Master Data Management - SR-010

Implement measures to ensure the uniqueness and consistency of core entities (individuals, households) within the Social Registry, including mechanisms to prevent the creation of duplicate records and to manage conflicting data during data import and updates.

Ensures data integrity, prevents duplicate records, and provides a single, authoritative source of truth for core entities within the SR. Note: The focus is on preventing duplicates within the SR, not necessarily resolving conflicts between the SR and external systems at the minimum level.

• Preventing the creation of duplicate individual records during registration.

• Ensuring that updates to an individual's information don't create conflicts with existing records.

• Identifying and flagging potential duplicate records during bulk data imports.

• Providing a mechanism for administrators to review and resolve potential duplicate records.

• Unique Identifier(s) for core entities (e.g., National ID, a combination of fields)

• Data fields for core entities (individuals, households)

• Data source information (for audit trail)

• Conflict flags (for potential duplicates)

• Audit logs of data changes and conflict resolution actions

• Data Uniqueness: The system must enforce uniqueness of core entities (individuals, households) based on configurable unique identifiers (e.g., a single field like National ID, or a combination of fields like name, date of birth, and address). This is typically achieved through database constraints.

• Conflict Prevention: During data entry and updates, the system must check for potential duplicates based on the defined unique identifiers. If a potential duplicate is found, the system must prevent the creation or update of the record and flag it for review.

• Conflict Resolution (Basic): The system must provide a mechanism for manual review and resolution of potential duplicate records flagged during data entry, updates, or bulk imports. This could involve comparing records side-by-side and choosing which to keep/merge.

• Data Storage: Relational database (or equivalent structured data storage) capable of enforcing uniqueness constraints.

• Logging: Basic audit trail of data changes (who, what, when).

• Data Uniqueness: Support for fuzzy matching algorithms to identify potential duplicates even with minor variations in data.

• Conflict Prevention: Real-time duplicate checks during data entry.

• Conflict Resolution (Enhanced): Rule-based conflict resolution with machine learning recommendations to assist in resolving duplicates.

• Data Storage: Document database (or equivalent) to allow for more flexible data structures.

• Logging: Centralized logging with integration with monitoring tools.

• Data Uniqueness: Integration with external identity verification services (e.g., national ID system) for enhanced deduplication.

• Conflict Prevention: AI-powered duplicate detection and prevention.

• Conflict Resolution (Automated): Automated conflict resolution based on configurable rules and machine learning models.

• Data Storage: Graph database to represent complex relationships between entities and facilitate deduplication.

• Logging: Blockchain-based immutable audit trails.

• Access to conflict resolution tools must be restricted to authorized personnel.

• Audit trails of all conflict resolution actions must be maintained.

• Data used for deduplication and conflict resolution must be handled in accordance with data protection regulations (e.g., GDPR).

• The system should be designed to handle large volumes of data and efficiently identify potential duplicates.

• At higher maturity levels, consider distributed processing and optimized matching algorithms.

• At higher maturity levels, consider integration with external identity verification services and data quality tools.

Compliance with GDPR for data consistency and conflict management.

• A user interface must be provided for administrators to review and resolve potential duplicate records flagged by the system. This interface should:

  • Clearly display the conflicting data.

  • Allow side-by-side comparison of potential duplicates.

  • Provide options for merging, deleting, or marking records as non-duplicates.

  • (At higher levels) Provide confidence scores or recommendations to assist in decision-making.