profilecombining

Profile combining, also known as profile fusion or entity resolution in some contexts, is the process of identifying records across disparate data sources that refer to the same real-world entity and merging them into a single, unified profile. The aim is to create a richer, more accurate representation by aggregating attributes, histories, and relationships from multiple systems, while preserving data provenance and governance. It is a key step in data integration, customer data platforms, and identity resolution workflows.

Methods used to perform profile combining typically fall into deterministic and probabilistic approaches. Deterministic methods apply fixed rules, such as exact matches on name, date of birth, and identifiers. Probabilistic or machine learning methods assign a likelihood that two records refer to the same entity based on multiple attributes, handling partial matches and noisy data. Techniques like blocking or indexing reduce the computational burden by limiting pairwise comparisons, followed by record linkage, clustering, and attribute fusion. During merging, conflicting attribute values are reconciled according to predefined rules or confidence scores, and a provenance trail is maintained so the source of each attribute is traceable.

Data preparation is essential: data must be standardized, schemas aligned, and quality checks performed; missing values and outliers are addressed; lineage and auditability are recorded for governance and compliance. The resulting profile may be enriched with external data or derived attributes, while preserving privacy controls and access policies.

Applications include customer data platforms, fraud detection, personalized recommendations, healthcare patient records, and research that benefits from a unified view of an entity. Challenges include accuracy tradeoffs, privacy and consent considerations, regulatory compliance, data bias, and security risks. See also entity resolution, data fusion, and record linkage.