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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.
- Sales Rank: #938120 in Books
- Published on: 2009-01-09
- Released on: 2008-12-19
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x 1.40" w x 7.40" l, 2.05 pounds
- 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:
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Model the most prevalent data modeling constructs such as ways to model roles, hierarchies, classifications, statuses, contact information, and business rules
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Re-use a powerful library of core patterns for data modeling
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Model at different levels of generalization
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Evaluate the pros and cons of specific versus generalized models
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Apply the patterns in many types of data modeling efforts, such as prototypes, applications, enterprise data models, data warehouses, and master data management efforts
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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).
Most helpful customer reviews
29 of 30 people found the following review helpful.
An important contribution to our field
By Steve Hoberman
As an analyst for a large manufacturing company's ERP implementation, I was responsible for a very complex and critical area called Classifications. Classifications was the place where all products, vendors, or customers were grouped into buckets based on similar behavior. For example, if this company manufactured vehicles, there could be classifications for hybrids, sports cars, SUVs, minivans, etc. To better understand classifications, I dived into screens, help files, and actual database tables and after several weeks, completed a classifications data model. The model I produced was very similar to the data model that appears on page 224 of "The Data Model Resource Book Volume 3: Universal Patterns for Data Modeling" by Len Silverston and Paul Agnew.
This book contains a collection of patterns, which are general building blocks that could be used as the basis for just about any type of data modeling within any industry. Classifications is one example, and there are a collection of others such as roles, statuses, and contact mechanisms. Whereas Volumes 1 and 2 in The Data Model Resource Book series contained models for common business processes or industries, this volume contains patterns that cross through all processes and industries. Consistent with the series however, the purpose of this text is to save the modeler time so instead of starting from scratch, the modeler can start from a reliable and proven foundation. Realizing these patterns exist and making them work for your particular modeling assignment can result in a higher quality data model and a greater level of consistency within your organization.
A majority of the book is dedicated to chapters which describe how to model a pattern at different levels of generalization. Level 1 is the most concrete and this is where terms and rules a business analyst are familiar with are shown, such as email address and telephone number. Level 2 through 4 go through increasingly more generized levels with Level 4 being the most generalized. The Classifications example I encountered in the ERP package for example was a Level 3 model, very generalized so that it can be leveraged by any industry. The book makes an important point that there are situations where one level is more appropriate than another, and sometimes the modeler must trade the familiarity and business rule enforcement of a Level 1 with the flexibility available in a Level 2, 3, or 4. For example, a phone number and email address from a Level 1 model would be generalized into contact mechanism data in a Level 2 model. This extra flexibility allows for accommodating other ways of contacting someone that may not have been specified (for example, via a person's "voice over IP" or Skype number). The book also makes the point that sometimes on a single model you can combine different levels for the same requirement (i.e. a hybrid approach).
Chapter 1 introduces the concept of a universal pattern as well as the terms and symbols used throughout the book. The goals for the book are also clearly articulated, in addition to the intended audience and a summary of each chapter. There is a wonderful furniture analogy used to distinguish a universal data model from a universal pattern. Universal data models (the subject of the first two volumes of The Data Model Resource Book), are similar to already constructed standard tables and chairs. The consumer can obtain this furniture instead of build the tables and chairs from scratch. Similarly, the modeler can reuse an inventory or claims universal data model instead of building it from scratch. Universal patterns are similar to the dovetail joints of the furniture, common pieces that exist in already built tables and chairs as well as custom furniture. Universal patterns are the building blocks such as the roles and statuses behind any modeling project.
Chapters 2 through 8 each focus on a particular pattern. Chapters 2 and 3 focus on parties and roles; Chapter 2 on declaration roles and Chapter 3 on contextual roles. A party is a person or organization of importance to the business, and declaration roles are those roles that are independent of any business event while contextual roles are dependent on a particular business event. For example Bob the person can have a declarative role of `Doctor', yet when an insurance claim is filed, they can also have the contextual role of `Primary Care Physician'. Chapter 4 focuses on similar structures for relating data including hierarchies, aggregations, and peer-to-peer relationships. Chapter 5 focuses on taxonomies and classifications, and Chapter 6 on patterns for states that business concepts go through. Chapter 7 contains patterns for getting in touch with parties, such as those patterns for modeling telephone number and email address. Chapter 8 focuses on how to model business rules including the rule itself, the factors involved in the rule, and the outcomes of the rule.
I was impressed with the consistency and comprehensiveness of each of these chapters. These chapters follow a similar format of demonstrating each of the four levels of detail. Each chapter begins with an explanation of the pattern and a discussion of its importance. Then for each of the four levels, there is a section on the reason for the level, how the pattern works (with lots of examples), when the pattern should be used, and the weaknesses of the pattern. I found the charts and tables to be extremely useful in the text, especially the Summary of Patterns table at the end of each chapter.
Chapter 9 focuses on how to apply these patterns in many types of efforts including both relational and dimensional modeling efforts and both application and enterprise areas of scope. As with the other chapters, there is a great summary at the end on the strengths and weaknesses of patterns with each type of effort. Chapter 10 adds the human dynamics side to incorporating patterns, as success or failure is heavily connected with people's perception or trust. Four principles are discussed, that will help acceptance and usage of the patterns: Understand motivations and work toward meeting them, Develop a clear, common, compelling vision, Develop trust, and Manage conflict effectively.
To summarize, under every data model is a set of common building blocks, clearly explained in "Universal Patterns for Data Modeling". I would recommend this book for every analyst, modeler, or architect who is striving for a level of information consistency within their organization. Whether you are just starting your modeling adventure or have been in the modeling for decades, you will find these patterns invaluable tools for every modeling effort.
15 of 16 people found the following review helpful.
A Comprehensive Study of Key Data Model Patterns
By Robert P. Hoeting
Unlike the world of Object Oriented programming, there are very few pattern books devoted to data modeling. This is unfortunate because most business applications suffer from over-specialization & inflexibility in the data model, where change is very expensive. The few data model patterns books that do exist do not explore the depths of subject to the extent that this book does.
Based on their many years of experience, Silverston & Agnew chose to focus on the most common areas of interest (Parties, Roles, Relationships, Statuses, Classifications, Contact Mechanisms, & Rules) and offer a set of data model pattens for each. Each area has a set of "pattern levels" ranging from specific to general, listing benefits, drawbacks, & usage guidelines for each. The modeler can choose which pattern level best suites the enterprise based on perceived needs.
This book can be thought of as the "gang of four" book for data modeling. I would recommend this book for anyone engaging in any form of data modeling. You may not use the exact patterns, but it will at least serve as a catalyst for future thought.
25 of 29 people found the following review helpful.
Two surprises I didn't like
By G. Pond
The text of this book was generally good - although it seemed padded out with a massive listing of model metadata. The book comes with a CD. The first surprise was, the CD does not include the Data Definition Language (DDL) for its sample models... there's an extra cost for that. My question to the publisher would be, what good is the CD you provide for free?
The second surprise is the illustrations. There are plenty of them, but they look like they were done in a primitive graphics package - not in an enterprise modeling tool. They author seems to have invented his own wierd set of conventions, including "foreign keys do not appear in the entities... that is duplicate information". Before you buy this book, take a look at the illustrations of the models. If you can live with the notation, maybe consider buying it.
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