How Big Data Brings BI, Predictive Analytics Together
Big data is breathing new life into business intelligence by putting the power of prediction into the hands of everyday decision-makers.
Thu, September 20, 2012
CIO — For as long as anyone can remember, the world of predictive analytics has been the exclusive realm of ivory-tower statisticians and data scientists who sit far away from the everyday line of business decision maker. Big data is about to change that.
As more data streams come online and are integrated into existing BI, CRM, ERP and other mission-critical business systems, the ever-elusive (and oh so profitable) single view of the customer may finally come into focus. While most customer service and field sales representatives have yet to feel the impact, companies such as IBM and MicroStrategy are working to see that they do soon.
Big Data Moves Analytics Beyond Pencil-Pushers
Imagine a world in which a CSR sitting at her console can make an independent decision on whether a problem customer is worth keeping or upgrading. Imagine, too, that a field salesman can change a retailer's wine rack on the fly based on the preferences that partiers attending the jazz festival next weekend have contributed on Facebook and Twitter.
Big data is pushing a tool more commonly used for cohort and regression analysis into the hands of line-level managers, who can then use non-transactional data to make strategic, long-term business decisions about, for example, what to put on store shelves and when to put it there.
However, big data is not about to supplant traditional BI tools, says Rita Sallam, Gartner's BI analyst. If anything, big data will make BI more valuable and useful to the business. "We're always going to need to look at the past…and when you have big data, you are going to need to do that even more. BI doesn't go away. It gets enhanced by big data."
How else will you know if what you are seeing in the initial phases of discovery will indeed bear out over time? For example, do red purses really sell better than blue ones in the Midwest? An initial pass through the data may suggest so—more red purses sold last quarter than ever before, therefore, red purses sell better.
But this is a correlation, not a cause. If you look more closely, using historical transaction data gleaned from your BI tools, you may find, say, that it is actually your latest merchandise-positioning-campaign that's paying dividends because the retailers are now putting red purses at eye level.
That's why IBM's Director of Emerging Technologies, David Barnes, is actually more inclined to refer to the resulting output from big data technologies such as Hadoop, map/reduce and R as "insights." You wouldn't want to make mission-critical business decisions based on sentiment analysis of a Twitter stream, for example.