Big Data Limitations - We Still Need the Human Element

Big Data Limitations - We Still Need the Human Element

Big data is a powerful tool for any organization that wants to know how to improve its processes. When used correctly, big data reveals unprecedented results. And, like any tool, big data has its limitations.

This seems obvious, right? We know that technological tools all have limitations, but it seems so easy for tech blogs and publications to get so completely enamored with the latest piece of shiny evolution that we don’t take the time to spell out where the issues might arise. It’s not even that we have to trash big data to say where its limitations might be. We don’t hate the hammer because it’s not a good screwdriver. We just use different tools as the need arises.

Big Data and Hiring

The New York Times just conducted an interview with Laszlo Bock, senior vice president for people operations at Google, and the conversation looked at how Google analyzes its hiring decisions. Obviously Google is madly in love with data, so the company would love to know if there is a more efficient means of completing the hiring process. If certain key indicators could reveal which new hires would make fantastic employees, then the company could save itself the time and expense of hiring people who aren't a good fit.

The result?

Google learned which characteristics don’t make a difference. After a few years from graduation, an employee’s college G.P.A. and test scores make no difference in that individual’s performance at work. The data simply doesn't relate (or correlate). Skills needed in the work world are entirely different than the ones needed in university.

The Shifting Need for the Human Element

If Google had found a magic bullet for hiring, then getting a job at a new company would require no real interaction with people at all. You'd simply submit your scores and walk in the next day if you qualified. For now, at least, that won't be the case. We still need people to make decisions based on their best impressions of others. They be right, and they might be wrong. The only way we can know is to have them try it.

This reliance on human decision-making brings up an interesting point. As jobs fall by the wayside as new technologies step in, it doesn't mean that all industries have to be obliterated by robots and algorithms. It will mean that we have to shift to where we can create the most value within each industry. The days of easy tasks are disappearing. We actually have to be engaged to show our abilities.

Paul Bunyan
Paul Bunyan

Image from Disney Wiki

I grew up watching a lot of old Disney cartoons, and one that always stuck with me was the portrayal of Paul Bunyan. Old Paul was a mighty man who could fell a dozen trees with the swing of an axe, but that old tycoon with his fancy sawing machine started to rival Paul. In a mighty showdown, the two cut as fast as they could (this was before the big push to conserve resources, of course) to see who was the best.

As it turned out, the man with the mighty machine won out, and Paul Bunyan wandered off in shame. Of course, Disney had to pad the story a bit and say that Paul was still off being mighty somewhere or another, but the story bugged me. Yes, I wanted the hero to win, but the result of the competition did not have to mean that Paul would feel the need to exile himself. How amazing would Bunyan have been with the world's largest chainsaw. The Amazon wouldn't stand a chance.

This seems to be where people are at today: stumped at the appearance of an old man with his newfangled sawing machine. Society still needs people at the helm. We still need to actively create value, and we can do it by utilizing the tools we have available. Because, really, the tools can't do it themselves.

What Do You Think?

Have you seen your job greatly affected by recent technological pushes? How have employees had to adapt?

Featured photo by Thomas Hawk

Creating the Power of the Shift – Why Self-Education Is a Must

Creating the Power of the Shift – Why Self-Education Is a Must

Big Data: Correlation Is Not Causation