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Data Integrity

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

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Data Integrity

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  1. # Best Practices & LessonsLearned. Data Integrity Does It Fit YourOrganization?

  2. 1: Definition data integrity • 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.

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

  4. 2: Validation & Qualification • 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.

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

  6. 3: Security of Datamanagement • 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.

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

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

  9. 4: Data management • 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.

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

  11. 4: Data management • 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.

  12. About Us: Visitour website to learn more… www.itsbestpractices.com

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