What about things like "Stuck on XYZ because no one fixes it because it doesn't show any good metrics and management doesn't care about it. So I fixed it at the expense of my own time."

>What about things like "Stuck on XYZ because no one fixes it because it doesn't show any good metrics and management doesn't care about it.

I'm confused by your comment. How did you decide this was something worthy of fixing if it "doesn't show any good metrics"? If you can't quantify the issue in any manner, how do you determine it's worth doing?

Let's say you've got a logging system that sometimes drops lines. And this sometimes makes debugging things hard, because you can't say whether that log line is missing because the code didn't run, or because the log line was lost.

Impact on end users? Nothing measurable. Impact on developers? Frustrating and slows them down, but by how much? It's impossible to say. How often does it happen? Well, difficult to count what isn't there. Would fixing the issue lead to a measurable increase in stories completed per week, or lines of code written, or employee retention? Probably not, as those are very noisy measures.

Nonetheless, that is not a fault I would tolerate or ignore.

Generally, using one's brain.

This is especially important when metrics would not be expected to be available -- for example, if you're designing a nuclear reactor, you need to think hard about ways to prevent a meltdown in advance, rather than collecting meltdown statistics and then fixing the piping problems that correlated with the most nuclear meltdowns.

This is also necessary when the true metric that matters is very hard to evaluate counterfactually. For example, perhaps your real task is "maximize profit for the company", but you can't actually evaluate how your actions have influenced that metric, even though you can see the number going up and down.

And necessary as well when a goal is too abstract to directly capture by metrics, resulting in bad surrogate metrics: for example, "improve user experience" is hard to measure directly, so "increase time spent interacting with website" might be measured as a substitute, with predictable outcomes that bad UI design can force users to waste more time on a page trying to find what they came for.

All of these problems are faced by metric designers, who need to pick directly-measurable metric B (UX design metric) in order to maximize metric A (long-term profits) that the shareholders actually care about, but they cannot evaluate the quality of their own metrics by a metric, for the same reason that they were not using metric A directly to begin with.

There is also the classic story about returning war planes. You can try to make sense of the damage, the bullet holes, and try to create strategies around how to improve affected areas. The problem is that the damage you actually want to inspect and prevent is on the planes that did not make it back, the ones you do not have an abundance of data on.

The classic war planes story was about Abraham Wald - https://en.wikipedia.org/wiki/Abraham_Wald

> I'm confused by your comment. How did you decide this was something worthy of fixing if it "doesn't show any good metrics"? If you can't quantify the issue in any manner, how do you determine it's worth doing?

Because sometimes some things without metrics are incidental to the actual thing you set out to do.

E.g. a large refactor that switches libraries which is necessary for your new service that give 10% lower latency. But that library refactor will need to be done and it will take 2 months.

This is a challenge often encountered by product owners. At times, what appears to be a problem may not actually be one. Conversely, you might discover that a perceived issue holds greater significance than the current circumstances or priorities suggest.

During my tenure as a product manager at Rakuten, I consistently grounded the initiation and culmination of all product endeavors in data. This approach dictated that prior to embarking on any project modifications, the presence of data was imperative to validate (or refute) the underlying hypotheses. This mean that if we just have an hypothesis and no data, we first add hooks to understand the situation throught data (ex. GA events, etc.).

For instance, when executives contested the efficacy of our registration form for X reasons, it became incumbent upon us to ascertain its current standing. This involved computing the ratio of registrations to site visits and conducting an in-depth analysis of the errors that users encountered while interacting with the form. We achieved this by integrating Google Analytics events with error messages, among other techniques. We provided first a picture, then we proceeded to make the changes.

This systematic approach enabled us to gain a comprehensive understanding of the situation, facilitating the implementation of purposeful changes. Subsequently, we gauged the impact of these alterations using key performance indicators (KPIs) such as the registration-to-visits ratio, and the previous events tracking changes too to understand if we really made a difference.