Data Integrity
Quick facts
| Category | Trustworthiness and reliability of records across their lifecycle |
|---|---|
| Used by | Pharmaceuticals, medical devices, biotechnology, food, and other regulated industries |
| Also called | ALCOA, ALCOA+ |
| Related standards | FDA 21 CFR Part 11, GMP, ICH Q10 |
| Related processes | Audit trail, electronic signature, validation, document control |
| Semantic match | data integrity, ALCOA ALCOA+, data trustworthiness, regulated data accuracy |
What is Data Integrity?
Data integrity is the principle that data must accurately and completely reflect what actually occurred, remaining trustworthy from the moment it is created through however long it must be retained. Compromised data integrity undermines every decision, submission or investigation built on that data.
The ALCOA+ framework provides a practical way to evaluate whether data meets this standard. Data should be Attributable to a specific person and time, avoiding shared logins; Legible and readable in a durable format; Contemporaneous, recorded at the time an activity actually occurs; Original, with raw data preserved rather than altered; and Accurate, reflecting validated, correct information.
The "+" extensions add that data should also be Complete, including any repeated or aborted attempts, not just successful outcomes; Consistent across related records; Enduring, retained for the required period; and Available for review or inspection when needed.
Why is Data Integrity important?
Data integrity underlies every quality and regulatory decision an organization makes, since decisions based on inaccurate, incomplete or manipulated data can lead to unsafe products reaching patients or customers.
Regulators treat data integrity as a central inspection focus, since evidence of falsified, altered or selectively reported data undermines confidence in an organization's entire quality system, not just the specific record in question.
Strong data integrity practices also protect organizations from costly consequences, including warning letters, product holds or rejected regulatory submissions that can result from data integrity failures.
How does Data Integrity work?
A typical data integrity program addresses:
- Attribution. Enforce unique user IDs and avoid shared logins.
- Contemporaneous recording. Capture data at the time an activity actually occurs.
- Original data preservation. Retain raw data and clearly identify true copies.
- Accuracy verification. Validate calculations, instruments and master data.
- Completeness. Include all attempts, deviations and repeated tests, not just successful results.
- Availability. Ensure records remain accessible and retrievable throughout their required retention period.
ALCOA vs. ALCOA+
| Comparison | ALCOA | ALCOA+ |
|---|---|---|
| Core principles | Attributable, Legible, Contemporaneous, Original, Accurate | Adds Complete, Consistent, Enduring, Available |
Real-world examples of Data Integrity
A laboratory enforces unique user logins for its testing equipment, ensuring every data entry can be attributed to a specific analyst rather than a shared departmental account.
A manufacturer's quality team reviews audit trail data during an internal audit, confirming records were entered contemporaneously rather than reconstructed after the fact.
An FDA inspector cites a data integrity finding after discovering that a company's records excluded failed test attempts, violating the completeness principle within ALCOA+.
Regulations and standards related to Data Integrity
FDA and international agencies including the EMA and MHRA have issued specific data integrity guidance, and PIC/S has published Good Practices for Data Management and Data Integrity guidance reinforcing global expectations around ALCOA+ principles.
Data integrity underlies the entirety of FDA 21 CFR Part 11, GMP regulations and ICH Q10's pharmaceutical quality system model, since every control these frameworks require ultimately supports the trustworthiness of the underlying data.
Required by
How QT9 helps with Data Integrity
QT9 QMS and ERP data integrity capabilities
- Enforce unique user IDs and role-based access controls across the platform.
- Capture data contemporaneously with automatic, synchronized timestamps.
- Preserve original data and complete change history without exception.
- Support validated calculations and controlled master data.
- Maintain records in durable, readable, retrievable formats.
- Provide instant availability of records for audits and inspections.
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Common mistakes with Data Integrity
Common mistakes include recording data after the fact rather than contemporaneously, undermining confidence that the record reflects what actually happened at the time.
Other problems include excluding failed attempts, out-of-spec results or aborted runs from records, violating the completeness principle even when the excluded data was never manipulated, only omitted.
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