Glossary

What is Data verification?

Data verification is the process of confirming that data is accurate and faithful to its source, that it was collected, transferred, or stored without being altered or corrupted. It answers a different question from validation: is this data actually correct, not just well-formed? Common methods include checksums, double entry, cross-checking against the source, and round-trip verification.

Verification matters most where accuracy is critical: data migration, backup and recovery, scientific records, and financial data. Together with validation, which checks that data follows defined rules, verification forms the two pillars of a data quality pipeline. For AI, verification also underpins traceability, confirming that the data behind a result is the data you think it is.

Frequently asked questions

How is data verification different from data validation?

Verification confirms data is accurate and faithful to its source; validation checks that data follows defined rules such as format and range. Verification asks 'is it correct?', validation asks 'is it well-formed?'

What methods are used for data verification?

Checksums, double entry, cross-checking against the original source, and round-trip verification that compares data after it moves. These confirm data was not altered or corrupted in transit or storage.

Where is data verification most important?

In data migration, backup and recovery, scientific records, and financial data, anywhere accuracy is critical. For AI, it supports traceability by confirming the data behind a result is the intended data.