- Publisher: Wiley; 1 edition (February 2001)
- Language: English
- ISBN-10: 0471388289
- ISBN-13: 978-0471388289
- Product Dimensions: 5.4 x 0.6 x 7.5 inches
- Shipping Weight: 3.2 ounces (View shipping rates and policies)
- Average Customer Review: 80 customer reviews
- Amazon Best Sellers Rank: #2,714,483 in Books (See Top 100 in Books)
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The Data Model Resource CD, Vol. 1: A Library of Universal Data Models for All Enterprises 1st Edition
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This CD-ROM, a companion to Len Silverston's The Data Model Resource Book, Revised Edition, Volume 1, arms you with a powerful set of data models and data warehouse designs that you can use to jump-start your database development projects. It will save you countless hours and thousands of dollars in database development costs by providing the SQL code you'll need to implement the models described in the book or reverse-engineer them into your CASE tool.
You'll get actual proven models for such common business functions as ordering and managing products, handling shipments, invoicing, accounting and budgeting, managing human resources, contact management, and project management. Plus, you'll find new and expanded models for call center management, product customization, shipping and receiving, payments and collections, budgeting scenarios, and employee qualification and performance. New data mart designs are also provided in this revised edition, including financial analysis, inventory management, and shipping logistics.
You'll also want to check out The Data Model Resource Book, Revised Edition, Volume 2 (0-471-35348-5), and its companion data model sets (each sold separately), which feature universal data models tailored to various industries and applications. A free demonstration of the Volume 2 Data Models is available on this CD-ROM product.
Minimum System Requirements:
32-bit operating system such as Windows 95, Windows NT 4.0 or higher.
Hard Drive Space:
25 MB required space for installation of files.
CD-ROM drive. You also will need to have the following applications to make full use of the CD-ROM: a browser such as Microsoft Internet Explorer or Netscape Navigator to open the readme.htm and navigate the demo; Visio 2000 required to view the images of the models in native format, although they can alternatively be viewed as JPEGs in a browser or another application.
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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.
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.