Embed Size (px)
DESCRIPTION
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
Citation preview
Data Integrity
# Best Practices & Lessons Learned.
• 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
Visit our website to learn more…
www.itsbestpractices.com
About Us:
