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Course Outline

  • Data Quality Governance
    • Alignment of Data Quality Governance with the DAMA DMBoK Framework
    • Strategic Objectives and Operational Drivers for Data Quality Governance
  • Foundations of Data Quality
    • Core Principles and Definitions
    • Definition of Data Quality Governance
    • Operational Contexts for Data Quality Management
    • Consequences of Inaccurate Data Assets
    • Comprehensive Knowledge Base for Data Quality Governance
    • Strategic Approach to Data Quality Governance
    • Framework for Enhancing Data Integrity
  • Data Profiling and Measurement Methodologies
    • Metrics for Evaluating Data Quality
    • Evaluating Data Integrity Through Profiling
    • Standard Outputs from Data Quality Profiling
    • Validation-Based Monitoring Protocols
    • Integrating Data Quality Monitoring Within the DQM Framework
  • Implementation Tools and Techniques
    • Distinguishing High-Quality from Low-Quality Data
    • Data Quality Dimensions (per DMBoK)
    • Strategies for Achieving Data Accuracy
    • Principles of Data Correction
    • Processes for Data Cleansing
    • Determining Acceptable Levels of Data Quality
    • Addressing Systemic Issues Beyond Data Correction
  • Operational Impact and Quality Dimensions
    • Economic and Operational Implications for Agency Mission
    • Key Dimensions of Data Quality
    • Data Quality Facets (DMBoK)
    • DMBoK Defined Dimensions of Data Quality
    • Application of Data Quality Dimensions in Federal Operations
    • Regulatory and Policy Requirements for Data Quality
    • The Relationship Between Data Governance and Data Quality
    • Measurement Methodologies for Data Quality
    • Strategies for Achieving Data Accuracy
    • Attributes of Data Quality Indicators (DQI)
    • Software Tools: Essential Functional DQ Capabilities
  • Root Cause Analysis Methodologies
    • Identifying Root Causes and Implementing Remediation Strategies
    • Definition of Root Cause Analysis
    • Contributory Factors in Root Cause Analysis
    • The Root Cause Analysis Process for Government Applications
  • Strategic Approaches, Assessments, and Roadmaps
    • The 5-Why Analytical Method
    • Application of Theory of Constraints
    • Frequently Encountered Data Quality Errors
    • Fiscal Implications of Data Quality Initiatives
    • Developing a Data Quality Strategic Roadmap for government initiatives
    • Overall Maturity Assessment of Data Quality Capabilities
  • Summary and Conclusions
    • Primary Takeaways

Requirements

Fundamental Principles of the CDMP Framework for government agencies

 21 Hours

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Price per participant

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