MatchLogic Blogs

Evaluate entity resolution software for enterprise use. Compare matching approaches, deployment models, and selection criteria for accurate, auditable record unification.

Data integration is the process of combining data from multiple disparate sources into a unified, consistent view that supports analytics, operations, and decision-making. The integration lifecycle in

Data deduplication is the process of identifying records within a dataset that refer to the same real-world entity and merging or removing the redundant entries to produce a clean, non-redundant data

Data standardization is the process of converting data from multiple sources into a consistent, uniform format that follows defined rules for structure, naming, and values. It includes format normaliz

Data cleansing (also called data cleaning or data scrubbing) is the process of identifying and correcting inaccurate, incomplete, improperly formatted, or duplicate records in a dataset.

Entity resolution is the process of determining when different data records refer to the same real-world entity, such as a person, organization, product, or location, and linking those records into a

Data matching is the process of comparing records across one or more datasets to identify entries that refer to the same real-world entity, such as a person, organization, product, or location. It is

Fuzzy matching techniques are string similarity algorithms that measure how closely two text values resemble each other, producing a numerical score rather than a binary match/no-match result. The fiv

Database matching software compares and links records across two or more separate databases that store information about the same entities but lack shared unique identifiers. Unlike a simple SQL JOIN