Business intelligence and analytics: systems for decision support
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| Previous Title: | Decision support and business intelligence systems / Efraim Turban |
|---|---|
| Main Author: | |
| Format: | Book |
| Language: | English |
| Published: |
Boston
Pearson
[2014]
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| Edition: | tenth edition, global edition |
| Series: | Always learning
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 686 Seiten Illustrationen, Diagramme |
| ISBN: | 9781292009209 1292009209 |
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Titel: Business intelligence and analytics
Autor: Sharda, Ramesh
Jahr: 2014
CONTENTS
Preface 21
About the Authors 29
Part I Decision Making and Analytics: An Overview 31
Chapter 1 An Overview of Business Intelligence, Analytics, and
Decision Support 32
1.1 Opening Vignette: Magpie Sensing Employs Analytics to
Manage a Vaccine Supply Chain Effectively and Safely 33
1.2 Changing Business Environments and Computerized
Decision Support 35
The Business Pressures-Responses-Support Model 35
1.3 Managerial Decision Making 37
The Nature of Managers' Work 37
The Decision-Making Process 38
1.4 Information Systems Support for Decision Making 39
1.5 An Early Framework for Computerized Decision
Support 41
The Gorry and Scott-Morton Classical Framework 41
Computer Support for Structured Decisions 42
Computer Support for Unstructured Decisions 43
Computer Support for Semistructured Problems 43
1.6 The Concept of Decision Support Systems (DSS) 43
DSS as an Umbrella Term 43
Evolution of DSS into Business Intelligence 44
1.7 A Framework for Business Intelligence (Bl) 44
Definitions of Bl 44
A Brief History of Bl 44
The Architecture of Bl 45
Styles of Bl 45
The Origins and Drivers of Bl 46
A Multimedia Exercise in Business Intelligence 46
? APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboards
and Analytics 47
The DSS-BI Connection 48
1.8 Business Analytics Overview 49
Descriptive Analytics 50
? APPLICATION CASE 1.2 Eliminating Inefficiencies at Seattle
Children's Hospital 51
? APPLICATION CASE 1.3 Analysis at the Speed of Thought 52
Predictive Analytics 52
? APPLICATION CASE 1.4 Moneyball: Analytics in Sports and Movies 53
? APPLICATION CASE 1.5 Analyzing Athletic Injuries 54
Prescriptive Analytics 54
? APPLICATION CASE 1.6 Industrial and Commercial Bank of China
(ICBC) Employs Models to Reconfigure Its Branch Network 55
Analytics Applied to Different Domains 56
Analytics or Data Science? 56
1.9 Brief Introduction to Big Data Analytics 57
What Is Big Data? 57
? APPLICATION CASE 1.7 Gilt Groupe's Flash Sales Streamlined by Big
Data Analytics 59
1.10 Plan of the Book 59
Part I: Business Analytics: An Overview 59
Part II: Descriptive Analytics 60
Part III: Predictive Analytics 60
Part IV: Prescriptive Analytics 61
Part V: Big Data and Future Directions for Business Analytics 61
1.11 Resources, Links, and the Teradata University Network
Connection 61
Resources and Links 61
Vendors, Products, and Demos 61
Periodicals 61
The Teradata University Network Connection 62
The Book's Web Site 62
Chapter Highlights 62 • Key Terms 63
Questions for Discussion 63 • Exercises 63
? END-OF-CHAPTER APPLICATION CASE Nationwide Insurance Used Bl
to Enhance Customer Service 64
References 65
Chapter 2 Foundations and Technologies for Decision Making 67
2.1 Opening Vignette: Decision Modeling at HP Using
Spreadsheets 68
2.2 Decision Making: Introduction and Definitions 70
Characteristics of Decision Making 70
A Working Definition of Decision Making 71
Decision-Making Disciplines 71
Decision Style and Decision Makers 71
2.3 Phases of the Decision-Making Process 72
2.4 Decision Making: The Intelligence Phase 74
Problem (or Opportunity) Identification 75
? APPLICATION CASE 2.1 Making Elevators Go Faster! 75
Problem Classification 76
Problem Decomposition 76
Problem Ownership 76
2.5 Decision Making: The Design Phase 77
Models 77
