datalinked

Datalinked is a term used in information management to describe data that has been connected across sources to enable integrated analysis and discovery. Used as an adjective, it characterizes datasets in which records share semantic or identifying links, allowing entities, relationships, and attributes to be queried across boundaries. The concept grew with data integration, master data management, and the semantic web, where linking data via identifiers, ontologies, and metadata enhances interoperability rather than isolation. No formal standard defines datalinked; the term is used variably by practitioners and vendors to describe a state rather than a single technology.

Implementation typically rests on three pillars: entity resolution to identify the same real-world object across datasets; schema alignment and metadata normalization to map fields; and linking mechanisms such as foreign keys, APIs, or RDF URIs to establish relationships. Data provenance and quality controls are essential to ensure trust in the linked data.

Applications include creating customer 360 views, integrating biomedical and clinical datasets, consolidating supply chain information, and enabling advanced analytics and fraud detection through cross-source insights.

Challenges include privacy and governance concerns, data quality and deduplication, scalability, and varying licensing terms. Datalinked data often requires robust access controls and clear data lineage to remain usable and compliant.

See also: data linkage, data integration, entity resolution, master data management, knowledge graph, linked data.