TDWI World Conference

Prerequisite: None

David Loshin

The growing recognition of data quality’s importance—due to critical issues such as regulatory compliance, data integration and sharing, and operational improvement initiatives—has engendered a greater readiness to dedicate resources to data quality, data governance, and data stewardship. The 2006 TDWI Best Practices Report Taking Data Quality to the Enterprise through Data Governance states that the number of respondents who said their organization had suffered losses, problems, or costs due to poor data quality grew to 53 percent in 2005.

This course introduces the fundamentals for developing an enterprise data quality program and guides managers and practitioners in aspects of data quality improvement, including data quality assessment, measurable dimensions of data quality, data quality tools, data issues tracking, data standards, and visualization and reporting.

You Will Learn

• Building a business case for establishing a data quality program
• Data quality assessment
• Developing a strategy for enterprise data quality management
• Developing policies and procedures for data quality assessment, data quality metrics, and ongoing monitoring and reporting
• Using data quality tools and technology
• Data standards management
• Data quality tracking and performance trending

Geared To

• Anyone with a role in a DW, BI, data reengineering, data integration, or information quality project