McKinsey just released a list of the 10 most popular articles from the third quarter of McKinsey.com, and one that had caught our eye made the list. “People Analytics Reveals Three Things that HR May Be Getting Wrong” takes a look at how the analysis of big data is helping companies identify, recruit, and reward the best personnel, often with results that are counterintuitive.
Chief financial officers use real-time, forward-looking, integrated analytics to better understand different business lines. And now, chief human-resources officers are starting to deploy predictive talent models that can more effectively—and more rapidly—identify, recruit, develop, and retain the right people. Mapping HR data helps organizations identify current pain points and prioritize future analytics investments. … Surprisingly, however, the data do not always point in the direction that more seasoned HR officers might expect.
The authors cite three examples of this trend:
As a result, new organizational structures were built around key teams and talent groups; in many cases, previous assumptions about how to find the right internal people were turned on their heads: The bank had always thought top talent came from top academic programs, but data analysis showed that the most effective employees came from a wider variety of institutions. Further, a correlation was evident between certain employees who were regarded as “top performers” and those who had worked in previous roles, indicating that specific positions could serve as feeders for future highfliers. These findings have changed the way the bank recruits, measures performance, and matches people to roles, resulting in a 26 percent increase in branch productivity and 80 percent higher conversion of new recruits.
The algorithm adapted by HR took into account historical recruiting data, including past applicant résumés and, for those who were extended offers previously, their decisions on whether to accept. When linked to the company’s hiring goals, the model successfully identified those candidates most likely to be hired and automatically passed them on to the next stage of the recruiting process. Those least likely to be hired were automatically rejected. With a clearer field, expert recruiters were freer to focus on the remaining candidates to find the right fit. The savings associated with the automation of this step, which encompassed more than 55 percent of the résumés, delivered a 500 percent return on investment. What’s more, the number of women who passed through automated screening—each one on merit—represented a 15 percent increase over the number who had passed through manual screening.
The authors’ conclusion: “When well applied, people analytics is fairer, has greater impact, and is ultimately more time and cost effective. It can move everyone up the knowledge curve—often times in counterintuitive ways.”