Decision Support Systems for Business Intelligence E-Book

Vicki L. Sauter

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 903

Veröffentlichungsjahr: 2014

Beschreibung

Praise for the First Edition "This is the most usable decision support systems text. [i]t is far better than any other text in the field" --Computing Reviews Computer-based systems known as decision support systems (DSS) play a vital role in helping professionals across various fields of practice understand what information is needed, when it is needed, and in what form in order to make smart and valuable business decisions. Providing a unique combination of theory, applications, and technology, Decision Support Systems for Business Intelligence, Second Edition supplies readers with the hands-on approach that is needed to understand the implications of theory to DSS design as well as the skills needed to construct a DSS. This new edition reflects numerous advances in the field as well as the latest related technological developments. By addressing all topics on three levels--general theory, implications for DSS design, and code development--the author presents an integrated analysis of what every DSS designer needs to know. This Second Edition features: * Expanded coverage of data mining with new examples * Newly added discussion of business intelligence and transnational corporations * Discussion of the increased capabilities of databases and the significant growth of user interfaces and models * Emphasis on analytics to encourage DSS builders to utilize sufficient modeling support in their systems * A thoroughly updated section on data warehousing including architecture, data adjustment, and data scrubbing * Explanations and implications of DSS differences across cultures and the challenges associated with transnational systems Each chapter discusses various aspects of DSS that exist in real-world applications, and one main example of a DSS to facilitate car purchases is used throughout the entire book. Screenshots from JavaScript® and Adobe® ColdFusion are presented to demonstrate the use of popular software packages that carry out the discussed techniques, and a related Web site houses all of the book's figures along with demo versions of decision support packages, additional examples, and links to developments in the field. Decision Support Systems for Business Intelligence, Second Edition is an excellent book for courses on information systems, decision support systems, and data mining at the advanced undergraduate and graduate levels. It also serves as a practical reference for professionals working in the fields of business, statistics, engineering, and computer technology.

Leseprobe

Contents

PREFACE

I INTRODUCTION TO DECISION SUPPORT SYSTEMS

1 INTRODUCTION

WHAT IS A DSS?

