Data Integrity

# Best Practices & Lessons Learned.

Does It Fit Your

Organization?

• 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

• 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

• 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

• 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

• 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

• Prevent unauthorized use of by installing automatically logoff.

• Never publicly post passwords. • Limit access control for systems.

3: Security of Datamanagement

• Audit trail activated on electronic records.• Understand where settings are originated.• Make sure physical and /or system security is

implemented.

3: Security of Datamanagement

• 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

• 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

• 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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