Course Outline


      • What is data management and why is it critical.
      • What are the different disciplines of data management?
      • DAMA & the DMBoK 2.0, and its relationship with other frameworks (TOGAF/COBIT…).
      • Overview of available professional certifications focusing on DAMA CDMP.

      • What is Data Governance and why it is important. A typical data governance reference model.
      • The main data governance roles: owner, steward, custodian.
      • The role of the Data Governance Office (DGO) and its relationship with the PMO.
      • What is the difference between Data Governance and IT Governance, and does it matter?
      • Overview of the Data Management implications of a selection of other regulations.
      • The key steps that organizations can take to prepare for compliance with current and future regulations.
      • How to get started with data governance and sustaining and building data governance.

      • Proactive planning for the management of data across its lifecycle.
      • Differences between data life cycle and a Systems Development Lifecycle (SDLC).
      • Data governance touch points throughout the data lifecycle.

      • What is metadata and why it is important?
      • Types of metadata, their uses and their sources.
      • Metadata and business glossaries. What is the connection?
      • How metadata provides the essential glue for data governance and metadata standards.

      • Starting the Data Governance Program, what you must get in place early. How to produce a realistic business case for DG linked to business objectives?

      • Why document and records management is important.
      • Taxonomy vs. ontology… what’s the difference.
      • Legal and regulatory considerations impacting records and content management.

      • Types of data models, their use and how they interrelate.
      • The development and exploitation of data models, ranging from enterprise, through conceptual to logical, physical and dimensional.
      • Maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the System Development Life Cycle (SDLC).
      • Data modeling and big data.
      • Why data modeling plays a critical part in data governance and BP case study.

      • The different facets of data quality, and why validity is often confused with quality.
      • The policies, procedures, metrics, technology and resources for ensuring data quality.
      • A data quality reference model and how to apply it.
      • Why data quality management and data governance are interconnected and case studies.

      • Core roles and considerations for data operations.
      • Good data operations practices.

      • Identification of threats and the adoption of defenses to prevent unauthorized access, use or loss of data and particularly abuse of personal data.
      • Identification of risks (not just security) to data and its use.
      • Data management considerations for different regulations, e.g. GDPR, BCBS239.
      • The role of data governance in data security management.

      • The differences between reference and master data.
      • Identification and management of master data across the enterprise.
      • 4 generic MDM architectures and their suitability in different cases.
      • How to incrementally implement MDM to align with business priorities.
      • Statoil (Equinor) case study.

      • What is data warehousing and business intelligence and why do we need it.
      • The major data warehouse architectures (Inmon & Kimball).
      • Introduction to dimensional data modeling.
      • Why master data management fails without adequate data governance.
      • Data analytics and machine learning and data visualization.

      • What are the business (and technology) issues that data integration is seeking to address?
      • Data integration and data interoperability - What's the difference?
      • Different styles of data integration and interoperability, their applicability and implications.
      • The approaches and guidelines for provision of data integration and access.



  35 Hours


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