4 Types of Data Analytics to Improve Decision-Making

We live in a world where the amount of data collected is rising every second. When such a high volume of data is being generated, it’s only natural to have tools that will help us handle all of this information. Raw data is often a pile of unstructured information.

Data analysts use their expertise to derive statistically significant information from the data. This is where different types of data analytics come into play. Data-driven insights play an integral role in helping businesses form new initiatives.

Based on the phase of workflow and the kind of analysis required, there are 4 major types of data analytics. These include: Descriptive analytics, Diagnostic analytics, Predictive analytics, and Prescriptive analytics

Let me take you through the main types of analytics and the scenarios under which they are normally employed.

1. Descriptive Analytics

As the name implies, descriptive analysis or statistics can summarize raw data and convert it into a form that can be easily understood by humans. They can describe in detail an event that has occurred in the past. This type of analytics is helpful in deriving any pattern if any from past events or drawing interpretations from them so that better strategies for the future can be framed

This is the most frequently used type of analytics across organizations. It’s crucial in revealing the key metrics and measures within any business.

2. Diagnostic Analytics

The obvious successor to descriptive analytics is diagnostic analytics. Diagnostic analytical tools aid an analyst to dig deeper into an issue at hand so that they can arrive at the source of a problem.

In a structured business environment, tools for both descriptive and diagnostic analytics go hand-in-hand!

3. Predictive Analytics

Any business that is pursuing success should have foresight. Predictive analytics helps businesses to forecast trends based on current events. Whether it’s predicting the probability of an event happening in the future or estimating the accurate time it will happen can all be determined with the help of predictive analytical models.

Usually, many different but co-dependent variables are analyzed to predict a trend in this type of analysis. For example, in the healthcare domain, prospective health risks can be predicted based on an individual’s habits/diet/genetic composition. Therefore, these models are most important across various fields.

4. Prescriptive Analytics

This type of analytics explains the step-by-step process in a situation. For instance, a prescriptive analysis is what comes into play when your Uber driver gets the easier route from Gmaps. The best route was chosen by considering the distance of every available route from your pick-up route to the destination and the traffic constraints on each road.

A data analyst would need to apply one or more of the above analytics processes as a part of his job.

After reading the above post, are you left wondering how to become a data analyst, then this blog post is for you.

If you are keen on pursuing a career in the field of data analytics, you can apply for these courses offered by Indepth Research Institute. On completion of these courses, you would be eligible for some of the most challenging roles in the domain.

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