Big Data vs Business Analytics?

Big Data can change your world and we aim to tell you how over the next few weeks.

But before we get into that, lets take a step back and understand what the terms surrounding Big Data really mean.

Big Data, Business Analytics, and Business Intelligence are terms that are generally used to refer to processing data.

But is ‘big data’ just another way of saying ‘business analytics’?

The answer is an emphatic NO. 

Wikipedia defines Business Analytics as the “discovery and communication of meaningful patterns in data”. Business analytics has been in use since the late 1960s, when computer based information systems were used to support decision-making activities. Firms commonly apply analytics to business data, to describe, predict, and improve business performance.


For instance, Banks have long been using business analytics to differentiate customers by characteristics such as credit risk and usage. Similarly, Financial services firms have been using analytics to identify future growth trends on the basis of historic data.

Business Analytics needs data to identify trends. But the traditional business analytics software struggles with Big Data. Its tools were not designed to sift through massive data warehouses storing enormous data in a variety of different formats. Millions of dollars and enormous amounts of time and energy have been spent to develop these software programs, but it is still a Herculean task to identify meaningful data and ignore the useless bits.

The term Big data is self explanatory – it is literally data that is too big to be handled by conventional data management softwares. According to Edd Dumbill of O’Reilly media, Big Data is defined as “data that exceeds the processing capacity of conventional data processing systems. The data is too big, moves too fast, and doesn’t fit the structure of conventional database architectures. To gain value form this data, you must choose an alternative way to process it.”

In the coming posts we will start digging deep into the big data universe and explore other aspects of the big data landscape…

Stay tuned!

What is Big Data again?

Big Data is defined – literally – as large amounts of data that companies worldwide are gathering today. It is ubiquitous, prevalent in every industry, every sector and there is a world of technology that has been created to simplify all of this data in order to create value for the users.

But if all of this is just data, what is all the hype about it for, and why is this relevant to your life?

Imagine you walk into a coffee place that you visit frequently. However, unlike before, you walk in and a pretty blonde waitress greets you with your first name, immediately seats you at your favourite location (right by the window that looks onto a beautiful street), which happens to be empty. While you are still blinking and wondering how she knows your preferences so well, she surprises you yet again and asks you if you would like your favourite Irish nut coffee with whipped cream, exactly the way you always have it!

No, this is not a scene straight out of a movie.

This is exactly what Big Data has set out to do.

Did you know that 12 terabytes of tweets created each day are turned into improved product sentiment analysis? 

And that the best video games are produced after analyzing over 500GB of structured data and 4TB of operational logs daily?

Big Data has become an exciting scene in the world of technology.

It is about utilizing a world of information in the simplest manner to add to the user experience.

It is about speed; making processes efficient, especially for real-time applications. Think stock markets.

It is about innovation – adding variety and creativity to existing processes. Remember Google glasses?

And last but not least, Big Data is for YOU, it is for the future leaders, managers and CEOs of today. Big Data will give you the ability to make decisions quickly, rationally and the power to generate unique solutions for the rapidly changing global scenario.

Big Data is about adding value. Stay tuned in to our blog to find out how.