Data Integrity
The completeness, consistency, and accuracy of data throughout its lifecycle, ensuring that information remains trustworthy and reliable for decision-making.
What is Data Integrity?
- Data Integrity
- Data integrity refers to the accuracy, completeness, and reliability of data throughout its entire lifecycle—from creation and storage to retrieval and archival. In regulated laboratory environments, data integrity is not just a best practice but a fundamental regulatory requirement that ensures the quality and safety of products and services.
Core Components of Data Integrity
Accuracy
Data correctly reflects the actual observation or measurement
Completeness
All required data is present with no missing elements
Reliability
Data can be trusted and verified for its authenticity
Common Data Integrity Risks
Human Error
Transcription mistakes, incorrect data entry, or misinterpretation of results
System Vulnerabilities
Unsecured systems allowing unauthorized access or modifications
Inadequate Audit Trails
Insufficient tracking of who made changes and when
Poor Training
Staff unaware of proper data handling procedures
Data Manipulation
Intentional falsification or selective reporting of results
Inadequate Backups
Risk of data loss due to system failures
Regulatory Framework
Multiple regulatory bodies have issued guidance on data integrity requirements:
Data Integrity and Compliance With Drug CGMP (2018)
Guidance on Good Data and Record Management Practices
Data Integrity Guidance
GxP Data Integrity Guidance
Good Practices for Data Management and Integrity
GAMP Records and Data Integrity Guide
Best Practices for Data Integrity
Implement Proper Access Controls
Unique user IDs, role-based permissions, and secure authentication
Maintain Complete Audit Trails
Automatic capture of who, what, when, and why for all data changes
Validate Electronic Systems
IQ/OQ/PQ validation and periodic revalidation of computerized systems
Train Personnel
Regular training on data integrity requirements and procedures
Regular Audits
Periodic self-inspections and data integrity assessments
Document Everything
Clear SOPs for data handling, corrections, and archival
LIMS Role in Data Integrity
A properly implemented LIMS is essential for data integrity compliance. Key features include electronic signatures, comprehensive audit trails, secure data storage, automated calculations, and controlled workflows that prevent unauthorized modifications.