Mathematical (Quantitative) Models 77
The Benefits of Models 77
Selection of a Principle of Choice 78
Normative Models 79
Suboptimization 79
Descriptive Models 80
Good Enough, or Satisficing 81
Developing (Generating) Alternatives 82
Measuring Outcomes 83
Risk 83
Scenarios 84
Possible Scenarios 84
Errors in Decision Making 84
2.6 Decision Making: The Choice Phase 85
2.7 Decision Making: The Implementation Phase 85
2.8 How Decisions Are Supported 86
Support for the Intelligence Phase 86
Support for the Design Phase 87
Support for the Choice Phase 88
Support for the I mplementation Phase 88
2.9 Decision Support Systems: Capabilities 89
A DSS Application 89
2.10 DSS Classifications 91
The AIS SIGDSS Classification for DSS 91
Other DSS Categories 93
Custom-Made Systems Versus Ready-Made Systems 93
2.11 Components of Decision Support Systems 94
The Data Management Subsystem 95
The Model Management Subsystem 95
? APPLICATION CASE 2.2 Station Casinos Wins by Building Customer
Relationships Using Its Data 96
? APPLICATION CASE 2.3 SNAP DSS Helps OneNet Make
Telecommunications Rate Decisions 98
The User Interface Subsystem 98
The Knowledge-Based Management Subsystem 99
APPLICATION CASE 2.4 From a Game Winner to a Doctor! 100
Chapter Highlights 102 • Key Terms 103
Questions for Discussion 103 • Exercises 104
? END-OF-CHAPTER APPLICATION CASE Logistics Optimization in a
Major Shipping Company (CSAV) 104
References 105
Part II Descriptive Analytics 107
Chapter 3 Data Warehousing 108
3.1 Opening Vignette: Isle of Capri Casinos Is Winning with
Enterprise Data Warehouse 109
3.2 Data Warehousing Definitions and Concepts 111
What Is a Data Warehouse? 111
A Historical Perspective to Data Warehousing 111
Characteristics of Data Warehousing 113
Data Marts 114
Operational Data Stores 114
Enterprise Data Warehouses (EDW) 115
Metadata 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 115
3.3 Data Warehousing Process Overview 117
? APPLICATION CASE 3.2 Data Warehousing Helps MultiCare Save
More Lives 118
3.4 Data Warehousing Architectures 120
Alternative Data Warehousing Architectures 123
Which Architecture Is the Best? 126
3.5 Data Integration and the Extraction, Transformation, and
Load (ETL) Processes 127
Data Integration 128
? APPLICATION CASE 3.3 BP Lubricants Achieves BIGS Success 128
Extraction, Transformation, and Load 130
3.6 Data Warehouse Development 132
? APPLICATION CASE 3.4 Things Go Better with Coke's Data
Warehouse 133
Data Warehouse Development Approaches 13 3
? APPLICATION CASE 3.5 Starwood Hotels Resorts Manages Hotel
Profitability with Data Warehousing 136
Additional Data Warehouse Development Considerations 137
Representation of Data in Data Warehouse 138
Analysis of Data in the Data Warehouse 139
OLAP Versus OLTP 140
OLAP Operations 140
3.7 Data Warehousing Implementation Issues 143
? APPLICATION CASE 3.6 EDW Helps Connect State Agencies in
Michigan 145
Massive Data Warehouses and Scalability 146
3.8 Real-Time Data Warehousing 147
? APPLICATION CASE 3.7 Egg Pic Fries the Competition in Near Real
Time 148
3.9 Data Warehouse Administration, Security Issues, and Future
Trends 151
The Future of Data Warehousing 153
3.10 Resources, Links, and the Teradata University Network
Connection 156
Resources and Links 156
Cases 156
Vendors, Products, and Demos 157
Periodicals 157
Additional References 157
The Teradata University Network (TUN) Connection 157
Chapter Highlights 158 • Key Terms 158
Questions for Discussion 158 • Exercises 159
? END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies High
with Its Real-Time Data Warehouse 161
References 162
Chapter 4 Business Reporting, Visual Analytics, and Business
Performance Management 165
4.1 Opening Vignette:Self-Service Reporting Environment
Saves Millions for Corporate Customers 166
4.2 Business Reporting Definitions and Concepts 169
What Is a Business Report? 170
? APPLICATION CASE 4.1 Delta Lloyd Group Ensures Accuracy and
Efficiency in Financial Reporting 171
