Business intelligence: a managerial perspective on analytics
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston, Mass.
Pearson
2014
|
| Edition: | 3. ed., global ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | 410 S. graph. Darst. |
| ISBN: | 9781292004877 9780133051056 0133051056 |
Staff View
MARC
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| 245 | 1 | 0 | |a Business intelligence |b a managerial perspective on analytics |c Ramesh Sharda ; Dursun Delen ; Efraim Turban |
| 250 | |a 3. ed., global ed. | ||
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Record in the Search Index
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|---|---|
| adam_text | BRIEF CONTENTS
Preface 17
About the Authors
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Glossary 393
Index 401
23
An Overview of Business Intelligence, Analytics,
and Decision Support 27
Data Warehousing 61
Business Reporting, Visual Analytics, and Business
Performance Management 119
Data Mining 169
Text and Web Analytics 225
Big Data and Analytics 299
Business Analytics: Emerging Trends
and Future Impacts 351
CONTENTS
Preface 17
About the Authors 23
Chapter 1 An Overview of Business Intelligence,
Analytics, and Decision Support 27
1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage
a Vaccine Supply Chain Effectively and Safely 28
1.2 Changing Business Environments and Computerized
Decision Support 30
The Business Pressures-Responses-Support Model 30
1.3 A Framework for Business Intelligence (Bl) 32
Definitions of Bl 32
A Brief History of Bl 33
The Architecture of Bl 34
The Origins and Drivers of Bl 34
► APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboards
and Analytics 35
A Multimedia Exercise in Business Intelligence 36
1.4 Intelligence Creation, Use, and Bl Governance 37
A Cyclical Process of Intelligence Creation and Use 37
Intelligence and Espionage 38
1.5 Transaction Processing Versus Analytic Processing 39
1.6 Successful Bl Implementation 40
The Typical Bl User Community 40
Appropriate Planning and Alignment with the Business Strategy 40
Real-Time, On-Demand Bl Is Attainable 41
Developing or Acquiring Bl Systems 42
Justification and Cost-Benefit Analysis 42
Security and Protection of Privacy 42
Integration of Systems and Applications 42
1.7 Analytics Overview 43
Descriptive Analytics 44
Predictive Analytics 44
► APPLICATION CASE 1.2 Eliminating Inefficiencies at Seattle
Children s Hospital 45
► APPLICATION CASE 1.3 Analysis at the Speed of Thought 46
Prescriptive Analytics 46
► APPLICATION CASE 1.4 Moneyball: Analytics in Sports
and Movies 47
► APPLICATION CASE 1.5 Analyzing Athletic Injuries 48
Analytics Applied to Different Domains 48
8 Contents
► APPLICATION CASE 1.6 Industrial and Commercial Bank of China
(ICBC) Employs Models to Reconfigure Its Branch Network 49
Analytics or Data Science? 50
1.8 Brief Introduction to Big Data Analytics 51
What Is Big Data? 51
► APPLICATION CASE 1.7 Gilt Groupe s Flash Sales Streamlined by
Big Data Analytics 52
1.9 Plan of the Book 53
1.10 Resources, Links, and the Teradata University Network
Connection 55
Resources and Links 55
Vendors, Products, and Demos 55
Periodicals 55
The Teradata University Network Connection 55
The Book s Web Site 55
Key Terms 56
Questions for Discussion 56
Exercises 56
End-of-Chapter Application Case 57
References 59
Chapter 2 Data Warehousing 61
2.1 Opening Vignette: Isle of Capri Casinos Is Winning with
Enterprise Data Warehouse 62
2.2 Data Warehousing Definitions and Concepts 64
What Is a Data Warehouse? 64
A Historical Perspective to Data Warehousing 64
Characteristics of Data Warehousing 66
