin Patel.

Creating tailor-made content as per the stage at which your buyer stands is crucial because it’s meant for a specific goal. Without this, you could be showing educational content to a buyer in the decision stage when they’re fully aware of the problem but need to be convinced that you’re the end and ultimate solution to their problem.

This will also make your content marketing strategy much more specific and achievable, ensuring that your customers are targeted the right content at the right time.

Not Experimenting with Content Formats #

Content marketing is not defined by nor is restricted to just blogs and social media posts. There are ample content formats that should be included in your content marketing strategy. Why?

Because it prevents your content on multiple channels from being monotonous. Have you ever thought of using letterhead and graphics in your email instead of just a text newsletter?

You can always experiment and combine different content formats to make your content more appealing and engaging for the audience.

Content formats and including multimedia in content is an essential part of user experience which cannot be missed out on.

Let’s explore some content formats you can include in your content marketing strategy:

Blogs

Your good old-fashioned blogs are an important part of your content strategy because they account for a significant part of SEO and help establish thought leadership by catering to the target audience’s pain points and major struggles.

With blogs, remember to optimize it for SEO while writing for humans by including white spaces, using short sentences, and using storytelling to evoke emotion. For content topics, you can use Quota and Answer the public to find important yet less-answered questions.

Infographics

Infographics help relay important information in a detailed format through visual aid. It attractively communicates the message to help the audience understand the topic much better. You can find plenty of templates on Venngage and Canvas, and tweak them to create your infographics.

If you’re answering an important question, it can also be used as a lead magnet to get email sign-ups.

eBooks

eBooks have recently taken momentum as one of the most popular lead magnets and free resources, with brands using eBooks to educate their audience in a more profound sense by providing extensive value. These can be a mix of text and multimedia, addressing an important topic in your industry, which would help many people solve their problems. Since an eBook can be downloaded and read later, people are slowly prioritizing them over other forms of content to educate themselves on relevant topics.

Videos

With the rise in video marketing, more and more companies are now including videos in their content marketing strategy because they help establish a one-on-one connection with the audience. These can be bite-sized videos on social media or long videos embedded in blogs and uploaded on YouTube.

You can also repurpose your blogs and social media posts into videos to provide better reach to your content.

You can incorporate many other formats in your content marketing strategy like podcast, checklists, cheat sheets, and email newsletters, giving a significant edge to your strategy. It’s inevitable for your audience to be everywhere and like all your content. To give your content greater reach and provide added value to your audience, adapting and using these formats is a great way to get ahead in the game.

Not Promoting Content Effectively #

Original diagram for Techfastly by Vin Patel.

Many people think that the execution part of a content marketing strategy ends after you hit the ‘publish’ button, but that’s where the real work starts.

If the content is king, then engagement is queen- and for people to engage with your content, you need to promote it excessively and through different strategies to refresh your content and introduce it to newer audiences.

It’s ideal to have a content promotion strategy in place to know the first few steps you can take to ensure that a published piece of content doesn’t die soon.

With so much content creation and consumption online, it’s easy for your content to get lost, so it would be best if you promote your post the right way within 24 hours of it getting published.

Here are some content promotion strategies you can follow:

Share your posts on social media in different formats. A tweet on Twitter, a carousel post on Instagram, and a video on Facebook are some ways you can begin promoting your content.
Send out an email blast to your email list as soon as you hit ‘publish.’
Include the link to your latest post(s) in your email signature. Since we send many emails every day, there’s a high chance, someone or the other would be curious enough to click on the link in the signature.
Leverage your community and share your content on social media groups, since these groups comprise like-minded people, they will definitely share it with their network if your content is good.
Connect with influences and talk to them about promoting your content or endorsing your product.
Leverage guest posting. It allows you to publish content on niche-specific websites that help your content reach their audience, thus expanding the reach.

There are numerous other ways to promote your content, but these top the list. You can always get innovative with your approach and brainstorm how you can ensure a wider reach for your content.

This is an important part and impossible to miss out on because creating awesome content won’t make a difference if it’s not in front of the audience. For your efforts to yield results, your content needs to be visible, and so content promotion needs to be prioritized as much as content creation.

Not Keeping Up With Trends #

While creating evergreen content that helps your target audience solve their problems is ideal, keeping up with trends is also essential to stay relevant. The audience participates in trends, and keeping up with them means that you’re listening to them and adopting new strategies.

You cannot use the same strategy repeatedly, because content marketing is always evolving. It requires you to assess gaps, make peace with new technologies, and keep up with trends to stay relevant along with being result-driven.

Not keeping up with these trends would give your competitors an edge over you. Every customer you come across will want to associate themselves with brands working on themselves in new and improved ways. You can help customers all you want, but you won’t stick for long unless you listen to them and form your brand on those values.

Let’s look at some emerging content marketing trends that you can include in your strategy:

Videos are taking the front seat #

It’s no surprise that the most prominent brands are now allocating their budget to videos, both shot, and stock. This is because of one very simple reason- videos keep the audience hooked and engaged for a more extended period than any other form of content. You can publish your content on various platforms like social media, YouTube, your website, and Video.

Moreover, you don’t always have to shoot videos. You can interview an expert on a live stream, host webinar and workshops, and do Q&A to interact with the audience. When you show your face on a screen, the audience gets to know about the person behind the brand.

So, to engage with your audience more and humanize your brand, videos are a must! Rand Fishkin, the founder of Moz is doing a great job at videos.

User-generated content is becoming a hit #

User-Generated Content or UGC is the content created by your customers and fans about your brand without paying them. According to research, over 83% of people trust other customers’ content than the brand itself.

When you share their content, they feel special, and it starts a loop for word of mouth as a result of which more people start creating this content. Moreover, it invites new customers because it acts as social proof and what’s better than customers showing their satisfaction on social media. The most significant part about it is that it creates a community, and turns followers into customers.

Original diagram for Techfastly by Vin Patel.

Conversational marketing is evolving What’s better to understand your audience and customers than talking to them directly?

We live in an age where chatbots are ruling but with robotic answers that do not give customers a personalized experience even though it helps them.

Today, when there’s a lot of competition around, the need to understand your audience’s concerns about your brand, product, or service is greater than ever. You can practice conversational marketing by using human scripts or real-time conversations in chatbots to assist customers.

Original diagram for Techfastly by Vin Patel.

Influence marketing is making strides on social media In 2020, there’s been a massive increase in the number of influences online. There has also been a corresponding rise in their fan following, given the unique content creation these influences from diverse fields are bringing to the table.

Moreover, 73% of marketers have a budget allocated for influence marketing, which tells us how much potential it holds.

Influences have the power to show people certain products and brands by endorsing them so that their massive audience is convinced to buy or at least give the product a chance. This acts as social proof, and everyone wants to use the brand that a social media influence is using, right?

It’s a win-win situation. Various other trends are gearing to take the front seat in content marketing like voice search, interactive content, automated content etc. Adapting to these trends means being updated, and there’s always brownie points for that!

Not Paying Attention to CTAs #

One of the most common content marketing mistakes is not paying attention to your lead generation auto-machine, your Call-to-Action buttons.

You can create the most awesome content in your industry, but if you don’t tell the audience what to do after reading the content, you’re making a blunder, but don’t worry, we can fix that!

Wherever you are creating content, be it on social media, emails, your website, or podcast- you need to tell your audience what they should do very clearly.

Do you want them to share your content?
Do you want them to subscribe to your newsletter or sign up for your course?
Do you want them to book a call with you?

Whatever is your end goal, it needs to be communicated very clearly and explicitly.

Here are some things to keep in mind when it comes to CTAs:

Use contrasting colors for CTA on your website to make them very evident.
Highlight CTAs multiple times across the course of your website and content.
Don’t turn around words. Make it straight cut.
Use an innovative approach for CTA copy like ‘Sign up for awesomeness’ instead of ‘Sign up now.’
Repeat your offer in different ways across channels.
Strategically place your CTA along the reader’s natural reading path.
Make the buttons optimized for both mobile and laptop.
Practice A/B testing because, with CTA, a slight change can bring a higher click-through rate if only you pay attention to it and keep experimenting.

CTAs can never be perfect, but paying attention to them and tweaking them in between courses can generate a lot of difference.

ReferralCandy Blog has a great list of CTA examples you can look up to for inspiration.

Not Tracking Performance #

Every strategy requires analysis, and content marketing is no different. Imagine spending months executing a plan and not analyzing what results it brought you or if the strategy was right for your brand.

Analyzing your performance tells you about the gaps or challenges you faced in execution or strategy to determine how you can fill them up next time.

Data plays an important role here, and if you hate numbers, you may want to fall in love with them for this part because this data determines your brand position in the market.

Say, you find that Instagram is performing well in terms of carousel content while videos are performing well on LinkedIn, you can tweak your strategy to focus on these platforms more and create more of this content. If you don’t analyze it, you could be focusing on Facebook when it hasn’t been working all this while.

Therefore, it’s imperative that your team reviews your marketing performance in intervals to ensure you’re going in the right direction.

Here’s what you should analyze: Website traffic The amount of traffic you’re driving to your website says a lot about how your content performs. You can use Google Analytic to track the source of your website traffic.

This will help you understand which platform generates the most traffic and what type of content or optimization supports the increase in traffic. This is important because the more people visit your website, the higher your chances are at converting them.

Social shares Social media accounts for a significant part of your content marketing strategy. Analyzing the number of shares your audience is making tells you if they like your content and if it’s relatable enough for them to share it with their network.

This will also give you an idea about what type of content is performing better on which platform so you can create more or less of that after tweaking your strategy. You can use Hootsuite to analyze social shares.

Conversions and sales You can analyze conversions by looking at the number of people who respond to your CTA and buy your product/service, book your call or sign up to your email list. This measure is important to track as it says a lot about how you’ve been performing collectively as a brand, irrespective of your digital presence or marketing efforts. You can use Google Analytic and as per the analysis, you can reform certain things to increase the conversion rate.

If you start analyzing everything in your strategy execution, it will take a lot of time. So, it’s best to define your KPIs beforehand to know what you need to focus on for achieving your goals.

Failing to measure results will not bring you growth, because then you’ll be following a stagnant content strategy. Results come when you analyze results, acknowledge success and failure, and reform gaps to make the plan better and your campaigns more effective.

At the end of the day, everyone wants to focus on how many conversions did you generate and where did those conversions come from. This makes it one of the most important reasons to analyze your efforts.

Wrapping It Up #

If you’re investing your resources in a timeless marketing strategy like content marketing, you cannot afford to make mistakes. The key is planning!

Plan for your worst-case scenarios and maintain agility to include spontaneous campaigns and content buckets if need be for trend jacking and moment marketing. When you’ve carefully planned your strategy, there’s no chance of messing it up.

Keep these 8 mistakes in mind while formulating your strategy and take notes from content marketing resources to be up to date with trends. With content marketing, consistency and patience are important because it’s a long-term game, and you cannot expect overnight results.

Rushing in implementing your strategy or being a part of the rat race makes a brand prone to these mistakes.

Don’t run; content marketing is here to stay.

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Transforming Global Economy through Disruptive Cloud

Vin Patel — Tue, 22 Nov 2022 12:49:40 +0000

Key takeaways

In an age of emerging technology, staying abreast is impossible.
Other examples of companies that evolved through digital transformation include Walmart, Disney, PayPal, National Geographic, and Starbucks.
With time, technology has evolved faster than anything else and has outspread uncountable intelligent ways to create businesses that bring disruptive changes on the global economic scale.

In an age of emerging technology, staying abreast is impossible. It is common to find that the leading value creators of recent years are all reliant on technology. It is undoubtedly a major disruptive force across the entire global economy. Almost every factor that contributes to the global economy is affected by technology. Instead of being a layer of the economy, technology is becoming the heart of it. The most significant evolutions in the economy are often accompanied by process of “creative destruction,” in which profit pools shift, industries restructure, and incumbent firms are replaced. According to Joseph Schumpeter, an economist from the early 20th century, such changes entail “creative destruction.” As you read further in this article, you will see how emerging technologies have a disruptive byproduct on the global economic scale and a brief on the growth of cloud infrastructure in non-technical companies.

Disruptive technologies often showcase a rapid change in price and performance compared to alternative approaches. They change how business is done, and needs are met despite entering an established market. Disruptive technology allows people to break down barriers and empower people who may have been ineffective. It can also spread more interaction and responsiveness to communications platforms.

Technology Drives The Future of All Industries! #

When you think about all leading industries, the only buzzword that comes to mind as the driving force is ‘Technology’. Technology is in itself the number one industry. Any industry’s expected success is largely due to the way technology has altered and will continue to alter. In the past decade, cloud computing, machine learning, artificial intelligence, and big data have been cited as the sub-sectors that have made the biggest impact and are continuing to do so. In terms of massive changes witnessed among multiple industries, technology is leading in almost every one of them. In the energy industry, technology has shown miracles in the form of energy trading. It increases the use and efficiency of sustainable energy sources. On the other hand, in financial companies, it’s financial technology or cryptocurrency that’s leading the way. Healthcare industries hold a strong output due to implementing technology using subsectors such as biotechnology, health data management, personalized health solutions, advanced insurance solutions, etc. Also, disruptive technological methods have introduced growth and sustainability in retail, construction, and hospitality.

Digital Transformation: A Disruptive Technology Method That Brings Massive Change To All Industries #

Digital transformation, also known as digital business transformation, refers to the strategic use of digital technologies for streamlining and accelerating business processes. Many technical and non-technical companies are accepting digital transformation for big growth. For example, IKEA has recently put one step forward in the field of technologically growing by digitally transforming through an online platform. Without this transformation, IKEA would not have opened another sales channel. Other examples of companies that evolved through digital transformation include Walmart, Disney, PayPal, National Geographic, and Starbucks. A study performed on 1,200 mid to large-sized US companies found that the average budget for digital transformation in 2018 was $14 million, and spending on digital transformation worldwide is expected to reach $1.78 trillion by 2022. Digital transformation is expected to add $100 trillion to the global economy by 2025. Digital transformation can have varying impacts on your company. However, digitization often brings these benefits:

Reduced operating costs
Optimized operations
Increased revenue
Improved customer satisfaction
Lowered fragmentation

Cloud-based Infrastructure #

Since the mid-2000s, cloud technology and platform business models have driven the technology industry globally. Among all technological advancements, cloud technology is one such strategy that has now spread to every big and small business and has played a major role in the economic sector. In the past few years, cloud computing has become increasingly popular for many reasons. It allows almost any application or service to be delivered over the Internet or a network, without the need for expensive local hardware or software. The market value of top cloud-native infrastructure software vendors is increasing rapidly. With cloud-based services, businesses can streamline performance and support growth and development with a much more scalable and reliable IT infrastructure. Historically, cloud computing has gone through multiple phases - it started with grid (or utility) computing, then came the “application service provision” phase, followed by what is now called SaaS. Software companies using cloud-native infrastructure can pay for cloud computing resources as they use them, rather than making expensive capital investments. Every new trend in cloud services has possibly changed the way a business runs in terms of product consumption and services, data storage, and communication. Additionally, cloud computing allows users to access their data remotely by moving data to the cloud. The enterprise to consumer expansion of cloud services does not appear to be slowing down any time soon. Companies can launch new ventures within the cloud without spending more on the infrastructure or employees. This strategy allows competing with larger, existing incumbents. Companies that use cloud technology and platform business models mainly apply them using data and analytic. The ability to monitor and analyze large data sets can help businesses better respond to real-time consumer feedback and improve their product offerings. Apart from cloud-based services, a few latest disruptive technologies are reported to have developed over the last decades. Such disruptive technologies include artificial intelligence, energy storage, next-generation genomics, blockchain, E-commerce, streaming entertainment, social media networks, GPS systems, smartphones, ride-sharing apes, etc.

Conclusion #

In summary, you now know that high-technology production was limited to financially developed countries in the past. Today, disruptive technology acts as a force that is more easily transferred to developing nations, where high-teach products can be combined with low costs. This displays the extremely rapid transitions of time and technology. With time, technology has evolved faster than anything else and has outspread uncountable intelligent ways to create businesses that bring disruptive changes on the global economic scale.

Related reading

Predictive Analytic AI-Powered SaaS Platform: Explained

Vin Patel — Tue, 22 Nov 2022 11:09:45 +0000

Key takeaways

The world as we know it is changing thanks to predictive analytic enabled by AI technology.
Attempting to ‘predict’ previous events can demonstrate its usefulness.
Developers that want to stay ahead of the curve should consider this and brainstorm ways to include predictive analytic AI into their products.

The world as we know it is changing thanks to predictive analytic enabled by AI technology. Today, every industry has access to a huge amount of data. Finding gems that solve your business challenges, on the other hand, is as tough as sailing across the ocean in a fleet. The majority of existing technologies lack the ability or speed to analyze large data crowds and get insights from them. Predictive analytic tools that are AI-driven come in handy in this situation. These solutions have the potential to provide vital data insights for each company. Such SaaS technologies can be extremely beneficial for businesses with limited finances and time-sensitive initiatives.

In general, SaaS systems are diversified and growing in popularity with each passing day. Customer success teams are common in SaaS organizations, which is a well-known fact. And developers believe that boosting client satisfaction will boost income. Predictive analytic and SaaS systems can boost your company’s success.

Key Statistics #

According to McKinsey, AI may unlock $2.6 trillion in marketing and sales value from $7.2 billion in 2020, representing an almost 200 percent growth.
The global predictive analytic industry is expected to expand to $21.5 billion by 2025.

AI-Powered Predictive Analytic SaaS Platforms’ Techniques #

Here are six of the most popular predictive analytic SaaS platforms to get you started.

Quantitative Research #

Quantitative research is a technique for identifying patterns of behavior that relies on mathematical and statistical modelling. It is numerically based and can be applied to any field in which numbers can be calculated.

Machine Learning #

All advanced AI technology is built on this foundation. Machine learning helps machines to increase their ’thinking’ abilities by learning from the patterns they process.

Neural Networks #

Neural networks are systems that technologically imitate human neural networks. They’re utilized to solve difficult AI challenges. The system is named after how it is arranged, comparable to human brain networks. It facilitates machine learning by utilizing specific algorithms.

Predictive Modelling #

Predictive modelling employs past event statistics to forecast the future. It’s vital to keep in mind that this type of modelling can be used in any scenario. Attempting to ‘predict’ previous events can demonstrate its usefulness.

Google Analytic #

Google Analytic is a widely used data analytic platform that uses machine learning from Google to provide insights into your data. The tool’s AI-powered advanced analytic form the foundation of many firms’ reporting.

Helix #

Helix creates technology and SaaS products that connect diverse datasets and extract advanced research insights from big data using artificial intelligence, machine learning, and other new technologies.

Predictive Analytic Platforms Powered by AI Are the Future of Technology #

AI-driven technology is causing a lot of buzz right now, and for a good reason. Although most people are unaware of it, AI is present in almost every aspect of our life, including technology. This presence is only growing because, according to McKinsey research, AI can handle more than 400 long-standing business difficulties, even at the current stage of development.

Small businesses and entrepreneurs can use the SaaS predictive analytic system to take advantage of AI processing power. In today’s highly competitive economy, this service is very important. Without a doubt, technology will advance in response to the increased need.

It’s vital to remember that new platforms must be compatible with existing technology. There are also legal ramifications to take into account. To protect both the developer and the user from legal action, all SaaS systems must include a well-thought-out agreement. As AI technology becomes more widely used, the problem becomes more complex.

How Do Predictive Analytic SaaS Platforms Deliver Insights? #

The main benefit of using predictive analytic SaaS tools for your business is that you might receive results that go beyond merely showing you what’s happening right now. They also advise on how to deal with the circumstance. As a result, a predictive analytic AI-powered platform generates real-time insights that may be deployed to achieve a competitive advantage.

The following are some examples of how predictive analytic insights might be applied in business situations:

Creating tailored marketing efforts for a variety of audiences
Developing product enhancements that anticipate changes in client needs allows you to stay ahead of the curve.
Analyzing the competitors to come up with a unique selling proposition
Predicting business results in the face of changing global economic conditions
Identifying areas for improvement and automating many operations within the company.
Getting the most out of available resources

Bottom Line #

From e-commerce to healthcare, AI is revolutionizing every industry. It’s difficult to imagine a company succeeding without big data. AI-powered SaaS services will continue to gain popularity as it is impossible to derive strong insights from data using only human intelligence. With machine learning technology improving with each application, we can expect breakthroughs soon. Developers that want to stay ahead of the curve should consider this and brainstorm ways to include predictive analytic AI into their products.

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Artificial Intelligence, Making its way in the Aviation

Vin Patel — Fri, 18 Nov 2022 11:38:48 +0000

Key takeaways

Passengers should anticipate greater touchless check-in and security processing, symptom screening, heat maps to preserve social separation, and other automated standards and new functions of the airport in the post-Covid-19 crisis.
Using this data, AI systems may recognize dangers in a variety of contexts.
The aviation sector has great potential to grow and become safer thanks to artificial intelligence.

Passengers should anticipate greater touchless check-in and security processing, symptom screening, heat maps to preserve social separation, and other automated standards and new functions of the airport in the post-Covid-19 crisis. In other words, they will encounter more robots and fewer people.

Smart technologies will be crucial in helping airports, and airlines get through this crisis, which is expected to hasten the use of automation and AI in the aviation industry.

Whether you’re ready or not, artificial intelligence (AI) and machine learning (ML) is being used in aviation. According to experts in the field, the usage of AI/ML can boost productivity, safety, workload, and speed to enable more advanced technology like data ecosystems and autonomous vision-based navigation.

AI in the Aviation Sector #

AI technologies and their computational abilities for data interpretation may be useful in a wide range of processes and activities, including analytic, software configuration, customer support, and many more. A number of industries, including finance, marketing, retail, and health, continue to show potential for AI.

Artificial intelligence (AI) can help businesses in the aerospace industry speed up production while also addressing safety problems. In addition, AI systems process massive amounts of data considerably faster than humans by examining data from several sources.

This allows aerospace companies to examine various elements more effectively and efficiently. AI in airplanes also helps with creating applications that monitor and conserve fuel, pinpoint problem areas, and support air traffic control. For corporate executives, understanding and utilizing the applications of AI have become essential.

Applications of Artificial Intelligence in the Aerospace Industry #

The applications that employ AI technology to disrupt the aviation industry are listed below:

Better Fuel Economy

Aerospace industries place a premium on fuel quality, and even a slight decrease in aircraft fuel consumption can significantly impact a business’s profitability and sustainability.

The deployment of AI-powered devices may result in a reduction in fuel use. For instance, a French startup, Safety Line’s machine learning application, can help pilots improve their climbing trajectories before each flight. The ascending process uses the most gasoline, so enhancing this stage results in significant financial savings.

2. Practices & Training #

AI can be used to construct flight training. AI simulations can be combined with interactive virtual frameworks to give pilots a complete simulation environment.

Simulators with artificial intelligence capabilities may also be used to collect and assess academic data, such as biometrics, to develop customized training regimens based on students’ academic performance.

The next significant application of AI will be to help pilots while they are flying. AI-powered cockpit changes can gradually improve a flight path by monitoring and alerting the pilot to crucial conditions such as fuel levels, aircraft conditions, weather, and other factors.

In the future, aircraft may be supplied with stunning cameras that are powered by image processing techniques, expanding the pilots’ field of vision and improving the quality of their performance.

3. Using AI To Enhance Air Traffic Management (ATM) #

The ATM becomes one of the most important factors when it comes to air safety because there are so many flights taking off, flying, and landing at the altitude that these planes can travel. In areas like flight planning, conflict prediction, and safety evaluations, the ATM can profit from AI.

In addition to minimizing delays, AI technologies can enhance trajectory prediction and safety. This trajectory prediction can aid in creating safer air travel routes in congested airspace. To prevent deadly incidents, researchers are creating conflict and complexity prediction technologies that can foresee unsafe circumstances based on the historical data that is supplied to them. By reducing the occurrences of ionosphere mistakes, where the atmospheric shift might impact the satellite navigation system’s signals, AI will also aid in improving the prediction.

4. Improved Client Experience #

Commercial aviation places a specific emphasis on client satisfaction and service excellence. AI is one way for airlines to increase consumer engagement and provide first-rate customer service. For instance, AI-based automated platforms called chatbots can react to customer inquiries in real time and in a manner resembling human interaction. Online chatbots can save clients time and effort by automating customer care. There are several ways to do this, including:

Personalized and accurate advice on the customer’s purchase choices
Chatbots with AI assistance provide quick and helpful support
24-hour automated assistance simplify client interactions

5. Identification of Travelers #

Security is a critical responsibility for commercial airlines, and AI can offer effective safeguards to ensure passenger safety. Smart cameras with AI capabilities may use facial recognition to detect suspicious people at airports.

To train AI systems for this, images of people with criminal records can be employed. Smart cameras with AI capabilities could be deployed in airports to spot criminal activity.

6. Threat Identification #

Using computer vision systems, advanced analytic, and geospatial signal processing, AI can discover and classify hazards and dangers. AI systems can be trained for this purpose using images and videos from a range of aerial vehicles and satellites.

The images and videos taken could be categorized as normal or suspicious. Using this data, AI systems may recognize dangers in a variety of contexts. AI-based solutions can be useful for business, civic, and commercial purposes.

Corporate leaders may help pilots make informed decisions based on spatial and situational information by integrating AI into the aircraft industry.

7. Fraud Prevention #

Airlines reportedly lose about $1 billion a year as a result of payment fraud, according to IATA estimates. Several fraudulent tactics include:

Using stolen credit cards.
Requesting compensation for “lost” luggage.
Setting up phony online travel companies, and so forth.

Both fraud management strategies and fraud cases are becoming increasingly sophisticated. Airlines use sophisticated, machine learning (ML)-based analytic tools to evaluate data from numerous sources, identify fraudulent transactions, and improve passenger authentication and payment security.

8. Automated Messaging #

Travellers become anxious when a disruption like a flight delay or a luggage loss happens. Customers will probably not choose this airline for their subsequent journey if they don’t receive a response or explanation of an issue from an airline representative in a timely manner. It is important to respond to consumer inquiries quickly, even more so than the actual procedures done to resolve a problem.

By applying algorithms for interpreting natural language or unstructured text, AI software accelerates and streamlines the operations of customer support representatives.

Creating chatbots is another method to automate and enhance customer care. Today, several airlines use AI-powered chatbots to improve customer service by assisting travellers with flight booking and management, luggage tracking, queries, and other needs. Bots can update flight information, lead consumers to certain services or outlets, and more, freeing up humans to focus on more worthwhile tasks and minimizing human contact.

Customer service automation, such as chatbots, can understand basic inquiries and answer in a conversational, informal manner. Airports may offer 24/7 customer support and lessen the human touch by using chatbots.

Famous Airlines using AI to Improve Operations #

As you can see, there are many ways that airlines may employ artificial intelligence (AI) and data science to automate their processes, cut costs, and improve customer happiness. Here, we’ll merely briefly discuss a few actual instances.

Alaska Airlines

According to numerous industry-recognized rating systems, including the American Customer Satisfaction Index and JD Power North America Airline Satisfaction, Alaska Airlines has consistently ranked among the top carriers for customer satisfaction (ACSI). Since the 1990s, it has also been a leader in innovation, beginning with the introduction of self-service kiosks, GPS technology, and online ticket sales. And it continues to pave the path.

Alaska Airlines inked the agreement for the usage of the Flyways AI technology in May 2021. A flight monitoring and routing application help dispatchers create fresh, effective routes and make informed judgments. The technology generates predictions based on data and offers aircraft operations and routing suggestions. Flyways helped the airline cut miles, conserve 480,000 gallons of fuel, and avoid producing 4,600 tonnes of carbon emissions during the course of the six-month trial period.

2. Delta Airlines #

In May 2017, Delta revealed that it would invest an estimated $600,000 in four automated self-service bag-checking kiosks, one of which would use facial recognition software. The airline said facial recognition technology would be used to authenticate customer identity by matching customer faces to passport images and choosing Minneapolis-St. Paul International Airport to debut the four self-service kiosks.

3. United Airlines #

A partnership between United Airlines and Amazon Alex, dubbed “United skill,” was revealed in September 2017. According to reports, the app enables Alex users to converse in natural language to obtain answers to the most typical questions concerning United flights.

4. Southwest Airlines #

Southwest Airlines, based in Dallas, is the biggest domestic airline in the US and the biggest low-cost airline worldwide. TripAdvisor’s 2020 Travelers’ Choice Awards named Southwest the top US airline.

Achieving high-level customer service requires you to listen to your consumers and make adjustments to your operations in response to their feedback. In its 2014-opened Listening Center, Southwest does precisely that. Forty professionals from the Customer Relations, Marketing, and Communication divisions keep an eye on social media feeds to help the airline address new problems as soon as they arise.

The team can monitor social media sentiment regarding Southwest, its rivals, and the airline industry as a whole, analyze the most frequently discussed business-related topics on social media and keep up with news from traditional media thanks to sophisticated software that collects relevant keywords from across the web. The airline uses social media insights to stay up to date on trends and offer consumers tailored promotions.

Summary #

AI and machine intelligence can deliver the best client experience imaginable through automation and self-service apes. Utilizing data also enables aircraft companies to contemplate larger discounts and competitive positioning.

One of the most important issues facing the aviation sector is air safety. It may be the only situation in which individuals are willing to compromise their privacy to ensure the highest level of air safety. AI is advancing aviation safety and assisting airlines to enhance their customer service, lessen the enormous problem of delays, enhance the environment of the airspace, and enhance passenger security. The aviation sector has great potential to grow and become safer thanks to artificial intelligence.

Related reading

Acceleration in Adaptation of IoT in Modern Day Businesses

Vin Patel — Thu, 17 Nov 2022 12:33:38 +0000

Key takeaways

It’s difficult to leave the “Internet of Things” out of a list of cutting-edge and paradigm-shifting technologies in a world dominated by artificial intelligence, data, and ever-evolving connection technology.
Sharing of perceptions and data** Almost every organization relies on data gathering and transmission, and the advent of IoT has completely changed how data is processed.
The fact that IoT technology is still in its infancy and its applications are expanding across various industries suggests the possibility of introducing additional features and advantages in the future.

It’s difficult to leave the “Internet of Things” out of a list of cutting-edge and paradigm-shifting technologies in a world dominated by artificial intelligence, data, and ever-evolving connection technology. IoT is actually one of the most significant technologies in use today because it is essential to the development of numerous other technologies, including machine learning.

Nearly every element of our life creates data, which is captured, shared, and tracked in order to provide us with fresh perspectives, increase efficiency, and enable businesses to make wise decisions. Enterprise leaders are beginning to grasp the value of the Internet of Things in enterprises. Healthcare, energy, automotive, manufacturing, agriculture, and other sectors are implementing IoT. It has numerous uses, including an air track, doorbell cameras, security systems, thermostats, and many others. The Industrial Internet of Things industry will continue to expand annually as more businesses adopt the IoT.

Why is the Internet of Things (IoT) so Important? #

IoT has emerged as one of the most significant 21st-century technologies in recent years. Continuous communication between people, processes, and things is now possible thanks to the ability to connect commonplace items—such as household appliances, automobiles, thermostats, and baby monitors—to the internet via embedded devices.

Low-cost computers, big data, the cloud, analytic, and mobile technologies enable sharing and collecting of data by physical objects with minimal human intervention. Digital systems can monitor, record, and modify every interaction between connected entities in today’s hyper-connected environment. The physical and digital worlds collide, but they work together.

What Technologies Have Made IoT Possible? #

Although the concept of IoT has been around for a while, it has only become a reality thanks to various recent technological advancements.

Availability of inexpensive, low-power sensor technology: More manufacturers may now use IoT technology thanks to reasonably priced and trustworthy sensors.
Connectivity: It is now simple to link sensors to the cloud and other “things” for effective data transfer thanks to a variety of network protocols for the internet.
Cloud computing infrastructures: Businesses and consumers may now get the infrastructure they need to scale up without having to manage it, all thanks to the expansion of cloud platforms.
Analytic using machine learning: Businesses may acquire insights more quickly and easily thanks to machine learning and analytic improvements, along with access to diverse and enormous volumes of data stored in the cloud. The development of these complementary technologies pushes the limits of IoT, and the data generated by IoT feeds these complementary technologies.
Artificial intelligence with speech (AI): Natural language processing (NLP) is now available on the Internet of Things (IoT) devices, including digital personal assistants like Alex, Cortana, and Sir. This has made IoT devices more appealing, practical, and inexpensive for usage at home.

Long-Term Adoption of IoT Will Change the IT Workforces of Businesses #

The operational technology (OT) and information technology (IT) teams are naturally merging together as businesses concentrate on accomplishing this transformation. Due to the increased enterprise adoption of IoT, both domains are increasingly collaborating, which has an impact on the priorities and workload of internal technology teams.

The transition to Industry 4.0 is continuing, and it appears that corporations are more at ease than ever with their part in it. Companies are increasingly incorporating IoT into their business strategies, according to new data from the Information Services Group (ISG), and they’re not only swiftly updating their IoT plans—their scope is also “becoming more ambitious” as a result. Now, businesses are receiving long-term IoT care.

In practice, this implies that companies are investing in connected device sensors, whether they are in automobiles, buildings, or tools and equipment, and utilizing the layers of analytic available for each use case to produce actionable data sources that “derive higher-level business insights.”

The value of the IoT technology market is anticipated to reach $1.39 trillion by 2026, according to Mordor Intelligence. Several things are probably to blame for this phenomenal increase, including:

The COVID-19 epidemic has expedited the development of data analysis, smart home technology, and remote monitoring.
Businesses are vying with one another to create superior artificial intelligence products. These frequently call for an IoT-related network of cutting-edge sensors and edge computers.
IoT networks are more effective compared to centralized systems at completing some jobs.

Industries, including manufacturing, logistics, oil and gas, transportation, energy, mining, aviation, etc., have all been significantly impacted by IoT. In fact, a fourth industrial revolution has already begun, marking a new phase in the management and structure of the industrial value chain. The fourth industrial revolution (industry 4.0) is based on caber-physical systems, also referred to as smart machines. By the end of 2023, 94% of organizations are expected to be using IoT.

Acceleration in IoT Adoption #

The survey claims that 79% of firms intend to invest sizable sums of money into at least one IoT project in the next two years. In fact, in the next two years, the typical large organization plans to invest in nine different IoT use cases. The majority of businesses want to invest in IoT for asset/plant performance improvement, with 42% of those surveyed planning significant investments. It should come as no surprise that the willingness to invest (more) and overall ROI have a strong association. 97% of businesses that have implemented IoT solutions for asset/plant performance optimization—technologies that let users operate and maintain assets at higher levels of equipment effectiveness—have already demonstrated a profitable return on investment.

But even less common use cases are anticipated to experience significant growth. For instance, more than one-fifth of businesses want to make significant operational investments in augmented reality (augmented operations). The conclusion is clear: IoT technology will be adopted more widely in the upcoming years, according to all indications.

Benefits of the Internet of Things (IoT) for Businesses #

Let’s examine a few strategies firms can use to enhance key areas of their operations by utilizing the Internet of Things and its cutting-edge technologies in the future.

Cost reduction

Internet of Things technology will be more specifically designed to support the success of those organizations as they deploy IoT devices to optimize operations and boost profitability. IoT devices are already having an effect on business profits, from cybersecurity to workplace productivity.

Maintenance expenses can be reduced when IoT devices and sensors are used to maintain corporate equipment at peak performance. Office equipment on-the-fly troubleshooting detects issues before they have an impact on staff and employees, avoiding the inconvenience and expense of significant repairs.

One advantage the Internet of Things delivers to your operations and maintenance process is the reduction of expensive extended downtime for repairs.

2. Sharing of perceptions and data

Almost every organization relies on data gathering and transmission, and the advent of IoT has completely changed how data is processed. IoT apes allow for broader access to personal data and track user connection patterns with the device. As the application learns from the patterns, it gets wiser and offers a better user experience.

IoT products let businesses analyze this data at the same time in order to grow their operations. The information can be used to study consumer wants, the buying cycle, the scope for advancements and innovations, and marketing and advertising tactics.

3. Moving real-time insights #

Real-time data from processes, devices, and people via sensors is revolutionizing business since it can be a game-changer in the visibility into what is happening. The important industries are already experiencing innovation, particularly in the retail sector.

Retailers can increase profitability by managing sales and inventory effectively by collecting real-time buyer insights. In order for devices like systems and smart cameras to work together, the Internet of Things is crucial. A corporation can then make well-informed decisions and acquire novel business models that aid in achieving its goals.

4. Deliver multimedia services more easily #

An omnichannel sales strategy streamlines the entire purchasing process, making it popular nowadays. This service is significantly aided by the Internet of Things (IoT), which also enhances the user experience.

Thanks to the installation of sensors on internet-connected equipment, businesses or manufacturers can monitor operations, status, and service levels from remote places and offer prompt support as and when consumers require it.

5. Accessibility, effectiveness, and output #

These advances will enable enterprises to operate more affordably, resulting in a significant landscape expansion due to the affordability of insertion and the increased viability of production. Smart desks, enabled by IoT, provide workers with the optimum workspace. To guarantee that people are seated for maximum comfort and efficiency, smart desks monitor user preferences and learn from them.

These upgrades will make it possible for businesses to run more efficiently, leading to a huge landscape expansion as manufacturing and insertion become more feasible. For instance, IoT-enabled smart desks give staff the ideal workstation. Smart workstations adjust and vary based on individual preferences, guaranteeing constant user comfort and efficiency.

6. Enhanced security and safety #

IoT has a huge potential in high-risk EHS businesses where events like collisions with large items, falls, and electrocutions put human lives in danger. Due to their ability to support remote operations, enable collaboration between human employees and robots, and give rich low-level data, such as high or low temperatures, IoT applications are equipped to offer safe, dependable, and practical solutions to improve safety conditions in the workplace.

IoT technology also provides a creative design for an autonomous system that localizes, watches, and warns site employees who work in dangerous areas, particularly in hostile and distant areas where the difficulties and risks associated with these facilities are raised. Assuring security and defense against physical threats like fire accidents in the workplace, the IoT also sends notifications for abnormalities or other acts that risk the enterprise.

Drawbacks of IoT #

The Internet of Things has advantages that are particularly wanted for boosting business profitability, but it also poses some risks. Let’s take a quick look at the drawbacks of IoT for the purpose of fairness.

Security holes

The most pervasive issue impeding IoT development as a whole is insufficient security measures. Because smart gadgets capture and transmit private information that may have serious repercussions if revealed, there is always a concern about data leaks. However, IoT solutions rarely adhere to all applicable data protection standards, encryption protocols, and other laws and technologies intended to prevent unwanted access to sensitive data. They also rarely have sufficient anti-tampering measures. Inadequate data protection may have expensive, terrible, or even catastrophic results, such as identity theft, the theft of company secrets, the loss of tools or products, sabotage, etc. Because of this, experts who can guarantee that the installed hardware and software systems have no vulnerabilities and are well-protected against any hacking attempts must create and execute IoT solutions in the company.

2. Connection and workload

With many gadgets linked to the internet, network and bandwidth limitations are becoming a problem. The majority of IoT features require lower latency for efficient performance, and others may call for local servers or service providers to offer new bandwidth and QoS to handle workloads with specific needs. Even without considering the cost of labor, putting this into practice could not be economical.

By using edge data centers to handle some of the computing workloads, businesses may increase the devices’ processing power and boost connectivity performance. Due to their proximity to the network edge, they will assist decrease latency and speeding up information processing. The ability to address connection concerns by extending network services into remote locations is another benefit of edge data centers.

3. Dependence on a network #

The extensive network of connections between different devices and accessibility to the global network are the essential components of the Internet of Things. IoT devices consequently also need an infrastructure that guarantees continuous cable and wireless connection with high throughput, low latency, and ongoing Internet access. Therefore, to use IoS, a corporate firm must first offer the full complement of necessary networking hardware, including cables, routers, hubs, local data storage devices, etc.

4. Demands for high skill levels #

IoT solutions need responsible, knowledgeable experts who are well aware of the scope and potential repercussions of their job. IoT solution deployment, setup, upkeep, and scale changes in a commercial operation require highly trained administrators, who may be difficult to discover and hire given their high salary expectations. All staff members should obtain the appropriate training and manuals before dealing with the implemented network of smart devices. Therefore, even though IoT decreases the need for human resources, the staff that is still on-site must be well-trained to avoid interfering with the operation of smart devices and causing a “snowball effect.”

Conclusion #

By offering data-driven insights into the effectiveness and performance of their processes and systems, opening up fresh perspectives on customer behavior and pain spots, and generating new business prospects, IoT enables organizations to innovate and flourish.

Because of this, businesses have a lot to gain from implementing IoT, despite the fact that there are difficulties before, during, and after deployment. However, organizations can use the technology’s disruptive potential by combining technological know-how, support infrastructure, and cybersecurity protocols. The fact that IoT technology is still in its infancy and its applications are expanding across various industries suggests the possibility of introducing additional features and advantages in the future.

Related reading

Social Media Analytic using NLP

Vin Patel — Thu, 17 Nov 2022 12:13:10 +0000

Key takeaways

We all use a language that both parties understand to communicate.
Few businesses develop their own platforms for sentiment research.
Anyway, considering that NLP is way faster than humans can give it an edge start in the world of the future, and who knows, in the future, machines might be able to ‘read between the lines’.

Understanding Natural Language Processing(NLP) #

We all use a language that both parties understand to communicate. What happens, though, when humans wish to talk to computers?

Today, computers play a crucial role in our lives, yet they only comprehend machine language. Because it is highly challenging for humans to understand the meaning of so many 1s and 0s, it makes it very challenging for us to communicate with them.

The idea of Natural Language Processing (NLP) was first brought to the field of technology in the 1950s. It is a part of artificial intelligence that aids a computer in interpreting or manipulating the natural language that humans use when they write or speak.

NLP is one of the technologies that is evolving the fastest. It uses a combination of machine learning, deep learning, and statistical models, and is made possible by the abundance of big data, powerful hardware, and sophisticated algorithms.

Today, social media has become an integral part of everyone’s life, and the amount of unstructured data produced by social media every day is massive and insane! It is not possible to consume and understand this data manually.

The volume and nature of information available to NLP researchers today have undergone a radical change due to the emergence of social media. It has altered how big data can be used.

It is feasible to identify correlations between demographic data, language use, and human engagement using data from social media sites like Twitter, Facebook, YouTube, blogs, and discussion forums. Researchers may learn to recognize these data, follow current topics and forecast the spread of illness using statistical and ML approaches.

Here are some applications of NLP that make it necessary for social media:

NLP aids researchers in determining which subjects are popular on social media.
Information extraction and retrieval are aided by NLP.
NLP is useful for screening adult content or inappropriate content.
NLP aids in compiling all the data necessary to draw any conclusions.
NLP is useful for spotting rumors.
Sentiment analysis and detection are made easier by NLP.

Role of NLP in Sentiment Analysis #

We must first comprehend sentiment analysis or sentiment detection to appreciate NLP’s function in this process. Sentiment analysis is a sort of text mining technique that finds and extracts subjective information from the source material. By keeping an eye on online interactions, businesses may use sentiment analysis to understand better the social sentiment surrounding their brands, products, or services.

Simply said, sentiment analysis identifies the public’s opinions, beliefs, feelings, orientation, etc., on social media. Determining the sentiment is often reliant on syntactic information; for example, the words’ sad’, ‘bad’, ‘difficult,’ etc. may convey negative emotions, and words like ‘successful’, ‘great’, and ‘interesting’ convey positive sentiments.

Few businesses develop their own platforms for sentiment research. Large training data sets and in-house expertise are needed. However, it can be profitable for companies that have highly unique needs that cannot be satisfied by current systems. In certain circumstances, businesses frequently create their own tools using open-source libraries.

HuggingFace, SpaCy, Flair, and AllenNLPare NLP libraries that can do sentiment analysis. Additionally, some low-code machine language tools, like PyCaret and Fast. AI, support sentiment analysis. Transformer models like BERT, XLNet, and GPT3 can be used for deep learning sentiment analysis. GPT3 can even analyze sentiment without any practice data.

Since there have been so many privacy scandals and data breaches in recent years, data privacy has become one of the most hotly debated topics. The widespread usage of AI models makes user privacy even more vulnerable since a sizable chunk of the user data that AI models consume is expressed in natural language.

NLP and privacy have come into contact during the past several years due to the numerous academics that have offered NLP-based solutions to these data privacy issues.

Everyone may engage with others and express their opinions freely on social media, but we are frequently unaware of the amount of sensitive and private information exposed. The user’s ignorance of privacy dangers is the leading cause of this.

How Does NLP Assist in This Circumstance? #

A client-side privacy awareness mechanism may warn users of potentially private information leakages in their communications, and privacy protection frameworks are constructed where ML models are designed to recognize messages that hold sensitive information.

During data pre-processing, NLP techniques are used to eliminate stop words, replace each word with a widely used synonym using the WordNet lexical database, and stem each word to condense the dictionary of terms to words in their root form.

According to a method and supporting tool provided by Canfora et al*.*, it is possible to automatically intercept the private information supplied in a social media post. They employed syntactic structures to identify unique recurrent patterns used by users to convey particular privacy leakage classes in natural language, and they automatically categorized the classes.

Tokenization, lowercase conversion, stop-word elimination, and stemming are the characteristics they utilized. They guarantee that sentence classification performance beats cutting-edge ML approaches and is unaffected by the features used or the training set. They also created a browser add-on to protect users’ privacy when they post on Twitter.

Recent developments in deep learning and machine learning (ML) have made it possible for computers to do many valuable tasks with NP.

Additionally, technology has made it possible for humans to create computer programs that can carry out tasks like language translation, semantic comprehension, and emotion recognition.

The fact that computers don’t yet have the same innate comprehension of natural language as humans do provides a drawback. Anyway, considering that NLP is way faster than humans can give it an edge start in the world of the future, and who knows, in the future, machines might be able to ‘read between the lines’.

Related reading

Which MPP Platform Is Right for You?

Vin Patel — Fri, 11 Nov 2022 10:02:54 +0000

Key takeaways

Most companies looking for a new data warehouse or analytic platform start their searches with Azure Synapse and Snowflake.
You can also employ Virtual Warehouses to achieve unrestricted growth and concurrency while avoiding downtime.
Azure Synapse, on the other hand, is what you should be looking at more closely for larger businesses.

Most companies looking for a new data warehouse or analytic platform start their searches with Azure Synapse and Snowflake. Why? - Because these two cloud services support massively parallel processing (MPP), which enables data computation to be distributed over multiple cloud nodes with ease.

The comparison between these two options is inevitable as both these market leaders are growing at a rapid pace. Azure Synapse and Snowflake are two popular and highly recommended ETL technologies for enterprises that need to handle massive amounts of data. Choosing between the two will be based on the qualities and the unique strengths of each service and the needs of your company.

Azure Synapse Overview #

When it comes to data management, Synapse is considered one of the best. Synapse provides an easy-to-use master repository for all forms of data using the Azure Data Lake. All you have to do now is submit your data to the lake and begin constructing your analytic on top of it.

A dedicated and serverless SQL pool are both available in Synapse, enabling you to grow your computational power without regard for storage capacity. The unit of scale for a dedicated SQL pool is a data warehouse unit, which is an abstraction of computer power. Because a serverless SQL pool is serverless, it automatically scales to meet query resource requirements.

Snowflake Overview #

Snowflake is a lot easier to set up and operate, and the zero-copy cloning feature is always beneficial. Snowflake’s automation is what sets it apart from the competition. Database optimization, partitioning, and indexing, among other things, can be all done automatically, which implies less time wasted on data warehouse administration. Snowflake also has a fee system that is dependent on usage. You will not be charged if you choose not to use Snowflake.

Azure Synapse Vs Snowflake Comparison #

Cost #

Snowflake’s costs are calculated on a per-second basis and are billed on a pay-as-you-go basis. The minimum duration of time required to auto-suspend and auto-resume operations is 60 seconds. So, if your query takes three minutes to process, you’ll only be charged for three minutes if the virtual data warehouse is suspended after the query is finished.

Azure Synapse, on the other hand, charges on an hourly basis for computing. As a result, if your data warehouse is only operational for 12 hours every month, you will only be billed for those 12 hours. Even if the data warehouse is only active for 30 minutes, you will be charged for an hour.

Scalability #

When it comes to scalability, Snowflake dominates due to its mufti-cluster and shares data design. Snowflake allows you to isolate diverse workloads into a common data layer at the same time. You can also employ Virtual Warehouses to achieve unrestricted growth and concurrency while avoiding downtime.

Both dedicated SQL pools and serverless SQL are available in Azure Synapse. The former has a per-defined scale unit (called a Data Warehouse Unit (DWU)), whereas the latter scales automatically to match your scaling requirements.

Admit and Management #

Snowflake’s main goal has always been to require minimal maintenance. This is fantastic news for data managers because it eliminates the need to recruit and train a specialized Snowflake account manager. It is user-friendly and thrives on automated solutions.

The admit side of things will be much more involved with Azure Synapse. Because performance monitoring is not automated, everything from tuning to concurrency control will require human intervention.

When it comes to administration, Snowflake is the clear victor because it is as close to hands-free as it can be.

Performance and Future-proofing #

Performance and Future-proofing is another area where the similarities between Azure Synapse and Snowflake make it tough to choose between the two. Both are ultra-fast, cutting-edge MPPs that provide real-time data access.

Both Synapse and Snowflake continue to evolve, but neither has forgotten about the cloud’s core requirements. Both have put in a lot of effort to set the bar for data warehousing.

Azure Synapse and Snowflake’s Limitations #

The following are some of Azure Synapse’s limitations: If you’ve never used it before, there’ll be a steeper learning curve. Queries that span multiple databases are not supported by Azure Synapse. The setup procedure is lengthy.

The following are some of the Snowflake’s limitations: Some database use cases, such as online transactional processing situations, are not suitable for it. For newbies, Snowflake might be challenging to utilize. The user interface is not user-friendly. Snowflake is too pricey for most users and small businesses.

Conclusion #

There isn’t much of a difference between the two most popular MPPs in the market. Both are highly optimized, simple to use, and capable of handling large workloads.

Snowflake should be considered before Azure Synapse for small-sized businesses with significant growth expectations. It’s made to make scaling easier, which can save you a lot of time and effort. Azure Synapse, on the other hand, is what you should be looking at more closely for larger businesses.

Related reading

The Kingmaker at NETFLIX: Data-Driven Predictive Modeling

Vin Patel — Fri, 11 Nov 2022 09:59:55 +0000

Key takeaways

The day was May 19 of the year 2019, and the very last episode of Game of Thrones was just about to air.
With an ever-increasing global member base and Content catalog, Netflix looks up to data and predictive modeling techniques for employing quality checks accurately and efficiently.
- Capture and keep user attention for as long as possible by providing a highly addictive and hyper-personalized user experience.

The day was May 19 of the year 2019, and the very last episode of Game of Thrones was just about to air. Emotions ran high and wild as the opening credits streamed in for an audience of eight that night.

A few seconds in, and we realized that the audio and video were off by a couple of seconds.

It was anger, despair, and irritability wrapped in one.

Since we were too invested in the finale by then (mainly Jon Snow), we faithfully cursed the streaming service in mufti-colored tones, vowed never to use it again, and decided to battle through by turning ON the subtitles.

This incident occurred, way before I started subscribing to Netflix, and now I just can’t seem to get enough. Netflix is my window into new lives, cultures, and experiences. Its high-quality content, great user experience, and on-point recommendations seem nothing less than magic.

So, How did Netflix actually make all of this happen? And more importantly, How does Netflix (a comparatively costlier alternative) manages to poach a subscriber amidst the sea of free, pirated, and cheaper video-on-demand alternatives?

Simple Answer: Data-driven Predictive Modeling Techniques. #

Need Details? Read ahead. #

Predictive modeling is a way to answer ? What is most likely to happen?, based on what has already happened (Historical data) and happening right now (Current data). It essentially maps out different probable outcomes that are likely to occur, using Machine Learning, Artificial Intelligence, Data Mining, Statistics, and whatnot.

