Data Modeling Masterclass

Data Modeling Masterclass


Steve Hoberman - the world's leading data modelling instructor - presents a Best Practices Approach to Developing a Competency in Data Modeling

9-11 June 2020 (9.30-17.30h)
Location: Live Online Event (@YOUR DIGITAL WORKPLACE)
Presented in English by Steve Hoberman
Price: 2250 EUR (excl. 21% VAT)
Register Now » AGENDA » SPEAKERS »


  • Due to the covid-19 pandemic and the uncertainty that we can run this in a physical face-to-face format any time soon, we have decided to turn this 3-day masterclass into a live online workshop
  • Workshop facilitator has run several of these masterclasses online and is well-prepared for this
  • This live online is 300 euro less expensive that its physical counterpart
  • We also decided to change the workshop starting time to 9h30, so that we can finish around 17h30, Brussels and Central European time (CET).

5 (Now 6) Reasons to Attend this Masterclass + a 5-Minute Video Introduction:

  • Steve has trained more than 10,000 people in data modeling since 1992
  • Entertaining and interactive teaching style (watch out for flying candy!)
  • His Data Modeling Masterclass is recognized as the most comprehensive data modeling course in the industry
  • Steve wrote 9 books on data modeling, including the bestseller Data Modeling Made Simple - you get 3 of them, plus superb course notes
  • His Data Model Scorecard technique is now the industry standard for assessing the quality of data model
  • There is now a 6th reason: Steve is giving away his brand new book "The Rosedata Stone: Achieving a Common Business Language using the Business Terms Model" (published March 9th, 2020) free for all participants !

Learning Objectives

This Master Class is a complete data modelling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models.

After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know not just how to build a data model, but how to build a data model well.

Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.

Top 10 Objectives:

  1. Explain data modeling components and identify them on your projects by following a question-driven approach
  2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book
  3. Validate any data model with key “settings” (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard®
  4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing
  5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions
  6. Practice finding structural soundness issues and standards violations
  7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start
  8. Use a series of templates for capturing and validating requirements, and for data profiling
  9. Evaluate definitions for clarity, completeness, and correctness
  10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture

Prerequisites:

This course assumes no prior data modeling knowledge and, therefore, there are no prerequisites. This course is designed for anyone with one or more of these terms in their job title: "data", "analyst", "architect", "developer", "database", and "modeler".




This is a very brief overview of the programme of this unique workshop:

  • DAY 1: 9.30h - Registration, warm welcome and explanation of the workshop
  • - 1. Modeling Basics
  • - 2. Overview to the Data Model Scorecard®
  • - 3. Ensuring the model captures the requirements
  • - 4. Validating model scope
  • DAY 2 - 5. Understanding conceptual, logical, and physical data models
  • - 6. Following acceptable modeling principles
  • - 7. Determining the optimal use of generic concepts
  • - 8. Applying consistent naming standards
  • DAY 3 - 9. Arranging the model for maximum understanding
  • - 10. Writing clear, complete, and correct definitions
  • - 11. Fitting the model within an enterprise architecture
  • - 12. Comparing the metadata with the data
  • - Summary and Conclusions
  • 17.30h - End of three-day workshop
  • Every day, there is a lunch break around 12.45h
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