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