The time comes during a technology’s evolution into the general consciousness when its actual definition comes into question. That’s especially true with something as amorphously named as big data.
I’ve increasingly noted that some people take it literally, equating “big data” with “lots of data.” That’s in direct contradiction to how the industry defines it, which is generally the combination of “structured data” and “unstructured data.”
I’ve especially noted this recently, with this month’s dual cultural lollapaloozas of both the Super Bowl and the Academy Awards. The attempts to predict the outcomes of these events beg the question: what is big data analysis really supposed to achieve? Can it really predict discrete outcomes? Or is it really designed to identify patterns and potential outcomes?
By Howard Baldwin. The ultimate answer probably lies within the “big” part of big data. Big, in this case, encompasses multiple gradations of data.