People analytics reveals three things HR may be getting wrong

People analytics reveals three things HR may be getting wrong

People analytics reveals three things HR may be getting wrong
More sophisticated analyses of big data are helping companies identify, recruit, and reward the best personnel. The results can run counter to common wisdom.

Bill James, the factory watchman turned baseball historian and statistician, once observed, “There will always be people who are ahead of the curve, and people who are behind the curve. But knowledge moves the curve.” Some companies are discovering that if they employ the latest in data analytics, they can find, deploy, and advance more people on the right side of the curve—even if the results at first appear counterintuitive.

Over the past decade, big data analytics has been revolutionizing the way many companies do business. Chief marketing officers track detailed shopping patterns and preferences to predict and inform consumer behavior. 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 (exhibit). Surprisingly, however, the data do not always point in the direction that more seasoned HR officers might expect. Here are three examples.

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A bank in Asia had a well-worn plan for hiring: recruit the best and the brightest from the highest-regarded universities. The process was one of many put to the test when the company, which employed more than 8,000 people across 30 branches, began a major organizational restructuring. As part of the effort, the bank turned to data analytics to identify high-potential employees, map new roles, and gain greater insight into key indicators of performance.

Thirty data points aligned with five categories—demographics, branch information, performance, professional history, and tenure—were collected for each employee, using existing sources. Analytics were then applied to identify commonalities among high (and low) performers. This information, in turn, helped create profiles for employees with a higher likelihood of succeeding in particular roles.

Further machine learning–based analysis revealed that branch and team structures were highly predictive of financial outcomes. It also highlighted how a few key roles had a particularly strong impact on the bank’s overall success. As a result, executives built new organizational structures around key teams and talent groups. In many instances, previous assumptions about how to find the right internal people for new roles were upended.

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Whereas the bank had always thought top talent came from top academic programs, for example, hard analysis revealed that the most effective employees came from a wider variety of institutions, including five specific universities and an additional three certification programs. An observable 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. Both of these findings have since been applied in how the bank recruits, measures performance, and matches people to roles.

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