Data Governance and Management (Database Management System)
Definition:
The Data Governance and Management function encompasses the overarching policies, processes, and technical infrastructure for the effective and responsible management of Social Registry data throughout its lifecycle. It focuses on data storage, retrieval, security, access control, data history, and the implementation of a comprehensive Data Governance Framework to ensure data integrity, compliance, and sustainable data management practices.
Functions:
Implements master data management practices for core entities
Manages secure data storage and retrieval mechanisms
Establishes and enforces data governance policies and procedures
Maintains a complete history of data changes for audit and analysis
Supports data lifecycle management from creation to archiving or deletion
Where Used:
Data Governance Committees for policy oversight and enforcement
Data Architecture and Database Administration Teams
Compliance and Audit Units for regulatory adherence
System Development Teams for implementing governance controls
Data Sharing and Integration Initiatives for data access management
Why Required:
Ensures responsible and ethical data handling practices
Supports compliance with data protection regulations and legal frameworks
Maintains long-term data integrity, reliability, and usability
Facilitates efficient data access and retrieval for authorized users
Establishes clear accountability and ownership for data assets
Implemented Through:
[SR-010] Master Data Management (Core)
[SR-008] Data History Manager (Core)
[SR-018] Data Governance Framework (Optional)
No specific Detailed Requirement mapped for "secure data storage and retrieval mechanisms" - this is implicitly part of the Core Infrastructure.
Requirements | Description | Functions | Links to | Why Core / Why Optional | Implementation Considerations |
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Essential function to implement master data management practices for core entities (individuals, households) with processes for resolving conflicts between different data sources | Master data entity definition, conflict resolution workflows, data source prioritization, data harmonization processes | Data Management Capability Area, Data Collection and Intake Capability Area | Master data management is crucial for ensuring data consistency and a single, authoritative view of core entities across the SR. Without MDM, data silos and conflicting information from different sources would undermine data integrity and the reliability of all SR operations. |
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Essential function to maintain a complete history of data changes, allowing reconstruction of registrant status at any point in time | Change tracking, historical data storage, audit trail maintenance, data reconstruction capabilities | Data Management Capability Area, Reporting and Analytics Capability Area, Security and Privacy Capability Area | Maintaining a data history is essential for auditability, accountability, and understanding data evolution over time. Without this function, the SR would lack transparency and the ability to trace data changes, hindering compliance, analysis, and trust in the data. |
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Function that ideally should implement a Data Governance Framework that can interface with external compliance and audit systems to maintain the integrity and reliability of registrant data | Policy enforcement, compliance monitoring, audit trail integration, external system interfacing for governance | Data Management Capability Area, Security and Privacy Capability Area, Interoperability and Integration Capability Area | Basic data management policies can be implemented manually in early SR stages. However, as data volume, complexity, and stakeholder involvement increase, a formal Data Governance Framework becomes increasingly crucial for systematic policy enforcement, compliance monitoring, and ensuring sustainable data management practices across the ecosystem. |
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