Business intelligence and analytics: systems for decision support

Cover......Page 1
Title Page......Page 2
Contents......Page 5
Preface......Page 22
About the Authors......Page 30
Part I Decision Making and Analytics: An Overview......Page 32
 Chapter 1 An Overview of Business Intelligence, Analytics, and Decision Support......Page 33
  1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely......Page 34
   The Business Pressures–Responses–Support Model......Page 36
   The Nature of Managers’ Work......Page 38
   The Decision-Making Process......Page 39
  1.4 Information Systems Support for Decision Making......Page 40
   The Gorry and Scott-Morton Classical Framework......Page 42
   Computer Support for Structured Decisions......Page 43
   DSS as an Umbrella Term......Page 44
   A Brief History of BI......Page 45
   Styles of BI......Page 46
   A Multimedia Exercise in Business Intelligence......Page 47
   Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics......Page 48
   The DSS–BI Connection......Page 49
  1.8 Business Analytics Overview......Page 50
   Descriptive Analytics......Page 51
   Application Case 1.2 Eliminating Inefficiencies at Seattle Children’s Hospital......Page 52
   Predictive Analytics......Page 53
   Application Case 1.4 Moneyball: Analytics in Sports and Movies......Page 54
   Prescriptive Analytics......Page 55
   Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network......Page 56
   Analytics or Data Science?......Page 57
   What Is Big Data?......Page 58
   Part I: Business Analytics: An Overview......Page 60
   Part III: Predictive Analytics......Page 61
   Periodicals......Page 62
   Chapter Highlights......Page 63
   Exercises......Page 64
   End-of-Chapter Application Case Nationwide Insurance Used BI to Enhance Customer Service......Page 65
   References......Page 66
 Chapter 2 Foundations and Technologies for Decision Making......Page 68
  2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets......Page 69
   Characteristics of Decision Making......Page 71
   Decision Style and Decision Makers......Page 72
  2.3 Phases of the Decision-Making Process......Page 73
  2.4 Decision Making: The Intelligence Phase......Page 75
   Application Case 2.1 Making Elevators Go Faster!......Page 76
   Problem Ownership......Page 77
   The Benefits of Models......Page 78
   Selection of a Principle of Choice......Page 79
   Suboptimization......Page 80
   Descriptive Models......Page 81
   Good Enough, or Satisficing......Page 82
   Developing (Generating) Alternatives......Page 83
   Risk......Page 84
   Errors in Decision Making......Page 85
  2.7 Decision Making: The Implementation Phase......Page 86
   Support for the Intelligence Phase......Page 87
   Support for the Design Phase......Page 88
   Support for the Implementation Phase......Page 89
   A DSS Application......Page 90
   The AIS SIGDSS Classification for DSS......Page 92
   Custom-Made Systems Versus Ready-Made Systems......Page 94
  2.11 Components of Decision Support Systems......Page 95
   The Model Management Subsystem......Page 96
   Application Case 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data......Page 97
   The User Interface Subsystem......Page 99
   The Knowledge-Based Management Subsystem......Page 100
   Application Case 2.4 From a Game Winner to a Doctor!......Page 101
   Chapter Highlights......Page 103
   Questions for Discussion......Page 104
   End-of-Chapter Application Case Logistics Optimization in a Major Shipping Company (CSAV)......Page 105
   References......Page 106
Part II Descriptive Analytics......Page 108
 Chapter 3 Data Warehousing......Page 109
  3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse......Page 110
   A Historical Perspective to Data Warehousing......Page 112
   Characteristics of Data Warehousing......Page 114
   Operational Data Stores......Page 115
   Application Case 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry......Page 116
  3.3 Data Warehousing Process Overview......Page 118
   Application Case 3.2 Data Warehousing Helps MultiCare Save More Lives......Page 119
  3.4 Data Warehousing Architectures......Page 121
   Alternative Data Warehousing Architectures......Page 124
   Which Architecture Is the Best?......Page 127
  3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes......Page 128