Components of the Business Reporting System 173
? APPLICATION CASE 4.2 Flood of Paper Ends at FEMA 174
4.3 Data and Information Visualization 175
? APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollars
with Simplified Information Sharing 176
A Brief History of Data Visualization 177
? APPLICATION CASE 4.4 TIBCO Spotfire Provides Dana-Farber Cancer
Institute with Unprecedented Insight into Cancer Vaccine Clinical
Trials 179
4.4 Different Types of Charts and Graphs 180
Basic Charts and Graphs 180
Specialized Charts and Graphs 181
4.5 The Emergence of Data Visualization and Visual
Analytics 184
Visual Analytics 186
High-Powered Visual Analytics Environments 188
4.6 Performance Dashboards 190
? APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau and
Teknion 191
Dashboard Design 192
? APPLICATION CASE 4.6 Saudi Telecom Company Excels with
Information Visualization 193
What to Look For in a Dashboard 194
Best Practices in Dashboard Design 195
Benchmark Key Performance Indicators with Industry Standards 195
Wrap the Dashboard Metrics with Contextual Metadata 195
Validate the Dashboard Design by a Usability Specialist 195
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 195
Enrich Dashboard with Business Users' Comments 195
Present Information in Three Different Levels 196
Pick the Right Visual Construct Using Dashboard Design Principles 196
Provide for Guided Analytics 196
4.7 Business Performance Management 196
Closed-Loop BPM Cycle 197
? APPLICATION CASE 4.7 IBM Cognos Express Helps Mace for Faster
and Better Business Reporting 199
4.8 Performance Measurement 200
Key Performance Indicator (KPI) 201
Performance Measurement System 202
4.9 Balanced Scorecards 202
The Four Perspectives 203
The Meaning of Balance in BSC 204
Dashboards Versus Scorecards 204
4.10 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 4.8 Expedia.com's Customer Satisfaction
Scorecard 208
Chapter Highlights 209 • Key Terms 210
Questions for Discussion 211 • Exercises 211
? END-OF-CHAPTER APPLICATION CASE Smart Business Reporting
Helps Healthcare Providers Deliver Better Care 212
References 214
Part III Predictive Analytics 215
Chapter 5 Data Mining 216
5.1 Opening Vignette: Cabela's Reels in More Customers with
Advanced Analytics and Data Mining 217
5.2 Data Mining Concepts and Applications 219
? APPLICATION CASE 5.1 Smarter Insurance: Infinity P C Improves
Customer Service and Combats Fraud with Predictive Analytics 221
Definitions, Characteristics, and Benefits 222
k APPLICATION CASE 5.2 Harnessing Analytics to Combat Crime:
Predictive Analytics Helps Memphis Police Department Pinpoint Crime
and Focus Police Resources 226
How Data Mining Works 227
Data Mining Versus Statistics 230
5.3 Data Mining Applications 231
k APPLICATION CASE 5.3 A Mine on Terrorist Funding 233
5.4 Data Mining Process 234
Step 1: Business Understanding 235
Step 2: Data Understanding 235
Step 3: Data Preparation 236
Step 4: Model Building 238
» APPLICATION CASE 5.4 Data Mining in Cancer Research 240
Step 5: Testing and Evaluation 241
Step 6: Deployment 241
Other Data Mining Standardized Processes and Methodologies 242
5.5 Data Mining Methods 244
Classification 244
Estimating the True Accuracy of Classification Models 245
Cluster Analysis for Data Mining 250
? APPLICATION CASE 5.5 2degrees Gets a 1275 Percent Boost in Churn
Identification 251
Association Rule Mining 254
5.6 Data Mining Software Tools 258
? APPLICATION CASE 5.6 Data Mining Goes to Hollywood: Predicting
Financial Success of Movies 261
5.7 Data Mining Privacy Issues, Myths, and Blunders 264
Data Mining and Privacy Issues 264
? APPLICATION CASE 5.7 Predicting Customer Buying Patterns—The
Target Story 265
Data Mining Myths and Blunders 266
Chapter Highlights 267 • Key Terms 268
Questions for Discussion 268 • Exercises 269
? END-OF-CHAPTER APPLICATION CASE Macys.com Enhances Its
Customers' Shopping Experience with Analytics 271
References 271
Chapter 6 Techniques for Predictive Modeling 273
