TDWI World Conference

Prerequisite: None

Arkady Maydanchik

More and more companies initiate data quality programs and form data stewardship groups every year. The starting point for any such program must be data quality assessment. Yet in the absence of a comprehensive methodology, measuring data quality remains an elusive concept. It proves to be easier to produce hundreds or thousands of data error reports than to make any sense of them.

This course gives comprehensive treatment to the process and practical challenges of data quality assessment. It starts with the systematic treatment of various data quality rules, and proceeds to the results analysis and building of an aggregated data quality scorecard. Special attention is given to the architecture and functionality of the data quality metadata warehouse.

You Will Learn

• The what, why, when, and how of data quality assessment
• How to identify and use data quality rules for assessment
• How to ensure completeness of data quality assessment
• How to construct and use a data quality scorecard
• How to collect, warehouse, and use data quality metadata

Geared To

• Data quality practitioners—those in the trenches who are responsible to manage, maintain, and deliver high quality data and to continuously improve the quality of data