Business Intelligence, Analytics, and Data Science: A Managerial Perspective

Cover......Page 1
Title Page......Page 2
Copyright Page......Page 3
Brief Contents......Page 4
Contents......Page 6
Preface......Page 20
Acknowledgments......Page 23
About the Authors......Page 26
Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science......Page 30
 1.1. Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics......Page 31
 1.2. Changing Business Environments and Evolving Needs for Decision Support and Analytics......Page 38
 1.3. Evolution of Computerized Decision Support to Analytics/Data Science......Page 40
 1.4. A Framework for Business Intelligence......Page 42
  The Origins and Drivers of BI......Page 43
  Application Case 1.1: Sabre Helps Its Clients Through Dashboards and Analytics......Page 45
  Transaction Processing versus Analytic Processing......Page 46
  Appropriate Planning and Alignment with the Business Strategy......Page 47
  Developing or Acquiring BI Systems......Page 48
 1.5. Analytics Overview......Page 49
  Application Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities......Page 51
  Predictive Analytics......Page 52
  Prescriptive Analytics......Page 53
  Application Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates......Page 54
  Analytics or Data Science?......Page 55
  Analytics Applications in Healthcare—Humana Examples......Page 56
  Analytics in the Retail Value Chain......Page 60
  What Is Big Data?......Page 62
 1.8. An Overview of the Analytics Ecosystem......Page 64
  Data Management Infrastructure Providers......Page 66
  Data Service Providers......Page 67
  Analytics-Focused Software Developers......Page 68
  Application Developers: Industry Specific or General......Page 69
  Analytics Industry Analysts and Influencers......Page 70
  Academic Institutions and Certification Agencies......Page 71
  Analytics User Organizations......Page 72
 1.9. Plan of the Book......Page 73
  Resources and Links......Page 74
  The Book’s Web Site......Page 75
 Questions for Discussion......Page 76
 Exercises......Page 77
 References......Page 78
Chapter 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization......Page 80
 2.1. Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing......Page 81
 2.2. The Nature of Data......Page 84
 2.3. A Simple Taxonomy of Data......Page 88
  Application Case 2.1: Medical Device Company Ensures Product Quality While Saving Money......Page 90
 2.4. The Art and Science of Data Preprocessing......Page 92
  Application Case 2.2: Improving Student Retention with Data-Driven Analytics......Page 95
 2.5. Statistical Modeling for Business Analytics......Page 101
  Descriptive Statistics for Descriptive Analytics......Page 102
  Arithmetic Mean......Page 103
  Measures of Dispersion (May Also Be Called Measures of Spread Decentrality)......Page 104
  Quartiles and Interquartile Range......Page 105
  Box-and-Whiskers Plot......Page 106
  The Shape of a Distribution......Page 107
  Application Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems......Page 111
 2.6 Regression Modeling for Inferential Statistics......Page 113
  How Do We Develop the Linear Regression Model?......Page 114
  How Do We Know If the Model Is Good Enough?......Page 115
  What Are the Most Important Assumptions in Linear Regression?......Page 116
  Logistic Regression......Page 117
  Application Case 2.4: Predicting NCAA Bowl Game Outcomes......Page 118
  Time Series Forecasting......Page 123
 2.7. Business Reporting......Page 125
  Application Case 2.5: Flood of Paper Ends at FEMA......Page 127
  A Brief History of Data Visualization......Page 128
  Application Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online......Page 130
  Basic Charts and Graphs......Page 133
  Specialized Charts and Graphs......Page 134
  Which Chart or Graph Should You Use?......Page 135
 2.10. The Emergence of Visual Analytics......Page 137
  High-Powered Visual Analytics Environments......Page 139
 2.11. Information Dashboards......Page 144
  Application Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion......Page 145
  Application Case 2.8: Visual Analytics Helps Energy Supplier Make Better Connections......Page 146
  Wrap the Dashboard Metrics with Contextual Metadata......Page 148
