Should big data be used to fill C-suite openings?

(Editor’s note: This post originally published on Aug. 4, 2015.)

Many enterprises are turning to big data and analytics to help them manage their work forces. Some rely on analytics to retain valuable employees, while others use analytics to help find the most promising job candidates to hire.

But hiring a new product manager or sales professional is one thing; can analytics be used by an enterprise to find the right person for the corner office? Absolutely, according to Forbes contributor and analytics author Bernard Marr.

In a Forbes post, Marr makes the case that hiring for C-suite positions is a task best “done with as little guesswork as possible.” And for good reason, as Marr notes: “When mistakes are made appointing people at this level, disaster is a distinct and clear likelihood.”

One huge corporate headhunter, Korn Ferry, has gone all-in on analytics for both high-level recruitment and professional development. The company has amassed a database of more than 7 million executives worldwide, from which it has mined data that has allowed it to construct an analytics-based platform called the Korn Ferry 4 Dimensions of Leadership and Talent (KF4D).

KF4D enables Korn Ferry to pinpoint the specific key traits of successful organizational leaders. Marr writes, “The data revealed some strong patterns about the importance of traits and qualities required for C-level positions, including being a lifelong learner, higher levels of emotional intelligence (for example, empathy), communication skills and a tolerance for risk.”

Beyond that, Korn Ferry’s analytics platform can help clients determine if a candidate has the right experience and acquired skills to succeed in the C-suite position available (or soon to be available) and if he or she would be a good fit within the client organization’s culture.

Perhaps this type of analytics process might have spared J.C. Penney the costly mistake in late 2011 of hiring Apple retail veteran Ron Johnson, whose 14-month reign was exactly the type of disaster Marr refers to in his Forbes post. As Fortune‘s Noel Tichy writes, “Pure and simple, the [J.C. Penney] board had fatally misjudged the quality of Johnson’s credentials and the relevance of his prior experience.”

Hiring never will be an exact science — certainly not with humans involved on both sides of the equation. But predictive analytics and comparative analytics can eliminate much of the guesswork and enable enterprises to zero in on the candidates best qualified for a position and most likely to be a good cultural fit.

Does your enterprise rely on analytics to find candidates for its top positions?


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