Agile BI: Just Enough Design, Data Modeling with Agility Training in Russia

• Data warehouse architects, designers, developers, and administrators
• Business intelligence practitioners
• Business analysts and product owners

• Agile modeling values and principles
• Techniques for determining the right level of up-front design
• How to avoid overbuilding solutions by designing for what is needed
• Domain modeling
• Data model patterns
• Data smells and the impact of technical debt
• Continuous integration
• Safe refactoring techniques for making incremental design changes
• How to determine the right level of design documentation that is needed
• Effective collaborative modeling practices for cross-functional teams
• How to minimize the amount of unnecessary rework through reference and conceptual designs
• How to establish iteration zero as an agile practice that gives teams the runway to start delivering

1. Introduction

• Agile Overview
• Benefits of Evolutionary Design

2. Agile Modeling Mindset

• The Challenge
• Purpose of Modeling
• Agile Modeling Goals
• Modeling in the Agile Project Management Lifecycle
• Agile Modeling Guiding Principles

3. Agile Documentation

• Why, When and What to Document
• Creating Self-Evident Implementations
• Documenting with a clear purpose for the right audience
• Minimally sufficient documentation
• Negative Value Documentation and Document End of Life

4. Domain Modeling

• Why, When and Who
• Story-Gathering Process
• Defining and Refining user roles
• User Personas
• Domain Modeling Exercise

5. Use Cases and User Stories

• Why, When and Who
• Use Cases
• User Stories
• Use Case Exercise

6. Sufficient Design Up Front

• How Buildings Learn
• Iteration Zero
• Conceptual Data Modeling
• Establishing Test Automation
• Continuous Integration Infrastructure
• Sufficient Design Guiding Principles

7. Data Model Patterns

• What is a Pattern?
• Pattern References
• Why Use Patterns?
• Drawbacks of Patterns
• Patterns, Seed Models, and Archetypes

8. Technical Debt

• Overview
• Design and Technical Debt
• Indicators of Technical Debt
• Prioritizing Debt
• Technical Debt exercise

9. Database Refactoring

• What is Refactoring?
• Refactoring Categories
• How Refactoring Works
• Preparing to Refactor
• How to Refactor

10. Agile Modeling with Data Vault

• Complexity of Changeb
• Introduction to Data Vault
• Hubs, Satellites and Links
• Data Vault example
• Data Vault Strength: Adapting to Change
• Data Vault Principles
• Why is Data Vault agile?
• Data Vault Exercise