Big Data is Big Pile of Junk without Proper Analysis

Big Data - Big Pile of JunkEverybody who is anybody has been quick to hop on the big data bandwagon. But, as Entrepreneur contributor Vin Gupta is quick to point out, big data can be just a big pile of junk if it’s not properly analyzed. He serves up three prime cases in point – along with a solution.

Wal-Mart

Wal-Mart commands one of the largest databases ever established, rife with info on their customers, competitors, products, prices and everything else remotely linked to the business. Gupta guesses they spend upwards of $1 billion per year on their data collection endeavors.

Yet Wal-Mart’s revenue is not growing. Not by $1 billion, $1 million or even $1. What gives? Gupta guesses again that they probably have no idea. So what’s so great about the company’s great mounds of data if they’re not somehow using it to increase revenue?

Best Buy

Best Buy is another company that spends oodles of cash on big data every year. In their case, their revenue is actually declining. The reasons behind their decrease in revenue are pretty clear. Their website is awkward and clunky, no match for Amazon’s more sophisticated technology. Again, lots of big data is doing absolutely nothing for the company’s bottom line.

The US Government

Not only does the government have the biggest database of people, but they enjoy access to our phone chats and emails. Yet all the insider big data knowledge did absolutely nothing to prevent the Boston Marathon bombing. Big data shows its value once again, namely as a big trash heap if no one knows what they’re supposed to do with it.

The Reality and the Solution

Big data is the big trend, no doubt. Dozens of software and analytics companies are pushing their latest, greatest software for effective marketing. Folks are billing themselves as expert data miners and offering their services to anyone who’ll bite.

Jumping on the bandwagon can easily be akin to jumping into a meaningless black hole – unless companies are equipped with people smart enough to ferret out the nuggets of data that really matter. That task, alas, is easier said than done, as all the true experts are already manning their own companies to the tune of a tidy profit.

Instead of focusing on more and more random data and trying to find someone genius enough to analyze it, Gupta suggests going for less data that actually has meaning. Here you’re more apt to find the nuggets that matter, the nuggets that can make a difference and increase your bottom line.

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