The success or failure of such predictive analytic is heavily biased towards the abundance of quality data availability - which is used to select, train, and test various predictive modeling techniques. And, with its millions of subscribers streaming countless hours every single day,

“Netflix is Data-rich, Very rich!”

Some of its data sources include Member Ratings, Member Information (location, language, watch duration), Video data (play duration, box office performance, critic reviews), User device data (type, features), Metadata (actors, directors, genre, parental rating), Real-time user actions (scrolls, mouse-overs, clicks, time spent on a given page, etc.), Social network data, Search terms entered by members, etc. [Sources as listed in various blogs, interviews and Quota questions answered by Netflix data engineers]. This abundant and versatile data is utilized by Netflix to understand, predict, and optimize its various product features using data-driven predictive modeling techniques, to enhance and personalize your experience.

Everyone Loves Netflix Recommendations #

Netflix knows its customers? expectations intimately!

The Million Dollar Netflix prize (launched in 2006), first brought attention to Netflix’s committed focus on improving its recommendations engine. Flashforward to 2021, and today Netflix personalizes millions of different products for every single one of its million subscribers using data-driven predictive modeling techniques.

This user-experience optimization which started with member ratings has evolved to optimize content titles, the imagery used to portray these titles, synopses, metadata, trailers, the number of titles displayed in a row, etc. And, all of this happens while competing continuously with the new incoming titles and changing user behavior. Given the global scale of Netflix’s audience, this engine is fine-tuned to be in line with the cultural, racial, and overall societal sensitivities of the audience as well. For example, an American staple might not sit well with an Indian audience and vice-a-verse. Xavier Amatriain, Former Engineering Director at Netflix, says this in a WIRED interview:

? We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior. All that data is fed into several algorithms, each optimized for a different purpose. In a broad sense, most of our algorithms are based on the assumption that similar viewing patterns represent similar user tastes. We can use the behavior of similar users to infer your preferences.?

Predictive Quality Control #

Butcher this and the churn rate skyrockets in no time!

When it comes to monitoring content quality (audio, video, subtitles, closed captions, etc]), Netflix sets the bar quite high. It imposes several Quality Checks (QC) consisting of various automated and manual inspections across its massive digital supply chain. Checks such as missing translation, censored content, incorrect frame rate, luminance shift, and audio loudness are some of the issues identified and replaced during QC to avoid any disruptive and disturbing streaming experience.

With an ever-increasing global member base and Content catalog, Netflix looks up to data and predictive modeling techniques for employing quality checks accurately and efficiently. The machine learning model is trained using the past QC data and predicts the probability of an asset not meeting the Netflix quality standards.

Global Expansion #

As of 2021, Netflix is available for streaming in over 190 countries.

As Netflix continues to expand globally, a variety of new content categories, catering to different languages (dubbed audio and subtitles) and cultural preferences has to be added to its library every single day. Apart from the massive data and engineering effort that goes into installing and maintaining such data servers throughout the world, a global expansion of such a humongous scale also adds to the diversity of the receiving networks and devices. This poses a myriad of new technical challenges. How do Netflix deals with such expansive exertions has been elaborated in the succeeding paragraphs?

Personalized Streaming Experience #

Not only ‘WHAT, but Netflix also optimizes ‘HOW you watch your content?!

Apart from suggesting what to watch, Netflix also tries to optimize another key QoE (Quality of Experience) metric that is ‘How you watch your content?. It accomplishes this by utilizing video performance data such as playback failures, rebuffed rate [Time required to replenish local client buffer from the server], nitrate, etc. This data is used to study and train predictive models determining the impact that each performance parameter on an individual member’s streaming experience. Ultimately, these predictions are used to fine-tune algorithms governing the QoE of a member for delivering a high-quality streaming experience.

Chaitanya Ekanadham, the Senior Research Scientist at Netflix, elaborates:

‘As we expand rapidly to audiences with diverse viewing behavior, operating on networks and devices with widely varying capabilities, a ‘one size fits all? solution for streaming video becomes increasingly suboptimal. For example: Viewing/browsing behavior on mobile devices is different than on Smart TVs. Cellular networks may be more volatile and unstable than fixed broadband networks. Networks in some markets may experience higher degrees of congestion. Different device groups have different capabilities and fidelities of internet connection due to hardware differences.?

Optimizing Content Caching #

Even before the credit rolls on, Netflix caches and plays Next!

Now, as the credits start rolling, a user will always have a choice to continue with Netflix’s recommendations or let Netflix choose the ‘Next?. To reduce the time latency between consequent video plays and facilitate a smooth binge-watching experience, Netflix employs predictive caching models. These models are trained using historical member viewing data and other contextual variables, to predict the most likely content, that might have been played next by the user. Since caching is largely dependent on the size and bandwidth available at an intermediate internet hop, the use of a predictive model makes an intelligent data-driven guess as to ‘What to cache next? from the massive Netflix catalog.

Original diagram for Techfastly by Vin Patel.

Mohit Vohra, Director, Content Delivery, says:

‘Maximizing caching efficiency at the closest possible locations translates to lesser network hops. Lesser network hops directly improve user streaming quality and also reduces the cost of transporting network content for both ISP networks and Netflix. Furthermore, maximizing caching efficiency makes responsible and efficient use of the internet.?

Predictive Content Delivery #

No one loves the buffer wheel!

Internet video-on-demand streaming services are akin to tunnel delivery, where both sending and receiving devices/networks in question play a crucial role. And, Netflix heavily invests in utilizing state-of-the-art predictive modeling algorithms and technologies for optimizing user experience from both ends. However, some of the factors that usually impede Quality of Experience (QoE) at the user end are the poor quality of a subscriber’s Internet connection, characteristics of the delivery network, algorithms being run on the playing device, etc. To overcome these, Netflix makes use of the historical user data pertaining to the network (bandwidth, round-trip time, stability, predictability) and device (firmware update, UI changes) for a better characterization of the user device and network, and adjusts accordingly.

Original diagram for Techfastly by Vin Patel.

Netflix has 203.66 million subscribers worldwide*,* with 5,415 content titles in their US library alone. One of the major factors contributing to this success is Netflix’s utilization of its abundant data combined with extensive use of predictive modeling techniques to learn, predict, and improve its member experience continuously. The various QoE (Quality of Experience) metrics discussed above impact each other as well as member interaction.

To summarize, By using predictive modeling techniques, Netflix is able to:

Scale various processes to cope up with the growing demand.
Re-allocate crucial resources to further improve the member experience.
Personalize content even in the face of poor quality networks or devices (Important when dealing with new and uncertain markets).
Capture and keep user attention for as long as possible by providing a highly addictive and hyper-personalized user experience.
And much more, which we might not know about.

So, the next time you wonder as to why Netflix continues to be on the top or why you simply can’t stop watching it, recall this:

Netflix is continuously using data and algorithms to outspend and outproduce its competition, and the scale of hyper-personalization it provides to your viewing experience keeps on pumping those nasty endorphins!

Related reading

Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World eBook: Iansiti, Marco, Lakhani, Karin R.: Amazon.in: Kindle Store

Top Five AI-ML Books For Business Leaders

Vin Patel — Fri, 11 Nov 2022 09:23:21 +0000

Key takeaways

Lakhani Released: 2020 The central idea of the whole book is to provide information about AI and how it has been changing the business industries.
It would be helpful for readers to use these codes as references and develop new codes.
After reviewing all these books, I want to conclude that these are fantastic resources to understand AI power and how we can use it in business transformations.

According to a recent new forecast by Gartner, Inc., the worldwide hyper-automation would enable the software market up to $596.6 billion by 2022. The prediction is enough to say that technologies become a dire need for the highly competitive market. Among emerging technologies, Artificial Intelligence (AI) and Machine Learning (ML) are positioned at the top to bring automation to businesses. Business leaders are seeking to implement AI & ML to transform their traditional business processes. They are looking for resources to understand how it works. Here is a list of books that would help understand how AI and ML could be game-changers for their business. #

(1) Competing in The Age of AI: Strategy & Leadership When Algorithms And Networks Run The World #

Author: Marco Lansiti & Karin R. Lakhani

Released: 2020

Review & Features: #

The central idea of the whole book is to provide information about AI and how it has been changing the business industries. The authors have explained that AI, networks, and data analytic can remove traditional business constraints like scope, scale, and learning and support companies to design new business models.

The book has covered the concept “Age of AI” to point out that AI has been transforming businesses’ working styles and changing economic models. Authors urged that “AI is becoming the universal engine of execution”. AI is bringing new companies operating execution models by integrating different software, algorithms, and operating systems. The authors used the example of “Amazon” to articulate this concept and compared it with Walmart.

The transformation in competition is possible through the emergence of various digital operating models. The book has provided examples of some companies competing with each other. Snapchat, Facebook, Tencent (WeChat), and TikTok have adopted scalable digital operating models to stay in the competition.

The explanation about other concepts like AI factory, Strong and Weak AI, Framework, Strategic Collisions, and Ethics has been described with real-life examples. The authors took three industries, including entertainment: YouTube, Netflix; banking: Ant financial; and hospitality: Airbnb, to explain the recurring pattern. The author pointed out that Weak AI is more impactful to enhance business growth.

The book is perfect for every business leader who is thinking of transforming the business to compete in the market.

Amazon Link: #

(2) The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You #

Author: Mike Walsh

Released: 2019

Review & Features: #

The book is not like a traditional book. It has been considered a practical approach to learning AI, automation, and algorithms to transform everyday operations. According to the author, we live in a world where wonders happen every day because of technology. Still, we assume that robots are replacing human jobs. The author argues that it is not true; the fact is that human beings are unwilling to adopt the changes.

The author has extensively researched, interviewed business leaders, industry experts, and scientists to explore the potential of human intelligence in the future. He provided a simple definition of algorithm leader, “An algorithm leader is someone who has successfully adapted their decision making, management style, and creative output to the complexities of the machine age.”

To encourage human beings and push them to become ‘smart’, the author developed the “10 Principle” shown in the picture. The author explored varied industries, cultures, and history to understand how to get success using AI. His principles are surrounded by futuristic ideas such as machine learning, probabilistic thinking, disruptive innovation, decentralization, and digital ethics.

Through this practical knowledge, the author pointed out that first, change your mind, change the work style, and then you can change the world. This change is not stuck for business purposes only but also applicable to personal life.

The book articulated the power of algorithms and how it plays a vital role in every aspect of the business. This book is best for anyone who wants to learn from real-life experiences and those who want to learn only specific principles.

Amazon Link: #

The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You eBook: Walsh, Mike: Amazon.in: Kindle Store

(3) Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions #

Author: Matt Daddy

Released: 2019

Review & Features: #

“Business data science refers to a modern standard for data analysis to implement it for businesses”. The book aims to provide an essential concept of machine learning for solving business problems and help grow the business economy. The author has provided a toolset for business data science by integrating machine learning, statistics, and economics concepts.

The uncertainty, regression, regularization, validation, classification, controls, and factorizations are building blocks of machine learning. All of these concepts have been explained with various examples.

According to the author, “Machine learning is the field that thinks about how to automatically build robust predictions from complex data”. It has been pointed out that ML and statistics are closely related to each other, which is inspiring new ideas for designing new ML algorithms to solve any business problems.

Another feature of the book is snippet codes of R language to explain the data analysis methods. It would be helpful for readers to use these codes as references and develop new codes. Technologies have been changing everything, and it is forcing business leaders to make wise decisions. Applying the prediction concept and developing a system would help understand the future risk and make better decisions.

Henceforth, the book is an excellent resource for learning the principles, applications, and practices to explore business data sciences.

Amazon Link: #

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions eBook: Daddy, Matt: Amazon.in: Kindle Store

(4) Artificial Intelligence for Business Leaders #

Author: Ait K Jha

Released: 2020

Review & Features: #

Readers do not require prior knowledge of AI and relevant topics. They do not need to learn complex algorithms and code to apply AI to drive their business. The book has covered all essential theoretical concepts to explain AI and how it can change every business. The author has provided a lucid definition of all concepts. For instance, “Machine Learning is a kind of Artificial Intelligence that provides computers the ability to learn without being explicitly programmed.” This simple definition of Machine Learning is easily understandable by anyone.

Another exciting feature of the book is that the author explained AI use-cases and practical examples to find out how AI makes business operations easy. Strategy formation before technology implementation is challenging for every business leader. The book has covered “Enterprise and Economic strategy” to help business leaders create effective business strategies.

Apart from this, most business leaders try to find other details about the technology, such as how disruptive it is, how it generated results in the past, where it failed, and what would be the future, or who is providing the services. The author has used examples of big companies like Amazon, Apple, Microsoft, and Google to describe all these.

All this information would be beneficial for readers to make a wise decision before adopting AI. In my opinion, the book is best not only for business encaustics but also for technology leaders to get the updated information in one place.

Amazon Link: #

Artificial Intelligence for Business Leaders: ARTIFICIAL INTELLIGENCE and MACHINE LEARNING BOOK FOR MANAGERS, LEADERS || ZERO CODING WITH SIMPLE Explanation… Intelligence for managers and leaders 1) eBook: Jha, Ait K: Amazon.in: Kindle Store

(5) The AI-First Company: How to Compete and Win with Artificial Intelligence #

Author: Ash Montana

Released: 2021

Review & Features: #

The author wrote this book to encourage the formation of AI-First companies with capabilities of data gathering, processing, and using it in the required place. The author himself founded, The AI-First Company to support this encouragement.

The author highlighted that AI-First companies are trillion-dollar companies that would take top place among all industries. These companies follow the design principle to develop predictive models which perform prediction and automation of business functions. And, the book would help them to understand the practical implementation of AI.

The cornerstone of the book is its framework which consists of eight parts. The author explained each part through a single chapter. For instance, the first part brought a new concept, “Data Learning Effects (DLE)” designed using network, scale, and learning effects. The author has designed the following formula:

DLE = Economics of scale to data + data processing capabilities + data network effects #

Also,

Output = function (data, data processing capabilities, and data network effects) #

Besides this part, the next parts are related to lean AI, strategy formation, team building, execution, and advantages. All these parts would help readers to integrate AI with existing business processes to automate it. The recent conversation of Author Christian Lute, founder of Crate.io) has also pointed out the importance of AI and ML in business.

The book is unique to find out the systematic steps of AI implementation in the existing business.

Amazon Link: #

The AI-First Company: How to Compete and Win with Artificial Intelligence eBook: Montana, Ash: Amazon.in: Books

#

Final Thoughts #

The availability of thousands of books always gets us confused, especially those who do not have a strong background in technology. I have recommended five AI and ML books for business leaders, executive managers, data scientists, and engineers through this article. All these five books cover theoretical concepts, original use-cases, original principles, and real examples related to ML and AI. After reviewing all these books, I want to conclude that these are fantastic resources to understand AI power and how we can use it in business transformations.

Related reading

Tangible Industry Problems and Its Solutions: Industry 4.0

Vin Patel — Thu, 10 Nov 2022 13:12:03 +0000

Key takeaways

Researchers, entrepreneurs, and representatives of government agencies and public groups address Industry 4.0-related issues consistently.
Skill development, which will result in demographic and social changes, is one of the essential underlying components for the effective adoption and implementation of the Industry 4.0 framework.
Digitization and connectivity of industrial processes (Industry 4.0) have the potential to improve sustainability in all three aspects.

Researchers, entrepreneurs, and representatives of government agencies and public groups address Industry 4.0-related issues consistently. Economists are paying increasing attention to the effects of the Industry 4.0 paradigm on global and national economies and capital markets. As a result of technological advancements and inventions, the global industrial environment has changed drastically in recent years. Industry 4.0 may be likened to three industrial revolutions in earlier centuries and represent technological advances’ most critical disruptive transformations in production.

The steam engine accelerated the First Industrial Revolution in Britain in the mid-18th century. The Second Industrial Revolution occurred in Europe and the US in the late 19th century. Mass manufacturing and electrical energy replaced steam engines in this revolution. Several technologies and mechanization increased output to meet demand. Integrated circuits started the Third Industrial Revolution (microchip). In the late 20th century, several industrialized nations used electronics and computer technology to automate industry.

Productivity has driven every industrial revolution. The first three industrial revolutions used steam engines, electricity, and digital technologies to improve productivity and efficiency. Industry 4.0, the fourth industrial revolution, has been extensively explored and discovered. Since it enhances smart and future factories, it affects the industrial sector. Industry 4.0 encompasses Cyber-Physical Systems (CPS), the Internet of Things (IoT), Internet of Services (IoS), Robotics, Big Data, Cloud Manufacturing, and Augmented Reality. Using these technologies, which will unite the digital and physical worlds by embracing a variety of future industrial advancements, is crucial for creating intelligent industrial processes. This adoption encompasses equipment, machines, production modules, and products capable of exchanging data and independently controlling each other, resulting in a smart manufacturing environment. This new paradigm will provide a range of commercial and social prospects for businesses that embrace it.

Beyond The Hype of Industry 4.0: Lean’s Idea #

The industry will be the first sector to see the consequences of Industry 4.0. This new industrial paradigm will create a picture of decentralized and digitalized manufacturing, with production elements that can independently govern themselves, trigger processes, and react to environmental changes. In addition, the emerging paradigm suggests completely integrating goods and techniques, transforming the industrial perspective from mass production to mass customization, resulting in a rise in complexity. Therefore, innovative technologies and the construction of intelligent factories will substantially influence production processes and operations, allowing for more operational flexibility and more efficient resource usage. Industry 4.0 will significantly impact manufacturing systems, supply chains, and industrial operations. This new paradigm is transforming the current industrial environment in three ways:

Production digitalization,
Automation, and
Manufacturing site integration with an extensive supply chain.

Industry 4.0 involves total network integration and real-time data sharing. Increasing productivity is the driving force behind each industrial revolution. In addition to boosting productivity, the fourth industrial revolution will impact the whole supply chain, from product design and production to outbound shipping.

In recent years, business models and marketplaces have changed rapidly; imaginative new business models have emerged. In the context of Industry 4.0, the advent of new disruptive technologies has changed how products and services are marketed and delivered, disrupting traditional businesses and offering new business opportunities and models. The value chains are becoming more responsive as Industry 4.0 promotes integration between producers and customers, enabling deeper customer connections and business model adaptation in response to market needs. The increasing digitization of industrial production, along with system integration and complexity, will result in the construction of increasingly complex and digital market models, enhancing competitiveness by reducing barriers between information and physical structures.

Skill development, which will result in demographic and social changes, is one of the essential underlying components for the effective adoption and implementation of the Industry 4.0 framework. In the future vision of work, new capabilities will be required, and it will be essential to provide chances for developing these skills through high-quality training. This new industrial paradigm will substantially influence the labor market and professional positions, and it will be crucial that more jobs are created than destroyed.

Five Lean Concepts That Are Crucial for Value Creation #

In 1997, the founders of Lean Enterprise Institute first articulated the lean principles, which serve as the foundation for the whole approach. In their book on project management, “The Machine That Changed the World,” Womack and Jones characterized the five principles of lean as the ultimate formula for enhancing workplace productivity.

1. Identify value #

The first lean principle is about recognizing customer wants and expectations. Managers need to assess the actual demands of their consumers through in-depth interviews and analysis. In the case of unique items, determining value is particularly essential.

2. Map the value stream #

Managers are accountable for keeping consumers interested and understanding their expectations, as customers are typically insufficiently articulate to convey their fundamental requirements.

The value stream is the identification of all the actions necessary to finish the project effectively. This is the second phase in lean project management, which refers to the values stated in the previous step.

By accurately mapping the entire value chain, managers may identify inefficient regions and strive toward their elimination. This enables project teams to optimize customer value while conserving business resources and maximizing profitability.

3. Establish a flow #

After the lean wastes have been eliminated, a workflow must be developed to facilitate the implementation of the project. The entirety of the project management process is subdivided into distinct delivery units or milestones that occur consecutively until the process’ conclusion.

To minimize bottlenecks and unanticipated delays in the project, it is essential to guarantee that each value stream event occurs without friction and that all necessary resources are accessible on time.

4. Generate pull #

Maintaining an inventory is crucial to any operation, and you cannot afford to squander time awaiting the arrival of essential resources or materials after each milestone. However, retaining a surplus of inventory increases expenses and waste. The fourth lean principle focuses on the two primary sources of waste and value in the lean list and works in progress.

By instituting a pull system and guaranteeing that nothing is processed or manufactured in advance, the expenses associated with storing inventory may be drastically minimized. This strategy eliminates the old method of depending on projections and authorized timetables, thus the name “just in time.”

5. Continuous improvement #

The last premise of lean project management is pursuing perfection without compromise and end. By improving incrementally and learning from each failure, managers ensure their teams’ future performance will be significantly enhanced.

Conclusion

The defining characteristics of Industry 4.0 are horizontal and vertical scheme integration and cooperation. Information and Communication Technology (ICT) is incorporated into multiple organizational hierarchical levels, ranging from floor-level control to production, operations, and management. This vertical integration networking enables the utilization of Industry 4.0 components for manufacturing in response to demand disparities or supply variations. ICT is utilized in horizontal integration to facilitate the sharing of information between several players. Complicated is the scenario of integrating various technologies for seamless cooperation, integration, and data sharing with all stakeholders. Implementing Industry 4.0 applications aids in cost reduction, increase productivity, efficiency, and adaptability, and enhances product customization.

Innovation and technological advances have a crucial role in businesses, industries, and nations. However, digital transformation advancements and increased interconnectivity will provide new difficulties to societies since Industry 4.0 will drastically alter the design, processes, operations, and services of goods and industrial systems. Industry 4.0 employs several cutting-edge tools and technologies, contributing to the redefinition of conventional industrial methods. Industry 4.0 will impact the entire value chain, enhancing production and engineering processes, enhancing product and service quality, optimizing customer-organization relationships, bringing new business opportunities and economic benefits, modifying educational requirements, and transforming the work environment. Digitization and connectivity of industrial processes (Industry 4.0) have the potential to improve sustainability in all three aspects.

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Cloud Gaining Traction in the Financial Services Industry

Vin Patel — Thu, 10 Nov 2022 09:41:11 +0000

Key takeaways

Capital One surprised technology and business analysts by shutting down its physical data centers and moving them all to Amazon Web Services.
Additionally, the cloud allows firms to test new apes and online services more cost-effectively, as well as reduce their costs by eliminating the need for on-premises equipment.
If you choose to only play on-prom, you will miss out on the cutting edge.

Capital One surprised technology and business analysts by shutting down its physical data centers and moving them all to Amazon Web Services. Even at that time, it was highly unlikely that Capital One would be the last major bank to make the transition to an all-cloud IT environment.

The cloud offers lower costs and greater scalability, which are attracting more and more financial services firms. A wide range of resources can be spun up elastically and run at scale, allowing financial services firms to develop better digital offerings and customer engagements, as well as keep pace with growing customer bases.

Cloud adoption also enhances productivity, attracts talent, and provides security. While most financial services organizations have remained on the sidelines for many years, they are finally recognizing the cloud’s benefits.

As a result, they accept cloud computing more readily, have seen its successful deployment in the financial services industry, and are becoming more adept at managing risk as well as leveraging it.

Banks have moved ahead with plans for digital transformation to provide a more interactive user experience in response to the pandemic. Banks are migrating applications to the cloud, preferably using cloud-ready apes.

Cloud mainframe emulators exist, but they don’t match up to software developed specifically for the cloud. Banks should view moving to the cloud as an opportunity to upgrade technology.

Customers are finding that analytic on-prom and processing data is becoming increasingly challenging. Some financial firms have created huge data lakes to circumvent product silos. The data can quickly become swamped if there is no common taxonomy. A cloud-based solution can facilitate these processes.

Use Cases #

Several financial services firms are exploring cloud technology to implement innovative new services and practices. Connecting to third-party apes, for instance, is likely to result in new customers and revenue.

The payment process for ridesharing services has historically relied on connecting passengers’ peer-to-peer payment apes or debit cards. This kind of connectivity and then the ability to use fintech apes require the cloud.

The cloud is being used to host complicated technologies such as data marketplace and data warehouses. Organizations can create a consistent data delivery strategy quickly by leveraging this technology.

Additionally, the cloud allows firms to test new apes and online services more cost-effectively, as well as reduce their costs by eliminating the need for on-premises equipment.

On the other hand, you can expect banks to expand into new markets and compete in ways they couldn’t imagine before, boosting their revenue and scaling up at previously unimaginable rates.

Answers to critical business questions are urgently needed by financial services firms. The answers might not be ready for business the next day if a firm even ran a nightly job to answer daily questions before cloud computing. Despite this, this scenario happens more frequently than you might think, and the delay in getting the required information can have serious financial consequences.

AI and Machine Learning #

Also, a large amount of interest is being shown in the use of fast and large-scale databases for machine learning. With the emergence of AI and machine learning, cloud computing has also shown great promise, including automated loan processing and fraud mitigation chatbots for customer service, a field where cloud computing provides great value because it turns unstructured data into structured data.

As the mortgage industry grows, clients are increasingly turning to the cloud for AI solutions and document management for servicing and mortgage origination.

Cloud-based data storage can make it easier for investment professionals and data scientists to work efficiently and get access to fast and scalable data storage.

On the other hand, the cloud gives financial service firms an opportunity to update ageing systems without investing in costly capital projects. Several of the payment systems in use today are quite archaic and are still being modernized rapidly.

With cloud-native applications, adopters have access to the latest technology and talent, which enables them to rebuild transaction processing systems that can handle very high volumes with low latency. By moving their platforms to the cloud, they become leaner and more agile.

Many use cases are no longer off-limits to the cloud as they used to be. #

In the cloud, even core banking has found a home. Yet, controlling and protecting sensitive data is still a major concern that may impede a move to cloud management and control. Fintech firms are embracing both private and public clouds. While cloud service providers developed their security and compliance offerings and increased transparency, the private cloud was the natural progression from traditional server-based infrastructure. Because of the diverse array of cloud-native services now available on the public cloud, plus the associated compliance and resiliency, the public cloud offers a compelling value proposition.

Takeaway #

Some of the world’s most guarded organizations are financial services firms, mostly due to stringent regulations and the security threats they face as global economy players. Despite these challenges, financial services firms have demonstrated that it is possible to benefit from all the benefits of public cloud infrastructure and still remain highly compliant and secure.

Last but not least, a lot of retail banking innovation is now taking place in the cloud. Fintech companies of the new generation are primarily cloud-based. If you choose to only play on-prom, you will miss out on the cutting edge.

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Current Strategies, Challenges, and Best Practices

Vin Patel — Thu, 10 Nov 2022 09:16:57 +0000

Key takeaways

The incorporation of the cloud has brought about a considerable transformation in data handling and storage.
Of course, there are security mechanisms to keep them at bay, but they are insufficient.
This infrastructure can be realized by embedding suitable security controls in the development process to eliminate common bottlenecks and increase the efficiency of cloud-native security.

The incorporation of the cloud has brought about a considerable transformation in data handling and storage. The cloud is essentially a huge network of remote servers worldwide connected together, operating as a single framework. These servers help run major applications, are used for data storage and management, video streaming, or delivering social media services. This consequently demands the need for a systematic approach that helps build and run applications across public, private, and hybrid clouds. This approach is referred to as ‘cloud-native’.

In a nutshell, when an application is said to be ‘cloud-native’, it means that it is designed to be developed and managed in a cloud environment. More and more companies are using the cloud-native approach to increase app development and delivery and accordingly meet user expectations.

What is Cloud-native Security? #

The advent of cloud-native technologies has replaced conventional software development models but has also ushered in the critical challenge of cloud-native security. Cloud-native application security has become vital to ensuring that application architecture remains protected. This is challenging in itself on account of the highly dynamic nature of cloud-native application architectures. Cloud apes shift through multiple cloud platforms, move between on and off-premise frameworks, and expanding and shrinking, making this task rather challenging.

To implement cloud-native security solutions, one must understand its key elements. Some of these include:

Data security

Workload security

Network security

Vulnerability management

Automated response

Inventory & classification

IAM security

Compliance management

While working toward implementing cloud-native security, it is important to understand where responsibilities lie? even more, important to know that responsibilities may be varied based on the services. Public exposure of cloud storage services, missing critical patches, accepting traffic from random sources, and compromising important accounts are problems in a cloud architecture that are relatively common.

In order to combat these issues in consistently changing digital ecosystems, it is important for enterprises to adopt an all-inclusive cloud-native security platform which incorporates threat detection capability, data analytic, artificial intelligence (AI), and automation. It is also vital to deploy the best of native cloud security tools to tackle diagnostic difficulties, lack of fixed perimeters, and similar problems.

Can Existing Security Approaches Work for Cloud-Native Architecture? #

Considerable cloud-native security controls exist to protect cloud-native architecture. This is basically a methodology that helps increase efficiency and productivity and builds better scalability with the help of cloud solutions. It is also quite highly collaborative. it helps sand off the rough edges as the finished app code is transferred into production. In order to ensure that cloud-native architecture continues to reign, several cloud-native security approaches are being used.

Multilayered Security #

A cloud service comprises seven layers - network, OS, hardware, facility, user, middleware, and application. Using multilayered security, one can monitor each one of these layers to identify risks and eliminate them accordingly. Multiple native cloud security tools are used for this purpose, such as end-to-end encryption and cloud firewalls.

Cloud-native Security Platforms #

This has seemingly come up as one of the most effective cloud-native security strategies in recent times. Using these platforms reduce cloud vendor lock-in and deliver visibility across ecosystems. Cloud-native security platforms also help establish tools and reminders for overburdened security teams.

Shared Responsibility Models #

These models dictate that cloud providers themselves will be responsible for some security parameters, while customers will be responsible for others. This concept basically provides a base for all the other cloud-native security approaches.

Common cloud-native security tools include CWPP (Cloud Workload Protection Platforms) and CSPM (Cloud Security Posture Management). CSPM is a collection of security tools that helps enable risk assessment and visualization, DevOps integration, compliance monitoring, and incident response. CWPP on the other hand is a workload security protection technology and is used in hybrid data centers, where, with the help of an agent, it helps address the requirements of server workload protection. This also includes providing cloud-native container security.

Although these approaches are good enough, they are not seamless. Managing cloud-native security tools, for example, can become rather inconvenient with time. In the shared responsibility model approach, it is likely that customers may be irresponsible. according to Gartner, a minimum of 99% of cloud failures is likely to be the customer’s fault, by 2025.

Lack of communication between security teams and business leaders is a major issue regarding cloud-native security. While stakeholders focus on cost, customer experience, and business needs, they neglect the infrastructure, which is observed by security leaders later on. Another problem with current security approaches is that perimeter security does not apply when the perimeter dissolves. Even certain scanner and firewall approaches lack the ability to provide adequate security that leads to the detection of false positives.

Organizations will require more efficient and powerful tools in order to deal with the issues that crop up with conventional security approaches. To that end, cloud-native security companies are coming up with unique techniques to address the inconsistencies in the existing controls. They are working to shift from traditional approaches that depend on network-based tools. Instead, they are hoping to do some pioneering work in serverless and cloud-native technologies that will enhance security efficiency at reduced costs.

One of the popular cloud-native security companies, PerimeterX Inc., is focusing on the same. It hopes to secure a serverless web application and make security smart, inexpensive, and portable. Although the perimeter dissolution issue may still persist with this one, it is a vital solution that may help introduce security into many environments.

Challenges with Detecting and Managing Vulnerabilities #

Despite the number of fairly good cloud-native security approaches, there persist a number of challenges in vulnerability detection and management.

No fixed perimeters #

The lack of fixed perimeters is a major problem in detecting vulnerabilities. With cloud-native applications, security personnel can no longer establish a static firewall surrounding an application across multiple clouds that is likely to scale a million workload instances at one time and mere hundreds at another.

Less IT expertise #

It’s surprising to know that many companies still haven’t migrated to the cloud due to a lack of IT expertise. The ‘Cloud Adoption Practices & Priorities Survey Report’, by the Cloud Security Alliance claims that around 34% of companies avoid the cloud as they believe their IT managers don’t have the experience to handle the demands of cloud computing and management. What’s more, even enterprises that are already on the cloud have apprehensions regarding their security due to a lack of experienced personnel.

Insider threats and data breaches #

The problem of data breaches and cyberattacks is well-known, but few companies are doing anything solid and concrete about it. Of course, there are security mechanisms to keep them at bay, but they are insufficient. Not dealing with data breaches on time leaves the business vulnerable to malicious threats and compliance risks.

Insider threats are rather commonplace as well. In fact, a research report by Intel states that insider threats may be responsible for a shocking 43% of data breaches. The public cloud environment is also a highly appealing attack surface for hackers. If not secured with the highest possible techniques, enterprises are at risk of exposing their cloud data and workload to malicious threats. Despite effective strategies, this remains a major challenge in vulnerability management in the cloud-native security ecosystem.

Difficulty in diagnostics and the presence of complex environments #

Cloud-native application architecture can be rather complex, in the presence of which it can sometimes become rather challenging to quickly diagnose the reason for a security breach. There is a considerable lag between the threat occurrence and the time security teams take to diagnose and address the same. Complex cloud environments require specific tools to ensure seamless security and protection across private and public cloud providers and on-premise deployments - even for edge networks. However, it can get tedious at times when the cloud environment is too complex and layered, making vulnerability detection quite a challenge.

Enterprises that have adopted the DevOps ecosystem are required to deploy appropriate security controls at the beginning of the development phase. Implementing security tools later can severely cripple the organization’s security mechanisms. Given that individual services can now be handled without having to disrupt other functions of the application, DevOps teams can expedite new updates and releases. However, this in itself may pose issues on account of outdated policy management processes and manual provisioning.

Cloud Migration Issues #

Although many enterprises are eager to move to the cloud, not all of them consider doing the same in an efficient, well-planned manner. Cloud migration has to be handled very carefully, or else the business is exposed to vulnerabilities even before its operations are shifted to the cloud. Using straightforward migration strategies that involve breaking down the process into stages may help achieve the transition efficiently without pressurizing IT personnel and reduce the risk of critical errors leading to vulnerabilities.

Lack of Visibility #

The absence of visibility and tracking is commonplace in the IaaS model, where cloud providers refrain from exposing the infrastructure layer to customers. This is, however, being extended to the PaaS and SaaS models as well, preventing customers from identifying their cloud assets.

Open Source

This is rather obvious. indeed, using open-source software for app development is far more vulnerable than anything. Most hackers attack the well in the Git rep and wait for developers to use the packages. Soon enough, they use an attack vector to compromise the application.

Cloud Compliance #

Organizations have to adhere to numerous federal regulations when migrating on the cloud. There are several international mandates that have to be addressed, such as HIPAA, PCI DSS, FERPA, FISMA, etc. Although enterprises do their best to comply with all the necessary regulations, this can still be a tedious process that can leave a business vulnerable to attacks. There has to be granular-level attention paid to the technical capacity needed to adhere to these mandates.

Unsecured APIs #

The cloud is a massive storage system with a fragmented surface attack area, and there are consequently many entry points for attacks to make their presence felt. While APIs are great, they have to be highly secured, as attackers can easily infiltrate the system by hacking into unsecured APIs.

Best Practices for Cloud-native Security #

In order to ensure that cloud-native security is as seamless as possible, security personnel must attempt to follow the best practices, some of which are outlined below:

Ensure to start the security process early in the development phase - right at the container level.
Ensure consistencies while implementing security. Cloud-native security controls are not a one-time thing. They have to be periodically checked and rechecked and new policies have to be implemented to ensure that vulnerabilities haven’t been introduced.
Use as many helpful native cloud security tools and techniques. Stay updated with the latest in the industry.
Introduce mufti-factor authentication and strong, zero-trust identification to ensure protection.
Educate stakeholders and liaison with DevOps stakeholders at the beginning of the security implementation cycle.
Encrypt data with top-of-the-line computing technologies and follow best security practices.
Enable threat detection for databases, IoT, and virtual machines. Integrate threat intelligence into the security platform to make faster decisions.
Enable an excellent firewall and Distributed Denial of Service (DDoS) protection.
Work to eliminate false positives during vulnerability detection. Also, prioritize vulnerabilities by risk.
Maintain transparency in shared responsibility.
Enable a cloud security solution that helps maintain the visibility of the entire ecosystem. Granular-level security strategies can be implemented level to mitigate risks.
Ensure to have a very strong password protection policy in place.

Where is Cloud-native Security Going? #

There are already several advanced strategies in place to make cloud-native security tight and seamless. In the future, cloud-native security is likely to be characterized by powerful API security, highly secure open source and ML, simplicity; rather, simplicity at scale, and adopting an excellent DevSecOps approach. Cloud-native security companies are making every effort to mitigate loopholes in existing security approaches and have been coming up with newer strategies to protect cloud-native architecture.

Recently, for example, cloud security solutions provider Barracuda Networks, Inc. announced the launch of a SASE platform. This cloud-native security platform is designed to help businesses reign control over data access from anywhere, anytime. It will help achieve SASE convergence by bringing together Secure Web Gateway, Secure SD-WAN, Zero Trust Network Access, and Firewall-as-a-Service from Barracuda.

Self-healing infrastructure is another doctrine that is expected to define the future of cloud-native security. Modern software teams will come to adopt Infrastructure-as-code (IaC) to enhance the security review process. In essence, IaC will help security teams to work on threat modelling and assessments even before the actual deployment, enabling DevOps teams to receive immediate feedback and troubleshoot accordingly.

This, in turn, will require a self-healing infrastructure to be in place so that problems can be autonomously identified and fixed without the interference of manual effort. This infrastructure can be realized by embedding suitable security controls in the development process to eliminate common bottlenecks and increase the efficiency of cloud-native security.

Related reading

Questions and answers

Can Existing Security Approaches Work for Cloud-Native Architecture? #

Where is Cloud-native Security Going? #

Are You Using the Correct Data to Power Your Digital

Vin Patel — Tue, 08 Nov 2022 08:43:20 +0000

Key takeaways

Data isn’t a new term to us, but its ability to produce commercial value in today’s world is unprecedented.
Consumers are becoming increasingly aware of how their data is collected and utilized and are concerned about it.
So it’s critical to optimize and safeguard data everywhere it travels and provides teams with simple tools to get the job done.

Data isn’t a new term to us, but its ability to produce commercial value in today’s world is unprecedented. The commercial models of digital platforms like Facebook, Amazon, and Google demonstrate this strength. However, this unprecedented potential of data remains untapped for the great majority of legacy enterprises operating with value-chain-driven business models. Their traditional method of collecting and utilizing data is one of the significant factors holding them back.

Digital transformation is integrating digital technology into all elements of a company. This is because it has a significant impact on how a company runs. The advantages of digital transformation can be found in various industries: It enables firms to upgrade legacy processes, streamline operations, improve security, and boost profitability.

90% of businesses operate in the cloud. Many firms are replicating old services in a digital version as they shift data to the cloud. Regarding actual digital transformation, there is far more to it. With the right technology in place, these services and data can be funneled into actionable insights, enhancing nearly every part of a company’s operations.

Let us understand the benefits of Digital transformation:

1. Accurate Customer Insights #

Customer insights can be unlocked with data. First, you may develop a customer-centric business strategy by knowing your consumers and their demands. Then, by integrating structured data (personal customer information) with unstructured data, these insights can help businesses flourish (social media analytic).

Data enables strategies to deliver more relevant, tailored, and adaptable information.

2. A Better User Experience #

Consider how your digital transformation can assist your employees in becoming more effective while also providing smoother, more intuitive experiences for your customers. Email communications to user portals, digital products, and how frequently you contact new leads fall under this category.

Customers have high expectations regarding digital interactions. Consumers are accustomed to a plethora of options, reasonable costs, and quick delivery. So the next battleground for brands is customer experience (CX).

Demonstrating that you cherish your consumers’ privacy is one approach to set your brand apart. Customers should be given control over how their data is collected and utilized and the freedom to make data-related decisions to gain their trust.

3. Improved Data Collection #

The ultimate value of customer data reports is interpreting the data to drive the firm forward. In the digital age, data may be gathered and integrated in a way that provides a better level of business insight.

Various organizational and functional units are established to transform raw data into helpful information across various touchpoints. Thus, it provides a single view of the customer journey; operations; production; finance; and business prospects.

As an element of your digital transformation, think about how you can give your consumers more control over their data and how you can utilize data privacy as a commercial differentiator. Consumers are becoming increasingly aware of how their data is collected and utilized and are concerned about it. By enforcing rigorous privacy regulations and giving customers the option to opt-out or change their views at any moment, you can earn the trust of your customers.

Using The Correct Data For Digital Transformation #

Most organizations use serialized data generated by different events and draw reports basis on the stats. On the other hand, few organizations use interactive data like Facebook, Google, etc. Now let us see which data is suitable for digital transformation.

1. Business Insights #

Episodic data stats are minimal and restricted. They only provide what brands are doing for brand presence in different geographical areas and segments. This is not the case with interactive data. Interactive provides us insights into how well the user enjoys the product the brand is endorsing. So, for example, if we have to get insights into the new launch of Cadbury chocolate, with serialized data, you will only know how many sales have happened across the globe, but with interactive data, you will also know-how is the user experience on a real-time basis which is very important for your business to grow and have a way forward.

Individual user profiles can be fine-tuned with ongoing streams of interactive data. Cloud technology enables businesses to manage massive profiles archives and continuous real-time data sourcing from each sensing equipment. In addition, AI, machine learning, and data analytic help each profile’s insight-building process even further.

As a result, businesses can use new insights from interactive data to provide more customized product features and new experiences for customers and develop new value-generating opportunities.

Organizations face issues sharing it with the public with serialized data as it is used only for competitive purposes.

On the other hand, real-time interactive data can be disseminated extensively, including third-party entities. Therefore, sharing it increases its worth. Furthermore, real-time interactive data sharing is also conceivable because of the current boom of connected IoT devices. There are estimated to be around 30 billion linked objects in the future, opening up significant opportunities to harness interactive data for enhanced consumer experiences and corporate scope development.

For example, home insurance firms might expand their business beyond covering losses and preventing them. Risk estimate capabilities or lucrative policies don’t bind future revenues. Instead, they develop new data-driven services that help reduce losses and provide prompt repairs.

Conclusion #

Digitalization is the weapon in 2022. Your productivity and workflow can be improved by using the right IT technologies that work together. As a result, it improves productivity by automating and integrating data from across the organization.

These tools make it easier to collaborate, but they also assist the entire organization in the future digitally. Companies are having a hard time adjusting to the new digital culture. You can realize the benefits of a digital transformation only if your employees have the proper training and education.

There is nothing like a digital transformation department or functional unit. It affects every aspect of a company and results in process innovation and efficiency across departments. Every department uses sensitive data, from sales and marketing to finance. So it’s critical to optimize and safeguard data everywhere it travels and provides teams with simple tools to get the job done.

Related reading

The Healthcare Industry Endorses Predictive Analysis Now!

Vin Patel — Mon, 07 Nov 2022 12:21:49 +0000

Key takeaways

Statistics, machine learning, data mining, and other statistics-related technologies have already accomplished the inconceivable in the IT industry and are quickly developing every day, extending their wings across many technological areas.
These biosensors are discretely attached to the patient’s chest and collect, store, measure, and transmit respiratory and heart rates as well as contextual data including posture, activity level, and ambulation every minute.
This article focuses on the use of predictive analysis in monitoring a patient’s health.

Statistics, machine learning, data mining, and other statistics-related technologies have already accomplished the inconceivable in the IT industry and are quickly developing every day, extending their wings across many technological areas. Predictive analysis has also been used in the healthcare business to embrace these technologies.

Predictive analytic is a field that evaluates historical and real-time data to create predictions about the future using diverse approaches such as modelling, data mining, statistics, and artificial intelligence (AI). Predictive analytic in healthcare may benefit the sector in a number of ways, and some of the use cases have practically changed the industry.

How does predictive analysis function in healthcare? #

Every case that healthcare staff deal with requires them to handle a vast quantity of data from many sources. Patients’ electronic health records, medical imaging, screening findings, and different administrative data are all included. Furthermore, they are frequently required to make quick judgments based on this knowledge. Every time they connect with their physician, patients want a consistent, informed experience. They want a smooth, customized experience whether they’re getting a basic check-up or visiting a specialist.

Predictive analytic tries to warn physicians and caregivers about the likelihood of events and consequences before they happen, allowing them to prevent rather than treat health problems. We now have algorithms that can be supplied with historical as well as real-time data to generate meaningful predictions, thanks to the emergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Predictive algorithms may be used to help clinicians make better decisions for individual patients as well as to influence treatments at the cohort or community level. Basically, predictive analysis solves functional and management problems in hospitals.

Use cases of predictive analysis: #

Advanced predictive analysis methods can perform wonders like improving chronic disease management, reducing overhead costs, being a great assistant in medical research, and many more. Here we read about the use cases of predictive analysis in the healthcare industry:

Predictive analysis detects early signs of patient health deterioration: #

In the ICU, where a patient’s life may rely on quick action when their health is likely to deteriorate, predictive insights can be very useful. The number of patients requiring acute care in the ICU has increased since the coronavirus pandemic, highlighting the need for technology to assist caregivers in making quick decisions.

Because patients’ vital signs are continually monitored and evaluated, predictive algorithms can assist in identifying patients who are most likely to require care within the next 60 minutes. This enables caretakers to intervene at an early stage in the patient’s condition, depending on minor symptoms of deterioration. Predictive analytic may also estimate the likelihood that patients will die or return to the ICU within 48 hours if they are discharged from the ICU, assisting caregivers in deciding which patients can be discharged.

In a hospital’s general ward, where patients’ deterioration sometimes goes unreported for lengthy periods of time, predictive analytic can assist discover early warning indicators of unfavorable outcomes. As wearable biosensors become more widely used, care professionals may be able to detect early indicators of patient deterioration as patients travel through different acuity levels in the hospital. These biosensors are discretely attached to the patient’s chest and collect, store, measure, and transmit respiratory and heart rates as well as contextual data including posture, activity level, and ambulation every minute.

Predictive analysis helps in chronic disease prevention and management: #

Predictive techniques like remote patient monitoring and machine learning can help hospitals make better decisions by providing risk grading and threshold alarms. This feeds into personalized messaging with the predictive analytic data cloud, reminding patients to refill medicines or assisting them if they have problems getting refills or going to the pharmacy.

Predictive analysis uses big data to find solution to the problem. The system may provide a specific risk score to a person based on test results, patient-generated data on their lifestyle, and biometric data. This score indicates the likelihood of a problem occurring in the near future. It’s also more likely to spot early indicators of deterioration and report them to a doctor.

Furthermore, AI may notify a care manager when a patient is lagging behind on their treatment plan and suggest targeted outreach. These are the types of flawless patient experiences that encourage better compliance plans and, as a result, better health results.

Predictive analysis delivers predictive care for at-risk patients in their houses: #

Many patients are now discharged without long-term health monitoring following a hospital stay, putting them at risk of adverse events and hospital readmission that may have been averted with proper preventative measures. Falls at home are particularly prevalent among the fragile and old, and they are a primary cause of fatal and non-fatal injuries. It’s yet another way for predictive analytic to help turn a reactive healthcare strategy into a proactive one.

Predictive analytic may leverage data from a variety of sources, such as hospital-based electronic medical records, fall detection pendants, and previous usage of medical alert systems, to identify seniors who might need emergency transportation in the upcoming month. This enables healthcare practitioners to contact a senior citizen prior to a fall or other medical crisis, avoiding needless hospital readmission and lowering transportation, acute care, and rehabilitation expenditures.

Predictive analysis can make predictions based on genetic data: #

Adults with genetic abnormalities account for at least 10% of the population. Early detection of certain of them can aid in their management and avert issues later in life. However, because the human genome is a complex system, assessing genetic data is a difficult task.

Predictive analytic may be used to examine and compare a person’s genetic data to a database of potential faults and disorders. It can even be utilized as early as the newborn stage to alert parents to the problem their kid may be suffering from.

Bottom-line: #

Predictive analytic in healthcare is projected to grow in popularity since it has the ability to improve clinical care delivery and equipment maintenance. But, since as useful as predictive algorithms are, their influence is ultimately determined by domain specialists? physicians, nurses, engineers, and hospital managers? who understand how to assess probability in the context of a patient or healthcare system. As a result, professional input, as well as cutting-edge analytical capabilities, are required in the creation and deployment of such algorithms. This article focuses on the use of predictive analysis in monitoring a patient’s health.

Related reading

How to Use Intelligent Document Processing

Vin Patel — Thu, 03 Nov 2022 13:02:52 +0000

Key takeaways

In each stage of document data integration; data science methods such as computer vision, text recognition, machine learning, and natural language processing, increase human understanding of unstructured data.
Any data that doesn’t match up is detected and sent to a person to be reviewed and corrected.
As we re-imagine how we work, new workflows become revolutionary business partners.

In each stage of document data integration; data science methods such as computer vision, text recognition, machine learning, and natural language processing, increase human understanding of unstructured data.

Intelligent Document Processing (IDP) is gaining popularity because it offers game-changing methods for automating data extraction projects that were previously difficult, if not impossible, to complete.

What Has Changed Recently in Document Processing? #

What’s new is integrating various tools into a single platform solution, completely changing how we work. New data sources improve business outcomes and open the road for human-driven innovation.

This is a novel method of gathering and extracting data. All of the major technology companies are developing sophisticated tools, but the difficulty is that they aren’t all available on one platform.

Intelligent Document Processing platforms are advanced software machines that classify data from any text-based source and feed it into the data supply chain.

What Makes an Intelligent Document Processing Platform So Special? #

Every step required to convert paper or digital documents into appropriately labelled data is included in intelligent document processing technologies.

IDP platforms must meet the following criteria:

They must be sector agnostic.
Accommodate both structured and unstructured data.
Integrate with cloud and on-premises content management systems
Scale to process billions of extractions every day
Create a graphical user interface for training and categorization

Each Stage of Document Data Integration and How Intelligent Document Processing Platforms Handle It? #

Document capture #

The platform integrates with scanning hardware to digitize tangible material such as paper or microforms. Because not all documents are digital, a solution to speed up historically sluggish scanning operations is necessary.

Data is ingested from digitally born content such as text files, PDFs, and office productivity documents via built-in integrations.

Image processing #

Computer vision algorithms provide image processing, which prepares a document for effective OCR and preservation. The IDP platform will produce two versions of digitized documents: one for machine reading and one for on-screen viewing in a content management system.

OCR (Optical Character Recognition) #

Accurate OCR is required for machines to read text on documents. The utilization of several OCR engines is one of IDP’s most essential features. By synthesizing the data from numerous engines until near-perfect accuracy is attained, a “layered” method eliminates the need for better OCR.

NLP #

Find paragraphs, sentences, or other language pieces in your papers that express specific meaning using Natural Language Processing (NLP). NLP uses techniques including sentiment analysis, part-of-speech tagging, named entity tagging, and feature-based tagging to speed up data discovery.

Classification #

The majority of business documents are collections of pages containing various types of information. Machine learning and other intelligence-based techniques are used to train IDP classification engines to recognize documents.

Automatic document recognition is a crucial step in deciphering the contents of a document. The days of manual data entry for categorization are long gone.

Extraction #

The artificial interpretation of information by the software is critical to successful data extraction. Because artificial intelligence (AI) is only as knowledgeable as its training, the system must be able to find and classify all expected information within a page. This includes recognizing parts of natural language documents and extracting certain data items such as dates, names, and numbers, among other things.

Data Validation #

To be trustworthy, every extracted data must be verifiable. IDP solutions are unique because they validate data using external databases and pre-configured lexicons. Any data that doesn’t match up is detected and sent to a person to be reviewed and corrected.

Integration #

The needs for data integration are quite varied. IDP platforms must integrate with all downstream applications since they are essential sources in the data supply chain. This comprises databases and document repositories in the cloud and on-premises. For portability, labelled data and metadata are connected to human-readable versions of the data.

How to Use Intelligent Document Processing to Your Advantage? #

The development of document data literacy is the key to success with IDP platforms. Before training software to integrate data, a significant amount of work must be spent learning what data is provided and the business risks that are associated with that information.

It is necessary to engage subject matter experts who use the information to do work to attain document data literacy. Their in-depth knowledge of the business value and interpretation of the data on the documents they work with guarantees that the correct data is extracted and that the appropriate action is taken with it.

