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November 11, 2005
Customer Data Integration Challenges
Two basic challenges associated with a customer data integration (CDI) application are how to determine linkage between two records and determine that they refer to the same entity, and the ability distinguish non-connectivity between two data records. In other words, to accurately identify a 'match', and a 'non-match'. One way of finding a solution to this issue is to realize that we do not have to rely solely on names as criteria for the process. Explicit knowledge, inferred knowledge and embedded knowledge can help to make decisions regarding matches and non-matches. Explicit knowledge considers entity attribution that is clearly presented in the data instance. The challenge for customer data integration for this is to identify linking fields across data sets that may be represented differently. As human beings, we may be able to look at two contacts and figure out whether or not they refer to the same individual. However, for a computer system, implicit/inferred knowledge means relying on more complex algorithms to compute the degree of similarity and then combining it with other inputs to derive the inference that solidifies the link. CRM Today reports:
The challenges in exploiting embedded knowledge is threefold: Being able to recognize that embedded knowledge is present; being able to extract those values from the fields, in which they are embedded; and understanding how to build a “connectivity model,” in which the embedded information can be made explicit for use in linkage.
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