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Data matching is the task of identifying, matching, and merging records that correspond to the same entities from several databases. The entities under consideration most commonly refer to people, such as patients, customers, tax payers, or travellers, but they can also refer to publications or citations, consumer products, or businesses. A special situation arises when one is interested in finding records that refer to the same entity within a single database, a task commonly known as duplicate detection. Over the past decade, various application domains and research fields have developed their own solutions to the problem of data matching, and as a result this task is now known by many different names. Besides data matching, the names most prominently used are record or data linkage, entity resolution, object identification, or field matching.
A major challenge in data matching is the lack of common entity identifiers in the databases to be matched. As a result of this, the matching needs to be conducted using attributes that contain partially identifying information, such as names, addresses, or dates of birth. However, such identifying information is often of low quality. Personal details especially suffer from frequently occurring typographical variations and errors, such information can change over time, or it is only partially available in the databases to be matched.
There is an increasing number of application domains where data matching is being required, starting from its traditional use in the health sector and national censuses (two domains that have applied data matching for several decades), national security (where data matching has become of high interest since the early 2000s), to the deduplication of business mailing lists, and the use of data matching more recently in domains such as online digital libraries and e-Commerce