BOOK
Business Intelligence: A Managerial Approach, Global Edition
Ramesh Sharda | Dursun Delen | Efraim Turban | David King
(2017)
Additional Information
Book Details
Abstract
For courses on Business Intelligence or Decision Support Systems.
A managerial approach to understanding business intelligence systems.
To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Title Page | 1 | ||
| Copyright Page | 2 | ||
| Brief Contents | 3 | ||
| Contents | 5 | ||
| Preface | 19 | ||
| Acknowledgments | 22 | ||
| About the Authors | 25 | ||
| Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science | 29 | ||
| 1.1. Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics | 30 | ||
| 1.2. Changing Business Environments and Evolving Needs for Decision Support and Analytics | 37 | ||
| 1.3. Evolution of Computerized Decision Support to Analytics/Data Science | 39 | ||
| 1.4. A Framework for Business Intelligence | 41 | ||
| Definitions of BI | 42 | ||
| A Brief History of BI | 42 | ||
| The Architecture of BI | 42 | ||
| The Origins and Drivers of BI | 42 | ||
| Application Case 1.1: Sabre Helps Its Clients Through Dashboards and Analytics | 44 | ||
| A Multimedia Exercise in Business Intelligence | 45 | ||
| Transaction Processing versus Analytic Processing | 45 | ||
| Appropriate Planning and Alignment with the Business Strategy | 46 | ||
| Real-Time, On-Demand BI Is Attainable | 47 | ||
| Developing or Acquiring BI Systems | 47 | ||
| Justification and Cost–Benefit Analysis | 48 | ||
| Security and Protection of Privacy | 48 | ||
| Integration of Systems and Applications | 48 | ||
| 1.5. Analytics Overview | 48 | ||
| Descriptive Analytics | 50 | ||
| Application Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities | 50 | ||
| Application Case 1.3: Siemens Reduces Cost with the Use of Data Visualization | 51 | ||
| Predictive Analytics | 51 | ||
| Application Case 1.4: Analyzing Athletic Injuries | 52 | ||
| Prescriptive Analytics | 52 | ||
| Analytics Applied to Different Domains | 53 | ||
| Application Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates | 53 | ||
| Analytics or Data Science? | 54 | ||
| 1.6. Analytics Examples in Selected Domains | 55 | ||
| Analytics Applications in Healthcare—Humana Examples | 55 | ||
| Analytics in the Retail Value Chain | 59 | ||
| 1.7. A Brief Introduction to Big Data Analytics | 61 | ||
| What Is Big Data? | 61 | ||
| Application Case 1.6: CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Service | 63 | ||
| 1.8. An Overview of the Analytics Ecosystem | 63 | ||
| Data Generation Infrastructure Providers | 65 | ||
| Data Management Infrastructure Providers | 65 | ||
| Data Warehouse Providers | 66 | ||
| Middleware Providers | 66 | ||
| Data Service Providers | 66 | ||
| Analytics-Focused Software Developers | 67 | ||
| Application Developers: Industry Specific or General | 68 | ||
| Analytics Industry Analysts and Influencers | 69 | ||
| Academic Institutions and Certification Agencies | 70 | ||
| Regulators and Policy Makers | 71 | ||
| Analytics User Organizations | 71 | ||
| 1.9. Plan of the Book | 72 | ||
| 1.10. Resources, Links, and the Teradata University Network Connection | 73 | ||
| Resources and Links | 73 | ||
| Vendors, Products, and Demos | 74 | ||
| Periodicals | 74 | ||
| The Teradata University Network Connection | 74 | ||
| The Book’s Web Site | 74 | ||
| Chapter Highlights | 75 | ||
| Key Terms | 75 | ||
| Questions for Discussion | 75 | ||
| Exercises | 76 | ||
| References | 77 | ||
| Chapter 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization | 79 | ||
| 2.1. Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing | 80 | ||
| 2.2. The Nature of Data | 83 | ||
