A few years ago, when I spoke to a group of people analytics leaders, much of the conversation was usually about their ambitions – namely to deploy predictive or prescriptive analytics. They also claimed they were interested in machine learning and novel visualization tools.
How times have changed.
Now, the topic that often seems most top of mind is data management.
So why is this? Well, it’s certainly the case that as complexity increases, analytics leaders are consumed with the many difficulties of acquiring data, keeping it clean, integrating data from different systems, creating a data warehouse that will allow them to run useful reports, and managing data privacy issues.
Yes, that’s a whole lot of issues to be consumed with!
But while those issues clearly need to be tackled, it strikes me that none of that is really analytics, it’s just the data management work needed to build an infrastructure for some types of people analytics. And yes, it feels far less exciting than the days when we were all dreaming about powerful insights.
You’ll notice that I said the “infrastructure for some types of people analytics”. There will be many HR/business issues that don’t need this type of data infrastructure and other business issues where this data won’t even help.
To me this implies that putting too much of our attention into data management is misplaced.
Evidence-based management
I keep returning to the mantra of evidence-based management, which is to seek out the best available evidence.
Evidence might be academic research, an in-house experiment, interviews with knowledgeable employees, or an ad hoc survey. None of those sources of evidence require or even benefit from a big data management program.
The challenge for people analytics leaders is that solid data management feels almost like a compliance issue. If you don’t have that then you’ll be continually beaten up by the business. Furthermore, it’s such a big challenge that you won’t have the resources to do that along with other work.
Have a consulting team instead
One possible approach is to spin off a part of the people analytics budget into a consulting team that tackles projects that don’t rely on a strong data management infrastructure.
These people can still do useful analytics, but the bulk of the budget continues towards the expected infrastructure work. If business leaders want faster improvements to the data infrastructure, then they can fund that; but it shouldn’t have an impact, either way, on the analytics consulting team’s budget.
People analytics has matured an incredible amount in the past decade. By and large, that’s a good thing. However, being stuck in the relatively unrewarding work of building data infrastructure is not the best place to be. People analytics leaders need to find a way to invest in good analytics work that stands apart from data management.