  Provide for Guided Analytics......Page 149
 Key Terms......Page 150
 Exercises......Page 151
 References......Page 153
Chapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing......Page 154
 3.1. Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing......Page 155
 3.2. Business Intelligence and Data Warehousing......Page 157
  What Is a Data Warehouse?......Page 158
  A Historical Perspective to Data Warehousing......Page 159
  Characteristics of Data Warehousing......Page 160
  Data Marts......Page 161
  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 162
 3.3. Data Warehousing Process......Page 164
 3.4. Data Warehousing Architectures......Page 166
  Alternative Data Warehousing Architectures......Page 169
  Which Architecture Is the Best?......Page 171
 3.5. Data Integration and the Extraction, Transformation, and Load (ETL) Processes......Page 172
  Application Case 3.2: BP Lubricants Achieves BIGS Success......Page 173
  Extraction, Transformation, and Load......Page 175
 3.6. Data Warehouse Development......Page 177
  Application Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery......Page 178
  Data Warehouse Development Approaches......Page 180
  Representation of Data in Data Warehouse......Page 183
  OLAP versus OLTP......Page 185
  OLAP Operations......Page 186
 3.7. Data Warehousing Implementation Issues......Page 187
  Massive Data Warehouses and Scalability......Page 189
  Application Case 3.4: EDW Helps Connect State Agencies in Michigan......Page 190
 3.8. Data Warehouse Administration, Security Issues, and Future Trends......Page 191
  The Future of Data Warehousing......Page 192
 3.9. Business Performance Management......Page 197
  Closed-Loop BPM Cycle......Page 198
  Application Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years......Page 200
  Key Performance Indicator (KPI)......Page 202
  Performance Measurement System......Page 203
  The Four Perspectives......Page 204
 3.12. Six Sigma as a Performance Measurement System......Page 206
  Balanced Scorecard versus Six Sigma......Page 207
  Effective Performance Measurement......Page 208
  Application Case 3.6: Expedia.com’s Customer Satisfaction Scorecard......Page 209
 Chapter Highlights......Page 210
 Questions for Discussion......Page 211
 Exercises......Page 212
 References......Page 214
Chapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms......Page 216
 4.1. Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime......Page 217
 4.2. Data Mining Concepts and Applications......Page 220
  Application Case 4.1: Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining......Page 221
  Definitions, Characteristics, and Benefits......Page 223
  How Data Mining Works......Page 224
  Application Case 4.2: Dell Is Staying Agile and Effective with Analytics in the 21st Century......Page 225
 4.3. Data Mining Applications......Page 230
  Application Case 4.3: Bank Speeds Time to Market with Advanced Analytics......Page 232
 4.4. Data Mining Process......Page 233
  Step 1: Business Understanding......Page 234
  Step 3: Data Preparation......Page 235
  Application Case 4.4: Data Mining Helps in Cancer Research......Page 236
  Other Data Mining Standardized Processes and Methodologies......Page 239
  Classification......Page 242
  Estimating the True Accuracy of Classification Models......Page 243
  Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions......Page 250
  Cluster Analysis for Data Mining......Page 252
  Association Rule Mining......Page 254
 4.6. Data Mining Software Tools......Page 258
  Application Case 4.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies......Page 260
 4.7. Data Mining Privacy Issues, Myths, and Blunders......Page 264
  Data Mining Myths and Blunders......Page 265
 Chapter Highlights......Page 268
 Questions for Discussion......Page 269
 Exercises......Page 270
 References......Page 272
Chapter 5: Predictive Analytics II: Text, Web, and Social Media Analytics......Page 274
 5.1. Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson......Page 275
 5.2. Text Analytics and Text Mining Overview......Page 278
  Application Case 5.1: Insurance Group Strengthens Risk Management with Text Mining Solution......Page 281
