Some numbers are more important than others – this is one of my key points in 7 Ways to Make Data Work for You. In becoming data driven, you need to make sure your folks are focused on a few key metrics and not unless numbers.
Today, I want to go a step further. Let’s say you’ve picked a key metric. And now you’re seeing variances. It’s not enough to say “variances are bad, fix it.” You need to understand why they're happening.
Here’s an example: A non-profit I know is becoming more data driven (a good thing). One of the first things they looked at was excessive overtime. This non-profit runs many residence facilities that are very, very labor intensive. In fact, 75% of their expense is payroll, and some units had tremendously large overtime charges.
When looking at the numbers, the manager (rightly) was quick not to jump to the most obvious conclusion: “We need to enforce are overtime policies.” Instead, he asked a few questions: Were facility managers using overtime as a way to compensate staff for lower-than-industry-standard wages? Were they using it as a type of bonus to keep good people and the standard of care high? If management chose to abruptly stop this practice – without reviewing pay scales and doing some serious thinking about how to retain good people – will it hurt more than help?
To me, this manager took the right approach. Too often, people focus on a single explanation for numbers when there can be multiple causes. When you do this, you risk getting locked out. People come to see the number not as the beginning of a conversation, but as a club to punish.
This is not to say all overtime was warranted in this case. Indeed, much of it could be attributed to lack of management oversight. But that wasn’t the only reason.
When people use numbers in your organization, do they use them to start a conversation and reach an understanding of a shared reality? Or are they used to punish and control?