What’s the Difference Between Document Capture and Intelligent Document Processing? #

The most significant difference between IDP and standard capture is the potential of development in document processing. Over a decade ago, the main players in traditional document capture stopped inventing solutions.

There are two explanations for this:

To begin with, such technologies were developed during a time when preserving compute was a priority. Their software architecture was not designed to handle the scalability requirements of today’s data-intensive applications. And, because many of these platforms have evolved through acquisition, a platform-wide software re-build to match IDP’s needs would be prohibitively costly.
The second reason is that traditional document capture firms have a huge customer base. They’re successful as is, and they don’t want to disturb their customers’ present operations by requiring an upgrade. Instead of focusing on capture innovation, they have shifted their focus to other technologies such as robotic process automation or rebranding to look to have IDP capabilities.

Categories of IDP Vendors #

To make it easier for you to explore the landscape, we’ve divided the vendors into categories:

Vendors of innovative IDPs Vendors of legacy IDPs
IDP merchants who specialize in a specific niche
Systems integrators who provide IDP services
Providers of technology with IDP components

Vendors in each category have produced solutions that are distinct from one another because they approach the IDP dilemma through different lenses.

Suppliers of IDP Technology #

General-purpose technology components like OCR and computer vision are available on the major technology platforms. Because these components aren’t whole IDP solutions, we’ve merely included them in this piece because some businesses prefer to develop capabilities internally using existing technological components.

Extract by Amazon
Google Cloud Vision is a service provided by Google.
Computer Vision in Microsoft Azure

The Engine for Transformation is Intelligent Document Processing. #

Data plays a key role in transformation in all organizations. They obtain important insight needed to disrupt their market by producing something new by gaining new sources of data or finding new techniques of analysis.

The most crucial aspect of “becoming digital” is data. It has been predicted that data will become the new trend, and that moment has come. Because digital transformation produces value-added services, products, operational strategies, and capabilities, data is the single most crucial component for innovative success.

Final Thoughts #

Data is a key enabler of digitalization, and companies that invest in intelligent document processing will remain on the cutting edge of innovation and growth. IDP supports the modern industry by integrating a steady stream of useful data into software applications. As we re-imagine how we work, new workflows become revolutionary business partners.

Related reading

Questions and answers

What Has Changed Recently in Document Processing? #

What’s new is integrating various tools into a single platform solution, completely changing how we work. New data sources improve business outcomes and open the road for human-driven innovation.

What Makes an Intelligent Document Processing Platform So Special? #

Every step required to convert paper or digital documents into appropriately labelled data is included in intelligent document processing technologies. IDP platforms must meet the following criteria: - They must be sector agnostic.

How to Use Intelligent Document Processing to Your Advantage? #

What’s the Difference Between Document Capture and Intelligent Document Processing? #

Can Industry 4.0 Save The Semiconductor Industry?

Vin Patel — Mon, 31 Oct 2022 05:50:35 +0000

Key takeaways

Industry 4.0 transforms manufacturing from a linear process to a more collaborative and networked approach.
In the past year, Samsung has released several new semiconductor chips offering significantly improved performance.
As we enter this new era of industrialization, it is clear that the semiconductor market has a bright future ahead.

Industry 4.0, or the Fourth Industrial Revolution, is a term used to describe the trillions of dollars worth of investments made by manufacturers worldwide in digital technologies such as Artificial Intelligence (AI), 3D printing, Virtual Reality (VR), and robotics. Industry 4.0 transforms manufacturing from a linear process to a more collaborative and networked approach. With the help of data analytic and sensors, manufacturers can collect real-time data on the production processes and make adjustments on the fly. This allows for greater flexibility and responsiveness to customer demands. As a result, Industry 4.0 is helping manufacturers become more agile, efficient, and profitable.

According to the SIA, Semiconductor Industry Association, the global semiconductor market is expected to reach $451 billion by 2025. This growth is driven by the continued demand for electronic devices and the need for more sophisticated and powerful chips to power these devices. In addition, the semiconductor market is also being propelled by the rise of new applications for semiconductor technology, such as autonomous vehicles and AI.

According to Design News, the following forces exist:

More than 75 billion IoT devices are expected to be installed globally by 2025.
A “smart car” will produce more than 4,000 GB of data in a single day.
The data produced will multiply by ten between 2016 and 2025, reaching 163 zettabytes daily.
By 2023, 1.3 billion people will have 5G subscriptions.
By 2030, the market for new “smart” technology (such as tablets, drones, and smartphones) will be $6.9 trillion.

The new digital era provides opportunities for companies to increase productivity and efficiency and also offers a unique platform for semiconductor companies to reach new customers. In the past, the customer base was limited to the electronics industry. However, with the rise of Industry 4.0 applications, such as autonomous vehicles and AI, the potential customer base for semiconductor companies is now comprehensive.

A Little About Smart Factories #

Factories have long been an essential part of the world economy, and their role increased as global trade became complex. As a result, factories increasingly need to adapt to shifting circumstances. Smart factories are equipped with advanced technologies that allow them to operate more efficiently and flexibly than traditional factories.

Delivery systems play a crucial role in smart factories because they allow materials to be moved swiftly and effectively between stations. In addition, they can be used to create highly customized production lines tailored to each factory’s needs. As a result, smart factories can operate at peak efficiency with the correct delivery system and produce the highest quality products possible. The most common type of delivery system is a conveyor belt, but many other types include robotics, pneumatics, and hydraulics.

Companies Working In The Semiconductor Sector #

The semiconductor sector is a rapidly growing industry with immense potential. Companies in this sector constantly innovate and develop new technologies that can revolutionize our lives and work. Some of the most cutting-edge companies working in the semiconductor sector include:

Intel:

Intel is currently working on a new project in the semiconductor sector. This project aims to create a new type of computer chip that is more energy efficient and faster than current chips. This would represent a tremendous advancement for the industry, and Intel is confident it can make it happen.

In addition to this project, Intel is also working on other initiatives to improve its products and services. For example, they are working on a new type of flash memory that is faster and more reliable than current models. Additionally, they are exploring innovative approaches to chip production that will lower costs and boost quality. As a result, Intel is making significant progress in the semiconductor sector, and its work will significantly impact the industry in the coming years.

TSMC:

TSMC is the world’s largest contract semiconductor manufacturer. The firm has been operating for over 35 years and is responsible for manufacturing some of the world’s most popular chips, including those used in Apple’s iPhone and iPad. In recent years, TSMC has been working closely with the semiconductor sector to help meet the ever-growing demand for chips. TSMC has invested heavily in new facilities and equipment and has also been working to improve its manufacturing process. In addition, the company is also working on developing new types of chips that can be used in various applications, helping to drive innovation and bring new products to market.

Samsung:

Recently, Samsung has been working closely with the semiconductor sector to produce chips that are faster and more efficient than ever before. To stay ahead of the curve, the corporation has made significant investments in R&D, and those investments are beginning to pay off. In the past year, Samsung has released several new semiconductor chips offering significantly improved performance.

For example, the company’s newest DRAM chips can speed up to 3.2 gigahertz, making them one of the fastest on the market. In addition, Samsung’s NAND flash memory chips are now up to twice as fast as previous generations. Thanks to these advances, Samsung is well-positioned to continue leading the way in the semiconductor industry.

Qualcomm:

Qualcomm is a leading semiconductor company that designs and markets wireless telecommunications products and services. Founded in 1985, Qualcomm has become one of the largest smartphone chipmakers in the world. Qualcomm has recently worked closely with the semiconductor sector to develop new technologies and products. Qualcomm’s Snapdragon line of chipsets is used in a wide range of smartphones and tablets, and the company is continually innovating to stay ahead of the competition. In addition to its work with the semiconductor sector, Qualcomm is also active in developing 5G technology. It has been working with mobile operators worldwide to roll out 5G networks. With its vast experience and expertise, Qualcomm is well-positioned to continue playing a significant role in shaping the future of wireless communications.

These are just a few of the major companies in the semiconductor industry. Together, they are helping to drive innovation and bring new products to market. As the demand for semiconductor chips continues to grow, these companies will work hard to meet the market’s needs.

Recent Semiconductor Financing Activity #

Over the past year, fundraising activity in the semiconductor business has increased significantly due to several factors, including the growing demand for semiconductors, the increasing complexity of semiconductor manufacturing, and the need for more specialized equipment.

In addition, the ongoing trade dispute between the United States and China has increased demand for semiconductors from countries outside China. As a result, investment in semiconductors has increased dramatically.

In the past few months, there has been a lot of semiconductor financing activity. Companies such as Maidu, Alibaba, and Tencent have all received funding for their projects. Let’s take a look at some of these companies and their projects.

Maidu:

Maidu is a Chinese internet company that provides various services, including search engines, maps, and cloud storage. The company has received $600 million in funding for its semiconductor business. The funds will be used to develop chips for artificial intelligence (AI) applications.

Alibaba:

Alibaba is a Chinese e-commerce company that offers online shopping, banking, and other services. The company has received $450 million in funding for its semiconductor business and to develop chips for AI applications.

Tencent:

Tencent is a Chinese internet company that offers various services, including social networking, gaming, and payments. The company has received $700 million in funding for its semiconductor business. According to resources, most funds will be used to develop chips for AI applications.

The Future of Semiconductor Manufacturing #

The semiconductor industry is in a state of flux. More’s Law, which predicted that the number of transistors on a chip would double every two years, has held for several decades. However, chipmakers are now running into the process of making tiny things by using modern technology. As chips continue to get smaller and smaller, producing them is becoming increasingly complex and expensive. In addition, demand for personal computers and other devices has stagnated in recent years, raising doubts about the future viability of the semiconductor industry.

Nevertheless, there are reasons to be optimistic about the future of semiconductor manufacturing. New technologies such as 3D printing and nanotechnology are beginning to be used in semiconductor production, and there is still strong demand for chips from the automotive and industrial sectors. As the semiconductor industry adapts to these changes, it will likely remain a vital part of the global economy for years.

Conclusion #

In the quest for data, speed, and storage, the semiconductor industry is booming as new firms emerge to stake their claim in the digital age. The future of this market looks bright as the world moves towards Industry 4.0, the next phase of industrialization that will see greater integration of physical and digital technologies. This shift will present new opportunities for semiconductor firms to provide the components that power Industry 4.0 applications such as Big Data, artificial intelligence, and the Internet of Things. As we enter this new era of industrialization, it is clear that the semiconductor market has a bright future ahead.

Related reading

The Fuel to Organizational Conflict or A Boon for Teamwork?

Vin Patel — Mon, 31 Oct 2022 05:47:37 +0000

Key takeaways

The 18th century witnessed a watershed event in the history of the world’s industries.
Lastly, a considerable amount of R&D is necessary to implement the concept at a ground level and establish a data pool that everyone can refer to.
Addressing these issues should solve the prevailing problems and enable a smooth transition to industry 4.0.

The 18th century witnessed a watershed event in the history of the world’s industries. The time was ripe for something revolutionary, and Great Britain provided the right platform, abundant with natural resources, capital, labor supply and technology. Thus started the industrial revolution with Great Britain as its flagbearer. Consequently, the country also became one of the leading economies of the time. Essential inventions of the era, such as the steam engine, internal combustion engine, and electricity production, formed the backbone of most technological developments. Steam power enabled the development of power looms and better techniques of steelmaking. The century also witnessed the invention and evolution of power tools. The first industrial revolution started a paradigm of mechanization that improved the workers’ output by more than 40%. This formed a baseline for further industrial innovations and the coming centuries saw the second and third iterations of revolution.

The second industrial revolution was marked by the use of oil and gas to power industrial processes. This era also witnessed the development of better communication technology like the telephone and telegraph. The entry of computers marked the third industrial revolution into the system, and a new era of automation started. Today, we are in a transition phase where industry 4.0 is the next big thing. Industry 4.0 uses the power of data analytic and more automation through AI, IoT, the involvement of smart machines, supercomputers, cloud storage, etc. However, with each one of these revolutions, we have seen significant changes in labor laws and workplace dynamics as well. After the Covid-19 pandemic, we are seeing more cases of moonlighting and quiet quitting; workers, having realized the importance of personal life and being there for the family, are opting for a better work-life balance. Hence, it is yet to be seen whether industry 4.0 fuels organizational conflict or is a boon for teamwork.

Industry 4.0 Implementation #

The first industrial revolution came as a drastic change, but industry 4.0 cannot be separated by a well-defined line. It is a gradual change and, if seen broadly, an evolution of industry 3.0 with the latest technologies. It represents an ever-increasing efficiency, collaboration and integration within the industrial processes. However, considering that most industries already have a fixed workflow, it is hard to evolve and implement new technologies without interrupting that workflow. SaaS, ML, and AI integration primarily establish a software infrastructure. However, the application of advanced robotics and implementation of the IoT framework does need an overhaul of the physical infrastructure. There are many levels to an industry 4.0 overhaul.

Firstly, the company should have an excellent understanding of ICT and industry 4.0. Secondly, they need to make sure that the necessary hardware infrastructure is in place. These include components that enable autonomy in the process - RFID, network connections, sensors, microprocessors and actuators. These are programmable components and can be left alone once programmed according to workflow, which also helps standardize the processes. We are talking about infinite possibilities here; industrial processes can be connected to the cloud and can be controlled from a centralized system. This makes the work easier for the administration when controlling multiple factories or offices. Lastly, a considerable amount of R&D is necessary to implement the concept at a ground level and establish a data pool that everyone can refer to.

Industry 4.0 Limitations #

Integrating ICT within the industry requires changes in the whole ecosystem – from workers to upper management. Effective decision-making and adaptability at the apex level can serve as a model for holistic application for better skill development of lower-level workers. Similarly, a bottom-up approach can lead to better administrative decisions.

This is because there are several limitations; the workers who look up to higher management should feel confident about the leadership, and inversely, the leadership should consider the workers’ concerns before making a decision. Industry 4.0, with its greater-than-ever automation, means robots will take the place of unskilled/low-skilled workers, creating a vacuum of employment opportunities. Mid-level employees who operate the machines need to be technically adept, and higher-level officers need to have theoretical and technical knowledge of ICT. This leads to expenditure on training costs not only at the corporate level but also at the government level because they will have to focus on making policies that promote skilled labor, increased expenditure on skill development, etc.

Finally, an associated risk with greater “ICTization” is data security. The increased implementation of cloud computing and processes being done through software means they are more vulnerable to caber-attacks.

The Human Factor #

Teamwork plays a crucial role in making all the value chains of an industrial system work in tandem. This boosts efficiency and ensures that the organization works at its best capacity. However, Covid-19 has created disruption in that area. With work-from-home and hybrid modes being increasingly popular, peer connect has taken a toll and relationships between co-workers have degraded.

When we talk about algorithms and software replacing humans, it is extremely necessary to consider the human factor. As of 2020, approximately 44% of the global workforce still lies in the low-skilled or unskilled category. Moreover, among the skilled workforce, very few are employable as they don’t have specialized skills. When the paradigm shift happens, these workers, who otherwise were an indispensable part of the industry, will be replaced by computer programs. Companies will focus on hiring programmers, data scientists and domain experts, and governments will focus on feeding these needs. This will lead to financial and social degradation, envy and demotivation among low-skilled and unskilled workers. For some, it will be too costly to upskill and, for some, too late. Although, the concept of this latest industrial revolution is aimed at increasing collaboration, it also brews a dangerous concoction of workplace conflict.

Conclusion #

Industry 4.0 is the next big thing and the rapid technological advancement year after year makes its scope really wide. However, we need to understand the current shortcomings and act accordingly. As of now, Switzerland is the only country fully prepared for an upgrade to industry 4.0. We need to keep in mind that Covid-19 has delayed the process of implementation by many years. A lot of industries have taken huge hits. For the companies to recover from this hit first, then put money into modernization, will take a lot of time. For now, we should focus on two important things - First, do not hurry; gradual adaptation is the key to a successful implementation. Second, a gradual shift to “back to the office” or hybrid models of work and third, promote team building exercises to encourage peer-to-peer and even subordinate-boss bonding. Addressing these issues should solve the prevailing problems and enable a smooth transition to industry 4.0

Related reading

Industry 4.0 Changing Lives: The Training Workers' Case

Vin Patel — Mon, 31 Oct 2022 05:28:57 +0000

Key takeaways

The expansion of digitization is making the global transition to Industry 4.0.
It makes them better communicators, improves the influx of innovative ideas, and increases their adaptability and ability to embrace new career challenges.
It is a revolution organizations need to prepare for so that their competition in the cut-throat marketplace doesn’t outrun them.

The expansion of digitization is making the global transition to Industry 4.0. Termed as the future factory, it is changing how organizations manufacture, enhance, and deliver their products by integrating smart technologies like cloud, artificial intelligence, IoT, and analytic.

Let’s dive in and understand how the new industrial revolution will affect us or if it is just another marketing buzzword.

Introduction to Industry 4.0 #

Industrial revolution 4.0, or the fourth industrial revolution, is the advancement of the industrial 3.0 revolution with the addition of smart devices and systems, virtual reality, and wireless connection.

It vividly describes digital transformation with technological developments and advancements, including machine learning, artificial intelligence, smart cCities, IoT, edge computing, cloud computing, and smart automation.

The four foundational technologies that constitute the building blocks of Industry 4.0, according to McKinsey are:

Data, connectivity, and computation power (includes cloud, blockchain, sensors, and Internet of Things)
Intelligence and analytic (includes machine learning, artificial intelligence, and advanced analytic)
Interaction of humans and machines (includes automation, robotics, augmented reality, virtual reality, and chatbots)
Advanced Engineering (includes renewable energy, nanoparticles, and additive manufacturing processes like 3D printing)

The first three industrial revolutions focused on inventing and innovating novel machines and advancing electronics and production. The advancements in automation and wireless connectivity will revolutionize the workflow, enable better connections with smart devices, and level up production and living standards.

Impact on Workforce #

Since the first robot Animate was created, employees have expressed concerns about losing their jobs to robots. Several sci-fi movies helped visualize robots running factories and businesses, but the reality is far different and is not the reflection of reality.

The Hays Global Skills Index shows how middle-skilled jobs would disappear. Still, it also describes the creation of jobs in higher and lower-skilled occupations, proving that automation would create more jobs instead of deleting them.

Research by the World Economic Forum supports these findings, which showed that automation creates around 133 million jobs and only cuts 75 million jobs. The employees in the phased-out jobs could be unskilled and assigned new roles in alignment with the new industrial revolution.

Global Trends Associated with Industry 4.0 #

Industry 4.0 promotes several projects, but the most vital trend is increased localization. This means that the production houses, manufacturing, and factories are getting closer to their customer base. Other global trends to look out for include:

Enhanced Cybersecurity and privacy
Industrial robots
3D printing
AR
Increased transparency
Cloud computation

Training Employees for Industry 4.0 #

Some methods for training your employees to adopt Industry 4.0 are:

Upskilling and Deskilling Employees #

The idea of deskilling employees might seem intimidating, but it offers more benefits than hiring a new workforce. Besides increasing employee satisfaction, it is a more cost-effective and faster process.

Searching for new employees, onboarding, and making them familiar with the workspace is time-consuming. In contrast, with a few inputs, it is easier to train existing employees within the organization.

Re-training for Soft Skills #

Soft skills prove as crucial as other skills in defining the competitiveness of the employees in their job. It makes them better communicators, improves the influx of innovative ideas, and increases their adaptability and ability to embrace new career challenges.

Some soft skill domains for training can include:

Emotional Intelligence
Collaboration
Creativity
Persuasiveness

Enhance Training Techniques #

To prepare to adapt to the digital revolution, your organization needs to improve its training methods. Two examples of achieving this goal are using AR and online performance support.

Implementing novel training techniques can give employees a better idea of what they would work with, improve the appeal of their prospective careers, and increase employee satisfaction.

How to Meet the Employees? #

Industry 4.0 training involves addressing individuals where they are, irrespective of their countries or cities.

Some of the training patterns we see in version 4.0 are just upgraded versions of training we’ve already seen. The following are some of them.

AR Training

In 2017, a study published in the Harvard Business Review discovered that augmented reality was already enhancing factory workers’ productivity.

Microlearning

Mobile devices are used to give quick information and instruction that is intended to help workers acquire new skills as they are required. These technologies provide information precisely when and where employees need it since they are made to be delivered in the manner in which the brain learns best.

On-the-job Training #

Employees learn while working by undergoing on-the-job training. Employ a strategy that is cumulative so that workers can gradually acquire the various skills they need.

As workers adapt to the requirements of industry 4.0 technology, this kind of training might also lead to decreased downtime.

Online Checklist #

Using digital checklists, employees can learn the procedures and systems required for a specific job or location.

Digital Support #

Mobile-first digital performance support gives employees immediate access to industry 4.0 technologies.

VR Training

While upholding quality standards and increasing output, virtual reality training can assist in lowering manufacturing costs. This training is urgent, given the global shortages and rising demand.

Companies that Implemented Training Modules for Workers for Smart Manufacturing #

Several organizations deny the impact of the fourth industrial revolution on how they conduct business. Even if companies understand the importance of Industry 4.0, they are either unprepared or struggling to identify talent, knowledge, and opportunities that come with the new industrial evolution.

Only some firms understand the essence of embracing industry 4.0 and have implemented training modules for workers for smart manufacturing. There are two ways of deskilling the existing workforce in manufacturing: By doing it within the organization or by outsourcing the training process.

Some organizations that have embraced Industry 4.0 and training methods for their employees are Schneider Electric, Bosch, SAP SE, ABB, and Hitachi.

What’s Next? #

According to Custom Market Insights (CMI), the market cap of Industry 4.0 was 118.11 billion US dollars in 2021 which is projected to reach approximately 155.76 billion US dollars by 2030, showing a CAGR growth rate of 14.3 percent.

These statistics are proof that Industry 4.0 isn’t just a buzzword. It is a revolution organizations need to prepare for so that their competition in the cut-throat marketplace doesn’t outrun them.

Related reading

Become a Successful Data Analyst from Scratch with These

Vin Patel — Fri, 19 Aug 2022 14:42:41 +0000

Key takeaways

Currently, data analysts are one of the most in-demand jobs due to the digital revolution, which has resulted in a massive amount of data that many organizations are not familiar with.
The story should be brief, straightforward, and accurately depict the subject matter.
Including project work experience and new skills on your data analyst resume will help you stand out from the crowd and land the job you want.

Currently, data analysts are one of the most in-demand jobs due to the digital revolution, which has resulted in a massive amount of data that many organizations are not familiar with. As a result, the data analyst will be able to provide meaningful insight into the data and apply it to the company’s advantage. As a result of the broad scope of this job, it should come as no surprise that companies are actively seeking data analysts. After reading this article, you should have a better understanding of what it takes to become a data analyst.

What exactly is the role of a Data Analyst? #

In order to provide information that can be used to make decisions, data analysts must collect, clean, transform, and model data. The best use of available data, making the best investment decisions, analyzing customer behavior, and identifying potential hazards are all tasks that data analysts perform for every data-driven organization they are part of.

Are you interested in learning how to become a Data Analyst? #

As a data analyst, you must have the necessary technical skills to perform more effectively daily, dealing with various problems in data analysis and within the organization. The information provided here may help choose a Data Analytic course for working professionals.

Learn how to become an expert data analyst from the ground up by following these simple steps!

Completion of the Academic Prerequisites #

A STEM (Sciences, Technologies, Engineering, and Math) background is the best place to begin learning the necessary skills for this vocation. Data analysts, on the other hand, must have a bachelor’s or master’s degree in one of the following fields:

● Business and information technology

● Mathematics\sStatistics

● Marketing\sFinance

● Information Technology

Develop a Solid Understanding of Programming Languages #

Data analysts require less coding knowledge and comprehension than data scientists. You must, however, be able to sort through and evaluate large datasets. However, the majority of businesses cannot afford these tools. As a result, Python and its numerous data visualization tools are the most popular choice. If you’re new to programming, Python is your best bet.

● Working knowledge of SQL fundamentals

As a data analyst, you’ve undoubtedly heard about this particular skill. You will be able to obtain data from various sources if you understand SQL. Understanding data recovery and integration may help you manage your data more effectively.

● Hands-on experience with Data Visualization Tools

Data analysts frequently employ data visualization tools. These data visualization tools do not require any technical knowledge to use. All you have to do is click and drag to get things to happen most of the time. These technologies necessitate a thorough understanding of the numerous characteristics required to evaluate and present data. A diverse skill set increases your chances of landing fascinating, one-of-a-kind, and potentially game-changing projects.

Despite its ease of use, Microsoft Excel is used in every industry and by every type of professional. Excel features such as pivot tables, equations for data manipulation, and graphs for visualization can be used to create genuinely exceptional dashboards.

● Be a Story-teller

Dashboards must be not only visually appealing but also functional. Each board item should have a solution-based narrative. This implies that business analysts must be able to present their findings clearly and concisely. The story should be brief, straightforward, and accurately depict the subject matter.

● Know about Machine Learning

When compared to traditional business intelligence solutions, machine learning enables organizations to gain more insights from both structured and unstructured data. It indeed provides leaders with the tools they require to plan and execute. Using machine learning and analytic in a broader sense allows you to respond to changing situations more quickly and extract more value from the rapidly growing treasure mine of data, making it an unavoidable tool for data analysis.

● Improve your Analytical Capabilities

Analytical abilities are, of course, essential for data analysts. Using these abilities, raw data can be collected, categorized, and evaluated. To be a successful professional data analyst, you must broaden your analytical skills and thinking style.

● Gain knowledge on a specific subject

Becoming a competent domain expert necessitates a thorough knowledge of the industry and the business for which you work. A company with an online store may require you to be familiar with the nuances of e-commerce for you to do your job correctly. For example, if you’re crunching numbers on mechanical systems, you might need to understand how they work.

As for the wide variety of industries, specialized knowledge varies from one to another. Working in the data analysis industry necessitates extensive domain knowledge, which is a required skill for all data analysts, regardless of location or business.

● Refresh Your Reasoning and Logic Skills

To comprehend complex algorithms and algorithms, a data analyst must have a strong understanding of logic. As a result, native solutions such as CRM and ERP systems can be easily analyzed, and complex reports can be generated. Working on your thinking skills will also make dealing with commercial issues easier.

● Make decisions and find solutions

These abilities serve as the foundation for analytical approaches. The primary responsibility of a data analyst is to provide reliable data for decision-making and problem-solving. As a result, it appears to be an excellent skill for a data analyst.

● Communicate well

Data analysts must be able to communicate effectively both verbally and in writing. To prepare, present, and explain data, you must communicate effectively with co-workers. When working with people, it is critical to communicate effectively.

Bottom-Line #

A data analyst’s job is very lucrative and well-paying, making it a very desirable career. Consider enrolling in the best courses to learn Data Science from Great Learning. Suppose you want to work as a data analyst but don’t have any prior experience. In that case, you should learn the fundamentals of data analysis, get involved in some data analysis projects, and build your résumé. Including project work experience and new skills on your data analyst resume will help you stand out from the crowd and land the job you want.

Related reading

The Role of Big-Data in Healthcare sector

Vin Patel — Thu, 04 Aug 2022 06:50:16 +0000

Key takeaways

The collected data could be efficiently utilized to reveal additional insight and to address business issues that were previously unsolvable using traditional analytic or software.
It can identify methods and processes that provide inferior results or are too expensive in comparison to the results.
On a broad level, healthcare stakeholders may depend on big data and predictive analytic to address critical problems like as readmission rates, high-risk patient care, staffing difficulties, and medication mistakes.

What is Big Data?

Big data is a broad term that refers to an extensive collection of complicated data, whether unstructured or organized. The collected data could be efficiently utilized to reveal additional insight and to address business issues that were previously unsolvable using traditional analytic or software. Typically, data scientists use artificial intelligence-powered analytic to examine these massive databases constructively to find relationships and correlations that might give valuable business insights.

Big Data in Healthcare Sector #

In healthcare, big data refers to the application of prescriptive, predictive, and descriptive analytic services to derive actionable insights from healthcare data. Three objectives underpin big date’s use in healthcare:

Improve clinical results through the use of patient data;
Boost employee productivity by using operational data;
Utilize financial data from healthcare to help hospitals or healthcare institutions enhance their income stream.

The “Three Vs” are the pillars of big data technology - volume, velocity, and variety. These three characteristics or aspects define effective big data analytic in healthcare.

Volume refers to the massive quantity of data generated by healthcare organizations via their applications, portals, websites, and EHRs (electronic health records)
The term “velocity” relates to the rate at which datasets are created and processed.
Variety refers to the numerous sorts of data that we may currently produce, collect, and evaluate.

Apart from these three, big data has two new Vs: veracity and value.

The term “value” refers to the monetary value of the data being created, gathered, or evaluated.

Finally, but certainly not least, veracity refers to the reliability, integrity, or quality of healthcare organizations’ data generation, collection, and analysis.

Big data is projected to enter the healthcare industry more quickly and deeply than it did in other sectors such as media, financial services, and manufacturing. This is unsurprising given that healthcare is the largest private sector employer in the United States and accounts for 20% of GDP.

Indeed, it is anticipated that the worldwide healthcare big data market would expand gradually at a compound annual growth rate (CAGR) of 22.07 percent to reach around $34 billion by 2022.

Another comprehensive analysis predicts that big data in healthcare will grow at an astounding 36 percent compound annual growth rate through 2025.

The term “big data in healthcare” refers to the massive amounts of health data gathered from a variety of sources, including electronic health records (EHRs), medical imaging, genetic sequencing, mayor records, pharmaceutical research, wearable, and medical devices. It differs from typical computerized medical and human health data used for decision-making in three ways: It is available in unprecedented volumes; data travels at a rapid rate and encompasses the vast digital universe of the health business; and, because it originates from a variety of sources, it is extremely varied in structure and character.

Applications of Big data #

The major application of big data in the field of health care sector are discussed below:

Maintaining patient health and preventing illness and disease should always be a top concern:

Consumer gadgets such as the Titbit activity tracker and smart watches, monitors an individual’s physical activity and can also report on particular health trends. The resultant data is already being uploaded to cloud servers, where it is being used to tell clinicians about their patients’ general health and wellness.
Titbit has already collaborated with United Healthcare, which pays its insured members with up to $1500 per year for frequent exercise. The One Drop app from Informed Data Systems for Android and Apple is causing substantial improvements in A1c levels in persons with diabetes. Meanwhile, Apple’s HealthKit, CareKit, and ResearchKit applications utilize the technology built into Apple’s mobile devices to assist patients in managing their diseases and to enable researchers to collect data from hundreds of millions of users globally.

Expanding diagnostic services enables patients to receive expert treatment more easily: Apes for mobile devices, such as Etna’s Triage, advise people about their medical conditions based on aggregated data and even recommend them to seek medical treatment depending on their input.

Apple has teamed up with Stanford researchers to examine if the Apple Watch’s heart sensor can be used to detect atrial fibrillation, a disease that claims roughly 130,000 lives each year in the United States. If the gadget is successful in identifying the illness, Apple can alert wearers to the need for medical treatment.
Propeller Health utilizes a Bluetooth-enabled sensor that connects to inhalers and spirometer used by individuals with asthma or COPD. The business monitors ambient conditions at sensor locations and delivers information directly to patients’ phones, enabling them to gain a better understanding of the reasons of their symptoms and take preventative steps. Additionally, the business sends reminders when prescriptions should be taken. Propeller says that patients are reporting 79% fewer asthma episodes and 50% more symptom-free days as a result of its 34 peer-reviewed papers.

Cost savings: Medical data provides clinicians with more insight, which translates into improved patient care, shorter hospital stays, and fewer admissions and readmission.

Additionally, analysis of healthcare big data helps to a better understanding of patient cohorts at highest risk for sickness, allowing for a proactive approach to prevention. In brief, study of healthcare big data enables the identification of outlier individuals who spend much more health services than the average. It can identify methods and processes that provide inferior results or are too expensive in comparison to the results.
The knowledge gained through big data analysis provides healthcare practitioners with clinical insights that would not be available otherwise. It enables them to prescribe therapies and make clinical decisions more precisely, removing much of the guesswork associated with therapy and resulting in cheaper costs and improved patient care.

Importance of big data in health care sector #

Yes, there is growing enthusiasm for the potential of big data in healthcare, and investment in analytic is expanding. However, according to a recent Dimensional Insight research, 56% of hospitals and healthcare institutions lack adequate big data governance or a long-term analytic strategy. Here are some of the reasons why big data is critical for healthcare:

Inadequate data governance results in record duplication, unclaimed reimbursements, financial benchmarking issues, and other operational inefficiencies. That is something that big data can correct!
Patient care is also becoming more complicated, and without adequate analytic, it becomes increasingly impossible to offer high-quality, safe, and effective patient care. Numerous healthcare companies have had inconsistencies between their clinical and accounting departments as a result of data errors and incompatibilities.
In healthcare, big data is widely utilized to assist in identifying and managing patients who are both high-risk and expensive.
Indeed, 71 percent of respondents to the aforementioned research reported seeing discrepancies between data from several sources inside their company, most notably financial, clinical, and administrative information. Additionally, 51% of executives discovered discrepancies between data from several clinical departments.
Additionally, big data is utilized to identify high-risk regions where patients may get more efficient treatment, thus lowering costs and increasing patient satisfaction.
By assisting payers and healthcare providers in identifying high-risk and costly patients, big data and analytic technologies enable these people to get appropriate intervention and save money on preventative treatment well in advance.

Challenges to big data: #

The Difficulties of Data Aggregation To begin, patient and financial information is often dispersed among many payers, hospitals, administrative offices, government organizations, servers, and file cabinets. Bringing it all together and preparing for future collaboration amongst all data providers when new data is generated takes considerable preparation. Additionally, each participating business must comprehend and agree on the kinds and formats of big data to be analyzed.
Management Obstacles Finally, achieving the promise of big data analytic in healthcare would require companies to change their business models. Data scientists will very certainly be required, as will IT personnel with the necessary expertise to operate the analyses.
The scalability of data is also a problem in this sector, that needs to be taken care of.

Road ahead #

Big data is just now starting to transform healthcare and propel the sector ahead on a number of fronts. The advances in medicine, technology, and finance that big data in healthcare offers will enable healthcare companies to enhance patient care and generate value. However, it will need stakeholders — providers, payers, pharmaceutical manufacturers, government and politicians, as well as scientific and research communities — to cooperate and innovate in order to reinvent their systems’ design and performance. They must develop the technical infrastructure necessary to store and consolidate the enormous amount of healthcare data expected to reach 2,314 exabytes by 2020, according to industry experts. Additionally, they must invest in human capital—information technology professionals, data scientists, data architects, and big data engineers—to lead us into this new and exciting frontier of human health and well-being.

Final verdict #

Big data has the ability to completely transform healthcare. Healthcare companies should make significant investments in big data in order to improve patient outcomes, reduce costs, and increase efficiency across all departments. More importantly, big data will enable doctors and hospitals to provide more focused treatment with improved outcomes. For pharmaceutical firms, big data is a catalyst that will aid in the creation and development of more creative medicines and products.

On a broad level, healthcare stakeholders may depend on big data and predictive analytic to address critical problems like as readmission rates, high-risk patient care, staffing difficulties, and medication mistakes.

Related reading

Blockchain Interoperability in Healthcare Industry

Vin Patel — Thu, 04 Aug 2022 05:46:40 +0000

Key takeaways

Introduction to Electronic Health Record The digital counterpart of the patient’s paper chart is commonly known as “Electronic Health Record” -EHR.
Most organizations and academics believe blockchain technology to be one of the frontiers to address the EHR interoperability issue.
That implies that blockchain is not far away as an essential component of the medical sector.

Introduction to Electronic Health Record

The digital counterpart of the patient’s paper chart is commonly known as “Electronic Health Record” -EHR. EHRs are patient-centered records in real-time that provide information for authorized users immediately and securely. EHR systems are constructed to go beyond conventional clinical data gathering and provide a wider perspective for patient care. EHRs are a crucial component of IT health and can:

Include patient history, diagnosis, medicines, treatment plans, vaccination dates, allergies, radiological imaging, laboratory findings, and test results.
Allow the proof-based methods physicians may utilize for deciding the treatment of a patient.
Provide transparent information available in a structured manner which lowers the clinical errors and administrative costs.

An EHR is characterized by its ability to digitally generate and manage health information by authorized personnel that can be shared with other physicians across various healthcare organizations.

EHR Interoperability #

The lack of access to medical records has been recognized as a barrier to transparent and efficient treatment for patients for a long time. While systems with electronic health records (EHR) assist in resolving this problem to an extent, many of these systems are heterogeneous and exhibit minimal interoperability within platforms, and demonstrate varying success integrating into clinical workflows. Many EHR systems are thus unable to provide key advantages of digital technologies, like data sharing capabilities, streamlining user experience, and advanced analytic. The lack of interoperability with various EHR platforms is very challenging as complex patients are present with multiple healthcare facilities in different healthcare jurisdictions.

The Lack Of Interoperability Causes Many Problems: #

• Restricted exchange of data: Currently, the EHR solutions available in the market limit patient information’s open flow across different platforms. For data transfer, most vendors impose interfacing costs. A lack of data sharing also encourages repetitive investigations, consuming additional healthcare resources and potentially delaying definitive care.

• The lack of a unified perspective of patients: The existing solutions provided by EHR vendors are compartmentalized, which fails to capture the entire medical journey of the patient. There is no common platform or API which enables smooth communication across different proprietary and older systems.

• Lack of insights: The advantages of data mining and data storage are significantly limited by the lack of full data and reluctance on the part of EHR suppliers to provide customized interfaces.

Challenges in Enabling Interoperability for EHR Systems #

Lack of interoperability leads to massive losses, yet the number of efforts the healthcare ecosystem is making for solving the issue is far lower than expected. Much contributes to the parties’ unwillingness to interoperate with EHR systems:

Failure to share data: Data sharing between payers and suppliers has always been a problem primarily because they operate independently and do not share common data exchange standards. This divide has produced cultural inertia in interoperability adoption for these parties. Due to the present drive towards value-based care, numerous efforts are underway to integrate care and delivery, which needs a seamless exchange of information across stakeholders.
Data security and integrity: The view of a single interoperable platform, which makes possible access to all health information in one place, raises significant issues about safety and privacy. The recent Anthem violation of medical data and WannaCry ransomware is evidence that a caber assault is threatened. When there is interoperability, the database becomes the target for caber attackers if the whole healthcare database is housed on a single central platform. Furthermore, it is essential for accurate health data not to be manipulated by any authorities.
Misaligned incentives: The motivations of stakeholders are not aligned with the interoperability objectives.

Suppliers: Currently, each EHR vendor offers its own systems. When interoperability becomes a reality, consumers will no longer find their own lock-in and will be free to pick any seller. This would weaken the competitive advantage of EHR companies and cannibalize their current income sources.
Buyers: Current users of EHR systems have spent trillions of dollars to purchase and set up traditional EHR systems. The use of an interoperable platform may force them to replace their current systems, resulting in the possible loss of trillions of dollars.

Government regulations: Forcing system interoperability is not an option with huge numbers of stakeholders in a complex information processing environment. An actual implementation would need the buy-in of all the parties involved who should be prepared to accept specific principles of governance and commercial agreements. Nevertheless, the involvement of public authorities would be essential in the timely implementation of advice and certification criteria and standards.

Why Chose Blockchain To Enable Interoperability #

Most organizations and academics believe blockchain technology to be one of the frontiers to address the EHR interoperability issue. Blockchain is designed to address the issue of health interoperability challenge by means of distributed ledgers, an information ecosystem similar to the healthcare landscape that spreads across different nodes (payers, providers, EHR platforms, etc.). Simply said, blockchain is an encrypted or encoded ledger that is a transaction database in the form of chain blocks. Multiple users verify these blocks via consensus methods over a public or private network. The cryptographic designs built-in blockchain technology prevents data tampering and unauthorized access.

Using blockchain technology to make data accessible for patients, providers, research institutes, and public entities offer several possible advantages. The use of healthcare at the patient level is a well-known trend that has led to a large number of patient data produced through mobile apes and other digital technologies. Patients can have an active and involved role in their treatment by making healthcare information more accessible. In addition, all pertinent health information would be sent to doctors at every meeting to allow effective and customized treatment plans and eliminate the chances for duplicate investigations. Enhanced access for scientists to the anonymized medical information on a scale may lead to more rigorous studies and better evidence-based decision-making. In addition, innovative efforts from developers and pharmaceutical companies could lead to lower research and development costs by making more accessible information and consent management via a patient-facing platform, thus providing patients as well as providers with more time-to-market and cheaper products and services.

Despite the advantages mentioned above, many major problems need to be addressed before using them safely and effectively. Like any technology that disrupts healthcare, blockchain must be properly assessed in light of their requirements, and providers must equip themselves to make good use of the technology. While blockchain may provide a better information platform, it is simple to believe that the aforementioned advantages are not immediately achieved when implementing a blockchain system. Although a blockchain system could facilitate sharing of information between healthcare systems, patients, providers, and governmental organizations, many technological, legal, and institutional obstacles restrict its full potential in the healthcare sector.

Barriers To Blockchain Adoption #

The use of blockchain as an interoperability platform will need the resolution of several issues:

• Scalability: The EHRs include the full health records for each person, and the database continues to grow with time. Traditional blockchain have limits on the maximum transaction pace because of the fundamental nature of blocks continuing to be added to a chain structure. Due to the complicated transaction logic, Blockchain transactions need sophisticated hardware. This begs the question: can blockchain be extended quickly enough to include EHR data?

• Micropayments: The healthcare industry is being inundated with related medical equipment in the near future. Micropayments have to be enabled across various IoT manufacturers to make sure these devices constantly communicate with one other and share real-time medical data. Because the conventional blockchain requires paying a fee to the miners for their computational power, it might mean paying a fee greater than the transaction value itself when processing the micropayments through blockchain.

• Power play of miners: Validation of network members known as miners is carried out in conventional blockchain. Some of the EHR prototypes based on blockchain have stimulated the participation of public health authorities as miners and offer them access to aggregate anonymized data as mining income. This provides a way to filter or postpone specific transactions for the privileged group.

Interoperable Ecosystem – An Ideal Setup #

The ecosystem utilizes both the forces of blockchain and the power of computation, but removes the constraints of current technologies simultaneously.

The ultimate platform should be a government or a public health agency distributed public leader, which allows all ecosystem members to connect and exchange data. The public ledger will be linked to every node family, including clinics, doctors, hospitals, and medical equipment; however, data on their systems will be on the private, licensed network or in the proprietary EHR. The fundamental concept of an authorized private network is identical to that of an authorized blockchain.

Blockchain platforms have a balanced set of rules to enable wider usage and flexibility to meet variations in the local practice to realize their full potential. In addition, technological solutions can be emphasized, but human aspects must be taken into account. Just as Blockchain essentially opposes a silted, decentralized approach, solutions should be decentralized as well and involve a broad range of multidisciplinary experts, including health care professionals, legal professionals, developers of technology and patients, so that information can be exchanged optimally while safeguarding patient safety.

The advantages of utilizing blockchain to make EHR more effective are undeniable. It will lead to customized and participatory therapy alongside results-based models; helps researchers develop better goods for less expense; helps to verify claim information quickly and accurately and improves settlements; reduces fraud, and helps to comply with regulations.

Conclusion #

The industry continues to focus on numbers — more patients, more consultations, additional testing, more operations and more hospitalizations. If the balance is translated into result-based charges, patient records are encouraged – interoperability becomes a compelling offer, and blockchain is the desired answer. It is already widely agreed that changes to the business model can only solve healthcare problems. That implies that blockchain is not far away as an essential component of the medical sector.

Related reading

Quantum Computing's Disruption In Finance Industry

Vin Patel — Wed, 03 Aug 2022 20:53:29 +0000

Key takeaways

(GIA)’s study shows that quantum computing has already brought changes in the market post-COVID-19.
All these are unbreakable and secured if it is used through conventional computers and advanced hardware systems.
They must be ready to handle challenges as soon as quantum-based solutions would take place at a high level.

Did you know that global quantum computing is expected to reach $411.4 million by 2026? Global Industry Analyst Inc. (GIA)’s study shows that quantum computing has already brought changes in the market post-COVID-19. The drastic transformation demanded by the pandemic in banking & finance, healthcare, transporting, space, defense, and other sectors has given an edge to the quantum computing market. Google has already got a quantum computer that can perform a calculation in 200 seconds only, while a traditional supercomputer would need 10000 years to solve the same problem.

Two main characteristics of quantum computers, superposition, and entanglement do things simultaneously and can transform anything. Because of high performance and speed, quantum computers can run complex algorithms and simulations. This feature is rising the quantum computing usages in various industries, especially finance.[R1] [Vs2]

Let’s discuss what problems took the attention of experts to initiate uses of quantum computing, how quantum computers are assisting the industries in solving their complex issues, how the industry is benefitted after adopting quantum computing, and if any banks are using and benefitting from it.

Why does finance/banking need high power quantum computers? #

Do you know, a quantum computer needs 1.3 hours (10 12 steps) to run a million operations per second using a quantum walk algorithm, whereas a classical computer needs 5 billion years (10 29 steps) to run trillion operations per second using a best classical computer or a cluster computer.

Globalization within finance industry has tremendously increased the number of daily business operations. Banks are expanding their networks and services around the world. Ordinary people become dependent on mobile banking, internet banking, and digital transactions. More than 50% of financial transactions occur over the internet today.

The applications of big data, artificial intelligence, and the cloud in finance have been improving with time. It is not only bringing transformation but also bringing challenges in different applications: investment banking, asset management, corporate banking, and retail banking.

Classical computer features provide ordinary business solutions. It’s one main limitation: “Doing one operation at one time” has been decreasing its usages.[R3] [Vs4] Solving real-world banking problems need high power and time. Classical computers can’t solve this intractable problem, but quantum computers can do that more accurately and faster.

Simulation, Machine Learning (ML), and optimization are the three most common concepts used in finance to implement various financial services and operations. All these support banks in different aspects including, risk management, economical pricing instruments, finding the best investment strategy, managing cash in ATM networks, capital management, credit scores, and fraud detections.

A recent paper, “Quantum Computing for Finance: State of the Art and Future Prospects” published in 2020 has highlighted problems, benefits, and various challenges. The authors of the paper have also pointed out the issues in these three categories. The following table shows multiple problems during the stage of the customer life cycle:


Customer life cycle	Simulation problems	Optimization problems	Machine learning problems
Customer identification	How to obtain new revenue sources?	How to improve supply chain efficiency, minimize risks, avoid late payment, and enhance liquidity?	Refining customer ratings, KYC, and how to avoid non-compliance annual penalties.
Financial products	Better management of value at risk, balance sheets, etc.	How to reduce operation costs, capital requirements, and systematic risks?	How to perform non-performing loan, improve recommendations systems, and customer retention?
Monitor transactions	How to optimize risk using a more precise calculation process?	How to keep relevancy in the portfolio based on market changes?	How to assess false alerts and suspicious notifications?
Customer retentions	How to improve risks analysis?	How to avoid customer churns?	How to maximize customer engagement?

In short, dependency on classical computers for the execution of operations linked with optimization, prediction, and simulation is not a good idea. Because it needs high power and high performance, and classical computers are unable to provide both. Finance industry needs supercomputers like quantum computers to process accurate data within seconds.

In a recent live discussion, a panel of experts discussed the value of quantum computing, quantum strategies, and the industry’s quantum readiness to adopt it. SGInnovate had initiated the session with the support of the High Commission of Canada.

Quantum computers have the potential to solve real and complex problems by offering better solutions. Various algorithms and methods have been used to provide specific solutions based on each problem. For instance, sampling methods are the best way to resolve the simulation problems as quantum computers need fewer samples than classical samples. Similarly, other algorithms have been used along with quantum computers to solve problems related to optimization, simulation, and machine learning.

Positive Impacts of Quantum Computing #

After discussing the problems and understanding that quantum-based solutions can solve these real-world banking problems, you can measure the benefits of quantum computing in finance. Here are some of the benefits quantum computing could offer the finance industry:

Reducing regulatory penalty costs
Avoiding human labor
Improving customer retention, interaction, and satisfaction
Increasing financial activities so fast
Improving cash flow
Reducing capital needs
Offering new investment methods
Effective risk identification and management

IBM has also pointed out these benefits after adopting quantum computing. The company has classified the use cases for finance industries into three areas: targeting and prediction, trading optimization, and risk profiling. We can consider these benefits, a positive impact of quantum computing, which lowers the risks in finance industry because of uncertainties.

Side Effects of Quantum Computing in Finance #

Cryptography is a well-known security method in finance. Symmetric keys, asymmetric keys (public keys), and hashing cryptography algorithms have protected sensitive banking data and provided integrity security checks. All these are unbreakable and secured if it is used through conventional computers and advanced hardware systems. Although, the same security algorithms become vulnerable and risky if superpower quantum machines would use them.

The quantum computers one characteristic: solving mathematical problems exponentially faster than the conventional computer, makes the standard cryptographic algorithms outdated and reduces the strength of public keys and hashes. In other words, a quantum computer is threatening the most regulated industry and asking to compromise in all aspects: client data, software/websites used for transaction and interaction with clients, and hardware that perform authentications during the payment process.

The leading magazine, Global Banking and Finance Review has pointed that quantum computing threat to cybersecurity. The National Security Agency had already warned of this quantum computing threat in 2016. After this, the agency has started researching new encryption algorithms and data protection methods to defeat the quantum threat. After spending four years, the National Institute of Standards and Technology’s post-quantum cryptography program has been chosen as the first standard to counter quantum decryption threat. This global effort would support overcoming the challenge of quantum threats.

How Are Real Banks Leveraging Quantum Computing #

You have already seen how diverse segments in banking & finance have increased not only data & operations but also realistic problems. When realistic banks found that investment in quantum computers and technologies could solve the intractable problems, they started making decisions. To date, many banks have already leveraged quantum computing to make their complex computing easy.

The leading global resource about quantum technology, The Quantum Daily, has revealed how 11 global banks have adopted quantum technologies. The list covered banks in US, UK, and Japan. JPMorgan Chase & Co., HSBC, City group, Barclay, Goldman Sachs are some names of those financial services firms. Some of them are in the initial stage of quantum programs, whereas some banks have completely adopted to quantum computing methods. According to their study, quantum technologies can solve problems like portfolio volatility optimization and value at risk calculation.

The Royal Bank of Canada (RBC) announced a $1.78 million investment for developing advanced cybersecurity systems and privacy tools in 2018. The study is taking place at the University of Waterloo. An enhanced education program, CrypotWorks21, is a part of this research to bring post-quantum cryptosystems to their bank.

Multiple companies and working groups are developing quantum computing systems to support the finance industry. Theworking group, Multiverse Computing, is one of those companies that has already acquired working experience with various banks like Banka, BBVA. The big names like Forbes, McKinsey, IBM, The Economist, and Boston Consulting Group have already declared this company a world leader in quantum computing service offering. [R5] [Vs6]

The company brought Singularity, the first quantum & quantum-inspired toolbox for this industry. Recently, the company has resolved a real-world problem, dynamic portfolio optimization, faced by Banka using D-Wave quantum computers. We can say the problem was known as quantitative finance in simple language, and classical computers could not resolve it. The experts find an optimal trading trajectory for an investment portfolio of assets over time by considering constraints and account transactions.

Final Thought #

Various experts have declared that the era needs quantum computing to solve intractable problems faster. The director of the Quantum Alliance Initiative, Arthur Herman, has also highlighted that Q-Day is on a threshold, and it is taking place of classical computers in his recent article. Here, Q-day refers to a term when large-scale quantum computers can easily calculate prime numbers used in public encryption systems to protect sensitive data of banking, finance, and other significant sectors. Implementing quantum-based solutions is a time-taking and complex process. Multiple software[Vs7] and interfaces are available in the market, which might confuse experts; thus, experts must understand the specific problems first. And then decide the appropriate quantum application to solve the problem. They must be ready to handle challenges as soon as quantum-based solutions would take place at a high level.

