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. Data Integrity
# Best Practices & Lessons Learned.

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.

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