 5.3. Natural Language Processing (NLP)......Page 282
  Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World......Page 284
  Security Applications......Page 288
  Application Case 5.3: Mining for Lies......Page 289
  Biomedical Applications......Page 291
  Application Case 5.4: Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Rethink Its Redesign......Page 293
 5.5. Text Mining Process......Page 295
  Task 2: Create the Term–Document Matrix......Page 296
  Task 3: Extract the Knowledge......Page 298
  Application Case 5.5: Research Literature Survey with Text Mining......Page 300
 5.6. Sentiment Analysis......Page 303
  Application Case 5.6: Creating a Unique Digital Experience to Capture the Moments That Matter at Wimbledon......Page 304
  Sentiment Analysis Applications......Page 307
  Sentiment Analysis Process......Page 309
  Using a Lexicon......Page 311
  Using a Collection of Training Documents......Page 312
  Identifying Semantic Orientation of Documents......Page 313
 5.7. Web Mining Overview......Page 314
  Web Content and Web Structure Mining......Page 316
 5.8. Search Engines......Page 318
  1. Development Cycle......Page 319
  Search Engine Optimization......Page 321
  Methods for Search Engine Optimization......Page 322
  Application Case 5.7: Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sales Increase......Page 324
 5.9. Web Usage Mining (Web Analytics)......Page 325
  Web Analytics Technologies......Page 326
  Web Site Usability......Page 327
  Traffic Sources......Page 328
  Conversion Statistics......Page 329
  Social Network Analysis......Page 331
  Application Case 5.8: Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy......Page 332
  Distributions......Page 335
  Social Media Analytics......Page 336
  How Do People Use Social Media?......Page 337
  Best Practices in Social Media Analytics......Page 338
 Chapter Highlights......Page 340
 Key Terms......Page 341
 Exercises......Page 342
 References......Page 343
Chapter 6: Prescriptive Analytics: Optimization and Simulation......Page 346
 6.1. Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts......Page 347
  Prescriptive Analytics Model Examples......Page 349
  Application Case 6.1: Optimal Transport for ExxonMobil Downstream through a DSS......Page 350
  Model Categories......Page 351
  Application Case 6.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions......Page 352
  The Components of Decision Support Mathematical Models......Page 355
  The Structure of Mathematical Models......Page 356
  Decision Making under Certainty......Page 357
 6.5. Decision Modeling with Spreadsheets......Page 358
  Application Case 6.3: Primary Schools in Slovenia Use Interactive and Automated Scheduling Systems to Produce Quality Timetables......Page 359
  Application Case 6.4: Spreadsheet Helps Optimize Production Planning in Chilean Swine Companies......Page 360
  Application Case 6.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes......Page 361
 6.6 Mathematical Programming Optimization......Page 363
  Application Case 6.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians......Page 364
  Linear Programming Model......Page 365
  Modeling in LP: An Example......Page 366
  Implementation......Page 371
  Multiple Goals......Page 373
  Sensitivity Analysis......Page 374
  Goal Seeking......Page 375
 6.8. Decision Analysis with Decision Tables and Decision Trees......Page 376
  Decision Tables......Page 377
  Decision Trees......Page 378
  Major Characteristics of Simulation......Page 379
  Application Case 6.7: Syngenta Uses Monte Carlo Simulation Models to Increase Soybean Crop Production......Page 380
  Advantages of Simulation......Page 381
  The Methodology of Simulation......Page 382
  Simulation Types......Page 383
  Monte Carlo Simulation......Page 384
  Application Case 6.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation......Page 385
  Visual Interactive Simulation......Page 386
  Simulation Software......Page 387
  Application Case 6.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment......Page 388
 Key Terms......Page 391
 Exercises......Page 392
 References......Page 394
Chapter 7: Big Data Concepts and Tools......Page 396
