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Data that is sufficiently accurate in one context may not be accurate enough for another context.For example, a value for "age" may suffice for marketing purposes, whereas a legal document may require a date of birth.
Matching typically involves creating a candidate list of possible matches (also referred to as bucketing) based on the data already in the system and then comparing these candidates against the incoming record.It is reprinted here with permission from IBM Press; Copyright 2008.Read the chapter below to learn about the business benefits of managed master data, or download a free of "Business benefits of managed master data to read later.Even this is a fraction of the cost of having to deal with data quality issues after they have caused business problems.
In reality, the 0 measurement is probably not even close to covering the cost of bad data. The cost of bad data manifests itself in misplaced shipments, product returns, lost marketing opportunities, cost for immediate and near-term system repairs, loss of customer trust, loss of customers, and an adverse impact on sales and thus market share.By calculating a score for each comparison, the matches can be ranked.