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Agile Data Warehouse Design Masterclass with Lawrence Corr

The next course has yet to be scheduled.  We expect Lawrence to visit again once he completes his next book.  Lawrence last visited NZ in March 2019.

Perhaps our 1 day course ‘An Optimal introduction to defining data requirements‘ would suit you?


Join Lawrence Corr, author of the bestseller “Agile Data Warehouse Design” for a three-day BEAM✲ training workshop and data modelstorming masterclass covering the latest agile techniques for systematically gathering Business Intelligence (BI) requirements and designing effective DW/BI systems.

Read our blogs about what is BEAM*, and how it can help you.

Visual BI Requirements Gathering & Collaborative Dimensional Modeling

Discover how modelstorming (modeling + brainstorming) directly with business stakeholders overcomes the limitations of traditional BI requirements analysis and data modeling to create a shared data language across business and IT.

Over three days of engaging class room sessions, quizzes, games and team exercises, Lawrence will build on Kimball method, industry-standard dimensional modeling and go beyond the books to provide you with practical tools and techniques for BI data design.



  • Understand BI vs. APP requirements
  • Know the BI lifecycle
  • Learn a common BI-specific language
  • Handover without re-translation
  • Repeatable framework
  • Document the right level of detail


  • Requirements that look dimensional
  • Framework to ask clarification questions
  • Cumulative build (less rework)
  • Know the context of data requirements
  • Framework for prioritising needs
  • Better estimates of effort to deliver


  • Upfront input into the data model
  • Enable self-service BI
  • Build on others reports
  • Faster reporting based on dimensions
  • Learn how to gather BI requirements
  • Enable better communication with users

Course Overview

You will learn how to:

  • Model BI requirements with stakeholders using business-friendly tools and techniques
  • Rapidly translate BI data requirements into efficient, flexible data warehouse designs
  • Identify and solve common BI problems using dimensional design patterns
  • Plan, design and develop BI solutions incrementally with agility

Agile Dimensional Modeling Fundamentals

  • BI/DW design requirements, challenges and opportunities: the need for agility
  • Modeling for measurement: the case for dimensional modeling, star schemas, facts & dimensions
  • Modelstorming with BI stakeholders: the case for collaborative data modeling
  • Thinking dimensional using the 7Ws (who, what, when, where, how many, why & how)
  • Business Event Analysis and Modeling (BEAM✲): an agile approach to dimensional modeling

Dimensional Modelstorming Tools

  • Data Stories, Themes and BEAM✲ Tables: modeling detailed BI data requirements by example
  • Timelines: modeling process sequence measurement
  • Hierarchy Charts: modeling dimensional drill-downs and rollups
  • Change Stories: capturing historical data requirements (slowly changing dimension rules)
  • BEAM✲ Matrix: Storyboarding multiple business events planning and estimating for agile BI development
  • Business Model Canvas: aligning DW/BI design with business model definition, measurement and innovation
  • BEAM✲ (BI Model) Canvas: a systematic approach to BI & star schema design
  • Test-driven design: agile data profiling for validating and improving requirements models
  • Data warehouse reuse: identifying, defining and developing conformed dimensions and facts
  • Balancing ‘just enough design up front’ (JEDUF) and ‘just in time’ (JIT) data modeling
  • Designing flexible, high performance star schemas: maximising the benefits of surrogate keys
  • Refactoring star schemas: responding to change, dealing with data debt
  • Lean DW documentation: enhanced star schemas, Data Warehouse matrix
  • How Many: Designing facts, measures and KPIs
  • Fact table types: transactions, periodic snapshots, accumulating snapshots
  • Fact additivity: additive, semi-additive and non-additive measures

Who & What patterns for modeling customers, employees, products and services

  • Large populations with rapidly changing dimensional attributes: mini-dimensions & customer facts
  • Customer segmentation: business to business (B2B), business to consumer (B2C) dimensions
  • Recursive customer relationships and organisation structures: variable-depth hierarchy maps
  • Current and historical reporting perspectives: hybrid slowly changing dimensions
  • Mixed business models: heterogeneous products/services, diverse attribution, ragged hierarchies
  • Product and service decomposition: component (bill of materials) and product unbundling analysis

When & Where patterns for modeling dates, times and locations

  • Flexible date handling, ad-hoc date ranges and year-to-date analysis
  • Modeling time quantitatively and qualitively as dimensions and facts
  • Multinational BI: national languages reporting, multiple currencies, time zones & national calendars
  • Understanding journeys and trajectories: modeling event sequences with multiple geographies

Why & How patterns for modeling cause and effect

  • Causal factors: trigging events, referrals, promotions, weather and exception reason dimensions
  • Fact specific dimensions: transaction and event status descriptions
  • Multi-valued dimensions: bridge tables, weighting factors, impact and ‘correctly weighted’ analysis
  • Behaviour Tagging: modeling causation and outcome, dimensional overloading, step dimensions


NZD $4,200(+gst)

Early Bird Discount NZD $3,600 (+gst)


OptimalHQ, Level 4

139 The Terrace Wellington