| 2.3. A Simple Taxonomy of Data | 87 | ||
| Application Case 2.1: Medical Device Company Ensures Product Quality While Saving Money | 89 | ||
| 2.4. The Art and Science of Data Preprocessing | 91 | ||
| Application Case 2.2: Improving Student Retention with Data-Driven Analytics | 94 | ||
| 2.5. Statistical Modeling for Business Analytics | 100 | ||
| Descriptive Statistics for Descriptive Analytics | 101 | ||
| Measures of Centrality Tendency (May Also Be Called Measures of Location or Centrality) | 102 | ||
| Arithmetic Mean | 102 | ||
| Median | 103 | ||
| Mode | 103 | ||
| Measures of Dispersion (May Also Be Called Measures of Spread Decentrality) | 103 | ||
| Range | 104 | ||
| Variance | 104 | ||
| Standard Deviation | 104 | ||
| Mean Absolute Deviation | 104 | ||
| Quartiles and Interquartile Range | 104 | ||
| Box-and-Whiskers Plot | 105 | ||
| The Shape of a Distribution | 106 | ||
| Application Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems | 110 | ||
| 2.6 Regression Modeling for Inferential Statistics | 112 | ||
| How Do We Develop the Linear Regression Model? | 113 | ||
| How Do We Know If the Model Is Good Enough? | 114 | ||
| What Are the Most Important Assumptions in Linear Regression? | 115 | ||
| Logistic Regression | 116 | ||
| Application Case 2.4: Predicting NCAA Bowl Game Outcomes | 117 | ||
| Time Series Forecasting | 122 | ||
| 2.7. Business Reporting | 124 | ||
| Application Case 2.5: Flood of Paper Ends at FEMA | 126 | ||
| 2.8. Data Visualization | 127 | ||
| A Brief History of Data Visualization | 127 | ||
| Application Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online | 129 | ||
| 2.9. Different Types of Charts and Graphs | 132 | ||
| Basic Charts and Graphs | 132 | ||
| Specialized Charts and Graphs | 133 | ||
| Which Chart or Graph Should You Use? | 134 | ||
| 2.10. The Emergence of Visual Analytics | 136 | ||
| Visual Analytics | 138 | ||
| High-Powered Visual Analytics Environments | 138 | ||
| 2.11. Information Dashboards | 143 | ||
| Application Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion | 144 | ||
| Dashboard Design | 145 | ||
| Application Case 2.8: Visual Analytics Helps Energy Supplier Make Better Connections | 145 | ||
| What to Look for in a Dashboard | 147 | ||
| Best Practices in Dashboard Design | 147 | ||
| Benchmark Key Performance Indicators with Industry Standards | 147 | ||
| Wrap the Dashboard Metrics with Contextual Metadata | 147 | ||
| Validate the Dashboard Design by a Usability Specialist | 148 | ||
| Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard | 148 | ||
| Enrich the Dashboard with Business-User Comments | 148 | ||
| Present Information in Three Different Levels | 148 | ||
| Pick the Right Visual Construct Using Dashboard Design Principles | 148 | ||
| Provide for Guided Analytics | 148 | ||
| Chapter Highlights | 149 | ||
| Key Terms | 149 | ||
| Questions for Discussion | 150 | ||
| Exercises | 150 | ||
| References | 152 | ||
| Chapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing | 153 | ||
| 3.1. Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing | 154 | ||
| 3.2. Business Intelligence and Data Warehousing | 156 | ||
| What Is a Data Warehouse? | 157 | ||
| A Historical Perspective to Data Warehousing | 158 | ||
| Characteristics of Data Warehousing | 159 | ||
| Data Marts | 160 | ||
| Operational Data Stores | 161 | ||
| Enterprise Data Warehouses (EDW) | 161 | ||
| Metadata | 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 | 161 | ||
| 3.3. Data Warehousing Process | 163 | ||
| 3.4. Data Warehousing Architectures | 165 | ||
| Alternative Data Warehousing Architectures | 168 | ||
| Which Architecture Is the Best? | 170 | ||
