The most expensive way to avoid fixing your organizational problems: migrate to a new data platform.
A client was convinced their data problems were a platform issue. The stack was “outdated.” Databricks would “solve everything.” Five months, EUR200K, and a full migration later - the pipelines still broke on the same schedules, the same data quality issues persisted, and the same people were firefighting the same problems.
The root cause was never the platform. It was no ownership model, no data contracts, no testing, no monitoring beyond “did the job run.” These problems follow you to any tool. Databricks, Snowflake, Fabric - none of them fix undocumented transformations or missing data owners.
I’ve changed my mind on this over the years. I used to think better tooling would naturally improve quality. It doesn’t. Better tooling just lets you make the same mistakes faster and more expensively.
The fix that actually worked: making producing teams accountable for what they ship downstream, assigning a single owner per data domain, and agreeing on what “correct” looks like before anything moves between teams. Could’ve done all of it on the old stack for a fraction of the cost.
What organizational problem are you trying to solve with a technology purchase?