Measure what matters—not everything.

the problem with data-ism

One of my biggest criticisms of modern society is data-ism: an obsession with tracking every possible metric in your life and business. We pay attention to numbers that can be fudged, substantiated in one way or another, but are ultimately just fungible and gameable.

Take the software world. You can easily manipulate how many commits you make, how many pull requests you open, how quickly they merge into main, the velocity at which code is added or removed. But ultimately, these are crude proxies for productivity. Maybe that's the best we've got, and we roll with it. Fine. But the numbers only tell a piece of the story.

You see it in day-to-day life too—wearables tracking heart rate, steps walked, stairs climbed. That's not to say it's useless. What you measure tends to matter to you, and perhaps measuring your steps helps you make sure you get enough.

Hell, I use numbers myself. I track how long I jump rope (15 to 30 minutes), and during strength training, it's reps and sets. These numbers are orienting. We need this basic level of mathematics to tell us the stories of our day-to-day lives.

Maybe what I'm saying is obvious to others. I speak it for those who might be in a similar pocket: don't measure everything. Don't obsess over every single number. Measure the things that actually matter and focus on those.

when the numbers clicked

I'll be honest—I used to roll my eyes a little every time we talked about ARR and moving this KPI upward. Numbers can be manipulated, and when people take them too seriously, it raises skeptical flags.

What shifted my orientation was building a tool for my company. My boss suggested I add analytics—track how many times people used it, unique visitors, daily visits, file exports. As I watched the numbers climb, I saw a story. A value story. Without it, I couldn't go to my peers and superiors and say, "Hey, look at the impact I've made."

From there, you can build proxies. Each exported deck probably saved someone 1-2 hours of work. Span that across hundreds of exports and you've got hours saved. Average the salary range, deduce an hourly rate, and suddenly you've got back-of-the-napkin math—the kind you'd see in a McKinsey case study.

the balance

Those numbers tell stories. In a GTM organization, that's the language they speak.

Are these numbers scientifically airtight? No. Can they be manipulated? Sure. But they're better than nothing—and they let you articulate value in a way that actually lands.

The balance isn't avoiding metrics. It's being selective about which ones you let tell your story