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Paper Section 1: Reflection and Literature ReviewUsing Microsoft Word and Professional APA format, prepare a professional written paper supported with three sources of research that details what you have learned from chapters 3 and 4. This section of the paper should be a minimum of two pages. Paper Section 2: Applied Learning ExercisesIn this section of the professional paper, apply what you have learned from chapters 3 and 4 to descriptively address and answer the problems below. Important Note: Dot not type the actual written problems within the paper itself.Research some data warehouse vendors and obtain information about their products. Give special attention to vendors that provide tools for multiple purposes, such as Cognos, Software A&G, SAS Institute, and Oracle. Free online demos are available from some of these vendors. Download a demo or two and try them and describe your learning experience and review of this products.Download an information visualization tool, such as Tableau, QlikView, or Spotfire. If your school does not have an educational agreement with these companies, then a trial version would be sufficient for this exercise. Use your own data (if you have any) or use one of the data sets that comes with the tool (they usually have one or more data sets for demonstration purposes). Study the data, come up with a couple of business problems, and use data and visualization to analyze, visualize, and potentially solve those problems.Important Note: With limited time for a college class, perfection is not expected but effort to be exposed to various tools with attempts to learn about them is critical when considering a career in information technology associated disciplines.Important Note: There is no specific page requirement for this section of the paper but make sure any content provided fully addresses each problem.Paper Section 3: ConclusionsAfter addressing the problems, conclude your paper with details on how you will use this knowledge and skills to support your professional and or academic goals. This section of the paper should be around one page including a custom and original process flow or flow diagram to visually represent how you will apply this knowledge going forward. This customized and original flow process flow or flow diagram can be created using the “Smart Art” tools in Microsoft Word.Paper Section 4: APA Reference PageThe three or more sources of research used to support this overall paper should be included in proper APA format in the final section of the paper.Paper Review and Preparation to submit for GradingPlease make sure to proof read your post prior to submission. This professional paper should be well written and free of grammatical or typographical errors. Also remember not to plagiarize!!!!!!!!!!! it445_book_edit_bi_.pdf Unformatted Attachment Preview BUSINESS INTELLIGENCE AND ANALYTICS RAMESH SHARDA DURSUN DELEN EFRAIM TURBAN TENTH EDITION .• TENTH EDITION BUSINESS INTELLIGENCE AND ANALYTICS: SYSTEMS FOR DECISION SUPPORT Ramesh Sharda Oklahoma State University Dursun Delen Oklahoma State University Efraim Turban University of Hawaii With contributions by J.E.Aronson Tbe University of Georgia Ting-Peng Liang National Sun Yat-sen University David King ]DA Software Group, Inc. PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editor in Chief: Stephanie Wall Executive Editor: Bob Horan Program Manager Team Lead: Ashley Santora Program Manager: Denise Vaughn Executive Marketing Manager: Anne Fahlgren Project Manager Team Lead: Judy Leale Project Manager: Tom Benfatti Operations Specialist: Michelle Klein Creative Director: Jayne Conte Cover Designer: Suzanne Behnke Digital Production Project Manager: Lisa Rinaldi Full-Service Project Management: George Jacob, Integra Software Solutions. Printer/Binder: Edwards Brothers Malloy-Jackson Road Cover Printer: Lehigh/Phoenix-Hagerstown Text Font: Garamond Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text. 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This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458, or you may fax your request to 201-236-3290. Many of the designations by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Turban, Efraim. [Decision support and expert system,) Business intelligence and analytics: systems for decision support/Ramesh Sharda, Oklahoma State University, Dursun Delen, Oklahoma State University, Efraim Turban, University of Hawaii; With contributions by J. E. Aronson, The University of Georgia, Ting-Peng Liang, National Sun Yat-sen University, David King, JOA Software Group, Inc.-Tenth edition. pages cm ISBN-13: 978-0-13-305090-5 ISBN-10: 0-13-305090-4 1. Management-Data processing. 2. Decision support systems. 