Just as integrity is an important characteristic of a human being, data integrity lays the foundation for valid analyses and reports. The integrity of data includes the following important components:
- Accuracy: truly reflects source records and free of transcription errors. It mirrors the presence of honesty and truthfulness.
- Consistency: free of logical errors, consistency across domains, visits, and devices etc. (e.g., collecting the same measurement for multiple times during the course of study) Consistency between data entries and data extraction such as data types, formats, conventions etc. This requirement of consistency corresponds to the personality of dependability and accountability.
- Security: defines roles, access levels, scopes, and activities etc. It’s important to allow the right role to perform the right tasks and prevent the other way around. This talks about the characteristics of self-control and self-discipline.
- Traceability: who did what at what time, and/or why. Just like in reality, keeping a track-record is essential for data integrity.
How to protect data integrity?
Protecting data integrity requires commitment of 4 Ps.
- Platform: a secured system that is Part 11 compliant and adequate to provide good front end for data entries, discrepancy management, and other end-user interfaces. It also provides a strong back end to allow programmers to design/implement a database with required edit checks, metrics reports and other important/study specific functions in an efficient and effective way.
- Process: a rigorous process specifies roles, functions, workflows, team-work, and responsibilities etc. It should include change control management.
- People: having committed people is the key in the whole picture of protecting data integrity. Paying attention to details, being sensitive to any potential deviations that might compromise data integrity and taking proactive steps before any possible mistakes might take place demonstrate the needed commitment. Timely and ongoing training efforts would help promote more people to become committed to data integrity.
- Passion: protecting data integrity requires corporate-level consensus and collaborative efforts.