How big data brings BI, predictive analytics together

By Allen Bernard, CIO |  Big Data, Analytics

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.

Reviewing Unstructured Data in Social Media Reaps Immediate Rewards

There is value in social media, though. What if you learn, as the buyer for a retailer, that Justin Bieber fans really loved the jacket he was wearing at the concert last night-and, oh, by the way, someone tweeted he got it from one of your stores? You could then make a snap decision to stock up on that jacket just in that city since you know it's about to become a very hot item, albeit for a very limited time.

Without a predictive analytics (PA) package looking for patterns in the Twittersphere that correlate your brand with geographic location and factors such as the number of mentions, you could miss out on a great but small window of opportunity to move merchandise.

"In the past, we would have based [our decisions] on historical data-and, by the time we did it, that trend may have already passed us," says Barnes. "So that's PA on steroids, at warp speed."

Slideshow: 10 Trends Driving Big Data in Financial Services

How this is accomplished is a marriage of open source technologies (where most of the Big Data platforms are coming from these days), Moore's Law, commodity hardware, the cloud and the ability to capture and store huge volumes of non-transactional data that was once discarded because no one knew what to do with it.

Unstructured data such as video and email, often cited as a driving force behind big data, barely plays a part in this. Scour blog posts and user forums, though, then correlate that information with geographic data, couple it with flat files of your existing structured customer data and bring in streams from new sources such as the MicroStrategy Wisdom engine, which tracks what some 14 million Facebook users are saying about your brand, and now you've got a new and powerful tool.


Originally published on CIO |  Click here to read the original story.
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