3. Expert systems (Compute r science) 4. Business intelligence. I. Title. HD30.2.T87 2014 658.403801 l-dc23 2013028826 10 9 8 7 6 5 4 3 2 1 PEARSON ISBN 10: 0-13-305090-4 ISBN 13: 978-0-13-305090-5 BRIEF CONTENTS Preface xxi About the Authors xxix PART I Decision Making and Analytics: An Overview PART II 1 Chapter 1 An Overview of Business Intelligence, Analytics, and Decision Support 2 Chapter 2 Foundations and Technologies for Decision Making Descriptive Analytics 77 Chapter 3 Data Warehousing Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management 135 PART Ill Predictive Analytics 78 185 Chapter 5 Data Mining Chapter 6 Techniques for Predictive Modeling Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis Chapter 8 Web Analytics, Web Mining, and Social Analytics 186 PART IV Prescriptive Analytics Chapter 9 37 243 288 338 391 Model-Based Decision Making: Optimization and MultiCriteria Systems 392 Chapter 10 Modeling and Analysis: Heuristic Search Methods and Simulation 435 Chapter 11 Automated Decision Systems and Expert Systems 469 Chapter 12 Knowledge Management and Collaborative Systems 507 PART V Big Data and Future Directions for Business Analytics 541 Chapter 13 Big Data and Analytics 542 Chapter 14 Business Analytics: Emerging Trends and Future Impacts 592 Glossary Index 634 648 iii CONTENTS Preface xxi About the Authors xxix Part I Decision Making and Analytics: An Overview 1 Chapter 1 An Overview of Business Intelligence, Analytics, and Decision Support 2 1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely 3 1.2 Changing Business Environments and Computerized Decision Support 5 The Business Pressures-Responses-Support Model 1.3 Managerial Decision Making The Nature of Managers Work The Decision-Making Process 5 7 7 8 1.4 Information Systems Support for Decision Making 1.5 An Early Framework for Computerized Decision Support 11 The Gorry and Scott-Morton Classical Framework Computer Support for Structured Decisions Computer Support for Semistructured Problems 13 13 The Concept of Decision Support Systems (DSS) DSS as an Umbrella Term 14 A Framework for Business Intelligence (Bl) Definitions of Bl 14 14 A Brief History of Bl 14 The Architecture of Bl Styles of Bl 13 13 Evolution of DSS into Business Intelligence 1.7 11 12 Computer Support for Unstructured Decisions 1.6 9 15 15 The Origins and Drivers of Bl 16 A Multimedia Exercise in Business Intelligence 16 ~ APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboards and Analytics 17 The DSS-BI Connection 1.8 18 Business Analytics Overview Descriptive Analytics ~ 20 APPLICATION CASE 1.2 Eliminating Inefficiencies at Seattle Childrens Hospital ~ 21 APPLICATION CASE 1.3 Analysis at the Speed of Thought Predictive Analytics iv 19 22 22 Conte nts ~ APPLICATION CASE 1.4 Moneybal/: Analytics in Sports and Movies ~ APPLICATION CASE 1.5 Analyzing Athletic Injuries Prescriptive Analytics 23 24 24 ~ APPLICATION CASE 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network 1.9 Analytics Applied to Different Domains 26 Analytics or Data Science? 26 Brief Introduction to Big Data Analytics What Is Big Data? 27 ~ 25 27 APPLICATION CASE 1.7 Gilt Groupes Flash Sales Streamlined by Big Data Analytics 29 1.10 Plan of the Book 29 Part I: Business Analytics: An Overview Part II: Descriptive Analytics 30 29 Part Ill: Predictive Analytics 30 Part IV: Prescriptive Analytics 31 Part V: Big Data and Future Directions for Business Analytics 31 1.11 Resources, Links, and the Teradata University Network Connection 31 Resources and Links 31 Vendors, Products, and Demos 31 Periodicals 31 The Teradata University Network Connection The Books Web Site 32 Chapter Highlights 32 Questions for Discussion ~ • Key Terms 33 • 32 33 Exercises 33 END-OF-CHAPTER APPLICATION CASE Nationwide Insurance Used Bl to Enhance Customer Service 34 References 35 Chapter 2 Foundations and Technologies for Decision Making 2.1 2.2 Opening Vignette: Decision Modeling at HP Using Spreadsheets 38 Decision Making: Introduction and Definitions 40 Characteristics of Decision Making 40 A Working Definition of Decision Making Decision-Making Disciplines 41 2.3 2.4 41 Decision Style and Decision Makers 41 Phases of the Decision-Making Process 42 Decision Making: The Intelligence Phase 44 Problem (or Opportunity) Identification 45 ~ APPLICATION CASE 2.1 Making Elevators Go Faster! Problem Classification 46 Problem Decomposition Problem Ownership 46 46 45 37 v vi Contents 2.5 Decision Making: The Design Phase Models 47 Mathematical (Quantitative) Models The Benefits of Models Normative Models Suboptimization 47 47 Selection of a Principle of Choice 48 49 49 Descriptive Models 50 Good Enough, or Satisficing 51 Developing (Generating) Alternatives Measuring Outcomes Risk 47 52 53 53 Scenarios 54 Possible Scenarios 