Listen. Get Data. Think. Get Data. Evaluate. Get Data. Analyze. Get Data. Synthesize. Decide. Transform.


Given that the analytics function has increasingly higher levels of sponsorship and responsibility in forward-thinking organizations today, it is hard to imagine that just about fifteen years back, it was a very different story. Till about the mid-nineties, the word "analytics" was non-existent. The function, if I may call it that, was buried within "MIS" teams or a complementary part of the data-warehousing group. If you needed a report that outlined what products you were selling in which markets and what price points you sent a request to the "IT team", and you would receive a tabular report after a couple of days (we cannot imagine waiting more than a few seconds today!). This was the time of centralized databases, report requisitions, MIS specialists and the occasional external consultant who would make sense of it all.

Fast forward a few years, and we had the first real industry-impacting shift information federation. This was made possible by the relative ease of availability of corporate data in structured data repositories coupled with 3 critical factors. First, the willingness of organizations to take hard business decisions based on data (and not just "gut"); second, the introduction of the "business analyst" to the job market and finally the search engine. You now had some very smart people looking at up-to-the-day data (their own, competition and the market), and building relationships with your customers, gaining new clientele, making obscene amounts of money for their employers and then making sure that anyone with a web-enabled browser could find out about it. The banks and mass-market retail corporations led this wave and the rest of the financial industry wasn't far behind. Over the last 10-12 years, Enterprise BI has focused on areas like maximizing monetization on marketing campaigns, increasing customer loyalty and purchase propensity, risk and fraud.

Several enterprises across the world are investing in this capability using a variety of engagement models in-house, captive, specialized outsourcing and generic outsourcing. The largest proportion of analytics consulting work is still centered on these "traditional" application areas where increasingly advanced techniques are being used to identify ever-elusive target groups and increase monetization. Indian companies are leading the way in providing analytics-as-a-service to global audiences.

Indian businesses have traditionally focused more on increasing penetration through expanding their logistics chains but as the markets become more saturated, they are beginning to understand the value of real-time customer and market information, and the need to drive profitability through data-driven insights. We should see these organizations build out their analytics capability over the next 1-2 years.