A Confusion Worth Fixing
Data validation and data verification are often treated as synonyms. They are not. Confusing them leads to workflows that catch the wrong errors at the wrong stage.
Data Validation
Validation checks whether data conforms to a defined format, rule, or schema. It asks: is this data structurally correct?
Examples:
- Is this email address formatted correctly?
- Does this date fall within the allowed range?
- Is this numeric field within acceptable bounds?
Validation happens at the point of entry or ingestion. It is largely automated.
Data Verification
Verification checks whether data accurately reflects the real-world state it is supposed to represent. It asks: is this data factually correct?
Examples:
- Does this reported conversion rate match the source analytics platform?
- Is this clinical outcome figure consistent with the trial protocol?
- Does this market size claim correspond to a dated, citable study?
Verification requires judgment, source-tracing, and often human review. It cannot be fully automated.
Why the Distinction Matters
A dataset can pass all validation checks and still be wrong. A column of perfectly formatted percentages can contain figures copied from an outdated competitor benchmark. Validation would not catch this. Only verification would.
Building both into your workflow — automated validation at ingestion, structured verification before any claim leaves the organisation — closes the gap where most consequential errors enter.