Course Outline
- Data Quality Management
- Data Quality Management and the DAMA DMBoK Wheel for Government
- Goals and Business Drivers of Data Quality Management for Government
- Data Quality Overview
- Key Points and Definitions in Data Quality
- Understanding Data Quality Management for Government
- Scenarios of Data Quality Management for Government
- The Impact of Poor Data Quality on Government Operations
- The Body of Knowledge for Data Quality Management for Government
- The Approach to Data Quality Management for Government
- A Simple Framework for Improving Data Quality in Government
- Measuring and Data Profiling
- Methods for Measuring Data Quality for Government
- Evaluating Data Quality Through Profiling for Government
- Typical Outputs of Data Quality Profiling for Government
- Validation Rules in Monitoring Data Quality for Government
- Data Quality Monitoring within the DQM Framework for Government
- Tools and Techniques
- Distinguishing Good from Poor Data Quality for Government
- Data Quality Facets According to DMBoK for Government
- The Path to Achieving Accuracy in Data for Government
- Understanding the Process of Data Correction for Government
- Clarifying Data Cleansing Techniques for Government
- Evaluating the Required Level of Data Quality for Government
- Addressing Data Issues Beyond Simple Fixes for Government
- Impacts and Dimensions
- Costs and Efforts of Business Impact from Poor Data Quality for Government
- The Multiple Dimensions of Data Quality for Government
- Data Quality Facets as Defined by DMBoK for Government
- DMBoK Dimensions of Data Quality for Government
- Applying Data Quality Dimensions in Government Operations
- Business Rules Governing Data Quality for Government
- The Relationship Between Data Governance and Data Quality for Government
- Measuring Data Quality for Government
- The Path to Achieving Accuracy in Government Data
- Characteristics of Data Quality Indicators (DQI) for Government
- Software Tools: Functional DQ Capabilities for Government
- Root Cause Analysis
- Addressing Root Cause Analysis Problems and Remediation for Government
- An Overview of Root Cause Analysis for Government
- Identifying Associated Causes in Root Cause Analysis for Government
- The Process of Conducting Root Cause Analysis for Government
- Approaches, Assessments, and Roadmap
- The 5-Why Approach to Problem Solving for Government
- Understanding the Theory of Constraints for Government Data Quality
- Common Mistakes in Data Quality Management for Government
- Financial Costs Associated with Poor Data Quality for Government
- Developing a Data Quality Roadmap for Government
- Maturity Assessment of Overall Data Quality for Government
- Wrap Up
- Key Takeaways from Data Quality Management for Government
Requirements
The Certified Data Management Professional (CDMP) Fundamentals program is designed to equip professionals with essential skills and knowledge in data management. This certification is crucial for individuals working in or aspiring to work in the field of data management, particularly for government agencies where accurate and efficient data handling is paramount. The program covers core topics such as data governance, data quality, and data security, ensuring that participants are well-prepared to meet the rigorous standards required for government operations. By completing this certification, professionals will be better positioned to contribute to effective data management practices, enhancing transparency, accountability, and efficiency in public sector workflows.
Testimonials (2)
It's a hands-on session.
Vorraluck Sarechuer - Total Access Communication Public Company Limited (dtac)
Course - Talend Open Studio for ESB
I generally enjoyed the knowledge of the trainer.
