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The Data Model Resource Book, Vol. 1: A Library of Universal Data Models for All Enterprises
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A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM.
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Product details
Paperback: 542 pages
Publisher: Wiley; Revised edition (March 6, 2001)
Language: English
ISBN-10: 0471380237
ISBN-13: 978-0471380238
Product Dimensions:
7.2 x 1.6 x 9 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
78 customer reviews
Amazon Best Sellers Rank:
#551,458 in Books (See Top 100 in Books)
It is worth repeating what this reviewer indicated in his review of the second volume in this series of three texts on data models: although there are quite a few positive reviews for this series, there are also a nontrivial number of dissenters as well, who cite use of Barker's notation rather than Crow's Foot notation (i.e. Information Engineering notation) as well as unexpected contents within the accompanying CD. When first introduced to Richard Barker's "Entity Relationship Modeling" text during graduate school, this reviewer does admit that becoming familiar with his notation did take some, but all told it does not deviate significantly from Crow's Foot, and after a short amount of time the reader will actually notice some advantages in using it, one of which is the reduced space that substantive models need to consume. Sure, additional information not available with the CD can be purchased on Silverston's companion web site at Wiley, but it really is not necessary. Not only are these downloads not necessary, these may detract from the process of understanding the material that Silverston is attempting to present. And Silverston presents very well.Silverston explains in this volume that clients have inquired on numerous occasions where they can find texts showing standard ways to model data constructs, and "based on numerous experiences of using template or 'universal data models' and customizing them for various enterprises, we have concluded that usually more than 50 percent of the data model (corporate or logical) consists of common constructs that are applicable to most organizations, another 25 percent of the model is industry specific, and on average about 25 percent of the enterprise's data model is specific to that organization. Of course, as John Zachman indicates in the second volume, "Let's get pragmatic. Starting with a universal data model does not absolve anyone of the responsibility of knowing his or her own enterprise intimately, at even an excruciating level of detail! Nor does it absolve anyone from the responsibility of learning how to build data models! What you have to do is start with the universal model, and then understanding data modeling and understanding your own enterprise, make the necessary modifications to make the universal model your own."Barker's notation is presented along with basic modeling in the introduction to this text, after which the author presents chapters on modeling people and organizations, products, ordering products, shipments, work effort, invoicing, accounting and budgeting, and human resources. Five chapters on data warehousing modeling follow, including explanations on how to create the data warehouse data model from the enterprise data model, as well as star schema designs for sales analysis, human resources, inventory management analysis, purchase order analysis, shipment analysis, work effort analysis, and financial analysis. When determining which of the three volumes you might purchase, be aware that there is some overlap between the volumes. For example, the second volume in this series contains models for products and people and organizations. In respect to these subject areas, however, the second volume in this series presents this information in respect to specific industries, while this first volume discusses them in a manner universal to all industries, significantly expanding upon any overlapping areas of the second volume while at the same time remaining industry neutral. Remember though that all of these models are to be used simply as input to your enterprise modeling efforts rather than as end states in themselves.This reviewer recommends this text just as wholeheartedly as the second volume in this series. It can never hurt to get additional insight from other industry practitioners, and compared to other available resources the cost of this text is trivial. Note also that the second volume in this series refers to this volume in a number of different areas, so it makes sense to acquire these two volumes together.
