“Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.”

― H. James Harrington

The human mind is an exceptional pattern detection engine. In fact, it’s so good that even in purely random data, the human mind will detect patterns that aren’t there. Gamblers are the most famous examples of these, relying on card streaks and dice patterns, but businessmen and scientists are as easily deceived as the gambler if the deceiver is their own pattern-generating mind.

Indeed, when scientists expect a result—and they measure the result—they are often deceived by their own minds into seeing results that are not present at all. The scientific technique of double blind studies exists because even science, when performed by motivated humans, is suspect. When you start with an opinion about what you want to be true, it’s easy to accidentally measure in a way that makes the opinion look true.

None of us are good at extracting true patterns from false patterns and when sampling data—and using memory—we are worse. Our default analytic capabilities are easily defeated by random irregularities in the data. The most effective solution yet discovered by humankind is the use of metrics. We decide how we will measure the data we wish to keep track of and then measure said data regularly and methodically, without human judgment interfering.

And so measurement is born. We must measure data in a repeatable fashion in order to control our work. But even so—even if we measure data reliably—we are able to tell ourselves stories about our data that are deceptive after the measurement. “Oh, that’s just Bob trying to solve problem X.” If we’re going to use our data correctly, we have to tell ourselves before measurement what we intend to do with the data.

And thus the genesis of metrics.