Related reading

Cognitive Manufacturing and The Beginning of a Revolution

Vin Patel — Wed, 03 Aug 2022 11:08:41 +0000

Key takeaways

Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes.
Our factories will become more efficient and productive, as well as less wasteful, as a consequence of the support of smart machines that continue to grow wiser as they have access to more data.
Manufacturing must grow into cognitive manufacturing in order to fully advance to Industry 4.0 and beyond.

Introduction #

Have you ever imagined a world without technology or new inventions? Let us try to think of one such world, where there were no such inventions to revolutionize mankind or to ease the lives of people. Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes. Imagine a world functioning on needles and thread. Does it sound weird? Yes, it does. Do you know what’s even worse? Imagine a world without online shopping. In the time that I am finishing this line, I’m sure a thousand more orders have already been placed on various online shopping apes in my locality alone. If I tell you to imagine these, then basically I am telling you to travel through time, and it won’t be a time travel back to just a few years, but you would have to go centuries back. But here the thing is, we talk about technology and its effects in almost everything, especially in easing our lives but then we just forget about one thing that has really been made easier by technology and inventions: it is the manufacturing of products.

When we talk about manufacturing, we need to talk about a kind of technology that is ready to revolutionize the industries and also this whole world, “Cognitive Computing.” Implementation of cognitive computing has bought a new revolution alongside, i.e., Industry 4.0.

Let us dive in now and try to understand what cognitive computing is.

What is Cognitive Computing? #

#

Cognitive computing is the application of thinking, language processing, machine learning, and human capabilities to assist ordinary computers to solve issues and analyzing data more effectively. A computer system can handle complicated decision-making processes by learning patterns and behaviors and becoming more intelligent.

A cognitive computing technology platform adapts and makes sense of information, including unstructured information such as natural speech, using machine learning and pattern recognition.

Now that we know what cognitive computing is, let us try to know what cognitive manufacturing is.

What is Cognitive Manufacturing? #

#

Cognitive manufacturing employs cognitive computing, the Industrial IoT, and sophisticated analytic to optimize manufacturing processes in previously unimaginable ways. It assists enterprises in improving basic business indicators like productivity, product reliability, quality, safety, and yield while minimizing downtime and costs.

Why Does Cognitive Manufacturing Matter? #

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Cognitive technologies delve deeply into a manufacturing process and business environment to extract information with real-world value for a company.

Cognitive manufacturing uses cognitive computing, the Industrial Internet of Things, and sophisticated analytic to digitize, analyze, and improve manufacturing processes in previously unimaginable ways.

Cognitive manufacturing is effective because it integrates sensor-based data with machine learning and other artificial intelligence skills to discover patterns in structured and unstructured data from plant, company, and industrial systems. It collects pertinent data in real-time and applies analytic to produce unparalleled levels of understanding and insights into the production process. It automates reactions based on its findings and provides actionable information as well as continually updated knowledge to production decision-makers.

Cognitive manufacturing has revolutionized the industry. It is said that it has made the world ready for industry 4.0. Let us dive in a bit more and try to understand what industry 4.0 is.

What is Industry 4.0 #

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We are in the midst of a substantial transition in the way we make products as a result of factory digitalization. This change is so compelling that it is being dubbed Industry 4.0 to signify the fourth manufacturing revolution. From the first industrial revolution (mechanization via water and steam power) to mass production and assembly lines via electricity in the second, the fourth industrial revolution will build on what was started in the third with the adoption of computers and automation, enhancing it with smart and autonomous systems powered by data and machine learning.

Now, and in the future, as Industry 4.0 develops, computers are linked and communicating with one another in order to make choices without the assistance of humans. Industry 4.0 and the smart factory are made feasible by a mix of caber-physical systems, the Internet of Things, and the Internet of Systems. Our factories will become more efficient and productive, as well as less wasteful, as a consequence of the support of smart machines that continue to grow wiser as they have access to more data. Finally, it is the network of these digitally linked machines that produce and exchange information that results in the ultimate power of Industry 4.0.

Cognitive Manufacturing and Industry 4.0? #

The transcontinental railroad, the cotton gin, electricity, and other technologies had long-lasting effects on civilization. The same thing happened when we learned about the breakthrough Artificial Intelligence and its applications, and Industry 4.0 was in the works. Businesses all across the globe were striving to implement the concept of Enterprise Cognitive Computing (ECC). It employed artificial intelligence (AI) to improve corporate processes by embedding algorithms into apes that assist the organizational process. Cognitive Computing applications can automate repetitive, formulaic operations and give accurate results in large magnitudes.

Cognitive Computing paved the door for a technology known as Cognitive Manufacturing, which is notably valuable for manufacturing firms. It is also a cognitive method to integrate AI algorithms with industrial procedures in order to accomplish desired results.

Cognitive Manufacturing uses cognitive computing, industrial IoT, and sophisticated analytic to digitize, comprehend, and improve manufacturing processes. It enables manufacturing processes to collect data from various sources and analyze this data in order to provide important knowledge about processes, machine work efficiencies, employee efficiency, and workload statistics with a thorough analysis that can render suggestions to optimize each department of the manufacturing paradigm.

By utilizing AI algorithms on diverse operational applications, it enhances productivity, scalability, dependability, and security. Cognitive manufacturing completely leverages data across systems, equipment, and processes to gain actionable information throughout the whole value chain from design to support. Manufacturing must grow into cognitive manufacturing in order to fully advance to Industry 4.0 and beyond.

End-Note #

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A few hundred words ago, I made you imagine a world without technologies, online shopping, and necessary inventions. Let us now end that time travel and think about the delivery of online shopping goods with drones. That is what we call a future in front of our eyes. One such future is cognitive computing and this when combined with the manufacturing sector gives rise to an industrial revolution. This is what we call the future. The beginning of industry 4.0. And cognitive computing is what is making this world ready for a kind of time travel we couldn’t have imagined. So, however turbulent it is going to be, trust the power of teach. It is going to be a safe journey.

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Questions and answers

Have you ever imagined a world without technology or new inventions? #

Let us try to think of one such world, where there were no such inventions to revolutionize mankind or to ease the lives of people. Imagine a world without cloth mills and without the invention of those big machines that are used nowadays to stitch clothes.

Do you know what’s even worse? #

Imagine a world without online shopping. In the time that I am finishing this line, I’m sure a thousand more orders have already been placed on various online shopping apes in my locality alone.

Digitalization to Decarbonization

Vin Patel — Tue, 02 Aug 2022 12:18:05 +0000

Key takeaways

The daunting issue of global warming has plagued major industrial sectors all over the globe.
Recently in October, as a part of its decarbonization goal of 2030, Apple declared that the number of its suppliers committed to deploying 100 percent clean energy has more than doubled since 2020.
As private and public entities collaborate and work on technological developments that can be harnessed to achieve zero carbon emissions, we can perhaps, have hopes to expect a carbon-free future sooner than envisioned.

The daunting issue of global warming has plagued major industrial sectors all over the globe. In a world characterized by increasing industrialization, urbanization, and a host of other issues including financial crises and food security, increasing carbon emissions stand out as a pressing problem that requires immediate action.

According to the World Meteorological Organization, there has been a considerable increase in temperature even in 2021 – the global mean temperature for this year was approximately 1.09°C more than the 1850-1900 average, indicating urgency for a solution.

Climate Change and Importance of Decarbonization #

Climate change has been a persistent problem since several years now. Excessive deforestation, improper waste management, and oceanic neglect are among the many reasons that have led to fluctuating climatic cycles, bringing about shrinking glaciers, heavy precipitation, rising maximum and minimum temperatures, and higher ocean temperatures. Carbon emissions are at an all-time high, brought about majorly by built environment.

According to the Urban Land Institute, built environment, i.e., man-made infrastructure is responsible for almost 40% carbon emissions worldwide. Think tank Chatham House propagates a similar line of thought, claiming that cement is responsible of around 8% of global carbon emissions.

In a scenario as grave as this, decarbonization has become more significant than ever before. Decarbonizing man-made structures with carbon-negative construction technologies is just one of the many ways to lower emissions and create a healthier environment. Energy-efficient buildings characteristic of enhanced HVAC systems, smart energy management systems, light upgrades, and better insulation are more than likely to have reduced carbon footprints, increasing energy efficiency by 20%, to say the least. As per energy.gov, U. S. consumers are said to have saved around USD 14.7 billion in energy costs merely by making the switch to LED lighting.

Decarbonization goes well beyond constructing carbon-negative buildings though. Creating a decarbonized economy involves a host of strategies such as energy management with analytic solutions, alternative fuel usage in vehicles (or better still, to go full steam with electric vehicles), using renewable more often than not, and adopting sustainable procurement practices. Going carbon-free has a slew of advantages, significant among these including longer life expectancy and better economic output as a whole.

How to Become Carbon-neutral with the Help of Technology #

In a bid to mitigate global warming and achieve considerable reduction of greenhouse gases, authorized bodies the world over have been collaborating to bring about vital initiatives. One such major initiative is the Paris Agreement. It is a legally binding international treaty pertaining to climate change, signed by 196 Parties on 12 December 2015 in Paris. It came into force on 4 November 2016. Its implementation involves considerable social and economic transformation, aided by technological developments to achieve climate change and reduce GHG emissions.

In a manner of speaking, massive advancements in technology may be one of the ways why our planet is currently in so much turmoil. The same technologies and the ingenuity however, can be easily deployed to ensure that we become carbon-neutral soon enough. The Internet of things, digitization, and artificial intelligence, in addition to numerous other technologies, will help lead the way to a decarbonized economy.

The Internet of Things (IoT) for example, can be effectively utilized to collect, store, and analyze data and information pertaining to buildings, plants, energy systems, and the like. In essence, this will enable businesses to adopt solutions that can measure actual device usage, control over/underused, match consumption to targets, etc. Adopting AI on a large scale will help measure and control a company’s carbon usage and help set sustainable targets for the future.

Speaking of AI, adopting Artificial Internet of Things (AIoT) will help in realizing a carbon-free economy, as it helps source real-time data from numerous systems, helping companies organize, collect, and collate data into detailed reports. Eventually, these reports help in monitoring and measuring emissions and enhancing data quality.

In an era where globalization and industrialization are rapidly shaping developments worldwide, digitalization may emerge as a double-edged sword, depending on where man chooses to put it to use. That is to say, some estimates claim that global data traffic is expected to triple to approximately 400 billion gigabytes monthly through 2022 – which is equal to the storage space of 100 billion DVDs. In essence, digitalization may be causing more carbon emissions than ever.

Having said that, digitalization can be conveniently deployed to better monitor and understand the ecological footprints of various products, although, this would require modern technologies to be completely integrated into the society. Digital solutions can be used to ensure clean air, lesser pollution, preservation of biodiversity, climate protection, and more. In the form of smart textiles for instance, digital solutions can be used to convert disposable items into products that are durable and recyclable. In the wake of COVID-19, with the work-from-home culture at the forefront, home office solutions such as video conferencing tools and the like gained precedence, greatly reducing unnecessary travel.

In a nutshell, technological solutions can be actively pursued to reduce carbon emissions and accomplish the net-zero emission targets promised by private and public entities. To ensure that carbon management solutions remain a major part of emission reduction agendas, technologies supporting offset integration and real-time measurement must be adopted on a large scale.

The Trend of Corporate Decarbonization – What Does the Scenario Look Like? #

Climate protection is no longer an option or a fad; it is real and imperative and requires immediate attention. Corporate firms, to that end, have been doing everything in their capacity to bring about fundamental changes in the energy ecosystem.

According to the World Energy Outlook 2020, emission reductions of around 2 billion tons can be achieved in 2030 by employing minor changes such as walking shorter distances, flying less, or resetting air conditioners. To that end, presently, big teach companies have been working toward carbon offsetting, direct air capture technology, other carbon capture, and utilization and storage (CCUS) technologies. In addition, many corporate entities have been working to scale up vital clean energy technologies such as wind and solar energy, geothermal, advanced nuclear energy, and green hydrogen.

A noteworthy example of corporate decarbonization is that of Kalmia Cement. The leading Indian cement maker apparently manufactures the lowest carbon intensity cement in the world – around 20 percent lesser than the cement average the world over, and is further planning to scale up its targets. The company has committed to become carbon negative by 2040.

Speaking of yet another instance defining the current scenario, Levi Strauss & Company has identified the significance of furthering its climate protection target, not only to adhere to the aims of the Paris Agreement, but also to ensure that its supply chain remains protected from the impact of climate change. In 2018, Levi Strauss & Co. committed to a target of 90 percent emission reductions in all its owned and operated facilities the world over and a 40 percent emission reduction in its supply chain, by 2025.

Sustainability Efforts by Companies #

Decarbonization efforts at the corporate level have not been limited to Kalmia Cement and Levi Strauss & Company; in recent times, a score of companies have been working to achieve the zero emissions target. A gist of the same is provided below:

McDonald’s Corporation #

Globally renowned food chain, McDonald’s Corporation announced its decarbonization commitment long back, having pledged to achieve net zero emissions spanning its operations worldwide, by 2050. As part of this commitment, the company has joined the SBTi’s (Science Based Targets initiative) Business Ambition for 1.5°C campaign. To that end, the firm plans to increase the levels of emission reduction in its current 2030 science-based target. Since 2018, McDonald’s has already achieved 8.5% reduction in restaurant and office emissions and 5.9% decrease in supply chain emissions.

Google #

Teach giant Google, since long, has been committed to reduce its carbon footprint. In 2007, the company became carbon-neutral and has recently claimed that with extensive investments in high-quality carbon offsets, it has erased its entire carbon footprint. Last year in September, it announced that it is going a step ahead with an approach called 24/7 Carbon Free Energy.

In effect, Google will now match electricity usage by the hour with locally sourced zero-carbon energy, thereby operating on carbon-free electricity 24/7, by 2030. Recently in May 2021, Google partnered with The AES Corp. for the supply of 24/7 carbon-free energy to its data centers in Virginia.

Microsoft #

Teach Corporation Microsoft recently declared that it will leverage the best of modern technologies to be completely carbon negative by 2030. By the year 2050, Microsoft aims to mitigate all the carbon it has generated since its inception in 1975. In addition, the firm will also launch an initiative to use Microsoft technology to help its clients and suppliers lower their own carbon footprints.

Microsoft has also come up with a new climate innovation fund worth USD 1 billion, to speed up the global development of carbon capture, reduction, and removal technologies. It plans to make carbon reduction a major part of its procurement processes as well.

Apple #

Last year in July, iPhone giant Apple announced that it plans to make its entire manufacturing supply chain and product life cycle, carbon neutral by 2030. As a matter of fact, Apple is already carbon neutral across its operations worldwide, but with this mission, the firm will ensure that every one of its products sold will have net zero climate impact by 2030. The company has been taking every possible initiative in this regard.

Recently in October, as a part of its decarbonization goal of 2030, Apple declared that the number of its suppliers committed to deploying 100 percent clean energy has more than doubled since 2020. Close to 175 Apple suppliers will transition to using clean energy, as Apple plans to bring over 9 gigawatts of renewable power online, globally. Over time, this action will help mitigate over 18 million metric tons of CO2 emissions annually.

Amazon #

In 2019, Amazon was the co-founder of The Climate Pledge, i.e., a commitment targeted toward net-zero carbon across all its operations by 2040. The retail giant encouraged many other companies to join its pledge, claiming that cooperation is the only way toward global decarbonization. To that end, the company has generated an innovative carbon system of record that measures the sources of carbon across all Amazon operations, thereby enabling the firm to concoct ways to decrease emissions.

Amazon also has plans to deploy 100,000 electric delivery vehicles and is further looking out for methods to enable sustainability in freight operations. In addition, it has invested in sustainable building technologies, such as carbon-sequestering cement, and is using the same in its construction projects.

American Airlines #

American Airlines has recently joined the bandwagon of companies aiming to lower carbon emissions through their operations. Early this year, the firm collaborated with Deloitte to increase the use of sustainable aviation fuel (SAF) for lowering aviation emissions. The agreement demands that both companies reduce CO2 life cycle emissions from aviation by around 3,050 metric tons of carbon dioxide – which apparently amounts to around 10,000 passengers flying from New York City to Los Angels (one-way).

Further, the company has pledged to mitigate greenhouse gas emissions by 2035, thereby supporting its current commitment to reach net-zero emissions by 2050. To that end, it has joined the Race to Zero and the Business Ambition for 1.5°C campaign. In September this year, American Airlines has also invested close to USD 100 million in a collaborative effort to increase the development of clean energy technologies for achieving a net zero economy by 2050.

BT Group #

UK-based telecoms company BT Group has pledged to achieve net-zero carbon emissions by 2045. In the year 2016, the company achieved a target it had set earlier, four years ahead of schedule. The target was to lower carbon emissions intensity by 80% on 1996-97 levels. Since 2009-10, BT has managed to save USD 328 million, and during 2017-18, the company cut its energy bill by USD 37 million.

It has accomplished this by bringing about efficient setups in its data centers, networks, and buildings. Further, BT also claimed that it aims to decarbonize by 87 percent, against the 2016-17 levels.

The IKEA Group #

The IKEA Group – one of the best furnishings giant worldwide, was indeed among the first ones to be a part of The Climate Groups EV100 initiative to fast-track the roll-out of electric transport last year. IKEA now uses electric vehicles (EVs) to further its goal of tackling emissions.

The company had pledged to transition its “last mile” home deliveries spanning key cities such as Los Angels, Paris, Amsterdam, Shanghai, and New York, to zero-emission transport by 2020. By 2025, the company aims to have all its “last mile” home deliveries to be in the zero emissions bandwagon.

NRG #

U. S.-based energy company NRG is also a part of the global decarbonization move, and has raised the bar by announcing to help its customers decarbonize their power needs through reduced emissions and delivery of renewable energy. The utility aims to reduce 50% absolute emissions by 2030 as opposed to the 2014 baseline – so far, it has already achieved 70% of this goal. NRG has also managed to reduce absolute emissions by almost 20 million tons.

Signify #

Philips Lighting – now Signify, has also joined the carbon neutral bandwagon. The firm has pledged to transition to 100% renewable energy with RE100. In addition, it will accelerate the transition to charging infrastructure and electric vehicles. Both these goals will be led by The Climate Group. Signify has also committed to using net-zero carbon buildings for its owned and operated sites – around 300 of them, to say the least.

Efforts Undertaken by Government Bodies #

As the issue of climate change becomes more and more pressing by the day, governments the world over have been charting out effective plans and policies to ensure carbon neutrality at the earliest. While some have already commenced decarbonization strategies, many others have joined carbon neutrality programs and have been collectively working toward a carbon free future. The paragraphs below enumerate the steps some governments have been taking in this regard.

The UK Government #

The UK government has been very proactive in this regard; indeed, emission reduction has been a major target for the nation since two decades. During 2000 and 2014, energy-related CO2 emissions in the UK reduced from 591 to 470 million metric tons, while GDP grew from USD 2.1 to USD 2.7 trillion. Recently in 2021, the UK government unveiled a Net Zero Strategy that laid down a plan on how the country will create 440,000 well-paid jobs in addition to unlocking GBP £90 billion in investment in 2030.

The City of Miami #

The Miami City Commission recently approved the City’s new comprehensive GHG reduction plan called Miami Forever Carbon Neutral, in a bid to achieve decarbonization in the state by 2050. Its main aim is to mitigate citywide GHG emissions from 2018 levels by 60% by 2035. In order to accomplish the same, the City plans to implement policies that address renewable energy usage, EV adoption, energy efficiency, and promoting a new green economy.

The Indian Government #

India aims to achieve Net Zero emissions by 2070. To that end, the Indian Prime Minister recently declared a five-fold strategy at the 26th Conference of Parties (CoP26). As a part of the same, the Indian government aims to obtain its non-fossil energy capacity to 500 gigawatts (GW) by 2030. The country has goals to meet 50 per cent of its energy requirements from clean energy by 2030 and it will reduce the total projected carbon emissions by 1 billion tons till the same year. In addition, India aims to mitigate the carbon intensity of its economy be lesser than 45 percent.

The U. S. Government #

The U. S. has been involved in decarbonization strategies since a very long time. Recently in April 2021, President Biden announced a new target for the U. S. for achieving a 50% to 52% reduction in economy-wide net greenhouse gas pollution by 2030, as opposed to the 2005 levels. In order to accomplish the same, the U. S. will focus on deploying clean and green technologies. In addition, it will invest in new technologies to mitigate emissions associated with the construction sector, say like, high-performance electrified buildings.

The U. S. will also help in reducing carbon pollution from the transportation sector by enhancing the efficiency of vehicles, providing funds for charging infrastructure, boosting R&D efforts, spending on low carbon new-generation renewable fuels, and bringing about cutting-edge transportation technologies.

Examples of a few other nations camping up their efforts with regards to decarbonization: #

In October 2020, the government of Japan declared its plans to reach net zero carbon emissions by 2050. Further in April 2021, the nation raised its earlier goal of emission reduction by 2030, to a massive 46% from its 2013 levels.
In June 2021, Germany passed a new Climate Action Law that pushed ahead the deadline for achieving climate neutrality, to 2045. The Law also states that the target for greenhouse gas emission reduction will be 65% by 2030 as compared to 1990, while a new interim target of 88% reduction applies until 2040.
Switzerland already has an ambitious zero emissions started, and recently, it has declared that it will need to spend USD 14 billion annually (around 2% of gross domestic product), over the next thirty years, to achieve its 2050 carbon-zero goal.
In 2009, Singapore committed to reducing emissions by 16% from business-as-usual (BAU) levels, by 2020. In 2015, it pledged to reduce GHG emissions by 36% from 2005 levels, by 2030. On 31 March 2020, Singapore claimed that it plans to halve its emissions by 2050, from its peak to 33MtCO2e to accomplish carbon neutrality by 2050.

The adoption of advanced technologies such as machine vision and artificial intelligence to mitigate emissions will soon become mainstream considering the pace at which governments and corporate entities are working toward decarbonization. As private and public entities collaborate and work on technological developments that can be harnessed to achieve zero carbon emissions, we can perhaps, have hopes to expect a carbon-free future sooner than envisioned.

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Green Cities: A Pathway to Sustainable Future

Vin Patel — Tue, 02 Aug 2022 11:17:57 +0000

Key takeaways

One of the goals is to make these cities into smart ones capable of providing long-term opportunities for cultural, economic, and social development within a safe, exciting, and dynamic setting.
In recent decades, heated debate around green technology and its equitable access have aroused international scholars’ concern.
Economic growth, population, energy pricing, weather, and technology influence energy usage.

Introduction

Increased reliance on smart policies for sustainable mobility, energy-efficient building retrofits, renewable energy production expansion, better waste management, and ICT infrastructure implementation is becoming more common in cities around the world in response to rapid social and environmental change at all scales. One of the goals is to make these cities into smart ones capable of providing long-term opportunities for cultural, economic, and social development within a safe, exciting, and dynamic setting. In order to provide a broad reference for the identification of urban development sustainable plans and programs within the current legislative framework, an analysis of the most interesting international initiatives pursued by cities is provided.

Green Technologies #

In today’s world, the more technologically advanced a city is, the more environmentally friendly it can and should be. With Facebook and Google in the neighborhood, San Francisco is assuming a worldwide leader regarding green technology investment. An analysis of the most forward-thinking cities came to this result.

Innovation encompasses a wide range of topics, from artificial intelligence and big data to biotechnology and the creation of new financial services, all of which do not necessarily aim to improve our quality of life. However, by examining the amount of money invested by corporations in green technology, it is possible to determine which towns throughout the world are doing the most to influence the future world.

It looks like San Francisco is leading the pack in this regard. The city is more than simply a haven for the most forward-thinking, cutting-edge technology enterprises in the world. Additionally, the city is home to the most significant number of businesses engaged in environmental initiatives, including those in the fields of green technology, eco-building, environmentally-friendly consumer goods, and alternative energy. Green technology’s business ecosystem in San Francisco has the largest size, according to a survey by FINOM, a start-up offering financial services in France, Germany, and Italy. This American city was determined to be the best in the sector based on the number of participating firms and the amount of financing provided to those organizations.

However, one of the most cutting-edge destinations is investing more in research. The British capital is a world-class location for higher education with the most highly-ranked research universities globally, from University College London to City University London.

London is also the most dedicated to promoting the growth of startups, out of the 100 cities in the survey. New York has gotten more start-up capital than any other U. S. city between 2010 and 2015, according to research that looked at how many firms were formed in each city following that year.

Green Cities #

We have compiled the list of innovative cities for their remarkable efforts in green technologies:

Oslo, NorwayOslo, Norway’s capital and most populous city, is one of many Scandinavian cities frequently ranked among the greenest in the world. This city’s strategy has included sustainable environmental initiatives for years. Its carbon footprint is smaller because the government has a committee solely devoted to sustainable development plans and actively safeguards wild and natural regions from development.

An aluminum frame and netting of Roof-barrier by Safedesign, easy-to-install, and adaptable safety solution popular in Oslo. It is erected on the roof edge to keep employees and other things from falling. Roof-barrier saves up to 85% of the time required by traditional scaffolding for rooftop construction and maintenance. Transport and storage costs are also cut by 90%. 2. Stockholm Stockholm has several environmental planning projects and is one of the cleanest cities in the world. With a target date of 2050, the city hopes to be fossil-fuel-free. A study by HouseTrip found that nearly three-quarters of people either walk, bike, or utilize public transit to go to work.

By 2040, Stockholm aspires to be completely fossil-fuel-free, and it sees eco-policies as the most brilliant thing for the city and the entire globe.

This city is already quite environmentally friendly. Fuel stations around the city sell bio-fuel made from sewage, which is frequently utilized by taxis and private vehicles. 3. Amsterdam Amsterdam is one of the world’s most bicycle-friendly cities, with a well-developed network of bike paths. According to HouseTrip study, Amsterdam is one of the water-conserving cities in the world. Eco-friendly hotels may also be found in the city. Cisco and the city of Amsterdam announced a deal in 2014 to transform Amsterdam into the “Internet of Everything” metropolis and one of Cisco’s showcase cities.

In recent decades, heated debate around green technology and its equitable access have aroused international scholars’ concern. Consequently, researchers and planners must take further steps and develop effective instruments to improve equitable access to green technologies and achieve long-term urban social sustainability. 4. VancouverVancouver is one of North America’s greenest cities and the most environmentally friendly in Canada, making it a popular destination for tourists. Since a Greenest City 2020 Action Plan was put in place some years ago, the government has set a goal to cut carbon emissions by 33% and is a world leader in hydroelectricity usage.

Renewable energy sources are used in green technologies. One of the most recent developments in green technology is green nanotechnology, which uses green engineering and green chemistry. The disposal of trash is a major contributor to environmental contamination in Vancouver. That is another area where green technology may help. A greener future is possible if we use it to alter our waste and production patterns in a way that does not affect our home planet. 5. CuritibaOnly one city in the whole South American continent scored above average in environmental sustainability on the South American Siemens Index. There has been a rapid-transit bus system in Curitiba for many years, a robust recycling program, and plans for a better bus system and additional bike lanes.

This city’s carbon footprint is relatively modest compared to that of its neighbors. The rapid bus system is trying to utilize the green technologies for its functioning to its fullest- which an also be seen as an example for other states. 6. DenmarkWith most citizens living within walking distance of public transit and half utilizing a bicycle to get to work, Copenhagen is frequently ranked as Europe’s greenest city. The city’s carbon impact is relatively minimal, despite its size. Additionally, residents compost, recycle and do their best to save electricity. By 2025, Copenhagen aims to become carbon neutral.

At least 775,000 electric cars will be purchased in Denmark by 2030, thanks to $407.6 million in financing from the country’s parliament on Friday (that includes hybrids). According to a larger plan, more than one million electric and hybrid vehicles will be on the road in Denmark by that time. That will minimize greenhouse gas emissions by more than 2 million tonnes. Denmark wants to meet its objective of reducing greenhouse gas emissions by raising taxes and levies on fossil-fuel vehicles based on their CO2 emissions. A mileage-based method will be phased out in favor of this one. 7. MinneapolisMinneapolis has been featured on numerous “green city” indexes and rankings these last several years. Cities in Minnesota have a program called Minnesota GreenStep Cities, which implements sustainable practices and activities. It also has 92 miles of on-street bikeway and 85 miles of off-street trails, decent air quality, and a well-maintained park system, making Minneapolis an environmentally friendly city.

By 2050, greenhouse gas (GHG) emissions must be reduced by 80 percent from a 2005 baseline, making it one of the US’s most severe climate action pledges. Taking advantage of falling costs and recognizing solar resources as the state’s most plentiful and pervasive energy resource, made new pledges and established targets for the deployment of solar energy in 2013. After 13 years, Minnesota’s efforts to cut emissions in the power sector have been most effective, with a 29% reduction in power sector emissions and a significant increase in renewable energy installations.

Conclusions #

The emissions from power generation and transportation might be reduced by using renewable energy technologies instead of fossil fuels. Developing and promoting renewable energy supply technologies and demand for renewable energy is imperative because of some detrimental and irreversible externalities in conventional energy production. More renewable energy sources should be used to lower the cost of generating electricity. Economic growth, population, energy pricing, weather, and technology influence energy usage.

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IoT-Enabled Data Acquisition System For Electric Vehicle

Vin Patel — Sat, 30 Jul 2022 13:47:10 +0000

Key takeaways

IoT has emerged as one of the most significant technological developments in recent years.
Considering how high-teach an electric vehicle is, technological issues are inevitable.
However, it is only feasible if the global auto sector complies with all the regulations.

IoT has emerged as one of the most significant technological developments in recent years. Through embedded systems, everyday items can be connected to the web, enabling smooth interaction between people, processes, and objects. The connectivity of sensors and software on a network is referred to by the 1999 term ‘IoT.’ It aids in managing linked devices, and a user interface allows for the collection and visualization of pertinent data from such devices.

IoT gives you the ability to manage and control things remotely. For example, it allows charging stations to cater services to EV drivers and react in real-time to unforeseen circumstances.

Why Is IoT Implementation Critical For The EV Industry? #

The Internet of Things (IoT) emerges as a critical solution that enables businesses to maintain the lead in an era of Big Data when real-time information and cutting-edge connectivity play vital roles in many industries. IDC (International Data Corporation) projects that global spending on IoT-powered technology will reach $1.2T in 2022, growing at a 13.6% CAGR (Compound Annual Growth Rate).

The figures, as mentioned earlier, show that global connectedness is increasing. Along with digitalization activities, the proliferation of “smart devices” will impact all facets of human existence and spur new business ideas to help firms remain competitive and meet global problems. For example, EV charging stations are now more effective and practical thanks to IoT technology, benefiting drivers and service members. Urban areas search for solutions to this issue as environmental degradation and air pollution levels rise. Reducing vehicle pollution is one of the most important approaches to managing air pollution in urban areas. The creative move in this area is represented by electric vehicles (EVs). Electric cars are high-teach machines that rely on a tonne of data to work at their best. Some of the performance parameters are monitoring speed, battery management, acceleration, mileage, charging, problem alarm, and preventative maintenance systems. IoT has a significant impact on how these electric vehicles are monitored.

How Data Is Acquired Using IoT Sensors? #

The extent of data collection and curation becomes even more essential for proper EV maintenance with a futuristic vision for automated EVs. The suggested platform makes use of the cloud platform ThingSpeak and data collection via the Internet of Things (IoT) sensors. IoT sensors attached to ESP2866 (Wi-Fi Module) and monitored by a laptop or smartphone gather data and send it to a cloud platform. We used ThingSpeak, a cloud platform built on MATLAB for graphical data visualization and sped-up analysis.

Battery Management System #

The battery management system (BMS) is essential for any electric or self-driving car. The Internet of Things (IoT) technology offers a huge amount of promise for developing and improving electric vehicle battery management systems. The Battery Management System (BMS) ’s primary duties are to observe and manage the battery’s operations. This includes the charging and discharging cycle, ensuring battery health and lowering the risk of battery damage by ensuring the vehicle receives the best possible energy delivery. The monitoring circuit tracks the critical battery parameters in the BMS. Both charging and discharging scenarios comprise the voltage, current, and temperature. The Internet of Things (IoT) is crucial for monitoring and controlling since it makes it possible to log data remotely for battery metrics, conditions, etc.

Due to its longer lifespan and improved range of high energy density, high-quality Li-ion battery packs are becoming more and more popular among EV makers. The data from the inboard sensors collected through IoT can assist in managing the difficulties in cases where the battery fails. These can then be put through AI-based performance assessment frameworks. On various Li-ions, tests can be performed to evaluate the patterns of partial and complete charging and discharging. The information gathered throughout each phase is used to describe models. Before being deployed on a server, these are AI-integrated. The EV transmits important sensor data to the server, providing information on the performance and next steps. We can say that the server keeps track of the EV’s condition.

Different types of sensors have already been incorporated into the vehicles of significant automakers like Tesla and MG. Therefore, after changing a battery, you are informed that the battery has been removed from and reinstalled in a car. Additionally, these batteries include an electronic control unit (ECU) with its own digital identity and attributes.

Preventive Maintenance System and Fault Alert #

Considering how high-teach an electric vehicle is, technological issues are inevitable. It notifies drivers of vehicles of any defects or malfunctions using IoT Fault alert systems, giving them time to take appropriate action. Using IoT technology, we can measure the car’s charge cycle, miles driven, battery temperature, and other parameters. We can then use this information for routine tyre maintenance, battery life monitoring, and replacement of other worn-out elements of the vehicle. This could prevent harm to the car and extend the life of both the car’s batteries and the car itself.

Safety and Smart Driving #

We can track the real-time data of the cars and their essential components utilizing the Internet of Things. It gives users a tonne of information the technology provides, increasing their trust in it. For example, users of electric vehicles may receive the following information via IoT devices embedded in those vehicles:

It assists in keeping an eye on the driver’s performance as well as essential factors like the vehicle’s speed, acceleration, and temperature and offers suggestions in real-time to ensure improved performance.
Real-time tracking, geo-fencing, and immobilization can help us stop car theft. IoT improves safety and security and lessens the need for insurance as a result.
IoT will track car performance information so that EV and battery manufacturers may enhance their wares. The information offered by IoT consists of the range for each charge, the battery life cycle, the use of a vehicle, performance variations based on location, weather, age, and changes in range for each charge over time.

Telematics Data #

Data is gathered when connected to vehicle sensors using IoT-based telematics technology, and it may be quickly presented through widgets, sent as fast notifications, and used to create automatic reports.

The following are advantages of employing telematics for distant monitoring of electric vehicles:

Battery Usage InformationUsers of electric vehicles with telematics can monitor current battery usage information. This data analysis offers information that enhances battery life. In order to prevent battery failure, it also enables the monitoring of crucial variables, including voltage, current, and temperature.
Driver Behavior DataElectric vehicle remote monitoring systems with telematics focus on the behavioral data of EV drivers in addition to monitoring and analyzing performance data.
Charging ReportsThe integration of telematics in electric car technology enables the creation of reports on all of the vehicles’ possible lifetime’s worth of full charging sessions. The charging report details the amount of time spent charging, the location of the charging station, and the percentage of charge that each car received.
Alert for Nearby Charging StationsElectric car owners must consider their state-of-charge (SOC) while deciding when and where to charge their vehicles. Telematics-based monitoring systems for electric vehicles alert the user to low battery levels in the cars as well as the location of nearby charging outlets. In addition, through IoT smartphone apes, telematics provides fleet managers with immediate input on changes in driver behavior to increase vehicle safety.

IoT Challenges in Electric Vehicle Monitoring and Management #

The utilization of a large volume of data sent across a network and the addition of so many devices to the Internet make this data more susceptible to hacker assaults and data theft. Cybercriminals may get access to the system, take user information, and utilize it inappropriately. Therefore, it is necessary to reinforce and make more hack-proof the IoT devices attached to the EV and gather the whole data. Because the IoT systems employed in electric vehicles are extremely sophisticated and have substantial installation and operating costs, the entire cost of an EV is increased. Increased R&D in this field has the potential to lower total expenses in the near future.

The overall design of EVs gets increasingly complex and difficult to handle in the IoT System because it is challenging to design, develop, maintain, and enable the comprehensive technology. Furthermore, for proper operation and real-time data, IoT devices used in EVs need to be connected to the Internet and have constant power. The gadget and anything connected will cease to function if either goes offline. Therefore, it will also have an impact on EVs’ complete performance.

Future Prospects of Internet of Things in Electric Vehicles #

In the years to come, the Internet of Things (IoT) in the EV sector will have a more promising future. The automotive IoT market will have a global market value of $106.32 billion by the end of 2023, predicts Netscribes Market Research Report. Many governments worldwide intend to replace conventional automobiles powered by gasoline or diesel engines with electric ones. However, it is only feasible if the global auto sector complies with all the regulations.

There will be several benefits once IoT technology is used globally for electric vehicles, including lower battery costs, safer and more secure driving, reduced manufacturing costs, easy maintenance, employment opportunities, and a reduction in air pollution.

Related reading

How Are Technology Leaders Using Data and Machine Learning

Vin Patel — Fri, 29 Jul 2022 18:55:15 +0000

Key takeaways

Businesses need to be ultra- vigilant in the contemporary world embodied by cutthroat competition to stay ahead in the race.
Identification of Tangible Goals – Identifying new business prospects with the help of big data necessitates a well-defined goal.
The key is to understand how machine learning and data can be appropriately used to meet each challenge and goal.

Businesses need to be ultra- vigilant in the contemporary world embodied by cutthroat competition to stay ahead in the race. Adopting, employment and mindful dissemination of upcoming technologies like AI, big data, and machine learning can help companies stay afloat. Data science and machine learning have impeccable impacts on businesses all over the globe, and they are rapidly turning critical for the differentiation and survival of technology bigwigs. The big claims about big data and machine learning as transformative instruments in the hands of businesses have accuracy and honesty in them. So, let us analyze the role of big data and machine learning in helping technology leaders to help identify new business opportunities.

The Implication of Big Data in Gauging New Business Opportunities #

The valuable data insights that help a company or individual understand their target audience and anticipate their preferences are known as big data. In 2020, 44 zettabytes of data were generated. Large-scale companies are incredibly valuable because they have the ability to merge diverse data sets into useful sources for AI algorithms to affect and comprehend human behavior. Thus, there is no question as to why technology leaders use big data in various ways to elevate their businesses and identify novel business opportunities. Experts believe that big data can help technology leaders outperform their competition through innovation. A myriad of companies is co-opting big data to discover their flaws.

By 2017, the adoption of big data in companies had reached 53 percent. In 2022, data will play a huge role in enabling companies to understand their target demographics and preferences. Every entry point in digitalization and modernity is inundated with new data that describes human behavior more accurately. Even more importantly, audio-visual mediums such as videos and phone calls are being used to collect more meaningful data. An in-depth examination of these data points can shed light on consumer habits, personality traits, and significant life experiences. Technological giants have leveraged such strategies for product improvement, business strategy, and marketing campaigns to cater to their target clientèle.

Since its proliferated use in 1997, big data has helped businesses and technology giants pave out new growth opportunities. Some of the veritable ways through which big data can help enterprises to include:

Creation of New Revenue SystemsBig data provides companies with invaluable insights that are premised on the market and consumer analysis. Technology giants and leaders have used big data to make additional revenue by selling non-personalized trend data to massive corporations operating within the same domain. Thus, big data provides an ideal additional route for revenue.
Technological InnovationOne of the best parts about adopting and scrutinizing big data is the plethora of backup technologies available at the company’s dispense. For instance, a relatively simple cloud-based phone system enables companies to easily store and retrieve extensive communication-related data such as call logs, customer information, client conversations, feedback, and the like. The availability of cost-effective ancillary digital mechanisms such as Office 365 for extensive data collection and storage makes its utilization even more feasible for businesses. Likewise, the ample presence of analytical tools can help companies at various stages of their functioning. For example, technological pioneers combine big data and analytic tools to understand the most viable way to spend money on customer acquisition, determine how much each customer is spending on the services, and so forth.
Data Safety and Risk AnalysisTechnology leaders appropriate big data tools and technologies to map out a company’s complete data landscape and use it to analyze all the internal and external threats to their business model even before they materialize. Knowledge is one of the most potent weapons businesses have to ace within their domain. The much-needed knowledge can be derived from big data and its various tools, making a company better equipped in all aspects, including safety. More and more industries are now focusing on big data, making attaining security more doable and within reach.
AutomationThrough robotic process automation, Big Data has the incredible ability to solve internal shortcomings and operations. Companies may create a highly automated and efficient decision-making system using enormous data sets. Automating data collection and storage and data security are now possible thanks to scalable IT infrastructure and declining cloud computing prices.

How to Harness the Power of Big Data #

Since 2012, a whopping 40 percent of the large global corporations led by technological leaders have made considerable investments in big data strategies. However, for newbies, the utilization of big data can be overwhelming and perplexing. Nevertheless, fret now as we have a few tips to share. Companies need to evaluate the following factors before finalizing big data solutions.

Putting Together an Experienced TeamThe first step in being successful in the precarious game of utilizing big data is to put together a team of expert data collectors, analyzers, and strategists. Going by the example of technological leaders, curating a diverse group that is equipped to deal with the nuances of modern analytic methods, manipulate big data sets, and understand overarching business goals is essential.
Identification of Tangible Goals – Identifying new business prospects with the help of big data necessitates a well-defined goal. Data and analytic should be used to support the company’s overall objectives, such as increasing profit margins, building brand recognition, and gaining market share.
Capturing the Right Data – Once the underlying business goal is identified, the next step is to have a concise and lucid understanding of the data itself before its application. Identifying, capturing, and tracking the correct data is the backbone of the entire big data mechanism. Consequently, relying on inaccurate data can wreak havoc and push the company to an abysmally new low.
Application of Proper Analytic Methods – Pioneer data analysts are skilled in translating big data into valuable insights with mind-blowing efficiency and accuracy. Visual summaries and crisp data reports that are easy to understand by laypersons facilitate the supporting teams to process the data and make meaningful business decisions quickly.

Now that we have discussed the intricate relationship between big data and business opportunities, let us move towards machine learning and observe its impact on new business avenues.

The Significance of Machine Learning #

The nearly-infinite quantity of data available to humankind coupled with factors like affordable data storage, growth of less expensive and more powerful processing tools, and so forth have propelled the development of machine learning or ML. Technological leaders and novices worldwide are compiling more innovative ways of analyzing mammoth-sized complex data with faster delivery, better accuracy, and seamless results. Machine learning tools enable organizations to identify profitable opportunities and potential risks. So, let us look at the top advantages of machine learning in the business sphere.

Better Customer Experience #

Machine learning helps companies attract more people to their products and services and help increase conversions. In other words, machine learning facilitates an organization to better understand their customers’ browsing experience and behavior so that the company can provide them with precisely what they need. For example, the recommendation engine in the renowned OTT platform Netflix helped the steaming site attract a vast audience and increase profits by effectively encouraging binge-worthy shows. A 2018 research reveals that 80 percent of Netflix viewers follow the recommendations extended by machine learning. In a nutshell, some of the benefits of personalized customer experience by machine learning include:

More extensive revenue generation, courtesy of higher customer engagement and retention
More pronounced customer satisfaction that facilitates brand loyalty
Less time invested in research through traditional methods
Cost-effectiveness by erasing office staff and pieces of equipment from the process

Effective Automation #

Delegating mundane and repetitive tasks to the machine increases production speed. Moreover, opting for machine learning also eliminate manual data errors data duplication and ensures a higher quality of work. Furthermore, the company does not even need a hi-teach developer to reprogram the systems repeatedly whenever there is a change in the workflow and process. Thus, the combination of machine learning and automation has various advantages for a business, including –

Faster service
Reduction in spending on software management
Saving money by distributing repetitive tasks to machines
Use human resources for more critical jobs
Removal of manual error
Reduction in mundane administrative tasks

Robust Predictive Abilities #

ML predictions have two significant advantages to help a business identify new avenues. The first is customer choice predictions that are premised on customer data. The machine learning system recognizes typical and atypical behaviors using customer data, which is then utilized to predict the changing demand for a company’s products and services. Likewise, large enterprises can tweak their ML systems to process enormous amounts of market data to arrive at unique and insightful forecasts. Thus, a company will identify trends faster and attain the fat-mover advantage.

Reasonable Resource Planning #

Through predictions made via machine learning, a company can estimate the resources needed to meet changing demands for its products and services. Advance knowledge can help a company in inventory and process management. As such, a company can viably have enough products during high-demand periods, decrease the risk of wastage, save money by knowing the optimal type and amount of product to keep on hand, and likewise.

Better Internal Company Changes #

The scope of machine learning is not just limited to customer acquisition and marketing campaigns. Machine learning is a tool that can help a company set and manage workflow, follow staff progress, and maintain internal corporate values. Few of the fundamental merits that machine learning offer in terms of internal adaptability include –

Wiser task prioritization and work distribution
Higher transparency of work among employees
Heightened productivity
Seamless integration of new technologies into the existing systems

Better Adaptation to Market Fluctuations – Technology leaders like Google, Amazon, and Apple are always a step ahead of the rest because they adapt quickly to market changes. As we have already discussed, the ML predictions provide a company the opportunity to veritably improve its business model and a competitive edge over peers. Some of the advantages include

• Machine learning extends insights into the market movement, allowing a company to know the global trends well in advance.

• ML ensures the basis of efficient sales and marketing stratagems in the future

• It helps companies save money, time, and human resources in advance.

• Lastly, machine learning allows a company to adapt to changing environments faster.

Efficient Data Management – Big data means that manual collection and data usage is complex and nearly impossible. In such a challenging context, machine learning comes into the picture. Machine learning helps a company distinguish relevant data from peripheral and ancillary information and spam. Through this mechanism, Mail was able to eliminate 100 million spam messages per day in 2019. Some of the benefits of machine learning for extensive data management include –

• Saving of time by dividing important work from subsidiary work for employees

• Increase in productivity as employees can focus on higher priority tasks

• Heightened user engagement by offering customers only relevant information

End Note

In conclusion, we can safely assert that machine learning and big data can help technology leaders and novices identify new business opportunities. The provision of an efficient automation solution coupled with other phenomenal advantages like saving time, money, and resources, predictive competencies, and so forth are undeniably advantageous for businesses of all types and sizes. A wide assortment of industries such as the healthcare sector, manufacturing sector, and so on are co-opting the combination of machine learning and big data to avoid mistakes and better cater to their customers. Thus, machine learning and big data are here to say. The key is to understand how machine learning and data can be appropriately used to meet each challenge and goal.

Related reading

How To Build Safer Virtual Worlds in the Web 3.0 Era?

Vin Patel — Mon, 04 Jul 2022 13:45:23 +0000

Key takeaways

“As we move into a new era of digital interactions, we must build a safer virtual world.” - Tim Burners-Lee. As we enter Web 3.0, it’s essential to be aware of the potential cybersecurity risks that come with it.
However, there are also some potential risks associated with a machine-readable web.
We can make sure that the benefits of Web 3.0 outweigh the risks.

“As we move into a new era of digital interactions, we must build a safer virtual world.” - Tim Burners-Lee.

As we enter Web 3.0, it’s essential to be aware of the potential cybersecurity risks that come with it. While the internet has brought us many captivating things, it has created a new playing field for criminals and hackers. In addition, with the rise of virtual worlds and online gaming, we need to be extra diligent about safeguarding our information. For example, Web 3.0 applications are more complex than their predecessors, making them more challenging to secure. In addition, these applications often handle sensitive data, such as financial information and personal identity data. As such, they are attractive targets for cybercriminals. In this article, we’ll look at some of the risks associated with web 3.0 and how we can protect ourselves from them.

Web Security Risks in The Age of Big Data #

The internet has come a long way since its inception in the late 1960s. The World Wide Web, or “web” for short, was created in 1989 and has since revolutionized how we communicate and share information. As a result, the web is now an integral part of our lives, with over three billion people using it every day.

However, as the saying goes, with great power comes great responsibility. As the web has become more central to our lives, it has also become a prime target for criminals and hackers. In recent years, we have seen a surge in cybercrime, with data breaches becoming increasingly common. This is especially true in the age of big data, where sensitive information is often stored in large databases that can be hacked.

Web 3.0 Cybersecurity Features #

Web 3.0 promises a more secure and trustworthy internet. However, to build safer virtual worlds, we need to understand these risks and take steps to mitigate them. Some of the most important features of Web 3.0 cybersecurity include:

End-to-End Encryption: #

End-to-end encryption is one of the most critical aspects of web security since it ensures that only the sender and intended recipient of communication can read it. This means that even if someone were to intercept your communication, they would not be able to read it because they would not have the key needed to decrypt it. End-to-end encryption is essential for communications that contain sensitive information, such as financial data or personal health information. It is also essential for communications that are meant to be private, such as intimate conversations between friends or family members. The good news is that many popular messaging apes already use end-to-end encryption, including WhatsApp, Signal, and iMessage.

User Authentication: #

Another important security feature is user authentication, a process of verifying someone’s identity. It requires the user to enter a username and password but can also be done with biometric data, such as fingerprint or iris scan. User authentication is essential because it helps to prevent unauthorized access to information or systems. It is also necessary for user privacy protection because it assures only those who are permitted to read or use specific information.

Data Encryption: #

Data encryption is another crucial security measure that can be used to protect information. Data encryption is a way of turning information into a code that can only be decrypted by someone who has the correct key. This is important for preventing unauthorized access to data, as well as for ensuring the privacy of data. Encryption is often used in conjunction with user authentication, as it can help to ensure that only authorized users can access encrypted data.

Blockchain Technology: #

Blockchain is a type of distributed ledger technology that is often used for security purposes. Blockchain allows for the secure storage of data and traceability of transactions. It’s an excellent tool for cybersecurity, as it can assist avoid data breaches and ensuring integrity. Blockchain is also being used to create secure virtual worlds, as it can help ensure the safety of user data and transactions.

Reputation System: #

Reputation systems are essential for cybersecurity as they can help identify and prevent attacks from malicious actors.

How Do We Build Safer Virtual Worlds? #

So what can we do to build safer virtual worlds? First, we need to educate ourselves about the risks associated with web 3.0; then, we need to put in place the necessary security measures to protect our data and identities. Finally, we need to be vigilant in our use of the internet and report any suspicious activity to the authorities. By working together, we can make the web a safer place for everyone.

Why does a Machine-Readable Web Matters For Security? #

The internet today is essentially a human-readable web. This means that most of the information on the internet is designed to be read and understood by humans. However, there is a growing trend towards a machine-readable web, where information is designed to be read and understood by computers.

This trend is being driven by the rise of artificial intelligence (AI) and the need for machines to be able to understand and process large amounts of data. A machine-readable web will allow AI systems to better understand the world around them and make more informed decisions.

However, there are also some potential risks associated with a machine-readable web. One of the most significant risks is that it could make it easier for malicious actors to exploit vulnerabilities.

Web 3.0 Security Risks #

Cybersecurity risk is nothing new, but it’s constantly evolving as technology advances. Here are ten of the significant cybersecurity risks in Web 3.0:

The Dangers of “Hacktivism”:“Hacktivism” is a term used to describe the use of computer hacking for political or social purposes. While some hacktivists may engage in online vandalism, others may go so far as to commit severe cybercrimes. In either case, their actions can pose a serious threat to both individuals and organizations.
**Data and Information Breaches:**A data reach occurs when unauthorized individuals gain access to or steal sensitive, personal, or confidential information. These breaches can occur when hackers gain access to a company’s computer systems, databases, or networks. They can also occur when employees accidentally expose data through email, social media, or other means.
**Malware, Viruses, and Other Threats:**Malware is a computer program designed to harm the user or impair computers. For example, email attachments, downloads, and websites can infect your PC. Once downloaded on your system, they can steal sensitive information, damage files, or take control of your computer.
**Phishing Scams:**Phishing is a fraud that employs email or other electronic contact forms to deceive people into providing personal information like passwords, credit card numbers, or bank account information. These scams often arrive at the victims end by misusing a legitimate source, such as a financial institution or reputed businesses.
**Spam:**Spam is unsolicited emails typically sent in bulk. They can be commercial, promoting products or services. Spam clogs up email inboxes and can be a nuisance.
**Cyberstalking:**Cyberstalking uses electronic communications to stalk, harass, or threaten someone. This can include sending threatening or obscene messages, posting private information about the victim online, or even impersonating the victim to damage their reputation.
**Financial Losses:**One of the most significant risks in Web 3.0 is the loss of personal data. With the rise of decentralization, hackers have more opportunities to access sensitive information. In addition, the increased use of smart contracts and other automated processes raises the stakes for caber attacks. If a hacker gains access to a contract, they may be able to wreak significant financial harm. As we enter this new Web era, it’s essential to be vigilant about cybersecurity risks.
**Ransomware:**Ransomware is a computer virus equipped with files on a computer system and then demands a ransom to decode the files. This can be a costly attack for businesses, leading to data loss and interruption. In addition, victims have been asked to pay the ransom via Bitcoin in certain situations, which might be challenging to track.
**Cyberbullying:**Cyberbullying is electronic communication to bully, harass, or threaten someone. This can include sending threatening or obscene messages, posting private information about the victim online, or even impersonating the victim to damage their reputation.
**Online Predators:**An online predator is a person who utilizes the internet to exploit children or adults sexually. These predators typically use social networking sites, chat rooms, or email to build relationships with their victims. They may then attempt to meet their victims in person, often to assault them sexually.

