Programme overview
Technical depth: Practitioner · Practical mode: Case study
Introduction
Organisations invest heavily in platforms, dashboards and AI, yet the same customer appears three times, finance and operations report different figures and nobody can say who owns a definition. Without agreed ownership, policies and quality controls, every new system reproduces the problem. This Core Concept course equips managers to set up data management and data governance that the business will actually run, using DAMA-DMBOK2, DCAM and the ISO data standards. Working on case material from several sectors, participants produce a Data Governance Operating Model and Roadmap for their own organisation.
Course Objectives
- Assess data management maturity against DAMA-DMBOK2 and DCAM and prioritise the gaps that matter to the business
- Design a data governance operating model with a council, data owners, data stewards and clear decision rights
- Draft data policies, standards and business glossary entries that business and IT teams can apply
- Implement metadata, data quality and master data practices using ISO 8000-61 and ISO/IEC 25012
- Control data risk through classification, lineage, issue management and governance of data used by AI
- Produce a Data Governance Operating Model and Roadmap ready for steering committee approval
Target Audience
- Data governance and data management managers establishing enterprise programmes
- Business managers appointed as data owners for customer, finance, asset or workforce data
- Data stewardship leads responsible for definitions, quality rules and issue resolution
- IT, architecture and platform managers accountable for data stores and integration
- Risk, compliance and information security managers overseeing data handling
- Analytics and reporting managers who depend on trusted enterprise data
Course Outline
Day 1: The Data Management Landscape and Current State
- DAMA-DMBOK2 Revised Edition Knowledge Areas and the DAMA Wheel
- Data Value and Risk Case: Business Drivers for Data Governance
- Data Landscape Inventory: Systems, Data Domains and Flows
- Data Management Maturity Baseline Using EDM Council DCAM v3
- Data Issue Log and Stakeholder Interview Guide
Day 2: Governance Frameworks and Data Standards
- ISO/IEC 38505-1 Governance of Data: Evaluate, Direct and Monitor
- Data Governance Operating Models: Centralised, Federated and Hybrid
- Data Governance Roles: Council, Data Owners, Data Stewards and Custodians
- Data Quality Characteristics in the ISO/IEC 25012 Model
- Data Lifecycle and Retention Controls Aligned to ISO 15489-1:2016
Day 3: Core Data Management Practices
- Data Policy, Standard and Procedure Hierarchy Drafting
- Critical Data Element Identification and Business Glossary Entries
- Metadata Management and Data Catalogue Population
- Data Quality Rules, Profiling and Scorecards Using ISO 8000-61 Processes
- Master and Reference Data Management Hub Styles
Day 4: Data Risk, Security and Problem Cases
- Data Classification Scheme and Access Controls Aligned to ISO/IEC 27001:2022
- Data Lineage Mapping for Management and External Reporting
- Data Issue Management and Escalation Workflow
- Governance of Data for AI Using ISO/IEC 42001:2023 and ISO/IEC 5259-1:2024
- Governance Programme Health Check and Recovery Plan
Day 5: Case Work and the Data Governance Operating Model
- Financial Services Case Study: Conflicting Customer Data Across Systems
- Public Service Case Study: Launching a Data Governance Council
- Data Stewardship RACI Build for Priority Data Domains
- Data Governance Operating Model and Roadmap Drafting
- Steering Committee Panel and Roadmap Defence
Skills You Will Gain
- Data Maturity Assessment
- Data Governance Design
- Data Stewardship
- Metadata Management
- Data Quality Management
- Master Data Management
- Data Risk Control
- Data Policy Writing
Why Attend This Course
- Return with a Data Governance Operating Model and Roadmap for your organisation, already challenged by a peer steering panel
- Settle disputes over definitions and ownership with an agreed decision process rather than repeated escalation
- Show sponsors where poor data is costing money or creating risk, in terms they will fund
- Compare governance approaches with data managers from other sectors and countries
Conclusion
Data becomes an asset only when someone owns it, its meaning is agreed and its quality is measured. This course moves from mapping the data landscape and baselining maturity, through the governance standards, operating models and roles that assign accountability, to the policies, glossary, metadata, quality and master data practices the business runs every day, and the classification, lineage and AI risks that test them. The final day turns that material into a Data Governance Operating Model and Roadmap that participants take to their steering committee.