1. Data Integrity # Best Practices & Lessons Learned. Does It Fit Your Organization?

2. Data integrity is the accuracy and consistency of stored data, indicated by an absence of any alteration in data between two updates of a data record. Data integrity is imposed within a system at its design stage through the use of standard rules and procedures, and is maintained through the use of error checking and validation routines. 1: Definition data integrity

3. Conduct periodic audits of the organization’s validated computer systems. Validation of configuration settings: Do not allow to reprocess without saving the results. 2: Validation & Qualification

4. Make sure all organization’s systems are validated and / or qualified. Include critical system test as part of the organization’s validation and/or qualification program: volume tests, stress tests, performance tests, boundary tests, compatibility tests. 2: Validation & Qualification

5. A validated system per applicable guideline will not automatically deliver 100% accurate printouts. Execute and document test protocols for stimulating worst case situations. 2: Validation & Qualification

6. How is guest login managed for systems and applications? Manage the version control of used software and applications. Assign correct level of access to users of the computerized systems. 3: Security of Datamanagement

7. Prevent unauthorized use of by installing automatically logoff. Never publicly post passwords. Limit access control for systems. 3: Security of Datamanagement

8. Audit trail activated on electronic records. Understand where settings are originated. Make sure physical and /or system security is implemented. 3: Security of Datamanagement

9. Choose the correct tool to follow-up on an identified GAP. Raw data misplaced or not retained because staff was not aware they should keep it. Remove or reduce duplication of data. 4: Data management

10. Always archive the organization’s source electronic records (raw data). Archiving copies of the source data is not acceptable. Printouts are never “raw data”. 4: Data management

11. Source electronic records or data must be reviewed. This includes the review of applicable meta data and audit trails. Review of audit trails must be build-in into the daily operations where electronic records are part of the process. 4: Data management

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