6.1 Opening Vignette: Predictive Modeling Helps Better
Understand and Manage Complex Medical
Procedures 274
6.2 Basic Concepts of Neural Networks 277
Biological and Artificial Neural Networks 278
? APPLICATION CASE 6.1 Neural Networks Are Helping to Save Lives in
the Mining Industry 280
Elements of ANN 281
Network Information Processing 282
Neural Network Architectures 284
? APPLICATION CASE 6.2 Predictive Modeling Is Powering the Power
Generators 286
6.3 Developing Neural Network-Based Systems 288
The General ANN Learning Process 289
Backpropagation 290
6.4 Illuminating the Black Box of ANN with Sensitivity
Analysis 292
? APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury Severity
Factors in Traffic Accidents 294
6.5 Support Vector Machines 295
? APPLICATION CASE 6.4 Managing Student Retention with Predictive
Modeling 296
Mathematical Formulation of SVMs 300
Primal Form 301
Dual Form 301
Soft Margin 301
Nonlinear Classification 302
Kernel Trick 302
6.6 A Process-Based Approach to the Use of SVM 303
Support Vector Machines Versus Artificial Neural Networks 304
6.7 Nearest Neighbor Method for Prediction 305
Similarity Measure: The Distance Metric 306
Parameter Selection 307
? APPLICATION CASE 6.5 Efficient Image Recognition and
Categorization with fcNN 308
Chapter Highlights 310 • Key Terms 310
Questions for Discussion 311 • Exercises 311
? END-OF-CHAPTER APPLICATION CASE Coors Improves Beer Flavors
with Neural Networks 314
References 315
Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis 318
7.1 Opening Vignette: Machine Versus Men on Jeopardy!: The
Story of Watson 319
7.2 Text Analytics and Text Mining Concepts and
Definitions 321
? APPLICATION CASE 7.1 Text Mining for Patent Analysis 325
7.3 Natural Language Processing 326
? APPLICATION CASE 7.2 Text Mining Improves Hong Kong
Government's Ability to Anticipate and Address Public Complaints 328
7.4 Text Mining Applications 330
Marketing Applications 331
Security Applications 331
? APPLICATION CASE 7.3 Mining for Lies 332
Biomedical Applications 334
Academic Applications 335
? APPLICATION CASE 7.4 Text Mining and Sentiment Analysis Help
Improve Customer Service Performance 336
7.5 Text Mining Process 337
Task 1: Establish the Corpus 338
Task 2: Create the Term-Document Matrix 339
Task 3: Extract the Knowledge 342
? APPLICATION CASE 7.5 Research Literature Survey with Text
Mining 344
7.6 Text Mining Tools 347
Commercial Software Tools 347
Free Software Tools 347
? APPLICATION CASE 7.6 A Potpourri of Text Mining Case Synopses 348
7.7 Sentiment Analysis Overview 349
? APPLICATION CASE 7.7 Whirlpool Achieves Customer Loyalty and
Product Success with Text Analytics 351
7.8 Sentiment Analysis Applications 353
7.9 Sentiment Analysis Process 355
Methods for Polarity Identification 356
Using a Lexicon 357
Using a Collection of Training Documents 358
Identifying Semantic Orientation of Sentences and Phrases 358
Identifying Semantic Orientation of Document 358
7.10 Sentiment Analysis and Speech Analytics 359
How Is It Done? 359
? APPLICATION CASE 7.8 Cutting Through the Confusion: Blue Cross
Blue Shield of North Carolina Uses Nexidia's Speech Analytics to Ease
Member Experience in Healthcare 361
Chapter Highlights 363 • Key Terms 363
Questions for Discussion 364 • Exercises 364
? END-OF-CHAPTER APPLICATION CASE BBVA Seamlessly Monitors
and Improves Its Online Reputation 365
References 366
Chapter 8 Web Analytics, Web Mining, and Social Analytics 368
8.1 Opening Vignette: Security First Insurance Deepens
Connection with Policyholders 369
8.2 Web Mining Overview 371
8.3 Web Content and Web Structure Mining 374
? APPLICATION CASE 8.1 Identifying Extremist Groups with Web Link
and Content Analysis 376
8.4 Search Engines 377
Anatomy of a Search Engine 377
1. Development Cycle 378
Web Crawler 378
Document Indexer 378
2. Response Cycle 379
Query Analyzer 379
Document Matcher/Ranker 379
How Does Google Do It? 381
? APPLICATION CASE 8.2 IGN Increases Search Traffic by 1500 Percent 383
