[lbhdc]

That is interesting. I have worked in the space too, and have seen similar attitudes.

I think it often comes down to who is responsible for making decisions with that data. If a product or business person is the one driving a feature, and looking for adoption, the engineers likely aren't going to be invested in building out sophisticated metrics. They get the metrics they are responsible for from their cloud provider (resource use/latency/scale).

I think that problem is compounded by the perception that these integrations are going to tank your products perf (may hurt the metrics engineers care about).

I think all of those dynamics change in really big companies with thousands of engineers. Then you can often end up in a situation where engineers are now required to maximize product metrics, and need visibility into their small slice of the pie.

So, I think its largely incentive, which is why I see all of the metrics vendors targeting product and sales people in small/mid sized companies.

[tiazm]

That’s an interesting point. One thing I’ve noticed, though, is that even the people who are directly exposed to incentives (usually product and marketing) tend to focus almost exclusively on the final KPIs they’re measured on, like revenue or conversion rate.

Because of that, the analytics layer is often seen as something secondary. As long as the top line numbers are moving, there’s little perceived urgency to invest in a structured analytics foundation that explains why those numbers move.

So even when incentives exist, they’re often too outcome focused. Analytics that helps understand mechanisms, not just results, struggles to justify itself until something breaks or growth stalls.

[lbhdc]

I think that is because they are being judge by their outcomes.

In the space I was in (ads) users were highly mistrustful of the data. They felt everything was kind of fuzzy (eg how well are you measuring unique users and their actions).

They would end up using multiple vendors (and we would have to spend a lot of time comparing and contrasting results). They really really want "apples to apples" comparisons.

At the end of the day they were trying to answer, does what I am spending my money on give me the results the business needs? To your point there is a lot of nuanced data, but their bosses definitely only cared about the top line, did it move the needle.