Programme overview
Course Overview
Enterprise data environments frequently fragment into isolated functional silos, where separate teams oversee analytical storage, master data hubs, and access controls without recognizing structural interdependencies. When baseline modelling, architecture, or metadata controls fail, secondary defects emerge downstream as inconsistent reporting figures, broken integration pipelines, and audit discrepancies. This training programme centers on the DAMA-DMBOK Data Management Framework, examining how all eleven knowledge areas interact to support reliable information assets across an organisation. Participants analyze structural dependencies, evaluate organizational maturity across key operational domains, and assemble a prioritized Data Management Capability Assessment and Improvement Roadmap designed by Core Concept.
Course Objectives
- Evaluate organizational data maturity across all eleven DAMA-DMBOK knowledge areas through structured diagnostic frameworks.
- Apply the Environmental Factors Hexagon and DMBOK context diagrams to define operational goals, institutional roles, deliverables, and implementation techniques.
- Review enterprise data architecture, conceptual data modelling, database operations, and analytical warehousing structures against established industry references.
- Trace root causes of downstream reporting failures back to upstream deficits within metadata capture, data quality dimensions, and master data management.
- Examine alignment between DMBOK, DCAM v3, and ISO 8000-61 to determine appropriate capability reference baselines.
- Draft an actionable Data Management Capability Assessment and Improvement Roadmap containing phased operational initiatives.
Target Audience
- Data office leaders and data management managers establishing an enterprise-wide operating capability
- Enterprise data architecture leads and data modellers maintaining shared structural standards
- Data warehousing, business intelligence, and integration specialists overseeing analytical platforms
- Data quality analysts, master data specialists, and metadata leads directing data stewardship initiatives
- Information security coordinators and corporate records managers enforcing lifecycle protection policies
- Technology leaders and delivery directors accountable for data-intensive enterprise systems
Course Outline
Day 1: Framework Foundations and Maturity Baselines
- DAMA-DMBOK2 revised structure, operational principles, and the DAMA Wheel
- Aiken Pyramid functional hierarchy and inter-area dependencies
- Henderson-Venkatraman Strategic Alignment Model applied to enterprise data
- Data management maturity assessment methodologies and diagnostic scoring criteria
- Current-state maturity diagnostic questionnaire execution
Day 2: Governance, Architecture, and Structural Design
- Environmental Factors Hexagon analysis and knowledge area context diagrams
- Data Governance operational configurations: centralized, federated, and distributed models
- Enterprise Data Architecture alignment using Zachman Framework viewpoints and TOGAF structures
- Conceptual, logical, and physical data modelling validation standards
- Comparative analysis of reference models: DMBOK, DCAM v3, and ISO 8000-61
Day 3: Operational Stores, Integration, and Core Repositories
- Database operations management, resilience controls, and service level agreements
- Data integration architectures: ETL, ELT, event replication, and API data services
- Document and records management alignment with ISO 15489-1 retention standards
- Reference and master data hub styles, golden record rules, and code set controls
- Data warehousing architectures comparing Kimball dimensional and Inmon normalized structures
Day 4: Cross-Functional Quality, Security, and Modern Data Platforms
- Metadata management, business catalog operations, and automated lineage capture
- Data quality dimensions, measurement metrics, and the Deming PDCA cycle
- Data security controls and classification schemes aligned to ISO/IEC 27001:2022
- Platform trade-offs across data lakes, lakehouse patterns, and data mesh concepts
- Ethical data handling frameworks and privacy by design under ISO/IEC 27701:2025
Day 5: Practical Diagnostics and Improvement Planning
- Retail banking scenario: diagnosing cross-area causes of regulatory reporting errors
- Healthcare provider scenario: resolving clinical integration and master patient indexing gaps
- Capability Heat Map synthesis across enterprise operational areas
- Data Management Capability Assessment and Improvement Roadmap formulation
- Roadmap walkthrough, peer challenge panels, and defensible sequence refinement
Skills You Will Gain
- Data management maturity scoring and diagnostic evaluation
- Enterprise data architecture and data modelling assessment
- Data integration pattern selection and pipeline review
- Metadata cataloguing and lineage tracking
- Data quality root-cause diagnosis and remediation
- Information security control alignment for data assets
- Cross-functional data management roadmapping
Why Attend This Course
- Develop a structured Data Management Capability Assessment and Improvement Roadmap directly applicable to workplace operations.
- Identify why isolated functional fixes stall without corresponding improvements in underlying data management areas.
- Adopt a standardized technical vocabulary that bridges communication across architects, operational stewards, security personnel, and auditors.
- Compare capability assessment methods against operational practices utilized across diverse international industry sectors.
Frequently Asked Questions
What is the primary deliverable produced during the course?
Participants construct an evidence-based Data Management Capability Assessment alongside a prioritized implementation roadmap, sequencing initiatives to address specific organizational capability gaps.
How does this course address relationships between different data management areas?
The curriculum utilizes the Aiken Pyramid and DMBOK context diagrams to demonstrate how weaknesses in foundational practices such as architecture, metadata, and quality cascade into failures across integration and reporting.
Does the course compare DMBOK with other industry models?
Yes, the sessions examine comparative alignments and structural differences between DMBOK, EDM Council DCAM v3, and ISO 8000-61 process models.
Conclusion
Sustainable data management requires treating technical and governance functions as an integrated operational system. By evaluating organizational maturity across the eleven DAMA-DMBOK knowledge areas, diagnosing structural interdependencies, and examining core integration, warehousing, and quality controls, participants establish a clear baseline for institutional improvement. The practical case exercises and maturity mapping culminate in a defensible Data Management Capability Assessment and Improvement Roadmap ready to guide systematic enterprise implementation.