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Business Intelligence: A Managerial Approach, Global Edition

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
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