Prerequisite: None

Course Outline

Data quality is one of the most difficult challenges for nearly every business, data management program, and BI and analytics team. Data resources power enterprise reporting, BI dashboards, self-service analytics, data science efforts, AI and machine learning, and more. The most common approach to data quality problems is reactive—a process of fixing problems when they are discovered and reported. But reactive data quality methods are not quality management; they are simply quality maintenance—a never-ending cycle of continuously fixing defects but rarely removing the causes. The only proven path to sustainable data quality is through a comprehensive quality management program that includes data profiling, data quality assessment, root cause analysis, data cleansing, and process improvement.

You Will Learn

• Techniques for column, table, and cross-table data profiling
• How to analyze data profiles and find the stories within them
• Subjective and objective methods to assess and measure data quality
• How to apply OLAP and performance scorecards for data quality management
• How to get beyond symptoms and understand the real causes of data quality defects
• Data cleansing techniques to effectively remediate existing data quality deficiencies
• Process improvement methods to eliminate root causes and prevent future defects

Geared To

• BI, MDM, and data governance program and project managers and practitioners
• Data stewards and data curators
• Data warehouse designers and developers
• Data architects and data modelers
• Data quality professionals