Preventive Measures

Organizations need to be aware of the following risks when operating in Web 3.0: Hacktivism, data and information breaches, malware and viruses, phishing scams, spam, cyberstalking, financial losses, ransomware, cyberbullying, and online predators. By understanding these risks, organizations can take measures to protect themselves and their users. Additionally, users should be educated on these risks and how to protect themselves when using the internet.

Some preventive measures against these risks include:

Encrypting data and information
Using strong passwords
Installing strong security software
Backing up data regularly
Restricting access to sensitive information
Monitoring activity for unusual behavior
Reporting suspicious activity immediately.

By taking these precautions, organizations and users can help to create a safer virtual world for everyone.

Conclusion

The evolution of the internet has reached a fork in the road. The way we build the virtual world of Web 3.0 will have profound implications for the future of our society. We must ensure that this world is built with security and privacy in mind, or we risk creating a playground for criminals and enabling mass surveillance.

There are many ways to build a safer virtual world, but it will require a coordinated effort from stakeholders. Governments, businesses, and individuals have a role in making the internet a safe place. We can make sure that the benefits of Web 3.0 outweigh the risks.

Related reading

Questions and answers

Why does a Machine-Readable Web Matters For Security? #

The internet today is essentially a human-readable web. This means that most of the information on the internet is designed to be read and understood by humans.

So what can we do to build safer virtual worlds? #

First, we need to educate ourselves about the risks associated with web 3.0; then, we need to put in place the necessary security measures to protect our data and identities. Finally, we need to be vigilant in our use of the internet and report any suspicious activity to the authorities.

Implementing Web 3.0 in Businesses

Vin Patel — Mon, 04 Jul 2022 13:09:25 +0000

Key takeaways

The internet is a spectacular technology that has delivered on all of its promises over the last few years.
Web 3.0 technology will provide people control over their data.
Overall, Web 3.0 allows businesses to operate more freely, develop faster, and provide a better customer experience.

The internet is a spectacular technology that has delivered on all of its promises over the last few years. The internet has transformed education, private communications, urban development and management, and, most importantly, the way we do business in a brief time. The best aspect is that the internet is still in its development stage, and the future potentials are bright.

If you ask any expert about the state of the web, they’ll tell you that we’re on the verge of Web 3.0. But what is Web 3.0, and how will it affect our lives, if at all? The third generation of online technology, known as Web 3.0, is here to stay. Although the technology is still in its early stages and there is no universally accepted definition of what it is or what it implies, it will be one of the most critical technologies for organizations in the future. So what impact will Web 3.0 have on how we use the internet and on businesses? Let’s find out.

What Is All The Buzz Around Web 3.0? #

The definition of Web 3.0 differs from situation to situation, although it is generally regarded as the third generation of the internet. Web 3.0 is often referred to as the semantic or geographical web. If you divide the interne’s growth into two halves, the first being plain online pages and the second being applications, social media, and mainstream adoption, the emerging blockchain-based web would be internet 3.0. It is a more user-friendly, safe, private, and well-connected internet.

Web 3.0 is distinguished by creating a virtual space within the internet via websites such as immersive digital shopping malls, which allow users to interact with one another and purchase things.

The ‘semantic web,’ which analyses and uses user data and behavior to provide a more personalized online surfing experience, has emerged due to leveraging the power of big data as well as machine learning. A fantastic example of this in action may be found in advertising, where advertisements for things you are actively interested in following you across the web. It allows personalization and the semantic web, which provides a foundation for information to be repurposed and shared more readily among programmed, communities, and businesses.

However, several experts predict that Web 3.0 will provide a quicker, more intuitive web experience, with the potential to forego command-line search keywords in favor of organically speaking to the likes of Google.

Critical Competitive Advantages of Web 3.0 #

1) Semantic Web #

The semantic web will help in ’educating’ the computer on what the data means, allowing artificial intelligence to grow and fully use the material. The main idea is to create a spider network of information throughout the internet to help people comprehend the meaning of words via search and analysis.

2) Improved Personalized Browsing Experience #

Web 3.0 gives everyone a significantly more personalized browsing experience. Websites may automatically change according to our device, location, and any accessibility needs we may have, and web apes will become extremely aware of our usage patterns.

3) Artificial Intelligence (AI) #

Along with blockchain technology, AI is the newest and most impactful technology on the market right now. In computer science, artificial intelligence, often known as machine intelligence, is simply intelligence or learning by computers. When combined with natural language processing in Web 3.0, computers can now discern information like people to give faster and more relevant results. Consequently, they grow more intelligent to meet the needs of various users.

4) The Advantages of Blockchain #

Integrating business technologies such as blockchain technology can improve the commercial applications of Web 3.0. For example, blockchain in Web 3.0 enables the cost-effective shifting and transfer of digital assets across company borders. Blockchain advantages include eliminating minor criteria such as gender, regional location, and many more.

5) IoT Smart Gadgets #

Smart IoT gadgets can only be improved by incorporating Web 3.0 for intelligent and smooth functionality. This Web 3.0 application gives new chances to become agile and adaptable to take risks efficiently and successfully. Company technology must provide new avenues for exploring and expanding the business sector.

6) 3D Graphics #

As the internet progresses from a simple two-dimensional web to a more realistic three-dimensional cyberworld, Web 3.0 will significantly affect its future. Web 3.0 websites and services, such as online gaming, e-commerce, and real estate, make extensive use of three-dimensional design.

7) Enhanced Customer Relations #

When implementing Web 3.0, every transaction is recorded in a decentralized ledger visible to all parties involved, making businesses directly accountable to their consumers. Businesses may leverage this transparency to foster trust and long-term consumer connections. In addition, customers know that the information is legitimate and has not been changed since blockchain data is immutable.

8) Insights Based on Data #

A Web 3.0 firm must have data-driven insights to satisfy the target audience based on market research and adequate data. Business technologies such as AI, machine learning, IoT, and other Web 3.0 applications make it simple to obtain essential information regarding the target audience.

How Have Major Companies Adopted Web 3.0 To Gain A Competitive Edge? #

Web 3.0, powered by today’s most disruptive technology - Blockchain – will revolutionize internet infrastructure and influence how organizations function. Web 3.0 technology will provide people control over their data. For example, people will be able to choose whether to share or sell their information. Advertising fraud will also be eliminated by Web 3 technologies that authenticate impressions and click with the user’s identification.

Streaming services like Amazon Prime and Netflix are top-rated and profitable these days. This sector, however, confronts hurdles linked to copyright concerns and equitable reward distribution to marketers and content providers. Smart contracts in web 3.0 can aid in the resolution of these difficulties. Unlike Web 2.0, Web 3.0 offers the prospect of filling the gap between a user’s online presence and their actual person, which means you’ll be able to leave anonymous comments on postings and make purchases without purchasers knowing your identity. This implies that users may make transactions and download information and photos without their true identity being revealed.

Blockchain technology streamlines the login process for your website’s visitors. For example, consider how often you’ve used your Facebook profile to sign in to websites. Blockchain technology works on the same principle as Facebook, but the difference is that, unlike Facebook, nobody owns your data but you. From a commercial perspective, your customers will still be able to sign in quickly on your website, and you will never have to worry about safeguarding their data for them because it’s already more secure.

Since blockchain technology is recognized for its vital security requirements, Web 3.0 may potentially prevent hacking. According to IBM, blockchain is very less likely to be hacked due to end-to-end encryption of documents. Another layer of protection that prevents hackers from attacking your website is making the data anonymous and demanding authorization to view it. Blockchain technology is already being used for security by companies like Apple, Google, and Facebook. Some companies already implementing Web 3.0 in their business models are as follows:

BitPay

BitPay is a crypt payment management tool for individuals and companies. Founded in 2011, BitPay enables retailers to accept Bitcoin payments for various goods and services. It offers hosted checkout, shopping cart plugins, billing solutions, mPOS software, etc. In addition, it provides payment cards and a digital wallet to customers for payment management.

Helium

Helium is a bitcoin mining solution based on hardware. It links low-powered equipment to a single gateway and transmits encrypted data over the internet. It also pays people who link-time machines and gateways. It has received stock backing from many renowned venture capital companies. Helium was founded in 2006 in San Francisco.

Terra

Terra is a payment infrastructure built on the blockchain. It provides a price-stable cryptocurrency that is utilized as a payment method. In addition, it provides a decentralized asset whose value is derived from transaction fees collected on the Terra network. This company was established in 2018 in Seoul, and it provides a reserve comprised of decentralized assets. The token may be used to pay with cryptocurrencies on e-commerce sites.

Web 3.0 is the future of business, and understanding how it works is crucial for entrepreneurs, corporations, and organizations. Businesses cannot afford to fall behind on current market trends. Business choices must be made with prudence, keeping in mind that the future is now and continually renewing.

Is Web 3.0 The Future of Businesses? #

In several ways, the internet has revolutionized the world. Web 3.0 promises a significant transformation to the internet, stressing user privacy – a problem that has been discussed but not effectively handled for some time. Web 3.0 is critical because it allows businesses to simplify their processes by eliminating intermediaries and connecting computers directly. This innovation improves collaboration and communication among employees, partners, and customers, resulting in a more efficient company.

Web 3.0 is all about web users creating more digital material than ever before. This is why businesses who are already on the Web 3.0 bandwagon first are more likely to win over their consumers by providing innovative, secure content solutions and an improved onboarding journey. Overall, Web 3.0 allows businesses to operate more freely, develop faster, and provide a better customer experience.

Related reading

Questions and answers

What Is All The Buzz Around Web 3.0? #

How Have Major Companies Adopted Web 3.0 To Gain A Competitive Edge? #

Is Web 3.0 The Future of Businesses? #

Customer service in the digital age. Do we still need human

Vin Patel — Mon, 04 Jul 2022 12:18:01 +0000

Key takeaways

Customer service has been of utmost importance for businesses for a long time, given how critical customers are to help a company reap successful long-term results and distinguish its brand from the competition.
Building customer relationships ensures increased employee morale, reduced customer attrition, excellent sales, and high turnover.
It is more than likely that customer service in the digital age may be characterized by a mixture of efficient bots and human staff, harnessing the best of both and helping businesses retain as many consumers as possible.

Customer service has been of utmost importance for businesses for a long time, given how critical customers are to help a company reap successful long-term results and distinguish its brand from the competition. What commenced in the form of written communication and telephone conversations to face-to-face interaction has today transformed into a full-fledged, value-driven service portfolio for customers.

Conventional strategies are no longer sufficient to address the changing needs of today’s market. With changing customer behavior, the stakes are significantly higher, and businesses need to work harder to provide increasingly beneficial yet cost-effective and, to a considerable extent, personalized services to customers.

In today’s world, driven by consistent digitalization, ensuring that customers are happy is more of a challenge than ever. With happier customers comes better trust in the brand and, consequently, more loyalty and advocacy. As per reliable reports, people may be four times more likely to buy a product referred by a known person. There’s an 18% increase in loyalty from a referred customer instead of the one obtained from a different method.

As of today, there are almost 4.4 billion active internet users worldwide, a number that will only serve to increase in the coming years. With a considerable percentage of the global population going online, businesses have access to a wide number of digital customers, paving the way for successful strategies to be coined – such as interacting with customers via live chat, virtually enhancing purchase experiences, and so on. Digitalization has revolutionized customer experience and can have a profound impact on customer behavior, in turn, impacting the brand value of a business.

Statistics About Companies Adopting Digitization #

Businesses had been adopting digitization way before the pandemic; however, it cannot be denied that the onset of COVID-19 acted as a tremendous trigger, pushing companies to rapidly scale up the deployment of digital strategies to accelerate their business. At present, digital transformation is at its peak and has led to the deployment of new business models implementing modern technologies such as machine learning, chatbots, data analytic, and blockchain. Companies across major end-use sectors have been rapidly integrating IT solutions in their ecosystem, generating new challenges and opening up numerous growth opportunities.

Back in 2021, IDC predicted that 30% of organizations would increase innovation and generate new models to digitize their companies. According to IDC, 60% of enterprise executives are of the opinion that IoT will have a major role to play in their companies’ digitalization.

Gartner had predicted that by 2023, close to 70% of organizations will use value stream management to enhance development and operations pipeline flow, leading to better, faster customer service. In addition, the highest growth of digital transformation was expected to be from devices (close to 14%) and enterprise software (approximately 10.8%), owing to companies providing innovative workplace solutions.

InformaTech predicts that AI software revenues may cross USD 100 billion globally by 2025. Close to 60% of companies may deploy artificial intelligence solutions to help employees in the near term, states Deloitte, while Forester says that digital transformation investments rose at over 3X the rate of overall IT expenditure.

With speedy internet becoming rather commonplace, more and more companies have been adopting digitization, investing heavily in business intelligence platforms to enhance customer experience. In 2021, Flexera claimed that around 37% of IT initiatives undertaken by companies globally focus on improving customer experience. The software company had also stated that 49% of companies across the globe prioritize cybersecurity when it comes to technology initiatives, while around 54% prioritize digital transformation.

According to a survey of 1,200 mid to large-scale US companies, the average digital transformation budget was pegged at USD 14 million in 2018, while digital transformation expenditure worldwide may cross USD 1.78 trillion in 2022. In addition, IDC claims that around 65% of global GDP may be digitalized by 2022.

With most industrial enterprises and startups having already adopted strong digital transformation strategies and a substantial percentage planning to adopt one soon enough, the digital ecosystem worldwide is set to proliferate at an appreciable rate. As customers form the nucleus of digitalization, businesses are expected to continue the adoption of digital solutions to enhance the customer experience in an increasingly competitive marketplace.

How Has Pandemic Affected Digitization and Its Adoption? #

The impact of the coronavirus pandemic on the adoption of digitization has been overwhelming. Many companies had strong security and IT mechanisms in place and had already been working to transition to a digital ecosystem. For these establishments, the onset of the pandemic made it easier to transition to a virtual working environment quickly. For many others, especially the ones with rigid legacy systems in place, an aging infrastructure, and bandwidth constraints, the pandemic was a brutal wake-up call to quickly digitalize their business models and create a new ecosystem in place for the future. This rang true in the case of government firms with severe capacity issues and the lack of a mobile-friendly presence. In effect, the pandemic forced several companies to accelerate the adoption of digital solutions, which included the deployment of virtualization and the cloud.

According to a report by Celerity, around 63% of industry leaders claimed that the pandemic prompted them to adopt digital transformation strategies sooner than they had planned. A similar report by IBM states that a mammoth 96% of leaders reported COVID-19 to fast-track their digital transformation plans by around 5.3 years.

One of the biggest changes sparked by the COVID-19 pandemic was observed in the payments industry. With social distancing becoming the norm due to nationwide lockdowns, the online payments sector depicted a meteoric rise, paving its way to becoming one of the fastest-growing markets worldwide. Having observed consumer interest in virtual payment models, several companies, even the ones with an originally conventional model, transformed their payment systems into consumer-friendly, digitalized versions.

In essence, the pandemic played a crucial role in speeding up the adoption of digital solutions, as contactless payment volumes witnessed an exceptional rise in demand across retail outlets, pharmacies, grocery stores, departmental stores, vegetable and fruit sellers, recharge vendors, and many others including prominent players across the and EdTech sector.

Given the dire situation of the healthcare ecosystem post the onset of the pandemic, the demand for telehealth and online consultations depicted an upsurge as well. Hospitals and medical establishments raced ahead to embrace digital transformation to monitor the increasing influx of patients. As per McKinsey, during 2020, the use of digital tools for health support by customers saw a rise of 15%, while it was predicted that around 37%of consumers might very well use telehealth in the future.

In addition, the research firm asserted that around post the pandemic, the use of telehealth increased from 11% to 76%. While it has had devastating effects overall, the pandemic has certainly helped push the implementation of digital tools across several industrial sectors.

Importance of Relationships with Consumers #

As the popular old adage goes, ‘The customer is always right’. Well, technically, this need not be true; however, it brings home an important point – customer satisfaction is highly critical for a business to be successful. At the end of the day, retaining consumers for your product/service is essential if you want the business to make money at all. Establishing strong, loyal relationships with customers, to that end, is one of the most vital strategies companies must adopt.

Building customer relationships ensures increased employee morale, reduced customer attrition, excellent sales, and high turnover. However, just establishing a liaison with the customer does not produce successful results – maintaining customer relationships is important. To that end, it is critical that companies increase focus on customer relationship management (CRM) as a vital tool. Customer acquisition is important, yes, but customer retention is more critical today.

Having good relationships with customers mandates consistent improvisation in company services. Customer behavior and expectations change periodically with changing trends and a transforming marketplace. Alongside, continual improvisation is vital so as understand changing consumer needs and fulfill them accordingly. A single negative experience or a seeming lack of concern can completely phase the customer out of the transaction ecosystem and negatively impact the company as well.

According to a survey conducted by PwC a year ago, 1 in 3 customers out of the 15,000 that were surveyed, chose to abandon a good brand after just one bad experience, while close to 92% may completely abandon the business after two negative experiences. In consequence, sour customer relationships can very well kill the brand sooner than one may realize.

A vital point that forms a part of good customer relationship management is paying attention to customer support. Customer feedback about a product/service, no matter how trivial, must be placed on a high pedestal. At the end of the day, customers want to feel heard, want to feel that they matter. A company that ensures appropriate customer satisfaction has more of a chance of retaining customers than a company that ignores customer feedback.

In today’s bustling work culture, paying attention to changing customer needs and behavior is more important than ever before. Businesses need to adopt suitable CRM software to understand customer requirements and how to fulfill the same effectively while maintaining brand awareness. As per estimates, using effective customer relationship management tools can help increase sales conversion rates by an astonishing 300%.

How Have Chatbots and Other Services Taken Over Customer Services? #

With an increasing number of brands trying to make their presence felt amid a cutthroat marketplace, the landscape of customer service has entirely changed in the last half a decade. Companies across myriad industry verticals are leveraging AI to ensure effective customer support. To that end, chatbots – virtual assistants, have made quite a reputation for themselves by handling multiple customer service pain points at the same time. For instance, chatbots are used today to resolve customer queries without human intervention, help customers 24/7, and handle multiple customers at once.

These AI-powered bots are suitably developed to use existing information and resources to help resolve customer questions. They are designed to recognize and answer query variations and can also be trained to provide instant responses in various voices and tones.

Because of their 24X7 availability, chatbots have quickly become a popular channel for customer inquiries. Users can promptly engage with chatbots in a live session and get all their questions resolved without having to meet an actual customer service executive face to face. This also enables customers to get their queries quickly resolved on demand, reducing the load on companies’ service teams. In effect, chatbots can take over simple question responses that usually take up 70%-80% of customer service representatives’ day. This time can be efficiently spent on resolving a more detailed customer query, leading to proactive customer support.

A vital reason why chatbots are taking over customer support is the financial strain. By installing a chatbot just once, businesses are able to save tons of money by not having to hire numerous customer service personnel. This comes as a cost-effective solution for understaffed teams in companies.

The increasing proliferation of digitization indicates that the humanizing technology trend is well on its way to becoming mainstream. Take Alex, for example, the virtual assistant that has probably had more conversations with customers than they may have had with actual people. To that end, companies are finding a way to revolutionize customer service by adopting digital solutions and making them as personable and empathetic as possible.

Chatbots are thus being carefully programmed to understand the customer’s pain points and voice changes and then resolve queries accordingly, in a deeply sympathetic voice that resonates with the brand in question. Chatbots are reshaping the entire dynamics of customer services by discarding redundant traditional messaging platforms and being available round the clock to transform even first-time visitors into permanent customers.

Are Human Interactions Still Needed? #

Contrary to popular consensus, digitized tools and virtual assistants may not exactly phase-out actual customer service entirely; human interactions may still be the need of the hour. In the aftermath of the pandemic, numerous companies turned to computerized customer service platforms and chatbots; however, it has been observed that many users are getting increasingly annoyed at having to communicate with a mechanized voice.

As per a survey comprising 2,000 consumers by outsourcing company CGS, almost 40% of British survey respondents claimed that speaking with a human customer service representative was preferable to having to converse with a virtual assistant. The same viewpoint was shared by United States survey respondents, 38% of which wanted companies to bring back human customer service.

Despite convenient online access and reliability, close to 83% of consumers claim that they prefer human interaction as opposed to digital assistants, according to the Global Consumer Pulse Research study from Accenture Strategy. This is because no matter how efficient and knowledgeable a virtual assistant is, it cannot match the convenience and understanding provided by a human customer service representative. Many are of the opinion that bots may have ready-made answers for standardized questions, but they will never be able to fully understand what the customer wants or solve the query the way the customer asks.

The biggest reason, however, for the retention of human customer service is the fact that users still want to feel needed, understood, and valued. Personalization is important, and so is active listening and empathetic communication that will help a company understand their customers well. Human interactions not only resolve the query in hand, but also help establish a long-term, personal relationship with users, thereby helping to increase customer satisfaction by up to 20% or more. It is more than likely that customer service in the digital age may be characterized by a mixture of efficient bots and human staff, harnessing the best of both and helping businesses retain as many consumers as possible.

Related reading

Questions and answers

How Has Pandemic Affected Digitization and Its Adoption? #

How Have Chatbots and Other Services Taken Over Customer Services? #

With an increasing number of brands trying to make their presence felt amid a cutthroat marketplace, the landscape of customer service has entirely changed in the last half a decade.

Are Human Interactions Still Needed? #

Contrary to popular consensus, digitized tools and virtual assistants may not exactly phase-out actual customer service entirely; human interactions may still be the need of the hour.

Architecture, Use Cases, and Examples

Vin Patel — Mon, 04 Jul 2022 12:00:50 +0000

Key takeaways

Using the term “edge computing” connotes a physical location.
A simple edge device can either collect or send data, or both, simultaneously.
These technologies will allow for virtualization and automation that has not been explored yet, such as better vehicle autonomy and workload migrations to the edge, while making wireless networks more flexible and cost-effective.

Edge computing brings AI and analytic workloads closer to where data is created, and actions are taken—reducing latency, decreasing network demands, increasing the privacy of personal and sensitive information, and improving operational resilience.

*Rob High,*IBM Fellow, VP & CTO, Networking & Edge Computing, IBM Cloud

Introduction

The edge architecture is a distributed computing architecture that incorporates every component active in edge computing—everything from devices to sensors to servers to clouds—wherever data is handled or utilized in remote areas of the network.

Using the term “edge computing” connotes a physical location. Instead of depending on the “bigger” clouds to perform all the work, it is computing that is done at or near the source of the data. An advantage of edge computing is that it brings corporate applications closer to the areas where data is generated and action is required. What is the source of all this information?

Devices that govern data flow at the network boundary are edge devices. With the rise of cloud computing and the Internet of Things (IoT), the importance of edge devices has increased, necessitating more intelligence, computation, and AI services at the network’s edge.

The term “edge computing” refers to processing data at the place closest to the source or destination of the data in a system—where it comes from or goes to. By lowering latency and lag, edge design speeds up processing. As a result, applications and programs running at the edge can perform better for the user and enhance overall performance. Each dock where goods are loaded and unloaded and inspecting the product before it leaves the pier is an example of an “edge” for a shipping firm. Even if your company’s primary data center is remote, the edge is where your apes are being processed.

What is Edge Computing? #

With edge computing, you not only get a lot of data from a lot of different devices, but you also have to make sense of all that data and let the devices decide for you. We will talk about IBM’s offer and how to design an edge solution, and the different parts that make it up. Use cases from different industries that deal with scalability, latency, and security will be shown.

Edge computing architecture is an ecosystem of infrastructure components that have been spread out from the central location of an enterprise’s data center or main server to all of the company’s other locations. This is part of the company’s overall deployment. Computing and storage capabilities, applications and devices, and sensors are all part of this. They work together with IoT devices in a central data center or cloud.

When we talk about edge computing, they have enough bandwidth, memory, and processing power to collect, process, and act on data in real-time without the help of other parts of the network, so they do not need to ask for help from other parts of the network. Some connection with the network lets the device talk to a database in a single place. It is easy to move scaled-down, on-premises edge servers or data centers around and fit them into small remote places. Red Hat has a wide range of flexible topology options that can be used in small spaces or different environments, such as power/cooling and intermittent network connectivity. This is just one example of what Red Hat can do with edge computing.

Key Components of Edge Ecosystem #

Cloud

Clouds can be public or private, and they can be a place to keep the container-based workloads like applications and machine learning models that run on them. Also, these clouds run and host the applications that are used to manage and orchestrate all of the different edge nodes. There will be many interactions between the workloads on edge and the workloads on these clouds. The cloud can also be a source and a destination for any data that other nodes need to do their jobs well.

Loads are apes that need to run on this architecture layer because they cannot run on the other edge nodes or the management layers. Both application and network workloads need to be moved to different edge nodes by using suitable orchestration layers.

Edge Hardware #

An edge device is a piece of equipment made for a specific purpose and has a lot of computing power. Many exciting things can be done with edge devices. These include an assembly machine on the factory floor, a cash machine, an intelligent camera, or a car that can see things. People often see edge devices with ARM or x86 class CPUs with 1 or 2 cores, 128 MB of memory, and maybe 1 GB of storage that is not on the device itself. Edge devices can be more powerful, but they are not the norm now.

They are the systems that are on-site or at the edge of the network. They can be separate physical or virtual servers in different places, or they can be combined in a system called a hyper-converged system. There are two main parts to this layer of the architecture. It will be home to both the parts of the systems that will run these applications in these architecture layers and the applications on the edge of these devices.

Application Layer: People who want to run big apes that cannot run at the edge because they are too big for the device will run these apes in this layer. Video analytic and IoT processing are two examples of complex applications.

Network Layer: Physical network devices aren’t usually used because it’s hard to keep them up and running. Almost all of the network layer has been virtualized or containerized. These could be routers, switches, or any other network parts needed to run the local edge.

The Edge Node #

An edge node is a general term for any edge device, edge server, or edge gateway that can be used for edge computing. It can be used to refer to any of these things, such as cameras, sensors, and other physical devices that run on-site at the edge are called edge devices. A simple edge device can either collect or send data, or both, simultaneously. The more complex edge devices have more processing power to do more things. In either case, it is essential to be able to put and manage apes on these edge devices so that they can work. Specialized video analytic, AI models that learn quickly, and simple real-time processing apes are some examples of applications that use this type of technology. IBM uses its IBM edge computing solutions to run and manage containerized applications on these edge devices.

Edge Cluster or Server #

An edge cluster/server is a general-purpose computer that is used in a place where people work, like a factory, retail store, hotel, distribution center, or bank. An edge cluster/server is usually made from an industrial PC or a computer rack. People often find edge servers with 8, 16, or more cores of computing power, 16GB of memory, and much storage, like hundreds of GBs. An edge cluster or server is usually used to run enterprise application workloads and other shared services, such as email.

Edge Gateway #

“Edge gateways” are usually edge clusters or servers that, in addition to running enterprise applications and sharing resources, also have services that can do things like translate protocols or connect to networks. A few edge devices can act as a limited gateway or host network function, but most edge gateways are separate from edge devices. Fixed-function IoT sensors collect and send data to an edge/cloud, but they do not have inboard computer memory or storage.

Let’s discuss a few use cases to understand the power of edge computing and how it will rule the world in the future.

Real-life Case Studies: #

There is a plethora of problems to be solved in every complicated workplace. On an individual basis, it will be necessary to decide if an edge computing solution is warranted for a given business challenge or use case. The following are some possible industrial use cases, as well as real-life solutions, for reference.

Case Study #1: Smart Cities #

Due to aging infrastructure, operational inefficiencies, and increased demands from inhabitants, operating and regulating cities has become an arduous undertaking. New technologies, such as IoT, edge computing, and mobile connection have made it easier for innovative city solutions to gain traction with residents and municipal officials.

IoT is the foundation of every smart city solution. These devices may be embedded within the city’s infrastructure and assets to monitor the functioning of these assets and offer meaningful information about their behavior. Edge computing is a prerequisite for delivering the city’s mission essential solutions, such as traffic, flood, security and safety, and critical infrastructure monitoring, due to the need to give a real-time decision and avoid transferring a significant quantity of sensor data.

Case Study #2: Manufacturing Sector #

As a result, a quick reaction is vital for reducing product failures and increasing efficiency in production. To maximize the efficiency of each machine, analytical algorithms keep track of how well it’s performing and adjust the settings—predicting when a failure will occur and scheduling maintenance between runs may be done with the use of analytic algorithms. Using different edge devices, it is possible to send important information to many edges, including the cloud.

Take a look at this: Reducing downtime is a priority for an electric bicycle manufacturer. Factory floor equipment includes presses, assembly machines, robots for painting, and conveyors. You lose $250,000 per hour if there is a problem with a manufacturing run. Making a profit is the difference between a company operating at maximum efficiency and experiencing unanticipated interruptions. The following are required to keep the factory running at maximum efficiency for it to function smoothly:

Methods for analyzing equipment performance and adjusting its operational settings to maximize efficiency.
To plan maintenance on equipment in between runs, you’ll need algorithms that can identify and forecast failures.
To accurately predict failure, models specific to each use case must be developed.

Conclusion

Edge computing is constantly changing and getting better. It uses new technologies and practices to improve its abilities and performance. Edge availability is one of the most important trends, and edge services are expected to be available worldwide by 2028. There are many different ways that edge computing can be used right now, but the technology is expected to become more common and change how the internet is used in the future. This will bring more abstraction and use cases for edge technology. In fact, there have been a lot of new products for edge computing, like computers, storage, and network appliances that are made for this type of technology. More multivendor partnerships will make it easier for products to work together and be flexible at the edge. There is an example of this, which is when AWS and Verizon work together so that the edge can get better connectivity.

Wireless communication technologies, such as 5G and Wi-Fi 6, will also impact edge deployments and use in the coming years. These technologies will allow for virtualization and automation that has not been explored yet, such as better vehicle autonomy and workload migrations to the edge, while making wireless networks more flexible and cost-effective.

Related reading

Edge Computing - The Key To IoT Success

Vin Patel — Mon, 04 Jul 2022 11:06:50 +0000

Key takeaways

Edge computing systems are accelerating the invention or assistance of real-time applications.
Further sensor data, image technologies, and the need to correlate all of this results in terabytes and megabytes of data being generated each second.
As these technologies are embraced by many sectors in the IoT ecosystem, there is still a lot to discover about them.

#

The manner in which organizations manage data, interpret it, and distribute it via millions of devices across the world is changing, owing to edge computing. The primary reason behind this widespread advancement in the field of edge computing is IoT or the Internet of Things. Edge-computing systems are being driven by the increasing proliferation of internet-connected gadgets (the IoT), as well as new software that demands real-time computing services.

Edge computing systems are accelerating the invention or assistance of real-time applications. These are video and graphics processing, analytic, self-driving cars, cognitive computing, artificial intelligence, and robotics, to name a few.

IoT - The First Wave of Edge Computing #

Edge computing has recently gained a lot of attention as a source of potential. Bold concepts like “the edge will devour the cloud” and “real-time automation will extend throughout healthcare, commerce, and manufacturing” are circulating in the business.

Experts agree that edge computing will serve a critical role in just about every company’s digital transformation. However, development has been sluggish. Organizations have been hesitant to fully utilize the edge for real-time decision-making and resource utilization due to outdated perceptions.

The concept of the edge has been closely identified with the initial wave of the Internet of Things in various sectors. Much of the focus at the time was on gathering data from little sensors mounted to everything and then transmitting that data to a central point, such as the cloud or major data center.

After then, the data flow had to be linked into what is known as sensor-fusion. Sensor economies, battery life, and pervasiveness at the time frequently resulted in data streams that were too narrow and had inadequate quality. Furthermore, upgrading old equipment with sensors was sometimes prohibitively expensive. While the sensors themselves were affordable, the installation took time and needed the use of qualified individuals.

Finally, the knowledge base of the workforce across enterprises was integrated with the skills required to interpret data utilizing sensor fusion. As a result, IoT adoption has slowed.

How Can Edge Computing Benefit Internet of Things? #

Processing power closer to the physical device or data source helps to improve the Internet of Things. The data which the IoT devices create must be examined at the edge, rather than travelling back to a central site, in order for it to respond faster or reduce issues.

For the data and computational demands of IoT devices, edge computing provides a local source of processing and storage. Here are some of the advantages that we derive from combining IoT with an edge:

Reduced Latency #

In cases where latency is an issue, edge computing can help. Since it does not travel over a network to the cloud or data center for processing, it minimizes latency. The time it takes to process and analyze data is referred to as latency. A connected device’s reaction time to the internet is less than a second. The answer may be delayed or halted in numerous scenarios.

It occurs when there is a problem with the internet connection or when the data center is located far away from the device. In such instances, the data processing speed is slowed. In order to compensate for the delay of data transmission across long distances, computational resources should be local.

Bandwidth

Cloud computing is used by businesses to handle and analyze large volumes of data. For the purpose of transferring data to the cloud, large quantities of electricity and bandwidth are required in the case of IoT. To gather and analyze data, IoT devices that create data and run software need to be linked to the cloud. Businesses may use edge computing to reduce their internet bandwidth use. Large volumes of data may be managed in close proximity to the source.

Another issue that companies had to deal with was the cost of operation. IoT devices create massive amounts of data and deliver minor updates to the cloud that aren’t essential. Because this procedure needs more bandwidth, the cost of bandwidth rises. The cost of the gadget to get to that bandwidth and the price of storage and analysis rises. This data may be collected and analyzed locally before being sent to the cloud via edge computing. It will be far less expensive than transmitting unfiltered data across expensive WAN networks.

Underlying Security Issues in IoT #

Organizations are committed to protecting the privacy of their essential data, regardless of the fact that the cloud provides great protection for their IoT applications. They fear that the data will be insecure once it reaches the cloud and departs their company’s premises. Edge computing can overcome this very obstacle by introducing additional security at the edge. This will increase the security of data storage and operations against hackers and assaults. Security can be adjusted on edge devices, for example, any outage will be limited to that particular device and its applications.

Taking IoT 1.0 to the Next Level #

The edge is present in every aspect of our lives: where we reside, operate, and where human activities take place. Improved and far more flexible sensors have overcome the problem of sparse sensor coverage. New assets and technologies come with a variety of built-in sensors. Sensors are now frequently supplemented with high-resolution/high-fidelity images (x-ray equipment, liar).

Further sensor data, image technologies, and the need to correlate all of this results in terabytes and megabytes of data being generated each second. Compute firepower is increasingly being installed near where the data is created to drive outcomes from these massive data flows.

The explanation is quite simple: between the edge site and the cloud, there is just insufficient bandwidth and time. In the short term, the data at the edge is most important. Data may now be evaluated and used in real-time at the edge rather than being processed and analyzed later in the cloud. Computing at the edge is required to achieve the next level of efficiency and operational excellence.

Proper understanding of massive data streams across a large number of geographically scattered sites is what the edge is all about. To properly comprehend what is now possible with edge computing, one must embrace this new perspective of the edge.

The Present Scenario of Edge Computing #

Instead of a few sensors at the edge, data may now be generated from a large number of sensors and cameras. Following this, the data is then evaluated at the edge using computers that are thousands of times more powerful than those used only two decades ago — all at affordable prices.

Real-time edge analytic is now a reality because of the availability of high-core-count CPUs and GPUs, as well as high-throughput networking and high-resolution cameras. Companies may better understand their processes and respond faster by deploying real-time analytic at the edge (where business action occurs). Some basic benefits of edge computing for IoT are:

Increased operational efficiency and faster reaction times.
Network bandwidth has been increased.
When a network connection is lost, the system continues to operate offline.
Analytic algorithms and machine learning provide local data processing, aggregation, and quick decision-making.
An IoT gateway can send data back to the cloud or a centralized data center from the edge, or it can deliver data to edge equipment to be processed locally.

By 2023, more than half of the new company IT infrastructure deployed will be at the edge rather than business data centers, rising from less than 10% now. By 2024, the number of applications installed at the edge will have increased 800%.

Anti-fraud Measures in Supermarkets #

Self-checkout is now widely used in supermarkets, and sadly, it has resulted in an upsurge in fraud. A shady shopper might exchange a lower-priced bar code for higher-priced goods, resulting in a lesser charge. Stores are increasingly deploying high-powered cameras to identify this form of fraud by comparing scanned products and weights to what they should be. Despite the fact that these cameras are very affordable, they create a large amount of data. Data may be evaluated in real-time by bringing computers to the edge. This implies that instead of waiting until the “buyer” has exited the parking lot, shops may identify fraud in real-time.

Food Production Monitoring System Using IoT #

At each stage of the manufacturing process, a manufacturing plant may now be outfitted with a slew of cameras and sensors. Real-time analysis and AI-driven inference can indicate if something is incorrect or if the process is straying in milliseconds, if not microseconds. Perhaps a camera indicates that too much sugar is being added or that an item is being covered with too many garnishes. Production lines may be modified to halt the drift or even stopped if repairs are needed, utilizing cameras and real-time analysis — all without generating disastrous wastage.

Edge AI in the Healthcare Industry #

The current healthcare business is being shaped by edge computing. As previously stated, there are several advantages to using Edge AI. It may be extremely important for both patients and healthcare practitioners in the healthcare business. It provides autonomous monitoring of hospital rooms and patients using computer vision and data from other sensors.

Consider the case of an accident due to fall detection. Several wearable now have the capacity to detect whether a person falls unexpectedly by utilizing particular technology. These gadgets’ edge AI can be trained to detect falls in real-time and even notify caretakers. In most circumstances, this can save a person’s life.

Role of Edge Computing in Autonomous Vehicles #

Autonomous cars are now conceivable because of affordable and widely accessible cameras that provide 360-degree stereoscopic vision. Analytic also enables accurate picture identification, allowing the computer to distinguish between a tumbleweed and the neighbor’s cat – and determine whether it’s safe to stop or steer past the obstruction. The real-time pattern recognition and vector planning that is the driving intellect of autonomous cars is enabled by the cost, availability, and compactness of the high-powered GPUs and CPUs.

The Future of Edge Computing in IoT #

IoT, or the Internet of Things, is driving the demand for Edge Computing due to very useful applications. Edge computing helps to bring the cloud closer to the edge of the network. Edge Computing is not a replacement for the cloud; rather, the two work together in a symbiotic way. Despite the fact that several companies are creating tools to manage this new infrastructure, there is still no standardization. Edge Computing will undoubtedly continue to expand and become increasingly common. As these technologies are embraced by many sectors in the IoT ecosystem, there is still a lot to discover about them.

Related reading

Where is Streaming Data Stored Temporarily? The Role

Vin Patel — Mon, 04 Jul 2022 10:00:43 +0000

Key takeaways

Many organizations across various industries now store hundreds of terabytes of data, known as big data.
The majority of today’s smartphones have storage capacities ranging from 32GB to 512GB.
Worse yet, you don’t want to find yourself in a situation where data loss is a real possibility without a backup plan in place.

Where is Streaming Data Stored Temporarily? The Role of Storage in Streaming Media #

-Baba Nadeem #

Many organizations across various industries now store hundreds of terabytes of data, known as big data. Still, there is a big difference between big data sets composed of static, archived files and big data containing business-critical streaming media files. These two types of big data are often known as big data at rest, and big data in motion.

It’s important to consider how files will be used when deciding what type of storage will be used for a big data set. In addition to object storage and scale-out network-attached storage (NAS), companies that rely on data in motion should consider high-performance storage options like solid-state drives (SSDs).

What is Media Storage Device? #

A media storage device can store large media files and access them quickly with a high amount of bandwidth and is also used to store data in real-time.

Based on your application requirements, its storage capacity ranges between 28 terabytes and multiple petabytes. Data can be transferred at the rate of 2 GBPS, 4 GBPS, or higher and can be accessed using a LAN cable. There are also various media storage devices that can be accessed via fire cable. Fire cable, on the other hand, adds to the complexity. It has a higher bandwidth than conventional cables and can carry data across longer distances. Much of the world’s internet, cable television, and telephone services are supported by fire cable connections.

The Nature of (Streaming) Media Files #

Due to the size of media files, modern browsers and protocols allow them to begin playing after only a tiny portion of the file has been downloaded. They also allow you to skip ahead to a certain point in the music. While this is going on, the data that will be played in the following few seconds or minutes is buffered ahead of time so that the user can have a smooth, uninterrupted playback experience. While some protocols, like HLS, effectively break a video stream into small distinct segments (files), this necessitates a CPU-intensive conversion step, which adds even another complexity. While this is still an option in some cases, as long as the file is in a current media format, the major focus is on serving the original content in the most efficient way possible without any processing.

Role of Storage in Streaming Media #

A multimedia storage system plays a crucial role in the performance and scalability of streaming media apes. As users increasingly access on-demand multimedia streaming apes on a daily basis, new storage solutions are required to handle the additional server load. Data placement, scheduling, file management, continuous data delivery, memory buffering, and prefetching are challenges faced by multimedia storage systems, as are storage and retrieval of data from storage devices.

Storage systems have long been regarded as a significant bottleneck in high-data-rate multimedia systems for two reasons. For starters, multimedia apes place a far greater demand on the storage system than prior applications. Second, storage devices have only become modestly quicker despite improved processor and network performance. Because of the growing speed gap, researchers are looking for new storage structures and file system storage and retrieval algorithms.

Features of Using a Media Storage Device #

Some of the main features of using a media storage device:

**Large storage capacity measured in petabytes:**A petabyte is equal to 1,000 terabytes, which is roughly one quadrillion bytes. The majority of today’s smartphones have storage capacities ranging from 32GB to 512GB. On the other hand, computer storage discs come in much bigger sizes, which leads us to the next unit.

We are able to store a surprising amount of video, images, audio, and other data on our phones and computers that would have been unthinkable just a few decades ago as storage technology has advanced rapidly.

You won’t be able to buy a storage drive that’s measured in petabytes or larger for a while, but you now have a rough idea of how much these devices can hold. 2. **High data transfer rates of tens of gigabits per second:**The data transfer rate (DTR) represents the amount of digital data that is transferred from one location to another in a given period of time. In other words, it is the speed at which data is transferred from one place to another. The higher the bandwidth of an existing path, the higher the transfer rate.

Data transfer in telecommunications is usually measured in bits per second, so an internet connection at low speed will likely be 33.6 kilobits per second (Kb’s). Ethernet local area networks can transfer data at speeds of 10 megabits per second; network switches able to transfer data in the terabit range are in development. In older systems, data transfer was measured in characters or blocks (of a specific size) per second, and the amount of time it takes data to travel between a CPU or RAM and such devices as a hard disk or CD-ROM is typically measured in milliseconds. 3. **Solid-state drives (SSD) are also available for improved performance:**Solid-state drives (SSDs) store data persistently using integrated circuit assemblies and are usually incorporated into computers as secondary storage in the computer storage hierarchy. Even though SSDs lack the physical spinning discs and moving read-write heads seen in hard disc drives (HDDs) and floppy discs, it is frequently referred to as a semiconductor storage device, a solid-state device, or a solid-state disc.

SSDs are often more resistant to physical shock, run silently, and have higher IOPS and lower latency than electromechanical drives. Data is stored in semiconductor cells in solid-state drives (SSDs). 4. **RAID protection is supported:**To preserve data in the event of a drive failure, RAID (redundant array of independent disks) stores the same data in numerous locations on multiple hard discs or solid-state drives (SSDs). However, there are several RAID levels, and not all of them are designed to provide redundancy.

RAID improves performance by distributing data across several discs and allowing input/output (I/O) processes to overlap in a balanced manner. Because storing data redundantly increases fault tolerance by increasing the mean time between failures, employing multiple drives is recommended. 5. **Component redundancy is also available:**Data security and high-availability computing are based on the notion of redundancy. Without redundancy, there would be no way to recover from even the smallest mistake or component failure, such as a disc drive failure. A data center with tens of thousands of disc drives can expect drive failures on a frequent basis during normal operations. Each of these disc drive failures could be disastrous if there is no data redundancy.

Device failures, of course, are only a small fraction of the overall demand for data redundancy. Large-scale disasters do happen, and they must be prepared. Using storage networks, you can store redundant data at another sufficiently removed location from the primary storage to protect data from threats that may impact the primary storage or processing.

Conclusion

Storage requirements in the streaming media industry will significantly expand due to continuing technological innovation, more usage of computer-generated and computer-altered imagery, and a higher demand for high-definition films.

Preserving key data and media assets is essential for ensuring the continuity and stability of your data center and network infrastructure during normal business operations. The objective is that your company will not need to utilize disaster recovery for its data and media files, similar to insurance. Worse yet, you don’t want to find yourself in a situation where data loss is a real possibility without a backup plan in place.

Related reading

DataStax, The Real-time Data Company, Unveiled “Change Data

Vin Patel — Mon, 04 Jul 2022 09:42:22 +0000

Key takeaways

The best part about the new CDC for Astray DB is that it’s now powered by advanced streaming technology and is built on the latest Apache Pulsar.
In regard to this, the enterprise architect of Alpha Ori Technologies, Praveen Viswanath, said that Alpha Ori Technologies develops real-time data solutions for maritime shipping.
Through DataStax, every business could be able to muster live data and rapidly construct the smart, top-notch apes needed to come to be a data-driven firm.

The best part about the new CDC for Astray DB is that it’s now powered by advanced streaming technology and is built on the latest Apache Pulsar. It uses the event streams to process and deliver database changes in real-time. This real-time data changes then becomes available for use across data lakes, search, artificial intelligence, data warehouses, and machine learning.

With this advanced and powerful integration of event streaming with high-performance, high-scale Astray DB mufti-cloud databases, any organization gets enabled to build more reactive and smarter applications. Applications that are fueled by real-time or, we can say, connected data.

According to the chief product officer at DataStax, Ed Snuff, the CDC for Astray DB is an entirely new and unique concept. The major areas it taps for the performance of the world’s most scalable database and combines with the next-generation event streaming. Undoubtedly, working with the next-generation event streaming technology will fuel today’s data-intensive applications with the best solutions for a seamless workflow.

Currently, only a fraction of real-time data is being leveraged, even though businesses have a huge urgency to use it. This is major because of the failure of legacy architectures that cannot meet the performance and scale requirements of real-time data processing. Due to this, we see the final result as data uploaded in batches which ultimately creates chronically stale data.

In regard to this, the enterprise architect of Alpha Ori Technologies, Praveen Viswanath, said that Alpha Ori Technologies develops real-time data solutions for maritime shipping. Also, he added that they are extremely excited about the availability of CDC for DataStax Astray DB. This will help real-time analytic by providing a real-time source of streaming data. He said CDC help our maritime clients save money on fuel, mitigate maintenance-related risks, and reduce carbon emissions by enabling them to alert and deliver analytic rapidly.

According to the report made by McKinsey, one of the seven characteristics predicted to define data-driven enterprises is the Data-Driven Enterprise of 2025, where data is processed and delivered in real-time. However, companies often choose between speed and computational intensity, which can delay more sophisticated analyses and inhibit the implementation of real-time use cases. WorkJam is the place of the business governor for actual forerunner orchestration: plan, duty administration, activities, communications, staffing, and several others.

The director of the R&D at WorkJam had earlier stated in an interview that they’re always hunting for different means to simplify their functions and operations with the help of Change Data Capture (also known as CDC). This will allow them to produce smarter and more responsive connected applications; live data would power.

With the help of Change Data Capture for Astray DB, companies could homogenize on the enormous scalable with no downtime Astray DB whatsoever. Apart from this, any modifications to the database are distilled and furnished in real-time to different portions of the data system without having any sort of downtime between rate, scale, and accessibility. This smooth incorporation facilitates corporations to initiate their real-time data to raise accumulative business significance from regions encompassing e-commerce, crime detection, consumer satisfaction, trades, stock and transport, and logistics.

The principal analyst at DBInsight stated that Through the CDC, Astray DB also unites functional and streaming data to evolve to be a bidirectional data center for live data. He added that the CDC would enable their consumers to combine Astray DB with cloud analytic places such as the Snowflake or the junction for Android application with further limited resistance.

DataStax is a live data corporation. Through DataStax, every business could be able to muster live data and rapidly construct the smart, top-notch apes needed to come to be a data-driven firm. DataStax Astray DB differently mixes the strength of Apache Cassandra—the highly scalable database across the globe—with the developed Apache Pulsar streaming teach in a harmonious bunch provided as assistance on any cloud.

Several giant corporations, comprising names such as ESL Gaming, Verizon, Audi, and several others, depend on DataStax to express the strength of real-time data to provide instant virtual experiences that could gain a victory in modern markets and shift businesses.

Apache Cassandra, Apache, Cassandra, Apache Pulsar, Pulsar, and Apache Kafka are either enrolled brand names or brands of the Apache Software Foundation or its associates in the Canadian region, the United States of America, and several different regions.

Related reading

Demystifying Machine-Learning Systems

Vin Patel — Wed, 06 Apr 2022 22:10:36 +0000

Key takeaways

Machine learning is increasingly infiltrating everything, including self-driving cars, voice recognition, chatbots, smart cities, and so on.
Reinforcement Learning: Reinforcement learning is seen to be the last chance for real artificial intelligence.
There is no question that machine learning will revolutionize numerous industries, including education, business, and health care, making the world a safer and better place.

Machine learning is increasingly infiltrating everything, including self-driving cars, voice recognition, chatbots, smart cities, and so on. Because of the amount of big data and the importance of data analytic and predictive analytic, machine learning has become a must-have technology.

When computers are exposed to new data, they are intended to learn independently and complete tasks without the need for human interaction. It means that when a computer or system developed with machine learning encounters a new pattern of data, it will detect, analyze, and adjust accordingly, producing the intended result without the need for humans.

The intricate and powerful ‘pattern recognition’ algorithms lead them where to seek what is at the heart of machine learning’s self-identification and analysis of new patterns. As a result, the need for machine learning programmers with considerable experience dealing with complex mathematical computations and applying them to big data and AI is increasing year after year.

Machine learning’s evolution #

Machine learning is not the same as it used to be due to advances in computer technology. It evolved from pattern recognition and the concept that computers might learn without being programmed to do specific tasks; artificial intelligence researchers wanted to see if computers could learn from data. Because models may change autonomously when they are exposed to new data, the repetitive feature of machine learning is critical. They use past computations to make consistent, repeatable judgments and consequences. It is not a new science, but it is gaining popularity.

While many machine learning techniques have been around for a long time, the ability to automatically apply complex mathematical computations to massive quantities of data repeatedly and faster each time is a relatively new phenomenon. Here are a few well-known machine learning applications that you may be familiar with:

The self-driving Google car is an example of the fundamentals of machine learning.
Online recommendation services like Amazon and Netflix are examples of machine learning applications in everyday life.
Combining machine learning with language rule generation helps Twitter understand what consumers are saying.
Fraud detection is much easier with ML algorithms.

What are the types of challenges that machine learning can solve? #

There are three types of machine learning problems:

1. Supervised Machine Learning: Supervised Machine Learning methods are used when you have previous data with results (labels in machine learning language) and wish to predict the outcomes for the future. Supervised Machine Learning challenges may be further classified into two types:

Classification Issues: When you wish to categorize outcomes into multiple groups. For example, determining if the floor needs to be cleaned/mopped is a categorization challenge. The outcome can be classified as either Yes or No. Similarly, whether a consumer will fail on their loan or not is a categorization challenge that every bank is interested in.

