The information age is upon us, and it’s producing information at such an enormous rate that many businesses and users have a hard time grasping it all. Big data is the term applied to this glut of data sets that are too long to manage without some kind of software or engineering to filter through it all efficiently.
So in summary: We’ve built tools that generate more information, which then require us to build more tools to interpret and analyze that information. Have things always been this complicated?
Chuck Hollis, vice president of global marketing at EMC, thinks big data isn’t all that new of a concept. In fact, it’s been at work in several industries for some time, he writes.
When considering big-data analytics, it's a relatively easy mental exercise to go vertical-by-vertical and come up with a half-dozen extremely compelling use cases that could be enabled by data science, data scientists and supporting infrastructure.
It's relatively new, and it's certainly extremely cool.
But the generic model of using big data — in its broader form — to support advanced knowledge workers is nothing new. Energy exploration. Weather modeling. Engineering simulations. Media and entertainment. Drug research.
No, these aren't the sexy web-scale companies that are the darling of the media; but they are pretty cool nonetheless when you get to understand what they do.
In one sense, the notion of creating incredible value from smart people working with enormous amounts of data is not really that new. The only thing that's new is the much broader applicability.
For more on Hollis’ thoughts on big-data analytics, read the full post on his blog.
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