Data validation checks whether data meets defined rules before it is accepted, stored or used in a business process. It can test format, completeness, range, consistency, uniqueness and whether a value agrees with an authoritative source.
Which validation rules are common?
- Required-field and permitted-value checks
- Length, format and data-type checks
- Range and reasonableness checks
- Cross-field and cross-record consistency
- Duplicate and identifier checks
- External verification against trusted sources
How does validation fit into a workflow?
- Define the rule and the risk it controls.
- Run the check at data entry or ingestion.
- Reject, quarantine or warn on failure.
- Route exceptions to an accountable reviewer.
- Record the result, source and timestamp.
- Revalidate when material fields change.
Validation vs. verification
Validation asks whether a value conforms to the expected rules. Verification asks whether the value is factually correct, often by comparing it with a document, registry or independent source. A correctly formatted bank account can still belong to the wrong recipient.
What makes a validation rule useful?
The rule should be specific, testable and tied to a decision. It should distinguish a blocking failure from a warning and explain how an exception can be resolved without silently weakening the control.
How should performance be monitored?
Track failure rate, false positives, false negatives, override frequency, resolution time and downstream incidents. Review rules when products, jurisdictions, systems or source formats change.
