In-depth Actionable Training from the Comfort of Your Own Desk

Prerequisite: None

Deanne Larson, Ph.D.

DM, CBIP, President

Larson & Associates

Deanne Larson, Ph.D., is an active data science practitioner and academic. Her research has focused on enterprise data strategy, agile analytics, and data science best practices. She holds Project Management Professional (PMP), Project Management Agile Certified Practitioner (PMI-ACP), Certified Business Intelligence Professional (CBIP), and Six Sigma certifications. Deanne attended AT&T Executive Training at the Harvard Business School focusing on IT leadership, Stanford University focusing on data science, and New York University focusing on business analytics. She has presented at multiple conferences including TDWI, TDWI Europe, PMI, and other academic conferences. She is a faculty member at Purdue Global, has consulted for several Fortune 500 companies, and has authored multiple research articles on data science methodology and best practices.

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
• Data warehouse designers and developers
• Data quality professionals