8.5 Search Engine Optimization 384
Methods for Search Engine Optimization 385
? APPLICATION CASE 8.3 Understanding Why Customers Abandon
Shopping Carts Results in $10 Million Sales Increase 387
8.6 Web Usage Mining (Web Analytics) 388
Web Analytics Technologies 389
? APPLICATION CASE 8.4 Allegro Boosts Online Click-Through Rates by
500 Percent with Web Analysis 390
Web Analytics Metrics 392
Web Site Usability 392
Traffic Sources 393
Visitor Profiles 394
Conversion Statistics 394
8.7 Web Analytics Maturity Model and Web Analytics Tools 396
Web Analytics Tools 398
Putting It All Together—A Web Site Optimization Ecosystem 400
A Framework for Voice of the Customer Strategy 402
8.8 Social Analytics and Social Network Analysis 403
Social Network Analysis 404
Social Network Analysis Metrics 405
? APPLICATION CASE 8.5 Social Network Analysis Helps
Telecommunication Firms 405
Connections 406
Distributions 406
Segmentation 407
8.9 Social Media Definitions and Concepts 407
How Do People Use Social Media? 408
? APPLICATION CASE 8.6 Measuring the impact of Social Media at
Lollapalooza 409
8.10 Social Media Analytics 410
Measuring the Social Media Impact 411
Best Practices in Social Media Analytics 411
? APPLICATION CASE 8.7 eHarmony Uses Social Media to Help Take the
Mystery Out of Online Dating 413
Social Media Analytics Tools and Vendors 414
Chapter Highlights 416 • Key Terms 417
Questions for Discussion 417 • Exercises 418
? END-OF-CHAPTER APPLICATION CASE Keeping Students on Track with
Web and Predictive Analytics 418
References 420
Part IV Prescriptive Analytics 421
Chapter 9 Model-Based Decision Making: Optimization and
Multi-Criteria Systems 422
9.1 Opening Vignette: Midwest ISO Saves Billions by Better
Planning of Power Plant Operations and Capacity
Planning 423
9.2 Decision Support Systems Modeling 424
» APPLICATION CASE 9.1 Optimal Transport for ExxonMobil
Downstream Through a DSS 425
Current Modeling Issues 426
? APPLICATION CASE 9.2 Forecasting/Predictive Analytics Proves to Be
a Good Gamble for Harrah's Cherokee Casino and Hotel 427
9.3 Structure of Mathematical Models for Decision Support 429
The Components of Decision Support Mathematical Models 429
The Structure of Mathematical Models 431
9.4 Certainty, Uncertainty, and Risk 431
Decision Making Under Certainty 432
Decision Making Under Uncertainty 432
Decision Making Under Risk (Risk Analysis) 432
? APPLICATION CASE 9.3 American Airlines Uses
Should-Cost Modeling to Assess the Uncertainty of Bids
for Shipment Routes 433
9.5 Decision Modeling with Spreadsheets 434
? APPLICATION CASE 9.4 Showcase Scheduling at Fred Astaire East
Side Dance Studio 434
9.6 Mathematical Programming Optimization 437
? APPLICATION CASE 9.5 Spreadsheet Model Helps Assign Medical
Residents 437
Mathematical Programming 438
Linear Programming 438
Modeling in LP: An Example 439
Implementation 444
9.7 Multiple Goals, Sensitivity Analysis, What-lf Analysis,
and Goal Seeking 446
Multiple Goals 446
Sensitivity Analysis 447
What-lf Analysis 448
Goal Seeking 448
9.8 Decision Analysis with Decision Tables and Decision
Trees 450
Decision Tables 450
Decision Trees 452
9.9 Multi-Criteria Decision Making With Pairwise
Comparisons 453
The Analytic Hierarchy Process 453
? APPLICATION CASE 9.6 U.S. HUD Saves the House by Using
AHP for Selecting IT Projects 453
Tutorial on Applying Analytic Hierarchy Process Using Web-HIPRE 455
Chapter Highlights 459 • Key Terms 460
Questions for Discussion 460 • Exercises 460
? END-OF CHAPTER APPLICATION CASE Pre-Positioning of Emergency
Items for CARE International 463
References 464
Chapter 10 Modeling and Analysis: Heuristic Search Methods and
Simulation 465
10.1 Opening Vignette: System Dynamics Allows Fluor
Corporation to Better Plan for Project and Change
Management 466
10.2 Problem-Solving Search Methods 467
Analytical Techniques 468
Algorithms 468
Blind Searching 469
Heuristic Searching 469
? APPLICATION CASE 10.1 Chilean Government Uses Heuristics to
