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Course Outline
INTRODUCTION TO DAMA
- Defining data management and explaining its critical importance.
- Exploring the distinct disciplines within data management.
- Examining DAMA and the DMBoK 2.0, as well as its relationship with other frameworks (such as TOGAF, COBIT, etc.).
- Overview of professional certifications available, with a focus on the DAMA CDMP.
DATA GOVERNANCE
- Understanding Data Governance, its importance, and reviewing a typical data governance reference model.
- Identifying primary data governance roles: owner, steward, and custodian.
- Exploring the function of the Data Governance Office (DGO) and its relationship with the PMO.
- Distinguishing between Data Governance and IT Governance, and assessing the relevance of this distinction.
- Reviewing data management implications relevant to a selection of other regulations.
- Identifying key steps organizations can take to prepare for compliance with current and future regulations.
- Strategies for initiating data governance, as well as sustaining and expanding it.
DATA LIFECYCLE MANAGEMENT
- Proactive planning for managing data throughout its lifecycle.
- Differentiating between the data lifecycle and the Systems Development Lifecycle (SDLC).
- Identifying data governance touchpoints throughout the data lifecycle.
METADATA MANAGEMENT
- Defining metadata and explaining its importance.
- Exploring types of metadata, their uses, and sources.
- Examining the connection between metadata and business glossaries.
- Understanding how metadata serves as the essential connector for data governance and metadata standards.
DG MINI PROJECT
- Initiating a Data Governance Program: establishing critical components early and creating a realistic business case for DG aligned with business objectives.
DOCUMENT RECORDS & CONTENT MANAGEMENT
- Understanding the importance of document and records management.
- Differentiating between taxonomy and ontology.
- Addressing legal and regulatory considerations impacting records and content management.
DATA MODELING BASICS
- Exploring types of data models, their uses, and interrelationships.
- Developing and utilizing data models across the enterprise, from conceptual and logical to physical and dimensional.
- Conducting maturity assessments to evaluate how models are used in the enterprise and integrated into the System Development Life Cycle (SDLC).
- Examining the relationship between data modeling and big data.
- Understanding why data modeling is critical to data governance, including a business case study.
DATA QUALITY MANAGEMENT
- Analyzing the different facets of data quality and why validity is often mistaken for quality.
- Identifying the policies, procedures, metrics, technology, and resources required to ensure data quality.
- Introducing a data quality reference model and demonstrating its application.
- Exploring the interconnection between data quality management and data governance, supported by case studies.
DATA OPERATIONS MANAGEMENT
- Defining core roles and key considerations for data operations.
- Outlining best practices for effective data operations.
DATA RISK & SECURITY
- Identifying threats and adopting defensive measures to prevent unauthorized access, use, or loss of data, particularly the misuse of personal data.
- Identifying risks to data and its use, extending beyond just security concerns.
- Addressing data management considerations for various regulations, such as GDPR and BCBS239.
- Examining the role of data governance in managing data security.
MASTER & REFERENCE DATA MANAGEMENT
- Distinguishing between reference data and master data.
- Identifying and managing master data across the enterprise.
- Evaluating four generic MDM architectures and their suitability for different scenarios.
- Implementing MDM incrementally to align with business priorities.
- Case study: Statoil (Equinor).
DATA WAREHOUSING, BUSINESS INTELLIGENCE & DATA ANALYTICS
- Defining data warehousing and business intelligence and understanding their necessity.
- Reviewing major data warehouse architectures, including Inmon and Kimball.
- Introducing dimensional data modeling.
- Explaining why master data management often fails without adequate data governance.
- Covering data analytics, machine learning, and data visualization.
DATA INTEGRATION & INTEROPERABILITY
- Addressing the business (and technology) issues that data integration aims to solve.
- Differentiating between data integration and data interoperability.
- Examining various styles of data integration and interoperability, their applicability, and implications.
- Outlining approaches and guidelines for providing data integration and access.
35 Hours
Testimonials (7)
Very engaging
Samieg - Vodacom
Course - Certified Data Management Professional (CDMP)
it was very interactive and although I was not exposed to some modules before, Gaurav made it easy to understand. Good Participation in the team
UVASH - Vodacom
Course - Certified Data Management Professional (CDMP)
The training covered all the areas that were required. Very Insightful.
Carol - Vodacom
Course - Certified Data Management Professional (CDMP)
Material was covered according to the weight of the exam's marks. gave a better understanding of this course. Quizes helped a lot
Saika - Vodacom
Course - Certified Data Management Professional (CDMP)
Quizzes to test our knowledge and white board work kept us engaged.
Paula Dunsby - Vodacom
Course - Certified Data Management Professional (CDMP)
The instructor was very simple and clear on the point of the course
Mohamed - Dubai Government Human Resources Department - DGHR
Course - Certified Data Management Professional (CDMP)
Practical knowledge of the trainer