Data Marts 67
Operational Data Stores 67
Enterprise Data Warehouses (EDW) 68
► APPLICATION CASE 2.1 A Better Data Plan: Well-Established TELCOs
Leverage Data Warehousing and Analytics to Stay on Top in a
Competitive Industry 68
Metadata 69
2.3 Data Warehousing Process Overview 70
► APPLICATION CASE 2.2 Data Warehousing Helps MultiCare Save
More Lives 71
2.4 Data Warehousing Architectures 73
Alternative Data Warehousing Architectures 76
Which Architecture Is the Best? 79
2.5 Data Integration and the Extraction, Transformation, and Load
(ETL) Processes 80
Data Integration 81
► APPLICATION CASE 2.3 BP Lubricants Achieves BIGS Success 81
Extraction, Transformation, and Load 83
Contents 9
2.6 Data Warehouse Development 85
► APPLICATION CASE 2.4 Things Go Better with Coke s
Data Warehouse 86
Data Warehouse Development Approaches 88
► APPLICATION CASE 2.5 Starwood Hotels 8i Resorts Manages Hotel
Profitability with Data Warehousing 89
Additional Data Warehouse Development Considerations 91
Representation of Data in Data Warehouse 92
Analysis of Data in Data Warehouse 93
OLAP Versus OLTP 93
OLAP Operations 94
2.7 Data Warehousing Implementation Issues 97
► APPLICATION CASE 2.6 EDW Helps Connect State Agencies in
Michigan 99
Massive Data Warehouses and Scalability 100
2.8 Real-Time Data Warehousing 101
► APPLICATION CASE 2.7 Egg Pic Fries the Competition
in Near Real Time 102
2.9 Data Warehouse Administration, Security Issues,
and Future Trends 105
The Future of Data Warehousing 107
2.10 Resources, Links, and theTeradata University Network
Connection 110
Resources and Links 110
Cases 110
Vendors, Products, and Demos 111
Periodicals 111
Additional References 111
The Teradata University Network (TUN) Connection 111
Key Terms 112
Questions for Discussion 112
Exercises 113
End-of-Chapter Application Case 114
References 116
Chapter 3 Business Reporting, Visual Analytics, and Business
Performance Management 119
3.1 Opening Vignette: Self-Service Reporting Environment Saves
Millions for Corporate Customers 120
3.2 Business Reporting Definitions and Concepts 123
What Is a Business Report? 124
► APPLICATION CASE 3.1 Delta Lloyd Group Ensures Accuracy
and Efficiency in Financial Reporting 126
Components of Business Reporting Systems 127
► APPLICATION CASE 3.2 Flood of Paper
EndsatFEMA 128
3.3 Data and Information Visualization 129
► APPLICATION CASE 3.3 Tableau Saves Blastrac Thousands of Dollars
with Simplified Information Sharing 130
A Brief History of Data Visualization 131
► APPLICATION CASE 3.4 TIBCO Spotfire Provides Dana-Farber Cancer
Institute with Unprecedented Insight into Cancer Vaccine Clinical
Trials 133
3.4 Different Types of Charts and Graphs 134
Basic Charts and Graphs 134
Spedalized Charts and Graphs 135
3.5 The Emergence of Data Visualization and Visual Analytics 138
Visual Analytics 140
High-Powered Visual Analytics Environments 140
3.6 Performance Dashboards 143
Dashboard Design 145
► APPLICATION CASE 3.5 Dallas Cowboys Score Big with Tableau
and Teknion 145
► APPLICATION CASE 3.6 Saudi Telecom Company Excels with
Information Visualization 146
What to Look For in a Dashboard 148
Best Practices in Dashboard Design 148
Benchmark Key Performance Indicators with Industry Standards 149
Wrap the Dashboard Metrics with Contextual Metadata 149
Validate the Dashboard Design by a Usability Specialist 149
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 149
Enrich Dashboard with Business-User Comments 149
Present Information in Three Different Levels 149
Pick the Right Visual Construct Using Dashboard Design Principles 150
Provide for Guided Analytics 150
