Master and Reference Data Management

The Entity shall publish its master & reference data management policy following the below guidelines:

• Review the draft policy with the Entity’s Data Committee or equivalent body
• Incorporate Data Committee or equivalent body’s feedback
• Review draft policy for auditability
• Incorporate internal audit feedback
• Seek policy review and guidance from National Data Governance Office and incorporate the provided feedback
• Publish the policy on Entity’s digital channels (e.g. The entity website)
• Socialize the policy within the Entity to drive adoption and change management

• The Entity shall identify and document the authoritative data source for each master & reference data attribute, ensuring that it refers to and utilizes the Reference Data published by the National Planning Council (NPC), as it is the authoritative Entity for reference data.
• The Entity shall validate and obtain approval from NPC for the business specific Reference Data created internally. The Reference Data should only be consumed by the Entity after it has been officially approved and shared by NPC.
• For attributes where the Entity is not the authoritative data source nor the data custodian, the Entity shall define a request process for acquiring necessary master & reference data from the respective external authoritative sources
• For attributes where the Entity is the authoritative data source and/ or custodian, the Entity shall define a process to make these attributes available to external entities as required. This process for sharing the data shall ensure adherence to National Data Sharing, Integration, and Interoperability Standards

The Entity shall develop a detailed master and reference data management plan, in alignment with the Entity’s data strategy, that will be used and monitored to implement and activate master and reference data management initiatives across the Entity. The plan shall include, at a minimum:

• List of activities and milestones. The plan should account for quick wins and longer-term strategic changes
• Owners and stakeholders
• Interdependencies, assumptions, and risks
• Implementation timeline and cost

• The Entity shall adopt a common architecture archetype for its master & reference data, in alignment with the enterprise data model, the data catalog & metadata standard, and the data architecture and modeling standard. Examples of common architecture archetypes include:
  • Registry: The registry model is the simplest form of MRDM architecture. It links multiple systems through a central registry that maintains a reference to Master & Reference data without storing the data itself. This model provides a consolidated view of Master & Reference data but doesn't centralize data storage
  • Consolidation: In the consolidation model, data from various source systems is aggregated into a central MDM hub. The MRDM system cleanses and deduplicates this data to create a "golden record" that serves as a read-only reference (data is not updated back to the source systems)
  • Coexistence: The coexistence model allows for the bidirectional flow of data between the MRDM hub and the source systems. This model both feeds the consolidated, cleansed data back into the source systems and receives ongoing updates from them
  • Centralized: Under the centralized model, the MRDM system acts as the primary source of Master & Reference data. All data is entered directly into the MRDM system and then distributed to other systems. This model provides the highest level of control over Master & Reference data

The Entity shall enhance its master and reference data management tooling capabilities to support the following:

• API integration support (i.e.: facilitate integration with external systems through APIs)
• Dynamic data synchronization (i.e.: facilitate real-time data exchange across distributed systems)
• Dataset version mapping (i.e.: provide capabilities to map and manage transitions between dataset versions)
• Hierarchical datasets support (i.e.: support management of data in hierarchical structures)
• Multilingual data handling (e.g.: manage and support reference data in multiple languages)
• Customizable user interface design (i.e.: offer customizable interfaces to meet diverse operational needs)

The Entity shall further enhance its data quality tooling capabilities to support the following:

• AI Powered Master and Reference Data Management (i.e.: providing an AI/ML approach to master and reference data management such as automated discovery and identification of master data, pre-trained ML models for entity resolution, etc.)

The Entity shall establish Key Performance Indicators (KPIs) to measure the effectiveness of Master & Reference Data Management efforts and guide continuous improvement initiatives. KPIs shall include, at minimum:

• Master and reference data quality metrics, as defined in the data quality domain (refer to DQM.3.2.1)

The Entity shall monitor and report on the Entity's master and reference data management efforts based on the defined KPIs. This shall include, at a minimum:

• Align master and reference data quality monitoring and reporting to all the data quality monitoring and reporting foundational sub-specifications as outlined in DQM.3.2.2
• A register recording all change requests for reference and master data, along with the decisions and rationale for each action taken in response to these requests`

The Entity shall enhance its master and reference data management monitoring and reporting capabilities as follows:

• Align master and reference data quality monitoring and reporting to all the data quality monitoring and reporting advanced sub-specifications as outlined in DQM.3.2.2

The Entity shall establish and implement service-level agreements (SLAs) that specify the performance criteria for Master & Reference Data Management. The SLAs shall include, at a minimum:

• Align master and reference SLAs to all the data quality SLAs foundational sub-specifications as outlined in DQM.3.2.3

The Entity shall enhance its master and reference data management SLAs capabilities as follows:

• Align master and reference SLAs to all the data quality SLAs advanced sub-specifications as outlined in DQM.3.2.3