data integrity # best practices & lessons learned. does it fit your organization?
TRANSCRIPT
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Data Integrity
# Best Practices & Lessons Learned.
Does It Fit Your
Organization?
![Page 2: Data Integrity # Best Practices & Lessons Learned. Does It Fit Your Organization?](https://reader036.vdocument.in/reader036/viewer/2022062805/5697bfd31a28abf838cac1fd/html5/thumbnails/2.jpg)
• 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
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• 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
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• 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
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• 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
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• 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
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• Prevent unauthorized use of by installing automatically logoff.
• Never publicly post passwords. • Limit access control for systems.
3: Security of Datamanagement
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• Audit trail activated on electronic records.• Understand where settings are originated.• Make sure physical and /or system security is
implemented.
3: Security of Datamanagement
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• 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
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• 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
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• 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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