| 3.5. Data Integration and the Extraction, Transformation, and Load (ETL) Processes | 171 | ||
| Data Integration | 172 | ||
| Application Case 3.2: BP Lubricants Achieves BIGS Success | 172 | ||
| Extraction, Transformation, and Load | 174 | ||
| 3.6. Data Warehouse Development | 176 | ||
| Application Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery | 177 | ||
| Data Warehouse Development Approaches | 179 | ||
| Additional Data Warehouse Development Considerations | 182 | ||
| Representation of Data in Data Warehouse | 182 | ||
| Analysis of Data in Data Warehouse | 184 | ||
| OLAP versus OLTP | 184 | ||
| OLAP Operations | 185 | ||
| 3.7. Data Warehousing Implementation Issues | 186 | ||
| Massive Data Warehouses and Scalability | 188 | ||
| Application Case 3.4: EDW Helps Connect State Agencies in Michigan | 189 | ||
| 3.8. Data Warehouse Administration, Security Issues, and Future Trends | 190 | ||
| The Future of Data Warehousing | 191 | ||
| 3.9. Business Performance Management | 196 | ||
| Closed-Loop BPM Cycle | 197 | ||
| Application Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years | 199 | ||
| 3.10. Performance Measurement | 201 | ||
| Key Performance Indicator (KPI) | 201 | ||
| Performance Measurement System | 202 | ||
| 3.11. Balanced Scorecards | 203 | ||
| The Four Perspectives | 203 | ||
| The Meaning of Balance in BSC | 205 | ||
| 3.12. Six Sigma as a Performance Measurement System | 205 | ||
| The DMAIC Performance Model | 206 | ||
| Balanced Scorecard versus Six Sigma | 206 | ||
| Effective Performance Measurement | 207 | ||
| Application Case 3.6: Expedia.com’s Customer Satisfaction Scorecard | 208 | ||
| Chapter Highlights | 209 | ||
| Key Terms | 210 | ||
| Questions for Discussion | 210 | ||
| Exercises | 211 | ||
| References | 213 | ||
| Chapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms | 215 | ||
| 4.1. Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime | 216 | ||
| 4.2. Data Mining Concepts and Applications | 219 | ||
| Application Case 4.1: Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining | 220 | ||
| Definitions, Characteristics, and Benefits | 222 | ||
| How Data Mining Works | 223 | ||
| Application Case 4.2: Dell Is Staying Agile and Effective with Analytics in the 21st Century | 224 | ||
| Data Mining versus Statistics | 229 | ||
| 4.3. Data Mining Applications | 229 | ||
| Application Case 4.3: Bank Speeds Time to Market with Advanced Analytics | 231 | ||
| 4.4. Data Mining Process | 232 | ||
| Step 1: Business Understanding | 233 | ||
| Step 2: Data Understanding | 234 | ||
| Step 3: Data Preparation | 234 | ||
| Step 4: Model Building | 235 | ||
| Application Case 4.4: Data Mining Helps in Cancer Research | 235 | ||
| Step 5: Testing and Evaluation | 238 | ||
| Step 6: Deployment | 238 | ||
| Other Data Mining Standardized Processes and Methodologies | 238 | ||
| 4.5. Data Mining Methods | 241 | ||
| Classification | 241 | ||
| Estimating the True Accuracy of Classification Models | 242 | ||
| Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions | 249 | ||
| Cluster Analysis for Data Mining | 251 | ||
| Association Rule Mining | 253 | ||
| 4.6. Data Mining Software Tools | 257 | ||
| Application Case 4.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies | 259 | ||
| 4.7. Data Mining Privacy Issues, Myths, and Blunders | 263 | ||
| Application Case 4.7: Predicting Customer Buying Patterns—The Target Story | 264 | ||
| Data Mining Myths and Blunders | 264 | ||
| Chapter Highlights | 267 | ||
| Key Terms | 268 | ||
| Questions for Discussion | 268 | ||
| Exercises | 269 | ||
| References | 271 | ||
| Chapter 5: Predictive Analytics II: Text, Web, and Social Media Analytics | 273 | ||