USES OF A DECISION SUPPORT SYSTEM

THE BOOK

SUGGESTED READINGS

QUESTIONS

ON THE WEB

2 DECISION MAKING

RATIONAL DECISIONS

NATURE OF MANAGERS

APPROPRIATE DECISION SUPPORT

APPROPRIATE DATA SUPPORT

GROUP DECISION MAKING

INTUITION, QUALITATIVE DATA, AND DECISION MAKING

BUSINESS INTELLIGENCE AND DECISION MAKING

ANALYTICS

COMPETITIVE BUSINESS INTELLIGENCE

CONCLUSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

II DSS COMPONENTS

3 DATA COMPONENT

SPECIFIC VIEW TOWARD INCLUDED DATA

CHARACTERISTICS OF INFORMATION

DATABASES

DATABASE MANAGEMENT SYSTEMS

DATA WAREHOUSES

CAR EXAMPLE

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

4 MODEL COMPONENT

MODELS AND ANALYTICS

OPTIONS FOR MODELS

PROBLEMS OF MODELS

DATA MINING

MODEL-BASED MANAGEMENT SYSTEMS

CAR EXAMPLE

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

4s INTELLIGENCE AND DECISION SUPPORT SYSTEMS

PROGRAMMING REASONING

UNCERTAINTY

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

5 USER INTERFACE

GOALS OF THE USER INTERFACE

MECHANISMS OF USER INTERFACES

USER INTERFACE COMPONENTS

CAR EXAMPLE

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

III ISSUES OF DESIGN

6 INTERNATIONAL DECISION SUPPORT SYSTEMS

INFORMATION AVAILABILITY STANDARDS

CROSS-CULTURAL MODELING

EFFECTS OF CULTURE ON DECISION SUPPORT SYSTEM

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

7 DESIGNING A DECISION SUPPORT SYSTEM

PLANNING FOR DECISION SUPPORT SYSTEMS

DSS DESIGN AND REENGINEERING

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

8 OBJECT-ORIENTED TECHNOLOGIES AND DSS DESIGN

KINDS OF DEVELOPMENT TOOLS

BENEFITS OF OBJECT-ORIENTED TECHNOLOGIES FOR DSS

SUGGESTED READINGS

QUESTIONS

ON THE WEB

9 IMPLEMENTATION AND EVALUATION

IMPLEMENTATION STRATEGY

IMPLEMENTATION AND SYSTEM EVALUATION

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

IV EXTENSIONS OF DECISION SUPPORT SYSTEMS

10 EXECUTIVE INFORMATION AND DASHBOARDS

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

11 GROUP DECISION SUPPORT SYSTEMS

GROUPWARE

GDSS DEFINITIONS

FEATURES OF SUPPORT

DISCUSSION

SUGGESTED READINGS

QUESTIONS

ON THE WEB

INDEX

Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Sauter, Vicki Lynn, 1955–

Decision support systems for business intelligence / Vicki L. Sauter. – 2nd ed.

p. cm.

Rev. ed. of: Decision support systems. 1997.

Includes bibliographical references and index.

ISBN 978-0-470-43374-4 (pbk.)

1. Decision support systems. 2. Decision making. I. Sauter, Vicki Lynn, 1955–Decision support systems. II. Title.

HG30.213.S28 2010

658.4′038011–dc22 2010028361

This book is dedicated, with love, to

My Late Father, Leo F. Sauter, Jr.,

My Husband, Joseph S. Martinich,

and

My Son, Michael C. Martinich-Sauter,

with thanks for their steadfast inspiration and encouragement.

PREFACE

Information is a crucial component of today’s society. With a smaller world, faster communications, and greater interest, information relevant to a person’s life, work, and recreation has exploded. However, many believe this is not all good. Richard S. Wurman (in a book entitled Information Anxiety) notes that the information explosion has backfired, leaving us stranded between mere facts and real understanding. Similarly, Peter Drucker noted in a Wall Street Journal (December 1, 1992, p. A16) editorial entitled “Be Data Literate—Know What to Know” that, although executives have become computer literate, few of them have mastered the questions of what information they need, when they need information, and in what form they need information. On that backdrop enters the awakening of business intelligence and analytics to provide a structure for harnessing the information to be a tool to help companies be more competitive.

This is both good news and bad news for designers of decision support systems (DSS). The good news is that if, as Drucker claims, the future success of companies is through the astute use of appropriate information, then DSS have a bright future in helping decision makers use information appropriately. The bad new is that where DSS are available, they may not be providing enough support to the users. Too often the DSS are designed as a substitute for the human choice process or an elaborate report generator.

Decision support systems, by definition, provide business intelligence and analytics to strengthen some kind of choice process. In order for us to know what information to retain and how to model the relationships among the data so as to best complement the human choice process, DSS designers must understand the human choice process. To that end, this book illustrates what is known about decision making and the different styles that decision makers demonstrate under different conditions. This “needs assessment” is developed on a variety of levels: (a) what is known about decision making (with or without a computer) in general; (b) how that knowledge about decision making has been translated into specific DSS needs; (c) what forms of business intelligence needs are associated with the problem or the environment; and (d) how does one actually program those needs into a system. Hence, all topics are addressed on three levels: (a) general theory, (b) specific issues of DSS design, and (c) hands-on applications. These are not separate chapters but rather an integrated analysis of what the designer of a DSS needs to know.

The second issue that drives the content and organization of this book is that the focus is totally upon DSS for business intelligence. Many books spend a significant amount of time and space explaining concepts that are important but ancillary to the development of a DSS. For example, many books discuss the methods for solution of mathematical models. While accurate solution methods for mathematical models are important for a successful DSS, there is much more about the models that needs discussion in order to implement a good DSS. Hence, I have left model solutions and countless other topics out of the book in order to accommodate topics of direct relevance to DSS.

