Information Technology Management with a Purpose

Jun 18 2014   6:30AM GMT

Big Data – Is it too big to comprehend ?

S R Balasubramanian Profile: S R Balasubramanian

Tags:
Big Data
Big Data analytics
Business Intelligence

Not so long ago, while we were still grappling with the implementation of Business Intelligence and trying to shape up for Business Analytics, the concept of ‘Big Data’ hit the market like many other technologies do. We know most new introductions come in with a high dose of publicity and shrill marketing which tries to sell but does very little to explain what it means. For example when ‘cloud computing’ arrived with all fanfare it did create a stir but it took quite a while for glamour to fade away and for the reality to hit the user. The constant bombardment of the scene with new terms leaves the users bewildered.

The truth is that I am also confused and trying to make sense of this new concept in town. Let me therefore try to arrive at some understanding to help remove the haze from our vision. I remember the time I had started with Business Intelligence long ago trying to support business with new found knowledge from analyzing business data and making information easily accessible. While I was happy with what we did, we were suddenly stumped with the term ‘business analytics’ which took us on a tizzy till we got back our senses. When we are adjusting to the new definition we were teased again with the new term ‘Big Data’. I am sure many of our friends are also trying to comprehend this new development. I will make an attempt to understand the differentiation between these terms.

Business Intelligence
BI is not a new concept. Data warehouses, data mining, and database technologies have existed in various forms for years and we have been using them merrily. Big data as a term might be new, but many IT professionals have worked with large amounts of data in various industries for years. Business Intelligence (BI) encompasses a variety of tools and methods that can help organizations make better decisions by analyzing “their” data. Therefore, Data Analytics in my opinion should be a part of BI. Is BI then the mother concept and the other off-shoots of it ?

Business analytics (BA)
BA is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user. These analytics solutions often come with prebuilt industry content that is targeted at an industry business process (for example, claims, underwriting, financial analysis or a specific regulatory requirement). BA therefore could be a specialized tool that analyses data in depth and makes multiple correlations to unravel stories that not visible from a simple analysis. This perhaps puts BA in a slightly different league and has a leg up when compared with BI.

Big Data
Big data may not just be about large amounts of data, as the name may suggest. Going beyond the conventional process of analyzing structured data as present in our databases, this approach involves digging and analyzing a lot of semi-structured and unstructured data. Fifteen years ago, we didn’t analyze email messages, PDF files, or videos. The Internet was just a fad; all information there was of a secondary nature there were no social networking sites or other discussion groups that give valuable information and insights into consumer behavior. Similarly, wanting to predict the future isn’t a new concept, but being able to access and store all the data that is created is new.
Various sources claim that 90 percent of the data that exists today is only two years old. And that data is growing fast. If 90 percent of all the data in the world was created in the past two years, what does that say about the data?

Now organizations with huge amounts of data would find it hard and time consuming to analyze and interpret them to generate meaningful information on time. Old technology platforms are slow and inefficient and now we need computer systems and technologies that can handle big data and solve the velocity-volume-variety problem. The new in-memory systems technologies provide a breakthrough solution for big data analytics.

The way ahead
We are therefore in a new era where information processing dons a new dimension and acquires the capability of accepting, storing, processing and huge and a diverse set of data and putting them through any complex analysis to generate meaningful information for business. It requires our skills in making use of this technology to our benefit.

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