   Application Case 3.3 BP Lubricants Achieves BIGS Success......Page 129
   Extraction, Transformation, and Load......Page 131
  3.6 Data Warehouse Development......Page 133
   Data Warehouse Development Approaches......Page 134
   Application Case 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing......Page 137
   Additional Data Warehouse Development Considerations......Page 138
   Representation of Data in Data Warehouse......Page 139
   Analysis of Data in the Data Warehouse......Page 140
   OLAP Operations......Page 141
  3.7 Data Warehousing Implementation Issues......Page 144
   Application Case 3.6 EDW Helps Connect State Agencies in Michigan......Page 146
   Massive Data Warehouses and Scalability......Page 147
  3.8 Real-Time Data Warehousing......Page 148
   Application Case 3.7 Egg Plc Fries the Competition in Near Real Time......Page 149
  3.9 Data Warehouse Administration, Security Issues, and Future Trends......Page 152
   The Future of Data Warehousing......Page 154
   Cases......Page 157
   The Teradata University Network (TUN) Connection......Page 158
   Questions for Discussion......Page 159
   Exercises......Page 160
   End-of-Chapter Application Case Continental Airlines Flies High with Its Real-Time Data Warehouse......Page 162
   References......Page 163
 Chapter 4 Business Reporting, Visual Analytics, and Business Performance Management......Page 166
  4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers......Page 167
  4.2 Business Reporting Definitions and Concepts......Page 170
   What Is a Business Report?......Page 171
   Application Case 4.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting......Page 172
   Components of the Business Reporting System......Page 174
   Application Case 4.2 Flood of Paper Ends at FEMA......Page 175
  4.3 Data and Information Visualization......Page 176
   Application Case 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing......Page 177
   A Brief History of Data Visualization......Page 178
   Application Case 4.4 TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials......Page 180
   Basic Charts and Graphs......Page 181
   Specialized Charts and Graphs......Page 182
  4.5 The Emergence of Data Visualization and Visual Analytics......Page 185
   Visual Analytics......Page 187
   High-Powered Visual Analytics Environments......Page 189
  4.6 Performance Dashboards......Page 191
   Application Case 4.5 Dallas Cowboys Score Big with Tableau and Teknion......Page 192
   Dashboard Design......Page 193
   Application Case 4.6 Saudi Telecom Company Excels with Information Visualization......Page 194
   What to Look For in a Dashboard......Page 195
   Enrich Dashboard with Business Users’ Comments......Page 196
  4.7 Business Performance Management......Page 197
   Closed-Loop BPM Cycle......Page 198
    Application Case 4.7 IBM Cognos Express Helps Mace for Faster......Page 200
  4.8 Performance Measurement......Page 201
   Key Performance Indicator (KPI)......Page 202
  4.9 Balanced Scorecards......Page 203
   The Four Perspectives......Page 204
   Dashboards Versus Scorecards......Page 205
  4.10 Six Sigma as a Performance Measurement System......Page 206
   Balanced Scorecard Versus Six Sigma......Page 207
   Effective Performance Measurement......Page 208
   Application Case 4.8 Expedia.com’s Customer Satisfaction Scorecard......Page 209
   Chapter Highlights......Page 210
   Key Terms......Page 211
   Exercises......Page 212
   End-of-Chapter Application Case Smart Business Reporting Helps Healthcare Providers Deliver Better Care......Page 213
   References......Page 215
Part III Predictive Analytics......Page 216
 Chapter 5 Data Mining......Page 217
  5.1 Opening Vignette: Cabela’s Reels in More Customers withAdvanced Analytics and Data Mining......Page 218
  5.2 Data Mining Concepts and Applications......Page 220
   Application Case 5.1 Smarter Insurance: Infinity P&C ImprovesCustomer Service and Combats Fraud with Predictive Analytics......Page 222
   Definitions, Characteristics, and Benefits......Page 223
   Application Case 5.2 Harnessing Analytics to Combat Crime:Predictive Analytics Helps Memphis Police Department Pinpoint Crimeand Focus Police Resources......Page 227
   How Data Mining Works......Page 228
   Data Mining Versus Statistics......Page 231
  5.3 Data Mining Applications......Page 232
