Data Model Resource Book: Universal Patterns for Data Modeling

Overview

"Universal Patterns for Data Modeling is essential reading for anyone undertaking commercial data modeling. The Data Model Resource Book series represents the most important contribution to the data modeling discipline in the last decade."
—Dr. Graeme Simsion, author of Data Modeling Essentials and Data Modeling Theory and Practice

This third volume of the bestselling Data Model Resource Book series revolutionizes the data modeling discipline by answering the question "How can you save significant time while improving the quality of any type of data modeling effort?" In contrast to the first two volumes, this new volume focuses on the fundamental, underlying patterns that affect over 50 percent of most data modeling efforts. These patterns can be used to considerably reduce modeling time and cost, to jump-start data modeling efforts, as standards and guidelines to increase data model consistency and quality, and as an objective source against which an enterprise can evaluate data models.

For each pattern, numerous alternatives are provided, ranging from very specific to very generalized ways of modeling. Len Silverston and Paul Agnew point out the pros and cons of these alternatives and provide guidelines to help you make appropriate decisions depending on the set of circumstances faced. In developing and documenting these patterns, the authors share an invaluable set of foundational tools for anyone involved in data modeling, from the novice to the expert. The authors show you how to:

• Model the most prevalent data modeling constructs such as ways to model roles, hierarchies, classifications, statuses, contact information, and business rules
• Re-use a powerful library of core patterns for data modeling
• Model at different levels of generalization
• Evaluate the pros and cons of specific versus generalized models
• Apply the patterns in many types of data modeling efforts, such as prototypes, applications, enterprise data models, data warehouses, and master data management efforts
• Gain buy-in regarding the use of patterns and/or standardizing on these patterns

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