Making Data Quality Management an Integral Part of Business Intelligence and Business Performance Management

Dan Chang
Professor
San Jose State University

Alok Arora
Founder
Data Grain

Thursday 1:00pm - 2:00pm

Level: All Levels

Data quality management traditionally has been treated as a separate concern from, or after thought of, business intelligence (BI) and business performance management (BPM). We advocate that data quality management should be made an integral part of BI/BPM to achieve the following benefits: BI/BPM results can be qualified with data quality indicators, BI/BPM techniques and tools can be used for data quality management, and data quality initiatives can be included as part of BI/BPM initiatives.

We present an approach to enable this, which includes making the following extensions to BI/BPM frameworks:

• Perform key data quality measurements at various coupling points on the enterprise information chain
• Extend the operational data store with data quality schema and measurements
• Extend the enterprise data warehouse and data marts with matching data quality schema and measurements
• Support data quality cubes and their use with business performance cubes
• Support data quality indicators and their use with business performance indicators
• Use data quality as tool to measure business process compliance

Dan has 20 years experience in architecting and developing software products, technologies and standards for large scale enterprises in the area of modeling tools, semantics tools, XML, business intelligence, object data access and expert systems. He has 10 years experience in teaching enterprise software technologies at graduate school.

Alok has 20 years experience in managing and architecting solutions for large scale enterprises in the area of Customer Data Integration, CRM, B2B payment systems and Geographic Information Systems.