   Application Case 5.3 A Mine on Terrorist Funding......Page 234
  5.4 Data Mining Process......Page 235
   Step 2: Data Understanding......Page 236
   Step 3: Data Preparation......Page 237
   Step 4: Model Building......Page 239
   Application Case 5.4 Data Mining in Cancer Research......Page 241
   Step 6: Deployment......Page 242
   Other Data Mining Standardized Processes and Methodologies......Page 243
   Classification......Page 245
   Estimating the True Accuracy of Classification Models......Page 246
   Cluster Analysis for Data Mining......Page 251
   Application Case 5.5 2degrees Gets a 1275 Percent Boost in ChurnIdentification......Page 252
   Association Rule Mining......Page 255
  5.6 Data Mining Software Tools......Page 259
   Application Case 5.6 Data Mining Goes to Hollywood: PredictingFinancial Success of Movies......Page 262
   Data Mining and Privacy Issues......Page 265
   Application Case 5.7 Predicting Customer Buying Patterns—TheTarget Story......Page 266
   Data Mining Myths and Blunders......Page 267
   Chapter Highlights......Page 268
   Questions for Discussion......Page 269
   Exercises......Page 270
   References......Page 272
 Chapter 6 Techniques for Predictive Modeling......Page 274
  6.1 Opening Vignette: Predictive Modeling Helps BetterUnderstand and Manage Complex MedicalProcedures......Page 275
  6.2 Basic Concepts of Neural Networks......Page 278
   Biological and Artificial Neural Networks......Page 279
   Application Case 6.1 Neural Networks Are Helping to Save Lives inthe Mining Industry......Page 281
   Elements of ANN......Page 282
   Network Information Processing......Page 283
   Neural Network Architectures......Page 285
   Application Case 6.2 Predictive Modeling Is Powering the PowerGenerators......Page 287
  6.3 Developing Neural Network–Based Systems......Page 289
   The General ANN Learning Process......Page 290
   Backpropagation......Page 291
  6.4 Illuminating the Black Box of ANN with SensitivityAnalysis......Page 293
   Application Case 6.3 Sensitivity Analysis Reveals Injury SeverityFactors in Traffic Accidents......Page 295
  6.5 Support Vector Machines......Page 296
   Application Case 6.4 Managing Student Retention with PredictiveModeling......Page 297
   Mathematical Formulation of SVMs......Page 301
   Soft Margin......Page 302
   Kernel Trick......Page 303
  6.6 A Process-Based Approach to the Use of SVM......Page 304
   Support Vector Machines Versus Artificial Neural Networks......Page 305
  6.7 Nearest Neighbor Method for Prediction......Page 306
   Similarity Measure: The Distance Metric......Page 307
   Parameter Selection......Page 308
   Application Case 6.5 Efficient Image Recognition andCategorization with kNN......Page 309
   Key Terms......Page 311
   Exercises......Page 312
   End-of-Chapter Application Case Coors Improves Beer Flavorswith Neural Networks......Page 315
   References......Page 316
 Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis......Page 319
  7.1 Opening Vignette: Machine Versus Men on Jeopardy!: TheStory of Watson......Page 320
  7.2 Text Analytics and Text Mining Concepts andDefinitions......Page 322
   Application Case 7.1 Text Mining for Patent Analysis......Page 326
  7.3 Natural Language Processing......Page 327
   Application Case 7.2 Text Mining Improves Hong KongGovernment’s Ability to Anticipate and Address Public Complaints......Page 329
  7.4 Text Mining Applications......Page 331
   Security Applications......Page 332
   Application Case 7.3 Mining for Lies......Page 333
   Biomedical Applications......Page 335
   Academic Applications......Page 336
   Application Case 7.4 Text Mining and Sentiment Analysis HelpImprove Customer Service Performance......Page 337
  7.5 Text Mining Process......Page 338
   Task 1: Establish the Corpus......Page 339
   Task 2: Create the Term–Document Matrix......Page 340
   Task 3: Extract the Knowledge......Page 343
   Application Case 7.5 Research Literature Survey with TextMining......Page 345
   Free Software Tools......Page 348
   Application Case 7.6 A Potpourri of Text Mining Case Synopses......Page 349
  7.7 Sentiment Analysis Overview......Page 350
   Application Case 7.7 Whirlpool Achieves Customer Loyalty andProduct Success with Text Analytics......Page 352
  7.8 Sentiment Analysis Applications......Page 354
