Registrylinked

Registrylinked is a design approach in data management in which a central registry serves as the authoritative source for mapping identifiers across multiple systems, enabling data items to be linked through canonical identities rather than relying on direct cross-system keys. This pattern supports decoupling of systems and improves the ability to correlate records that originate from different data sources.

Core components of a registrylinked architecture typically include a registry service, identity records that associate a canonical identifier with local system identifiers, and a linking layer that resolves queries to the canonical identity. The registry stores provenance, versioning, and governance metadata to track how mappings were created and updated, providing traceability for data lineage and change management. An identity resolution process ingests data from source systems, matches records to existing canonical identifiers or creates new ones, and registers or updates the mappings in the registry. Applications query the registry to resolve identities and retrieve linked representations of the same real-world item across systems.

Data models in registrylinked designs commonly include fields such as canonical_id, system_id, local_id, version, provenance, confidence scores, and relationships to other identities. Benefits of this approach include improved interoperability, consistent identification across heterogeneous environments, auditable linking, and stronger support for master data management and analytics. Challenges encompass system complexity, potential performance considerations, governance overhead, privacy and security concerns, and the need for robust matching rules and ongoing maintenance to prevent derivate or stale mappings.

Registrylinked concepts are applied in contexts such as healthcare patient records, financial account linkage, product catalogs, research data integration, and government registries. See also: master data management, entity resolution, identifiers, linked data.