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Big data analytics will place new burdens on data storage systems. Here are some of the key features those systems will need to meet the challenges of big data.

"Big data" refers to data sets that are too large to be captured, handled, analyzed or stored in an appropriate timeframe using traditional infrastructures. Big is, of course, a term relative to the size of the organization and, more importantly, to the scope of the IT infrastructure that’s in place.

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Big data also infers analysis, driven by the expectation that there’s value in all the information businesses are accumulating -- if there was just a way to pull that value out.

Perhaps it follows from the notion that storage capacity is cheap, but in the effort to cull business intelligence from the mountains of new data created every day, organizations are saving more of it. They’re also saving data that’s already been analyzed, which could potentially be used for trending against future data collections.

This was first published in April 2012

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