Data Modeling Made Simple 2nd Edition

Data Modeling Made Simple 2nd Edition
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  • Item #: 156
Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives:
  1. Know when a data model is needed and which type of data model is most effective for each situation
  2. Read a data model of any size and complexity with the same confidence as reading a book
  3. Build a fully normalized relational data model, as well as an easily navigatable dimensional model
  4. Apply techniques to turn a logical data model into an efficient physical design
  5. Leverage several templates to make requirements gathering more efficient and accurate
  6. Explain all ten categories of the Data Model Scorecard
  7. Learn strategies to improve your working relationships with others
  8. Appreciate the impact unstructured data has, and will have, on our data modeling deliverables
  9. Learn basic UML concepts
  10. Put data modeling in context with XML, metadata, and agile development

Johnny Gay 's review of Data Modeling Made Simple for The Data Administration Newsletter (TDAN.com)

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.

The chapters in the book are divided into 5 sections. The chapters in each section end with an exercise and a list of the key points covered to reinforce what you've learned. I find myself comparing the teaching structure of the book to the way most of us learn to swim.

SECTION I: Data Modeling Introduction

The first section is like the shallow end of the pool, where as a beginning swimmer, you can dip your toes in to test the water. These easy chapters are short and concise. Here the author uses very common objects to describe what a data model is, and why it is so valuable. His first examples made excellent use of what's truly a universal data model to millions of computer users in school and business: the spreadsheet. 

SECTION II: Data Model Components

In the second section, Steve Hoberman introduces you to the simplest components that make up a data model, and explains the important terms that we apply when we discuss them. By the end of section 2, you now have both feet comfortably in the water. You're ready and eager to plunge deeper into the depths of this pool of data model knowledge.

SECTION III: Subject Area, Logical, and Physical Data Models

You've made it to the deep end of the pool where you get a real workout as you lap through the 3 levels of data models: subject area (or conceptual), logical, and physical. Just as there are different strokes for different folks, there are different models for different audiences. By the end of section 3, you'll be able to swim through the intricacies of a data model like a barracuda.

SECTION IV: Data Modeling Quality

Just as swimmers can kick-start their movement through the water with the use of swimming aids (maybe a flotation device or fins will help), you can utilize Steve's 4 favorite templates to collect and organize the requirements that will define your data model. You may recall the scorecard the Olympic judges use to rate a dive. Steve introduces his Data Model Scorecard, which applies a quality rating to a data model.  It's an objective look at the quality of the model built. We are actually adopting this tool where I work, after applying our own weightings to his 10 criteria.

SECTION V - Beyond Data Modeling

Believe it or not, you're ready to leave the pool and jump head first into a small part of the ocean of outside influences that affect a data modelers' work. Bill Inmon tackles unstructured data with taxonomies. Here he simply provides the best explanation about taxonomies and ontologies that I've found. Michael Blaha, who literally wrote the book on the subject of the Unified Modeling Language (UML), follows with an introduction about UML. Steve ends by answering the 5 most frequently asked modeling questions that he has encountered.

This revision took the first edition up several notches from what some deemed a data modeling for dummies book, to what is now a full-fledged textbook. It's easy to see how it could quickly and easily light the way for many future data modelers in any classroom.  I have it on good authority that the author wrote this book to be the most easy-to-read and comprehensive data modeling text on the planet. I agree. This is in itself a wonderful way-finding tool for data modelers that's very easy on the eyes and complete in its coverage.

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Metadata explanation from Data Modeling Made Simple referenced on Wikipedia

About the author

Steve Hoberman is one of the world's most well-known data modeling gurus. He understands the human side of data modeling and has evangelized "next generation" techniques. Steve taught his first data modeling class in 1992 and since then has educated more than 10,000 people about data modeling and business intelligence techniques. He has presented at over 50 international conferences, authored three data modeling books, founded the Design Challenges group, and invented the Data Model Scorecard®.
  • Author: Steve Hoberman
  • Number of pages: 360
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