Business Problem: The client had to profile membership and claims data in order to assess the quality of data and continuously monitor the quality.
Our Solution: The work involved design and development of rules to profile membership and claim data based on business logic. Reviewed existing rules with respect to best practices for performance and maintenance and recommended changes. As a follow-on project implemented a custom Data Quality Monitoring Solution and integrated the DQ results into Business Glossary.
The initial project involved understanding the business logic implemented in SQL queries and defining equivalent IA rules to validate the data received from various partner organizations. For performance reasons, rule design included creating Virtual tables, Views in Netezza, etc. A custom Dashboard was developed to provide an enterprise view of Data Quality, aggregated to Business Glossary (BG) Categories and Data Source, based on the BG Terms associated with the IA Rules. Data Quality information extracted from XMETA using DataStage jobs is stored in a custom datamart in SQL Server
Technology: IBM Infosphere Information Analyzer 8.7, Netezza, Business Glossary 8.7, DataStage 8.7