Product Description

A Straightforward, No-Nonsense Guide to Building the Most Accurate, Complete, and Useful Data Models Possible. How do I know if my data model is accurate? When is a model really complete? Is it possible for a model to be both technically perfect and of no use to an organization, and what can I do to avoid that problem? This book provides answers to these and other crucial data modeling questions. While there are plenty of books that describe the characteristics of finished high-quality data models, only The Data Modeling Handbook gets down to the nitty-gritty of actually building one. Packed with real-world examples, annotated diagrams, and a wealth of rules and best practices, this field-tested guide provides experienced data modelers, architects, and engineers with hands-on guidance from two noted data management experts.

• The only book offering clear, straightforward rules and guidelines for judging model accuracy and completeness
• Presents all rules in several notations, including IDEF1X, Martin, Chen, and Finkelstein
• Compares and contrasts the most popular modeling styles and demonstrates how great models can be built using any type of notation
• Explains how to use an organization’s plans, policies, objectives, and strategies to build accurate, complete, and useful models
• Offers detailed guidance to establishing a continuous quality evaluation program that’s easy to implement and follow
• Packed with real-world examples and annotated diagrams illustrating each point covered
• Describes how to use Case tools most effectively to build high-quality models

Product Details

• Amazon Sales Rank: #741778 in Books
• Published on: 1994-12
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 384 pages

From the Publisher
A straightforward explanation of how to combine good technique with business context in a self-regulating modeling process that results in the creation of useable models. Contains a series of rules and best practices in an organized reference format. Addresses transition to systems development and model management, presenting each rule in several notations. Includes numerous examples drawn from practical experience.

From the Back Cover
A Straightforward, No-Nonsense Guide to Building the Most Accurate, Complete, and Useful Data Models Possible. How do I know if my data model is accurate? When is a model really complete? Is it possible for a model to be both technically perfect and of no use to an organization, and what can I do to avoid that problem? This book provides answers to these and other crucial data modeling questions. While there are plenty of books that describe the characteristics of finished high-quality data models, only The Data Modeling Handbook gets down to the nitty-gritty of actually building one. Packed with real-world examples, annotated diagrams, and a wealth of rules and best practices, this field-tested guide provides experienced data modelers, architects, and engineers with hands-on guidance from two noted data management experts.

• The only book offering clear, straightforward rules and guidelines for judging model accuracy and completeness
• Presents all rules in several notations, including IDEF1X, Martin, Chen, and Finkelstein
• Compares and contrasts the most popular modeling styles and demonstrates how great models can be built using any type of notation
• Explains how to use an organization’s plans, policies, objectives, and strategies to build accurate, complete, and useful models
• Offers detailed guidance to establishing a continuous quality evaluation program that’s easy to implement and follow
• Packed with real-world examples and annotated diagrams illustrating each point covered
• Describes how to use Case tools most effectively to build high-quality models

About the Author
MICHAEL C. REINGRUBER is Technical Leader for the development of the SRA Business Reengineering Methodology. WILLIAM W. GREGORY is Deputy Director of the Business Process Improvement Division of SRA.

Customer Reviews

Improving your Models
This book is not a database book, nor does it try to be that. For anyone who understands the role of logical data modelling, this book will prove to be a valuable addition to your professional bookshelf. It is filled with detailed examples of good & bad models, along with analyses that list the pros & cons of each approach. I'd recommend this to any intelligent professional who is moving into the data architect role, along with experienced modellers who are looking for analyses of data modelling implications.

Easy to understand
I have many books on data modeling but this one is probably the most understandable. The examples are well-formed and many diagrams are provided along with written explanations. They do an excellent job of going through 5th normal form and show how to resolve many different issues such as special cases in generalization hierarchies. I have found this book very useful in practice and it has served me well.

Excellent book!
This book is excellent. The subject matter is advanced but still easy to read and understand. The examples used in the book are varied and excellent illustrations of the problems discussed in the book. Many of the examples remind me of similar data modeling errors I have seen in my experience and give excellent methods of correcting the mistakes. I highly reccomend this book for the data modeler who is ready to go to the more advanced conecpts. (You must know basic data modeling concepts to understand this book, such as how to read diagrams, basic terminology, etc.)