Don’t Be Afraid of Big Data
You have to give it to big data analytics vendors; they’ve done a marvelous job of scaring us all into submission as we wonder, “Just what are we going to do with all of this data?”
The answer is easy. Read it, organize it, and analyze it. It’s not nearly as hard as we’ve all been led to believe.
IBM is on our side, announcing this week that it “has added nine new academic collaborations to its more than 1,000 partnerships with universities across the globe, focusing on big data and analytics."
The goal is to reach those looking to tackle this “big” problem and to give them the education and skills to fill the “4.4 million jobs that will be created worldwide to support big data by 2015,” according to SD Times.
By learning how to interpret big data and monetize their interpretations, software professionals could find themselves in line for a pay raise by eliminating the need for businesses to hire outside experts. It's likely that developers and testers—good ones—already have the analytical skills required to sift through mounting amounts of data.
This is the reason that big data has even become such an issue. For years, most companies have simply stored big data and then failed to organize or use it. Organizations that take the time to sift through it can realize a financial advantage. Perhaps if the data had been kept under control all along, it wouldn’t even be referred to as “big” and met with such fear of the task at hand.
Mike Wheatley at Silicon Angle references Dave Fowler when he explains that “data literacy” is not only not difficult, it’s really “just the next stage in the evolution of what we define as computer literacy.” Wheatley writes:
But isn’t Big Data like, really complicated? Isn’t that why we always leave it to the experts?
Fowler says that it doesn’t have to be, and indeed in most cases it isn’t. Organizations need to free themselves from this mindset, this overwhelming "hype" about Big Data, which in reality isn’t nearly as "big" as it’s made out to be.
With the scariness of big data starting to wane, and developers and testers starting to call the bluff of analytics experts by becoming experts themselves, perhaps the term “big” will be dropped altogether. Then we’ll look at gigabytes, terabytes, and petabytes as essentially what they are—data.
