Some things work quantitatively, some don't. I like using quantities for athletic training.

If I was training for a marathon I would set numerical goals for how much I would run a week coming into it. If I am recovering from an injury that's even more the case.

Lately my son and I have been learning a martial arts form that has about 80 steps in it. There is a lot new about it for us: I've never done a two-person form or a weapons form before, and he's never trained in a form at all. You learn this kind of thing by breaking it up into pieces: so we have a burndown chart on the wall for it, which I think is a good motivation.

Quantitative approach aka “noob gains” stops working fairly quickly even with athletic training

"Noob gains" doesn't have anything to do with setting goals or quantitatively measuring progress. It refers to the speed at which unadapted athletes are able to progress (e.g. add weight to the bar, run farther) each workout when they first start training, compared to athletes who have been training longer, are more adapted, and progress slower.

Most professional and highly adapted athletes are extremely quantitative about their training programs, much more so at the advanced stages than in the early stages. They just progress much slower because it takes more training for their bodies to continue adapting.

"Noob gains" just means that new quantitative measurements can take place over a short period.

Athletics is incredibly quantitative- even for the most advanced and experienced athletes.

Ah, yes, the numbers-or-it-doesn’t-count business people are rough. I like to call this strategy “ignoring empirical evidence that’s hard to do math with.”

I don't think that in general (even in organizations) the KRs of OKRs have to be quantitative, they just have to be more-or-less binary. For example "agree upon the spec for feature X" is a perfectly valid KR.

I think it’s a tension between useful metrics to help measure performance and stopping people who game the metrics. This is hard as vanity metrics are easy to pick and game.

I try to go for macro metrics that at least let people understand what’s important, bur also not directly rewarding specific values or targets. An example is that I’m trying to promote data hygiene in my organization so we have some guidelines for how to clean data, where to store it, etc. We a general metric for “Who is reading our guidance” to at least see what pages are read. We have another for “who is versioning their data.”

I don’t have goals tied to specific numbers of readers or data repos, but do use it to at least make sure the material is available.

I’m still testing to see, but the problem with no metrics, I think, is that sometimes people will get astray without an outside source to help test and prioritize.

Yes, yes and YES, thank you.