A formal data governance model will be established, assigning clear roles and responsibilities for data stewardship, oversight, and access control. Data integrity will be protected through audit logging, versioning, and rollback mechanisms, enabling transparent tracking of changes and rapid correction of errors. This is handled in the data model section above.

Automated validation rules will be implemented at all data entry points—both in the field and during integration—to enforce mandatory attributes, valid ranges, permitted formats, and spatial consistency. Error handling procedures will flag inconsistencies, missing values, or suspected duplicates for immediate review by data managers. This is handled in the data capturing specifications and addressing standard.

Regular data quality audits will be scheduled to assess completeness, accuracy, and conformity to the data model. Automated tools and manual sampling will be used to monitor address coverage, detect anomalies, and measure adherence to standards. Audit results will inform continuous improvement efforts and staff training needs.

For system migrations, a structured reconciliation process will compare legacy data with NAS records, identify discrepancies, and enable targeted correction. Scripts will be developed for data matching, and all changes will be logged for traceability.

To minimize risks, data migration and system deployment will follow a phased approach, starting with pilot testing in selected wilayats. Lessons learned from these pilots will guide process refinement before scaling to a national rollout.

Feedback mechanisms will allow users and integrators to report suspected data issues, which will be triaged and resolved according to documented protocols. The framework will be reviewed and updated regularly to reflect evolving best practices and technology advancements.