We assumed that institutional data would be clean. It was not. After six months of running Data-S with three government clients, I can tell you that the most important feature we built wasn't the visualization engine or the real-time pipeline — it was the data cleaning layer we almost didn't build.
The Reality of Legacy Systems
When we designed Data-S, we envisioned smooth API integrations and modern database schemas. Reality delivered CSVs from the 90s, inconsistent naming conventions, and data that had been handwritten and later digitized by a chain of different vendors.
The lesson: Design for the mess, not the ideal.
One Thing That Saved Us
The decision to implement a robust logging and observability layer early on saved us hundreds of man-hours. When a pipeline fails at 3 AM in a secure facility, you need more than just an error code. You need context.
We built a custom diagnostic tool that snapshots the data state before and after every transformation. It was a "nice-to-have" during development. It was essential in production.