Managing data quality is among the most vexing of information management issues. Most organizations have persistent and long-standing data quality problems. These troubles grow and propagate with the additional challenges of data redundancy, purchased applications and databases, legacy databases, multiple data providers and consumers, missing documentation, and uncertainty in defining data quality.
Stepping up to data quality improvement isn’t easy. It demands an understanding of quality management principles and practices, and the ability to apply those practices to a complex and continuously changing data resource. Whether your goal is a broad enterprisewide data quality program or a highly targeted data quality project, you must begin by understanding the practices and processes of data quality assessment and improvement.
You Will Learn
- Definitions and dimensions of quality
- How to create an actionable definition of data quality
- Typical causes of data quality problems
- Roles, responsibilities, and accountabilities in data quality management
- Roles, uses, and limits of data quality tools and technology
- Processes and techniques for data quality assessment and data quality improvement
- Data quality and data governance professionals
- BI/DW managers, architects, designers, and developers
- Data stewards, data architects, and data administrators
- Information systems analysts, designers, and developers
- Anyone with a role in data quality or information systems testing