  7.9 Sentiment Analysis Process......Page 356
   Methods for Polarity Identification......Page 357
   Using a Lexicon......Page 358
   Identifying Semantic Orientation of Document......Page 359
  7.10 Sentiment Analysis and Speech Analytics 359How Is It Done?......Page 360
   Application Case 7.8 Cutting Through the Confusion: Blue CrossBlue Shield of North Carolina Uses Nexidia’s Speech Analytics to EaseMember Experience in Healthcare......Page 362
   Key Terms......Page 364
   Exercises......Page 365
   End-of-Chapter Application Case BBVA Seamlessly Monitorsand Improves Its Online Reputation......Page 366
   References......Page 367
 Chapter 8 Web Analytics, Web Mining, and Social Analytics......Page 369
  8.1 Opening Vignette: Security First Insurance Deepens Connection with Policyholders......Page 370
  8.2 Web Mining Overview......Page 372
  8.3 Web Content and Web Structure Mining......Page 375
   Application Case 8.1 Identifying Extremist Groups with Web Linkand Content Analysis......Page 377
   Anatomy of a Search Engine......Page 378
   Document Indexer......Page 379
   Document Matcher/Ranker......Page 380
   How Does Google Do It?......Page 382
   Application Case 8.2 IGN Increases Search Traffic by 1500 Percent......Page 384
  8.5 Search Engine Optimization......Page 385
   Methods for Search Engine Optimization......Page 386
   Application Case 8.3 Understanding Why Customers Abandon Shopping Carts Results in $10 Million Sales Increase......Page 388
  8.6 Web Usage Mining (Web Analytics)......Page 389
   Web Analytics Technologies......Page 390
   Application Case 8.4 Allegro Boosts Online Click-Through Rates by 500 Percent with Web Analysis......Page 391
   Web Site Usability......Page 393
   Traffic Sources......Page 394
   Conversion Statistics......Page 395
  8.7 Web Analytics Maturity Model and Web Analytics Tools......Page 397
   Web Analytics Tools......Page 399
   Putting It All Together—A Web Site Optimization Ecosystem......Page 401
   A Framework for Voice of the Customer Strategy......Page 403
  8.8 Social Analytics and Social Network Analysis......Page 404
   Social Network Analysis......Page 405
   Application Case 8.5 Social Network Analysis HelpsTelecommunication Firms......Page 406
   Distributions......Page 407
  8.9 Social Media Definitions and Concepts......Page 408
   How Do People Use Social Media?......Page 409
   Application Case 8.6 Measuring the Impact of Social Media at Lollapalooza......Page 410
  8.10 Social Media Analytics......Page 411
   Best Practices in Social Media Analytics......Page 412
   Application Case 8.7 eHarmony Uses Social Media to Help Take the Mystery Out of Online Dating......Page 414
   Social Media Analytics Tools and Vendors......Page 415
   Chapter Highlights......Page 417
   Questions for Discussion......Page 418
   End-of-Chapter Application Case Keeping Students on Track with Web and Predictive Analytics......Page 419
   References......Page 421
Part IV Prescriptive Analytics......Page 422
 Chapter 9 Model-Based Decision Making: Optimization and Multi-Criteria Systems......Page 423
  9.1 Opening Vignette: Midwest ISO Saves Billions by Better Planning of Power Plant Operations and Capacity Planning......Page 424
  9.2 Decision Support Systems Modeling......Page 425
   Application Case 9.1 Optimal Transport for ExxonMobil Downstream Through a DSS......Page 426
   Current Modeling Issues......Page 427
   Application Case 9.2 Forecasting/Predictive Analytics Proves to Bea Good Gamble for Harrah’s Cherokee Casino and Hotel......Page 428
   The Components of Decision Support Mathematical Models......Page 430
  9.4 Certainty, Uncertainty, and Risk......Page 432
   Decision Making Under Risk (Risk Analysis)......Page 433
   Application Case 9.3 American Airlines UsesShould-Cost Modeling to Assess the Uncertainty of Bidsfor Shipment Routes......Page 434
   Application Case 9.4 Showcase Scheduling at Fred Astaire East Side Dance Studio......Page 435
   Application Case 9.5 Spreadsheet Model Helps Assign Medical Residents......Page 438
   Linear Programming......Page 439
   Modeling in LP: An Example......Page 440
   Implementation......Page 445
   Multiple Goals......Page 447
   Sensitivity Analysis......Page 448
   Goal Seeking......Page 449
   Decision Tables......Page 451
   Decision Trees......Page 453
