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Course Outline
- Data Quality Management
- Data Quality Management and the DAMA DMBoK Wheel
- Goals and Business Drivers of Data Quality Management
- Data Quality Overview
- Data Quality Key Points and Definitions
- What is Data Quality Management?
- Scenarios of Data Quality Management
- The Impact of Poor Data
- The Body of Knowledge for Data Quality Management
- The Data Quality Management Approach
- A Simple Framework for Data Quality Improvement
- Measuring and Data Profiling
- Measuring Data Quality
- Assessing Data Quality Through Profiling
- Typical Outputs of Data Quality Profiling
- Validation Rules of Based Monitoring
- Data Quality Monitoring in DQM Framework
- Tools and Techniques
- Good Data Quality vs. Poor Data Quality
- Data Quality Facets (DMBoK)
- The Path to Accuracy
- Understanding Data Correction
- Demystifying Data Cleansing
- How Good Does Data Quality Need to Be?
- Don’t Just Fix the Data
- Impacts and Dimensions
- Costs and Efforts of Business Impact
- The Many Dimensions of Data Quality
- Data Quality Facets (DMBoK)
- DMBoK Dimensions of Data Quality
- Applying Data Quality Dimensions
- The Business Rules of Data Quality
- Are Data Governance and Data Quality Linked?
- Measuring Data Quality
- The Path to Accuracy
- Characteristics of Data Quality indicators (DQI)
- S/W Tools: Functional DQ Capabilities
- Root Cause Analysis
- Root Cause Analysis (RCA) Problems and Remediation
- What is Root Cause Analysis?
- Associated Causes of Root Cause Analysis
- The Root Cause Analysis Process
- Approaches, Assessments, and Roadmap
- The 5-Why Approach
- What is Theory of Constraints?
- Common Data Quality Mistakes
- Financial Cost Associated with Data Quality
- Creating the Data Quality Roadmap
- Maturity Assessment (Overall) of Data Quality
- Wrap Up
- Key Takeaways
Requirements
CDMP Fundamentals
21 Hours
Testimonials (1)
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