Information Management Fundamentals

Course Outline

Introduction to Data Management, DMBoK & overview of the 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
• Overview of the DAMA CDMP professional certification, what are the levels and how can you progress from one level to the next.

Data Governance

• What is Data Governance.
• Why Data Governance is at the heart of successful Information Management.
• A typical Data Governance reference model.
• Data Governance roles & responsibilities.
• Organisation structures & type of Operating models to support Data Governance.
• Principles for Data Governance
• The role of the Data Governance Office (DGO) & its relationship with the PMO.
• How to get started with Data Governance.

Data Quality Management

• The different facets of Data Quality, and why “Validity” is often confused with “Quality”
• The different Dimensions of Data Quality.
• The policies, procedures, metrics, technology and resources for ensuring Data Quality is measured and ultimately continually
• A Data Quality reference model & how to apply it.
• Root cause analysis & 5-whys
• Capabilities & functionality of tools to support Data Quality management.
• Data Quality measures – guidelines for their creation & monitoring.

Master & Reference Data Management

• The differences between Reference & Master Data.
• Identification and management of Master Data across the enterprise.
• 4 generic Master Data Management architectures & their suitability in different cases.
• The different genres of Master Data Management solutions & pitfalls to avoid
• Different approaches for Master Data Management implementation
• The essential relationship between Master Data Management , Data Quality, and Data Governance
• The under looked but critical aspect of Reference Data Management

Data Warehousing & BI Management

• What is a Data Warehouse & why are they used.
• 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 & Kimball)
• Introduction to Dimensional Data Modelling
• Overview of slowly changing dimensions and why they are required

Data Modelling

• What are Data Models & why do we need them.
• What are the 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.
• Data modelling & Big Data – why data modelling is NOT just about Relational Database design
• The use of data models in Data Governance, and Data Quality Management.

Metadata Management

• What is (and isn’t) Metadata
• The provision of metadata repositories and the means of providing business user access and glossaries from these.
• Different types of Metadata & their uses
• Where is metadata found – the different sources of metadata
• What metadata do we need to manage
• Metadata & Business Glossaries. What’s the connection?

Data Integration & Interoperability

• Data integration & Data interoperability – What’s the difference?
• What are the business (and technology) issues that Data Integration is seeking to address?
• The different styles of Data Integration & Interoperability, their applicability and implications.
• The approaches, plans, considerations and guidelines for provision of Data Integration and access.
• Consideration of Data integration & interoperability approaches including: P2P, ETL, ELT, CDC, Hub & Spoke, Services Orientated Architecture (SOA), Data Virtualisation, and an assessment of their suitability in different cases.

Data Architecture & Data Lifecycle Management

• Types of Enterprise Architectures
• 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 & a Systems Development LifeCycle (SDLC).

Data Risk Management, Security, Privacy & Regulatory compliance

• Identification of threats and the adoption of defences to prevent unauthorised 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.

Data Operations Management

• Core roles & considerations for data operations
• Obstacles to performance
• Good Data Operations practices

Records & Content Management

• Why document & records management is important
• The records management lifecycle

OPTIONAL:  CDMP Sample Examination & Live Exam

• Delegates wishing to take the 90-minute (+20 minutes where English is a second language) CDMP Data Management Fundamentals exam, upon payment of the $300 exam fee will have access to a sample exam to practice on before taking the live exam. The live exam will be taken on the final afternoon.