Data Analytic & Decision Making - A Strategic Move
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.
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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 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.
Related reading
- How to collect data using Google Analytics
- The Evolving Role of Data Analytics in the Healthcare Industry
- Data Visualization For Business - All You Need To Know
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.