| 5.1. Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson | 274 | ||
| 5.2. Text Analytics and Text Mining Overview | 277 | ||
| Application Case 5.1: Insurance Group Strengthens Risk Management with Text Mining Solution | 280 | ||
| 5.3. Natural Language Processing (NLP) | 281 | ||
| Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World | 283 | ||
| 5.4. Text Mining Applications | 287 | ||
| Marketing Applications | 287 | ||
| Security Applications | 287 | ||
| Application Case 5.3: Mining for Lies | 288 | ||
| Biomedical Applications | 290 | ||
| Academic Applications | 292 | ||
| Application Case 5.4: Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Rethink Its Redesign | 292 | ||
| 5.5. Text Mining Process | 294 | ||
| Task 1: Establish the Corpus | 295 | ||
| Task 2: Create the Term–Document Matrix | 295 | ||
| Task 3: Extract the Knowledge | 297 | ||
| Application Case 5.5: Research Literature Survey with Text Mining | 299 | ||
| 5.6. Sentiment Analysis | 302 | ||
| Application Case 5.6: Creating a Unique Digital Experience to Capture the Moments That Matter at Wimbledon | 303 | ||
| Sentiment Analysis Applications | 306 | ||
| Sentiment Analysis Process | 308 | ||
| Methods for Polarity Identification | 310 | ||
| Using a Lexicon | 310 | ||
| Using a Collection of Training Documents | 311 | ||
| Identifying Semantic Orientation of Sentences and Phrases | 312 | ||
| Identifying Semantic Orientation of Documents | 312 | ||
| 5.7. Web Mining Overview | 313 | ||
| Web Content and Web Structure Mining | 315 | ||
| 5.8. Search Engines | 317 | ||
| Anatomy of a Search Engine | 318 | ||
| 1. Development Cycle | 318 | ||
| 2. Response Cycle | 320 | ||
| Search Engine Optimization | 320 | ||
| Methods for Search Engine Optimization | 321 | ||
| Application Case 5.7: Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sales Increase | 323 | ||
| 5.9. Web Usage Mining (Web Analytics) | 324 | ||
| Web Analytics Technologies | 325 | ||
| Web Analytics Metrics | 326 | ||
| Web Site Usability | 326 | ||
| Traffic Sources | 327 | ||
| Visitor Profiles | 328 | ||
| Conversion Statistics | 328 | ||
| 5.10. Social Analytics | 330 | ||
| Social Network Analysis | 330 | ||
| Social Network Analysis Metrics | 331 | ||
| Application Case 5.8: Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy | 331 | ||
| Connections | 334 | ||
| Distributions | 334 | ||
| Segmentation | 335 | ||
| Social Media Analytics | 335 | ||
| How Do People Use Social Media? | 336 | ||
| Measuring the Social Media Impact | 337 | ||
| Best Practices in Social Media Analytics | 337 | ||
| Chapter Highlights | 339 | ||
| Key Terms | 340 | ||
| Questions for Discussion | 341 | ||
| Exercises | 341 | ||
| References | 342 | ||
| Chapter 6: Prescriptive Analytics: Optimization and Simulation | 345 | ||
| 6.1. Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts | 346 | ||
| 6.2. Model-Based Decision Making | 348 | ||
| Prescriptive Analytics Model Examples | 348 | ||
| Application Case 6.1: Optimal Transport for ExxonMobil Downstream through a DSS | 349 | ||
| Identification of the Problem and Environmental Analysis | 350 | ||
| Model Categories | 350 | ||
| Application Case 6.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions | 351 | ||
| 6.3. Structure of Mathematical Models for Decision Support | 354 | ||
| The Components of Decision Support Mathematical Models | 354 | ||
| The Structure of Mathematical Models | 355 | ||
| 6.4. Certainty, Uncertainty, and Risk | 356 | ||
| Decision Making under Certainty | 356 | ||
| Decision Making under Uncertainty | 357 | ||
| Decision Making under Risk (Risk Analysis) | 357 | ||
| 6.5. Decision Modeling with Spreadsheets | 357 | ||
