False Positive: This occurs when a match is incorrectly identified, meaning records are deemed to match when they should not.
True Positive: This is a correct identification of a match, meaning records that should match are correctly identified as matching.
False Negative: This occurs when a match is not identified when it should have been, meaning records that should match are not matched.
True Negative: This is a correct identification of no match, meaning records that should not match are correctly identified as not matching.
Anomaly: This is a generic term that could refer to any deviation from the norm and does not specifically address the context of matching records.
Explanation:
The question asks about a scenario where two records should have matched but did not. This is the classic definition of aFalse Negative.
In data matching processes, this is a critical error because it means that the system failed to recognize a true match, which can lead to fragmented and inconsistent data.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
ISO 8000-2:2012, Data Quality - Part 2: Vocabulary.
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