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Analytics & Human Resources

Date posted: November 22, 2016

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:

  • Choosing where to cast the recruiting net
    An Asian bank had a longstanding practice of recruiting the best and brightest from the highest regarded universities. As part of a major organizational restructuring, they used data analytics to identify high-potential employees, map new roles, and gain better insight into key indicators of performance. What did they find?
  • Branch and team structures were highly predictive of financial outcomes.
  • A few key roles had a particularly strong impact on the bank’s overall success.

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.

  • Cutting through the hiring noise and bias
    Data analysis can also help organizations eliminate unconscious preferences and biases that can surface even when those responsible have the best of intentions. A professional services company that receives a quarter million job applications annually sought to reduce the costs associated with initial résumé screening—and improve its effectiveness—by introducing advanced automation. They were concerned that automating the process might compromise the goals they had set for hiring more women; but the assumption that screening conducted by humans would increase gender diversity more effectively proved to be incorrect:

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.

  • Addressing attrition by improving management
    A major U.S. insurer had been facing high attrition rates, and initially responded by offering bonuses to managers and employees who opted to remain. This yielded minimum success. Then it gathered data to help create profiles of at-risk workers; the intelligence included a range of information such as demographic profile, professional and educational background, performance ratings, and levels of compensation. By applying sophisticated data analytics, a key finding rose to the fore: Employees in smaller teams, with longer periods between promotions and with lower-performing managers, were more likely to leave. More effective than bonuses was providing greater opportunities for learning, development, and more support from a stronger manager. Consequently, funds were reallocated towards learning development for employees and improved training for managers. Performance and retention both improved, with significant savings to boot.

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.”