Finally, I believe in DSS and their contribution. Those who know me well know that when I believe in something, I share it with enthusiasm and zeal. I think those attributes show in this book and make it better. Writing this book was clearly a labor of love; I hope it shows.

MAJOR FEATURES OF THE BOOK

Integration of Theory and Practice: It is the integration of theory with practice and abstract with concrete that I think makes this book unique. It reflects a personal bias that it is impossible to understand these design concepts until you actually try to implement them. It also reflects a personal bias that unless we can relate the DSS concepts to the “real world” and the kinds of problems (opportunities) the students can expect to find there, the students will not understand the concepts fully.

Although the book contains many examples of many aspects of DSS, there is one example that is carried throughout the book: a DSS to facilitate car purchases. I have selected this example because most students can relate to it, and readers do not get bogged down with discussion of company politics and nuances. Furthermore, it allows a variety of issues to be compared in a meaningful fashion.

Focus on the “Big Picture”: The representation throughout the book focuses on “generic” DSS, which allows discussion of design issues without concern for whether it is a group system, an organizational system, or an individual system. Furthermore, it allows illustration of how seemingly specialized forms of DSS, such as geographic information systems, actually follow the same principles as a “basic” DSS.

Although I show implementation of the concepts, I do not overfocus on the tools. There are example screens of many tools appearing in the book. Where I show development, I create my examples using HTML, Javascript, and Adobe® Cold Fusion.® Most information systems students today have an understanding of HTML and Javascript. Cold Fusion commands are sufficiently close to these that even if you elect to use another tool, these examples can be understood generally by students.

Strong Common Sense Component: We technology folks can get carried away with the newest and greatest toy regardless of its applicability to a decision maker. It is important to remember the practicalities of the situation when designing DSS. For example, if we know that a company has a commitment to maintaining particular hardware, it would not make sense to develop a system relying upon other hardware. These kinds of considerations and the associated implications for DSS design are highlighted in the book. This is not to say that some of these very interesting but currently infeasible options are not discussed. Clearly, they are important for the future of management information systems. Someday, these options will be feasible and practical so they are discussed.

Understanding Analytics: Some research indicates that companies do not have enough people who can apply analytics successfully because they do not understand modeling well. In this book, I try to emphasize the questions that should surround the use of analytics to ensure they are being used properly and that the decision maker fully appreciates the implications of their use. The goal is not only to help the reader better understand analytics but also to encourage builders of DSS to be aware of this problem and build sufficient modeling support in their systems.

Integration of Intelligence: Over the years expert systems have evolved into an integrated component of many decision support systems provided to support decisions makers, not replace them. To accomplish such a goal, the expert systems could not be stand alone, but rather need to be integrated with the data and models used by these decision makers. In other words, expert systems (or intelligence) technology became a modeling support function, albeit an important one, for decision support systems. Hence, the coverage of the topic is integrated into the modeling component in this book. However, I do acknowledge there are some special topics needing attention to those who want to build the intelligence. These topics are covered in a supplement to Chapter 4, thereby allowing instructors to use discretion in how they integrate the topic into their classes.

International Issues Coverage: As more companies become truly multinational, there is a trend toward greater “local” (overseas) decision making that must of course be coordinated. These companies can afford to have some independent transaction processing systems, but will need to share DSS. If the DSS are truly to facilitate decision making across cultures, then they must be sensitive to differences across cultures. This sensitivity includes more than just changes in the language used or concern about the meaning of icons. Rather, it includes an understanding of the differences in preferences for models and model management systems and for trade-offs and mechanisms by which information is communicated and acted upon. Since future designers of DSS will need to understand the implications of these differences, they are highlighted in the book. Of course, as with any other topic, the international issues will be addressed both in “philosophical” terms and in specific technical (e.g.,coding) terms.

