Over the past several months, the web has been abuzz with articles about "leveraging data to inform HR" and "Big Data meeting HR". Articles from the Wall Street Journal, Forbes, The Economist, and HR blogs have been quick to report on this trend. The basic notion is simple - having HR and business outcome data, whether massively large, or of even more modest size, can help organizations make more informed decisions about their workforce from recruiting to retention and positively impact their bottom line.
Indeed, one can argue that nearly every aspect of talent acquisition and talent management appears to be moving towards evidence-based decisions premised on data driven metrics and analytics.
Though this is all well and good, there is also growing recognition that simply having access to large amounts of data and being able to summarize it or link different elements of it together at a click of a button is far different from ensuring that the data upon which analytics are based are of sufficient quality, or that the conclusions organizations attempt to draw on various analytics are valid. Such behind the scenes work is absolutely critical to an effective HR analytics function, yet something that enterprise-wide data warehouses and analytic tools alone will not provide.
Despite increasing appreciation of the “human element” of the HR analytics process, there has been little tangible discussion of the technical skill set beneficial to a modern HR data scientist. It is this skill set that can not only make one's HR analytics function truly hum, but also help organizations navigate the many potential pitfalls and opportunities along the way.
In this series of posts, I will take a critical look at this issue. The focus will be on intangibles that no data warehouse or software can provide - the people and skills behind the HR data machine.
So, what does the ideal HR data scientist look like to you?
Look for more in this series in the coming weeks…
About the Author
Dan Putka is a Principal Staff Scientist at HumRRO.