| Application Case 6.3: Primary Schools in Slovenia Use Interactive and Automated Scheduling Systems to Produce Quality Timetables | 358 | ||
| Application Case 6.4: Spreadsheet Helps Optimize Production Planning in Chilean Swine Companies | 359 | ||
| Application Case 6.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes | 360 | ||
| 6.6 Mathematical Programming Optimization | 362 | ||
| Application Case 6.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians | 363 | ||
| Linear Programming Model | 364 | ||
| Modeling in LP: An Example | 365 | ||
| Implementation | 370 | ||
| 6.7. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking | 372 | ||
| Multiple Goals | 372 | ||
| Sensitivity Analysis | 373 | ||
| What-If Analysis | 374 | ||
| Goal Seeking | 374 | ||
| 6.8. Decision Analysis with Decision Tables and Decision Trees | 375 | ||
| Decision Tables | 376 | ||
| Decision Trees | 377 | ||
| 6.9. Introduction to Simulation | 378 | ||
| Major Characteristics of Simulation | 378 | ||
| Application Case 6.7: Syngenta Uses Monte Carlo Simulation Models to Increase Soybean Crop Production | 379 | ||
| Advantages of Simulation | 380 | ||
| Disadvantages of Simulation | 381 | ||
| The Methodology of Simulation | 381 | ||
| Simulation Types | 382 | ||
| Monte Carlo Simulation | 383 | ||
| Discrete Event Simulation | 384 | ||
| Application Case 6.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation | 384 | ||
| 6.10. Visual Interactive Simulation | 385 | ||
| Conventional Simulation Inadequacies | 385 | ||
| Visual Interactive Simulation | 385 | ||
| Visual Interactive Models and DSS | 386 | ||
| Simulation Software | 386 | ||
| Application Case 6.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment | 387 | ||
| Chapter Highlights | 390 | ||
| Key Terms | 390 | ||
| Questions for Discussion | 391 | ||
| Exercises | 391 | ||
| References | 393 | ||
| Chapter 7: Big Data Concepts and Tools | 395 | ||
| 7.1. Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods | 396 | ||
| 7.2. Definition of Big Data | 399 | ||
| The “V”s That Define Big Data | 400 | ||
| Application Case 7.1: Alternative Data for Market Analysis or Forecasts | 403 | ||
| 7.3. Fundamentals of Big Data Analytics | 404 | ||
| Business Problems Addressed by Big Data Analytics | 407 | ||
| Application Case 7.2: Top Five Investment Bank Achieves Single Source of the Truth | 408 | ||
| 7.4. Big Data Technologies | 409 | ||
| MapReduce | 409 | ||
| Why Use MapReduce? | 411 | ||
| Hadoop | 411 | ||
| How Does Hadoop Work? | 411 | ||
| Hadoop Technical Components | 412 | ||
| Hadoop: The Pros and Cons | 413 | ||
| NoSQL | 415 | ||
| Application Case 7.3: eBay’s Big Data Solution | 416 | ||
| Application Case 7.4: Understanding Quality and Reliability of Healthcare Support Information on Twitter | 418 | ||
| 7.5. Big Data and Data Warehousing | 419 | ||
| Use Cases for Hadoop | 419 | ||
| Use Cases for Data Warehousing | 420 | ||
| The Gray Areas (Any One of the Two Would Do the Job) | 421 | ||
| Coexistence of Hadoop and Data Warehouse | 422 | ||
| 7.6. Big Data Vendors and Platforms | 423 | ||
| IBM InfoSphere BigInsights | 424 | ||
| Application Case 7.5: Using Social Media for Nowcasting the Flu Activity | 426 | ||
| Teradata Aster | 427 | ||
| Application Case 7.6: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse | 428 | ||
| 7.7. Big Data and Stream Analytics | 432 | ||
| Stream Analytics versus Perpetual Analytics | 434 | ||
| Critical Event Processing | 434 | ||
| Data Stream Mining | 434 | ||
| 7.8. Applications of Stream Analytics | 435 | ||
| e-Commerce | 435 | ||
| Telecommunications | 435 | ||
| Application Case 7.7: Salesforce Is Using Streaming Data to Enhance Customer Value | 436 | ||
