It’s often said every New Year is a time when a company is at their most vulnerable – because staff have had their Christmas and New Year breaks to really think hard about where they want to be and what they want to do in the coming year ahead.
But you know, HR people aren’t immune to this time of year being when they want to think about ‘their’ careers too.
And HR analytics professionals are no different also.
It’s my belief that we all need to take stock at some point, and now is as good a time as any for HR analytics experts to consider how they might want to drive their career.
Here are three paths to consider:
The proven status quo
The most obvious future for analytics pros is just to keep on doing what they’re doing.
There is a lot of ongoing work related to data management, routine reporting, dealing with ad hoc requests, upgrading technology, and automation.
This is the work managers want their analytics teams to do, so it’s the natural focus.
But maybe now, it just lacks some of the excitement that analysts first had when all this was new.
Data-informed decision consultant
One of the most impactful roles for HR analytics pros is to help managers make data-informed decisions.
This requires helping managers ask the right questions, going on data safaris to dig up relevant evidence wherever it might be, and then figuring out what that mixed bag of evidence means.
I think this is better positioned as “evidence-based management” (EBM) rather than “analytics” since traditional analytics are only one source of evidence for informing decision-making.
In the EBM paradigm, one should also be looking at academic evidence, interviews, and so on. Those more interested in this can learn about EBM from the Centre of Evidence-based Management (CEBMa.org).
The problem with this role is that managers often don’t want any help making decisions.
What can be even more difficult is when they only want evidence that supports what they’ve already decided to do.
If one goes down the route of being a data-informed decision consultant using an EBM approach, then ones has to work hard to build trust with managers and slowly demonstrate that it’s in their interests to partner with them in assessing the evidence.
Advanced methods
There are interesting new methods of analysis coming along.
The most obvious one to learn is causal modeling, which is already being used in some places.
It’s exactly what it sounds like. It builds a map of how A causes B, and so on.
The topic has been much advanced by the work of Judea Pearl and you can read about it in The Book of Why.
There is also something called Topological Data Analysis, which has a set of tools to analyze complex data sets that don’t conform to a neat linear graph.
It is based on the mathematical field of topology, hence the name.
Yet another advanced method is explainable AI, which will be a big advance over existing machine learning techniques because it will give more trustworthy results.
This has close ties with causal modelling so the two will go well together.
The trouble with investing effort in advanced methods is that most organizations are not ready for them, and to be honest, these methods are not fully ready to be used in most organizations.
Developing expertise in these advanced methods may pay off or it may just prove to be a hobby.
The big payoff is that if you can spend time learning about these methods it will stretch your mind.
Conclusion
Analytics is still an important and evolving area. But, it’s also the case that in many organizations it’s reasonably mature.
People working in that field should take a moment to consider where their career is going and what they want to focus on