Data quality is one of the core responsibilities of data stewardship. Data stewards play a hands-on role in data quality management. They discover and apply data quality rules, participate in defect detection and quality measurement, contribute to issue resolution, conduct and support root cause analysis, and work to prevent recurring defects. Doing that work well requires a solid foundation in data quality concepts, principles, and methods.

This course builds that foundation across seven modules. It begins with the basics — what quality means, what it means when applied to data, and where data quality defects come from. From there, it introduces data quality dimensions as a structure to organize data quality criteria and rules. Data quality rules are covered in some depth: what they are, how they're discovered, and how they're applied. The later modules of the course look at putting data quality rules into practice: the processes and practices of data quality management, how to assess data quality and communicate results through scorecards, and how to conduct root cause analysis to get to lasting fixes.

• Basic concepts, principles, and practices of data quality management
• Common causes of data quality defects
• How DQ criteria and DQ rules are used throughout data quality management
• DQ management methods ranging from defect detection to quality by design
• Concepts and techniques of data quality assessment
• Concepts and techniques of data quality scorecards
• Concepts and techniques of root cause analysis

• data stewards
• business or IT professionals who want to become data stewards
• business or IT counterparts working with data stewards
• information management professionals who want to learn about data quality