Regression Problem: These challenges fall under the Regression umbrella when you want to know the expected quantity. Determining how much cleaning is necessary, for example, is a regression problem. A regression problem is determining the expected amount of default from a client.

2. Unsupervised Machine Learning: There are situations when you do not want to anticipate an outcome precisely. You want to do some segmentation or grouping. A bank, for example, may want to categorize its customers in order to understand their behavior better. This is an unsupervised machine learning problem since we are not expecting any results.

Reinforcement Learning: Reinforcement learning is seen to be the last chance for real artificial intelligence. It is a somewhat more sophisticated topic than typical machine learning, but it is as important for the future. This is as excellent an introduction to reinforcement learning as you can find elsewhere.

How do machines learn? #

Machines learn in the same way that people do. Humans learn via their experiences, training, and teachers. They sometimes employ knowledge that is given into their brains, and other times they make judgments by analyzing the present circumstance in light of their previous experiences.

Similarly, machines learn from the inputs that tell them which is correct and which is incorrect. Then they are given data to analyze depending on the training they have received thus far. In other circumstances, people have no notion which is right or wrong and just make a conclusion based on their own experiences. We will investigate the many learning principles and methodologies.

Who is using it? #

Machine learning may be used for any business that deals with massive amounts of data and has several difficulties to solve. For example, machine learning has proved to be incredibly valuable to firms that are making the most use of the technology in the following domains:

Financial services #

Banks and other financial institutions use machine learning technology for two primary purposes: identifying relevant insights in data and preventing fraud. The data can help investors locate investment possibilities or decide whether to trade or not. Data mining may also be used to identify clients with high-risk profiles, and caber surveillance can discover early signs of fraud.

Government #

Government organizations have a particular need for machine learning for public safety and utilities since they have a variety of data sources that may be mined for insights. Analyzing sensor data, for example, identifies chances to increase efficiency and save money. Machine learning may also help detect fraud and reduce identity theft.

Medical care #

Machine learning is a rapidly rising trend in the health care business with the development of wearable gadgets and sensors that can analyze a patient’s health in real-time. Medical experts can also use technology to analyze data in order to find patterns or red flags to provide better diagnosis and treatment.

Retail #

Retail websites that propose things based on prior purchases use machine learning to examine your purchasing history. Retailers use machine learning to acquire data, evaluate it, and customize a shopping experience, create a marketing campaign, optimize pricing, item planning, and get consumer insights.

Oil and gas #

Oil distribution is being simplified in order to become more efficient and cost-effective. In this field, the number of machine learning application cases is large? and rising: discovering new energy sources, identifying and analyzing minerals in the earth, and predicting the failure of refinery sensors.

Transportation #

Transportation business relies on making routes more efficient and detecting possible difficulties to boost revenue; therefore, data analysis is critical to uncover patterns and trends. Machine learning’s modelling capabilities and data analysis are valuable tools for delivery firms, public transit, and other transportation enterprises.

Conclusion #

Machine learning is unique in its own way. While many experts are concerned about the rising reliance and visibility of machine learning in our daily lives, on the bright side, machine learning may accomplish marvels. And the world is already experiencing its magic - in health care, banking, automotive, image processing, voice recognition, and a variety of other industries.

While many of us are concerned that robots will take over the world, how we develop successful yet safe and manageable machines is entirely up to us. There is no question that machine learning will revolutionize numerous industries, including education, business, and health care, making the world a safer and better place.

Related reading

Questions and answers

What are the types of challenges that machine learning can solve? #

There are three types of machine learning problems: 1. Supervised Machine Learning: Supervised Machine Learning methods are used when you have previous data with results (labels in machine learning language) and wish to predict the outcomes for the future.

How do machines learn? #

Machines learn in the same way that people do. Humans learn via their experiences, training, and teachers.

Who is using it? #

Predictive Analytic in Combating Cybercrime

Vin Patel — Fri, 11 Feb 2022 01:30:00 +0000

Key takeaways

Today, cybercrime is a trending word across industries and has become a devastating threat.
Predictive analytic uses the method of attack graphs to handle such a heavy task.
For way better help in cybersecurity, companies can too accomplice with analytic firm to work and imply predictive analytic.

Today, cybercrime is a trending word across industries and has become a devastating threat. Cybercrime is on the rise mostly due to the uncountable and latest range of methods used for attacks. The rapid and dynamic development of methods to assault cybersecurity has made defending the network from this type of crime a harder task. Nevertheless, as an extract of the great saying “Darkness cannot drive out darkness, only light can”.

Predictive analytic has proved to be a ray of hope against cybercrime. Do you think that a prior warning can help prevent these caber-attacks? Fortunately, there is an advanced way of doing so. As a prelude, I would like to introduce you to the concept of predictive analytic.

What is Predictive Analytic? #

Analyzing historical data and using analytical techniques, such as statistical modeling and machine learning, to predict future outcomes is called predictive analytic. Predictive analytic is termed as a solution because it is designed to handle growing volumes of data and simultaneously predict the possibility of an attack. With the aid of sophisticated predictive analytic tools and models, people can predict the future of their businesses. In addition, predictive analytic can be used to detect and cease various types of criminal behavior before they cause serious damage. By studying user behavior and actions, an organization can notice anything out of the ordinary, such as cybercrimes. As predicted outcomes have grown, security professionals have begun to leverage predictive analytic. Individuals run multiple models simultaneously and combine the outcomes to find the most likely result. The greatest preferences of predictive analytic are its capacity to distinguish inconsistencies.

How Predictive Analytic Work? #

The most commonly used methods in predictive analytic include:

Identifying Loopholes in Network #

Cybercrime is a threat to all industries with varying impacts. For example, the methods used for attacking healthcare industries will differ from the methods used in software or aerospace industries. These variations depend on the data available, and it is always an important step to implement ways of protecting data together with restricting cybercrime from taking place. Predictive analytic use tools which are based on identifying network vulnerabilities. These tools help identify and observe malicious activities on a network and use this analysis to anticipate possible threats. It is important that these systems are up to date and specifically capable of identifying sophisticated attacks buried under the heaps of data in order for them to be effective.

Handling Massive Data with Attack Graphs #

Predictive analytic deals with a huge data collection which can be exceptionally difficult to cross through and then process, analyze for required insights. These data results as an overall collection from databases, programs, devices, and servers.

Predictive analytic uses the method of attack graphs to handle such a heavy task. All paths of entry and exit to a cybersecurity network are represented by a caber-attack graph. They delineate ways in which an attacker can misuse the vulnerabilities of a network.

Lists Preventions to Avoid Future Attacks #

Predictive analytic focuses more on preventive future attacks rather than explaining the reason and areas of attack in the past. It provides a smart move for you to identify weak areas along with solutions to identify patterns and detect abnormalities that can be easily avoided with certain preventions to save your data. Predictive analytic combines with hacker bots to detect and monitor network activity. It can also detect immediate irregularities in the data flow.

Predictive Analytic Tools #

Here are three of the most popular predictive analytic tools:

? SAP Predictive Analytic — SAS — Microsoft Azure Machine Learning

These tools use big data, statistical algorithms, and machine learning methods to predict the probability of real-time and future results based on past data. The main objective of predictive analytic tools can be summarized in four points:

? Detect cybercrime — Optimize the operation of network stream — Improve overall performance and protect data — Reduce future risks

Industries Using Predictive Analytic To Prevent Cybercrime #

Predictive analytic is widely used among all industries to improve business with merely a minimum or negligible percentage of existing risk. It is a highly valued technology in industries like Retail, Marketing, Healthcare and Banking. Among these, banking and financial industries use predictive analytic to avoid cybercrimes. There are huge amounts of data and money in the financial industries which face extreme risk on a daily basis. Apart from detecting cyberattacks and protecting data, the banking sector takes the help of predictive analytic for optimizing customer-based decisions, avoiding fraud, enhancing risk management, and providing better customer solutions.

What Does the Future of Predictive Analytic Hold? #

In conjunction with being a commanding subject for study and research at this time, predictive analytic has already made a position in terms of security in each large and small company. Security being the most elevated need of each company, predictive analytic have combined with branches of artificial intelligence, modelling etc., and have come up as the foremost successful shield for cybersecurity. There’s continuously less chance for such caber crimes to end totally in near future but the possibility and accessibility of these security technologies has multiplied up. These innovations have a mind-blowing potential to form our virtual and computerized world free of all wrongdoings.

Conclusion #

By involving predictive analytic as a potential part, an organization can recognize the wide range of cybercrime taking place every day. Predictive analytic apply statistical algorithms for organizations to predict future caber-attacks in real-time. Organizations can take an active part in sorting these cybercrime assaults by involving a group of experts and analysts who can research how often it is possible for cybercrime to occur. Analysts can gather internet data measurements and apply predictive analytic techniques to identify any vulnerabilities in their cybersecurity that could be exploited by cybercriminals. By combining machine learning with predictive analytic, companies can move forward with the efficiency of cybersecurity and intelligently dodge all approaching threats. This implies they are able to form a total analytic and visualization arrangement which helps hold up all information and reports. For way better help in cybersecurity, companies can too accomplice with analytic firm to work and imply predictive analytic.

Related reading

Questions and answers

What is Predictive Analytic? #

Analyzing historical data and using analytical techniques, such as statistical modeling and machine learning, to predict future outcomes is called predictive analytic.

What Does the Future of Predictive Analytic Hold? #

In conjunction with being a commanding subject for study and research at this time, predictive analytic has already made a position in terms of security in each large and small company.

How Human-centric AI Solutions can Help in Solving Business

Vin Patel — Fri, 21 Jan 2022 15:30:00 +0000

Key takeaways

Every day, you have to decide whether a quaint little coffee shop in some town deserves a loan or not.
We are only helping everyday decision-makers to be better at their jobs by using human-centric AI.
All the dangers and concerns of the spiralling growth of AI can be avoided by encouraging human-centric AI methodologies.

Imagine you run a bank that lends money to coffee shops. Every day, you have to decide whether a quaint little coffee shop in some town deserves a loan or not. If this task is assigned to a traditional AI system, the machine will probably check the financial statements, the credit rating of the owners, assets and liabilities of the shop, etc. It might also check analytic like the number of repeat customers and the coffee flavors that make the most profit.

But is that all the information required to make that all-important business decision to lend or not to lend? How about checking the commitment and enthusiasm of the owners to scale up their business into a multinational coffee shop chain? How about other factors like the camaraderie between the partners of the coffee shop?

Gauging such factors that come easy to a professional banker, is quite tough for most AI systems. This is one among the several instances where human-centric AI systems would help us in solving our complex business problems.

What is Human-centric AI? #

Human-centric AI never attempts to take the human being out of the business equation. Instead, it empowers humankind to be better at their jobs. You can be a better doctor, teacher, or like in the earlier example - a better banker by using human-centric AI systems.

There are two streams of research currently happening in this field. An ambitious long-term idea is to model deep learning systems by simulating how the human brain works. The other, more promising, stream of research is to include more human input during the entire life cycle of the AI system.

As a child, all of us were incapable of functioning properly in this society. We learned the rules of the world bit by bit from our parents, siblings, teachers, family members, friends, and strangers. We didn’t have to see pictures of all breeds of dogs to learn to differentiate a dog from a horse. It’s the same with a human-centric AI system. Rather than supplying the AI system with a brute force list of endless data, a curated set of data is provided to human-centric AI systems.

Human Beings and Learning Phase #

A standard machine-learning algorithm uses large chunks of data in its learning phase. Later, the decisions made by the AI are hugely influenced by the data that is used in the learning phase. So, the quality of data used becomes highly important. But who decides what data is supplied to the AI?

Let’s again consider the example of granting loans to coffee shops. In a human-centric AI system, expert bankers will be teaching the system how they decide to approve or reject a loan application. This list of experienced bankers should be diverse enough to represent all genders, ethnicity, and background to prevent the AI system from being biased towards any community.

In the learning phase of the AI system, experts from various fields can be used to improve the accuracy of the system. Fields like sociology, philosophy, economics and game theory would contribute towards improving human-centric AI systems.

Human Beings and Execution Phase #

Once the initial learning phase is complete, a human-centric AI system begins to arrive at decisions for each business scenario. However, we still need the discretion of a human being to validate the decisions made by the AI system. Any disagreement between the expert and the AI system provides an opportunity for perfecting the system to a better state.

Such validation steps are integral to any human-centric AI system. Remember, the goal is not to remove human beings from the decision-making process. We are only helping everyday decision-makers to be better at their jobs by using human-centric AI.

Human-centric AI makes explainable decisions. This makes it easy for humans to validate the decisions. Coming back to our earlier example of lending to coffee shop owners, consider a scenario in which the new AI system advises not to grant a loan to a small coffee shop owner named Martha. The system will also explain why Martha doesn’t deserve a loan. Let’s say the AI explains that it’s too risky to loan Martha because of her old age. A human being will be able to identify that it is not ethical to deny Martha a loan because of her age. Here comes the most important feature of human-centric AI - being ethically sound.

Adding Ethics to AI #

In addition to solving a business problem, human-centric AI ensures fairness in its operation. In fact, human-centric AI is the answer to all our dystopian concerns about AI taking over our planet.

All industries can benefit from human-centric AI systems. In the medical field, an inexperienced doctor can quickly identify even the smallest hints of pneumonia, which otherwise, could only be identified by senior doctors. This would bridge the gap between the rich and poor in terms of expert medical care. Telecoms engineers can detect patterns of problematic networks and take urgent actions without affecting emergency services using a human-centric AI system. Logistics managers can decide on the most efficient and environmentally friendly route to be taken for a particular consignment. The possibilities of human-centric AI to solve business problems ethically are endless.

In Sales and Marketing #

Even with extensive data collection and analytic, companies struggle to understand the needs of their customers. It’s not rare to see mufti-million dollar marketing campaigns that ticked all the right boxes fall off the cliff during execution. Often, this happens due to the over-reliance on numbers rather than emotions.

Many products sell due to subjective qualities like beauty, image, and brand perception. It’s not easy for a data analytic tool to measure such nonobjective data. Even for a traditional AI, efforts to measure the aesthetic appeal of a prospective product have not been successful in an industry environment. Human-centric AI can solve this prediction problem efficiently and help in determining the market perception of a product or service.

Most business problems are sales problems at the root of them. So if human-centric AI could determine whether what you’re doing is going to sell in the grander sense of the word, it could be of help in solving such business problems.

Conclusion #

Human-centric AI can solve complex business problems with high accuracy. We can also ensure ethically correct practices by pursuing this technology. All the dangers and concerns of the spiralling growth of AI can be avoided by encouraging human-centric AI methodologies.

Related reading

Cloud Banking: Leveraging Data For Valuable Insights

Vin Patel — Tue, 11 Jan 2022 19:49:32 +0000

Key takeaways

Financial institutions are progressing at a breakneck speed from a technology standpoint.
Working in a more efficient development environment allows banks to bring new goods and services to the market more quickly and simply upgrade them.
Data stored in the cloud can be as (or more) secure as data stored on-premises, regardless of the deployment type.

Financial institutions are progressing at a breakneck speed from a technology standpoint. In banking and finance, the need for and demand for improved technology is driving the system to modify strategy, establish new models, and ultimately satisfy client expectations. Banks are now discovering intriguing new methods to transform their data into actionable information. Cloud-based solutions offer the best storage and capabilities for managing large amounts of data while also making data and insights easily available to analytic and the business.

This article will help you in understanding the power of the cloud in management and how cloud-based platforms are helping in transforming and uplifting the banking industry. But first, let us properly go through the concept of cloud computing.

Understanding Cloud Computing #

Cloud computing is the on-demand distribution of computer services through the internet, including software applications, data storage, and processing power. Rather than investing in contemporary computer infrastructure, banks and credit unions can employ cloud computing solutions to supplement or replace the services provided by their present data centers. This saves businesses money and time in the long run by avoiding the upfront costs and complexity of owning and managing increasingly sophisticated IT systems.

Expenditure on cloud computing solutions continues to rise as a result of the demand for modernized back-office technology, while spending on conventional, in-house IT continues to decline. Vendor-provided public cloud services, private clouds constructed by a financial institution, and hybrid cloud solutions that combine a private cloud with one or more public cloud services, utilizing proprietary software to ease communication between the two, are all options.

Why is Cloud Better Than Traditional Banking Platforms? #

On many levels, analytic from a cloud-based platform may be beneficial. Real-time information on marketing campaign ROI, for example, is a crucial statistic for a chief marketing officer that is not readily available or actionable with today’s on-premises solutions. A chief information security officer, for example, may have access to data that improves a bank’s governance skills, resulting in security and compliance advantages. Banks are also using Google Cloud’s open APIs to authenticate and protect financial conversations with consumers, allowing for the transfer from face-to-face digital channels.

For data warehousing and data management, banks can employ a variety of cloud-native services. Furthermore, successfully integrated AI and machine learning tools may assist automate chores and creating personalized experiences. The elastic capabilities of cloud computing, for example, have been proven to reduce data model training time by more than half when compared to traditional on-premises systems. Data models converge more quickly, allowing for speedier service delivery to markets and better service to customers. Banks can also use high-performance computing (HPC) facilities to handle enormous amounts of data. Cloud computing offers banks with current options for HPC, which are manifested in containers and purpose-built hardware devices such as graphics processor units and tensor processing units.

Another significant advantage of migrating to the cloud and utilizing managed services is that programs may be handled uniformly across several settings. Working in a more efficient development environment allows banks to bring new goods and services to the market more quickly and simply upgrade them. Traditional and older on-premises data infrastructures can’t handle the volume of data or real-time data streams required for sophisticated business analytic and AI. It is necessary to have a contemporary data architecture. Furthermore, banks can flexibly scale infrastructure up or down to meet demand in a cloud environment, which is critical for digital success.

The Future of Banking with Cloud: #

Embracing the cloud is crucial to a successful digital banking revolution. While there have been concerns about cloud security and regulation in the past, cloud computing solutions are becoming more common in the market for both traditional and non-traditional financial institutions. All but the largest financial institutions lack the computing capacity needed to exploit data and apply sophisticated analytic, machine learning, and artificial intelligence. The good news is that numerous cloud-based solution providers, such as IBM, have developed industry-specific solutions for the banking sector.

In the future, banks may improve their customers’ experiences in a variety of ways by using cloud data:

When leveraging third-party apes and services to improve the client experience, a more secure environment is created.

The ability to deliver solutions directly to consumers’ messaging applications or to deliver quick solutions to the customer’s phone, resulting in personalized experiences.

Even during peak hours, quicker transaction speeds are possible.

Real-time checks, for example, may be accessed more quickly and easily.

Recommendations for investments and savings that are optimized.

Personalized offerings and solutions based on solid analytic.

According to IBM, ‘Organizations have an enormous opportunity to leverage cloud computing to drive innovation and improve their competitive position. Cloud computing? whether private, hybrid, or public? enables organizations to be far more agile while reducing IT costs and operational expenses. In addition, cloud models enable organizations to embrace the digital transformation necessary to remain competitive in the future.?

Not only is the cloud assisting in the innovation of IT strategy, but it is also becoming an engine for rapidly developing new capabilities and services to meet business needs. Many transformational solutions (for example, customer relationship management, finance, and enterprise resource management) are already cloud-based’they’re simply not advertised as such. A cloud business case should highlight how the bank can use cloud-delivered solutions to generate customer insights, experiences, and offerings, as well as grow revenue, reduce expenses, locate and inboard better personnel, and provide more uniform corporate operating platforms. It should also incorporate a baseline cloud value-assessment model to help with scenario planning by mapping the economics of shifting market dynamics, pricing, and business assumptions.

End Note #

The bank of 2030 will be substantially different from the one we have now. Banks and other financial services businesses are quickly realizing that the cloud is more than a technology; it is a place where they can store data and applications and access advanced software applications over the internet.

Business unit and IT executives who are used to working with on-premise data centers may find the notion of updating or replacing outdated systems with an enterprise-level cloud solution intimidating. Fortunately, banks may take this shift step by step. Depending on their organizational goals, maturity, and preparedness, they can mix and match hybrid and mufti-cloud solutions; most businesses opt for a mufti-cloud strategy. Data stored in the cloud can be as (or more) secure as data stored on-premises, regardless of the deployment type.

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Do You Need To Worry About Your Edge Computing System?

Vin Patel — Thu, 06 Jan 2022 15:30:00 +0000

Key takeaways

Edge computing refers to computing that brings intelligence, data processing, analytic, and communication capabilities down to endpoints or where data is gathered and provided to end-users, such as network gateways.
- Node software and operating systems can be changed or modified.
To assist ward off hardware-based assaults, purchasing an edge server from a manufacturer with a Counterfeit Protection Program (CPP) and other stringent quality inspection systems in place is strongly advised.

What Is Edge Computing, And How Does It Work? #

Edge computing refers to computing that brings intelligence, data processing, analytic, and communication capabilities down to endpoints or where data is gathered and provided to end-users, such as network gateways. By providing highly efficient networks and operations, improved user experiences, and service delivery, latency is reduced. Network performance and optime determine whether a company succeeds or fails. Year after year, a company’s reliance on performance and reliability will only grow. Cloud workloads will continue to migrate to the edge for this reason.

By 2027, the global market for edge computing is estimated to reach USD 43.4 billion. With new traffic demands and traffic patterns, 5G technology will help the edge computing business flourish. The edge is seen as a threat by cloud leaders, who have responded by spending substantially in the edge ecosystem alongside telecoms providers. Individuals or companies who refuse to go to the cloud and instead stick to their on-premises computer servers are known as server buggers. So, if you’re clinging to the cloud today, you might be tomorrow’s cloud hugger, i.e., rejecting the move to edge computing and all of its capabilities and benefits, including cost savings.

Edge computing is a brand-new way of doing business. It’s a decommissioned data center that’s physically closer to IoT devices, lowering costs and latency. Edge computing is a potent instrument that will usher in the future of autonomous vehicles, augmented reality, smart cities, and more. This data processing and analysis methodology will evolve in the future years. It’s nearly inescapable because of the potential and lower cost.

Millions of IoT devices are distributed over the world, gathering and transmitting petabytes of telemetry and audio/video to servers for analysis. In the consumer sector, the internet of things is referred to as the smart home,’ which includes internet-connected equipment such as lighting fixtures, thermostats, home security systems, cameras, and so on. The IoT brings together traditional domains such as embedded systems, wireless sensor networks, control systems, automation, transportation systems, vehicular communication systems, medical devices, elder care, and others. The bandwidth required to transport the data costs millions of dollars, and the distance involved causes latency problems. The data analytic computers were physically brought closer together to minimize expenses and increase quality, with only relevant and important data being transmitted through the cloud. This is what Edge Computing is all about.

Every new widely used computing technology comes with a learning curve for businesses and organizations. Edge computing, in which cloud computing is decentralized and deployed at or near data-generating devices, poses similar issues, not least of which is edge security, or the practice of understanding edge computing’s cybersecurity vulnerabilities and countermeasures.

Advantages of Edge Computing #

The following are the key advantages of edge computing over cloud computing:

Data handling that is better

Lower connectivity costs and improved security

Uninterruptible, dependable connection

Instead of sending sensitive data to a central data center, edge computing allows you to filter it at the source. Less sensitive data is sent between devices and the cloud, which means greater security for you and your customers. Most IoT projects can also be completed at a lower cost by decreasing data transit and storage requirements using traditional methods.

#

Hacking and Physical Manipulation #

In an edge-computing architecture, physical tampering of devices is a possible possibility, depending on their location and level of physical protection from adversaries.

By bringing processing resources closer to data sources, edge computing by its very nature increases the attack surface. Physical attackers aiming to breach entire edge networks have more territory to cover with an enlarged attack surface, but the fact that there are more devices in more places makes physical attacks that much easier to carry out.

After gaining physical access, attackers can:

Extract valuable and confidential cryptographic data
Node circuits can be tampered with.
Node software and operating systems can be changed or modified.

There’s also the chance that an attacker will physically harm or destroy edge nodes, jeopardizing the network’s effectiveness.

Injections of Harmful Hardware or Software #

Cybercriminals can use a variety of hardware and software-based tools to corrupt, steal, alter, or erase data travelling within edge networks, especially when it comes to infecting and manipulating edge nodes, or servers and devices located at the edge.

Caber attackers can inject unauthorized software and hardware components into the edge network, wreaking havoc on the efficacy of existing edge servers and devices and even allowing service provider exploitation, in which entities that provide the software and hardware solutions that enable edge computing to begin unwittingly executing hacking processes on behalf of the attacker.

Node replication, which involves adversaries inserting a malicious node into the edge network and assigning it an ID number that is identical to that of an existing node, is one such terrifying behavior identified by IEEE researchers. Assailants can then take data from within the network, much like a covert spy stealing valuable intelligence from the opponent. They can even use node-revocation protocols to delegitimize other nodes in the network.

There’s also camouflaging, in which attackers inject a fake edge computing node that looks and acts like any other - sharing, receiving, storing, processing, redirecting, and transmitting data packets - as well as hardware trojan injection, which gives attackers control over a node’s integrated circuits, and thus its data and software.

Threats against Data Routing #

A “routing information attack,” or simply “routing attack,” occurs at the communication level of an edge network and is another edge computing security concern to be aware of. Routing attacks influence throughput, latency, and data pathways by interfering with the way data is transported within a network.

In their work, the IEEE researchers discuss four different types of routing information attacks:

Black holds: Incoming and outgoing network data packets are simply erased during a black hole attack, guaranteeing that they never reach their intended destination. If the data needs to be retransmitted, this reduces throughput and can increase latency. The worse the network performs, the lower the throughput and the higher the delay.

Grey holes: A grey hole attack is similar to a black hole attack, except instead of removing data packets in a network all at once, it deletes them gradually and selectively. This is a more sophisticated attack than the black hole attack, and as a result, it can be more difficult to detect.

Wormholes: During a wormhole attack, packets are recorded at one network site, tunnelled to another, and then replayed. Research conducted at the University of British Columbia found that strategically placing a wormhole can disrupt 32% of all conversations across an ad hoc network.

Hello Flood: Finally, there’s the Hello Flood attack, which involves a malicious node broadcasting hello packets to nodes pretending to be their neighbors, causing network routing confusion.

DDoS (Distributed Denial-of-Service) Attacks #

Another edge computing security concern to be aware of is distributed denial of service (DDoS) attacks, in which an existing network resource is overwhelmed with traffic from other compromised systems inside the network. Outage attacks, sleep deprivation assaults, and battery draining attacks are three well-known DDoS attacks carried out on edge computing devices, according to IEEE researchers.

When a DDoS attack forces nodes to stop operating completely, it is called an outage attack. A sleep deprivation assault occurs when attackers flood nodes with legitimate requests, preventing them from entering a power-saving mode, resulting in a significant increase in power consumption. A battery-draining assault, also known as a barrage attack, can trigger an outage by depleting the battery life of certain nodes or sensors by repeatedly running energy-intensive programmed or applications. Researchers also point out the possibility of jamming attacks, which can be continuous or intermittent. The network is swamped with counterfeit messages and disturbances during a jammer assault, which can exhaust resources at all levels: communication, computing, and storage.

Final Thoughts #

The four edge computing security issues discussed in this article are just a few of the many that can affect an edge network and compromise sensitive data. It’s critical to stay up to date on the current dangers to your edge computing setup so that you can be ready in the event of a disaster. Edge computing is generally seen as a secure computing paradigm as long as adequate cybersecurity procedures are implemented across the network. It’s always a good idea to put your faith in manufacturers who have rules and practices in place to ensure that their edge servers and other edge computing solutions are secure.

Manufacturers who have obtained or are working toward obtaining their Cybersecurity Maturity Model Certification (CMMC), are DFARS/NIST SP 800-71-compliant and have partnered with leading cybersecurity companies to offer operating system (OS) hardening and data-at-rest encryption key management for self-encrypting drives fall into this category (SEDs). To assist ward off hardware-based assaults, purchasing an edge server from a manufacturer with a Counterfeit Protection Program (CPP) and other stringent quality inspection systems in place is strongly advised.

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Data Analytic & Decision Making - A Strategic Move

Vin Patel — Wed, 05 Jan 2022 22:20:00 +0000

Key takeaways

The amount of data available across businesses is staggering and gives rise to new trends every day.
It helped determine whether an investment in a particular location would succeed.
Identify new opportunities with a significant competitive advantage and boosts the confidence to brace the digital disruption.

The amount of data available across businesses is staggering and gives rise to new trends every day. It’s correct utilization can drive decision-making, impact strategies, and improve organizational performance. Despite this, a lot of data is riddled with redundancies, duplicates, and errors. It is imperative to have invaluable, tangible figures against which a business can measure its performance. As a result, demand for data analytic has soared. Yet only fewer insights are immediately actionable. Therefore, it could be equally valuable to think about how data analytic can enhance decision-making on a broad scale. No ado, let’s get into the business right away!

What is Data Analytic? #

Data analytic involves assessing data to uncover facts, find patterns, and extract insights. Data analytic is often referred to as business analytic in the business world. The process uses many frameworks, tools, and software to analyze data. Examples of these tools and technology are Google Charts, Microsoft Excel, Power BI, Infogram, Data Wrapper, Zoo Analytic, and Tableau. By using these tools, you can explore data in different ways and create visualizations that help illustrate your point.

Various Types of Data Analytic #

1. Descriptive Analytic #

A descriptive analysis is the most straightforward analytic method and the foundation for all other types. You can identify trends in raw data and succinctly explain ‘what happened or what is happening’.

2. Diagnostic Analytic #

Diagnostic Analytic evaluates “why did this happen?” The process compares coexisting trends and movements. It uncovers correlations between variables and pinpoints causal relationships where possible. Diagnostic analytic gets to the root of an issue.

3. Predictive Analytic #

With predictive analytic, you can forecast future trends or events and answer - What might happen? Historical data in conjunction with industry trends can help make informed predictions for the future. It helps to formulate strategies accordingly.

4. Prescriptive Analytic #

Prescriptive analytic answers - How do you proceed further? A scenario is examined from all angles with this analytic, and actionable conclusions are drawn. It directly aims to make data-driven decisions.

Who Needs Data Analytic? #

It is still an understatement to emphasize who needs data analytic. Anyone in business-making decisions needs a solid understanding of data analytic. Data is more accessible than ever before. By forming strategies and making decisions without considering the data, you may overlook essential opportunities or red flags. A data analytic skill can benefit professionals such as:

Marketers - Those who need a grip on industry trends, customer data, and past performance data can plan a fool-proof marketing strategy.
Product managers - Those who need to analyze various parameters of a product and improve its sales.
Finance professionals - Where historical performance data helps to forecast a business’s financial trajectories
Human resources - Those responsible for gathering insights into the opinions, motivations, and behaviors of employees and matching those insights with industry trends to bring about meaningful changes in the workplace

Every industry needs insightful data to change the whole business game, data-driven decision-making, and better business outcomes. So, what is this data-driven decision-making?

Data-driven Decision Making #

Data-driven decision-making is an approach to using data and extracting insights to determine the best course of action. In business, it takes many forms. For example, a business might:

Collect survey responses
Conduct user testing
Launch a new product or service
Analyze shifts in demographic data to find opportunities

All this happens over data that can pull some trends, give visibility, and act as an element bridging those gaps strategically. How exactly that happens will depend on numerous factors, like business goals, types of data, the quality required, and accessibility.

Data collection and analysis play an integral part in businesses. But with quintillion bytes of data produced every day across the globe, businesses have never had it easier to capture, process, and interpret data into real, actionable insights. Although data-driven decision-making has been around for centuries, it is a relatively new phenomenon.

Top Three Examples of Data-Driven Decision-Making #

Data enables today’s most prosperous and largest organizations to make high-impact business decisions. Consider references of these well-known businesses to better understand the role of data analytic:

Google

Google pays close attention to “people analytic.” As part of those initiatives, Project Oxygen, Google evaluated performance reviews of more than 10,000 employees and compared them with retention rates. As a result of this information, Google developed training programs to develop the competencies of high-performing managers.

Starbucks

Starbucks closed hundreds of locations in 2008, and Howard Schultz, then-CEO, promised to analyze future store locations. With the help of a location-analytic company, Starbucks identifies ideal store locations based on demographics and traffic patterns. It also consults regional teams before making decisions. It helped determine whether an investment in a particular location would succeed.

Amazon

Amazon uses data to find suitable product recommendations for the user base. Amazon’s recommendation engine works with machine learning and data analytic. It pulls insights into previous purchases and buying behavior. McKinsey reported that, in 2017, 35% of Amazon’s consumer purchases were the outcome of the recommendation tool.

Benefits of Data Analytic - A Strategic Move #

With such examples on your table, let’s reinforce why making a move towards integrating data analytic into businesses can give an edge in transformation.

Make smarter decisions #

Data plays many roles. It can be a product launch, marketing campaign, launching a new store, or something else entirely. With efficient data collection and analysis, you can confidently make business decisions. Additionally, it helps to benchmark your current strategy, providing a better understanding of the impact of any future decision. Furthermore, data is logical and concrete than instincts. You can make more objective business decisions. It allows you to commit to a vision or strategy without thinking twice fully. Moreover, it controls the data to monitor flaws and measure its accuracy easily, which is challenging in traditional methods.

Take advantage of customer patterns #

Selling the right products to the right people is the first step in business. Here comes the need for business analytic. In this way, your business can ensure that it offers the right products and services. How can Data Analytic help with that?

The first step is segmentation. The data is split into multiple categories using demographics, attitudes, and behavior.
The second step is forecasting. Predict specific patterns in the segmented category to determine the future of a product or service ahead.
The third step is pricing. Data gives an ability to understand the market value for a product or service and helps to set the right pricing.
The last step is customer satisfaction’data with the three steps mentioned above to meet customer expectations.

A performance driver #

Data analysis is a pivotal game-changer internally within a complex business scenario to make an informed decision for a holistic improvement. Operational costs, workforce planning, product development are the best examples. Streamlining these business operations will make them more time-efficient. An improved process results in enhanced profit margins.

Improved risk mitigation #

A strong reason businesses need a strategic data analytic team is to make better risk management decisions. A vast amount of unstructured data risks making a wrong decision unless adequately analyzed. Therefore, effective data analytic will predict risk and improve decision-making. It’s also possible to create an actionable plan.

End Note #

Data analytic is a disruptive technology. It’s time for businesses to keep their systems up and running for a glass-breaking transformation. The reason - data is an integral part of making business decisions in today’s market. Identify new opportunities with a significant competitive advantage and boosts the confidence to brace the digital disruption. Have you considered incorporating data analytic yet? You’re just one thought away that leads to sustained success.

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Questions and answers

What is Data Analytic? #

Data analytic involves assessing data to uncover facts, find patterns, and extract insights. Data analytic is often referred to as business analytic in the business world.

Diagnostic Analytic evaluates “why did this happen? #

" The process compares coexisting trends and movements. It uncovers correlations between variables and pinpoints causal relationships where possible.

With predictive analytic, you can forecast future trends or events and answer - ***What might happen? #

Historical data in conjunction with industry trends can help make informed predictions for the future. It helps to formulate strategies accordingly.

Prescriptive analytic answers - ***How do you proceed further? #

A scenario is examined from all angles with this analytic, and actionable conclusions are drawn. It directly aims to make data-driven decisions.

How Data Integrity and AI Work Together To Empower Trusted

Vin Patel — Mon, 03 Jan 2022 23:30:00 +0000

Key takeaways

Artificial intelligence (AI) and machine learning (ML) are unquestionably becoming more relevant to businesses looking to gain a competitive advantage via digital transformation.
Data visibility and observability are made possible by this form of automation.
Companies that wish to promote digital transformation and outperform competition must manage data integrity successfully.

Artificial intelligence (AI) and machine learning (ML) are unquestionably becoming more relevant to businesses looking to gain a competitive advantage via digital transformation. More than 75% of enterprises value AI and machine learning over other IT investments, and they’re employing data scientists in large numbers to help them succeed. However, most of these projects are compartmentalized within discrete business functions rather than tackling digital transformation across the company.

Traditional analytic cannot handle the magnitude and complexity of data that enterprises now have access to. We need new methods to analyze this deluge of data, and AI and machine learning can help us do just that. To educate ML models, we also require incredibly rich and complete data sets, which we now have thanks to the exponential rise of data. As of 2020, IDC expects 64.2 zettabytes of data were generated or replicated, with 10.6% of labelled data being relevant for analysis or input into AI/ML.

Nonetheless, before AI/ML can be used to transform an organization, organizations must first resolve the data integrity problems that are used to drive AI/ML results.

Challenges in Data Integration and Quality #

Let’s start with how a company saves its information. Customer and employee data, for example, are typically stored in difficult-to-access data stores. A conventional IT infrastructure for a company includes a range of enterprise applications and databases, several data centers, and fresh data generated in the cloud. These variables contribute to data silos, which are challenges to guaranteeing data consistency and accuracy. Many businesses are hampered by data silos, which restrict them from gaining quick and effective business insights from relevant data.

Even though business leaders use high-quality data to make choices, a survey found that more than 45 percent of freshly created data records contained at least one serious inaccuracy. Data bias and terrible business judgments are caused by incomplete, duplicate, and inaccurate data coming into machine-learning pipelines and analytic. Data can also be prone to inaccuracies, especially when it comes from a variety of sources and providers. Standardizing, verifying, and validating data before AI/ML can use it is typically a time-consuming, human process. Therefore, data scientists still spend most of their time preparing data rather than fine-tuning models or visualizing it.

As we discuss the necessity of data quality for AI, it’s also vital to discuss AI for data quality. The use of automation in data pipelines can improve the date’s correctness and consistency. Using AI/ML to identify data changes, alert data drift, and establish quality criteria, for example, can assist enhance data quality, automating data pipelines, and minimizing manual workload. Data visibility and observability are made possible by this form of automation.

Data governance is also crucial to address in the context of data quality. Trust in data stems from the capacity to demonstrate with absolute certainty how the data was prepared, track the lineage of data back to its raw source, and give rights management and auditing capabilities.

Enriching Data with Context and Meaning #

Data management has always worked on making data precise and consistent, but this isn’t enough to make data relevant. I’ve noticed that firms in various industries fail to take advantage of third-party data to provide essential context to their internal data. Before you can truly trust the market intelligence you gain from that data, you must evaluate it in context? not just who and what, but also where, when, and why.

Location is an essential category of third-party data. Insurance businesses, for example, use geography to underwrite policies, analyze and forecast risk involved with catastrophic events like hurricanes and wildfires, and set pricing strategies. Financial services firms use location data to confirm and accurately attribute transactions to a specific store or merchant and better understand subsidiaries and parent corporations. Telecommunications companies are preparing 5G rollouts based on location data, AI/ML, and analytic to provide a new location and context-aware services to millions of endpoints. Points of interest and mobility data are also included in data enrichment, allowing for the analysis of traffic movements into and around a location.

Demographics is another critical source of third-party data since they aid in client segmentation, tailored outreach, and the development of new services and products. By offering a more comprehensive view of clients, demographic data can also assist firms in reducing data bias. Retailers mix location with demographics and consumer insights to help comprehend their clients, give greater personalized experiences, and determine their purchasing proclivity and generate fresh product recommendations.

While it is true that enriching data using third-party enrichment data set benefits AI/ML models, it is also true that AI and ML are becoming increasingly vital for their development. The use of AI and machine learning speeds up and scales up the process of creating data sets as well as proposing which data sets to utilize for enrichment.

Obtaining Data Integrity To Get Confidence In Business Decisions #

These steps, taken collectively, make up data integrity: data integration, quality of data and governance, data enrichment, and location intelligence. Insufficient data has a massive negative influence on AI and machine learning. For business insights obtained from AI/ML to be trusted, data must be delivered with optimal accuracy, context, and consistency. It is impossible to rely on data and the data-driven business insights based on it without data integrity.

Final Words #

CEOs and business leaders are rightfully concerned about the data integrity to make decisions. Data integrity is essential for trusting the results of sophisticated analytic and the business decisions that follow. Companies that wish to promote digital transformation and outperform competition must manage data integrity successfully.

Related reading

Evolution of Data Visualization Techniques and Tools

Vin Patel — Thu, 30 Dec 2021 15:30:00 +0000

Key takeaways

Data visualization is merely a graphical representation of information and data.
This category groups together a set of techniques aimed at representing data by writing it on a map.
It is increasingly valuable for professionals to use this data to make decisions and use images to tell stories as data informs who, what, when, where, and how.

Data visualization is merely a graphical representation of information and data. Visuals like tables, charts, and maps are the data visualization tools for understanding trends, sticky values, and data trends.

In the stream of big data, data visualization tools and technologies are essential for analyzing massive amounts of information and making data-driven decisions.

Data Visualization: Our Eyes Are Attracted by Colors and Patterns #

Our eyes are drawn to colors and patterns. We can quickly identify the red from the blue, the square of the circle. Our culture is visual, ranging from art and advertising to television and movies. If you’ve ever looked at a spreadsheet full of data and couldn’t see a trend in it, you know how much more effective visualization can be.

Data visualization is thus another form of visual art that captures our interest by keeping our eyes captivated by the message. When we see a chart, we quickly translate it through the trends and values that emerge from it. So let’s go back to the basics.

What Is Data Visualization? #

If it is difficult to give a precise definition, it is partly because data visualization encompasses several fields of study. It is the result of combining several disciplines such as statistics, computer science, and visual communication. In addition to this, the term data visualization refers to several different visualization techniques, some of which are very distant from each other.

However, there is one common aspect with all of these techniques: the graphical representation of a set of data. It sounds obvious, but it’s important to remember it because data visualization is usually associated with charts and tables of data.

The term data visualization first appeared in 2001 when Edward Tuft published The Visual Display of Quantitative Information. This book popularized the idea that charts and tables of data could not be viewed as data visualizations but rather as a means of representing data.

A Keyword for Information Visualization #

Since then, the term data visualization has become a keyword to define information visualization. However, it is still used differently in academia and the professional world. In academia, it is often used synonymously with information visualization or business visualization. In the professional world, it mainly refers to interactive charts or dashboard charts.

To arrive at a precise definition of data visualization, we must consider all the stages of creating a graph. This involves thinking about all of the steps after obtaining and analyzing the data. In particular, we should be interested in the creative process, which is often neglected in books dealing with data visualization.

Data visualization is indeed also a creative discipline that includes several aspects such as the collection and analysis of data, the creation of charts and dolorimetry, the structuring of the visualization, the design of the narrative, and the visual design in general.

All Stages #

Indeed, it is essential to specify that data is the basis of all visualizations. In addition to this, it is crucial to clarify that data is not always directly used in a visualization. This is the case, for example, if the visualization structuring step allows additional data to be generated.

If we understand what data visualization is, we need to consider all the steps between getting the data and the result. It is important to note, for example, that data can be presented at several levels: at the raw data level, at the structured data level, and the advanced data level.

The data-visualization process usually begins with the collection of raw data. This step can be done by several methods, such as manual collection, automatic collection, or data extraction.

The Different Types of Data Visualization #

One of the difficulties of data visualization is to define the different types of visualization. The question is essential because this definition is the basis for understanding the work of Data Visualists.

To answer it, it is interesting to point out that data visualization is a creative discipline that encompasses several techniques and several types of visualization. To see more clearly, it is helpful to classify them into a few main categories: tables and charts, maps, infographics, and dashboards.

Tables and Graphs: #

This category groups together a set of techniques aimed at representing data in a table or graph. These are defined as visual representations of data. They have the particularity of allowing data visualization in many formats (proportions, ratios, frequencies, etc.). Examples of visualizations: bar, column, dashed charts, etc., data charts, crosstabs, cartogram, etc.

The Cards: #

This category groups together a set of techniques aimed at representing data by writing it on a map. Maps are visual representations of spatially organized data that allow data to be compared and from the same point of view. Examples of visualizations: map of the world, map of the metro in Paris, map of cafés in Madrid, etc.

Infographics: #

This category groups together a set of techniques aimed at representing data by inscribing them on a plane. Infographics are visual representations of spatially organized data that allow data to be compared and from the same point of view. Examples of visualizations: time scale, time diagram, etc. *

Dashboard #

The dashboard is a visual representation of the data. NASA developed it to help it achieve its goals. Today, the dashboard is an indispensable tool for businesses. The dashboard allows the different departments and the different teams of a company to have a fair and precise vision of the market, competitors, and objectives to be achieved. It also makes it possible to measure the effectiveness of the actions taken to achieve these objectives. Find out how you can build your dashboard based on your goals.

How To Create a Good Data Visualization? #

By reasoning “decision aid.”

By showing the invisible, data visualization facilitates and accelerates decision-making. It is a valuable tool that is more efficient than simple Excel tables. Thanks to data visualization, we have access to the essentials! Data visualization simplifies the dissemination of information. It provides points of comparison and analysis on trends. It then refines the predictions on future trends.

You certainly know that colors affect our emotions! These influence our decision-making. To convince, do not neglect the choice of your colors! One of the basic rules in data visualization is using a single color to represent the same type of data.

Data visualization makes it easier to understand the world. It is an excellent tool for analysis and projection into the future. Today it is becoming accessible to as many people as possible. It is imperative to have an adequate data visualization solution.

All must easily understand a good data visualization! It must also be intuitive so that the user can take ownership of the information. Finally, data visualization must be good simplicity so that everyone understands it. That said, data visualization remains a language!

Data visualization is not an end in itself! It must bring value to the user! Data visualization must fit the needs of the user. It must be used wisely. The important thing is not to get locked into a predefined pattern. Thus, needs change and data visualization must evolve!

By Focusing On The Dashboard #

The best way to have fun in data visualization is to have a dashboard! A dashboard is an efficient tool! It allows you to visualize at a glance the leading indicators of an ecosystem. Thus, it facilitates decision-making and the analysis of results.

In addition, data visualization must be interactive! It should allow the user to make choices or to analyze the data more finely. This will enable him to appropriate the information, understand it, and interpret it. Interactivity allows the reader to choose the data they wish to analyze.

By Seeking To Last #

Data visualization must remain simple and easy to use! Remember, you are dealing with users who are not data visualization experts. However, even if the user is not a data expert, he does have some good ideas. Setting up a dashboard will allow him to understand the data and quickly analyze the results!

Finally, good data visualization must be sustainable! Data visualization must be scalable to adapt to new data and to changing needs internally or externally.

Data Visualization: Not So Easy #

However, dressing up a graphic is not enough to make it more attractive. Effective data visualization is a delicate balance between form and function. The most straightforward graph might be too dull to notice or bring out a strong point. The most fantastic visualization might completely fail to convey the right message, or it could say a lot. Data and visuals have to gel together, and there is an art in combining good analysis with good storytelling.

Mastering Data Visualization: Important in Any Career #

Skills change to adapt to a data-driven world. It is increasingly valuable for professionals to use this data to make decisions and use images to tell stories as data informs who, what, when, where, and how.

Related reading

Questions and answers

What Is Data Visualization? #

How To Create a Good Data Visualization? #

By reasoning “decision aid.” By showing the invisible, data visualization facilitates and accelerates decision-making. It is a valuable tool that is more efficient than simple Excel tables.

Better Tools to Test and Build an API Swiftly

Vin Patel — Thu, 23 Dec 2021 20:30:00 +0000

Key takeaways

Organizations are moving to the microservices architecture model for successful software application projects because most microservices projects use APIs (Application Programming Interfaces).
Also, to create an API, we need to define a schema for input and output, whereas in Apiary, the API can be designed with simulated input and output.
The best way to experience this is to try and see what works best for your business needs.

Organizations are moving to the microservices architecture model for successful software application projects because most microservices projects use APIs (Application Programming Interfaces). Here we have to applaud Microservices as it has various advanced features compared to other models.

These have discrete datastores with separate commands to manage each datastore; in addition, it allows software vendors to deploy components more quickly. And if an application is updated, the remaining applications do not need to be updated; they can continue their functionality.

Now is the time to deliver fast results or services to end-users who use apes for their needs. Many APIs use REST services over HTTP to send and receive data. Many software testers automate their testing with UI-based testing and API testing. While comparing API testing with UI-based testing, API testing is much faster and more reliable than UI-based testing.

Required API testing is a kind of software test to develop APIs to ensure the functionality, performance, security, and reliability of the built application. It mainly focuses on the business layer of the software architecture. Testing is a crucial part of successful Continuous Integration and DevOps application operations.

Let’s look at the following tools to make our development tasks more manageable. #

Apogee #

Apig’e by Google Cloud enables API managers to design, secure, publish, analyze, monitor, and monetize APIs. It can be used in a hybrid cloud environment to perform digital acceleration. Apogee turns regular developers into API experts.

Apogee edge creates API proxies and uses them to get accurate analytic data. In addition, the proxies created by Apogee edge manage security and authentication to provide better services.

MuleSoft API #

API MuleSoft, otherwise known as AnyPoint API Manager, is a platform developer who can create, design, manage, and publish APIs. It allows organizations to integrate with popular cloud services like Salesforce, SAP, and many more. In addition, the AnyPoint platform uses Mule as its execution engine. API Manager ensures the security of each API and, in layman’s terms, complete API lifecycle management.

Apiary #

Apiary is a complete API platform where a user can design, build, develop and document the API. In addition, it provides a framework for faster developing, testing, and implementing a production-ready API. Also, to create an API, we need to define a schema for input and output, whereas in Apiary, the API can be designed with simulated input and output.

This dummy API will meet application specifications without changing any coding, while data can be integrated and tested. The built API has different stages of the workflow. However, the Apiary can allow each team step to work independently.

Tricentis Tosca #

Tricentis Tosca is a custom continuous testing tool for DevOps platforms because some central agencies haven’t come together in DevOps environments. A newbie can also understand the Tosca tool and instantly create commercially advanced API tests and integrate them into any scenario.

Tricentis Tosca is suitable for continuous testing and automation testing for mobile, web, user interface, SAP, etc.

Rest Assured #

Rest Assured Library is a custom API tool for the Java domain that uses people to test and validate REST services. It’s also used to test HTTP, JSON, and XML web services, and it gives us a lot of critical features like XPath validation, JSON path syntax, easy file uploads, and spec reuse. And also, he was mainly influenced by dynamic languages like Groovy, Ruby.

One of the great features that Rest Assured offers is that you don’t need to parse XML or JSON responses after getting the answer.

JMeter #

Meter is an uncomplicated yet powerful tool for automated testing. For example, you can perform performance testing of RESTFul services using JMeter scripts, and it can use different languages such as Java, JavaScript, and PHP. It is designed for testing web applications but later expanded to other testing functions.

Characteristics

JMeter has been rated as a rapid API testing tool because it performs range testing quickly.

Open-source and supports the installation of many plugins and extensions

JMeter has a separate feature called adding settings in the settings tab, which we can’t find in other programs

Postman #

A factor is a tool that allows you to build and test APIs. It is open-source and easy to install. For example, postman workspace has features like new, import, runner, my workspace, invite, collections, request tab, HTTP request and save, etc.

Characteristics

Write and run tests for each query using JavaScript.

When testing the API in Postman, you can choose the required HTTP methods like GET, PUT, POST, etc.

You can use Postman’s environment management features to give the input value of any API result.

Store associated endpoints in a collection.

Postman offers a lot more benefits for building an API faster.

Conclusion #

Either way, there would be the same functionality accessible in all API tools, but the approach is different. The best way to experience this is to try and see what works best for your business needs.

Related reading

Steps For Building Predictive Models

Vin Patel — Mon, 13 Dec 2021 14:32:00 +0000

Key takeaways

It is intended to drive increased sales and provide data to refine marketing strategies.
Specific points must also be finalized, such as the realization of the documentation, before the actual launch.
No, this type of platform isn’t the silver bullet to all of your problems, but it can lower your costs and alleviate the concerns that building an AP team can raise in terms of how much money to invest.

The use of predictive analytic (PA) models is on the rise. It is intended to drive increased sales and provide data to refine marketing strategies. The use of these models is thus becoming a common practice to measure, for example, consumers’ desire or purchasing capacity and, therefore, calibrate scales and communication campaigns.