Object-Oriented Concepts and Tools: Another feature of the book that differentiates it from others is a use of object-oriented technology. Many books either present material without discussion of implementation or use traditional programming tools. If students have not previously had experience with them, object-oriented tools can be tricky to use. However, we know that a reliance upon object-oriented technology can lead to easier maintenance and transfer of systems. Since DSS must be updated to reflect new company concerns and trends, designers must be concerned about easier maintenance. So, while the focus of the book is not on object-oriented programming, the nuances of its programming will be discussed wherever it is practical. In addition, there is a chapter that focuses upon the topic that can be included in the curriculum.

Web Support and Other Instructional Support Tools: There is a complete set of Web links that provide instructional support for this book. Example syllabi, projects, and other ideas can be viewed and downloaded from the Web. All figures and tables appear on the Web so you can use them directly in the class or download them to your favorite demonstration package to use in class. In addition, there are lots of Web links to sites you can use to supplement the information in the book. Some of those links provide access to demo versions of decision support packages for download and use of some sample screens. These provide up-to-date examples of a variety of systems that students can experience or instructors can demonstrate to bring the practice into the classroom. Other links provide access to application descriptions, war stories, and advice from practitioners. Still others provide a link to a variety of instructors (both academic and nonacademic) on the topic.

I strived to provide support for the class from a variety of different perspectives. You can see the information at http://www.umsl.edu/~sauterv/DSS4BI/. Further, there is information at the end of every chapter about the kinds of materials found in support of that chapter, and directions for direct access to the chapter information is given in those chapters. More important, in the true spirit of the Web, I will update these links as more information becomes available. So, if you happen to see something that should be included, please email me at [email protected]. In addition to the DSS support, I have accumulated links regarding automobiles and their purchase and lease. This Web page would provide support for people who want to explore the car example in the book in more depth or for students who want to use different information in the development of their own automobile DSS. You can link to this from the main page or go to it directly at http://www.umsl.edu/~sauterv/DSS4BI/automobileinformation.html.

ACKNOWLEDGMENTS

If a book is a labor of love, then there must be a “coach” to help one through the process. In my case, I am lucky enough to have a variety of coaches who have been there with me every step of the way. First, in a very real sense, my students over the years have provided a foundation for this book. Even before I knew I was going to produce this work, my students provided an environment in which I could experiment and learn about decisions, decision making, and decision support systems. It is their interest, their inquisitiveness, and their challenge that have led me to think through these topics in a manner that allowed me to write this book. I have particular gratitude to Mary Kay Carragher, David Doom, Mimi Duncan, Joseph Hofer, Timothy McCaffrey, Kathryn Ntalaja, Richard Ritthamel, Phillip Wells, and Aihua Yan for their efforts in support of this book.

Second, there are numerous people at John Wiley & Sons who helped me achieve my vision for this book. I am grateful to each one for his or her efforts and contribution. In particular, I would like to thank my editors, Beth Lang Golub, editor of the first edition, and Susanne Steitz-Filler, editor of the second edition. They each believed in this project long before I did, and continued to have faith in it when mine wore thin. I could not have produced this book without them. In addition, I want to thank my style editors, Elisa Adams and Ernestine Franco, who helped to make my ideas accessible through direct and constructive changes in the prose. In addition, I would like to thank the reviewers of the first and second editions who provided superb comments to improve the style and content.

Finally, I want to thank my friends and family for their support, encouragement, and patience. My husband, Joseph Martinich, has been with me every step of the way—not only with this book, but in my entire career. I sincerely doubt that I could have done any of it without him. My son, Michael Martinich-Sauter, has demonstrated infinite patience with his mother. More important, he has inspired me to look at every topic differently and more creatively. I have learned much about decisions, decision making, and decision support from him, and I am most grateful he has shared his wisdom with me. Finally, I want to acknowledge the sage Lady Alexandra (a.k.a. Allie—the dog), who made me laugh when I really needed it and whose courage made me appreciate everything more.