   Application Case 9.6 U.S. HUD Saves the House by Using AHP for Selecting IT Projects......Page 454
   Tutorial on Applying Analytic Hierarchy Process Using Web-HIPRE......Page 456
   Chapter Highlights......Page 460
   Exercises......Page 461
   End-of-Chapter Application Case Pre-Positioning of Emergency Items for CARE International......Page 464
   References......Page 465
 Chapter 10 Modeling and Analysis: Heuristic Search Methods and Simulation......Page 466
  10.1 Opening Vignette: System Dynamics Allows FluorCorporation to Better Plan for Project and Change Management......Page 467
  10.2 Problem-Solving Search Methods......Page 468
   Algorithms......Page 469
   Application Case 10.1 Chilean Government Uses Heuristics to Make Decisions on School Lunch Providers......Page 470
   Example: The Vector Game......Page 472
   How Do Genetic Algorithms Work?......Page 474
   Genetic Algorithm Applications......Page 476
   Application Case 10.2 Improving Maintenance Decision Making in the Finnish Air Force Through Simulation......Page 477
   Application Case 10.3 Simulating Effects of Hepatitis B Interventions......Page 478
   Major Characteristics of Simulation......Page 479
   Advantages of Simulation......Page 480
   The Methodology of Simulation......Page 481
   Simulation Types......Page 482
   Monte Carlo Simulation......Page 483
   Visual Interactive Simulation......Page 484
   Application Case 10.4 Improving Job-Shop Scheduling DecisionsThrough RFID: A Simulation-Based Assessment......Page 485
   Simulation Software......Page 488
  10.6 System Dynamics Modeling......Page 489
  10.7 Agent-Based Modeling......Page 492
   Application Case 10.5 Agent-Based Simulation Helps Analyze Spread of a Pandemic Outbreak......Page 494
   Key Terms......Page 495
   End-of-Chapter Application Case HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a MajorAward......Page 496
   References......Page 498
 Chapter 11 Automated Decision Systems and Expert Systems......Page 500
  11.1 Opening Vignette: InterContinental Hotel Group Uses Decision Rules for Optimal Hotel Room Rates......Page 501
  11.2 Automated Decision Systems......Page 502
   Application Case 11.1 Giant Food Stores Prices the EntireStore......Page 503
  11.3 The Artificial Intelligence Field......Page 506
   Experts......Page 508
   Features of ES......Page 509
  11.5 Applications of Expert Systems......Page 511
   Classical Applications of ES......Page 512
   Newer Applications of ES......Page 513
   Areas for ES Applications......Page 514
   Knowledge Acquisition Subsystem......Page 515
   Blackboard (Workplace)......Page 516
   Application Case 11.4 Diagnosing Heart Diseases by Signal Processing......Page 517
  11.7 Knowledge Engineering......Page 518
   Knowledge Acquisition......Page 519
   Knowledge Representation......Page 521
   Inferencing......Page 522
   Explanation and Justification......Page 527
  11.8 Problem Areas Suitable for Expert Systems......Page 528
  11.9 Development of Expert Systems......Page 529
   Selecting the Building Tools......Page 530
   Application Case 11.5 Clinical Decision Support System for Tendon Injuries......Page 532
  11.10 Concluding Remarks......Page 533
   Key Terms......Page 534
   End-of-Chapter Application Case Tax Collections Optimization for New York State......Page 535
   References......Page 536
 Chapter 12 Knowledge Management and Collaborative Systems......Page 538
  12.1 Opening Vignette: Expertise Transfer System to Train Future Army Personnel......Page 539
  12.2 Introduction to Knowledge Management......Page 543
   Knowledge......Page 544
   Explicit and Tacit Knowledge......Page 546
  12.3 Approaches to Knowledge Management......Page 547
   The Practice Approach to Knowledge Management......Page 548
   Knowledge Repositories......Page 549
   The KMS Cycle......Page 551
   Technologies That Support Knowledge Management......Page 552
   Characteristics of Groupwork......Page 554
   The Benefits and Limitations of Groupwork......Page 555
   An Overview of Group Support Systems (GSS)......Page 557
   Time/Place Framework......Page 558
   Groupware Tools......Page 559
   Web 2.0......Page 561
   Collaborative Networks......Page 562
   Group Decision Support Systems (GDSS)......Page 563
   How GDSS (or GSS) Improve Groupwork......Page 564
   Facilities for GDSS......Page 565
