Metadata
- Author: Emilie Schario
- Full Title:: I’ve Become the PM I Used to Hate
- Category:: 🗞️Articles
- URL:: https://locallyoptimistic.com/post/ive-become-the-pm-i-used-to-hate/
- Read date:: 2025-08-20
Highlights
When I query the production replica, I’m working with tables I helped design, fields I named, and relationships I understand intimately. (View Highlight)
But when I turn to the data warehouse, I’m suddenly a tourist in my own data. You go from a simple schema in postgres to some column in a table 15 levels removed from the raw data, transformed through a lineage DAG that would make a family tree look simple. (View Highlight)
The data warehouse abstracts away this complexity, which is usually a good thing. But sometimes, that abstraction removes exactly the context I need to interpret the results correctly. (View Highlight)
Data professionals typically ask questions like: • “What’s the monthly trend in user engagement across all cohorts?” • “How do retention rates vary by acquisition channel over time?” • “What’s the lifetime value distribution for users who signed up in Q3?” • “Can we build a reliable model to predict churn using this data?” • “What’s our data quality score for this critical business metric?” These are great warehouse questions. (View Highlight)
PMs (and other business stakeholders) ask questions like: • “Is the new checkout flow causing more payment failures?” • “Are users actually clicking the button we just moved?” • “Did fixing that bug yesterday actually fix the problem?” • “How many support tickets did we get about feature X this week?” These are operational questions where speed matters more than perfection (View Highlight)
if multiple teams are making decisions based on the metric, or if it’s consistently used in exec team-level reporting (consistently being a vague guideline, but if it’s used multiple quarters after launch), it probably needs data eyes on it. (View Highlight)
you can’t demand teams use only blessed metrics if you can’t keep up with the speed of the business, which hardly any data team can or should. The trick is finding the right balance between speed and rigor. (View Highlight)
whose job is it to actually worry about this transition? Most PMs probably don’t give a rip about it unless it causes pain in their day-to-day work. (View Highlight)