Data validation
Checking data to make sure it follows the right format and meets expected rules before it is used, like confirming that a date field actually contains a valid date.
The Simple Version
Checking data to make sure it follows the right format and meets expected rules before it is used, like confirming that a date field actually contains a valid date.
Detailed Explanation
Data validation operates at the point of data entry, ingestion, or transformation and is distinct from data verification: validation checks conformance to predefined rules (schema, range, format, referential integrity), while verification checks accuracy against an external reference. In AI pipelines, validation is applied to raw datasets before training to catch malformed records, out-of-range values, referential inconsistencies, and schema violations. Validation rules should be codified and versioned alongside training code so that any future data ingestion is subject to the same checks. ISO/IEC 5259 Part 4 covers data quality processes including validation within AI data pipelines.
Key Characteristics
- Rule-based: checks against predefined schemas, formats, ranges, and constraints
- Applied at ingestion, transformation, and loading stages of data pipelines
- Distinct from verification, which assesses accuracy against an external reference
- Validation rules should be codified, versioned, and reusable across dataset versions
Why It Matters
Automated validation gates in AI training pipelines prevent invalid data from silently corrupting model training, a standard engineering practice that significantly reduces debugging costs.
Real-World Analogy
Like passport control at an airport, it checks whether a document conforms to expected standards (correct format, not expired, biometric chip readable) but does not independently verify that the person is who they claim to be.
Common Misconceptions
- Validation and verification are interchangeable, validation tests conformance to rules; verification tests accuracy against reality.
- Valid data is necessarily accurate, data can pass all validation rules and still contain incorrect values that comply with the rules.