Make Decisions on School Lunch Providers 469
10.3 Genetic Algorithms and Developing GA Applications 471
Example: The Vector Game 471
Terminology of Genetic Algorithms 473
How Do Genetic Algorithms Work? 473
Limitations of Genetic Algorithms 475
Genetic Algorithm Applications 475
10.4 Simulation 476
? APPLICATION CASE 10.2 Improving Maintenance Decision Making in
the Finnish Air Force Through Simulation 476
? APPLICATION CASE 10.3 Simulating Effects of Hepatitis B
Interventions 477
Major Characteristics of Simulation 478
Advantages of Simulation 479
Disadvantages of Simulation 480
The Methodology of Simulation 480
Simulation Types 481
Monte Carlo Simulation 482
Discrete Event Simulation 483
10.5 Visual Interactive Simulation 483
Conventional Simulation Inadequacies 483
Visual Interactive Simulation 483
Visual Interactive Models and DSS 484
? APPLICATION CASE 10.4 Improving Job-Shop Scheduling Decisions
Through RFID: A Simulation-Based Assessment 484
Simulation Software 487
10.6 System Dynamics Modeling 488
10.7 Agent-Based Modeling 491
? APPLICATION CASE 10.5 Agent-Based Simulation Helps Analyze
Spread of a Pandemic Outbreak 493
Chapter Highlights 494 • Key Terms 494
Questions for Discussion 495 • Exercises 495
? END-OF-CHAPTER APPLICATION CASE HP Applies Management
Science Modeling to Optimize Its Supply Chain and Wins a Major
Award 495
References 497
Chapter 11 Automated Decision Systems and Expert Systems 499
11.1 Opening Vignette: Intercontinental Hotel Group Uses
Decision Rules for Optimal Hotel Room Rates 500
11.2 Automated Decision Systems 501
? APPLICATION CASE 11.1 Giant Food Stores Prices the Entire
Store 502
11.3 The Artificial Intelligence Field 505
11.4 Basic Concepts of Expert Systems 507
Experts 507
Expertise 508
Features of ES 508
? APPLICATION CASE 11.2 Expert System Helps in Identifying Sport
Talents 510
11.5 Applications of Expert Systems 510
? APPLICATION CASE 11.3 Expert System Aids in Identification of
Chemical, Biological, and Radiological Agents 511
Classical Applications of ES 511
Newer Applications of ES 512
Areas for ES Applications 513
11.6 Structure of Expert Systems 514
Knowledge Acquisition Subsystem 514
Knowledge Base 515
Inference Engine 515
User Interface 515
Blackboard (Workplace) 515
Explanation Subsystem (Justifier) 516
Knowledge-Refining System 516
? APPLICATION CASE 11.4 Diagnosing Heart Diseases by Signal
Processing 516
11.7 Knowledge Engineering 517
Knowledge Acquisition 518
Knowledge Verification and Validation 520
Knowledge Representation 520
Inferencing 521
Explanation and Justification 526
11.8 Problem Areas Suitable for Expert Systems 527
11.9 Development of Expert Systems 528
Defining the Nature and Scope of the Problem 529
Identifying Proper Experts 529
Acquiring Knowledge 529
Selecting the Building Tools 529
Coding the System 531
Evaluating the System 531
? APPLICATION CASE 11.5 Clinical Decision Support System for Tendon
Injuries 531
11.10 Concluding Remarks 532
Chapter Highlights 533 • Key Terms 533
Questions for Discussion 534 • Exercises 534
? END-OF CHAPTER APPLICATION CASE Tax Collections Optimization
for New York State 534
References 535
Chapter 12 Knowledge Management and Collaborative Systems 537
12.1 Opening Vignette: Expertise Transfer System to Train
Future Army Personnel 538
12.2 Introduction to Knowledge Management 542
Knowledge Management Concepts and Definitions 543
Knowledge 543
Explicit and Tacit Knowledge 545
12.3 Approaches to Knowledge Management 546
The Process Approach to Knowledge Management 547
The Practice Approach to Knowledge Management 547
Hybrid Approaches to Knowledge Management 548
Knowledge Repositories 548
12.4 Information Technology (IT) in Knowledge
Management 550
The KMS Cycle 550
Components of KMS 551
Technologies That Support Knowledge Management 551
12.5 Making Decisions in Groups: Characteristics, Process,
Benefits, and Dysfunctions 553
Characteristics of Groupwork 553
The Group Decision-Making Process 554
The Benefits and Limitations of Groupwork 554
