Certified Data Management Professional (CDMP)

Upon completion of the course, participants will be able to:

different categories of challenges, appreciate concepts including lifecycle management, normalization, dimensional modeling and data virtualization and appreciate why they are important, understand the critical roles of master data management and data governance and how to effectively apply them, understand the different facets (dimensions) of data quality and explore a workable data quality framework, describe the major considerations for successful data governance and how it can be introduced in bite-sized pieces, understand the different types of data models and their applicability, attend the DAMA certification exam.

• 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.