When I ask managers about useful people analytics applications, I’m often surprised by how simple the examples are.
This stands in sharp contrast to the stories of sophisticated analytics I hear about at conferences.
Here’s one example of a simple but useful analytics tool.
An analytics leader I recently spoke to shared just how much managers appreciated a tool that gave a graphic showing spans of control.
Quite simply, the tool being talked about helped managers plan reorganizations.
Thanks to its very visual look, they could see – at a glance – where spans of control were abnormally high or abnormally low.
What was good about it, was the fact the graphic involved data but there is no sophisticated mathematics. It was just an effective way to display counts.
The key takeaway here, I feel, is that what mattered to the managers was not whether the tools were sophisticated or not, merely whether it was useful in helping them think through a reorganization – and this tool did exactly that.
In our book, The CMO of People: Manage Employees Like Customers, Peter Navin and I shared a people analytics presentation that Peter gave to a CEO.
The presentation included a table of their sales goals in Europe, how many salespeople they would need to hire to achieve that goal, how many recruiters that would take, and how many recruiters they had now.
It was dead simple math, but it made a clear point that they needed to hire more recruiters if they wanted to hit their sales goals in Europe.
It was a simple but effective use of numbers to show what resources HR needed to support the business in achieving a critical goal.
At another client, their analytics team made a very rough estimate of the impact of benefits changes on the number of people likely to retire and who they were.
Here the takeaway was that the leadership didn’t ask for precision, they were delighted that the analytics team had taken them from a world of “we have no idea if we’ll face a tsunami of early retirement” to “it will be somewhere in this range, so no need to panic, and we ought to look more closely at this one department.”
I make these observations for one very important reason.
I worry that people analytics departments – upon hearing about sophisticated analytics – will overlook how much business value they can provide with simple analytics.
Managers rarely need precision.
They just need enough data to guide their decisions.
Managers rarely need analytics so advanced that it will tell them the answer – such as how to reorganize.
They simply need data that will help inform their thinking.
Furthermore, managers appreciate analytics they can easily grasp, such as those that only use high school math, rather than analysis they don’t fully trust because it involves tools like Monte Carlo simulations that they have a limited grasp of.
The takeaway is to pay attention to the sort of information managers value and don’t dismiss the importance of simple analytics.
Simple well-presented data can help provide insight into an issue your managers care about.
Delivering those easily understood insights may be the most valuable work you do.
