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The insight gained by data profiling can be used to determine how difficult it will be to use existing data for other purposes. It can also be used to provide metrics to assess data quality and determine whether or not metadata accurately describes the actual values in the source data. The data profiling process cannot identify inaccurate data; it can only identify business rules violations and anomalies.
Profiling tools evaluate the actual content, structure and quality of the data by exploring relationships that exist between value collections both within and across data sets. For example, by examining the frequency distribution of different values for each column in a table, an analyst can gain insight into the type and use of each column. Cross-column analysis can be used to expose embedded value dependencies and inter-table analysis allows the analyst to discover overlapping value sets that represent foreign key relationships between entities.
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