I

INTRODUCTION TO DECISION SUPPORT SYSTEMS

1

INTRODUCTION

Virtually everyone makes hundreds of decisions each day. These decisions range from the inconsequential, such as what to eat for breakfast, to the significant, such as how best to get the economy out of a recession. All other things being equal, good outcomes from those decisions are better than bad outcomes. For example, all of us would like to have a tasty, nutritional breakfast (especially if it is fast and easy), and the country would like to have a stable, well-functioning economy again. Some individuals are “lucky” in their decision processes. They can muddle through the decision not really looking at all of the options or at useful data and still experience good consequences. We have all met people who instinctively put together foods to make good meals and have seen companies that seem to do things wrong but still make a good profit. For most of us, however, good outcomes in decision making are a result of making good decisions.

“Good decision making” means we are informed and have relevant and appropriate information on which to base our choices among alternatives. In some cases, we support decisions using existing, historical data, while other times we collect the information, especially for a particular choice process. The information comes in the form of facts, numbers, impressions, graphics, pictures, and sounds. It needs to be collected from various sources, joined together, and organized. The process of organizing and examining the information about the various options is the process of modeling. Models are created to help decision makers understand the ramifications of selecting an option. The models can range from quite informal representations to complex mathematical relationships.

For example, when deciding on what to eat for a meal, we might rely upon historical data, such as those available from tasting and eating the various meal options over time and our degree of enjoyment of those options. We might also use specially collected data, such as cost or availability of the options. Our model in this case might be simple: Select the first available option that appeals to us. Or, we might approach it with a more complex approach: Use linear programming to solve the “diet problem” to find the cheapest combination of foods that will satisfy all the daily nutritional requirements of a person.1

In today’s business world, we might use models to help refine our understanding of what and how our customers purchase from us to improve our customer relationship management. In that case we might collect information from point-of-sale systems for all of our customers for multiple years and use data-mining tools to determine profiles of our customers. Those profiles could in turn profile information about trends with which managers could change marketing campaigns and even target some marketing campaigns.

The quality of the decision depends on the adequacy of the available information, the quality of the information, the number of options, and the appropriateness of the modeling effort available at the time of the decision. While it is not true that more information (or even more analysis) is better, it is true that more of the appropriate type of information (and analysis) is better. In fact, one might say that to improve the choice process, we need to improve the information collection and analysis processes.

DSS in Action DSS in Business

Equifax provides DSS and supporting databases to many of America’s Fortune 1000 companies which allow these businesses to make more effective and profitable business decisions. The system allows users access to more than 60 national databases, mapping software, and analysis tools so that users can define and analyze its opportunities in a geographic area.

The tool enables retailers, banks, and other businesses to display trade areas and then to analyze demographic attributes. In particular, this DSS integrates customer information with current demographic and locational data. For example, Consumer-FactsTM, offers information about spending patterns of more than 400 products and services in more than 15 major categories, with regional spending patterns incorporated. Further, it provides five-year projections that reflect the impact of dynamic economic and demographic conditions, such as income, employment, population, and household changes, on consumer spending that can be integrated with a corporation’s own customer information.

This coupling of data and analysis of reports, maps, and graphs allows decision makers to consider questions of customer segmentation and targeting; market and site evaluation; businessto-business marketing; product distribution strategies; and mergers, acquisitions, and competitive analysis. For example, the DSS facilitates consideration of crucial, yet difficult questions such as:

Who are my best customers and where are they located?Which segments respond positively to my marketing campaign?How will the addition of a new site impact my existing locations?How can I analyze and define my market potential?How can I estimate demand for my products and services accurately?What impact will an acquisition have on my locations?How is the competition impacting my business?

Increasingly corporations are attempting to make more informed decisions to improve their bottom lines. Some refer to these efforts to use better information and better models to

improve decision making as business intelligence. Others refer to it as analytics. In either case, the goal is to bring together the right information and the right models to understand what is going on in the business and to consider problems from multiple perspectives so as to to provide the best guidance for the decision maker.