The Data Modeling Handbook: A Best-practice Approach to Building Quality Data Models

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