Data Modeling Master Class Training Manual by Steve

This is the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman`s website.

The Master Class is a complete course on data modeling, containing four days of practical techniques for producing solid relational and dimensional data models. After learning modeling concepts and terms, you will apply a best practices approach to building and validating data models through the Data Model Scorecard™. You will learn not just how to build a data model, but also how to build a data model well. Challenging exercises and workshops will reinforce the material and enable you to apply these techniques in your current projects.

Steve is an excellent presenter, keeping his audience engaged while covering in-depth relational and dimensional modeling techniques. His entertaining style combined with his many real world examples and exercises, held our interest throughout the course.
S. DeCandia, Pfizer Global Manufacturing

In my long professional career, I have participated in many training seminars but I have never encountered a class in which the subject had been so thoroughly considered and presented in such a clear and engaging manor. As a fairly new, but full time data modeler, I expect to use the things that I learned in this class every day. My only regret is that the class had to end.
G. Schmid, Travelers Insurance

This was the most comprehensive, informative, energetic, interesting and just plain FUN class I have ever taken on the subject of Data Modeling.
G. Werner, Long Island Railroad

Part 1: Modeling Basics
Assuming no prior knowledge of data modeling, we will begin this section with an entertaining exercise that will illustrate an important gap filled by data models. Next, we will explain data modeling concepts and terminology. We will also explore each component on a data model and practice reading business rules.

Part 2: Overview to the Data Model Scorecard™
The Scorecard is a set of ten categories for validating a data model. We will explore best practices from the perspectives of both the modeler and reviewer, and you will be provided with a template to use on your current projects.

Part 3: Understanding subject area, logical, and physical data models
The subject area model captures a business need within a well-defined scope; the logical data model captures an application-independent business solution; and the physical data model captures the technical solution by focusing on factors such as performance and security.

Part 4: Ensuring the model captures the requirements
We will focus on techniques such as the use of spreadsheets and business assertions to ensure the data model meets the business requirements.

Part 5: Validating model scope
We will focus on techniques for validating that the scope of the requirements matches the scope of the model. If the scope of the model is greater than the requirements, we have a situation known as scope creep. If the model scope is less than the requirements, we will be leaving information out of the resulting application.

Other training modules include:

• Following acceptable modeling principles
• Determining the optimal use of generic concepts
• Applying consistent naming standards
• Arranging the model for maximum understanding
• Writing clear, correct, and consistent definitions
• Matching the model with the enterprise
• Comparing the meta data with the data

Steve Hoberman is a world-recognized innovator and thought-leader in the field of data modeling. He has worked as a business intelligence and data management practitioner and trainer since 1990. Steve is known for his entertaining, interactive teaching and lecture style (watch out for flying candy!) and is a popular, frequent presenter at industry conferences, both nationally and internationally. Steve is a columnist and frequent contributor to industry publications, as well as the author of Data Modeling Made Simple, Data Modeler’s Workbench, and Data Modeling for the Business. He is the founder of the Design Challenges group and inventor of the Data Model Scorecard.