DMBK3: Data Quality Management Training Course
This programme provides an in-depth exploration of Data Quality Management as outlined in the DAMA Body of Knowledge (DMBoK®). It examines the various components of Data Quality, clarifying why validity is frequently mistaken for quality. The course covers the dimensions of Data Quality, alongside the policies, procedures, metrics, technology, and resources necessary to ensure high-quality data. Participants will study a specific reference model, root cause analysis, tools that support Data Quality Management, and the creation and monitoring of Data Quality metrics. Additionally, the content addresses common myths and pitfalls associated with Data Quality Management.
Purpose
To help learners appreciate the significance, positioning, and role of Data Quality within organizations and for data professionals.
Outcome
- Gain understanding of the various areas of Data Quality
- Explore Data Quality policies and procedures
- Discuss data profiling, validation rules, facets, data cleansing, and other relevant topics
- Understand how Data Quality connects with other data disciplines
- Learn about the DMBoK dimensions of Data Quality
Course Outline
- Data Quality Management
- Data Quality Management and the DAMA DMBoK Wheel
- Goals and Business Drivers of Data Quality Management
- Data Quality Overview
- Key Points and Definitions of Data Quality
- 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
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
DMBK3: Data Quality Management Training Course - Enquiry
Testimonials (2)
The variety of the information shared and the clarity to explain terms in plain English.
Arisbe Mendoza - Fairtrade International
Course - GDPR Workshop
It's a hands-on session.
Vorraluck Sarechuer - Total Access Communication Public Company Limited (dtac)
Course - Talend Open Studio for ESB
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