At every HR conference during the past year, the importance of analytics has taken centre stage. Many hope analytics will be a “shiny new toy” that will vault HR professionals into their desired strategic role.
Nobody can disagree with the concept of using data-based decision-making. But let me contribute a few thoughts on what is needed for HR analytics to become integral to HR’s value-adding contribution to talent, leadership, and capability.
First, collect data about data. I was at a conference recently where a presenter stated that, without better data, HR was aimless. I asked him: “What data do you have that shows that data improves HR decision-making?” He was advocating, hypocritically, for better use of data, without supporting data. Proponents of HR analytics should conduct analysis to show HR decision-making is improved with analytics.
Second, effective analytics does not start with data but with a clear description of a phenomenon. In HR, this is how to help an organization succeed through its talent (people), leadership, and capability (culture). When statistics became a popular science in the 1930s, a criticism was “dust bowl empiricism” where datasets were analyzed using statistics, without theory. Scholars learned that theory should drive data, not the other way around. Today, The Cloud and big data provide another level of information, but increase the danger of returning to dust bowl empiricism.
HR’s ‘theory’ is the business challenge of delivering talent, leadership, and capability. Without a deep appreciation of these business issues, HR professionals may collect data that is not relevant. In one case, a team of HR analysts created a model that predicted, with high accuracy, which senior managers were at risk of leaving. But regrettable losses within the company were less than 2% and turnover of key talent was not within the top five business priorities. Realizing the value of analytics requires understanding of the business context and the choices in which HR can intervene to drive value. Analytics is not an end in itself.
Third, recognize that there are many forms of data. Data found in spreadsheets can be analyzed statistically to discover correlations, paths, and patterns. But qualitative insights can be equally valuable. Empirical analysis solves puzzles; qualitative observations deal with mysteries. Both are viable targets of HR analytics. Puzzle-solving requires becoming an analyst who scrutinizes data to find trends; mystery-investigation requires becoming an anthropologist who observes what is not obvious and asks questions about what might be missing. The latter should anticipate what might happen in future, not validate and replicate what has already happened.
Finally, HR analytics cannot be isolated within HR; customers want integrated solutions. HR is becoming more connected to marketing, finance, information, and other functions and HR analytics should become part of an integrated solution to solve business problems.
When business problems can be identified, creative solutions may be proposed and, through collaborative analytics, more granular, solution-orientated information generated.
HR analytics offers the prospect of HR becoming a discipline with an equal voice in business dialogues; dialogues in which our involvement was unimaginable 15 years ago. But, for this voice to be credible, HR professionals must develop a deep understanding of business so they can address and solve relevant problems. Let’s bring to the table rigorous analytical insights about talent, leadership, and capability, and do so in the right way.