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Data silos in big data analytics: Now you see them, now you don't?
This article is part of the May 2012 Volume 13 issue of CIO Decisions
Data silos have plagued business intelligence efforts for as long as businesses -- and their CIOs -- have been trying to extract meaningful BI from data. The existence of data silos means that the slaved-over, costly, single-version-of-the-truth database being tapped for great insights is really only a partial version of reality. And therefore the answers that database yields quite possibly are not the right answers after all. Garbage in, garbage out, as the BI pros say. We want to let people look at the data, but we have to make damn sure they can't change it. Enter big data analytics -- the buzzword for the data triple-V (variety, volume and velocity) that inundates most companies today. And the data silo plague grows --exponentially so, according to analyst Ted Friedman. "You've had silos for all time inside your company. Now, with the big data phenomenon, you have silos that reside across the universe -- inside your firewall, out on the Web, in the cloud, data that could be owned by other enterprises or by your customers and...
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Features in this issue
Most organizations sit on a gold mine of business intelligence. Extracting it requires knowing how analytics projects differ from day-to-day IT work.
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Big data makes the problem of data silos even bigger. Or does it? Either way, CIOs must make big changes to get the most out of big data analytics.