Under pressure to recruit and retain top talent, large employers are increasingly turning to predictive analytics as a key element in their hiring and retention practices. For now, the focus is on using personality tests to identify promising front-line employees, such as call center or sales people. But the use of Big Data—and the pressure on HR departments to harness it in measurable ways—is growing. Does that mean software engineers and other technology pros will face personality testing on the way to their next job? Click here to find analytics jobs. Yes and no. Though ultimately the hiring of any software professional hinges on human factors such as skills, experience and cultural fit, the use of Big Data is becoming more prevalent in HR’s approach to almost everything it does, from hiring to training. When it comes to hiring, the right analytics can help employers understand the talent pool in more meaningful ways. “You can’t assume all software engineers are the same,” said Dr. John Sullivan, a talent management consultant in Pacifica, Calif. “You need to find out what they want.” To do that, he added, companies are studying software professionals—often their existing employees—to learn how they look for a job, what makes an employer attractive to them, and how the company’s own hiring practices can be improved. A widely noted instance of this was Google’s abandonment of brainteasers after data showed they added nothing to the hiring process. Data, said John Reed, senior executive director for Robert Half Technology, can help employers “identify and resolve issues that may be getting in the way of recruiting top performers.”

What Makes a ‘Fit’

That doesn’t mean data is absent from the individual hiring process. While the decision to hire someone may still be based on the judgments of managers, team members and HR, analytics are often used to identify how well a candidate will fit into the company’s culture. For example, take the time that Google used data from performance reviews, surveys and other material to identify the characteristics of effective managers. The resulting list wasn’t surprising, at least on the surface; the identified traits, such as “have a clear vision and strategy for the team,” led internal critics to scoff that the project simply compiled a set of HR clichés. In reality, though, it disproved some of Google’s key assumptions about its workforce, such as the need for managers to be technical experts first and foremost. Engineers, it turned out, respond best to managers who can help them tackle problems, not someone who simply dictates what they should do next. By measuring such things, Sullivan believes, companies can predict how seamlessly a particular candidate will slot into their new role. Analyzing different traits of their workforce allows employers to get an idea of what strong performers—as well as average or poor ones—have in common. At a time where recruiting software talent is challenging at best, “to know this stuff is a competitive advantage,” he added. One thing’s for sure: The use of data among technical employers is only going to grow. According to Reed: “Companies are just discovering how to leverage their people data to manipulate it into something meaningful.”

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