Goodhart’s Law is the principle that “when a measure becomes a target, it ceases to be a good measure.” It describes the unintended consequences that occur when people alter their behavior to manipulate a specific metric in order to achieve a reward or avoid punishment, distorting the metric’s original purpose.. [1, 2, 3]
This quote has been popping up in feed quite ubiqitously lately. Usually, it refers to some company that has decided to implement an AI usage dashboard in order to track their employees’ use of AI tools. It reminds of the burn rate metrics during the dotcom heyday.
Most people with a little experience would most likely scratch their noggins in wonder and ask themselves the question: “but why?”.
The funny thing is, this is not really a new trend, we just hear more about it because of the AI hype. I think many software delivery people have been presented with all kinds of odd metrics they have to report on. At least, I have seen my fair share.
The most common metric I have encountered is time. How much time has a team spent on a task. This is a pervasive metric that most organizations use, and more to do with budgeting constraints than actual delivery or value. It is also a metric that often becomes the whole point. It is more important that we meet the estimate than we deliver robust software or, dare I say, actual customer value.
I get it though. It’s all about money in and money out. Of course, at the end of the day a team still costs what a team costs in terms of wages.
There are numerous of examples like this that I think illustrates the same problem. We tend to measure the wrong things. And in a age of data I think there are some very real opportunities for some organizations to improve their deliveries and their business value provided they can keep the the on the ball and stay clear of the wrong metrics.
We live in an age where data is abundant, but insight is rare. The organizations that thrive won’t be the ones with the most dashboards — they’ll be the ones that understand which numbers matter, and which ones are just noise. If we can shift our focus from tracking activity to understanding value, we might finally start measuring what truly counts.