By Jim Wong, CPA | March 28, 2014


“Big data” seems to be the term of the day, these days. But there’s a reason for that. In today’s digital world, where information streams at a rate most of us would have thought inconceivable even ten years ago, big data is no flash in the pan.

Big data describes “the exponential growth and availability of data, both structured and unstructured.” And many believe that big data will become as important to our business marketplace as the Internet.

The reason for that is simple. More data equals better analysis. And “better decisions can mean greater operational efficiencies, cost reductions and reduced risk.”

Today, we’re going to explore how you can bring big data into your human resources departments.

In a recent article for Inc.com, senior writer and author Ilan Mochari shared insights he gleaned from a recent talk by practice professor of operations and information management at Wharton, Cade Massey.

Now, let’s set some things straight right off the top: If you want to start benefiting from big data, you don’t necessarily have to be a “big business”. As Massey noted, “…(questions) regarding how to evaluate job candidates and employee performance – are relevant to all organizations, even if you’re light years away from hiring a data scientist.”

Here are three ways you can bring big data into your HR practices.

Evaluate How Effective Your Interview Process Is

We talked about Google last week, and Massey mentions them as well. They used big data to look at their past hiring processes, and they dug into the resulting hires. Then, Google did something somewhat counter to what most big organizations do. They cut the number of interviews they did per candidate down.

“They were spending hours of their managers’ time interviewing candidates — eight interviews, nine interviews, 10 interviews,” he revealed, stating that they realized those interviews didn’t do much to predict future performance. Google eventually said, “…let’s just cut that back. Let’s cut it down to the bare minimum. Let’s have three or four interviews.”

Do a deep dive into your hiring stats, and see if you can use the numbers to streamline your system.

Evaluate Your Unconscious Job–candidate Biases

We all have unconscious biases. A candidate went to a certain university, or worked at a prestigious company. Or, you might have an ‘experience bias’.

Writer Mochari shared an anecdote from Mukul Pandya, Executive Director/Editor–in–Chief of Knowledge@Wharton, about experience bias at Xerox. “One of the counterintuitive things they discovered was that, if somebody had a lot of experience working for different call centers, it was not necessarily a good thing,” he says. “It might just be that they had a high burnout rate. It was, in fact, a predictor of potentially bad performance.”

So, use numbers to evaluate how your organization is classifying candidates, and what your retention rates have been like over the past few years. You might find areas that can be adjusted.

Evaluate Your Performance Evaluations

Depending on your industry, positive performance evaluations will hang on different results. But are these the right results for you to base bonuses on? Consider what Massey says about the investment world.

“There have been studies in some places that sometimes — I am not going to say all places, all times — the relationship between a fund manager’s performance in one year is unconnected to his performance the next year. The idea is, if that is true, then there is a lot of chance in this process and these differences are not functions of skill. If they are not functions of skill, then maybe we should not be rewarding them heavily each year [their fund performs well].”

Big data, year to year, can help you evaluate whether results are due to hard skill – or to chance. And while it might be difficult to ratchet back bonuses when key performance indicators are being met, it might help you make changes moving forward, and realize your key performance indicators need some adjusting as well.

Big data doesn’t have to be scary. And you don’t have to be a data scientist to benefit from it. Now, start crunching those numbers.


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