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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

Fuzzy matching software identifies records that are similar but not identical by applying string similarity algorithms to compare field values and produce a match confidence score. Unlike exact matchi

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

Fuzzy name matching software identifies when different name records refer to the same person, even when the names are spelled differently, formatted inconsistently, or use variations like nicknames, t

Duplicate records cost enterprises millions annually across marketing, sales, compliance, and AI initiatives. Build the financial case for investing in deduplication

List matching software combines records from two or more lists, identifies entries that refer to the same person, household, or organization across those lists, and produces a clean, deduplicated mast

Learn how name standardization improves record matching through parsing, nickname resolution, cultural name handling, and phonetic encoding for enterprise data quality

Cloud isn't the only answer for enterprise data quality. On-premise data matching and entity resolution give regulated industries control, compliance, and cost predictability.

Record linkage software identifies and connects records from different data sources that refer to the same real-world entity, without requiring those sources to share a common unique identifier. Forma