Relationship: Analytics & Data-Driven Decision Making

analytics and data-driven decision making relationship

Data analytics is a topic that many data-driven organizations are becoming increasingly interested in. Data analytics often includes the process of analyzing data to find insights that can be used to make decisions. But what does this mean? How are different types of analytics related to data-driven decision-making? This blog post will explore how an organization’s use of data can help them make better, more informed decisions. Before getting into the details, lets quickly understand how business analytics is related data analytics.

There are a number of facets that business analytics and data analytics have in common. In both the cases, the common steps include dealing with gathering data from different sources, identifying trends in the data to produce insights, and acting on those insights to create decisions. Data analytics is based on collecting, analyzing, and presenting statistical data with the goal of producing findings and demonstrating how well processes are working or explaining why one outcome was better than another. On the other hand, business analytics relies on its advanced-analysis toolset to derive actionable insights from vast amount of information from disparate systems and display the same on graphically-rich dashboards.

What are the different types of analytics?

The following is a list of different types of analytics:

  • Descriptive analytics: Descriptive analytics focuses on describing what happened. This means taking data and summarizing it in a way that is easy to understand. Data visualization is an important part of descriptive analytics. For example, a retailer might use descriptive analytics to determine how many people shopped in their store last month. Another example is that of a company using descriptive analytics to determine how many times their website has been visited.
  • Diagnostic analytics: Diagnostic analysis focuses on the why. It helps to determine how to solve a problem or improve an existing process. For example, a company might want to determine why customers are canceling orders so that they can address the problem. A diagnostic analytics solution would help them do this by using data like order size, customer information, and product quality variables in an effort to find correlations with canceled orders.
  • Predictive analytics: Utilized to predict or estimate future trends or events, often with the goal of applying that knowledge for a specific outcome. It requires training predictive models based on historical data based on different machine learning algorithms. For example, a retailer might use predictive analytics to determine how many people will shop in their store next month.
  • Prescriptive analytics: Focuses on optimizing business processes through the use of optimization techniques, simulation models, and other advanced analytics. For example, a retailer might use prescriptive analytics to determine what prices they should set for their products in order to maximize revenue.

How is data-driven decision-making related to different forms of analytics?

A data-driven decision makes use of information from different forms of analytics to identify, understand and prioritize the actions needed. Analytics allows for a more informed understanding of current processes as well as how those processes can be improved upon or even replaced with newer, better ones. The relationship between these two factors becomes apparent when you consider the difference in data-driven decision-making vs. traditional decisions that are made with little to no data or understanding of processes and procedures involved.

There are different forms of analytics available, each one offering its own unique method for gathering data and extracting insights/information from the raw data. Each form of analytics is different and some focus on the bigger picture while others focus on the minutia.

  • Descriptive analytics: Descriptive analytics helps drive decisions by providing insights into what is going on. It allows the business to see how things are trending over time, from quarter to quarter or year over year. Descriptive analytics can also provide insight into why processes work well in some areas but not others by showing which factors contribute most significantly to positive outcomes. Descriptive analytics is suited for programmed decision-making.
  • Diagnostic analytics: Diagnostic analytics helps drive decisions by providing insights into why things are happening. It can help to identify the root cause of problems, allowing decisions makers to prioritize which issues they should address first. This form of analytics is suited for programmed decision-making but also allows room for unanticipated changes and outcomes that might require a change in direction or strategy down the road.
  • Predictive analytics: Predictive analytics helps drive decisions by identifying what will happen in the future. It allows businesses to identify, understand and prioritize actions needed now that will lead to profitable results down the road. It utilizes data, statistics, and models to forecast future behaviors/outcomes based on current conditions. This information can be used by businesses when making decisions that will affect their bottom line in order to determine the best course of action for increasing revenue or decreasing costs. Insights from predictive analytics can be suited to make unprogrammed decision-making.
  • Prescriptive analytics: Prescriptive analytics helps drive decisions by providing guidance on what to do next. It allows the business to understand which options will lead to the best results and how those actions can be prioritized, leading up to a decision that needs to be made now. Insights from prescriptive analytics can be suited to make unprogrammed decision-making.

Data-driven decision-making starts with the insight of data analytics, which can be grouped into four categories: descriptive, diagnostic, predictive, and prescriptive. Each category helps drive decisions by providing insights on what is going on or why things are happening. Once you have an understanding of all your data points through each type of analytics, it’s time to decide what to do next. Think about how much better informed you feel when deciding between two options that both seem like good ideas because you know exactly where they will lead based on past performance or future predictions? This feeling should help guide any unprogrammed decisions made after gathering enough information from different types of analytic tools.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.
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