Webinar: Next Gen QMS: Quality, Risk & Compliance Unified Through AI

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Webinar: Next Gen QMS: Quality, Risk & Compliance Unified Through AI

Data Integrity

Definition

Data integrity is the assurance that data is complete, consistent, and accurate throughout its entire lifecycle, from creation and processing through storage, retrieval, and eventual archival or deletion. It's the overarching principle that frameworks like ALCOA+ and requirements like audit trails exist to support.

Benefits

Strong data integrity practices are foundational to regulatory trust; data integrity failures are among the most serious findings a regulator can issue, because they call into question not just a single record but the reliability of an organization's entire quality system.

Use Case

A company migrating from paper batch records to an electronic system undertakes a formal data integrity risk assessment as part of implementation, evaluating each step of the data lifecycle — capture, review, storage, retrieval, to ensure the new system doesn't introduce integrity gaps that didn't exist on paper.

Frequently Asked Questions (FAQs)

  • No — data integrity applies equally to paper records, though electronic systems introduce both new risks (e.g., unauthorized edits) and new tools (e.g., automated audit trails) for managing it.

  • Backdating entries, sharing login credentials, or excluding unfavorable test results without documented justification are among the most commonly cited data integrity issues.

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