Dimensional Modelling for Business Intelligence

Dimensional models provide the underlying building blocks for a Business Intelligence (BI) initiative which means that Dimensional Modelling skills are one of the critical factors that contribute to the successful outcomes of BI projects.

This two-day course gives participants the skills required to develop Dimensional Models that can be applied to real business problems.

The course is based on Ralph Kimball’s approach to Dimensional Modelling which has become widely adopted to the point that it is now considered the “standard” approach to Dimensional Modelling.

The course is taught by an experienced Dimensional Modeller who offers an independent point of view that is free from vendor bias and tool preferences.

At the end of the course participants will be equipped to make a valuable contribution to BI projects.

Course Features

• Assumes no prior knowledge of dimensional modelling or business intelligence.
• Independent perspective of dimensional modelling free of vendor influence and preferred tool bias.
• Based on Ralph Kimball’s widely adopted approach to dimensional modelling.

Participant Benefits

• Ability to describe the principles of dimensional modelling and develop dimensional models that solve real business problems.
• Ability to design a series of data marts conforming to a planned architectural approach.
• Capable of making an informed and valuable contribution to Business Intelligence (BI) projects.
• Understand the relationship of dimensional modelling to business planning, measurement, and IT architecture.

Who Should Attend

• Anyone acting (or planning to act) in the role of Data Architect, Data Analyst, Business Systems Analyst, Systems Analyst, Business Analyst or Business Consultant.
• Experienced Data Architects who need to update their skills, attend a “refresher”, or simply get some new ideas.
• Other IT professionals and business intelligence stakeholders who need to understand dimensional modelling.

Course Duration

• 2 days full-time

Course Agenda

Introduction to Data Management

• What Is Data Management?
• Data Management Challenges
  • Update vs. Query Structured Data
  • Emergence of Big Data Un-Structured and Semi-Structured
  • Business Intelligence vs. Data Analysis
  • Machine Learning
• Responding to Challenges
• Technology
• Architecture
  • Classic Data Warehouse
  • Data Lake
  • Data Vault
  • Data Lakehouse
  • Data Pipeline
  • Data Fabric Conceptual Architecture
  • Data Mesh Architectural Principles
• Governance
• Data Quality
  • Proactive Data Quality Improvement
  • Reactive Data Quality Improvement

Defining Business Needs

• Defining Business Needs
• Business Drivers
  • Business Intelligence
  • Data Analytics
  • Data Source Availability
• User and Data Source Driven Requirements
• Identifying Business Activities
  • Traditional Approach
    • Enterprise Architecture
    • Survey Business Processes
    • Map Data to Business Processes
  • Agile Approach
    • Jobs To Be Done
    • Hierarchy of Jobs
      • Mission
      • Capabilities
    • Organisation Structure
    • Capabilities and the Organisation Structure
• Defining Business Outcomes
  • Identifying Business Outcomes
  • Drawing Concept Maps
• Measuring Performance
  • Identifying Performance Measures
  • Leading and Lagging Measures
• Step By Step Approach
  • Step 1: Define the Business Area Mission
  • Step 2: Brainstorm Jobs To Be Done
  • Step 3: Group Into Capabilities
  • Step 4: Identify Data Marts
  • Step 5: Develop a Data Mart Concept Map
  • Step 6: Define Performance Measures

Modelling Data

• Comparing Data Models
  • Data Storage Models
  • Normalised Data Models
  • Star Schemas
• Data Modelling Principles
  • Classification
  • Abstraction
    • Representation
    • Concepts
  • Reification
  • Entities
  • Attributes
  • Relationships
  • Cardinality (Multiplicity)
• Normalised Data Models
  • Roles As Attributes
  • Roles As Entities
  • Multiple Roles
  • Resolving Many-To-Many Relationships
• Dimensional Data Models
  • Star Schema
  • Dimensions
    • Role Dimensions
    • Roles as Views
    • Type Dimensions
• Relationship Between Normalised and Dimensional Models
• Dimensions
  • High Quality Verbose Attributes
  • De-Normalised Roles
  • Time Dimension
  • Location Dimension
  • Degenerate Dimensions
  • Optimising Large Dimensions
  • Time Mini-Dimension
  • Junk Dimensions
  • Slowly Changing Dimensions
  • Multi-Valued Facts
  • Snowflake Schema
• Facts
  • Fact Granularity
    • Time
    • People, Things (and Places)
  • Fact Aggregation
  • Fact-Less Facts
  • Facts as Dimensions
    • Conformed Measures
    • Conformed Dimensions
    • Line of Business Facts