12.6 Supporting Groupwork with Computerized Systems 556
An Overview of Group Support Systems (GSS) 556
Groupware 557
Time/Place Framework 557
12.7 Tools for Indirect Support of Decision Making 558
Groupware Tools 558
Groupware 560
Collaborative Workflow 560
Web 2.0 560
Wikis 561
Collaborative Networks 561
12.8 Direct Computerized Support for Decision Making:
From Group Decision Support Systems to Group Support
Systems 562
Group Decision Support Systems (GDSS) 562
Group Support Systems 563
How GDSS (or GSS) Improve Groupwork 563
Facilities for GDSS 564
Chapter Highlights 565 • Key Terms 566
Questions for Discussion 566 • Exercises 566
? END-OF-CHAPTER APPLICATION CASE Solving Crimes by Sharing
Digital Forensic Knowledge 567
References 569
Part V Big Data and Future Directions for Business
Analytics 571
Chapter 13 Big Data and Analytics 572
13.1 Opening Vignette: Big Data Meets Big Science at CERN 573
13.2 Definition of Big Data 576
The Vs That Define Big Data 577
» APPLICATION CASE 13.1 Big Data Analytics Helps Luxottica Improve
Its Marketing Effectiveness 580
13.3 Fundamentals of Big Data Analytics 581
Business Problems Addressed by Big Data Analytics 584
? APPLICATION CASE 13.2 Top 5 Investment Bank Achieves Single
Source of Truth 585
13.4 Big Data Technologies 586
MapReduce 587
Why Use MapReduce? 588
Hadoop 588
How Does Hadoop Work? 588
Hadoop Technical Components 589
Hadoop: The Pros and Cons 590
NoSQL 592
? APPLICATION CASE 13.3 eBay's Big Data Solution 593
13.5 Data Scientist 595
Where Do Data Scientists Come From? 595
? APPLICATION CASE 13.4 Big Data and Analytics in Politics 598
13.6 Big Data and Data Warehousing 599
Use Case(s) for Hadoop 600
Use Case(s) for Data Warehousing 601
The Gray Areas (Any One of the Two Would Do the Job) 602
Coexistence of Hadoop and Data Warehouse 602
13.7 Big Data Vendors 604
? APPLICATION CASE 13.5 Dublin City Council Is Leveraging Big Data
to Reduce Traffic Congestion 605
? APPLICATION CASE 13.6 Creditreform Boosts Credit Rating Quality
with Big Data Visual Analytics 610
13.8 Big Data and Stream Analytics 611
Stream Analytics Versus Perpetual Analytics 612
Critical Event Processing 612
Data Stream Mining 613
13.9 Applications of Stream Analytics 614
e-Commerce 614
Telecommunications 614
? APPLICATION CASE 13.7 Turning Machine-Generated Streaming Data
into Valuable Business Insights 615
Law Enforcement and Cyber Security 616
Power Industry 617
Financial Sen/ices 617
Health Sciences 617
Government 617
Chapter Highlights 618 • Key Terms 618
Questions for Discussion 618 • Exercises 619
? END-OF-CHAPTER APPLICATION CASE Discovery Health Turns Big
Data into Better Healthcare 619
References 621
Chapter 14 Business Analytics: Emerging Trends and Future
Impacts 622
14.1 Opening Vignette: Oklahoma Gas and Electric Employs
Analytics to Promote Smart Energy Use 623
14.2 Location-Based Analytics for Organizations 624
Geospatial Analytics 624
? APPLICATION CASE 14.1 Great Clips Employs Spatial Analytics to
Shave Time in Location Decisions 626
A Multimedia Exercise in Analytics Employing Geospatial Analytics 627
Real-Time Location Intelligence 628
? APPLICATION CASE 14.2 Quiznos Targets Customers for Its
Sandwiches 629
14.3 Analytics Applications for Consumers 630
? APPLICATION CASE 14.3 A Life Coach in Your Pocket 631
14.4 Recommendation Engines 633
14.5 Web 2.0 and Online Social Networking 634
Representative Characteristics of Web 2.0 635
Social Networking 635
A Definition and Basic Information 636
Implications of Business and Enterprise Social Networks 636
14.6 Cloud Computing and Bl 637
Service-Oriented DSS 638
Data-as-a-Service (DaaS) 638
Information-as-a-Service (Information on Demand) (laaS) 641
Analytics-as-a-Service (AaaS) 641
14.7 Impacts of Analytics in Organizations: An Overview 643
New Organizational Units 643
Restructuring Business Processes and Virtual Teams 644
The Impacts of ADS Systems 644
Job Satisfaction 644
Job Stress and Anxiety 644
