Identity Resolution for Data Quality and Master Data Management

Dataflux

David Loshin

Click here to download the whitepaper

Summary

The need to link and consolidate entity information with a high level of confidence depends on meeting the challenge of comparing identifying data within a pair of records to determine similarity between that pair or to distinguish the entities represented in those records. Identity resolution employs techniques for measuring the degree of similarity between any two records, often based on weighted approximate matching between a set of attribute values between the two records.
The selection of an identity resolution tool must be accompanied by a process to analyze the suitability of entity data elements as candidate identifying attributes. This assessment must take a number of factors into consideration, especially when observing how well the attribute selection helps meet the dual challenge associated with unique identification, entity differentiation, and record matching.

By applying approximate matching techniques to sets of those identifying attributes, identity resolution can be used to recognize when slight variations suggest that different records are connected, where values may be cleansed, or where enough differences between the data suggest that the two records truly represent distinct entities. Identity resolution is a critical component of most data quality, MDM, and business intelligence applications. The desire for customer centricity or a comprehensive product catalog is predicated on the capabilities provided by identity resolution to find all records that carry information about each unique entity and resolve them into a unified view.

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