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The ISO definition of Master Data quality focuses on the degree to which the data's characteristics meet the requirements of individual users. This implies that quality is subjective and depends on whether the data is suitable and adequate for its intended purpose, fulfilling the specific needs of its users.

References:

ISO 8000-8:2015 - Data quality — Part 8: Information and data quality: Concepts and measuring.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 13: Data Quality Management.

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.

Metrics tied to Reference and Master Data Quality generally include:

Data Sharing Usage: Measures how often master data is accessed and used across the organization.

Rate of Change of Data Values: Tracks how frequently master data values are updated or modified.

Service Level Agreements (SLAs): Monitors adherence to agreed-upon service levels for data availability, accuracy, and timeliness.

Data Sharing Volume: Measures the volume of data shared between systems or departments.

Excluded Metric - Operational Functions: While operational functions are important, they are not typically considered metrics for data quality. Operational functions refer to the various tasks and processes performed by systems and personnel but do not directly measure data quality.

References:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"