   Chapter Highlights......Page 566
   Exercises......Page 567
   End-of-Chapter Application Case Solving Crimes by Sharing Digital Forensic Knowledge......Page 568
   References......Page 570
Part V Big Data and Future Directions for Business Analytics......Page 572
 Chapter 13 Big Data and Analytics......Page 573
  13.1 Opening Vignette: Big Data Meets Big Science at CERN......Page 574
  13.2 Definition of Big Data......Page 577
   The Vs That Define Big Data......Page 578
   Application Case 13.1 Big Data Analytics Helps Luxottica ImproveIts Marketing Effectiveness......Page 581
  13.3 Fundamentals of Big Data Analytics......Page 582
   Business Problems Addressed by Big Data Analytics......Page 585
   Application Case 13.2 Top 5 Investment Bank Achieves Single Source of Truth......Page 586
  13.4 Big Data Technologies......Page 587
   MapReduce......Page 588
   How Does Hadoop Work?......Page 589
   Hadoop Technical Components......Page 590
   Hadoop: The Pros and Cons......Page 591
   NoSQL......Page 593
   Application Case 13.3 eBay’s Big Data Solution......Page 594
   Where Do Data Scientists Come From?......Page 596
   Application Case 13.4 Big Data and Analytics in Politics......Page 599
  13.6 Big Data and Data Warehousing......Page 600
   Use Case(s) for Hadoop......Page 601
   Use Case(s) for Data Warehousing......Page 602
   Coexistence of Hadoop and Data Warehouse......Page 603
  13.7 Big Data Vendors......Page 605
   Application Case 13.5 Dublin City Council Is Leveraging Big Datato Reduce Traffic Congestion......Page 606
   Application Case 13.6 Creditreform Boosts Credit Rating Quality with Big Data Visual Analytics......Page 611
  13.8 Big Data and Stream Analytics......Page 612
   Critical Event Processing......Page 613
   Data Stream Mining......Page 614
   Telecommunications......Page 615
   Application Case 13.7 Turning Machine-Generated Streaming Data into Valuable Business Insights......Page 616
   Law Enforcement and Cyber Security......Page 617
   Government......Page 618
   Questions for Discussion......Page 619
   End-of-Chapter Application Case Discovery Health Turns Big Data into Better Healthcare......Page 620
   References......Page 622
 Chapter 14 Business Analytics: Emerging Trends and Future Impacts......Page 623
  14.1 Opening Vignette: Oklahoma Gas and Electric Employs Analytics to Promote Smart Energy Use......Page 624
   Geospatial Analytics......Page 625
   Application Case 14.1 Great Clips Employs Spatial Analytics to Shave Time in Location Decisions......Page 627
   A Multimedia Exercise in Analytics Employing Geospatial Analytics......Page 628
   Real-Time Location Intelligence......Page 629
   Application Case 14.2 Quiznos Targets Customers for Its Sandwiches......Page 630
  14.3 Analytics Applications for Consumers......Page 631
   Application Case 14.3 A Life Coach in Your Pocket......Page 632
  14.4 Recommendation Engines......Page 634
  14.5 Web 2.0 and Online Social Networking......Page 635
   Social Networking......Page 636
   Implications of Business and Enterprise Social Networks......Page 637
  14.6 Cloud Computing and BI......Page 638
   Data-as-a-Service (DaaS)......Page 639
   Analytics-as-a-Service (AaaS)......Page 642
   New Organizational Units......Page 644
   Job Stress and Anxiety......Page 645
   Analytics’ Impact on Managers’ Activities and Their Performance......Page 646
   Legal Issues......Page 647
   Privacy......Page 648
   Recent Technology Issues in Privacy and Analytics......Page 649
   Ethics in Decision Making and Support......Page 650
   Data Infrastructure Providers......Page 651
   Data Warehouse Industry......Page 652
   Reporting/Analytics......Page 653
   Prescriptive Analytics......Page 654
   Application Developers or System Integrators: Industry Specific or General......Page 655
   Analytics User Organizations......Page 656
   Analytics Industry Analysts and Influencers......Page 658
   Academic Providers and Certification Agencies......Page 659
   Questions for Discussion......Page 660
   End-of-Chapter Application Case Southern States Cooperative Optimizes Its Catalog Campaign......Page 661
   References......Page 663
Glossary......Page 665
 B......Page 679
 C......Page 680
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 L......Page 683
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