Context
The task was straightforward. Migrate a large HR dataset, around 250,000 records, from the old system to the new. I was responsible for the transformation, validation, and import logic. On paper, everything looked good. For weeks, I processed file after file. Numbers lined up. Nothing flagged as wrong.
Until the final QA round.
The Drag
Roughly 2,000 data rows were missing from the target system. No errors, no rejection messages, just gone. On a set that large, the gap was small enough to escape notice. But it was real. I spent two weeks chasing it. Days of parsing logs, nights of step-by-step review, with no fault found.
The Twist
Eventually, the root cause surfaced. The original data was so poor that we had built an extensive correction engine into the migration logic. Every row passed through layers of sanitization and reformatting. The missing 2,000 rows?
They were the only ones that didn’t need fixing.
Clean rows. Untouched. And because the logic expected everything to be broken, the clean ones slipped through without being processed.
The Insight
Sometimes, the outlier is not the broken case. It’s the one that works.
And if your system assumes error as the norm, make sure it also knows what to do when there isn’t one.