Product Description

This third volume of the best–selling "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.

Praise for The Data Model Resource Book, Volume 3

"Len and Paul look beneath the superficial issues of data modeling and have produced a work that is a must for every serious designer and manager of an IT project."
—Bill Inmon, World–renowned expert, speaker, and author on data warehousing and widely recognized as the "father of data warehousing"

"The Data Model Resource Book, Volume 3: Universal Patterns for Data Modeling is a great source for reusable patterns you can use to save a tremendous amount of time, effort, and cost on any data modeling effort. Len Silverston and Paul Agnewhave provided an indispensable reference of very high–quality patterns for the most foundational types of datamodel structures. This book represents a revolutionary leap in moving the data modeling profession forward."
—Ron Powell, Cofounder and Editorial Director of the Business Intelligence Network

"After we model a Customer, Product, or Order, there is still more about each of these that remains to be captured, such as roles they play, classifications in which they belong, or states in which they change. The Data Model Resource Book, Volume 3: Universal Patterns for Data Modeling clearly illustrates these common structures. Len Silverston and Paul Agnew have created a valuable addition to our field, allowing us to improve the consistency and quality of our models by leveraging the many common structures within this text."
—Steve Hoberman, Best–Selling Author of Data Modeling Made Simple

"The large national health insurance company I work at has actively used these data patterns and the (Universal Data Models) UDM, ahead of this book, through Len Silverston’s UDM Jump Start engagement. The patterns have found their way into the core of our Enterprise Information Model, our data warehouse designs, and progressively into key business function databases. We are getting to reuse the patterns across projects and are reaping benefits in understanding, flexibility, and time–to–market. Thanks so much."
—David Chasteen, Enterprise Information Architect

"Reusing proven data modeling design patterns means exactly that. Data models become stable, but remain very flexible to accommodate changes. We have had the fortune of having Len and Paul share the patterns that are described in this book via our engagements with Universal Data Models, LLC. These data modeling design patterns have helped us to focus on the essential business issues because we have leveraged these reusable building blocks for many of the standard design problems. These design patterns have also helped us to evaluate the quality of data models for their intended purpose. Many times there are a lot of enhancements required. Too often the very specialized business–oriented data model is also implemented physically. This may have significant drawbacks to flexibility. I’m looking forward to increasing the data modeling design pattern competence within Nokia with the help of this book."
—Teemu Mattelmaki, Chief Information Architect, Nokia

"Once again, Len Silverston, this time together with Paul Agnew, has made a valuable contribution to the body of knowledge about datamodels, and the act of building sound data models. As a professional data modeler, and teacher of data modeling for almost three decades, I have always been aware that I had developed some familiar mental "patterns" which I acquired very early in my data modeling experience. When teaching data modeling, we use relatively simple workshops, but they are carefully designed so the students will see and acquire a lot of these basic "patterns" — templates that they will recognize and can use to interpret different subject matter into data model form quickly and easily. I’ve always used these patterns in the course of facilitating data modeling sessions; I was able to recognize "Ah, this is just like . . . ," and quickly apply a pattern that I’d seen before. But, in all this time, I’ve never sat down and clearly categorized and documented what each of these “patterns’’ actually was in such a way that they could be easily and clearly communicated to others; Len and Paul have done exactly that. As in the other Data Model Resource Books, the thinking and writing is extraordinarily clear and understandable. I personally would have been very proud to have authored this book, and I sincerely applaud Len and Paul for another great contribution to the art and science of data modeling. It will be of great value to any data modeler."
—William G. Smith, President, William G. Smith & Associates, www.williamgsmith.com

"Len Silverston and Paul Agnew’s book, Universal Patterns for Data Modeling, is essential reading for anyone undertaking commercial datamodeling. With this latest volume that compiles and insightfully describes fundamental, universal data patterns, 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

"Volume 3 of this trilogy is a most welcome addition to Len Silverston’s two previous books in this area. Guidance has existed for some time for those who desire to use pattern–based analysis to jump–start their data modeling efforts. Guidance exists for those who want to use generalized and industry–specific data constructs to leverage their efforts. What has been missing is guidance to those of us needing guidance to complete the roughly one–third of data models that are not generalized or industry–specific. This is where the magic of individual organizational strategies must manifest itself, and Len and Paul have done so clearly and articulately in a manner that complements the first two volumes of The Data Model Resource Book. By adding this book to Volumes 1 and 2 you will be gaining access to some of the most integrated data modeling guidance available on the planet."
—Dr. Peter Aiken, Author of XML in Data Management and data management industry leader VCU/Data Blueprint

Product Details

• Amazon Sales Rank: #73004 in Books
• Published on: 2009-01-02
• Original language: English
• Number of items: 1
• Binding: Paperback
• 648 pages

From the Back Cover

"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

About the Author
Len Silverston is the best–selling author of The Data Model Resource Book (Volumes 1 and 2), a speaker and data management consultant with more than 25 years of experience helping organizations integrate their information and systems. He is the owner and president of Universal Data Models, LLC.

Paul Agnew is an author and consultant with more than 17 years of experience in the data management field in many different industries. He is an expert in data modeling, architecture, and integration. He is a senior partner at Universal Data Models, LLC (www.universaldatamodels.com).

Customer Reviews

Both advanced and practical
A belated but welcome sequel to vol.s 1-2 in the series. The book is one-of-its kind, advanced (in parts: very!), dry (in parts: very!) and will probably confuse a novice modeler. On the other hand it is practical and takes relational modeling to a new level for experienced modelers. The key point so well explained and exemplified is that there are different levels of modeling of the same subject - each with a proper purpose and use. There is never one true and correct model, a data model is after all 'a model', a representation of something, not the thing itself. The true modeling professional must know when to choose one or the other model type, and when and how to switch between them. After all, data modeling may be an art and it may be a craft, but it is definitely not an exact science (though a great many other data modeling books may leave that impression).