Data validation may not be one of the hot topics in data management, but it plays a critical role in ensuring that data sent from one place to another for operational and analytic purposes is still the same data. For many organizations, reconciliation and auditing of such data is a painful task of manual comparisons or, worse, a tedious sampling of data from two systems to look at differences to create exceptions; in many cases people transfer the data into spreadsheets where even more mistakes can be made. Many organizations have gone to the trouble of custom-coding SQL and applications to compare the results of data integration and synchronization processes and ensure the integrity of their data.
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