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Course Outline
INTRODUCTION TO DAMA
- Understanding data management and its critical importance.
- Exploring the distinct disciplines within data management.
- Examining DAMA and the DMBoK 2.0, including its relationship with other frameworks such as TOGAF and COBIT.
- Reviewing available professional certifications, with a focus on the DAMA CDMP.
DATA GOVERNANCE
- Defining Data Governance, its importance, and a typical reference model.
- Identifying primary data governance roles: owner, steward, and custodian.
- Understanding the function of the Data Governance Office (DGO) and its interaction with the PMO.
- Distinguishing between Data Governance and IT Governance, and discussing the relevance of this distinction.
- Overviewing the data management implications of selected regulatory frameworks.
- Outlining key steps organizations can take to prepare for compliance with current and future regulations.
- Initiating data governance efforts, sustaining them, and building long-term capacity.
DATA LIFECYCLE MANAGEMENT
- Proactively planning for data management throughout its lifecycle.
- Differentiating between the data lifecycle and the Systems Development Lifecycle (SDLC).
- Identifying data governance touchpoints within the data lifecycle.
METADATA MANAGEMENT
- Defining metadata and explaining its importance.
- Classifying types of metadata, their applications, and sources.
- Exploring the connection between metadata and business glossaries.
- Demonstrating how metadata serves as essential infrastructure for data governance and metadata standards.
DG MINI PROJECT
- Launching the Data Governance Program: establishing early foundations and developing a realistic business case linked to 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 their interrelationships.
- Developing and leveraging data models, from enterprise and conceptual levels down to logical, physical, and dimensional models.
- Conducting maturity assessments to evaluate how models are utilized within the enterprise and integrated into the System Development Life Cycle (SDLC).
- Examining data modeling in the context of big data.
- Analyzing the critical role of data modeling in data governance, supported by a business case study.
DATA QUALITY MANAGEMENT
- Examining the various facets of data quality and clarifying why validity is often mistaken for quality.
- Identifying the policies, procedures, metrics, technology, and resources required to ensure data quality.
- Applying a data quality reference model.
- Understanding the interconnection between data quality management and data governance, illustrated with case studies.
DATA OPERATIONS MANAGEMENT
- Defining core roles and considerations for data operations.
- Establishing best practices for data operations.
DATA RISK & SECURITY
- Identifying threats and implementing defenses to prevent unauthorized access, use, or loss of data, with a focus on personal data abuse.
- Identifying risks to data and its usage beyond just security concerns.
- Addressing data management considerations for various regulations, such as GDPR and BCBS239.
- Exploring the role of data governance in data security management.
MASTER & REFERENCE DATA MANAGEMENT
- Distinguishing between reference 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 explaining their necessity.
- Reviewing major data warehouse architectures (Inmon & Kimball).
- Introducing dimensional data modeling.
- Explaining why master data management fails without adequate data governance.
- Covering data analytics, machine learning, and data visualization.
DATA INTEGRATION & INTEROPERABILITY
- Identifying the business and technological issues that data integration aims to resolve.
- Differentiating between data integration and data interoperability.
- Exploring different 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
