Data Quality Management (DQM)

Data quality management (DQM) is the pipeline process that checks the data for required values, valid data types, and valid codes. You can also configure DQM to correct the data by providing default values, formatting numbers and dates, and adding new codes.

Data quality management, along with name hygiene and standardization and address hygiene and standardization, is designed to optimize and enhance data quality. This data quality preparation is an essential step in entity resolution, because it increases the confidence in the resulting resolved entities and detected relationships.

To apply data quality management to the data loaded into the system, you configure data quality management rules (or DQM rules). DQM rules can perform a variety of repair, clean up, and standardization functions on incoming identity data values, such as properly formatting numbers, identifying and correcting clerical or transposition errors, and identifying and correcting intentional inaccuracies introduced by those intent on trying to conceal their identities.

The product comes pre-configured with several DQM rules by UMF segment that handle the most typical data quality issues for that UMF segment. But you can configure additional DQM rules, as needed. Before you do so, however, you must be familiar with the original quality of the data and the ETL (extract, transform, and load) process that was used to transform the identity data into UMF. After you know what further data enhancement is necessary, you can select the right DQM rules, functions, and values to apply to each type of identity data that needs further data quality optimization.