Why data quality beats model choice every time

When a model underperforms, the instinct is to try a fancier algorithm. In practice, the biggest gains almost always come from the data feeding it.

The usual suspects

Duplicate records, inconsistent categories, silent schema changes and labels that mean different things to different teams. None of these are glamorous, and all of them quietly cap your accuracy.

Make quality testable

Treat data checks like unit tests. Row counts, null rates, value ranges and freshness should be asserted automatically on every pipeline run, with alerts when they fail.

Fix it at the source

Cleaning data downstream is a treadmill. Where you can, fix the form, the integration or the process that produces the bad data in the first place.