Analytics' Impact on Managers' Activities and Their Performance 645
14.8 Issues of Legality, Privacy, and Ethics 646
Legal Issues 646
Privacy 647
Recent Technology Issues in Privacy and Analytics 648
Ethics in Decision Making and Support 649
14.9 An Overview of the Analytics Ecosystem 650
Analytics Industry Clusters 650
Data Infrastructure Providers 650
Data Warehouse Industry 651
Middleware Industry 652
Data Aggregators/Distributors 652
Analytics-Focused Software Developers 652
Reporting/Analytics 652
Predictive Analytics 653
Prescriptive Analytics 653
Application Developers or System Integrators: Industry Specific or General 654
Analytics User Organizations 655
Analytics Industry Analysts and Influences 657
Academic Providers and Certification Agencies 658
Chapter Highlights 659 • Key Terms 659
Questions for Discussion 659 • Exercises 660
? END-OF-CHAPTER APPLICATION CASE Southern States Cooperative
Optimizes Its Catalog Campaign 660
References 662
Glossary 664
Index 678 |
| any_adam_object | 1 |
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| dewey-full | 658.4030285 |
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| dewey-ones | 658 - General management |
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| dewey-sort | 3658.4030285 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | tenth edition, global edition |
| format | Book |
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| id | DE-604.BV041879142 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T04:06:01Z |
| institution | BVB |
| isbn | 9781292009209 1292009209 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027323304 |
| oclc_num | 881753845 |
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| physical | 686 Seiten Illustrationen, Diagramme |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Pearson |
| record_format | marc |
| series2 | Always learning |
| spelling | Sharda, Ramesh Verfasser (DE-588)132667118 aut Business intelligence and analytics systems for decision support Ramesh Sharda [und 5 weitere] tenth edition, global edition Boston Pearson [2014] © 2014 686 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Always learning Business Intelligence (DE-588)4588307-5 gnd rswk-swf Entscheidungsverfahren (DE-588)4637781-5 gnd rswk-swf Business Intelligence (DE-588)4588307-5 s Entscheidungsverfahren (DE-588)4637781-5 s b DE-604 Fortsetzung von Decision support and business intelligence systems / Efraim Turban (DE-604)BV041878755 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027323304&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sharda, Ramesh Business intelligence and analytics systems for decision support Business Intelligence (DE-588)4588307-5 gnd Entscheidungsverfahren (DE-588)4637781-5 gnd |
| subject_GND | (DE-588)4588307-5 (DE-588)4637781-5 |
| title | Business intelligence and analytics systems for decision support |
| title_auth | Business intelligence and analytics systems for decision support |
| title_exact_search | Business intelligence and analytics systems for decision support |
| title_full | Business intelligence and analytics systems for decision support Ramesh Sharda [und 5 weitere] |
| title_fullStr | Business intelligence and analytics systems for decision support Ramesh Sharda [und 5 weitere] |
| title_full_unstemmed | Business intelligence and analytics systems for decision support Ramesh Sharda [und 5 weitere] |
| title_old | Decision support and business intelligence systems / Efraim Turban |
| title_short | Business intelligence and analytics |
| title_sort | business intelligence and analytics systems for decision support |
| title_sub | systems for decision support |
| topic | Business Intelligence (DE-588)4588307-5 gnd Entscheidungsverfahren (DE-588)4637781-5 gnd |
| topic_facet | Business Intelligence Entscheidungsverfahren |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027323304&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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