Average Reviews:
(More customer reviews)I disagree that a person could become a "master" data modeler if the contents of this book are the complete set of skills in their arsenal. While the book outlines some good techniques for interviewing end users and basic data modeling skills, there is alot more involved in data modeling than what is covered here.
As an experienced data modeler who works with large, complex data models in a constantly changing business, I find I do not refer to this book at all. The book excludes common data modeling constructs that I have found very useful, including subtypes and supertypes. The book does not explain the difference between conceptual, logical, and physical data modeling. (It covers techniques used to capture conceptual/logical level data, but nowhere does it explain that or the difference between this type of model and a physical model, and why and when you'd need one or the other.)
The book does not cover normalization, which, once one leaves the interview with end users, one will need to understand. The book does not mention data integration with other systems or databases, how this topic is important and could (and often should) arise in interviews with end users.
Some of the topics covered I found shallow and incomplete, for example, how to name things in a data model. The authors take a parochial view by ignoring real world issues such as using consistent names across database and organizations, and avoiding naming things for what they are used for, not what they are.
As a practicing data modeler, I find my users aren't as naieve about data models as Carlis and Maguire assume them to be. I often am asked why I am modeling data in a given way. In my view, this book does not address the "why" - why do you model the data in the way suggested, and what happens if you don't. When I can answer these questions well for my customers, I earn approval, and this book doesn't equip one to do so.
In sum, my belief is that this book contains about 1/4 of the information a person needs to know to become a "master" data modeler. It's a good starter book if you are a novice data modeler or are having trouble gathering information from business subject matter experts, but if you really want to become an expert data modeler, I'd recommend continuing beyong this book. I prefer 'Data Modeling Essentials 2nd Edition' by Graeme Simsion
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This book introduces Logical Data Structures (LDS), a powerful new approach to database design that can dramatically improve the cost-effectiveness and business value of any enterprise database system or database-driven application. The authors introduce a new notation, new diagramming approach, and new user-centered, high-ROI techniques for modeling even the most complex, high-volume database applications. This book starts from first principles, asking and answering crucial questions like: "To best serve users, how should the process of data modeling work? To create good, economical software systems, what kind of information should be on a data model? To become an effective data modeler, what skills should you master before talking with users?" Next, it teaches data modeling using LDS, designed to encourage a user-centered, requirements approach that leads directly to more effective applications. The authors walk you through the entire process of creating and enhancing a data model. For all database administrators, analysts, designers, and architects, and for all IT managers responsible for enterprise database system management or deployment.
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