Entityconsult

Entityconsult is a term used in data management to describe platforms, services, or methodologies that focus on identifying, linking, and managing real-world entities across disparate data sources. It covers functions such as entity resolution, deduplication, canonicalization, and the construction of entity graphs to represent relationships between people, organizations, locations, and other objects.

Typical capabilities include ingestion of structured and unstructured data, similarity and rule-based matching, machine-learning–driven matching, confidence scoring, and survivorship rules to produce a unified view of an entity. Additional features often cover data governance aspects such as lineage, audit trails, privacy controls, and compliance reporting, as well as APIs and dashboards for exploration, monitoring, and data stewardship. Many implementations emphasize the creation of a canonical or golden record and the ability to propagate updates across connected data sources.

Deployment models for entityconsult solutions vary, including software-as-a-service, on-premises, and hybrid configurations. Architectural elements commonly seen are data ingestion pipelines, a matching or inference engine, an entity graph or master data store, and presentation or API layers. Integrations with data catalogs, master data management (MDM) systems, or knowledge graphs are typical to support broader data governance and analytics workflows.

Applications span customer data integration, supplier and product master data management, fraud detection, risk assessment, regulatory reporting, and personalized experiences. Industries that frequently deploy such capabilities include retail, financial services, healthcare, telecommunications, and manufacturing. The term is not standardized, and vendors may market similar functionality under related labels such as entity resolution, MDM, or data governance. Related concepts include entity resolution, master data management, and knowledge graphs.