The Organization of the PA Model #

The organizational model of the PA function is to build a complete PA team. An alternative to this model is to use tools to automate specific time-consuming tasks. Examples of this platform type include DataRobot, Apteryx, RapidMIner, and WTW Emblem for non-life insurance pricing. This type of tool aims to alleviate the need for specialized PA resources by facilitating the construction of models.

Regarding the development of a predictive model, the starting point is to identify the needs of the business, driven by the project owner. This will influence the choice of model used, all other things being equal.

Below is a list of the usual steps to follow for any PA task:

Integrate the PA initiative into a business context

Define data needs

Clean data

Please choose a model and develop it from the available data

Test the model

To throw it

The PA Models: What are the Benefits? #

This type of tool is not going to help solve business problems or find data. Nor will it replace field knowledge, the need for a data dictionary, or translate the “bad data, bad results” problem. On all these points, it is up to the user to do their job.

What the tool does is: #

Automate the first data cleaning (missing values, creation of age groups or others, a cloud of keywords to encode text into variables, detection of outliers, etc.),

Build all possible and imaginable models on a sample of data (PCA, ARMA, generalized linear model, decisional tree forest, gradient boosted trees classifier with early stopping [3], etc.,

Test and validate the second sample of data, and classify the models according to their prediction capacities.

The tool adds the appropriate panoply of additional features: visible code, technical documentation in MS Word, graphical representation of the predictive capacity of each variable under each model (with dependency analysis), and many more not listed here.

From there, it’s the business’s turn to take control. First, the model must indeed be understood to be fully assimilated. Then, the dark stateside must be revealed through the generated documentation, individual scenarios, and other graphics. These steps can be reused to communicate the model to different stakeholders and obtain their consent. Specific points must also be finalized, such as the realization of the documentation, before the actual launch.

Data #

You have data files from this department, but you may need more data from internal or external sources (e.g., a data broker…). Make sure you have the skills of a lawyer who will check the compatibility, with the GDPR or any other legal constraint, of transfers of data from one legal entity to another.

Target value #

What this means is the overwhelming need to define your needs. Let us mention two illustrations in this context:

In the first, we notice that we are not looking to qualify transactions but groups of transactions that fall within the scope of a single survey.

In the second, we construct an indicator highlighting the transactions covered by the survey and the result of the study (amount recovered net of the cost of the survey). The file can then have an indicator with three positions: “Not surveyed,” “Positive recovery,” “Negative recovery.”

Calculation With All Possible Predictive Models, Classification of Models, Additional Elements #

The modelling work can now begin. The data should be separated into several subfiles: one for calibration, one for testing, one for comparing models. As a series of tests are carried out, it is necessary to apply a more refined segmentation and verify that the data used on one side is not used.

Then comes the step of finding the correlated variables, doing a PCA, and testing linear models, or even somewhat complex models.

We must then choose the model that works best after having clarified the classification criteria.

Finally, it remains to document the chosen model and write the equations that underlie the model’s theory. All of these steps can be performed by API platforms.

But the model must be understood by the owner. You have to take ownership of what the tool has proposed. Did he show a correlation? la Nicolas Cage? Or is it an illustration of the parable of the blind and the elephant (Anekantavada) [4], in which everyone guesses the whole by touching a part (if one feels the leg, the elephant looks like a tree?; if you touch the ear, the elephant looks like a fan; if you touch the trunk, the elephant looks like a snake?)?

Does the model over-represent the data-rich aspects but forget the fundamental elements incorrectly covered by the file? Is the phenomenon stable enough for the results to be exploitable (for example, everyone who buys a yellow jacket owns a car and is risk-averse)?

The fraud detection team must then perform the launch of the model on the accurate data. Are the data still available? Will the model never be used out of context? Will the documentation be updated? Do stakeholders understand the model well enough to continue using it? This substantial work is critical for an exemplary deployment of the tool.

End Note #

No, this type of platform isn’t the silver bullet to all of your problems, but it can lower your costs and alleviate the concerns that building an AP team can raise in terms of how much money to invest.

Related reading

Questions and answers

The PA Models: What are the Benefits? #

This type of tool is not going to help solve business problems or find data. Nor will it replace field knowledge, the need for a data dictionary, or translate the “bad data, bad results” problem.

What the tool does is:? #

Automate the first data cleaning (missing values, creation of age groups or others, a cloud of keywords to encode text into variables, detection of outliers, etc.), Build all possible and imaginable models on a sample of data (PCA, ARMA, generalized linear model, decisional tree forest, gradient boosted trees classifier with early stopping [3], etc., Test and validate the second sample of data, and classify the models according to their prediction capacities.

Marketing Intelligence in Enhancing Customer Experience

Vin Patel — Wed, 08 Dec 2021 20:30:00 +0000

Key takeaways

Artificial Intelligence has progressed to the point that practically every industry is using it for better business and access to new technology.
Hence, it is a great way to get insights into the marketing performance through one analytic datapoint.
When it comes to their company’s performance, those questioned prefer the use of customer analytic above digital marketing, channel management, and advertising management.

Introduction #

Artificial Intelligence has progressed to the point that practically every industry is using it for better business and access to new technology. In every industry, there is constant competition between companies, and one of the most important factors in staying ahead of the competition is understanding the current state of the industry, keeping an eye on the positions of the closest competitors, and keeping track of the changing consumer terrain as a whole. Marketing Intelligence is one such technology that can help any business accelerate its customer services and provide a better customer experience, ultimately resulting in the growth of the company. Let us start by understanding what Marketing Intelligence is.

What is Marketing Intelligence? #

Marketing intelligence is information that is useful to a company’s marketing operations. After the data has been collected, it may be evaluated to properly and effectively guide the campaign decision-making process. Marketing Intelligence is used by marketers in accomplishing several marketing goals along with higher-level activities like consumer trends, the competitor’s behavior, product analysis, and many more.

Any business needs to understand the customer’s behavior to make the service more customer-centric. This is an essential activity as consumers play a huge role in the growth of any company. Brands may use AI and other marketing intelligence to provide instant customer support, which can help them increase conversions and create loyalty. Customers are significantly more inclined to connect with a company that answers fast, even if it costs more or comes with additional restrictions, such as limited variety or location.

All other marketing techniques will fall on deaf ears if the client experience is poor. Marketers may understand what the audience expects and how to improve the buyer’s journey by using marketing intelligence to better capture the customer’s route to purchase. Happy consumers contribute to increased market saturation and provide companies the potential to develop and create a reputation in their sector, in addition to improved sales and word-of-mouth referrals. Marketers are concentrating on the end first by continuously reviewing customer experience data, which helps them to work backward to decide what may be enhanced or incorporated for a better user reality.

Types of Marketing Intelligence in Customer Analytic #

There are several types of marketing intelligence techniques used by marketers to increase consumer traction. We have discussed some of them below:

Artificial Intelligence #

Artificial Intelligence plays a huge role in increasing customer interaction and creating a better impression. Chatbots are an excellent illustration of how AI marketing is assisting marketers in responding to more consumers faster, allowing them to devote more time and resources to directly assisting clients with complex requirements. This does not make the experience overwhelming for customers who are just looking for ways to return any product and get a refund, for example. Companies are able to help more sensitive consumers by utilizing AI to free up human customer care resources at a time when having a tailored encounter with another human is critical to long-term retention and referrals.

Unified Marketing Measurements #

The data that is collected with the help of AI leads to many higher-level data regarding consumer behavior that must be analyzed by the firm for a better customer experience. Unified Marketing Measurements (UMM) gathers and normalizes data from many devices, campaigns, and touchpoints to examine individual interactions as well as high-level patterns to show how these factors combined affect the buyer’s journey. Hence, it is a great way to get insights into the marketing performance through one analytic datapoint. Marketers may use UMM to communicate progress and outcomes to the leadership team. More importantly, UMM enables marketers to evaluate cross-campaign data, identify actionable insights and next steps, and continue to meet or exceed business objectives.

Real-Time Insights #

One of the best advantages of technology over traditional methods is the gain of real-time insights. Predicting a client’s behavior or response to a certain action made via customer service or during the engagement process is what timely insights are all about. One of the most common uses of right-time data in marketing is to course-correct campaigns based on how customers react to or engage with advertisements. Marketing teams will need marketing analytic tools that can process data from campaigns as they are being conducted to do this. Live data acquired through social media is also included in real-time insights. Understanding what a target audience is saying through social listening and brand reputation monitoring may help marketers understand their requirements and respond appropriately.

Use of Marketing Intelligence in Customer Experience #

As discussed earlier, customer analytic is very important for any business, and it increases customer retention and loyalty. A large part of this is due to the fact that customer analytic and data can be utilized to customize the customer’s experience, increasing the probability that they will not only buy again but that they will purchase in the first place.

If one has prior experience of online shopping in big eCommerce settings like Amazon, they would realize that they get product recommendations from the site on the basis of their search history. This helps in personalizing their feed and showing them products they are actually interested in, instead of irrelevant marketing.

When it comes to online experiences, customers have significantly raised their expectations. They anticipate a degree of personalization to break through the clutter of firms vying for their time, attention, and money. This is why marketers must understand more about their consumers using various marketing intelligence tools in order to tailor e-audience experiences.

EndNote #

In this market filled with consumer goods, it is very easy for any company to get overshadowed by its competitors. Hence, it is very important for businesses to understand the significance of Marketing Intelligence and implement them in real life. This way, not only will they gain a certain amount of trust and loyalty from their customer base, but will also experience faster growth and better profit.

According to an article by Mckinley & Co., A superior experience powered by marketing intelligence has become one of many businesses’ most prized sales and marketing tactics. When it comes to their company’s performance, those questioned prefer the use of customer analytic above digital marketing, channel management, and advertising management.

Related reading

How Artificial Intelligence is transforming the Retail

Vin Patel — Thu, 04 Nov 2021 19:25:00 +0000

Key takeaways

For years, the retail business has been undergoing a digital change.
In addition, AI can create rich metadata for any product, avoiding human fatigue and error.
Automation alone can increase efficiency, give retail units time to think creatively, and provide great online and offline experiences.

For years, the retail business has been undergoing a digital change. It has improved speed, efficiency, and accuracy across the board. The primary asset to run the business efficiently is analytic, and digital will provide the data, using which all retail businesses can draw innovative strategies.

Without artificial intelligence, none of these insights would be feasible. AI in retail has given firms access to high-level data and information, which they can use to improve retail operations and create new business prospects. In fact, over three years, AI in retail is predicted to have generated $40 billion in increased revenue. According to a KPMG report, 90 percent of retail industry leaders believe their personnel are ready for AI adoption and have the necessary capabilities. Furthermore, 53% of retail executives indicated the COVID-19 pandemic had accelerated their company’s implementation. Simultaneously, 49% say AI adoption in their industry is growing faster than it should. Let us know in brief how artificial intelligence transformed the retail sector.

Helps to make better business decisions: #

The data collected, labeled, and organized may now be analyzed to find trends and patterns to help retailers make better decisions. AI provides performance insights of their target audience to retailers and helps create personalized experiences for every customer. The simple thing that can be done with the data available to find trends is exploratory analytic. While this is straightforward, it cannot be used to automate decision-making processes.

Retailers can use AI to generate data models and, as a result, build prescriptive or predictive decision engines. This can aid demand forecasting and enable them to make more data-driven decisions. In addition, these predictive models can learn to make better decisions over time by incorporating more and more data, allowing them to make better conclusions.

Data structuring: #

Data structuring is a very hectic process. The bulk data received from the manufacturer should be analyzed, structured, and then transformed into valuable data, which requires enormous effort and a time-taking approach. For example, Brand A puts up a crop top on their platform with a festive sale label, and Brand B puts up another crop top for the Diwali sale. While searching for offers, only Brand A’s will appear, and Brand B will not get so much traction and visibility.

AI helps with structuring raw data into valuable insights. As a result, Algorithm helps in identifying potential customers. Moreover, AI helps in identifying patterns of target audience and boosts your platform accordingly. With AI, structuring data can be done easily.

Data creation: #

AI can solve inaccurate and inconsistent data problems. Creating and labeling AI-supported data can be done at the beginning of the digitization process. In addition, AI can create rich metadata for any product, avoiding human fatigue and error.

This can be done using a computer vision algorithm that can automatically identify and accurately label various attributes of the product. You can also use the same Algorithm

to generate product titles and descriptions, making it easy to search for rich SEO-enabled metadata.

SEO-ready tags, titles, and descriptions make finding products on search engines such as Google and Being. In addition, digitization of image recognition products improves search, browsing, and SEO, achieving 90? accuracy and over 20% conversion.

Boost visibility: #

Finding a product is the first and most crucial step in a buyer’s journey. A straightforward discovery process can help retailers earn the trust of their buyers. Shoppers who find exactly what they are looking for are much more likely to return to the same retail store for their next purchase. On a website, discovery begins with a site search. According to research, 58% of web visitors often use internal search engines to navigate their website.

Search engine users are aspiring buyers and an essential segment of e-commerce sites. Therefore, search results on your website should capture the intent of your search query and display products that match the items you searched for as closely as possible.

For accurate, consistent, and structured data, the relevance of each individual’s results is more important than the relevance of the results. In other words, are the search results tailored to each individual’s style preferences?

AI-powered personalized retail search helps adjust search results by increasing the specific affinity of each shopper. Each time you click, you can see the buyer’s preferences and go to the relevant search bar to understand your customers.

Increase in sales: #

Few people add their favorite items to the cart but never make a purchase. This is known as ‘Cart Abandonment.? To help recover this situation, emails are sent to the respective user to purchase as a reminder. According to research, the retailers have lost almost $4 trillion through abandoned sales. Cart recovery emails are the best way to cope up with the loss. More than 60% of retailers utilize it, and it’s shown to be the most effective approach to engage with customers and recoup their losses.

Since the shopper’s inbox is constantly bombarded with many emails, you need to drop mail creatively to stand out from the rest. AI-powered emails help recover loss and build customer relationships, which again helps in conversions and sales.

Conclusion #

Retailers today need automation to collect data; they also need to consolidate that data to speak to the buyer in a single voice. Shoppers should be able to switch from online to offline effortlessly and back online again. This means that the data collected on your website should be made available to store sellers, and your website’s recommendations should be improved with products purchased by your store’s customers. Once a complete story about each buyer has been created, the data cannot be separated.

Retail and e-commerce AI can completely change the way teams work. Automation alone can increase efficiency, give retail units time to think creatively, and provide great online and offline experiences.

Related reading

Understanding IoT and its Implementation in Business

Vin Patel — Wed, 27 Oct 2021 00:25:00 +0000

Key takeaways

Are you new or have doubts about the word, Internet Of things or the IoT devices.
It provides benefits by giving the customer the ease of doing things remotely from generating insights for business enterprises.
The IoT has become a part of our lives; we share and collect data every day without even knowing it sometimes.

Are you new or have doubts about the word, Internet Of things or the IoT devices. Then you have landed on the right page. By the end of the article, you will know all about IoT, its benefits, its implementation in business, and the challenge you may face while using an IoT device.

Let’s first understand what IoT is? IoT indicates billions of gadgets or things connected to the internet worldwide and continuously sharing and collecting data without any human intervention. All smart devices like TV, AC, speakers, cars, fans, doors, or any other tangible device connected to IoT can be controlled via remote and/or voice. IoT devices not only collect and share data but also help ventures and business enterprises record, stock, and analyze their data for a much longer time and systematically. Several companies have already adopted this new technology, while many are still learning the importance and implementation of IoT devices in their organizations.

Things connected have already surpassed the number of the human population. According to the reports, by 2020, about 21 billion things were connected via IoT technology, and the number is expected to reach 41 billion by the year 2025.

IoT Implementation in Business #

Big companies like Apple, Samsung, and others have already adopted the IoT technology. While some companies are at the early stages of adopting the IoT and putting it to use for its benefit. Here’s how you can kick start your IoT strategies for business. ? Data management: Companies and enterprises deal with enormous data for their business every day, and it becomes difficult to store and categorize them. The application of IoT technology helps you to categorize and stock essential and relevant data hassle-free.

? Use of analytic: Once the data is collected, companies can quickly analyze them. IoT gives an in-depth report of detailed data. It provides the analysis of demand and supply and also helps to generate insights for future business.

? Several device management: This technology makes devices more flexible and scalable. Businesses can sync and monitor several devices painlessly from anywhere connected to sensors or networks through IoT technology.

? Carrying out the back-end task: Organizations collect and generate a massive amount of data every day. And at the back, there is a gateway responsible for refining the unimportant and harmful data out. The refined data will further be modelled and analyzed for future or present use.

? Keeping track of data: With the use of IoT technology, keeping track of data becomes accessible. It tracks previous and ongoing data; with this feature, you can immediately know any topic related to your business with just a click.

Benefits of Using IoT #

IoT has revolutionized the business sector by enhancing the business and customer experiences. It provides benefits by giving the customer the ease of doing things remotely from generating insights for business enterprises. ? Increase in productive efficiency: Every organization wants to achieve its maximum productivity. With the implementation of IoT technology in the production sector. The manufacturing units can predict the output products and can use the inputs more efficiently. IoT technology also helps employees reduce workload by doing minimal tasks (which don’t require human intervention), increasing their work efficiency.

? Understanding consumer behavior: Companies? main motive is to meet the consumer demands and know their preferences, needs, and wants. Thanks to IoT technology, businesses can analyze and integrate data through various consumer activities like video monitoring, internet usage, and social media.

? Improve workplace security: IoT devices or things can help organizations reduce workers’ risks and make the overall workplace more secure. Using devices with installed sensors can help workers working in heavy environments and could be warned before any fall. The installation of cameras at the workplace will help businesses to monitor their surroundings.

Threats & Challenges of IoT #

Internet of things is the trend of the generation with several benefits, but it also comes with a set of cons that every business enterprise or individual must know before using them. ? Reduced security & privacy: The Internet of things is gaining popularity but also threats. Connecting your business with IoT gives a risk of organizational data exposure on the internet. Organizations willing to implement IoT must lookout for the best way to secure their devices. Multiple layers of security must be applied to these devices to prevent data breaches. There are several ways to protect your device, like the end’to’end encryption, firewall, and other security measures.

? Walk-in complexity: As the organizations get connected with IoT, the level of complexity also increases. Several devices connected together with added layers of security for data collection and sharing simultaneously can be overwhelming for the users.

? All-time network support: All IoT devices are connected via the internet, and they need internet access to work. Without internet or low-speed internet could result in considerable losses to the companies.

Last Words #

The Internet of Things has a scalable potential in the future. In the coming years, at least ten devices or things will be connected to a single person. Worldwide spending on IoT will reach the mark of $1trillion by the year 2022. The IoT has become a part of our lives; we share and collect data every day without even knowing it sometimes. The future is more exciting and innovative. The IoT has made the world seem smaller.

Related reading

Does Effective Forms Management Improves Healthcare Data

Vin Patel — Sun, 24 Oct 2021 00:04:00 +0000

Key takeaways

Quality supervision is crucial for governing the generation and dispersion of safe, productive, beneficial commodities that fulfill consumers’ expectations.
Several healthcare facilities depend upon the EHR platform to perform their work as a form engine, thinking that the facility doesn’t have any alternative.
These companies accomplish another level of standard process and outstanding efficiency and thus let the healthcare facility concentrate on the main objective.

What Does Quality Data Management Mean? #

Quality supervision is crucial for governing the generation and dispersion of safe, productive, beneficial commodities that fulfill consumers’ expectations. Data integrity management differs from it slightly, even though it is an identical theory. Healthcare corporations have admission to a vast quantity of patient information and data presented; however, this received data is beneficial for the health care facility.

Data quality management includes utilizing protocols, modern efficient technology, and equipment to regulate the integrity of data as well as other information to make sure that references are ascertained.

Productive data quality management encompasses incorporating credible data references, assessing data and information, working with and transmitting susceptible data, and enforcing modern and secured networks, which safeguard customers’ data. What can old quality data management do? Well, the old systems are outdated, as modern technology has prevailed.

The old system lacks efficiency, takes more time, and consumers might get frustrated with the poor network. Poor quality management will lead to low morale in the employees as they may receive poor quality data and information.

The data are valuable, but it can become complicated and lead to adverse effects if it is irrelevant. This curtailed inefficiency will lead to several mistakes in the system, resulting in improper treatment in patients and reduced job satisfaction.

Have you ever thought of forms strategy for your industry, which could help you improve the data quality? Making such strategies will help in growing the healthcare model. You can find several strategies that will help achieve the purpose, but the forms management strategy is underrated yet most important. Most of us don’t give enough credit to this strategy as much it deserves.

However, you must know that this form management strategy oversees several initiatives involving your client’s fulfillment, patient’s scores, and care co-operation analytic.

Nowadays, care co-operation analytic has become a trend for all the clinical databases as well as all the other tasks conducted by the healthcare data has been increasing significantly.

When you put so much effort into accumulating the data, data exchange plays a crucial role in exponential growth. This forms management strategy has stood up to its mark to aid in accumulating all the organized data in healthcare.

These are the forms that are needed to be filled out through a patient’s cell phone by the patient itself. They must do so before their arrival at the healthcare facility or after leaving the facilities.

Moreover, more details through the patient in the remote areas and in the office have been increasing substantially. Besides, the IT network needs extremely particular details of the patient, which cannot be received otherwise.

This way of collecting the data from the patient is quite smooth and simple. Well, it’s because the transfer of data happens between several systems without any interruption and remotely.

The healthcare industry has to accumulate all the organized information through organizational, institutional, and clinical forms filled on various devices and areas. The collection of these forms is worthless if the organization isn’t leveraging these data. The healthcare facility has to observe the forms along with the business.

The organization must acknowledge that these forms of management strategy can bring a lot of positive changes in their business model and will also result in increased output. The organization must think that forms that might seem worthless are a true asset that can improve the data quality.

Governing such a huge data of various categories of forms may feel demanding for a particular organization if they don’t have a proper forms management strategy and all the required mechanisms in position. Several healthcare facilities depend upon the EHR platform to perform their work as a form engine, thinking that the facility doesn’t have any alternative.

However, the engine within the EHR platform is excellent for the short-term purpose, but this implementation is a long-term liability. These forms are essential for many more purposes that include the workflow of the healthcare organization than what this EHR work engine can provide.

Moreover, these forms that are EHR based are not cellphone friendly and simple to transmit to all the areas. Many healthcare organizations are dissociating forms that are EHR based; these organizations are working on emptying these forms programming as well as supervision to the external affiliate that can offer exceptional abilities.

Several third-party forms management solutions offer a smooth experience with EHR forms and enable the automation of several workflows.

These forms that are EHR based are extremely costly to enforce as their implementation needs specialists, and hence, these third-party solutions are making the job easier for the organization.

Another advantage is that these forms can only be used at those places which are not being apprehended manually. Nowadays, this form of management strategy has become a crucial part of the healthcare facility, and hence, it is exponentially thriving and has become more important.

Nowadays, we see several healthcare companies are creating beneficial associations with the strategic tools that incorporate the EHRs. At one side of the spectrum, we could observe the internal institutional committees developed to offer every re-presents of the healthcare facility via forms mechanisms.

While on the other side, these healthcare facilities outsource the individual strategy as well as teach. Similarly, there happens to be several in the center through a blend of in-house form development and the governing jointly from the form creation that was outsourced.

The main part of the form management strategy is to evaluate your form needs, and more importantly, to see it as a good longtime IT investment, simplify the method and be flexible; that is the patient can access the forms at his desired place and time as per their convenience, moreover, to create a more beneficial technique that will do the work for the entire organization.

All that the patients expect from the healthcare facility is just a smooth workflow throughout the facility for all the cell phones. These forms must be ily accessible on mobile phones as many patients will try to provide the data through the same, hence it must work properly on the mobile phone.

These patients anticipate these forms to be digitalized and available on all the cell phones the patient chooses. Moreover, these forms should be smart enough and attached to their EHR because the information needs to be simply accessible to their provider throughout each and every system.

What Can You Do To Implement This To Your Current Teach? #

Nowadays, as technology is growing day-by-day, there is an increasing requirement for fine information that is just coming to be extremely essential. In case your organization is yet depending on severed, printed forms or defaulting to the forms, the organizations should take all the time they need to create a more efficient strategy.

The organization has come a long way with the old technology and now, it’s time to adopt modern technology and design a new system to collect and connect the forms and this cannot be offered by EHRs. If you yet don’t adopt this modern teach it will need more time to transfer from the old to the new one.

There are many sponsors of healthcare IT that have several years of experience. These are exclusive to help all the healthcare facilities and they have more than a thousand clients. They offer a smooth workflow and an efficient system for the facility.

These third-party companies design a software platform that is incorporated with the EHRs system and many more networks to eliminate the manual work for the needed documents. It must record the patient’s information as well as their provided data and speed the workflow for the same.

These companies accomplish another level of standard process and outstanding efficiency and thus let the healthcare facility concentrate on the main objective.

Related reading

Questions and answers

What Does Quality Data Management Mean? #

What Can You Do To Implement This To Your Current Teach? #

Nowadays, as technology is growing day-by-day, there is an increasing requirement for fine information that is just coming to be extremely essential.

Artificial Intelligence of Things (AIoT)

Vin Patel — Tue, 19 Oct 2021 18:04:00 +0000

Key takeaways

If you are a technology enthusiast or have a vague idea of what’s going in the world of technology, then you must be familiar with the popularity of artificial intelligence (AI) and the internet of things (IoT).
AI-based algorithms are used to remove trash data and utilize any business strategy in order to organize this unstructured data into a meaningful chunk of data.
AIoT is a one-time investment that yields lifelong benefits.

Introduction: Convergence of Internet of Things and Artificial Intelligence #

If you are a technology enthusiast or have a vague idea of what’s going in the world of technology, then you must be familiar with the popularity of artificial intelligence (AI) and the internet of things (IoT). There is no doubt that these two are individually powerful entities that have not only shaken the industries with their capabilities but have been performing as technological boons ever since. But what happens when these two technologies are clubbed together and become an exciting collaboration named Artificial Intelligence of things (AIoT)?

Speaking of what AIoT is, it is a new generation process of converging the source of intelligence received from artificial intelligence and making inter-device connections with the help of the internet of things. It is paving its way to being one of the most popular technical solutions for data analytic and processing. It is in the nature of IoT to aggregate things, particularly devices, producing huge amounts of data. When AI comes into the mix, the data-hungry algorithms of AI churn the data and process it throughout, making the analysis and action parts of it highly efficient. Now that we have a basic idea of AIoT, it is time for us to know its benefits.

Benefits of AIoT #

All the praise of AIoT would make no sense if you do not have a clear vision of its benefits. So, we have listed out the major benefits of AIoT:

Increase in Efficiency:

There is a saying, ‘Time is Money,? and these are the two factors that can drastically increase or decrease your efficiency. The employers of any company need to use their employee’s time as effectively and efficiently as possible. This will not only yield positive outcomes but also result in increased revenue. This is possible with the help of AIoT. Let us discuss some examples to prove this point. Preventive maintenance is a benefit of AIoT, which helps the maintenance team to recognize parts of any machinery that is not working correctly or even predict malfunction in advance. This saves the expenditure of replacing the entire machine and keeps the activities running smoothly, hence increasing efficiency.

Increase in Safety:

Safety is a very important factor while running any business, as any small mishap due to careless behavior can lead to huge losses. AIoT helps to boost safety in several ways. One example of this could be employing computer vision to monitor personnel on a production floor or using virtual or augmented reality in dangerous circumstances. This application of artificial vision can be used to get real-time alerts of drivers while driving any vehicle. It provides alerts to prevent accidents in cases like the driver falling asleep behind the wheel.

Better Customer Eexperience:****

It is very important for companies to look for ways to improve their customer service in any customer-centric business, and AIoT could be of great help in this area. For example, in any shopping mall or store, there are surveillance cameras used to understand their customers’ shopping patterns and other information like which aisle is getting more sales and which is not getting any customer visits. This way, they can plan accordingly and make the shopping experience more personalized for their targeted customers.

Smart Business Ddecisions:****

IoT devices collect huge amounts of data every day, making it difficult for personnel to differentiate between important and unimportant information. AI-based algorithms are used to remove trash data and utilize any business strategy in order to organize this unstructured data into a meaningful chunk of data. Chief technology officers (CTOs) and other decision-makers may now make sound business decisions based on the important insights gleaned from this data.

Uses of AIoT in Various Sectors: #

AIoT is used in many sectors in real life. Let us learn about a few of them.

Healthcare Devices:

Many companies are coming with smartwatches that monitor the users’ heart health, activities, blood pressure, etc. These are created with the concept of AIoT. These have revolutionized the healthcare industry and made people more aware of their health. IoT facilitates the connection of many device kinds; on the other hand, AI turns the collected data into insights that can play an essential part in making users healthier.

Fleet Management:

Fleet vehicles are clusters of motor vehicles owned or leased by the Government or any private company. AIoT is used for the tracking, management, and maintenance of fleet vehicles. This set of use cases was created by combining GPS, additional sensors, and an AI system. An example of this is autonomous vehicles, which utilize sonars, radars, cameras, and GPS to collect data on driving conditions and then use AI to make choices based on IoT’s data.

Use of Drones:

Drones are trendy devices used worldwide for many purposes. When used to monitor vast regions, drones may assist in monitoring traffic in real-time and make changes in the flow of traffic. AI may be used to evaluate data and then make choices on reducing traffic congestion by adjusting speed limits and traffic light timing - all without the need for human intervention. Drones are also used to deliver products to consumers, usually in the eCommerce and food delivery sectors. There are other fields that are ready for exploration for the use of drones as well.

Smart Homes:

It is no longer a fantasy to have houses that understand their owners and respond to their commands. With the help of AIoT, people are able to build smart homes with electronic devices that work with just one voice command. Infused with voice assistants and artificial intelligence that understands the user’s behavior and acts accordingly, like switching off lights after the user moves out of the room or automatically switching on electronic devices when required. Between 2020 and 2025, the AIoT-driven, smart home industry is expected to achieve a $246 billion valuation at a CAGR of more than 25%.

Conclusion #

So, it is fitting to say that the combination of artificial intelligence and IoT has been revolutionizing many industries and has proven itself beneficial till now. Engineers and scientists are still working on AIoT to make improve it further and make it more convenient for the users. AIoT is a one-time investment that yields lifelong benefits.

Related reading

The Evolving Role of Data Analytic in the Healthcare

Vin Patel — Mon, 30 Aug 2021 22:13:14 +0000

Key takeaways

Healthcare data analytic has become a vital part of healthcare, and in 2021, the projected revenue for this industry is close to $9 billion.
Logistic Regression: Used to determine potentially risky events through a series of analysis.
All in all, the future of healthcare data analytic is on an upward trend and will only impact more lives soon.

Healthcare data analytic has become a vital part of healthcare, and in 2021, the projected revenue for this industry is close to $9 billion. Yes, you read that right. $9 billion. It’s only projected to increase from here on forth and could potentially rake in $20 billion by the year 2027. If this isn’t proof that it’s one of the most trending topics in healthcare, we don’t know what is!

Data analytic in healthcare is not a new phenomenon but rather years of research that has culminated in intelligent systems being used across the globe. Without healthcare data analytic, we wouldn’t be able to retrieve our records at our whims and fancies. We won’t be able to diagnose complex diseases like pancreatic cancer in its early stages, and we definitely will not be able to get personalized plans for our treatment.

I mean, let’s think about the time when the COVID-19 pandemic hit us. We wouldn’t identify and isolate people (contact tracing) unless we had data about who was infected. How do you think we identified hotspots? What do you think your e-vaccination certificate is all about? They’re all examples of the use of data analytic in healthcare! In 2020, 59% of people were happy to use AI-based devices to diagnose their health issues, and with time, it’s increased.

But how has healthcare data analytic evolved over time? Let’s break it down for you.

What is Healthcare Data Analytic? #

So, what is healthcare data analytic? Simply put, it’s a field that involves using data to make informed decisions or how we popularly know it now as data-driven decisions. Big data refers to the sheer volume of the data generated and our inability to store them physically. Data can be in any form’text, audio, video, log files, or even annotated records. If the data contributes to the decision-making process, it is considered relevant. It’s what we know as an Electronic Health Record or EHR.

Original diagram for Techfastly by Vin Patel.

It’s has a significant impact on the healthcare industry. For example, it benefits doctors by allowing them to look at a problem from various perspectives and make better decisions for their patients. From the patient’s perspective, they receive incredibly personalized plans with reduced costs rendered impossible before big data analytic. From the administrative perspective, it has simplified the entire process. Instead of wasting time making manual written entries, they can focus on actually running the healthcare center.

Use of Predictive Modeling in Healthcare #

If you go to see, medicine is either about preventing health issues or treating health issues based on scientifically backed data. So, it’s not surprising that the next step in healthcare data analytic was predictive modeling. Doctors are humans, and there are only so many avenues they can think of at the end of the day. Predictive data analytic takes that burden off their shoulders and helps them make data-based decisions. It’s widespread to see the use of AI algorithms and the Internet of Things (IoT) to combine data from the past and present to find the best fitting solution.

A few examples of such algorithms include:

Artificial Neural Networks: Models the connections within our brain and is used for diagnostic purposes to identify patterns and develop patient-specific solutions.

Logistic Regression: Used to determine potentially risky events through a series of analysis.

Random Forest:Used to draw multiple trees or, in this case, possibilities of development of disease based on their medical history.

It has completely revolutionized the healthcare space, and now that we see it, there’s no going back.

How Has Data Analytic in Healthcare Evolved Over Time? #

It was not uncommon to see that every little medical record would be handwritten and stored decades ago. Even now, doctors are required to put in their ’notes? which are filed in the patient’s folder. But that was a dangerous way to store data. Why, you ask? Simply because it could get stolen or destroyed easily. Just imagine that there’s a fire in the hospital, and suddenly, years of data are lost in seconds. This is precisely what healthcare data analytic aims to solve.

The complete digitization of the medical workflow has allowed doctors and the administration alike to focus on much more important things. While initially, many companies were focused on creating the best technologies that can detect the most intriguing details within the shortest amount of time, something was amiss. Soon, in the 2010s, it was figured that while most of the big data in healthcare data analytic was solving a particular problem, the end user? the patient was not necessarily reaping the intended benefits.

Value-based healthcare has become a buzzword now, with many healthcare management systems focusing on the patient’s perspective. For example, there are many applications to book an appointment instead of waiting for hours on end to get one; you can voice your concerns about the treatment and service that you receive; hospitals can follow up using automated systems which saves hours on their part and the best of all, online customer relationship management (CRM) powered by Artificial Intelligence (AI) allows us to map a patient’s journey within the system,

A refreshing change, we must say! #

Another critical aspect that worries most about healthcare data analytic is cybersecurity. Initially, when scientists built these systems, security measures were sub-par at best. In 2021, the going rate for one EHR is $250. That’s a massive amount to pay for one record, but it also goes to show why healthcare data is so prone to be attacked. It’s one of the reasons why most hospital systems prevent the use of third-party apes and have increased network visibility. The increasing focus on security has only helped the overall image of healthcare data analytic improve over time.

The COVID-19 Push to Big Data Analytic in Healthcare #

Like every other technology, big data analytic also got the much-needed push during the pandemic. The true power of data analytic in healthcare was shown when umpteen papers were published describing the various ways in which the pandemic was being fought.

From the diagnostic perspective, it was used for pre-symptomatic detection of the infection, tracking the disease in real-time also monitoring discharged patients in real-time. Epidemiologists also used it to identify hotspots and the spread of variants through the use of genetic repositories. Scientists eventually used this information to identify risks scores and conduct contact tracing activities all across the globe. It helped epidemiologists and helped hospital administrators make data-driven decisions on how and when to quarantine, what their current capacity is, what their resource capacity was like, and which patients they need to prioritize. It most definitely saved millions of lives globally by using automated software powered by big data and AI.

What Does The Future Hold? #

We must work on improving the current systems to ensure better output shortly. Some initiatives include providing more data to algorithms for precision analysis; developing error-free and flawless algorithms that prevent bias; creating tighter security networks to protect data; and focusing on using comics data to provide personalized medical recommendations.

All in all, the future of healthcare data analytic is on an upward trend and will only impact more lives soon. The implementation of big data analytic in healthcare has really given a new meaning to the phrase?Prevention is better than cure.?

Related reading

Questions and answers

How Has Data Analytic in Healthcare Evolved Over Time? #

It was not uncommon to see that every little medical record would be handwritten and stored decades ago.

So, what is healthcare data analytic? #

Simply put, it’s a field that involves using data to make informed decisions or how we popularly know it now as data-driven decisions. Big data refers to the sheer volume of the data generated and our inability to store them physically.

But that was a dangerous way to store data. Why, you ask? #

Simply because it could get stolen or destroyed easily. Just imagine that there’s a fire in the hospital, and suddenly, years of data are lost in seconds.

Data Privacy Challenges in Building an IoT Solution

Vin Patel — Mon, 23 Aug 2021 20:34:30 +0000

Key takeaways

Today we live in a digital era where everything is getting digitized and connected to each other.
However, as IoT is an emerging technology, there are still several challenges in building IoT solutions for several applications.
Additionally, network firewall, custom security strategies and token-based authentication should be implemented on all aspects of every IoT application in the solution.

Today we live in a digital era where everything is getting digitized and connected to each other. As a result, modern technologies alter the way people used to live, work, commute and communicate. These technologies, like Artificial Intelligence (AI), Internet of Things (IoT), Information & Communication Technology (ICT), Machine Learning (ML), Computer vision, Blockchain, and Data Analytic, are still evolving but making remarkable transformations in industries. Among these technologies, IoT has proven potential to connect the physical and virtual world on a single platform and thus gaining popularity in every sector adopting advanced technologies.

IoT #

IoT stands for Internet of Things. The ’thing? in IoT can be any physical device such as smartphone, computer, washing machine, wearable device, television, headphone, lamp, building, vehicle or any other possible thing that can be thought of. The IoT technology will connect any systems, devices, objects that can be connected in the near future. These things are embedded with sensors, software, and other electronic components to transfer the data. The internet interconnects these devices and makes a giant IoT network of connected ’things?. There are three relationships in the IoT network viz

Things-things (machines to machines)

Things-people (devices to people)

People-people (people to people)

Significant Challenges in Building IoT Solution #

The integration of several devices connected through the network is prone to vulnerabilities, threats, and risks. Some standard IoT privacy and security problems are discussed below:

? Authentication and Authorization

Existing systems and software platforms are getting upgraded with technological advancements, thus increasing the date’s security and safety. But accordingly, hackers are becoming more dangerous in attacking or hacking systems leading to cybercrimes such as identity thefts, personal information misuse and others. This is because most of the devices have weak and predictable passwords. Additionally, many sensors, devices and systems have default authorization credentials that are easy to hack through the network.

? Transport Layer Insecurity

IoT infrastructure has different layers of configurations and systems such as hardware, communication, application, and communication layers. Systems in these layers communicate with each other to transmit and receive data. Transport layers must be secured to prevent data leakage. Data encryption is the best solution available today to transfer data safely over the internet, but most devices fail at this step. It leads to insecure data transfer and increases security issues of IoT solutions.

? Insecure User Interface

IoT devices such as smartphones, laptops, personal computers or even web interfaces have user interfaces to manage the device as well as to make appropriate use of the data collected. However, these devices are prone to security issues such as data leakage, cross-site scripting, or identity misuse. Therefore, it is vital to maintain proper security measures while operating and handling IoT solutions.

? Disregarding Privacy Concerns

There should be safe code practices and a proper set of rules while handling business logic and services data. Furthermore, private, confidential and sensitive information regarding the organization should be kept safe to avoid possible threats like cyberattacks. Therefore, organizations and governments must implement security methods while adopting IoT solutions in sensitive data handling.

? IoT Security and Privacy Laws

Businesses look for customer privacy and secure IoT devices in compliance with IoT security and privacy laws. A few rules and regulations have been passed globally, such as the California Consumer Privacy Act (CCPA), European Union General Data Protection Regulation (GDPR). These acts are dedicated to protecting consumer privacy, but there are differences in scope, requirements and penalties for noncompliance for both laws. Moreover, there is no single globally accepted rule or regulation regarding IoT solutions that make it difficult for IoT solution manufacturers to produce solutions in compliance with these acts.

IoT Data Privacy Challenges #

IoT enables uninterrupted and fast machine-to-machine communication that has already attracted several industries, governments, and enterprises. They have initiated developing and implementing IoT solutions and aim to enhance the productivity and growth of organizations. However, as IoT is an emerging technology, there are still several challenges in building IoT solutions for several applications. For example, there are no universal standards for IoT solutions. Moreover, concerns regarding data privacy and application security are not addressed yet. IoT solutions will be used to collect, store, exchange and analyze massive data efficiently and securely. Thus, it is important to implement strategies in protecting all components of IoT solutions from evolving security threats.

IoT interconnects billions to trillions of intelligent things with unique identities and addresses. These things process and communicate information about the physical environment and about themselves. These IoT systems are used in different applications such as advanced building management systems, pervasive healthcare, public surveillance, innovative city services, participatory sensing applications and data acquisition. The collection of vast amounts of data through these systems about people’s private lives and personal information raises serious data privacy concerns. These issues may have unwanted consequences like a failure of new services, costly lawsuits, reputation damage and so forth.

IoT-related projects have experienced colossal data privacy issues previously, like a public boycott of the Italian retailer Benetton in 2003, the revocation of the Dutch intelligent metering bill in 2009 and the recent outcry against the EU FP7 research project INDECT. IoT is dependent on different technologies like wireless sensor networks (WSN), mobile applications & platforms, web personalization and Radio Frequency Identification (RFID).

IoT features are changing with the latest technology developments. Simultaneously, the number of ways of interacting with smart things are growing rapidly and making networking more complex. Upcoming features will be exciting and attentive but will aggravate data privacy issues and introduce unknown threats. These known, as well as strange privacy issues, must be considered in reference models accounting for specific entities and data flows. In addition, the perspective of existing privacy legislation regarding evolving and unique IoT solution features should be considered while building IoT solutions.

Privacy is one of the most important majors for people, and it is becoming prominent with increased usage and efficiency of electronic data processing. It was first defined in 1968 by Weston as ’the right to select what personal information about the person is known to what people?, and it is still equally valid. Before electronic data usage, data privacy concerns were limited and primarily controllable. For example, it is challenging to prevent cyberattacks and hacks in electronic data over the internet. Therefore, IoT solutions should guarantee — Awareness of privacy risks by IoT solutions collecting data — Understanding and control of the use and dissemination of personal data — Individual control on collecting and processing personal information by IoT solutions

Thus, enterprises should focus on the security of IoT solution components like communication channels, embedded software, data, and other various devices that are prone to data privacy concerns. Multiple-factor authentication and authorization processes should be implemented to ensure user identity and reduce opportunities for hackers to misuse confidential user information. Additionally, network firewall, custom security strategies and token-based authentication should be implemented on all aspects of every IoT application in the solution.

Related reading

Use of Quantum Machine Learning in Data Analytic

Vin Patel — Thu, 22 Jul 2021 23:06:17 +0000

Key takeaways

Imagine sitting in front of your computer and finishing off a certain work by staying awake the whole night.
The phrase “data analytic” refers to the act of analyzing datasets to make conclusions about the information contained within them.
For the data science experts sitting out there, this was one such article that can make your lives a bit easier, all it will take is a pinch of quantum physics combined with machine learning.

Introduction #

Imagine sitting in front of your computer and finishing off a certain work by staying awake the whole night. The following day, when you wake up and open your teach news feed, you come across an article telling you how a particular new technology can help you do the same thing in half the time that you took to finish it off. You regret staying awake the whole night and not knowing about the new technology and its application in your life. The article you read might not be such a big thing, but it surely made you acquainted with something that can stay with you in the long run. Consider this as one such article which can make your life easier.

Today, our area of focus is quantum machine learning. QML is a new thing for the IT industry but a thing that can change the dimensions of the industry effectively. Let us first try to know what quantum machine learning is.

What is Quantum Machine Learning? #

Traditional computers struggle to handle massive computations when data points are projected in greater and higher dimensions. Even if a traditional computer could do it, it would take too long.

Simply said, traditional machine learning techniques might be too demanding for traditional computers at times. Quantum computers, fortunately, have the computing capacity to tackle these difficult algorithms. They solve problems quicker than their classical equivalents by utilizing strong principles such as superposition and entanglement.

Scientists can convert a conventional machine learning algorithm into a quantum circuit that can be performed effectively on a quantum computer using quantum machine learning.

So, in simple words, Quantum Machine Learning (Quantum ML) is an interdisciplinary field that brings together quantum physics and machine learning (ML). It’s a symbiotic relationship, with quantum computing being used to create quantum versions of ML algorithms and traditional ML algorithms being used to study quantum systems.

Now that we know what quantum machine learning is, let us try to look at its applications.

Applications of Quantum Machine Learning #

Quantum machine learning is a very new area with a lot of room for advancement. However, we can already begin to forecast how it will affect our future!

Here are some of the areas where QML will have an impact:

Understanding Nanoparticles
Molecular and atomic mapping is used to create novel materials
Medical research and molecular modeling to identify new medicines
Understanding the human body’s inner workings
Pattern recognition and categorization have been improved
Expanding the scope of space exploration
By combining IoT and blockchain, we can achieve total linked security.
In the analysis of data

QML will address more issues than we could have anticipated as more remarkable discoveries occur every day.

After looking at the applications, we can be assured of one thing, and i.e., if QML can help in such kinds of things in the early stages of its discovery, in a few years it can be the next big thing. In the applications above we came across a point that mentioned ‘data analysis?. Watching the things that Big Data can do and how fast the data science industry is growing, anyone will be curious about this very application. So let us dive deeper into the ocean of quantum machine learning and search for the desired fish, data analysis. For this, we must start with what data analytic is.

What is Data Analytic? #

The phrase “data analytic” refers to the act of analyzing datasets to make conclusions about the information contained within them. Data analysis techniques allow you to take raw data and derive important insights from it by uncovering patterns.

In their research, data scientists and analysts employ data analytic methodologies, and companies use them to influence their judgments. Data analysis may assist businesses in better understanding their consumers, evaluating their advertising efforts, personalizing content, developing content strategies, and developing new goods. Finally, firms may utilize data analytic to improve their bottom line and raise their performance.

Quantum Machine Learning in Data Analytic #

While solving real-world problems, quantum machine learning algorithms work with existing datasets and provide a simple and better understanding of them. It makes the work easier for a data scientist who spends hours analyzing data. The rise of QML will benefit the ones in the industry and will also make them shed a lot of load from their already crowded head.

Researchers examine the topic of quantum advantage in machine learning in a recent paper published in Nature Communications titled “Power of data in quantum machine learning.” Researchers attempted to quantify how the difficulty of an issue varies formally with the availability of data in the article, and how this might occasionally improve the competitiveness of classical learning models using quantum techniques. The data can raise classical models to rival quantum models, despite the fact that the quantum circuits generating the data are difficult to calculate classically.

To begin, they demonstrated how data-driven classical algorithms might match quantum output. They then established rigorous prediction error limits for training conventional and quantum machine learning algorithms to learn quantum mechanical models using kernel functions. Kernel techniques give verifiable guarantees and are also quite flexible in terms of the functions they can learn, therefore they took these steps.

Computational complexity classes are occasionally used to represent the idea of quantum advantage over a conventional computer. Bounded Quantum Polynomial (BQP) time tasks, such as factoring large numbers and modeling quantum systems, are predicted to be easier for quantum computers to handle than classical systems. Bounded probabilistic polynomial (BPP) problems, on the other hand, are simple to answer on conventional computers.

The researchers working on these showed that learning algorithms with input from a quantum process, such as fusion or chemical processes, generate a new class of problems known as BPP/Same, which can effectively execute tasks that standard algorithms without data can’t. It’s a type of problem that can be solved quickly with polynomial-sized advice. This shows that understanding the quantum advantage for different machine learning tasks needs a data analysis as well.

Going deeper into this, we will realize that all the applications of QML are somewhere related to data analytic. From analyzing human behavior to pattern recognition and space exploration, QML uses data analytic to function smoothly and vice verse.

Conclusion #

In the near future when many new quantum machine learning algorithms will come up with the exploration of this field, more and more applications of it will show up too. Data analytic with time will get easier and easier for an industry in so much demand.

For the data science experts sitting out there, this was one such article that can make your lives a bit easier, all it will take is a pinch of quantum physics combined with machine learning.

Related reading

Questions and answers

What is Quantum Machine Learning? #

Traditional computers struggle to handle massive computations when data points are projected in greater and higher dimensions. Even if a traditional computer could do it, it would take too long.

What is Data Analytic? #

Types of AI and Emerging Technologies

Vin Patel — Tue, 30 Mar 2021 00:07:53 +0000

Key takeaways

It has disrupted technology with a widening range of applications in everyday life.
The system uses large volumes of training data for deep learning.
The future of our race with cutting-edge AI technology is uncertain, but if appropriately implemented, these cogent systems will bring the dawn of progress for us.

Artificial Intelligence (AI) isn’t just a buzzword anymore. It has disrupted technology with a widening range of applications in everyday life. AI was acknowledged as a possible technology by John McCarthy in the year 1955. He believed machines could be made to think, behave and perform as humans. Renowned as the Father of AI, McCarthy created Lisp computer language in 1958. It is the programming language used even today to create standard AI applications.

AI is a multifaceted technology with a variety of types and categories. In this article, I have simplified the types of AI by dividing them into two broad categories:

Types of AI

The realm of AI includes machine learning, big data, cognitive intelligence, deep learning, natural language processing (NLP), and much more. Here is a broader categorization to help you easily navigate through the types of AI and analyze which fits your requirement.

Type 1- AI can be categorized into three subsets based on current utilization and future viability.

1. Narrow- Narrow AI is the simplest form of AI that is efficient at performing a single task. Weather forecast applications and speech-to-text converters are typical examples of Narrow AI. Though sophisticated, Narrow AI is referred to as ‘Weak AI? considering its capabilities are limited. Narrow AI-powered devices cannot perform functions beyond their scope. Autonomous cars, computer vision, Google Translate, and other NLP-enabled apes, which we use frequently, have reached only up to the stage of Narrow AI. It doesn’t perform on the standards of human intelligence but is considered adequate for routine jobs. Narrow AI can cut down human intervention from repetitive and monotonous jobs.

2. General- General AI is a more complex version of AI as it is predicted to work like the human brain. This human-level AI will be capable of understanding, reasoning, and reacting to its surroundings. It is known as ‘Strong AI?, and we are coming across several challenges as we strive to achieve it. Computers can process data and pull up relevant information from their memory. Unlike humans, it is difficult for them to react to unprecedented situations or innovate to create something that never existed. Teaching computer skills like planning, perception, and decision-making based on unrelated information is a difficult task. Though it isn’t unachievable, General AI is still distant from real-life applications.

3. Super- Artificial Super Intelligence (ASI) is a concept given by Nick Bostrom, a University of Oxford scholar and AI expert. He intends to create an AI better than the combined power of best human brains from every creative and scientific field. ASI would be a more capable form of General AI and is unachievable at the moment. It is predicted to be a fully capable form of AI. Scientists like Stephen Hawking believe it to be devastating for the human race, while others claim it will enable humans to do more.

Type 2- Another classification of types of AI can be done based on its cognitive capabilities and functionality. The four subsets of type 2 AI are:

1. Reactive- Reactive AI systems are the oldest form of artificial intelligence enabled machines with extremely limited capability. They do not have a memory and emulate the human mind’s ability to respond in the present situation only. They are programmed to perform a set of tasks in response to a stimulus. Reactive AI machines do not have the ability to ’learn? as they do not store experiences or refer to them when performing automatic actions. These machines can effectively respond to a complex combination of instructions. IBM’s Deep Blue is a reactive AI-based machine. It is renowned for being the only computer to beat a human at chess. The machine won chess matches against Grandmaster Garry Kasparov in 1997.

