One of the hottest topics among HR professionals is HR analytics. As an empirical scholar who has taught business statistics to numerous HR Master's students at the University of Minnesota, I should be pleased. But there are troubling aspects of the discourse on HR analytics.
For starters, proponents of HR analytics invariably start by denigrating traditional HR decision-making by equating it to a reliance on gut feeling and instinct. This was true in last year's New York Times, and is captured by a recent posting "HR is an Obsolete Way to Make Decisions" on the Evanta Leadership Network blog:
Human resources analytics. Talent analytics. People analytics. No matter what you name it, there's a tangible shift from gut decisions and intuition to analytic reports and metrics.... Human resources historically prides itself on a connection - and intuition - with people. It's one of the only business silos that has the liberty to operate (even if occasionally) purely on instinct. Personal experience and corporate beliefs run deep - but it's an obsolete way to make decisions. (Evanta Leadership Network blog, April 11, 2014)
Undoubtedly poor decisions have been made based on gut feelings and instinct in all areas of business, including HR. But for decades, the top Master's programs in HR, such as the University of Minnesota's HRIR program, have been equipping HR leaders to make decisions based on a rigorous, research-based understanding of what drives human behavior and therefore what works at work. Combine this with an HR professional's experience, and you have a rich basis for decision-making that should not be dismissed as some ill-informed gut feeling.
Can HR analytics help this decision-making process? Certainly. But there is a troubling undercurrent to the advocacy of HR analytics in which the entire field is painted with a broad brush of ignorance. There is a thoughtful side to the profession, supported by rigorous graduate study, that has been quietly successful for generations.
Moreover, today's advocacy of HR analytics can be quite mechanical. There is little discussion of underlying theories of human behavior that are necessary for making sense of empirical results. A recent presentation on HR analytics at Google explicitly emphasized the mindset of "what-if" over "why." But the "why" is critically important for truly understanding when and how to implement changes based on HR analytics. Without that understanding, results are likely to be misinterpreted and misapplied. That's not significantly better than decisions based on gut feelings. And what about those tough situations for which data are unavailable?
So where does this leave us? First, we need to distinguish between naïve gut feelings on the one hand, and a rigorous research- and experientially-based knowledge base on the other. To assume that all decision-making done before the supposed advent of HR analytics is the former is simply wrong. Second, we should embrace the trend of increased use of HR analytics, but not their mechanical use. That is, we need to continue to deepen our understandings of human behavior so that we can better interpret the results that HR analytics provide. Or more simply, we need theory and data. This should provide a more productive narrative on HR analytics than the oft-repeated rhetoric about replacing gut feelings with data-driven decision-making.