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(More customer reviews)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.
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The Data Model Resource Book, Vol. 3: Universal Patterns for Data ModelingThis 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
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