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Big Data by all accounts is supposed to help humans perform better by augmenting our limited brain power. Computers, after all, have the ability to crunch data with lightning speed, something humans just haven’t been built to do.

Conventional tech wisdom states that the more data you have, the better the outcome — even if that sounds counter-intuitive. That’s the thinking behind the NSA hoovering up as much data as they can get their hands on. With more data should come deeper understanding, but what happens when there’s too much data and it surpasses our human ability to understand it in a given moment?

Chances are that too much information running through our small brains clouds our thinking, making it more difficult to do our jobs. Computers can slice and dice data with great precision, coming up with meticulous details about a given situation, but it could be another matter for mere humans who are left to process that data and apply it to our work.

That’s because we can only deal with so much data, even when the machine is filling in for our limitations. We may have a handful of things we can keep in our brains at once, at least at the moment we are trying to be effective employees and do the job to the best of our abilities with the data the machines have been kind enough to supply for us.

By all accounts, machines can get us so far, but it is left to us as humans to take that information and process it to make meaningful connections.

As a great real-life example, baseball has become a sport dominated by data. Former major league baseball player, Tony Clark, who is currently head of the MLB Player’s Association put it well when he told Nick Carfardo of the Boston Globe, “There is value in having information. There is a danger in having too much of it.”

Baseball players are like the rest of us, except they have to hit a baseball being thrown at 90+ MPH. Despite their great talent, they have to take this great information provided by the stats geeks in their organization, and use it to the best of their ability to hit the baseball. The problem as Clark puts it, is that there is a case of too much data while trying to do the job.

Players, like all of us, have a plan on how to execute their jobs. It helps to have good data to build that plan, but you have to cut through the noise and get to the most important nuggets that will help you execute at the highest level.

As Clark put it, “Unfortunately, knowing what pitch he throws on Tuesday day games, on turf, the second week of each month, has no value to me. So you can put yourself in a position where I have so much information I can’t move, I can’t function, I can’t game plan in a way that allows me to perform the way I need to perform,” Clark told the Globe.

That kind of exaggeration has a ring of truth to it as we go forth into the world of increasing amounts of data. Yes, machines can process that data and spit out all kinds of esoteric data points, many of which actually have little value.

The challenge remains finding the wisdom in the data to do our jobs. Perhaps artificial intelligence will help in that regard, but ultimately it’s about finding the data that matters — whether that’s the right approach at the plate for a baseball player, the best customer to target or the new product idea to implement for a company or even government security agencies finding the greatest likelihood of a terrorist attack.

Whatever the outcome, the data has no inherent value unless it produces an outcome to help us perform better — and finding the data that matters most is still a huge issue.

This article was originally published on www.techcrunch.com and can be viewed in full

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