Superlatives are the norm when talking about Big Data. We all see Big predictions of the Big numbers and High growth of petabytes, connected devices, data containers, etc., and the Tidal changes they bring.
But I think the “Big” adjective best applies to Complexity. How do IT shops manage all these 1s and 0s and set the table for meaningful analytics?
At the most basic level, there are two approaches, seemingly contradictory, for laying the analytics foundation: consolidate data onto fewer platforms, or analyze it selectively across platforms in a decentralized fashion. These actually complement one another well as part of an iterative data management model in which IT continuously re-positions data sets to support evolving analytics needs.
Let’s examine each, then look at the squishy middle, where most companies will reside.
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Bold statement? I was a bit surprised at the conclusion myself. But, let’s take a deeper look.
I’ve obviously made certain choices regarding storage, and have done my own comparisons at certain points in time, but was prompted to update my thoughts after a recent tweet by Chris Mims of the WSJ:
Strong words from Chris, but they ring true. It’s easy to forget that your device can be stolen, and your typical protections (e.g. a lot of folks don’t even bother to set a device login) can be circumvented way more easily than a well-designed, well-funded cloud storage service. Historically, according to the Open Security Foundation, reported exposures of personally identifiable information (PII) resulting from lost or stolen devices and media made up more than 22% of all incidents, while hacking made up 30%. The numbers are comparable. While the percentage of incidents related to hacking has increased dramatically in the last year, the um, Target has been easier prey like retailers.
Looking more broadly than security, I wondered if a cloud storage service could actually be better than storage on a device.
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