Managing data quality is among the most vexing of information management issues. Most organizations have persistent and long-standing data quality problems—troubles that grow and propagate with the 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 enterprise-wide 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. This course is designed specifically to provide that foundational knowledge.
There are no prerequisites for this course.
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
Module One: Data Quality Concepts
Defining Data Quality
Dimensions of Data Quality
Common Causes of DQ Problems
Module Two: Data Quality Practices and Processes
Quality Management Practices
Quality Management and Data
Data Quality Organizations
Data Quality Processes
Data Quality Tools and Technology
Module Three: Data Quality Assessment
Planning & Preparation
Conducting the Assessment
Assessment Results
Applied Results
Module Four: Data Quality Improvement
Procedural Data Quality
Rule-Based Data Quality
IT Processes and Data Quality
Business Processes and Data Quality
Module Five: Summary and Conclusion
Join our public courses in our Hong Kong facilities. Private class trainings will be organized at the location of your preference, according to your schedule.