3.7 Business Performance Management 150
Closed-Loop BPM Cycle 150
► APPLICATION CASE 3.7 IBM Cognos Express Helps Mace for Faster
and Better Business Reporting 153
3.8 Performance Measurement 154
Key Performance Indicator (KPI) 154
Performance Measurement System 155
3.9 Balanced Scorecards 156
The Four Perspectives 156
The Meaning of Balance in BSC 158
Dashboards Versus Scorecards 159
3.10 Six Sigma as a Performance Measurement System 159
The DMAIC Performance Model 160
Balanced Scorecard Versus Six Sigma 160
Effective Performance Measurement 160
Contents 11
► APPLICATION CASE 3.8 Expedia.com s Customer Satisfaction
Scorecard 162
Key Terms 164
Questions for Discussion 165
Exercises 165
End-of-Chapter Application Case 166
References 168
Chapter 4 Data Mining 169
4.1 Opening Vignette: Cabela s Reels in More Customers with
Advanced Analytics and Data Mining 170
4.2 Data Mining Concepts and Applications 172
Definitions, Characteristics, and Benefits 173
► APPLICATION CASE 4.1 Smarter Insurance: Infinity P C Improves
Customer Service and Combats Fraud with Predictive
Analytics 174
How Data Mining Works 179
► APPLICATION CASE 4.2 Harnessing Analytics to Combat Crime:
Predictive Analytics Helps Memphis Police Department Pinpoint
Crime and Focus Police Resources 179
Data Mining Versus Statistics 183
4.3 Data Mining Applications 183
► APPLICATION CASE 4.3 A Mine on Terrorist Funding 186
4.4 Data Mining Process 187
Step 1: Business Understanding 187
Step 2: Data Understanding 188
Step 3: Data Preparation 188
Step 4: Model Building 190
Step 5: Testing and Evaluation 192
Step 6: Deployment 192
► APPLICATION CASE 4.4 Data Mining in Cancer Research 193
Other Data Mining Standardized Processes and Methodologies 194
4.5 Data Mining Methods 196
Classification 196
Estimating the True Accuracy of Classification Models 197
► APPLICATION CASE 4.5 2degrees Gets a 1275 Percent Boost in
Churn Identification 203
Cluster Analysis for Data Mining 204
Association Rule Mining 206
4.6 Data Mining Software Tools 210
► APPLICATION CASE 4.6 Data Mining Goes to Hollywood: Predicting
Financial Success of Movies 213
4.7 Data Mining Privacy Issues, Myths, and Blunders 216
Data Mining and Privacy Issues 216
► APPLICATION CASE 4.7 Predicting Customer Buying Patterns—
The Target Story 217
Data Mining Myths and Blunders 218
Key Terms 220
Questions for Discussion 220
Exercises 221
End-of-Chapter Application Case 223
References 223
Chapter 5 Text and Web Analytics 225
5.1 Opening Vignette: Machine Versus Men on Jeopardy!: The Story
of Watson 226
5.2 Text Analytics and Text Mining Overview 229
► APPLICATION CASE 5.1 Text Mining for Patent Analysis 232
5.3 Natural Language Processing 233
► APPLICATION CASE 5.2 Text Mining Improves Hong Kong
Government s Ability to Anticipate and Address Public
Complaints 235
5.4 Text Mining Applications 237
Marketing Applications 238
Security Applications 238
► APPLICATION case 5.3 Mining for Lies 239
Biomedical Applications 241
Academic Applications 242
► application case 5.4 Text mining and Sentiment Analysis Help
Improve Customer Service Performance 243
5.5 Text Mining Process 244
Task 1: Establish the Corpus 245
Task 2: Create the Term-Document Matrix 246
Task 3: Extract the Knowledge 248
► APPLICATION CASE 5.5 Research Literature Survey with Text
Mining 250
5.6 Sentiment Analysis 253
► APPLICATION CASE 5.6 Whirlpool Achieves Customer Loyalty and
Product Success with Text Analytics 255
Sentiment Analysis Applications 256
Sentiment Analysis Process 2 58
Methods for Polarity Identification 259
Using a Lexicon 260
Using a Collection of Training Documents 261
