Courtesy of ITVAR News

Big Data Analytics is the new buzzword today in the IT industry. It is slowly becoming a platform and an important consideration that has come to dominate the wish list of any CIO today.  Big Data is normally seen as a successor to Business Intelligence (BI), a tool almost similar to the former but yet appearing slightly different when seen from a usage standpoint. Vendors earlier offering Business Intelligence to its clients are now seen to be scaling up to Big Data Analytics given its ability to play with a huge amount of data, usually unstructured and generating predictive insights of it. And they cite the reason why…

Big Data v/s Business Intelligence

The basic difference between Big Data tools and normal BI tools can be better understood by showcasing people function in the respective processes. "The advent of Big Data has resulted in a new professional role created for analyzing the huge amounts of data on a real-time basis, known as data scientists to extract meaningful insights hidden in the avalanche of data found within and outside their organizations. A Data Scientist is different from a professional in the BI functions in a way that the former will go beyond the capabilities of what many would call 'traditional business intelligence (BI).' While traditional BI is interested in the 'what and the where,' data scientists are interested in the 'how and why'," explains Amit Mehta, Director - Marketing, EMC India & SAARC.  

Data Scientists in that case are seen as the new heroes of the Big Data era with their ability to do predictive analysis, posit the right questions, analyze massive data sets and their interest in inferring things that are not already present in the data. "People with Traditional BI skillset can only tell you how many widgets you sold in a region compared to last year. A data scientist on the other hand can tell you why sales plummeted in the Northwest compared to every other region - or at least would have a hypothesis," Mehta clearly puts it.

If we look at the traditional definition of Big Data, it is a set of unstructured data that requires algorithms and interactivity in order to find the patterns it contains, with the ways to derive the inferences determined only after the data has been collected. "Essentially, Big Data can handle more data and is way faster than BI, which means exploration and interactivity and in some cases delivering results in less time than it takes to load a web page," opines Subroto Das, VP - Storage Business, IBM, India & South Asia.

Also, in the traditional Business Intelligence model, since these tools are often used to simply create periodic reports, it is possible to first clean the data, cross-check before processing and analyzing it. "However, in the case of Big Data, structures vary often as different data sources are tapped in ad hoc fashion or because the sources are not from traditional RDBMS tables and so cannot be simply pushed into a structured repository. In many cases, the analysis needs to be run against the raw data before any data cleansing processes have been run. This further complicates the query process," points out Amit Malhotra, Director - Storage Sales, JAPAC Systems Division, Oracle.

The views of Harmeet Malhotra, Director - IDM & Storage Solutions Marketing - APJ, Dell are still however in favour of Business Intelligence who somehow feels that Big Data, though a known phenomenon to many has not picked up pace yet. "Traditional BI tools are designed to handle a structured database and one can run a query or analysis. But in case of unstructured data (i.e. Big Data) the information is not stored in the database and it first need to be extracted from different sources. So this is in fact a big challenge for the person has to be really skilled and also specific about what he is looking for."  

In years to come…

So does this mean extinction of the traditional Business Intelligence tools? Not exactly.

Though many believe Big Data to be fast replacing Business Intelligence, there is a rising inclination towards clubbing the richness of both these tools together, in relation to the existent structured and unstructured data, co-relating and integrating them in some way and then deriving some meaningful insights out of it. The power of Business Intelligence has only been enlarged with the inclusion of Big Data.

Big Data is also further believed to enhance the need for both BI and analytics in times to come, not just because of the volumes and velocity of the data involved but because of the different nature of analysis required. This will result in tools that extend from the core BI capabilities to handling such different analysis types coming to the fore.