| Law Enforcement and Cybersecurity | 437 | ||
| Power Industry | 437 | ||
| Financial Services | 437 | ||
| Health Sciences | 437 | ||
| Government | 438 | ||
| Chapter Highlights | 438 | ||
| Key Terms | 439 | ||
| Questions for Discussion | 439 | ||
| Exercises | 439 | ||
| References | 440 | ||
| Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics | 443 | ||
| 8.1. Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures | 444 | ||
| 8.2. Internet of Things | 445 | ||
| Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans | 446 | ||
| Application Case 8.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets | 447 | ||
| IoT Technology Infrastructure | 448 | ||
| RFID Sensors | 448 | ||
| Fog Computing | 451 | ||
| IoT Platforms | 452 | ||
| Application Case 8.3: Pitney Bowes Collaborates with General Electric IoT Platform to Optimize Production | 452 | ||
| IoT Start-Up Ecosystem | 453 | ||
| Managerial Considerations in the Internet of Things | 454 | ||
| 8.3. Cloud Computing and Business Analytics | 455 | ||
| Data as a Service (DaaS) | 457 | ||
| Software as a Service (SaaS) | 458 | ||
| Platform as a Service (PaaS) | 458 | ||
| Infrastructure as a Service (IaaS) | 458 | ||
| Essential Technologies for Cloud Computing | 459 | ||
| Cloud Deployment Models | 459 | ||
| Major Cloud Platform Providers in Analytics | 460 | ||
| Analytics as a Service (AaaS) | 461 | ||
| Representative Analytics as a Service Offerings | 461 | ||
| Illustrative Analytics Applications Employing the Cloud Infrastructure | 462 | ||
| MD Anderson Cancer Center Utilizes Cognitive Computing Capabilities of IBM Watson to Give Better Treatment to Cancer Patients | 462 | ||
| Public School Education in Tacoma, Washington, Uses Microsoft Azure Machine Learning to Predict School Dropouts | 463 | ||
| Dartmouth-Hitchcock Medical Center Provides Personalized Proactive Healthcare Using Microsoft Cortana Analytics Suite | 464 | ||
| Mankind Pharma Uses IBM Cloud Infrastructure to Reduce Application Implementation Time by 98% | 464 | ||
| Gulf Air Uses Big Data to Get Deeper Customer Insight | 465 | ||
| Chime Enhances Customer Experience Using Snowflake | 466 | ||
| 8.4. Location-Based Analytics for Organizations | 467 | ||
| Geospatial Analytics | 467 | ||
| Application Case 8.4: Indian Police Departments Use Geospatial Analytics to Fight Crime | 469 | ||
| Application Case 8.5: Starbucks Exploits GIS and Analytics to Grow Worldwide | 470 | ||
| Real-Time Location Intelligence | 471 | ||
| Application Case 8.6: Quiznos Targets Customers for Its Sandwiches | 472 | ||
| Analytics Applications for Consumers | 472 | ||
| 8.5. Issues of Legality, Privacy, and Ethics | 474 | ||
| Legal Issues | 474 | ||
| Privacy | 475 | ||
| Collecting Information about Individuals | 475 | ||
| Mobile User Privacy | 476 | ||
| Homeland Security and Individual Privacy | 476 | ||
| Recent Technology Issues in Privacy and Analytics | 477 | ||
| Who Owns Our Private Data? | 478 | ||
| Ethics in Decision Making and Support | 478 | ||
| 8.6. Impacts of Analytics in Organizations: An Overview | 479 | ||
| New Organizational Units | 480 | ||
| Redesign of an Organization through the Use of Analytics | 481 | ||
| Analytics Impact on Managers’ Activities, Performance, and Job Satisfaction | 481 | ||
| Industrial Restructuring | 482 | ||
| Automation’s Impact on Jobs | 483 | ||
| Unintended Effects of Analytics | 484 | ||
| 8.7. Data Scientist as a Profession | 485 | ||
| Where Do Data Scientists Come From? | 485 | ||
| Chapter Highlights | 488 | ||
| Key Terms | 489 | ||
| Questions for Discussion | 489 | ||
| Exercises | 489 | ||
| References | 490 | ||
| Glossary | 493 | ||
| Index | 501 | ||
| Back Cover | Back Cover |