2. Limited Memory- Limited memory machines have the capability to learn from historical data and make quick decisions. It derives information from stored data fed to the system earlier. Besides, it builds experiential knowledge by observing actions from its environment. The system uses large volumes of training data for deep learning. It creates reference models and uses them along with information stored in its memory for solving problems. All present-day AI systems fall under this subset. These include virtual assistants, chatbots, and autonomous vehicles. The process to train them includes image recognition AI and a wide variety of related images to act as training data. It scans thousands of pictures to identify and store them for future use. When the machine is exposed to a real environment, it makes predictions based on its ’learning experience? and stores them. The learning process continues to reach higher levels of accuracy. For instance, an autonomous vehicle is trained using images of routes, landmarks, and other vehicles. It stores the data and when on the road, it reconciles the information to make a decision. The vehicle also detects patterns and driving behavior of human-driven cars. It makes real-time adjustments based on these changes.

3. Theory of Mind- Theory of Mind is a proposed form of AI aimed at creating machines with the emotional capabilities of a human. These systems are complex and go beyond the memory or decision-making capabilities. They are focused on exhibiting human-like emotions to drive conversations. We can consider them the advanced versions of our current voice assistants or chatbots. The concept is aimed at building a thought process of the machine to derive a behavioral output. These machines are being trained to identify, understand, remember, and respond to emotional inputs. The applicability of the ‘Theory of Mind? has been noticed in robots Kismet and Sophia. Kismet was created in 2000 and could imitate human emotions through facial expressions. Sophia, launched in 2016, is a humanoid robot with human-like physical features. This bot has the capability to respond to human interaction with thoughtful responses and appropriate facial expressions.

4. Self-awareness- Self-awareness AI is the most advanced stage of AI development. It is a theoretical concept that perceives AI to reach the self explanatorily stage. An evolved form of AI that may possess human consciousness. Creating this type of AI is challenging, but all AI research by scientists aims to achieve this stage. AI systems based on self-awareness will possess emotions, understand human needs, and realize their own feelings. The system will be an advanced stage of Theory of Mind enabled machines, as it will not replicate human beliefs and feelings. It will be self-regulated with the capability to think, make inferences, and react. According to experts, this type of AI can be potentially dangerous for our existence or may boost our progress as a species. The dream is farfetched, and we cannot yet comment upon the outcome of implementing self-awareness AI-powered systems.

Way Forward

The rapid developments in the field have empowered companies to come up with innovative solutions backed by AI. Among the emerging types of AI and associated technologies are Generative AI, Federated Learning, and Neural Network Compression. Generative AI is a breakthrough development in the AI industry. It aids in content development by enabling computers to create 3D and 2D images using text, audio, and pictures. Federated Learning is an emerging machine learning technique that assists in training and improving AI-based tools. Neural Network Compression is another upcoming technology to address Big Data storage issues and facilitating access to Deep Neural Networks (DNN). It compresses data for easy storage and deployment on the cloud.

As we progress towards developing more competent AI systems, we inch closer to the most advanced tier of both Type 1 and 2 of AI. The future of our race with cutting-edge AI technology is uncertain, but if appropriately implemented, these cogent systems will bring the dawn of progress for us.

Related reading

Questions and answers

Though sophisticated, Narrow AI is referred to as ‘Weak AI? #

considering its capabilities are limited. Narrow AI-powered devices cannot perform functions beyond their scope.

It is known as ‘Strong AI? #

, and we are coming across several challenges as we strive to achieve it. Computers can process data and pull up relevant information from their memory.

Reactive AI machines do not have the ability to ’learn? #

as they do not store experiences or refer to them when performing automatic actions. These machines can effectively respond to a complex combination of instructions.

The applicability of the ‘Theory of Mind? #

has been noticed in robots Kismet and Sophia. Kismet was created in 2000 and could imitate human emotions through facial expressions.

How to collect data using Google Analytic

Vin Patel — Thu, 05 Nov 2020 07:30:00 +0000

Key takeaways

!Diagram: Diagram Original diagram for Techfastly by Vin Patel. Web analytic evaluates both website and web marketing actions performances.
It can be used to categorize interactions in reports that are specific to the website.
!Diagram: Diagram Original diagram for Techfastly by Vin Patel..

Original diagram for Techfastly by Vin Patel.

Web analytic evaluates both website and web marketing actions performances. It affords to understand how many users visit a website when they visit it and for how long they browse the website, which are the sources that carry the most traffic and more. There are several web analytic tools, and Google Analytic is always the first choice. Not only because it’s free, but it provides lots of features that help to test, track, and measure customer behavior on a website.

Let’s give a short checklist on how to choose a useful analytic tool:

-List the goals that need to be achieved from the information available on the tool. Check whether the tool can achieve the goals.

-Check if the features match your web analytic needs. Check if it is easy to understand and use.

-Check if it fits your budget, and it is in line with your expected ROI.

-Check if it has a free-trial period.

What is Google Analytic? #

Google Analytic is a free web analytic tool offered by Google that allows you to aggregate, filter, and visualize data to analyze how users interact with a website. It’s a tracking software of people browsing a website retrieving data with valuable insights into user behavior, interests, and more to take action to improve a product or a service.

Original diagram for Techfastly by Vin Patel.

Why Digital Analytic? #

A web analytic tool’s task is to make easily accessing data without having to read through online sales records, count how many times a page has been shared, or count comments on each post.

People usually purchase goods in stages. There are different stages that describe customer interactions. A basic purchase includes the following steps:

Acquisition when users build awareness and interest.
Behavior when users engage with the business.
Conversion when a user becomes a customer.

In the offline world, this process can be hard to measure. But in the online world, there is the opportunity to measure several steps of the purchase using digital analytic, tracking what online behavior led to purchases and exploiting that data to make decisions about how to reach new and existing customers.

How does Google Analytic collect data? #

The website data collection begins with a snippet of JavaScript tracking code that’s included on every webpage where information is collected. The tracking code trails each user’s interaction on the website. This code uses the domain of the website to track and put it in the analytic reports. In this way, Google Analytic will drop a cookie in the user’s browser for that website and easily track the trail on a single website URL domain by default.

With each user interaction on the website, the analytic tracking code sends a URL string, called “hit,” with useful information about users.

The URL string contains some useful data like:

the language of the user’s browser
the name of the page visited
the screen resolution of the user’s device
the Analytic ID that associates that hit to the right analytic account

The hit will also include other parameters like a randomly-generated user identifier. This will allow Google Analytic to understand new and returning users.

The three most common types of hits are:

“pageview” hits
“event” hits
“transaction” hits

A “pageview” hit is triggered every time a user opens a webpage with the tracking code. This is the most common type of hit sent to Google Analytic.

An “event” hit is triggered every time a user interacts with a particular element on the website. Event hits send four parameters of data in the URL: event action, category, label, and value. It can be used to categorize interactions in reports that are specific to the website.

A “transaction” hit can pass data to Google Analytic about commerce purchases.

Setting up the Enhanced Commerce function, you can also pass additional commerce data like product category, whether items have been added or removed from a shopping cart, and how many times users viewed a product on a website.

Additional hits such as “social hits” can pass likes, shares, or tweet data; and “page timing hits” that allow you to report on page timings.

Google Analytic widens those data using other sources such as IP address, server-log files, and other ad-serving data. And in this way, it can understand things like:

a user’s location
specifics about user’s browser
user’s age and gender
source/medium that referred users to a website

Once the hit is sent to Google Analytic and combined with additional data, they are ready for processing by the Analytic servers.

Original diagram for Techfastly by Vin Patel.

How to set up Google Analytic? #

Let’s look at how Google Analytic accounts are organized.

All company accounts can be grouped under an “Organization,” which is optional. This allows us to manage multiple Google Analytic accounts under one grouping. Large businesses could have multiple accounts, while small-sized companies generally use one account. When an account is generated, it automatically generates a property and a view for that account. But each Analytic account can have multiple properties and each property can have multiple views. This organizes data collection in the best way for a business.

The Google Analytic account determines how data is collected from websites and manages who can access that data. Generally, the best choice is to create separate accounts for distinct businesses or business units.

Each account has at least one “property,” and each property can collect data independently of each other using a unique tracking ID that appears in the tracking code. Just as each account can have multiple “properties,” each property can have multiple “views.” The Running Filters feature determines what data to include in the reports for each view.

The view level also affords you set Google Analytic “Goals.” Goals are a way to track business objectives from the website. For instance, a task could be how many users signed up for an email newsletter or how many users purchased a product. Pay attention when setting up your accounts, properties, and views because you can’t change data once it’s been collected and processed.

Start your Google Analytic trial.

Original diagram for Techfastly by Vin Patel.

Related reading

Questions and answers

What is Google Analytic? #

Google Analytic is a free web analytic tool offered by Google that allows you to aggregate, filter, and visualize data to analyze how users interact with a website.

Why Digital Analytic? #

A web analytic tool’s task is to make easily accessing data without having to read through online sales records, count how many times a page has been shared, or count comments on each post.

How does Google Analytic collect data? #

How to set up Google Analytic? #

Let’s look at how Google Analytic accounts are organized. All company accounts can be grouped under an “Organization,” which is optional.

The Use of Python in AI and ML

Vin Patel — Thu, 22 Oct 2020 20:22:04 +0000

Key takeaways

The open-source programming language Python has been around for a long time, but it continues to be a dominant force.
So when AI engineers and ML code with Python, they are sure about the product that’s built (and can move forward with confidence).
What’s more, the lack of a learning curve also makes Python a highly cost-effective solution.

The open-source programming language Python has been around for a long time, but it continues to be a dominant force. Its popularity in the digital age is attributed to the fact that you can build many cool things with Python (like basic QA Systems).

In recent years, Python’s dominance has also extended as a result of its ability to manage complex coding scenarios, especially in the domain of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

According to O’Reilly Media, Python’s popularity was fueled by ML development. In fact, Python was the most popular coding language on the platform, accounting for 10% of all usage. O’Reilly further states that Python’s popularity on its platform was driven by data scientists, AI engineers, and ML engineers.

For example, AI is leveraged to build prediction models to engage in data analytic, create robust strategies, boost productivity, enhance efficiency, and more. All this is achieved through programming in Python.

As oceans of data are generated every second of the day, AI and ML are used extensively for its processing and analysis capabilities (a feat that can’t be achieved by the human brain because of the volume of data it can absorb at any given moment).

What Are the Key Benefits of Using Python in AI and ML? #

When it comes to AI and ML, Python is like a super stack demanded by AI and ML-based experiments.

The key benefits of leveraging Python for your next AI and ML project are as follows:

Low barrier to entry
Extensive libraries
Enhanced flexibility
Consistency
Stability
Readability
Platform agnostic

For this post, let’s take a closer look at some of these benefits.

Low Barrier to Entry #

When working with lots of data, you have to find innovative ways to process it conveniently and effectively. As Python is easy to learn, most data scientists pick it up quickly and engage in AI development without external support.

This is because Python resembles simple English with a simple syntax that allows the seamless management of complex systems. The extensive documentation available within the Python community also helps accelerate this process.

Extensive Libraries #

It also helps that Python is supported by extensive libraries, structures, and thriving online communities. Python libraries are also one of the main reasons why the code has come into prominence within AI.

These libraries are collections of modules distributed across a variety of sources like PyPi. The latter is equipped with a pre-composed bit of code that enables clients to perform analytical activities. Code like this can form the foundation of AI-based projects, saving time and money.

For ML projects, Python libraries enable continuous information prepping and data processing. In this scenario, Python libraries allow you to access, manage, and transform the data seamlessly.

So when AI engineers and ML code with Python, they are sure about the product that’s built (and can move forward with confidence).

Some popular libraries used in AI and ML projects are as follows:

Caff (for DL that enables seamless switching between CPU and the GPU, while processing over 60 million images a day using a single NVIDIA K40 GPU).
Eras (for DL, enabling rapid calculations and prototyping)
Matplotlib (for 2D plots, charts, histograms, and more)
NLTK (for processing, computational linguistics, and natural language recognition)
Pandas (for high-level data structures and analysis)
PyBrain (for neural networks, reinforcement, and unsupervised learning)
PyTorch (developed by Facebook to support Computer Vision, NLP, and many other ML programs)
Scikit-image (for image processing)
Scikit-learn (for managing basic ML algorithms like classification, clustering, linear and logistic regressions, regression, and more)
Spark MLlib (developed by Apache, enables easy scaling of your computations quickly and provides seamless integration with other tools)
StatsModels (for data exploration and statistical algorithms)
TensorFlow (for working with DL by setting up, training, and utilizing artificial neural networks with enormous datasets)

Some libraries also make excellent visualization tools. This makes it easy to represent data in a human-readable format. As mentioned above, Matplotlib, for example, allows the creation of charts, histograms, and plots to enable simple comprehension. These libraries and visualization tools also make it much easier to create reports that are easily understood by a non-technical audience.

Enhanced Flexibility #

Python is highly adaptable. For example, you can use scripting or OOPs when coding. AI engineers can implement changes quickly without ever recompiling the source code. You can also combine it with other programming languages as needed (especially C and C++ code modules).

This creates a robust environment where developers choose coding styles they are comfortable with and can solve different problems in the best possible way.

For example, the commands made in the imperative style describe how a task is performed. As the program state changes, you can define the computations sequence that needs to happen with each change.

Or if ML engineers choose a functional or declarative style, the operations that need to be performed are ‘declared.? In this scenario, declared statements take the form of mathematical equations and don’t consider the program state.

One benefit that’s not mentioned often is Python’s ability to simplify the code and make it readable. This programming language focuses on cutting down the number of lines of code to execute a function. So when AI and ML projects are coded in Python, programmers won’t have to write a lot of code.

If you’re just starting your Python journey, you’ll soon realize that it’s a fantastic tool that is more than a general-purpose programming language. Its extensive libraries, community support, flexibility, and low barrier to entry makes it the go-to language for anyone thinking about a career in AI or data science.

Going forward, Python’s popularity also takes pressure off startups and recruiters. Whenever engineers and team members need to be replaced, it’ll be much easier to put warm bodies on seats.

It also easier for anyone in the company to quickly learn and engage in an AI or ML-based project. What’s more, the lack of a learning curve also makes Python a highly cost-effective solution.

Related reading

API in the Digital Transformation Context

Vin Patel — Mon, 24 Aug 2020 21:16:40 +0000

Key takeaways

APIs represent a strategic component of the Digital Transformation.
Sorting allows data to be ordered according to the time of modification.
Although APIs are born as a technical tool, they take on a crucial role in the Digital Transformation context as a strategic driver of business and innovation, allowing companies to rethink their corporate assets.

APIs represent a strategic component of the Digital Transformation. They seem like simple strings of code, but they go beyond the programming language. APIs simplify the dialogue between different applications, allow developers to avoid writing code from scratch, and open new digital services. This is precisely what is required for companies that want to accelerate Digital Transformation and understand the relevance of software in business innovation, especially in projects based on Artificial Intelligence, Big Data and IoT (Internet of Things).

Original diagram for Techfastly by Vin Patel.

To understand how and why APIs play a strategic role in supporting digital transformation, we can look at the more than 14 thousand public APIs used by various services and software. In addition to Facebook, the best-known APIs are those provided by Twitter, Google, eBay, or Amazon. For example, Google Maps is open to the developer’s community to build customized maps, integrate them into websites and offer geo-referenced search services, or even use them in mobile apes.

The API economy is the result of open-source projects and the increasingly comprehensive approach to open innovation. With API, companies can bring the change available to the market into their structure to enable and provide better services. Finally, the programming interface is becoming much more of an application(transition from the App Economy towards the API Economy) and will accelerate the migration to more flexible systems.

What is an API? #

API stands for Application Programming Interface, and it is defined as a system of tools and resources in an operating system enabling developers to create software applications. More generally, it can be considered as a set of functions that allow you to access the services of an application through a programming language. But these functions can be different: API can be a library that enables a developer to interact with a specific software or platform. Otherwise, it can act as a “call” from a program towards parts of another application. API enables communication between applications or software, and it’s a flexible and efficient tool for both data exchange and functionality sharing. They reduce development time and cost as well as mitigate the risk of errors.

Original diagram for Techfastly by Vin Patel.

An example comes from a Web API for either web server or web browser. When you navigate on a website in your browser, like “Twitter.com,” a request for data goes out to Twitter’s remote server. Once your browser receives the data and processes the code response, the browser displays Twitter. In this case, Twitter’s server is the API that enabled communication and allowed you to access the web page.

There are several types of APIs.

Private APIs are published internally for use by the company’s developers only to improve a company’s products and services. Public APIs are published publicly and can be used by any third party partner. Partners APIs are used within business relationships, often to integrate software between partners and companies.

API usually uses JSON (JavaScript Object Notation) as the primary language. JSON is an open-source JavaScript library of methods designed to extract and manipulate data stored in HTML documents, in this way, APIs can aggregate data and complete processes over totally disparate systems.

In this daily networking ecosystem, APIs can generate added value. It requires an API strategy based on operational efficiency and productivity, innovation and cooperation, revenue generation, and new business models.

What Features are Essentials to Make a Good API? #

A good API should allow us to search for data by specific criteria and detect changes in data by particular timestamps. Moreover, it should be able to practice paging and sorting. Paging means limiting the amount of data that can be received at one time and the frequency of requests for data. Sorting allows data to be ordered according to the time of modification. The use of REST API (Representational State Transfer) as a software architecture for a Web API is suggested because it supports many data formats such as TXT, CSV, XML, and JSON. Unlike granting access to the framework by an API key, it is a better choice to an Open Authorization framework (OAuth). It enables an end user’s account information to be used by third-party services, such as Facebook and Google, without exposing the user’s account credentials to the third party. Further strategic value is to provide reliable documentation because it is one of the elements that discounts project implementation time, and hence, costs for the project. Moreover, proper documentation adds to the popularity of an API.

What about APIs’ History? #

In the past, APIs were mostly relegated to software developers looking to fix interface and data sharing issues linked within compatible software solutions. The advent of APIs dates back to the dawn of the IT age, well before personal computers. At the time, an API was typically used as a library for an operating system. After about 30 years, APIs have emerged from their local environments, and in the early 2000s, they have become an important technology for remote data integration.

Original diagram for Techfastly by Vin Patel.

Deep Dive into Success Histories. #

In 2000, the eBay API was rolled out. The API purpose was to standardize how applications integrated with eBay, and make it easier for partners and developers to build a business around the eBay ecosystem.

In 2006, the social network Twitter platform introduced its TwitterAPI. Much like the release of the eBay API, Twitter’s API release was in response to the growing usage of Twitter by those scraping the site or creating rogue APIs. Twitter exposed the Twitter API via a REST interface using JSON and XML. In some years, Twitter’s API has become the center of many desktop clients, mobile applications, web apes, even by Twitter itself. In August 2006, Amazon EC2 or Elastic Compute Cloud, a new cloud computing service, was launched. After some months, the cloud storage service Amazon S3 was launched. Amazon EC2 provides computational capacity in the cloud by allowing developers to launch different sizes of virtual servers within Amazon data centers. Using the Amazon EC2 API, developers can launch small, large, and extra-large servers and pay for every hour that the server is running. Amazon EC2, combined with AmazonS3, has built a platform for the next generation of computing with APIs at the core.

Where can APIs be exploited? #

Artificial Intelligence and Big Data are the areas where APIs can be exploited. Examples come from chatbots as a link between these conversational agents and websites or customer care services. APIs are rethinking the cloud services market through Machine Learning and Deep Learning for facial recognition, document classification, voice and text recognition, sentiment analysis, and predictive modeling. IoT APIs are another example of an intersection between devices and internet networks or other applications connected with the network.

Original diagram for Techfastly by Vin Patel.

If properly managed, APIs provide a critical “innovation pool” of digital resources; they can become relevant tools to improve the user experience. Although APIs are born as a technical tool, they take on a crucial role in the Digital Transformation context as a strategic driver of business and innovation, allowing companies to rethink their corporate assets.

Happy Digital Transformation with APIs world.

Related reading

What is an API? #

API stands for Application Programming Interface, and it is defined as a system of tools and resources in an operating system enabling developers to create software applications.

What Features are Essentials to Make a Good API? #

A good API should allow us to search for data by specific criteria and detect changes in data by particular timestamps. Moreover, it should be able to practice paging and sorting.

What about APIs’ History? #

Where can APIs be exploited? #

Artificial Intelligence and Big Data are the areas where APIs can be exploited. Examples come from chatbots as a link between these conversational agents and websites or customer care services.

Data Engineers vs. Data Scientists

Vin Patel — Sat, 11 Jul 2020 17:30:00 +0000

Key takeaways

Original diagram for Techfastly by Vin Patel. Data Science is an emerging technology in this 21st century.
Data Scientists cannot directly use raw data with those errors.
After that, you can consider hiring a data scientist to find the hidden insights in your data.

The two positions are not interchangeable. #

Original diagram for Techfastly by Vin Patel.

What is the difference between Data Engineers and Data Scientists? #

Data Science is an emerging technology in this 21st century. Data Science involves all the processes which bring data into actionable insights. These processes include getting data, cleaning data, performing descriptive statistics,and building a model on data, implementing the insights to solve the given problem. Data Engineering is an aspect of Data Science that mainly focuses on how data is processed and analyzed practically.

There are several ways to define what a Data Scientist is. Let’s look at one of those definitions. A Data Scientist is a person who has a broad range of knowledge in multiple disciplines while specialized in one or two. He or She understands the domain knowledge of a particular field. For example, a Data Scientist needs to understand the business processes of a company, including marketing, strategy, and sales before doing any data science. A data scientist should have a good knowledge of Machine Learning and Statistics.

In contrast to a Data Scientist, a Data Engineer is a person who develops, constructs, tests, and maintains architectures, which are very useful for every step of the data science project lifecycle. For example, a Data Engineer practically develops, constructs, tests, and maintains databases and large-scale systems to store and manage data that are useful for Data Scientists.

Although Data Engineers and Data Scientists work together to get insights from large-scale data, their skills may vary, and the two positions are becoming more and more distinct. This article mainly focuses on those distinctions in detail between the two positions. Without further delay, welcome to ? Data Engineers vs. Data Scientists?.

What do Data Engineers and Data Scientists do? #

A Data Scientist performs actions on each step of the following Data Science project lifecycle.

Original diagram for Techfastly by Vin Patel.

At the start, a Data Scientist understands the business process. This understanding is often called getting domain knowledge. Then he or she defines the problem and sets up a goal and objectives to solve it.
Then, a Data Scientist acquires the data, performs some fundamental data analysis, and some visualizations to understand the nature of the data. If the data is not in the correct shape, he or she applies standard techniques such as data cleaning, dimensionality reduction, feature engineering to get data into shape.
After that, a Data Scientist decides which model gives the best output for the problem. In some situations, a traditional approach will not be able to solve the problem. Therefore, he or she needs to apply data-driven machine learning techniques for the modeling task.
Then, a Data Scientist will validate the model with assumptions and implement the model with the given problem.
Then, he or she sends the findings to the customer. Customer acceptance is required.
Finally, he or she writes a report about the entire process. This report should be clear and straightforward enough to understand the whole process and findings for a non-technical person.
A Data Scientist uses R or Python for data analysis.

Note that the data analysis process is not a one-time process. It often needs to go back to the previous steps if there is an error or change occurred.

A Data Engineer, contrary to a Data Scientist, performs the following tasks:

A Data Engineer deals with raw data that contains human or machine errors. Data Scientists cannot directly use raw data with those errors.
A Data Engineer discovers various opportunities to acquire relevant data. Data is everywhere, but a Data Engineer should use different techniques to collect data from various sources, for example, web data, sales records, etc.
A Data Engineer employes a variety of languages and tools (SQL, Hadoop) to process data and connects databases between different platforms. This process provides an opportunity for data scientists to do parallel computing, which speeds up data analysis tasks.
A Data Engineer recommends ways to improve data reliability, efficiency, and quality. Those data engineered by data engineers will give accurate results if the data scientists apply the correct techniques in their analysis.

What do Data Engineer and Data Scientists NOT do? #

Data Scientist and Data Engineers do NOT do the following things.

A Data Scientist does NOT do a data analysis task without defining the problem clearly:?A problem well defined is a problem half solved?. A Data Scientist often finds the domain knowledge to define the problem well and set up the goal.
A Data Scientist does NOT work alone even if he or she has all the necessary skills to complete a project: Data Scientists often prefer teamwork. Other team members are specialized in different fields. So, Data Scientists usually prefer to take advantage of those specializations.
A Data Engineer does NOT focus on the mathematical part of the model. Instead, he or she considers the efficiency, reliability of the model by improving the quality of the data.
A Data Scientist need NOT understand the inner working of a machine learning algorithm in terms of mathematics. Instead, a Data Scientist should know how to use which algorithm correctly for the given problem.
Data Scientists and Data Engineers do NOT do full work at once. Instead, they prefer to breakdown the big tasks into small pieces and make a Work Breakdown Structure (WBS).?You can not eat an apple at once, but you can eat the entire apple if you cut it into small pieces!? The same applies to the data science process.
Data Engineers and Data Scientists do NOT work separately. They should work together to deliver high-quality results for a real-life data science project.

How do I know when I need a Data Engineer or a Data Scientist? #

This question is a difficult one to give an exact answer since this depends on various factors. But I can provide some helpful facts to study for yourself and make the decision by yourself.

A Data Scientist is an expert in the following specified fields:

Advanced Statistics
Machine Learning
Python or R Programming
Big Data
Storytelling

In contrast to Data Scientist, a Data Engineer is an expert in the following specified fields and technologies:

Database Management
NoSQL
Hadoop
MongoDB
Web Scraping
Big Data

The question of ‘how do I know when I need a Data Engineer or a Data Scientist?’ should be answered from the perspective of the current company status. Suppose that you have a company that has clean, structured data in the company databases. If so, you can consider going straight to a data scientist with the above skills. The data scientist can do the rest of the work.

But what about if you have unstructured, messy data with missing values? What about if you need data from other external sources such as the web data? You should first hire a Data Engineer to get your data into shape and collect other necessary data. The Data Engineer will handle your unstructured data, which otherwise can’t be done in a traditional way like using SQL because the data is unstructured. The Data Engineer will use different technologies such as Hadoop, NoSQL, to handle unstructured data. The Data Engineer will also do web scrapping to get the necessary data from the web. After that, you can consider hiring a data scientist to find the hidden insights in your data.

Related reading

Questions and answers

What do Data Engineers and Data Scientists do? #

A Data Scientist performs actions on each step of the following Data Science project lifecycle. - At the start, a Data Scientist understands the business process.

The question of ‘***how do I know when I need a Data Engineer or a Data Scientist? #

’ should be answered from the perspective of the current company status. Suppose that you have a company that has clean, structured data in the company databases.

How to Calculate the ROI of AI Investment

Vin Patel — Fri, 26 Jun 2020 14:30:00 +0000

Key takeaways

As it requires a good deal of investment to include AI in your business, it is necessary to analyze and estimate the Return of Investment (ROI) before making the decision.
Some key elements that will help you with a good estimation of ROI: The first and most important thing is the purpose of implementing AI for your business.
If you have a budget, then implement AI in your business, even just for the sake of facilitation.

AI and data science are now leading the business world. As it requires a good deal of investment to include AI in your business, it is necessary to analyze and estimate the Return of Investment (ROI) before making the decision.

Cost of AI #

Implementing Artificial Intelligence requires skilled professional and smart tools. You will need to buy special tools and software to set up an AI-based business. These tools require smart investment and are technical. Graphic Processing Unit (GPU), which is the base of an AI-based system, alone costs hundreds of dollars to the business.

Also, AI tools are not easier to handle for traditional staff. So, you have to hire a new team or work on upskilling your current employees for an AI-based system, which is again a huge cost.

Here the question arises that is this investment in AI is profitable? Will it give the desired benefits? What will be its ROI? It is better to learn from experiences then to experiment.

Success and Failure of AI in Business World #

Large businesses and big brands are using Artificial Intelligence for years. Some projects failed to achieve their desired results, and some had to be abandoned. But there are a good number of businesses that get a good ROI by implementing AI in their businesses.

. Approx. 85% of AI business plans have failed in the past. Even big names, like IBM, Amazon, and Apple, were unsuccessful in getting their desired results.

. Watson for Oncology is the biggest failure of AI. It was a joint venture of IBM and The University of Texas to treat cancer with the help of AI. This project turned out to be a failure, and the investors had to shut it down after whooping 62 million dollars of investment.

. The exploitation of AI Cabot by Microsoft and the malfunction of Face Recognition feature of iPhone are other examples of AI failure.

Successful AI Implementation in Business #

But, before you drop your plan of investing in AI, have a look at these success stories as well. Even smaller businesses, like pizza outlets and bakeries, have increased their efficiency by using AI for their business.

. Amazon Go is one of the best examples of AI success. It smartly predicts the customers’ move and shows them what they are probably looking for. It increased the customer flow at Amazon and decreased the turnover rate.

. Barclay`s is successfully using AI to improve its security. Its AI system can detect and inform about any illegal activity.

. Domino`s Pizza has remarkably increased the accuracy of its estimated delivery time from 75% to 95%, which helps it in better customer service.

AI for Business Yay or Nay? #

The failure rate of AI projects is alarming, but its benefits are worth the risk. You can launch a successful AI plan for your business by carefully weighing the pros and cons and accurately estimating the Return of Investment.

What is the ROI? #

ROI or the Return of Investment is the profit and cost ratio of an investment. ROI estimates whether the investment will be fruitful for the business, or it will only be a financial burden for the company.

Calculating ROI #

Return of investment is the major concern of all businesses. In most cases, the ROI of Artificial intelligence is unpredictable and is challenging to calculate.

Key Elements of Calculating ROI #

Some key elements that will help you with a good estimation of ROI:

Analyze Your Goals #

The first and most important thing is the purpose of implementing AI for your business. AI has its limitations. If your goal of the task is within the realm of AI, go for it. But if it’s an abstract idea, be prepared for the risk of failure.

Calculate the Costs #

The investment of purchasing an AI-based system is not the only cost of your project. You will also need smart tools to boost this system further, and most importantly, need technical staff to manage the AI operations.

For the correct estimation of ROI, it is essential to calculate all the costs, including training of the staff and the cost of deployment of the project.

Retaining Talent #

A company invests a good chunk of money, time, and efforts to recruit and maintain its team. With the implementation of AI, a good number of your current team might not be functional. Wasting the efforts of creating the old team and investing in a new technical team, is another major cost for the business. It’s not easy to let go of your loyal employees, and you want to retain the talent. This will need intensive training and, again, a good investment.

Evaluate the Benefits #

After calculating the cost, your next step is to evaluate the benefits. List down the likely outcomes of implementing AI in your business, and critically examine its impact on your business. Just the tag that you are an AI-based business is not enough justification for investing in this system. There should be a profitable outcome for this system.

Weighing Cost against Benefits #

Once you have a clear picture of estimated costs and likely benefits of the project, you can calculate ROI.

If the cost of implementing AI is more than the estimated returns of the investment, then the project will be just a financial burden on your business.

But if the returns of investment are higher than the investment, then the project will likely give good ROI.

ROI percentage #

ROI percentage indicates the profitability percentile of the project.

The difference between your earnings and cost is the net revenue or net return of investment. You can calculate the percentage of ROI, by dividing the net return of investment with the cost of the investment and then multiplying it by 100.

A higher percentage of ROI indicates a higher probability of the success of the project.

Is ROI Enough to Evaluate the Worth of AI? #

ROI gives a clear picture of the monetary outcome of the business, but not all benefits of investment are financial. Boosting the soft image of the brand and facilitating customers is as important as generating revenues.

If the ROI of your AI investment is not higher, but it will facilitate your employees and clients, then it’s not a failure. If you have a budget, then implement AI in your business, even just for the sake of facilitation. It will definitely benefit your business in the long run.

Related reading

Questions and answers

AI for Business Yay or Nay? #

What is the ROI? #

Is ROI Enough to Evaluate the Worth of AI? #

Git and GitHub in the Open-Source Development World

Vin Patel — Tue, 23 Jun 2020 17:30:00 +0000

Key takeaways

Agile was formally launched in 2001 in the Agile Manifesto with the goal of better developing software by collaboration, improving documentation, interaction over processes and tools.
More specifically, it’s a web hosting service of the Git repository and allows you to collaborate much more easily with other people on a project.
Overall, these systems give quite the same services, but the GitHub developers community is more popular.

Agile was formally launched in 2001 in the Agile Manifesto with the goal of better developing software by collaboration, improving documentation, interaction over processes and tools.

Original diagram for Techfastly by Vin Patel.

Today agile development is embedded in almost every software development organization of an innovative company, as well as in a data science pipeline of a data-driven Company. Agile is an iterative approach to software development based on steps that afford easy changes on the project, continuous interactivity with stakeholders, reducing production costs, etc.

Git and GitHub are embedded in this process, focusing the attention on how to collaborate on code launching a new era of open source development.

What is Git? #

Original diagram for Techfastly by Vin Patel.

Launched in 2005 by Linus Torvalds, Git is a distributed version control system, it works for many services, such as GitHub and GitLab, but you can use Git privately or publicly and without other services.

A version control system is a software package that helps you to keep track of a file or a set of files (word document, excel spreadsheet, piece of HTML, etc.) over a time period in such a way that it allows you to tag all the changes that are happening on the file or a set of files. It allows you to look back to the original file or every previous level tagged whenever needed.

When a version control system is installed and launched, it builds a local directory at the same place where your files are stored, which it exploits to manage the whole chronology of the switches made to your files.

There are three types of version control systems at disposal.

The first one, the local version system, works through a locally installed database that records all file changes. It can then reproduces the file’s contents to the user’s workplace exactly at any given point in time by adding up all the relevant patches. However, if the computer holding the data is experiencing issues, it won’t be possible to recover them.

With the second one, the centralized version control system, users are able to work on the same project, because there is a commonplace (server) that contains all the files stored with any changes that everybody enters from their local machines (clients). However, if the server were to run into issues, no one can collaborate and save their changes.

The third one, the distributed version control system, is a combination of the previous two options described above. It combines the advantages of the local version control system and the advantages of the centralized version control system.

All users working on the same project have a complete copy of the repository (the whole history of the various files). In this way, each team member seems to have a backup without having the need to connect to the server to view the history of a project.

This third approach is the system used by Git, and it is, for this reason, it is safer and faster than the other systems. Safe because the complete copy of the repository is saved on the server and is owned by all users working on the project. Quick because it runs locally, so you can connect to the server only to perform a data update.

Why should you use Git? #

First of all, it’s free and open-source, Git is released under the GNU General Public License version 2.0, which is an open-source license.

You have the opportunity to check out a complete history of all the changes that happened in your project, so if you make a mistake, you can bounce back to recover an earlier version of your job. Not only that, but you are also confident of your changes because, with commit messages in Git, it’s easy to document for future reference why you’re making a change.

It’s useful in a collaborative job, as well as for programmers in sharing their code because it gives you the opportunity to have multiple streams of your history. It means you can create many branches of history to try several changes to your topic or to build out several features independently. Then, you can merge those back into the main project history (the master branch) once they are ended, or delete them if you don’t use.

Moreover, Git has the ability to solve conflicts in a programming job. It means when many people are working on the same file at the same time, and Git is able to merge changes automatically. If this does not happen, it means there are some conflicts, and Git will show the reasons.

Thanks to these and other features, Git has become extremely popular and it’s used by all the biggest Silicon Valley companies (Google, Facebook, Apple, etc.).

What is GitHub? #

Original diagram for Techfastly by Vin Patel.

GitHub, launched in 2008 by Tom Preston-Werner, Chris Wanstrath, and P. J. Hyett, has reinvented the collaboration on code and web. It’s a website where you can upload a copy of your Git repository. More specifically, it’s a web hosting service of the Git repository and allows you to collaborate much more easily with other people on a project. GitHub provides a centralized location to share the repository, a web-based interface to view it, and features which allow you to consult and review changes with your team more effectively.

GitHub’s interface is quite friendly to use, so even newbie programmers can take advantage of It’s features. Without GitHub, Git generally requires more technical experience and also the use of the command line. GitHub is so easy to use, that does not require any programming knowledge, you can use it for any types of files.

Why should you use GitHub? #

First of all, GitHub is much more than just a place to store your Git repositories. It provides some benefits as you can either report bugs or define new features to develop by your team.

The opportunity to collaborate on several branches or features allows you to review all the different features being worked on and latest changes, moreover looking through the commit history allows you to see what the team has been working on.

GitHub can be considered between a social network and a file repository. It offers solutions both for private projects and for open source projects.

GitHub gives you the opportunity to develop and implement a management strategy because you can manage the collaboration with colleagues in different locations in the drafting of documents or reports and in the same way it’s a social platform where professionals, from all sectors, meet to collaborate remotely on projects of all kinds.

Given GitHub is a repository, it allows your work to get out in front of the worldwide, providing exposure for your project and for yourself. Moreover you have the opportunity to have your code reviewed by the community.

Like any other social platform, GitHub allows you to meet new people and build relationships with them. Once an account is opened, it will be possible to upload projects and works to be shared with other users; look through the various repositories in search of an interesting project with which to collaborate; get in touch with other people and share projects and ideas with them.

GitHub is also a source to find talent to bring on board in a company because you can find programmers working on similar projects or who have skills that offer a good fit for your organization.

Many companies and organizations are moving to open-source solutions because these technologies allow for greater flexibility in a continuously changing environment and are able to satisfy more rapidly to market demands. GitHub is a community where programmers are working to solve current issues and making solutions available to the public.

It’s not surprising, in short, if GitHub ended up attracting the attention of the big names in the hi-teach space, and this attention blurred in the acquisition of it by Microsoft in 2018, paying $ 7.5 billion.

Original diagram for Techfastly by Vin Patel.

GitHub is a large repository of codes known among companies that are hosting their own projects, documentation, and codes, such as Apple, Amazon, and Google. Microsoft has increased the investment in open source technology since Satyr Nadella took over as CEO. Moreover, Microsoft is the website’s main contributor, and GitHub even hosts the original Windows File Manager source code and is using the open-source Git version control system for Windows development.

This acquisition probably marks a point from which will probably begin a closer integration between Microsoft’s development tools and the GitHub service.

What about GitHub with competitors? #

Now have a look at some GitHub features and competitors.

GitHub is able to offer several accounts:

-basic free account for team and developers with unlimited number of repositories/unlimited collaborators and 500MB storage per repository.

-then other payment accounts for team and enterprise.

GitLab is the main competitor of GitHub, offering similar services and, both GitLab and GitHub are a web-based Git repository.

GitLab offers free and payments accounts as GitHub, and it is the cheaper option; moreover, GitLab offers 10 GB storage per repository.

Bitbucket is a web-based version control repository hosting service. It is suitable for software projects that use Mercurial or Git. Bitbucket is a part of the Atlassian software suite, so it can be integrated with other Atlassian services such as Lira. Also, Bitbucket offers several plans and is cheaper compared with GitHub and GitLab, and offers 1 GB storage per repository in the free account.

Helix TeamHub is another web-based version control repository hosting service and is suitable for projects that use Mercurial, Git, or SVN (Subversion). It offers free plans for limited users with 10 GB storage per repository or payment plans.

Overall, these systems give quite the same services, but the GitHub developers community is more popular.

Happy sharing documents & coding on GitHub!

Related reading

Questions and answers

What is Git? #

Why should you use Git? #

First of all, it’s free and open-source, Git is released under the GNU General Public License version 2.0, which is an open-source license.

What is GitHub? #

GitHub, launched in 2008 by Tom Preston-Werner, Chris Wanstrath, and P. J. Hyett, has reinvented the collaboration on code and web.

Why should you use GitHub? #

First of all, GitHub is much more than just a place to store your Git repositories. It provides some benefits as you can either report bugs or define new features to develop by your team.

Ditch Your Rule-Based Chatbot Strategy for Conversational AI

Vin Patel — Tue, 26 May 2020 08:23:00 +0000

Key takeaways

Marketing Executives around the globe have a view to automating customer service using chatbots.
Think like you are running an image recognization like algorithm on natural conversations on chat to map the primary intent of the customer.
The challenges with rule-based bots get addressed by engaging the customers with an intent oriented conversation and engaging humans to perform validated tasks.

Marketing Executives around the globe have a view to automating customer service using chatbots. 91% of CEOs think about automating customer service, but only 52% can implement, and chatbot effectiveness is still questionable. Chatbots as a trend picked up in 2017 and 2018. 2017 had 84 tech startups whose primary focus area was chatbots. In 2018 the number decreased to 42 and in 2019 to 12. Gartner had predicted that 80% of enterprises would use chatbots in their applications by 2020. The fact in 2020 is that chatbots, in general, have failed to impress executives because of low Return of Investment (ROI) and the end-users on lack of providing relevant service. Most of the available chatbot engines have some rule-based engine combined with Artificial Intelligence (AI) working on the customer data of the enterprise. With the current trend, the best practice for Rules-based chatbot implementation is:

The CEO/CTO has to cultivate a data engineering culture first, meaning the enterprise should be ready to mine insights from the data assets they have collected over several years. The process involves collecting, consolidating, cleaning, and making the data available for analytic. The data that is capable of generating insights is the data that you need to input in the chatbots.
Start with a small proof of concept or a trial with long term strategic vision or business objective in mind. Making the objective clear makes you win half the battle. Example: " We need to increase our cross-sells by x% by reaching our customers on mobile apes by a chatbot in 3 months."
Embed the chatbot on the right delivery channel in your organization. The right digital channel is where your customers are engaged the most. Depending on your business, it can we web, mobile, or both.
Integration of proper authorization and data security. For example, you will like to authorize your existing customer by capturing his account number or his unique identifier and also encrypt his personally identifiable information so that it is not visible to all.

Original diagram for Techfastly by Vin Patel.

Most of the chatbot companies that currently provide a solution have a rule-based AI Mechanism to handle the responses, and they become efficient when customer data is leveraged. This process is quite time consuming and the reason why chatbots become a controversial topic in executive meetings. With the progress of deep machine learning technologies in the past two years, automated conversational chatbots are leading over rule-based chatbots. These chatbots, instead of being rule based on consumer data, they use the conversational data of entire social media to find the intent of the consumer. Matching with customer data is not required, as in the case with the rule-based chatbot. Think like you are running an image recognization like algorithm on natural conversations on chat to map the primary intent of the customer. The image recognition algorithms match an image rapidly with billions of mapped images to find the type of image. Similarly, a conversational chatbot map outlines a natural conversation to make it contextual based on conversations sets in social media.

Facebook is a pioneer of discussions on social media, and they have recently open-sourced their deep conversational machine learning intelligence. Facebook has pre-trained 9.4 billion neural networks on massive conversational data. It uses 1.5 billion existing open domain natural conversations to train conversations. The conversation seems like human interactions in a very contextual fashion. The algorithm( ) can handle a terabyte of conversation data sets quite quickly. The approach to bring your organization’s data to work will be to do speech to text conversation of your entire call center historical data set and include that as a training set on top of already available intelligent pre-trained neural network. The algorithm uses skills like knowledge, empathy & personality to engage a consumer in a natural conversation based on billions of trained conversations. It then blends all three skillets to keep the consumer more engaged in the conversation. This blending is known as blended skill talk. An example of a blended skill talk is as below:

Original diagram for Techfastly by Vin Patel.

The conversational chatbots can handle conversations like negotiations, chitchats, question and answer, empathetic dialogs, booking reservations, quizzes, and so on. This powerful conversational AI can be easily integrated into Facebook messenger to acquire customers, perform transactions, brand awareness, and excellent customer service.

This technology is easy to implement as compared to the enormous time, effort, and money required for rule-based custom chatbots. The key is to use powerful, open-sourced AI with an already available and integrable chat platform like Facebook messenger. The difference between rule-based chatbots and conversational chatbots is a faster time to market with better customer experience and engaging conversations. Also, Python is the language for this open-sourced framework, which is the preferred language for machine learning, and the skillet is easy to cultivate in your enterprise, large or small.

The library also provides seamless integration with Amazon Mechanical Turk so that human workers can do data collection, training, and evaluation. The human Turks and chatbots can all communicate in the same conversation. The group chat can have humans and bots talking to each other in a single chat thread. The challenges with rule-based bots get addressed by engaging the customers with an intent oriented conversation and engaging humans to perform validated tasks.

Original diagram for Techfastly by Vin Patel.

If conversational chatbots when used effectively, can

Provide detailed and expert level knowledgeable answers to complex questions
Enable 24x7 always-on customer service
Get your customer added to an email mailing list or subscribe to a newsletter
Make your customers agree on making a purchase or doing a cross-sell
Make and manage reservations
Resolve customer complaints with empathy

and many more use cases based on your industry.

Tweet to @parlay_parley

Related reading

Machine Learning 101 For Non-Programmers- I

Vin Patel — Mon, 11 May 2020 15:46:04 +0000

Key takeaways

This post is part-1 of a series of multiple posts that will help us get somewhat familiar with Machine Learning. In our work lives, many of our projects touch Machine Learning directly or indirectly.
The Training Classifier ingests this Training Data and detects patterns in the data.
Only when we feed in thousands of records, we will see our algorithm properly train itself and be able to provide reliable outcomes.

Writing an Algorithm with just six lines of Code- Inspired by a Google Developers’ YouTube Video From 2016 #

This post is part-1 of a series of multiple posts that will help us get somewhat familiar with Machine Learning.

In our work lives, many of our projects touch Machine Learning directly or indirectly. Apart from a few folks on the team, seldom do others get a chance to work on an algorithm. The very word, algorithm, brings up images of pages and pages of complicated code. The reality, however, is much different.

The kind of a Machine Learning model we will code today will be a Supervised Learning Machine Algorithm. What this means is that we will feed some sample data (referred to as ’training data’) to our 6 line algorithm and get the desired prediction.

As an example, if we were writing an algorithm that was going to predict if an email is a spam or not based on some rules we provide, the entire process would look like the image below.

Original diagram for Techfastly by Vin Patel.

The different emails coming in would be the input. The classifier or our algorithm would then classify emails as spam or not spam. The resulting segregation would be the output of the program.

In this transcription of a Google Video, I will walk you through all the steps I took to set up the right development environment and then understanding the six lines of the actual algorithm. The use case I have taken here is that of an algorithm that will determine if a new car insurance applicant is likely to be a Great/Average/Bad driver. From a business standpoint, of course, based on the prediction of how good a driver is, a different price can be provided to different applicants.

However, at the end of the exercise, you can save the algorithm and reuse it for any other use cases.

The steps I am describing below have been broken further into major categories:

Step 1? Setting up the Development/Coding Environment

Step 2? Writing the Algorithm

Step 3? Running the Algorithm

Let’s get started.

Step 1? Setting up the Development Environment #

We need to install two things:

A. A programming language to write the Machine Learning Code in and

B. A code editor that helps us detect errors easily as we write this code

You can achieve both objectives by visiting. Anaconda is an open-source data science platform that provides access to Python and R, two programming languages, and their editors in a convenient package.

Steps to download Anaconda and getting ready to write code in the Code Editor:

Go to the individual version of Anaconda page -
Choose the right version of Anaconda to download.

Original diagram for Techfastly by Vin Patel.

Suppose you are on a Windows machine, you can check whether you are on a 32 bit or 64-bit machine by typing in ‘system information’ in the Windows search bar at the bottom left.

Original diagram for Techfastly by Vin Patel.

Double click on the Anaconda software once you have downloaded it. Choose all the ‘recommended options’ as you install Anaconda.

Original diagram for Techfastly by Vin Patel.

Access the Anaconda Navigator by using the windows search bar.

Original diagram for Techfastly by Vin Patel.

Click to launch Spider- the code editor that you will be using to write the code

Original diagram for Techfastly by Vin Patel.

At this point, this is what your screen will look like

Original diagram for Techfastly by Vin Patel.

The Code Editor

As you write code in the left-hand panel, Spider will let you know if you have entered any wrong syntax. You are now ready to write code for your algorithm.

Step 2

Writing the Algorithm #

There are three key steps at a macro level to write the algorithm that we need to undertake:

Original diagram for Techfastly by Vin Patel.

The Training data collection exercise is where the algorithm gets the data from which it can learn. The Training Classifier ingests this Training Data and detects patterns in the data. At this point, the algorithm is ready to make predictions on any input data that it may get.

As a general rule, an algorithm usually gets 80% of all data. We use the remaining 20% of the data to test the algorithm. But more on this will be discussed much later in this series of posts.

Line 1

At this point, we need to import scikit. Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms.

In-Line 1, all you have to write is ‘import sklearn’. Don’t worry about the ‘alert’ that the code editor displays in orange. Any time you wish to run your program, click on the green arrow circled below.

Training Data #

Original diagram for Techfastly by Vin Patel.

To train the algorithm, we need to provide it with some relevant data. Here is an example of what this input data may consist of in our insurance example:

Original diagram for Techfastly by Vin Patel.

For this small example, we will stick with the four input variables of Zip Code, Age, Gender, and Car Type. On a live project, of course, there may be many other variables that can be relevant and required.

In terms of the terminology used in the field of Machine Learning, the four input variables of Zip Code, Age, Gender, and Car Type are called ‘Features’. The desired output of a Good/Average/Bad driver is called a ‘Label.

Let’s take a very small sample of 3 records:

Instead of filling in actual Zip codes, Age, Gender, and Car Type with actual values, using numeric values make it easy to write the algorithm. If that is not the case, we would have to deal with integers versus strings, etc. So let’s avoid that by using numeric codes for each feature field.

Original diagram for Techfastly by Vin Patel.

In our simple example, we will input the numeric codes above as a representative value for each feature.

Step 1? As we are getting ready to enter now important data, it is a good time to save this file with a real name. Use the file’s ‘Save As’ command and save it as ‘car-insurance’.

Original diagram for Techfastly by Vin Patel.

Please make sure that you change the file type to be as ‘Python’ when you are saving the file.

Line 2

Add in the features as line 2 into the algorithm.

features = [[1, 2, 1, 2],[1, 5, 1, 4],[3, 3, 2, 6]]

Your screen should now look like the following screenshot:

Original diagram for Techfastly by Vin Patel.

Line 3

Add in-line three, which is the ’labels’ for the algorithm to learn.

labels = [0,1,2]

Based on the code above, your excel file/Google Sheet of data should look like as follows:

Original diagram for Techfastly by Vin Patel.

The program now has the input data that it needs. We now need to train the algorithm.

Line 4

At this point, we need to import the Decision Tree module from scikit for data to be analyzed using Decision Trees.

A single line of code can easily achieve this-

clf = tree. DecisionTreeClassifier()

The word ‘clf’ refers to the ‘classifier’. Your screen should now look like this:

Original diagram for Techfastly by Vin Patel.

The red X on line 4 suggests an error. If you hover your mouse over the red X you see the code editor being helpful and providing you the reason for the error:

Original diagram for Techfastly by Vin Patel.

The error relates to ’tree’ not being a defined element. Line 1 needs to be revised from the sklearn to ‘import tree’ to resolve the error. As you can see from the screen below, the error has now gone away.

Original diagram for Techfastly by Vin Patel.

Line 5

At this point, we are ready to trigger the Training Classifier to ingest the rules and detect the patterns from the sample data. To do so, we need to add the following line of code-

clf = clf.fit(features, labels)

The ‘fit’ here refers to the command which instructs the algorithm to find patterns in the data.

Original diagram for Techfastly by Vin Patel.

The Prediction #

Your algorithm is now ready to accept input data for a sample case and provide a prediction of the kind of driver a prospect is likely to be, based on what it has learned.

Let’s assume that a new prospect has the following variables for their Zip Code, Age, Gender, Car Type - 3, 2, 3, 4

To output the prediction for the values above, please input the following line of code-

print(clf.predict([[3, 2, 3, 4]]))

At this point, your screen should look like this:

Original diagram for Techfastly by Vin Patel.

To run the program, click on the green arrow and see the output on the right side of the pane to view the prediction.

Original diagram for Techfastly by Vin Patel.

Congratulations! Your algorithm predicts that based on the values inputted, this new prospect driver will be a bad driver.

Based on this information, we can show this prospect as a higher car insurance quote.

This algorithm can now be saved and reused for other use-cases with modification. In the next post, we will examine how you can analyze results for thousands and tens of thousands of records. Only when we feed in thousands of records, we will see our algorithm properly train itself and be able to provide reliable outcomes.

Related reading