54 Errors in Decision Making 54 2.6 Decision Making: The Choice Phase 2.7 Decision Making: The Implementation Phase 2.8 How Decisions Are Supported Support for the Intelligence Phase Support for the Design Phase 57 Support for the Choice Phase 58 56 58 Decision Support Systems: Capabilities A DSS Application 55 56 Support for the Implementation Phase 2.9 55 59 59 2.10 DSS Classifications 61 The AIS SIGDSS Classification for DSS Other DSS Categories 61 63 Custom-Made Systems Versus Ready-Made Systems 63 2.11 Components of Decision Support Systems The Data Management Subsystem 64 65 The Model Management Subsystem 65 ~ APPLICATION CASE 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data ~ 66 APPLICATION CASE 2.3 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 68 The User Interface Subsystem 68 The Knowledge-Based Management Subsystem 69 ~ APPLICATION CASE 2.4 From a Game Winner to a Doctor! Chapter Highlights 72 Questions for Discussion ~ • Key Terms 73 • 70 73 Exercises 74 END-OF-CHAPTER APPLICATION CASE Logistics Optimization in a Major Shipping Company (CSAV) References 75 74 Conte nts Part II Descriptive Analytics Chapter 3 Data Warehousing 77 78 3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse 79 3.2 Data Warehousing Definitions and Concepts What Is a Data Warehouse? 81 A Historical Perspective to Data Warehousing Characteristics of Data Warehousing Data Marts 85 APPLICATION CASE 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry 85 Data Warehousing Process Overview ~ 3.4 83 84 Enterprise Data Warehouses (EDW) Metadata 85 3.3 81 84 Operational Data Stores ~ Data Warehousing Architectures Which Architecture Is the Best? 90 93 96 Data Integration and the Extraction, Transformation, and Load (ETL) Processes 97 Data Integration ~ 98 APPLICATION CASE 3.3 BP Lubricants Achieves BIGS Success Extraction, Transfonnation, and Load 3.6 87 APPLICATION CASE 3.2 Data Warehousing Helps MultiCare Save More Lives 88 Alternative Data Warehousing Architectures 3.5 102 APPLICATION CASE 3.4 Things Go Better with Cokes Data Warehouse 103 Data Warehouse Development Approaches ~ 103 APPLICATION CASE 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing 106 Additional Data Warehouse Development Considerations Representation of Data in Data Warehouse Analysis of Data in the Data Warehouse OLAP Versus OLTP OLAP Operations 109 110 11 0 Real-Time Data Warehousing ~ 113 APPLICATION CASE 3.6 EDW Helps Connect State Agencies in Michigan 115 Massive Data Warehouses and Scalability 3.8 107 108 Data Warehousing Implementation Issues ~ 98 100 Data Warehouse Development ~ 3.7 81 116 117 APPLICATION CASE 3.7 Egg Pie Fries the Competition in Near Real Time 118 vii viii Contents 3.9 Data Warehouse Administration, Security Issues, and Future Trends 121 The Future of Data Warehousing 123 3.10 Resources, Links, and the Teradata University Network Connection 126 Resources and Links 126 Cases 126 Vendors, Products, and Demos 127 Periodicals 127 Additional References 127 The Teradata University Network (TUN) Connection 127 Chapter Highlights 128 • Questions for Discussion Key Terms 128 • 128 Exercises 129 .... END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies High with Its Real-Time Data Warehouse References 131 132 Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management 135 4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers 136 4.2 Business Reporting Definitions and Concepts What Is a Business Report? 139 140 ..,. APPLICATION CASE 4.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting 141 Components of the Business Reporting System 143 .... APPLICATION CASE 4.2 Flood of Paper Ends at FEMA 4.3 Data and Information Visualization 144 145 ..,. APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing A Brief History of Data Visualization 146 147 .... APPLICATION CASE 4.4 TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials 149 4.4 Different Types of Charts and Graphs Basic Charts and Graphs Specialized Charts and Graphs 4.5 151 The Emergence of Data Visualization and Visual Analytics 154 Visual Analytics 156 High-Powered Visual Analytics Environments 4.6 150 150 Performance Dashboards 158 160 .... APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau and Teknion 161 Conte nts Dashboard Design ~ 162 APPLICATION CASE 4.6 Saudi Telecom Company Excels with Information Visualization 163 What to Look For in a Dashboard 164 Best Practices in Dashboard Design 165 Benchmark Key Performance Indicators with Industry Standards Wrap the Dashboard Metrics with Contextual Metadata 165 Validate the Dashboard Design by a Usability Specialist 165 Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard Enrich Dashboard with Business Users Comments Present Information in Three Different Levels 4.7 166 ~ 4.8 