How Google Uses Data to Build a Better Worker

For the search and advertising juggernaut, human resources is more than human

“I have charts and graphs to back me up. So f*** off.” 

Not the mantra you’d expect from your typical human resources representative. But every new hire for Google’s People Analytics department, part of the company’s HR function (which it calls People Operations), gets a laptop sticker emblazoned with this slogan. More than just an attention getter, it succinctly captures the profound difference in how Google addresses all matters related to people. Googlers maintain a firm belief that insights from data can systematically improve performance and leadership within the company’s ever-growing empire.

While the rest of the world is waking up to analytics and the power of Big Data, Google has spent years methodically building one of the most refined performance-management engines in the corporate world.  Similar to any of the company’s other departments, every People Operations project starts with a question to answer. Google’s People Analytics team has posed questions that range from tactical issues (“What if we could decrease ramp-up time for new employees by a month?”) to the existential (“What if all engineers were able to reach their potential for innovation?”) to the seemingly outlandish (“What if working at Google increased your life span by a year?”). The questions lie at the heart of solving fundamental business problems related to managing the employee life cycle, maximizing employee’s potential contribution, or improving Googlers’ wellness.

The impetus for the company’s data-based approach to the generally subjective domain of people decisions came from Laszlo Bock, Google’s SVP of People Operations. Bock, who joined the company in 2006 after stints with GE and McKinsey, found a like-minded ally when he hired Prasad Setty to run the company’s analytics department.  At first glance, Setty is an unexpected HR leader with his chemical engineering degree and quick admission that he found the softer courses in his MBA curriculum frustrating because people decisions tend to invite temperamental, emotional and subjective choices. Bock challenged Setty to bring the same rigor to people decisions that Google applied to its engineering analysis. 

Recalling how Google once tested forty-two different color shades for the Google toolbar to determine which hue optimized click-through rates, Bock put a stake in the ground that has since differentiated Google’s approach. As Setty recalled during our interview, Bock said plainly, “We need to be able to measure, to find out what does and doesn’t work at Google rather than just adopt best practices.”

Better-Than-Human Relations 

Over the past six years, Setty has built his People Analytics department into the company’s mainframe. While Google won’t disclose its exact size, estimates are that Setty has dozens of employees including PhDs, hardcore technologists and ex-consultants. As Setty told us, “the combined power of the group is having the business people make sure we’re solving the right problems, the stats folks ensure there’ s rigor in how we do it and the technologists who make the solutions scalable and transparent.”

Tackling Bock’s challenge, the team’s mission statement is for “all people decisions at Google to be based on data and analytics.” While many companies seek to make data-based talent decisions, Google deploys the kind of rigorous, scientific testing and statistical analysis that is more common in university labs. Running real experiments, according to Setty, helps describe “a small but significant percentage of the variance in human behavior.”

But Setty and his team learned early on while working on a project for Google’s engineering department that leaders didn’t want algorithms to replace human judgment. Rather than entrust important talent decisions to black-box calculations, Google’s leaders asked Setty to focus on providing insights that could help decision-makers improve the odds of getting complex decisions right. As Setty explained, the models that result from his department’s experiments explain normal or “average contexts” that won’t apply universally. The goal of People Analytics is to “complement human decision makers, not replace them.”

Presented by

Chris DeRose

Chris DeRose is a workforce-management consultant and the co-author of Judgment on the Front Line: How Smart Companies Win by Trusting Their People.

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