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The Data Model Resource Book, Vol. 2: A Library of Data Models for Specific Industries

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    Available in PDF - DJVU Format | The Data Model Resource Book, Vol. 2: A Library of Data Models for Specific Industries.pdf | Language: ENGLISH
    Len Silverston(Author)

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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 First Edition, while adding a companion volume to take care of more specific requirements of different businesses. Each volume is accompanied by a CD-ROM, which is sold separately. Each CD-ROM provides powerful design templates discussed in the books in a ready-to-use electronic format, allowing companies and individuals to develop the databases they need at a fraction of the cost and a third of the time it would take to build them from scratch.
With each business function boasting its own directory, this CD-ROM provides a variety of data models for specific implementations in such areas as financial services, insurance, retail, healthcare, universities, and telecom.

"I thought the models in the books were well thought out and adequately explained." (Computer Shopper, February 2002) "In addition to being an excellent resource for data modelers, this book will help managers, business analysts and architects gain a high level understanding of various industries and integration challenges facing IT professionals. Len's concepts, insights and models provide a valuable contribution to data architecture."--Regina Pieper Enterprise Architect, Sun Microsystems "Len Silverston has produced an enormously useful two-volume compendium of generic (but not too generic) data models for an extensive set of typical enterprise subject areas, and for various industries that any data modeler will likely encounter at some point in his or her career. The material is clearly written, well organized, and goes below the obvious to some of the more perverse and difficult information requirements in an enterprise. This is an invaluable resource for doing one's homework before diving into any modeling session; if you can't find it here, there is certainly a very similar template that you can use for just about any situation with which you might be faced."-- William G. Smith President, William G. Smith & Associates "In today's fast-paced e-oriented world, it is no longer acceptable to bury business constraints in hard-to-change data structures. Data architects must comprehend complex requirements and recast them into data architecture with vision for unforeseen futures. Len?s models provide an outstanding starting point for novice and advanced data architects for delivering flexible data models. These models position an organization for the business rule age. Their proper implementation and customization allows the organization to externalize and manage business policies and rules so that the business can proactively change itself. In this way, the data architecture, based on Len's models and procedures for customizing them, becomes by design the foundation for business change."--Barbara von Halle Founder, Knowledge Partners, Inc. Co-author of Handbook of Relational Database Design "These books are long overdue and a must for any company implementing universal data models. They contain practical insights and templates for implementing universal data models and can help all enterprises regardless of their level of experience. Most books address the needs for data models but give little in the way of practical advice. These books fill in that void and should be utilized by all enterprises."--Ron Powell Publisher, DM Review "I was first introduced to The Data Model Resource Book three years ago when I was hired by a firm that wanted an enterprise data model. This company did not believe the dictum that "all companies are basically the same;" they felt they were somehow unique. After a little analysis with Len Silverston's help, we found that we were actually quite a bit the same: we had customers, accounts, employees, benefits, and all the things you'd find in any corporation. All we had to do was adapt the product component of Len's book and we were ready to move ahead with a great framework for all of our data. A CD-ROM that accompanies the book provided scripts to build the model in Oracle very quickly. We then began mapping all of our detailed data types to the enterprise model and, voila, we could find a place for all of those various spellings and misspellings of Account Number. Volume 2 of this revised edition provided even more exciting features: models of industry-specific data. I began to see interesting patterns that permeated this volume. For example, a reservation is a reservation, whether you're an airline, a restaurant, or a hotel. (We even have something similar in the oil industry--the allocation.) Another concept from the book that has changed my thinking and vocabulary is the word "party." I recently managed a project in which an employee could also function as a customer and as an on-line computer user. The team was in disagreement regarding a name for this entity; but after checking The Data Model Resource Book, we realized that here we had a party playing three roles. Whether your job is to jump-start a data warehouse project or borrow ideas for any subject area in your next operational database, I highly recommend The Data Model Resource Books, Revised Edition, Volumes 1 and 2 as your bible for design."--Ted Kowalski Data Architect, Equilon Enterprises LLC Author of Opening Doors: A Facilitator's Handbook

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Book details

  • PDF | 576 pages
  • Len Silverston(Author)
  • Wiley; 1 edition (March 7, 2001)
  • English
  • 4
  • Computers & Technology

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Review Text

  • By David Solomon on June 17, 2005

    I bought this book primarily for information regarding accounting and general ledger systems. I read all the great reviews here and thought this would be a great book. I am very disappointed. The entire section on accounting is only 40 pages and contains only a cursory overview of accounting systems and structures. They include a CD with the book which is supposed to contain actual SQL data structures but you have to pay them $350.00 to unlock the CD. Given the depth of the book I would be reluctant to risk paying them that much for simple data structures which you can easily find elsewhere on the web for free.In all honesty I have not studied this book fully but in my opinion it was a waste of money and I will try to return it to Amazon.

  • By Erik Gfesser on April 15, 2010

    Although there are quite a few positive reviews for this series of three texts by Silverston, 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 time to become familiar with it, 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.As Silverston explains in Volume 1, 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 this text, "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."After presenting Barker's notation and basic modeling, the author presents models for the following industry areas in separate chapters: manufacturing, telecommunications, health care, insurance, financial services, professional services, travel, and e-commerce (the name of this last area is obviously a bit dated, since this book was published in 2001, but it does not detract in any meaningful way from the message). For each industry area, the author discusses issues of concern. For example, Silverston poses the following questions for the insurance industry: "How can we support the insurance coverage needs of our customers?", "How can we best sell products and services to meet those needs while maintaining proper risk levels?", "How can we improve customer service and maintain our customer relationships?", "How can we maximize revenues, minimize costs, and still maintain excellent service levels?", and "How can we ensure that our systems are flexible enough to allow us to change our business models quickly and easily?". And after explaining that information needs to be tracked in order to answer these types of questions, the author literally walks the reader through people and organizations in insurance, insurance products, insurance product coverage, insurance policies, premium schedules, and policy claims.Well recommended text, even if the reader is already familiar with the industry areas in which they are interested, because 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.

  • By Erik Gfesser on September 18, 2010

    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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