166 167 APPLICATION CASE 4.7 IBM Cognos Express Helps Mace for Faster and Better Business Reporting 169 Performance Measurement Key Performance Indicator (KPI) 170 171 Performance Measurement System 4.9 166 166 Business Performance Management Closed-Loop BPM Cycle 165 165 Pick the Right Visual Construct Using Dashboard Design Principles Provide for Guided Analytics 165 Balanced Scorecards The Four Perspectives 172 172 173 The Meaning of Balance in BSC 17 4 Dashboards Versus Scorecards 174 4.10 Six Sigma as a Performance Measurement System The DMAIC Performance Model 175 176 Balanced Scorecard Versus Six Sigma 176 Effective Performance Measurement 177 ~ APPLICATION CASE 4.8 Expedia.coms Customer Satisfaction Scorecard 178 Chapter Highlights 179 Questions for Discussion ~ • 180 Exercises 181 184 Part Ill Predictive Analytics Chapter 5 Data Mining 5.2 181 Key Terms END-OF-CHAPTER APPLICATION CASE Smart Business Reporting Helps Healthcare Providers Deliver Better Care 182 References 5.1 • 185 186 Opening Vignette: Cabelas Reels in More Customers with Advanced Analytics and Data Mining 187 Data Mining Concepts and Applications ~ 189 APPLICATION CASE 5.1 Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics 191 ix x Contents Definitions, Characteristics, and Benefits 192 ..,. APPLICATION CASE 5.2 Harnessing Analytics to Combat Crime: Predictive Analytics Helps Memphis Police Department Pinpoint Crime and Focus Police Resources 196 5.3 How Data Mining Works 197 Data Mining Versus Statistics 200 Data Mining Applications 201 .... APPLICATION CASE 5.3 A Mine on Terrorist Funding 5.4 203 Data Mining Process 204 Step 1: Business Understanding 205 Step 2: Data Understanding 205 Step 3: Data Preparation 206 Step 4: Model Building 208 .... APPLICATION CASE 5.4 Data Mining in Cancer Research Step 5: Testing and Evaluation 5.5 5.6 5.7 210 211 Step 6: Deployment 211 Other Data Mining Standardized Processes and Methodologies 212 Data Mining Methods 214 Classification 214 Estimating the True Accuracy of Classification Models 215 Cluster Analysis for Data Mining 220 ..,. APPLICATION CASE 5.5 2degrees Gets a 1275 Percent Boost in Churn Identification 221 Association Rule Mining 224 Data Mining Software Tools 228 .... APPLICATION CASE 5.6 Data Mining Goes to Hollywood: Predicting Financial Success of Movies 231 Data Mining Privacy Issues, Myths, and Blunders 234 Data Mining and Privacy Issues 234 .... APPLICATION CASE 5.7 Predicting Customer Buying Patterns-The Target Story 235 Data Mining Myths and Blunders 236 Chapter Highlights 237 • Key Terms 238 Questions for Discussion 238 • Exercises 239 .... END-OF-CHAPTER APPLICATION CASE Macys.com Enhances Its Customers Shopping Experience with Analytics References 241 241 Chapter 6 Techniques for Predictive Modeling 243 6.1 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures 244 6.2 Basic Concepts of Neural Networks 247 Biological and Artificial Neural Networks 248 ..,. APPLICATION CASE 6.1 Neural Networks Are Helping to Save Lives in the Mining Industry 250 Elements of ANN 251 Conte nts Network Information Processing 252 Neural Network Architectures 254 ~ APPLICATION CASE 6.2 Predictive Modeling Is Powering the Power Generators 256 6.3 Developing Neural Network-Based Systems The General ANN Learning Process 259 Backpropagation 260 6.4 Illuminating the Black Box of ANN with Sensitivity Analysis 262 ~ 6.5 APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 264 Support Vector Machines ~ 265 APPLICATION CASE 6.4 Managing Student Retention with Predictive Modeling 266 Mathematical Formulation of SVMs Primal Form 271 Dual Form 271 Soft Margin 271 Nonlinear Classification Kernel Trick 272 270 272 6.6 A Process-Based Approach to the Use of SVM Support Vector Machines Versus Artificial Neural Networks 6.7 Nearest Neighbor Method for Prediction Similarity Measure: The Distance Metric 276 Parameter Selection ~ 258 273 274 275 277 APPLICATION CASE 6.5 Efficient Image Recognition and Categorization with kNN 278 Chapter Highlights 280 • Key Terms 280 Questions for Discussion 281 • Exercises 281 ~ END-OF-CHAPTER APPLICATION CASE Coors Improves Beer Flavors with Neural Networks References 284 285 Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis 288 7.1 Opening Vignette: Machine Versus Men on Jeopardy!: The Story of Watson 289 7.2 Text Analytics and Text Mining Concepts and Definitions 291 ~ 7.3 Natural Language Processing ~ 7.4 APPLICATION CASE 7.1 Text Mining for Patent Analysis 296 APPLICATION CASE 7.2 Text Mining Improves Hong Kong Governments Ability to Anticipate and Address Public Complaints Text Mining Applications Marketing Applications Security Applications ~ 295 300 301 301 APPLICATION CASE 7.3 Mining for Lies Biomedical App ... 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