TDWI Data Quality Management: Techniques for Data Profiling, Assessment, and Improvement Training in Norway

There is no prerequisites.

BI, MDM, and data governance program and project managers and practitioners; data stewards; data warehouse designers and developers; data quality professionals

• 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

Module One: Data Quality Basics

• Data Quality Concepts 
  • Defining Data Quality
  • Integrity and Correctness
  • Data Quality and Metadata Quality
  • Rules as Metadata
  • Metadata and DQ Management
• Data Quality Processes
  • Quality Control, Assurance, & Management
  • Data Profiling
  • Data Quality Assessment
  • Data Cleansing
  • Root Cause Analysis
  • Process Improvement

Module Two: Profiling Data

• Data Profiling Concepts
  • Purpose and Processes
• Column Profiling
  • Extracting Values Metadata
  • Examining Values Metadata
• Table Profiling
  • Examining Dependencies
  • Keys and Uniqueness
  • Column Dependencies
• Cross-Table Profiling
  • Examining Redundancy and Relationships
  • Redundancy
  • Relationships
• Analyzing Data Profiles
  • Column Profiles
  • Values Frequency
  • The Story in the Profiles
  • Data Quality Rules
• Data Profiling in Practice
  • Profiling and Projects
  • People and Technology

Module Three: Assessing Data Quality

• DQ Assessment Concepts
  • DQ Assessment Defined
  • Subjective Assessment
  • Objective Assessment
  • Assessment in DQ Management
• Subjective Assessment
  • Subjective Assessment Process
  • A Survey of Data Quality Perceptions
• Objective Assessment
  • Objective Assessment Process
  • Planning and Preparation
  • Cataloging Data Quality Rules
  • Cataloging Data Quality Errors
  • Testing and Tuning Rules
  • Reporting and Analyzing Data Quality
• Assessment in Practice
  • Assessment and Projects
  • People, Roles, and Skills

Module Four: Fixing Data Quality Defects

• Data Cleansing Concepts
  • Data Cleansing Defined
  • Techniques
  • Cleansing Process Overview
  • Planning & Preparation
• Procedural Data Cleansing
  • Names and Addresses
  • De-Duplication and Consolidation
• Rule-Based Data Cleansing
  • Data Cleansing Rules
  • Rules Processing
• Data Cleansing in Practice
  • Data Cleansing Workflow
  • Cleansing and the Data Warehouse
  • Data Cleansing and Projects
  • People, Roles, and Responsibilities

Module Five: Preventing Data Quality Defects

• Root Cause Analysis
  • RCA Overview
  • The RCA Process
  • Causal Modeling
  • Five Whys
  • Fishbone Diagrams
  • Causal Loop Diagrams
• Process Improvement
  • Process Improvement Principles
  • Process Improvement Overview
  • Prerequisites
  • Target Processes
  • Change Planning
  • Action Planning
  • Executing Change

Module Six: Summary and Conclusion

• Sustaining Data Quality
  • Awareness, Accountability, Action
  • Mistakes to Avoid
• References and Resources
  • To Learn More