In week one of every platform review, I find the same five problems. Your company probably has at least three.
Here’s what I look for:
1. Pipeline ownership. Who gets paged when something breaks at 2am? If the answer is “whoever’s available” or “our senior engineer,” you have a single point of failure - and probably a burned-out employee.
2. Data quality monitoring. Not “do pipelines run?” but “is the output correct?” Most teams monitor job status. Few monitor whether the numbers make sense.
3. Schema change governance. Who decides when a schema changes? Who gets notified? If downstream systems break every time the source team ships, there’s no governance.
4. Documentation age. I ask to see the data dictionary. If it’s more than 6 months old or doesn’t exist, tribal knowledge is your architecture.
5. Time to first insight. How long from “we need this data” to “we can use this data”? Anything over two weeks points to process bottlenecks, not technical ones.
The problems aren’t surprising. What surprises teams is how much they’ve normalized. “That’s just how it works here” is the most expensive sentence in data engineering.
Week one reveals what six months of internal meetings can’t.
Details on how I run platform reviews: https://thomasnys.com/services/platform-review/
Which of these five would show up in your platform review? My guess is pipeline ownership - it’s the most common.