Every growing startup hits the same three data problems. Not similar. Identical.
Here’s what I keep finding:
Problem 1: The “temporary” pipeline that’s now load-bearing. Built for a one-time migration 18 months ago. Now processes 40% of revenue data. The person who wrote it? Left six months ago. Documentation? A Slack thread that got archived.
Problem 2: Schema changes announced via Slack DM. The notification system is whoever remembers to message whom. The team reading from the table finds out about Friday’s change on Monday morning. When dashboards break.
Problem 3: Quality checks that run after the dashboard updates. By the time you catch bad data, 50 people have already seen wrong numbers. The CEO included. Now you’re explaining instead of building.
Here’s the thing - these aren’t technology problems. They’re communication problems wearing technical costumes.
The fix isn’t a new tool. It’s three conversations:
- What happens when this breaks at 2am?
- Who needs to know before you change it?
- What does “correct” actually mean for these numbers?
I got this wrong early in my career. Spent months optimizing pipelines when the real issue was that two teams never talked to each other.
Somewhere in your stack is a table with 40 downstream consumers and zero documented owners. That’s not technical debt. That’s a time bomb.
↳ I wrote more about what a platform review actually uncovers: https://thomasnys.com/services/platform-review/