Information Management and CDMP Professional Certification

This course covers the information management disciplines as defined in the International Industry standard, DAMA body of knowledge (DMBoK) v2. It covers the entire information management spectrum, including how information architecture is applied. In this course, students prepare for the CDMP certification. The exam is taken on the last day of the course.

Why you should take this course

For users with an introductory knowledge of this topic, and are searching for additional information and its application.

Here are the topics we'll cover.

1. Introduction to Data Management, DMBoK and Overview of CDMP Certification

   • What is Data Management, the drivers and issues if it goes wrong
   • What is the DMBoK, its intended purpose and audience of the DMBoK
   • What are the disciplines of Data Management in the DMBoK
   • Changes in DMBoK 2., and the relationship of the DMBoK with other frameworks (TOGAF / COBIT etc.)
   • The DAMA CDMP professional certification overview and CDMP exam coverage by DMBoK 2 section, the different levels and how can you progress through them

2. Data Governance

   • What is Data Governance and why Data Governance is at the heart of successful Information Management.
   • A typical Data Governance reference model including Data Governance roles and responsibilities.
   • Organisation structures and types of Operating models to support Data Governance.
   • Principles for Data Governance and how to get started with Data Governance.
   • The role of the Data Governance Office (DGO) and its relationship with the PMO.
   • Data handling ethics and data sampling considerations.

3. Data Quality Management

   • The different facets of Data Quality, and why Validity is often confused with Quality
   • The different Dimensions of Data Quality and how to apply them.
   • The policies, procedures, metrics, technology and resources for ensuring Data Quality is measured and ultimately continually improved.
   • A Data Quality reference model and how to apply it.
   • Root cause analysis and the “5-whys” approach
   • Typical capabilities and functionality of software tools to support Data Quality management.
   • Data Quality measures – guidelines for their creation and monitoring.
   • The common myths and pitfalls about Data Quality management and how to avoid them.

4. Master and Reference Data Management

   • The differences between Reference and Master Data.
   • Identification and management of Master Data across the enterprise.
   • The 4 generic Master Data Management architectures and their suitability in different cases.
   • A Master Data Management maturity assessment to consider business procedures for Master Data Management and the provision and appropriateness of Master Data Management solutions per major data subject area.
   • How to incrementally implement Master Data Management to align with business priorities.
   • The genres of Master Data Management solutions and the common pitfalls if you select the wrong type;
   • Different approaches for Master Data Management implementation and why you must be careful in the approach selected;
   • The essential relationship between Master Data Management, Data Quality and Data Governance
   • The under looked but critical aspect of Reference Data Management

5. Data Warehousing, BI Management, and Big Data Analytics

   • What is a Data Warehouse and why are they used.
   • The difference between Data Warehouse and Data Lake and where each is appropriate.
   • Provision of Business Intelligence (BI) to the enterprise and the way data consumed by BI solutions and the resulting reports are managed. Particularly important if the data is replicated into a Data Warehouse.
   • The major DW architectures (Inmon and Kimball)
   • Introduction to Dimensional Data Modelling
   • Types of BI, DW, Analytics and Visualisations.
   • Data Analytics and Big Data – a brief overview.

6. Data Modelling

   • What are Data Models and why do we need them.
   • Different 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.
   • Entity subtypes and supertypes, and whey these help in data centric (vs application centric) approaches.
   • A maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the System Development Life Cycle (SDLC).
   • 10 different uses for Data models - why data modelling is NOT just about Relational Database design
   • Why Data Modelling is an essential component of Data Governance

7. Metadata Management

   • What is, and isn’t, Metadata
   • Provision of metadata repositories and the means of providing business user access and glossaries from these.
   • Types of Metadata and their uses
   • Sources of metadata
   • Metadata and Business Glossaries. What’s the connection?
   • The uncomfortable truth about Big Data technologies

8. Data Integration and Interoperability

   • Data integration and Data interoperability – What’s the difference?
   • What are the issues that Data Integration is seeking to address?
   • Different styles of Data Integration and Interoperability, their applicability, and implications.
   • The approaches, plans, considerations, and guidelines for provision of Data Integration and access.
   • Consideration of P2P, ETL, CDC, Hub and Spoke, Service-orientated Architecture (SOA), Data Virtualization and assessment of their suitability for the particular use cases.

9. Data Architecture and Data Lifecycle Management

   • Types of Architectures
   • Enterprise Architecture approaches and Process vs Data interaction.
   • Proactive planning for the management of Data across its entire lifecycle from inception through, acquisition, provisioning, exploitation eventually to destruction.
   • Considerations for Data across the value chain.
   • Differences between Data Life cycle and a Systems Development LifeCycle (SDLC).

10. Data Risk Management, Security, Privacy and Regulatory Compliance

    • Identification of threats and the adoption of defences to prevent unauthorized access, use or loss of data and particularly abuse of personal data.
    • Exploration of threat categories, defense mechanisms and approaches, and implications of security and privacy breaches.
    • Identification of risks (not just security) to data and its use, together with risk mitigation, controls, and reporting.
    • Adapting to the changing legal and regulatory requirements related to information and data.
    • Assessing the approach to regulatory compliance and understanding the sanctions of non-compliance.
    • Data Management considerations for different regulations (e.g., GDPR, BCBS239)

11. Data Storage and Operations Management

    • Core roles and considerations for data operations
    • Obstacles to performance
    • Good Data Operations practices
    • Balancing availability vs performance

12. Document Records and Content Management

    • Why document and records management is important
    • What are “significant” records of the organization and why they must be carefully managed
    • The records management lifecycle
    • Audit and records control