IdentityResolution

Identity resolution, also called entity resolution or record linkage, is the process of identifying and linking records that refer to the same real-world entity across one or more data sources. The goal is to produce a single, unified representation or golden record that reconciles conflicting or redundant attributes. It enables a coherent view of an entity despite data scattered across systems.

Techniques range from deterministic matching, which relies on exact attribute matches such as an identifier, email address, or phone number, to probabilistic matching, which assigns a likelihood that two records refer to the same entity based on attribute similarities. More recently, machine learning models and graph-based approaches are used to improve accuracy. Practices such as data normalization, blocking, and clustering help scale comparisons and reduce computational load.

Identity resolution is a core capability of master data management (MDM) and identity analytics. It involves data governance, lineage, and provenance, ensuring privacy and regulatory compliance. It also requires data quality management to address incomplete, inconsistent, and outdated information, and to manage conflicts between competing records.

Applications include tying customer records across channels in marketing and CRM, linking patient records in healthcare, reconciling supplier and product data for supply chains, and detecting fraud. The typical outcome is a single view of an entity, which supports better decision making, personalized engagement, and improved operational efficiency. As data landscapes grow more complex, robust identity resolution remains central to data integration and governance efforts.