Data Modeling Made Simple: A Practical Guide for Business & Information Technology Professionals Review

Average Reviews:

(More customer reviews)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..


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Ever have a bad data day? If you're a business user, architect, analyst, designer or developer, then you've probably had some bad data days. It comes with the territory. Overcoming these problems is much easier if you have an in-depth understanding of the actual data. That's where a data model comes in handy. It's a diagram that uses text and symbols to represent groupings of data, giving you a clear picture of your business and application environment. Data Modeling Made Simple provides the tools you need to read, create and validate models of your business and applications.

This book contains everything about modeling you need to know but were too afraid to ask, such as:
-What are the traditional and nontraditional uses of a data model?
-How do subject area, logical, and physical data models differ?
-When do I build a BSAM, ASAM, or CSAM?
-What is the easiest way to apply normalization?
-Where can I best leverage abstraction?
-How do I decide whether to use denormalization or dimensionality?
-What are primary, foreign, alternate, virtual, and surrogate keys?
-What is the best approach to building the models?
-How can I use the Scorecard system to validate a data model?

Plus over 30 exercises to reinforce concepts and sharpen your skills!

Reviews:
"What a great book—and a fun read too! Steve has captured the essence of data modeling and made it simple. For those who are not data modelers but need to work with them, this book is an excellent primer. For those who model data occasionally but not routinely, it is an invaluable quick reference. And for those of us who are experienced (and incorrigible) data modelers, Data Modeling Made Simple is a terrific reminder that we really can keep it simple!"
David Wells, Director of Education, Data Warehousing Institute

"An excellent introduction from someone who knows his subject and knows how to teach it"
Graeme Simsion, University of Melbourne

"Data Modeling Made Simple is a must read for all professionals new to data modeling as well as those that want to ‘speak the language' and understand the concepts.Steve writes as though he is right there with you, walking you through the terminology, explaining the symbols, and telling you what you should consider doing before, during and after you have modeled your data."
Robert S. Seiner, President, KIK Consulting & Educational Services, LLC andPublisher of The Data Administration Newsletter, tdan.com

"Data Modeling Made Simple is an excellent training guide for anyone entering the data modeling field. Steve Hoberman takes the fundamental concepts of data modeling and presents them in an easy to understand and entertaining manner that I'm sure you will find valuable."
David Marco, President, EWSolutions

"How does one who is not a formally trained ‘data modeler' understand the basics of data modeling?Steve Hoberman has created an informative, fun, easy to follow, and practical book sharing data modeling concepts which are essential for any professional involved in information technology. Mr. Hoberman clearly answers key questions behind the what, why and how of data modeling and reinforces the explanations with appropriate examples, analogies and exercises."
Len Silverston, Best-Selling Author of The Data Model Resource Book, Volumes 1 and 2

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Data Modeling Theory and Practice Review

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(More customer reviews)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.

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DATA MODELING THEORY AND PRACTICE is for practitioners and academics who have learned the conventions and rules of data modeling and are looking for a deeper understanding of the discipline.The coverage of theory includes a detailed review of the extensive literature on data modeling and logical database design, referencing nearly 500 publications, with a strong focus on their relevance to practice.The practice component incorporates the largest-ever study of data modeling practitioners, involving over 450 participants in interviews, surveys and data modeling tasks.The results challenge many longstanding held assumptions about data modeling and will be of interest to academics and practitioners alike.Graeme Simsion brings to the book the practical perspective and intellectual clarity that have made his Data Modeling Essentials a classic in the field.He begins with a question about the nature of data modeling (design or description), and uses it to illuminate such issues as the definition of data modeling, its philosophical underpinnings, inputs and deliverables, the necessary behaviors and skills, the role ofcreativity, product diversity, quality measures, personal styles, and the differences between experts and novices.Data Modeling Theory and Practice is essential reading for anyone involved in data modeling practice, research, or teaching.

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