Glossary · Data integrity
Data integrity (GxP)
Also known as: Data integrity, GxP data integrity, DI
German: Datenintegrität
In GxP regulation, data integrity is the extent to which data are complete, consistent, accurate and trustworthy, and remain so throughout the data lifecycle, from creation through processing, review, reporting and archiving to destruction. It applies to paper, electronic and hybrid records alike.
- GxP
- Data integrity
In one sentence
GxP data integrity: data that are complete, consistent, accurate and trustworthy across their whole lifecycle, on paper and in electronic systems.
Example
A contract laboratory maps the data flow of its chromatography system, restricts who can reprocess results, reviews audit trails before approving each batch result and keeps the raw data files, not just printed reports, for the retention period.
How it applies
- Guidance, not one law: The expectations come from GMP, GLP, GCP and GDP rules read together with guidance: MHRA “GxP Data Integrity Guidance and Definitions” (2018), PIC/S PI 041-1 (2021), FDA “Data Integrity and Compliance With Drug CGMP: Questions and Answers” (2018) and WHO TRS 1033 Annex 4 (2021).
- Principles: The attributes a record must have are summarized as ALCOA+: attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring and available.
- Data governance: Regulators expect a system of controls, not only individual checks: defined data owners, data flow maps, risk-based review of audit trails, access and role management, and a culture in which staff can report errors without fear.
- Computerized systems: Electronic data depend on validated systems and controls such as unique user accounts, audit trails, backup and archiving; see EU GMP Annex 11 and 21 CFR Part 11.
- Hybrid systems: Where an instrument produces electronic data and a paper printout is signed, the electronic data remain the original record and must be kept and reviewed.
- Technical documentation: Batch records, laboratory records and validation reports are evidence only if their integrity can be shown. Keep the raw data, metadata and audit trails that let an inspector reconstruct how a result was produced; see good documentation practice.
Data integrity vs. data security
Data security protects data against unauthorized access, loss or damage. Data integrity is broader: it asks whether the data are trustworthy as a record of what actually happened. A well-secured system can still hold data with poor integrity, for example when results are recorded after the fact or failed test runs are silently discarded.