If you simply skimmed the blogosphere and raced through a few news stories about trends in hiring and personnel, you might conclude that big data is taking the “human” out of “human resources.”
Not surprisingly, big data and data analytics are increasingly being deployed to figure out who to hire and how to best keep good employees happy while encouraging poor employees to hit the bricks.
NPR recently aired a story about companies like Pymetrics, RoundPegg and Knack, that are using brain games to measure aptitude, cultural fit and personality. The Wall Street Journal soon followed with a piece that talked about companies like Culture Amp and Ultimate Software Group that are working with big and well-known companies to identify valuable workers that some HR departments refer to as “flight risks.”
There is a lot at stake as companies work to hire the right fit and to hang on to their best workers. No one wants to spend money on recruiting, hiring and training only to have recent hires bailout quickly. And the cost in lost productivity alone is reason enough to keep track of which workers might be looking for other opportunities elsewhere. But those who understand human-relations data the best, say the current trend is not a wild departure from the sort of thinking hiring managers have always done.
“There is data that we use all day long, and historically, that we look at when we make a decision about whether to hire somebody — data around whether they have different skills, what their historical job history was; things that sit on the top of a resume,” says Jim Meyerle, who co-founded Evolv, a recently-acquired company that used big data to predict the best hires for hourly wage jobs. “That’s all data utilized in order to make a hiring decision that ultimately, hopefully, makes for a better match than just picking somebody at random.”
But as computers and software have become more powerful and adept at storing and sifting through data, more and more business practices and other activities are lending themselves to solutions that team up humans and machines.
In some ways, the examples of human resources professionals relying on human-plus-machine models point to the true value of data-driven systems: the ability to spread the power of data through a business or other organization. Some call it the democratization of data. Rather than requiring a hiring manager to ask a team of data analysts to work on a question and report back in days or weeks with an answer, new tools are moving toward virtually instant insights.
And as strides in artificial intelligence continue, the best tools will become smarter over time. They won’t simply report what is, but will offer predictive reports on what’s to come.
None of which cuts humans out of the equation.
Meyerle points out that when it comes to hiring, for instance, there is an endless amount of data that could be collected in order to make a decision.
“Part of that challenge is, what of that data is most relevant?” says Meyerle, co-founder of Evolv. Meyerle has moved on from Evolv, which was recently acquired by Cornerstone OnDemand. “There is a person that is an expert at determining what data we should be collecting and then it’s configuring or developing tools in order to capture that information in a systematic way.”
For instance, he says, it’s possible that in hiring for a given position, one particular data point is an incredibly strong predictor of the candidate’s success or failure. A good hiring manager might realize that on his or her own, but a machine is much more likely to use that intelligence consistently.
“When people are applying for positions, well, then you’re able to build that into the data model,” Meyerle says. “An individual hiring manager, an individual recruiter, might find something that is relevant and then forget about it or not keep it as a systematic thing that they’re looking for.”
A machine is also able to track how those factors lead to success — or not — over hundreds, thousands or many thousands of hiring decisions and employees.
“The power of the machine is it’s enabling that person to collect what has been found across, ideally, a lot of different people who have been hired over time,” Meyerle says. “It’s helping enable that person to get the best data, the most relevant data, to make a decision on — because ultimately, the machine helps cut down on the data that somebody is looking at and delivers that in a digestible format to a person who ultimately needs to make a decision.”
The “human” in human resources is safe for the foreseeable future, Meyerle adds.
“I think at the end of the day, the human always has to make the decision.”
Mike Cassidy is BloomReach’s storyteller. Contact him at firstname.lastname@example.org; follow him on Twitter at @mikecassidy.