Incredibly well done follow-up to the first two volumes of Silverston's data model series (see my reviews for "The Data Model Resource Book Volume 1 (Revised Edition): A Library of Universal Data Models for All Enterprises" and "The Data Model Resource Book Volume 2 (Revised Edition): A Library of Universal Data Models by Industry Types"). As discussed in the introduction to this book, while the first volume answered the question "Where can we find a book showing a standard way to model common data model structures?" and the second volume extended the template models presented in the first volume by adding additional data model constructs that are industry specific, this third volume answers the question "How can we quickly extend and customize these models for our organization and our needs to quickly develop any data model with higher quality, even if it is specific to our enterprise?"The patterns that Silverston and Agnew present are categorized into chapters entitled "Setting Up Roles: What Parties Do", "Using Roles: How Parties Are Involved", "Hierarchies, Aggregations, and Peer-to-Peer Relationships: The Organization of Similar Data", "Types and Categories: The Classification of Data", "Status: The States of Data", "Contact Mechanisms: How to Get in Touch", and "Business Rules: How Things Should Work". Each chapter is well laid out, similar in style to other books of this genre such as "Design Patterns: Elements of Reusable Object-Oriented Software" by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides. In this text, each pattern is laid out in its own section that answers the following questions: "Why Do We Need This Pattern?", "How Does This Pattern Work?", "When Should This Pattern Be Used?", and "What Are the Weaknesses of the Pattern?"In addition, an aspect of this book that this reviewer especially appreciates is a presentation of each pattern at different levels. While the data management industry habitually refers to conceptual, logical, and physical data models, because there are widely different views as to the definition and purpose behind each of these types of data models (this reviewer is all too familiar with this inconsistency from his consulting engagements), the authors devised four levels of patterns that span from the most static and the more specific (Level 1) to the most flexible and more "generalized" (Level 4). The authors explain that the two key purposes of a data model are (1) "to illustrate and communicate information requirements, and (2) "to provide a sound foundation for a database design", and because these purposes can obviously be at odds with each other, depending on the stakeholders involved, care needs to be taken during the modeling process to determine appropriateness of approach.While this reviewer offers no suggestions for alternative terminology, the decision of the authors to use the term "generalization" to describe the transformation of very specific data model structures to those which more flexibly support data requirements is interesting (especially given their explanation that they chose to do so since the object-oriented community uses the perhaps more intuitive term "abstraction" in a different way that has a different meaning) because this reviewer is well aware that software developers also use the term when referring to an object class superset that has object class "specializations". But despite this small matter, the consistent focus of the authors on the purpose of each level of data model presented throughout the text for each group of patterns (always discussing Level 1, Level 2, and Level 3, and sometimes discussing Level 4) is highly valued by this reviewer, especially when the authors discuss these levels in relationship to the Zachman Framework and other data model classification schemes.This reviewer also enjoyed the last two chapters, entitled "Using the Patterns" and "Socializing the Patterns", in which the authors answer the questions "How do I use, apply, and/or implement these patterns in my enterprise?", "What is an appropriate balance between requiring adherence to the patterns and allowing them to be used completely optionally and used if and when they are helpful to the modeler/designer?", "How do you get your enterprise and various people in the enterprise to adopt these patterns?", and "What types of policies or principles regarding use of the patterns would be most appropriate to get the most benefit from these patterns?"In the first of these last two chapters (Chapter 9), the authors demonstrate how the patterns can be used to create different data models that meet different needs, using the different levels of patterns as interchangeable components and combining them to solve common data modeling challenges for circumstances surrounding the building of prototyping and scoping data models, application data models, enterprise data models, data warehouse data models, and master data management (MDM) data models. In the list of additional considerations when using generalized patterns at the end of this chapter, this reviewer appreciates their remark that "generalized structures move the change process from the typical 'data architect to DBA to developer to tester' process to a data change process, and often organizations have no formal process for data change like they have for application change" and that "this can be addressed with organizational commitment to flexible data modeling structures", especially because this reviewer experienced firsthand the impact on culture when implementing database change management processes at his last two clients.Chapter 10 addresses getting these patterns accepted and used appropriately, discussing in detail many of the objections that one might face in the workplace as well as what the authors see as key to success in this regard: (1) understand motivations and work toward meeting them, (2) develop a clear, common, compelling vision, (3) develop trust, and (4) manage conflict effectively. In addition to understanding the motivations of others, the authors also indicate that understanding one's own motivations is also important. While this last chapter is focused on the data model patterns discussed throughout the text, in the opinion of this reviewer the psychological aspects discussed are applicable to a much broader context in the workplace, and it can only help the industry if more authors choose to address such matters. Well recommended book to all data architects and other data management professionals.
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