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Metrics, metrics, why must you be so complicated?

I’ve always thought that measuring stuff was pretty easy. If you’re just starting out trying to “implement some metrics” – it’s super easy to start. Measure whatever you can measure. Don’t start with “what you think you want to measure, if only you knew how to collect that data.” Start with what’s easy to measure. What can you learn from it? Learn how to use data for insight.

And as you learn how to measure, you start to get a sense not just of what to measure, but to what degree of depth those measurements are useful. So the “if we only knew how to collect that data” excuse starts to fade.

Mechanics aside, I always thought that metrics were pretty straightforward – even as your organization matures and your measures become more complex, you’ve grown along with that complexity, and built measure upon measure. It’s still straightforward.

But every truth has a counterpoint.

For support, rather than illumination.

You can use metrics to validate (i.e. prove you are right) and you can use them to learn. And if you are using metrics to learn, that inherently implies that you need to change what you measure continually. Look at it this way: You can learn what times people normally eat lunch by standing in a restaurant and counting the people buying lunch. But you have to track specific individuals to determine how likely people are to be repeat customers. Because wouldn’t you operate the business differently knowing that your busiest spike was at noon, but also knowing that all of your repeat customers typically show up at 12:30? (Yes, you would.)

Where you are -vs- where you want to be
I had epiphany today about some of our operational metrics. Yes, we are measuring the right objectives. Are we delivering what we say we’re going to deliver, on time, on budget, of appropriate grade and quality? Fine. But we’re measuring what we think we’re capable of. We set a target and expect to achieve it. How are we doing?

Well, it’s certainly valid to measure what we hope to achieve. But what about where we hope to improve? Obviously, we can pick something that we think we need to improve and start measuring the heck out of it until it has higher numbers and everything is fantastic. But that’s just validation. What about illumination? We also need to measure what we’ve never considered measuring – what can we learn from it? Not to say we should just start measuring everything that moves (or doesn’t.) But return to that infancy principle of “measure what you can.” What do we not see? What data looks like nothing? Because “nothing” may just indicate really, really poor performance.

So is measurement for improvement straightforward? Simple!

But measurement for discovery requires that you find new territory to search.