Identifying Semantic Orientation of Sentences and Phrases 261
Identifying Semantic Orientation of Document 261
5.7 Web Mining Overview 262
Web Content and Web Structure Mining 264
5.8 Search Engines 267
Anatomy of a Search Engine 267
Development Cycle 267
Contents 13
Web Crawler 268
Document Indexer 268
Response Cycle 269
Query Analyzer 269
Document Matcher/Ranker 269
Search Engine Optimization 270
Methods for Search Engine Optimization 2 71
► APPLICATION CASE 5,7 Understanding Why Customers Abandon
Shopping Carts Results in a $10 Million Sales Increase 272
5.9 Web Usage Mining (Web Analytics) 274
Web Analytic Technologies 274
► APPLICATION CASE 5.8 Allegro Boosts Online Click-Through Rates
by 500 Percent with Web Analysis 275
Web Analytics Metrics 277
Web Site Usability 277
Traffic Sources 278
Visitor Profiles 229
Conversion Statistics 280
5.10 Social Analytics 281
Social Network Analysis 282
Social Network Analysis Metrics 283
► APPLICATION CASE 5.9 Social Network Analysis Helps
Telecommunication Firms 283
Connections 284
Distributions 285
Segmentation 285
Social Media Analytics 285
How Do People Use Social Media? 286
► APPLICATION CASE 5.10 Measuring the impact of Social Media at
Lollapalooza 287
Measuring the Social Media Impact 288
Best Practices in Social Media Analytics 289
► APPLICATION CASE 5.11 eHarmony Uses Social Media to Help Take
the Mystery Out of Online Dating 290
Key Terms 293
Questions for Discussion 293
Exercises 293
End-of-Chapter Application Case 294
References 296
Chapter 6 Big Data and Analytics 299
6.1 Opening Vignette: Big Data Meets Big Science at CERN 300
6.2 Definition of Big Data 303
The Vs That Define Big Data 304
► APPLICATION CASE 6.1 BigData Analytics Helps Luxottica
Improvement its Marketing Effectiveness 307
6.3 Fundamentals of Big Data Analytics 308
Business Problems Addressed by Big Data Analytics 311
► APPLICATION CASE 6.2 Top 5 Investment Bank Achieves Single
Source of the Truth 312
6.4 Big Data Technologies 313
MapReduce 313
Why Use MapReduce? 315
Hadoop 315
How Does Hadoop Work? 315
Hadoop Technical Components 316
Hadoop: The Pros and Cons 317
NoSQL 319
► APPLICATION CASE 6.3 eBay s Big Data Solution 320
6.5 Data Scientist 321
Where Do Data Scientists Come From? 322
► APPLICATION CASE 6.4 Big Data and Analytics in Politics 325
6.6 Big Data and Data Warehousing 326
Use Cases for Hadoop 327
Use Cases for Data Warehousing 328
The Gray Areas (Any One of the Two Would Do the Job) 329
Coexistence of Hadoop and Data Warehouse 329
6.7 Big Data Vendors 331
► APPLICATION CASE 6.5 Dublin City Council Is Leveraging
Big Data to Reduce Traffic Congestion 333
► APPLICATION CASE 6.6 Creditreform Boosts
Credit Rating Quality with Big Data
Visual Analytics 337
6.8 Big Data And Stream Analytics 338
Stream Analytics Versus Perpetual Analytics 339
Critical Event Processing 340
Data Stream Mining 341
6.9 Applications of Stream Analytics 341
e-Commerce 341
Telecommunications 342
► APPLICATION CASE 6.7
Turning Machine-Generated Streaming
Data into Valuable Business Insights 342
Law Enforcement and Cyber Security 344
Power Industry 344
Financial Services 344
Health Sciences 344
Government 345
Key Terms 346
Questions for Discussion 346
Contents 15
Exercises 346
End-of-Chapter Application Case 347
References 348
Chapter 7 Business Analytics: Emerging Trends and Future Impacts 351
7.1 Opening Vignette: Oklahoma Gas and Electric Employs Analytics
to Promote Smart Energy Use 352
7.2 Location-Based Analytics for Organizations 353
Geospatial Analytics 353
► APPLICATION CASE 7.1 Great Clips Employs Spatial Analytics to
Shave Time in Location Decisions 355