 7.1. Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods......Page 397
 7.2. Definition of Big Data......Page 400
  The “V”s That Define Big Data......Page 401
  Application Case 7.1: Alternative Data for Market Analysis or Forecasts......Page 404
 7.3. Fundamentals of Big Data Analytics......Page 405
  Business Problems Addressed by Big Data Analytics......Page 408
  Application Case 7.2: Top Five Investment Bank Achieves Single Source of the Truth......Page 409
  MapReduce......Page 410
  How Does Hadoop Work?......Page 412
  Hadoop Technical Components......Page 413
  Hadoop: The Pros and Cons......Page 414
  NoSQL......Page 416
  Application Case 7.3: eBay’s Big Data Solution......Page 417
  Application Case 7.4: Understanding Quality and Reliability of Healthcare Support Information on Twitter......Page 419
  Use Cases for Hadoop......Page 420
  Use Cases for Data Warehousing......Page 421
  The Gray Areas (Any One of the Two Would Do the Job)......Page 422
  Coexistence of Hadoop and Data Warehouse......Page 423
 7.6. Big Data Vendors and Platforms......Page 424
  IBM InfoSphere BigInsights......Page 425
  Application Case 7.5: Using Social Media for Nowcasting the Flu Activity......Page 427
  Teradata Aster......Page 428
  Application Case 7.6: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse......Page 429
 7.7. Big Data and Stream Analytics......Page 433
  Data Stream Mining......Page 435
  Telecommunications......Page 436
  Application Case 7.7: Salesforce Is Using Streaming Data to Enhance Customer Value......Page 437
  Health Sciences......Page 438
 Chapter Highlights......Page 439
 Exercises......Page 440
 References......Page 441
Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics......Page 444
 8.1. Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures......Page 445
 8.2. Internet of Things......Page 446
  Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans......Page 447
  Application Case 8.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets......Page 448
  RFID Sensors......Page 449
  Fog Computing......Page 452
  Application Case 8.3: Pitney Bowes Collaborates with General Electric IoT Platform to Optimize Production......Page 453
  IoT Start-Up Ecosystem......Page 454
  Managerial Considerations in the Internet of Things......Page 455
 8.3. Cloud Computing and Business Analytics......Page 456
  Data as a Service (DaaS)......Page 458
  Infrastructure as a Service (IaaS)......Page 459
  Cloud Deployment Models......Page 460
  Major Cloud Platform Providers in Analytics......Page 461
  Representative Analytics as a Service Offerings......Page 462
  MD Anderson Cancer Center Utilizes Cognitive Computing Capabilities of IBM Watson to Give Better Treatment to Cancer Patients......Page 463
  Public School Education in Tacoma, Washington, Uses Microsoft Azure Machine Learning to Predict School Dropouts......Page 464
  Mankind Pharma Uses IBM Cloud Infrastructure to Reduce Application Implementation Time by 98%......Page 465
  Gulf Air Uses Big Data to Get Deeper Customer Insight......Page 466
  Chime Enhances Customer Experience Using Snowflake......Page 467
  Geospatial Analytics......Page 468
  Application Case 8.4: Indian Police Departments Use Geospatial Analytics to Fight Crime......Page 470
  Application Case 8.5: Starbucks Exploits GIS and Analytics to Grow Worldwide......Page 471
  Real-Time Location Intelligence......Page 472
  Analytics Applications for Consumers......Page 473
  Legal Issues......Page 475
  Collecting Information about Individuals......Page 476
  Homeland Security and Individual Privacy......Page 477
  Recent Technology Issues in Privacy and Analytics......Page 478
  Ethics in Decision Making and Support......Page 479
 8.6. Impacts of Analytics in Organizations: An Overview......Page 480
  New Organizational Units......Page 481
  Analytics Impact on Managers’ Activities, Performance, and Job Satisfaction......Page 482
  Industrial Restructuring......Page 483
  Automation’s Impact on Jobs......Page 484
  Unintended Effects of Analytics......Page 485
  Where Do Data Scientists Come From?......Page 486
 Chapter Highlights......Page 489
 Exercises......Page 490
 References......Page 491
Glossary......Page 494
Index......Page 502
Back Cover......Page 514