Definition

Data integrity is the degree to which data can be trusted as complete, accurate, consistent, and reliable throughout their lifecycle. In regulated life sciences, data integrity underpins product quality decisions, batch release, investigations, validation evidence, and regulatory submissions.

Data integrity applies to both paper and electronic records. It includes the data values themselves, metadata, audit trails, calculations, reports, system configuration, review evidence, and the procedural controls that govern how records are created, changed, reviewed, retained, and retrieved.

Why Is Data Integrity Important?

Data integrity is fundamental to product-quality and batch-release decisions because patient safety depends on the accuracy and reliability of regulated data.

• Supports trustworthy quality decisions and inspection readiness.
• Protects product quality and patient safety by ensuring records are reliable.
• Connects system controls, procedures, training, and human behavior.
• Helps prevent incomplete records, unauthorized changes, missing metadata, and weak review practices.
• Provides a foundation for Part 11, CSV, audit trail review, and data governance programs.
• Protects organizations from data manipulation, unauthorized changes, or accidental loss.

What Does Data Integrity Include?

Data integrity includes data governance, data-lifecycle management, electronic records, audit trails, user-access control, system security, and backup and recovery.

ALCOA+ expectations

Data should be attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available.

Lifecycle controls

Controls should address data creation, processing, review, reporting, correction, archival, retention, and retrieval.

Technical controls

Common controls include user access, audit trails, electronic signatures, backup and restore, time synchronization, metadata protection, and system validation.

Procedural and cultural controls

Procedures, training, quality oversight, deviation handling, and management commitment help ensure controls are used effectively.

Common Data Integrity Challenges

• Shared user accounts or weak access controls reduce attribution.
• Audit trails are enabled but not reviewed meaningfully.
• Metadata and original records are not retained with reports or exports.
• Manual transcription creates risk without adequate verification.
• Legacy systems lack modern controls and require procedural risk mitigation.
• Uncontrolled spreadsheets create risks to calculations, version control, access, and traceability.

Best Practices for Data Integrity

• Map data lifecycle processes before designing controls.
• Use risk-based assessments to prioritize remediation.
• Validate computerized systems according to intended GxP use.
• Review audit trails and critical records according to defined procedures.
• Maintain governance, training, and periodic review to keep controls effective.

How Mangan Biopharm Supports Data Integrity

Mangan Biopharm supports data integrity programs through assessments, remediation planning, CSV alignment, audit trail review strategy, lifecycle mapping, validation data management, and inspection-readiness support.