Validation
Checking values against rules, such as a date being in the past or a state code being on the allowed list, and reporting what fails.
Validation finds problems; it does not fix them. A value can also pass every rule and still be wrong, which is why valid is not the same as accurate.
Where it comes up
- Ambiguous day and month orderA date such as 03/04/2025 could mean March 4 or April 3, and nothing in the cell says which.
- Inconsistent category labelsA column meant to hold a few fixed values holds many spellings of them.
See also
- Data qualityHow well a dataset serves the purpose it is used for. Usually judged by accuracy, completeness, consistency, validity, uniqueness, and timeliness.
- SchemaThe declared structure of a dataset: its columns, their names and types, and the rules they must satisfy.
- Data cleaningFinding and correcting values in a dataset that are wrong, inconsistent, duplicated, or missing, so the data can be relied on.