Real-Time Location Intelligence 357
► APPLICATION CASE 7.2 Quiznos Targets Customers for its
Sandwiches 358
7.3 Analytics Applications for Consumers 359
► APPLICATION CASE 7.3 A Life Coach in Your Pocket 360
7.4 Recommendation Engines 362
7.5 The Web 2.0 Revolution and Online Social Networking 363
Representative Characteristics of Web 2.0 364
Social Networking 364
A Definition and Basic Information 365
Implications of Business and Enterprise Social Networks 365
7.6 Cloud Computing and Bl 366
Service-Oriented DSS 367
Data-as-a-Service (DaaS) 369
Information-as-a-Service (Information on Demand) (laaS) 370
Analytics-as-a-Service (AaaS) 371
7.7 Impacts of Analytics In Organizations: An Overview 372
New Organizational Units 373
Restructuring Business Processes and Virtual Teams 373
Job Satisfaction 374
Job Stress and Anxiety 374
Analytics Impact on Managers Activities and Their Performance 374
7.8 Issues of Legality, Privacy, and Ethics 376
Legal Issues 376
Privacy 376
Recent Technology Issues in Privacy and Analytics 376
Ethics in Decision Making and Support 379
7.9 An Overview of the Analytics Ecosystem 379
Analytics Industry Clusters 380
Data Infrastructure Providers 380
Data Warehouse Industry 381
Middleware/BI Platform Industry 381
16 Contents
Data Aggregators/Distributors 382
Analytics-Focused Software Developers 382
Reporting/Analytics 382
Predictive Analytics 382
Prescriptive Analytics 383
Application Developers or System Integrators: Industry Specific or General 383
Analytics User Organizations 385
Analytics Industry Analysts and Influences 385
Academic Provides and Certification Agencies 387
Key Terms 389
Questions for Discussion 389
Exercises 389
End-of-Chapter Application Case 390
References 391
Glossary 393
Index 401
|
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| illustrated | Illustrated |
| indexdate | 2024-07-10T01:00:59Z |
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| language | English |
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| spelling | Sharda, Ramesh Verfasser (DE-588)132667118 aut Business intelligence a managerial perspective on analytics Ramesh Sharda ; Dursun Delen ; Efraim Turban 3. ed., global ed. Boston, Mass. Pearson 2014 410 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Business intelligence Industrial management Delen, Dursun Verfasser aut Turban, Efraim 1930- Verfasser (DE-588)13563024X aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027058521&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Sharda, Ramesh Delen, Dursun Turban, Efraim 1930- Business intelligence a managerial perspective on analytics Business intelligence Industrial management |
| title | Business intelligence a managerial perspective on analytics |
| title_auth | Business intelligence a managerial perspective on analytics |
| title_exact_search | Business intelligence a managerial perspective on analytics |
| title_full | Business intelligence a managerial perspective on analytics Ramesh Sharda ; Dursun Delen ; Efraim Turban |
| title_fullStr | Business intelligence a managerial perspective on analytics Ramesh Sharda ; Dursun Delen ; Efraim Turban |
| title_full_unstemmed | Business intelligence a managerial perspective on analytics Ramesh Sharda ; Dursun Delen ; Efraim Turban |
| title_short | Business intelligence |
| title_sort | business intelligence a managerial perspective on analytics |
| title_sub | a managerial perspective on analytics |
| topic | Business intelligence Industrial management |
| topic_facet | Business intelligence Industrial management |
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