The Unglamorous Work of Data Quality

It's not a controversial claim anymore that most AI projects fail for reasons that have nothing to do with the model. Duplicate records, inconsistent formatting, undocumented business logic buried in spreadsheets — this is the terrain that determines whether a project succeeds, and it's rarely glamorous enough to get budget on its own.

The organizations pulling ahead this year treated data quality as infrastructure, not a one-time cleanup project. They built pipelines with validation baked in, assigned real ownership to datasets the way you'd assign ownership to a service, and measured data quality with the same rigor they measure uptime.

This matters more, not less, as AI tooling gets more powerful. A more capable model amplifies whatever it's given — including bad data, at scale, with total confidence. Garbage in, garbage out was always true; it's just louder now.

The unglamorous fix remains the same one it's always been: fewer sources of truth, clear ownership, and validation that runs before problems ship, not after.