Training on Data Quality Management

Module 1: Introduction to Data Quality Management

• Understanding Data Quality: Definitions and Importance
• Key Dimensions of Data Quality: Accuracy, Completeness, Consistency, Timeliness, and Uniqueness
• The Impact of Poor Data Quality on Business Operations
• Introduction to Data Quality Frameworks and Standards
• Case Study: Business losses caused by poor customer data management in an organization
• Practical: Evaluate a sample dataset against key data quality dimensions

Module 2: Data Governance and Data Quality

• Defining Data Governance and its Role in Data Quality
• Establishing Data Ownership and Accountability
• Data Stewardship: Roles and Responsibilities
• Creating and Implementing Data Governance Policies and Procedures
• Case Study: Implementing data governance in a multi-department organization
• Practical: Design a simple data governance structure for an organization

Module 3: Data Profiling and Data Quality Assessment

• Introduction to Data Profiling Techniques
• Identifying Data Quality Issues through Data Profiling
• Data Quality Assessment Methods and Tools
• Interpreting Data Quality Metrics and Reports
• Case Study: Detecting inconsistencies in a national survey dataset
• Practical: Perform data profiling on a sample dataset and interpret results

Module 4: Data Cleansing and Validation Techniques

• Techniques for Cleaning and Standardizing Data
• Data Deduplication and Matching
• Ensuring Data Integrity through Validation Rules
• Automating Data Cleansing Processes
• Case Study: Cleaning and standardizing customer records in a CRM system
• Practical: Apply data cleansing and validation techniques to a messy dataset

Module 5: Continuous Data Quality Improvement

• Implementing Data Quality Monitoring and Auditing
• Root Cause Analysis for Data Quality Issues
• Building a Data Quality Improvement Plan
• Best Practices for Sustaining Data Quality in Dynamic Environments
• Case Study: Continuous improvement of data quality in a financial reporting system
• Practical: Develop a data quality improvement and monitoring plan