Data Modeling: Theory and Practice [Perfect Paperback]

To me, this book's value is a bit like children being warned not to accept lollies from strangers; it's a pity we even have to give such warnings, but it's absolutely essential we do. I wish to congratulate Simsion for bravely tackling a subject of much heated controversy, and in a manner that obviously reflects both a solid practitioner's hard-won lessons, but that is supported by rigorous academic research.

So what's this important message? Simply that data modelling is a creative exercise, where multiple "solutions" may be generated, each with relative merits. The importance lies in practitioners consciously and deliberately generating alternatives. Without this open-minded view, I have personally witnessed heated debates where one modeller defends his/her model because they know it can be made to work, and therefore assumes anything different must be "wrong". But even more significantly, modellers may stop looking as soon as one "workable" model is tabled, and hence miss out on alternatives that may prove beneficial in a given business context.

And why is it even controversial? Apparently, some academics teach data modelling that way. Maybe because it's easier for them to have one "correct" answer to a problem so marking assignments is easier? Or maybe that was what they were taught, and any students who pass through their ranks and end up teaching without encountering real-world modelling may perpetuate?

One warning, though. This book is not the first text to be read by those interested in data modelling. I would recommend Simsion & Witt's "Data Modelling Essentials for such people, followed by one of many excellent books on "patterns". David Hay got the patterns topic going in the data modelling community, and Len Silverston's two volume series has taken it much further. And the object-oriented community also has contributions to make on patterns.

A minor criticism - Simsion largely dismisses the use of the Unified Modeling Language's class modelling notation, in part arguing that "Class diagrams are intended to represent data structures which might be directly implemented using an object-oriented database" and goes on to correctly note the struggle of such databases to gain significant database market share that their vendors initially might have predicted. I would simply comment that there is a difference between using a subset of the class modelling syntax to represent what is truly a data model, as compared to using class modelling notation to represent classes which, in some cases, may never have "persistence" i.e. may never have their data values stored in a database of any kind. And even if class diagram notation is used (some might say misused?) just to represent a data model, I have seen this approach used quite effectively. So on this point, it looks like Simsion and I have slightly different views. But at the very heart of his book, he encourages open debate on alternative views, with the understanding that all views may have something to contribute.

So let's thank Simsion for offering his views, and encouraging others to offer theirs. Well done, it's a great reference book (probably not easy reading for those not exposed to research styles - but don't let that put you off), and one that hopefully bridges the gap between academics and practitioners, and gives the practitioners "permission" to be creative as most know is the way to generate alternative solutions for consideration.

I found this book to be a valuable sequel to the beginner and intermediate modeling texts, such as Data Modeling Made Simple and Graeme's first book, Data Modeling Essentials. This book provides a great mix of both academic and practitioner approaches to data modeling. I have been an analyst and modeler for a number of years and found the side by side comparisons of experts' believes and approaches to be fascinating. While reading this book, I found myself asking questions on my own beliefs and approaches to modeling, and this really helps improve the consistency of my modeling decisions. This is an intermediate to advanced modeling text that is extremely well-written and provides valuable insight into how we do data modeling.

After spending 25 years practicing, lecturing, writing about, studying, and teaching data modeling, I was thrilled to discover new insights and gain more clarity in this field due to Dr. Simsion's diligent work that is shared in his latest book, "Data Modeling Theory and Practice". Like many data modelers, I have strong views on this subject and it was interesting to be able to see the broad range of opinions, experiences, and even to be able to see some of the diverse models produced in the research contained with this book. One of my views is that the more perspectives you can see, the greater the clarity. So, regardless of your views on data modeling, if you are interested in furthering your understanding of the discipline of data modeling, then I would highly recommend reading this material as I give my "two thumbs way up" to this revolutionary book.