This 2-day course addresses the core data management topic of data modelling. It also prepares delegates for the CDMP Data Modelling Specialist exam. Often misunderstood and relegated to just the technical aspect of “database design”, data modelling is one of the most important disciplines of data management. The course introduces delegates to data modelling, its purpose, the different types of models, how to construct and read a data model, and the wider use of data models beyond the traditional area of database design. It contains a wide-ranging clarification of data modelling concepts and terminology, together with techniques for producing usable data models. This course covers the CDMP Data Modelling specialist exam syllabus, practices with sample questions, and prepares candidates to take the DAMA CDMP specialist Data Modelling exam.

Data Modelling Basics

• What is Data Modelling and why does it matter
• What is the relationship between a data model and other types of models in the Enterprise Architecture
• What is a Conceptual Data model, why it’s important and the pivotal role it plays in all architecture disciplines
• The major differences between Enterprise, Conceptual, Logical, Physical and Dimensional data models
• Data vs MetaData; what’s the difference and why does it matter Data Model Components
• Data Modelling Basics; Entities, Attributes, Relationships
• How to identify Entities and Subtypes
• What are the differences between exclusive and nonexclusive subtypes?
• How do different data modelling notations represent subtypes?
• Basic standards that you can use right away
• Relationships: Cardinality & Optionality, Identifying, Nonidentifying, recursive, and many-to many
• How does cardinality and referential integrity lead to better data quality?
• Rules for handling Super types, subtypes, many to many and recursive relationships
• Keys: Primary, Natural, Surrogate, Alternate, Inverted, Foreign
• What are the alleged and actual benefits of surrogate keys?
• Attribute properties & attribute domains

Creating Data Models

• How to get started with data models
• What core information is needed to create a data model, how this can be easily communicated to business people, and what visual constructs to use to get their attention
• Templates and guidelines for a step-by-step approach to implementing a high-level data model in your organization
• How to capture requirements for data models
• Approaches for creating a data model (Top Down, Bottom
  Up, Middle out) and when to use them.

Using Data Models

• How to use high-level data models to communicate with business people to get the core information you require to build robust applications.
• The critical role played by Data Models in all disciplines of Information Management.
• Why Data Models are required for software package implementation
  Data models are not just for DBMS design, the other areas where models are critical.
• Maturity assessment to consider the way in which models are utilized in the enterprise and their integration in the System Development Life Cycle (SDLC).

Dimensional Data Modelling Basics

• Facts and Dimensions, the basics of Dimensional models
  The key differences between Dimensional & Relational models
• The use of Dimensional data models in Business Intelligence & Data Warehousing
• Inmon vs Kimball Data Warehouse approaches
• How to cater for change in Dimensional models; the different types of slowly changing dimensions
• Aggregation and Summarisation – what you really need to know
  Columnar Database & Data warehouse – a forgotten treasure?

Improving your Data Models

• Data Modelling Notations and tooling
• Normalisation: 1st, 2nd and 3rd normal form and a brief overview of other normal forms
• Ten steps for checking the quality of your data models Layout, presenting, and communicating a data model to non-modellers

This course explains the essential data modelling building blocks. It will help students to understand the differences between relational and dimensional models, and between the different levels of Conceptual, Logical and Physical models. On completion they will be able to:

• Describe the purpose of, Conceptual, Logical, and Physical data models
• Create a Conceptual and a Logical Data model
• Read and interpret a data model
• Understand different approaches for fact finding and how to apply normalisation techniques
• Understand how to validate a data model.

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

Level Set Understanding & Terminology:

• Learn about the need for and application of Data Models
  See the areas where Data modelling adds value to Data Management activities
• Understand the critical role of Data models in Master Data
• Management and Data Governance.

Pragmatic Learning:

• Understand the difference between, Conceptual, Logical,
  Physical and Dimensional Data models
• Learn the best practices for developing Data models that can be read by humans
• Through practical examples, learn how to apply techniques in Data modelling.

Practitioners who will need to read, consume or create data models to gain a better understanding of data during Information Management initiatives including:

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