Content area
Full Text
The emerging field of human capital analytics in organizations today is at an interesting point in its evolution. While enthusiasm and investment are high, results seem slow in coming. The optimism that the right algorithm will quickly and easily illuminate powerful insight is waning as the reality of the challenges of doing powerful talent analytics work sets in. Many organizations still struggle to consistently produce sound descriptive data, and very few (as of 2016 only 8 percent of companies in Deloitte’s global survey) report that they are fully capable of developing predictive models, let alone fully prescriptive models that directly outline specific action. A full 60 percent of companies indicate they are not ready to provide true future looking (prescriptive or predictive) analyses (Deloitte Global Human Capital Trends 2016). This gap is surprising given that only two years earlier 78 percent of large companies rated HR and talent analytics as urgent or important, placing it among the top three most urgent trends (Deloitte Global Human Capital Trends 2014). Clearly something is not happening the right way.
In this paper we argue that there are a number of factors that serve as barriers to the rapid development of effective HR analytics capabilities. Specifically “HR analytics” is used to refer to a too-wide array of measurement and analytical approaches, making strategic focus difficult; there is a misconception that doing more measurement of HR activities and human capital will necessarily lead to actionable insights; there is too much focus on incremental improvement of existing HR processes, detracting from diagnosing the problems with business performance; too much time is spent on mining existing data, to the detriment of model building and testing, including collecting new more appropriate data; too much energy is consumed with basic tasks of data management; and the all too common phenomenon of avoiding action by nitpicking the details of the data. We discuss each in turn.
Problem 1: the tent is too big
HR analytics today is not a crisp discipline with a sharp focus on a limited number of issues. It has come to include anything numerical about talent and HR work. Examples include simple data reports, analyzing data integrated from multiple systems (e.g. performance and compensation), dashboards, making data available “on demand,” and...