4 Types of Business Analytics That Your Business Needs

business analytics types

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Businesses ranging across various industries now have the option to access different types of data, something that seemed a distant thought a few decades ago.

It doesn’t matter what department you consider, our professional lives revolve around heaps of data that we knowingly/unknowingly provide to outside parties.

In such cases, the most crucial question is how this unstructured data could be used for business purposes and influence decision making. The answer is by leveraging big data analytics.

By dealing with the data through the entire business analytics cycle, the data applications fall into 4 types of business analytics that we will talk about in this article.

Overview: Types of Business Analytics

For the different types of analytics involved, a significant amount of data gets utilized at every step. Depending on the stage and the need for the data analysis, there are 4 main types of analytics:

  1. Descriptive Analytics
  2. Diagnostic Analytics
  3. Predictive Analytics
  4. Prescriptive Analytics

All these types of analytics involve all the data analytics activities a company needs. 

All 4 types of business analytics exist in a specific order where every single type is dependent on the other, and no kind is said to be better than the other. 

As data is an essential asset for a company, almost every company relies on either one or all these analytics types. So, let’s talk about these.

1. Descriptive Analytics

As data flows into a medium, it’s usually large and needs crunching into understandable chunks. That is when Descriptive Analytics is involved to summarize the findings of the data and understand it thoroughly.

If you have ever heard a company saying they are using advanced analytics, understand that descriptive statistics are used on the existing data. This is a crucial step to make the raw data understandable. Further, this very data is presented to investors and managers when needed. 

The two most common and important techniques that are involved are data aggregation and data mining which means that this method is the best for understanding the behavior of the data. 

When the historical data is mined, companies can also analyze consumer behaviors, which are further beneficial for targeted marketing and service improvement of an enterprise overall.

2. Diagnostic Analytics

If an enterprise has ever to determine the past behavior of a particular data set in the past, diagnostic analytics comes to the rescue. 

Some of the standard features involved in diagnostic analytics include data mining, data discovery, and drill-down. This type of analysis helps an enterprise understand the root cause of certain events. Along with that, it even helps in determining what the factors and the events that led to the outcome are.

For an enterprise using diagnostic analytics, they can determine the cause behind their increase/decrease in the sales and other departments. 

You need to realize that this type of analytics doesn’t give a lot of insights. 

3. Predictive Analytics

Judging by the name, predictive analytics gives us the ability to predict future outcomes. 

However, what’s important to realize is that predictive analytics doesn’t predict future events. All it can do is to forecast future events based on the existing data it extracts from you. 

The best part of predictive analytics is that it helps you find how certain parts of your organization could function based on the current data. 

Predictive analytics is also popular to build and validate the models that provide the right predictions. This type of analysis depends on machine learning algorithms that include SVM, random forests, and stats for learning/testing the data. Some of the popular tools used for predictive analytics are R, RapidMinder, and Python.

For your company to practice predictive analytics, you need dedicated data scientists and ML experts that could build such models for you. 

4. Prescriptive Analytics

Prescriptive analytics is based on predictive analytics. The difference is that it goes beyond all types of analytics to find the right solutions. It’s a “How to make it happen?” strategy for businesses.

Prescriptive analytics could provide the best outcomes as per the right course of action and suggest some of the best methods to acquire the desired outcomes. For this purpose, it uses a unique feedback system that learns from past situations.

Prescriptive Analytics even includes optimizing some of the most important functions related to the desired outcomes.

It even stimulates where the key performance areas are put together to find the solutions that could be worthy.

CONCLUSION

To figure out what is the best mix of data analytics types for your organization, it’s always advisable to identify the answer to the following questions:

  • What is the current scenario of data analytics in your company?
  • How thoroughly do you have to deal with the data?
  • How far are my current data insights from the insights I need?

All these analytics techniques might seem like these need to be implemented sequentially. However, in most cases, companies could usually jump to the last and most direct resort – prescriptive analytics. This is because it is one of the most effective methods to provide solutions a company is looking for in terms of business analytics.

If you are interested in Business Analytics and want to explore this domain more, Verzeo’s Business Analytics course is the right fit for you.

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