Course Outline

Introduction to the DMBoK

• What is the DMBoK, its intended purpose and audience.
• Changes in DMBoK 2.0, and the relationship of the DMBoK with other frameworks (TOGAF / COBIT etc.).
• DAMA CDMP professional certification overview & CDMP exam coverage by DMBoK section.

Data Governance

• Why Data Governance is at the heart of successful Information Management.
• A typical DG reference model
• DG roles & responsibilities, the role of the Data Governance Office (DGO) & its relationship with the PMO.
• How to get started with Data Governance.

Data Quality Management

• The Dimensions of Data Quality, policies, procedures, metrics, technology and resources for ensuring Data
• Quality is measured and ultimately continually improved.
• A DQ reference model & how to apply it.
• Capabilities & functionality of tools to support Data Quality management.

Master & Reference Data Management

• The differences between Reference & Master Data.
• Identification and management of Master Data across the enterprise.
• 4 generic MDM architectures & their suitability in different cases.
• MDM maturity assessment to consider business procedures for MDM and the provision and appropriateness of MDM solutions per major data subject area.
• How to incrementally implement MDM to align with business priorities.

Data Warehousing & BI Management

• Provision of Business Intelligence (BI) to the enterprise and the manner in which data consumed by BI solutions and the resulting reports are managed.  Particularly important if the data is replicated into a Data Warehouse.
• Types of BI, DW and Analytics.

Data Modelling & Metadata Management

• Provision of metadata repositories and the means of providing business user access and glossaries from these.
• The development, use 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 Integration & Architecture Management

• What are the business (and technology) issues that Data Integration is seeking to address, the different styles of Data Integration, their applicability and implications.
• The approaches, plans, considerations and guidelines for provision of Data Integration and access.
• Consideration of P2P, ETL, CDC, Hub & Spoke, Service-orientated Architecture (SOA), Data Virtualization and assessment of their suitability for the particular use cases.

Data Lifecycle Management

• Proactive planning for the management of Data across its entire lifecycle from inception through, acquisition, provisioning, exploitation eventually to destruction.  This IM discipline and its maturity assessment determine how well this is planned for and accomplished.

Data Risk Management, Security & Privacy

• 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, defence mechanisms & approaches, and implications of security & privacy breaches.
• Identification of risks (not just security) to data and its use, together with risk mitigation, controls and reporting.

Regulatory Compliance

• The polices and assurance processes that the enterprise is required to meet.
• Adapting to the changing legal and regulatory requirements related to information and data.
• Assessing the approach to regulatory compliance & understanding the sanctions of non-compliance.

Data Management Tools & Repository

• Examination of the categories of tools supporting the IM disciplines.
• How to select the appropriate toolset.
• Discussion of an example policy for use of specific technology to ensure consistency and interoperability across the enterprise.