Raw data holds a lot of power, but it must be sorted, filtered, and utilized in a strategic manner to gain meaningful and actionable insights. This is where the 4 types of data analytics emerge.
They are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Also Read: How Much Insights You Can Uncover from Unstructured Data
Discover the 4 types of data analytics to learn how to best transform and utilize raw data.
It is important for all businesses to harness the full potential of the data they gather. Understanding the 4 types of data analytics is a good start.
Type #1: Descriptive Analytics
It is the simplest, most basic form of data analytics. It feeds on historical data to summarize and provide information on past events. Analyzing past performances is necessary for businesses to locate improvement areas.
For example, a firm will use descriptive analytics to assess their website traffic or sales revenue. If it is the holiday season, then a clothing store might witness greater demand and steadily increasing sales.
Thus, it simply gives insights on what has happened.
Type #2: Diagnostic Analytics
Diagnostic analytics takes the insights and results of descriptive analytics one step further. How does it do so? By trying to deduce the reason, or the “why” behind an event.
From the previous example, if clothing sales drop during the festive season, then diagnostic analytics explores the reasoning behind it. It could be a change in customer demand or inadequate marketing efforts.
Diagnostic analytics can help firms understand the root causes behind the events descriptive analytics has identified.
Type #3: Predictive Analytics
As the name suggests, it is used to make future predictions by using past data, statistical modelling and algorithm, and machine learning (ML).
Naturally, predicting the future is neither an easy task nor is it always possible. But pattern recognition from past data with some smart mathematics can make it so.
For example, losing out on customers is no business’s dream. Predictive analytics is notably used to forecast customer churn rate.
Type #4: Prescriptive Analytics
Similarly to how diagnostic analytics develops on descriptive analytics insights, prescriptive analytics builds on predictive analytics.
It goes a step further to provide recommendations and action plans for the predictions made. Thus, it gives scenario-based best plan of actions and optimal actions.
Closing Thoughts
In a nutshell, these 4 types of data analytics build the framework for using data for better business decision making.
