Why data quality comes first in AI projects

In AI projects, attention usually goes to model selection and accuracy scores. Yet many failed projects fail not because of the model, but because of the data it was given.

Missing records, duplicated samples, imbalanced classes and inconsistent labels cause performance that looks strong in testing to drop quickly in the field.

That is why data readiness should be treated as a quality gate: profile the data, measure gaps, verify label quality and ensure traceability from source to model.

The “quality at the source” principle that quality engineering has applied in industry for decades holds just as true for AI projects.