Duration

5 days

Course Overview

This course addresses ALL the Information Management disciplines as defined in the International Industry standard, DAMA body of knowledge (DMBoK) v2 and the revised edition (V2 rev). Taught by an industry recognized DAMA DMBoK author and CDMP(Fellow) this course provides a solid foundation of the different disciplines across the complete Information Management spectrum. By attending the course, delegates will get a firm grounding of the core Information Management concepts and illustrate their practical application with real examples of how Information Architecture is applied. Additionally, this course provides a solid foundation for students wishing to take the industry professional certification the DAMA Certified Data Management Professional (CDMP). Following the course, students can optionally sit the Data Management Fundamentals examination, enabling students to attain their CDMP (Associate level) certification.

The course is designed and taught by the VP of Professional Development for DAMA International, author of “Data Modelling for the Business” an industry recognized DAMA DMBoK author, DAMA CDMP(Fellow) Past President of DAMA UK, author and examiner of the professional CDMP certification and recipient of the DAMA Lifetime Achievement Award for Data Management Excellence.

This course addresses all the Information Management disciplines as defined in the DAMA body of knowledge (DMBoK v2.0) and the revised edition (V2 rev).

Learning Objectives

This course provides students with the knowledge, methods and techniques required to analyse, mature and implement information management solutions within your organisation. It affords a solid grounding in all the different disciplines of Data Management. In addition, if students wish to take the CDMP certification they will gain familiarity with the CDMP examination format, types of questions and the most appropriate way of answering them. They will understand and revise the major syllabus points. Attempt practice questions in preparation for sitting examinations.

Areas covered include:

Data Governance
Data Quality Management
Master and Reference Data Management
Business Intelligence & Data Warehousing
The essential role of Data modelling
Data Lifecycle Management
Metadata Management
Risk, Security & Regulatory compliance
Data Operations
Content & Records management
Data Integration & Interoperability

Audience

Practitioners who seek to gain an overview of the different disciplines of Information Management and those seeking Professional recognition and certification for Information Management including:

Business Intelligence & Data Warehouse developers & architects
Data Modellers
Developers
Data Architects
Data Analysts
Enterprise Architects
Solution Architects
Application Architects
Information Architects
Business Analysts
Database Administrators
Project / Programme Managers
IT Consultants
Data Governance Managers
Data Quality Managers
Information Quality Practitioners

There are 4 levels of accomplishment for the CDMP certification, Associate, Practitioner, Master, and Fellow.

Associate Level:

Recommended 2+ years relevant Data Professional work experience

1 examination (Data Management Fundamentals). Pass mark is 60%

Practitioner Level:

Recommended 3-5 years relevant Data Professional work experience

3 examinations (Data Management Fundamentals + 2 specialist).

Pass mark for all 3 exams 70%

Master Level:

Required 10+ years relevant Data Professional work experience

3 examinations (Data Management Fundamentals + 2 specialist).

Pass mark for all 3 exams 80%

Written case study reviewed by CDMP Fellow

Fellow:

By appointment only

Completion of all previous levels,

Recognised exceptional works & contributions in the Data Management field

Published author

25+ years’ experience

The 2 specialist exams must be chosen from the following set:

Data Governance and Stewardship

Data & Information Quality

Data Modelling

Data Warehousing & Business Intelligence

Metadata Management

Data Integration & Interoperability

Master and Reference Data Management.

At the end of the course, delegates would have gained the following:

Level set understanding & terminology:

Learn about the need for and the application of Information Management disciplines for different categories of challenges.
Explore an Information Management framework and understand how it aligns with other architecture frameworks.
Explore concepts such as lifecycle management, normalisation, dimensional modelling and data virtualisation and appreciate why they are important.
Understand the critical roles of Master Data Management and Data Governance and how to effectively apply them.

Pragmatic Learning

Learn the different MDM architectures, their suitability for different needs and how best to implement Master Data Management approaches.
Understand the different facets (dimensions) of Data Quality and explore a workable Data Quality framework.
Discover the major considerations for successful Data Governance and how it can be introduced in bite-sized pieces.
Develop a set of usable techniques that can be applied to a range of information management challenges
Learn the best practices for managing Enterprise Information needs
Through practical examples, learn how to apply techniques in information architecture planning.

CDMP Certification

Understand the syllabus for the CDMP professional certification.
Revise all the areas for the Data Management Fundamentals examination.
Take practice questions for all areas of the exam syllabus.
Students can leave this course having attained their CDMP (Associate) certification. The Data Management Fundamentals exam may optionally be aken at the end of day-5 of a “physical” course, or at a time of the students choosing in the following week for a “virtual” course.

Course Synopsis:

Introduction to Data Management, the DMBoK & pathways through 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
Changes in DMBoK 2 revised edition (V2 rev)., and the relationship of the DMBoK with other frameworks (TOGAF / COBIT etc.).
DAMA CDMP professional certification overview & CDMP exam coverage by DMBoK section.
What is the CDMP, what are the levels, what are the DAMA specialist certifications and how can you progress from one level to the next.

Data Management Process

What is Data Management
The Information value chain
The Information life cycle vs the Systems Development life cycle
Drivers for data management
Components of data management

Data Quality Management

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

Data Modelling & Design

What are Data Models & 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.
Business conceptual data models.
Logical data models.
Normalisation.
Considerations for Physical data models & design
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).
Data modelling & Big Data - why data modelling is NOT just about Relational Database design.
How Data Modelling is an essential component of Data Governance

Master & Reference Data Management

The differences between Reference & Master Data.
How to identify and manage Master Data across the enterprise.
4 generic MDM architectures & their suitability in different cases.
An 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.
Categories of MDM solutions
Approaches for MDM implementation
The essential relationship between MDM, DQ and DG
The under looked but critical aspect of Reference Data Management

Data Warehousing & BI Management & Big Data

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
Types of BI, DW, Analytics & Visualisations.
Big Data (included in the DAMA DM Fundamentals examination)

Data Risk Management, Security, Privacy & 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, 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.
Risk appetite & how to determine this.
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 considerations for different regulations, e.g., GDPR, BCBS239

Document, Records & Content Management

Why document & records management is important.
The records management lifecycle
Significant and Vital Records
Audit and records control
Document control schemes
Taxonomy & ontology – what are they and why are they important.
Content Management & Content Distribution.

Metadata Management

What is (and is not) Metadata.
Metadata repositories and the relationship with Glossaries and Data Dictionaries.
Means of providing business user access to useful metadata.
Types of Metadata.
Uses of Metadata.
The different sources of metadata & what can be provided automatically.
Metadata container standards.
Industry metadata standards.
The uncomfortable truth about metadata & Big Data technologies.

Data Architecture & Data Lifecycle Management

Types of Enterprise Architectures & example frameworks.
The different Architecture domains.
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 Operations & Storage Management

Core roles & considerations for data operations
Obstacles to performance
Good Data Operations practices
The delicate balance of performance vs 4 core factors.
Data archiving and storage considerations.

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 Ethics (included in the DAMA DM Fundamentals examination)