When we carry our competencies across the measurement bridge, we enter a fake and dangerous world—as a tool of assessment, order and control, they are worse than useless. But as public signifiers for what we deem most important, they are another way we can cascade meaning in our organizations, and thereby help our leaders and teams understand what’s most important.

And, as we’ve seen in this chapter, the problem with almost all data relating to people—including you—is that it isn’t reliable. Goals data that reports your “percent complete”; competency data comparing you to abstractions; ratings data measuring your performance and your potential through the eyes of unreliable witnesses: it wobbles by itself, and fails to measure what it says it’s measuring.

The key to understanding performance is to stop thinking of it as a broad abstraction, and instead start finding elements of it that we can measure reliably and act on usefully.

...you want to be represented by data that simply, reliably, and humbly captures the reaction of your team leader to you. That’s not you, and it shouldn’t pretend to be you. It’s your leader, and what she feels, and what she would do in the future. And that’s enough. Truly.

Telling someone they’re smart, good at math, or a great presenter implies that their performance depends on a stable trait. If they did well on a test, they have that trait, but if they did badly, well, they’re just out of luck. They don’t have what it takes and there’s not much they can do to change it.

But rephrasing that feedback as process praise is more likely to have the intended effect. Telling someone they did well, or did a good job on a test or presentation, focuses less on stable traits and more on the particular instance at hand. Which means if things don’t go so well once in a while, it’s not a mark of failure or lack of ability. It’s just a misstep and a reminder to work harder next time around.