How to actually use metrics 😱

Happy Friday!

I have a question at the end of this email that I'd really love your answer to.

Beekeeping season has started, but I can't find my harness to attach my camera for videos, so until I find that, there won't be any new videos about that. If you're wishing for more, though, Discovery has an amazing 2-episode show called The Secret of Bees. I watched it last night on Disney+. It is gorgeous and fascinating.

In the background, I keep growing and working on my digital garden, and that leads me to thinking I should just start dumping my knowledge into it. That way, folks can see what I do, and more importantly, how I do it. Also, it'll be a great reference for me to get all of that stuff out of my head.

Since this is all being done in the style of my own digital garden, though, it's informal, messy, and living.

One thing in particular, though, that I was proud of was that I wrote a lot about metrics, measures, and signals. This isn't a new topic for me, but I thought I'd just write the main "Guide" that lives in my head. There is more to add, but I'm happy with it.

If you want to get better with actionable metrics and developing meaningful signals as a leader, I think you should take a look.

This isn't an article about what folks "Should" do or my ideas of what is good. This is what I do regularly for clients and is very battle-tested. In fact, my consulting engagements are based around goals backed with metrics. I'm not done until that goal is accomplished and the agreed-upon metrics match. My business depends on my ability to establish and track effective metrics.

In this newsletter I'll offer one additional bit of advice that I've seen folks struggle with all the time.

Metrics and signals aren't proof, they're indicators.

So many people get paralyzed with the idea of metrics because of conceptual imperfection. The metric doesn't paint a complete picture, the data quality is questionable, and the math may not mean what we think. All of these objections leave us feeling like we shouldn't trust what the data indicates.

The thing is, though, these metrics are meant to be something we create as an indicator to direct our actions. They're more like thermometers than anything else. A thermometer doesn't "prove" you're healthy, but they indicate you are sick.

That's how they should work. They're an indicator that is good enough to guide our actions. Don't let the idea of conceptual imperfection stop you from being more data-driven.

The Question:

What are the struggles you've had with establishing useful metrics or working with them?

Bonus Question:

Want me to teach you how to do this?

Sincerely,

Ryan