Data Modeling Made Simple: A Practical Guide for Business & IT Professionals

Think what you want, but to me there definitely IS a difference between books about data modeling written by nonpractitioners and practitioners. Same subject, but sometimes poles apart in terms of quality and readability. Theoretical background is certainly essential for a good data modeler, however many books written by nonpractioners overcomplicate things and fail to bring their points across in an easy to understand language. Likewise there are books by practitioners that seriously lack quality. The book at hand combines the best of both worlds and is a pearl among the entry-level books about data modeling. Easy to read and understand, concise, and straight to the point with "just enough" theory. If you are a beginner in data modeling, this book will help you in getting started quickly and in the right direction. If you are an advanced (or even expert) data modeler, this book is a good reread to refresh your knowledge.

This is one of my favorite data modeling books. Not because it provides the most detail into the differences betwen 4th and 5th Normal Form or because it explains in excruciating detail how to develop and Enterprise Data Model, but because it helps me to work with people who are not data architects. I was recently working with the VP of BI for the company I work for and was telling him that we needed to develop a subject area model for our data warehouse and that we would use it as a sort of road map in building the data warehouse. He asked a simple question, "What's a subject." I gave him some examples and he said, "Oh, you mean a table." Hmmm....not quite. More discussion ensued and I finally got the point across. I realized after that incident that I had a hard time communicating exactly what my deliverables would be and why they are important. I grabbed Steve's book off my shelf and re-read it that weekend (it's an easy book to read). As I looked at the list of things I planned to do in building a new data warehouse and pondered the help I would need from the business users in completing them, I saw more conversations in my future about what I do and why it's necessary. I promptly ordered 3 copies of the book for the business users I interact (including the aforementioned VP) with and 3 more for the junior data modelers on my team. The book has been very helpful in bringing non-technical folks up to speed to help with a DW initiative. We are currently working on definitions and Steve's examples and guidelines in this area are helping us put together solid definitions and I am doing a lot less editing than I expected. As someone who does data architecture every day, I sometimes forget why. I just know that I start with subject areas and work my down. I need definitions that are useful, but sometimes find it hard to explain to others what I mean by that. I have adopted Steve'w words and examples when communicating what I do and why I do it that way to others and the best part is that people are getting it. I think this is one of the best practical data modeling books I own at any price. And on a value/$ scale, this tops the charts. Nice work Steve!

Great book. Excellent job boiling things down to essentials. Reminded of one of a line from one of Pascal's letters - "I have made this letter longer than usual because I lack the time to make it shorter." i.e. It must have taken significant time and effort to distill things down so effectively! Its the beauty of the book that it covers a breadth of material but at the right level to keep it short and accessible. I liked the breakdown of subject area models into the 3 areas though it took me a little while to get over my previous use and experience (mental model) of "Subject Area Model" which is closest to the Business Subject Area Models (BSAM) in the book. Ironic that our industry suffers from ambiguous definitions of key terms. I've employed the equally problematic term "Conceptual Data Model" when referring to these, but Steve's breakdown into Business, Application and Comparison gives three key uses for these kind of models, all of which I've employed successfully in past engagements. The book is a target rich environment for complements, but here are a few specific ones. I loved the scope cube and will definitely employ that in the future. Nice tool in helping identify the appropriate scope for a model! I also appreciated the very practical framework for and tips on design reviews. Steve has a simple and effective way of explaining abstract concepts - e.g. the explanation of views as a hybrid between tables & SQL. He is very good at relating everyday experiences to the often mysterious and abstract world of data modeling (e.g. his introduction to normalization by relating it to his experience of sorting a trunk of baseball cards). Overall, the book is a masterful practical walkthrough of essential data modeling activities and concepts. Steve also does what many do not, adds the WHY to the how. The book is very readable (I did it in a few sittings) and employs an effective and understandable business card scenario for examples and exercises all the way through the book. The exercises give some good application opportunities for the material and there are practical tips and illustrative anecdotes throughout. I highly recommend the book to anyone interested in data modeling and though I wish I had written it, I'm glad to be able to have it and use it. Great job Steve! Wayne Little CEO/CTO Lit Data Solutions

I'm on the business side (I am not a techie) and this is the first technical book that I have actually read cover to cover. Steve in a very easy to understand manner walks through step-by-step the components of data models and the different types of models that exist. As a business user I found Chapter 7 (What is the Subject Area Model?) to be the most relevant for my day to day job. I also finally now understand the difference between normalization and abstraction. Chapter 11 on the Data Model Scorecard will be very useful to my team to ensure that data models are critiqued against the Scorecard's 10 categories. The only thing I can think of to make this book better would be to include an index, but the book is so concise and quick to locate topics that it is not really essential.

For over a year I'm looking for a good book to help business analysts to understand data models drawn by others and to train them in creating basic data models needed to cover business needs. I found a lot of good books but all too heavy, too many pages, too detailed and very nice if you want to become a real heavy duty data-guru. There is absolutely nothing wrong with data gurus, every organization needs a few of those, but it needs quite a few more of the 'casual' modellers. This book .. not too big.. a good read.. and even better reread.. It contains exactly everything that is needed for those modellers. So, if you're a Business Analyst, Information Manager and need a good understanding of Data Modelling, even occasionally need to make one yourself, without having to spend years in training: buy this book..