A Comprehensive Guide to Understanding the Four Types of Big Data Analytics

If you’re reading this post, chances are that you are well-versed with general terms such as “Data Analytics”, “Data Analysis”, and “Business Intelligence”, as they are frequently used in the context of Big Data.  Although ‘Big Data’ has been a buzzword for more than two decades, it is still one of the most misunderstood (and misused) terms. That’s why in this blog, we are going to disambiguate the term “big data”. 

What Do You Mean By “Big Data”?

In layman’s language, Big Data is a large set of structured, semi-structured or unstructured data that is so humongous and complex in nature that conventional data processing application software is not robust enough to deal with it. In other words, you can say Big Data is so voluminous in size that traditional data management software and tools are not able to collect, store and process it efficiently.  

Big data can be easily characterized by 3Vs:  velocity (the speed of data moving in and out makes it hard to decode), volume (humongous amount of data to handle easily), and variety (the type and range of data sources are too diverse to assimilate). By using the right data analytics tool, Big Data can fetch meaningful insight since it is gathered from varied sources to unveil hidden patterns and relationships.

There are Four Different Types of Big Data BI that Play a Vital Role in Business:

Prescriptive: This type of data analysis reveals what sort of actions need to be taken immediately. This is the most valuable kind of data analysis, since it shows recommendations for next steps.

Predictive: This type of data analysis reveals what is likely to happen in future. 

Diagnostic: This type of data analysis reviews the past performance to figure out what happened and why.

Descriptive: This type of data analysis reveals what is going on at present, based on incoming data. To mine the information, professionals typically use a real-time dashboard and/or email reports.

 

Big Data Analytics in Action

Prescriptive analytics: It is really important, but usually it is not used. Though big data analytics throws light on a subject, prescriptive analytics renders you a sharp focus to answer specific questions.

Predictive analytics: It uses big data to determine the past patterns to forecast the future.  For example, many companies out there are using predictive analytics for analyzing lead source, CRM data, social media, types of communications, and more.

Diagnostic analytics: It is used to find out what happened in the past. For example, you can use diagnostic analytics in a social media marketing campaign to measure the number of followers, reviews, page views, pins, and the number of posts.

Descriptive analytics: A typical example of descriptive analytics would be determining the credit risk of a person by analyzing his former financial performance to forecast his future financial behavior.  

As you can see, leveraging Big Data analytics can add significant value to any business.  That is Why Big Data analytics certification courses and online training is in huge demand.