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Business Intelligence, Analytics, and Data Science: A Managerial Perspective 4th edition [Mīkstie vāki]

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  • Formāts: Paperback / softback, 512 pages, height x width x depth: 255x200x18 mm, weight: 922 g
  • Izdošanas datums: 05-Jun-2017
  • Izdevniecība: Pearson
  • ISBN-10: 0134633288
  • ISBN-13: 9780134633282
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  • Formāts: Paperback / softback, 512 pages, height x width x depth: 255x200x18 mm, weight: 922 g
  • Izdošanas datums: 05-Jun-2017
  • Izdevniecība: Pearson
  • ISBN-10: 0134633288
  • ISBN-13: 9780134633282
Citas grāmatas par šo tēmu:
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.
Preface xix
About the Authors xxv
Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science 3(50)
1.1 Opening Vignette: Sports Analytics-An Exciting Frontier for Learning and Understanding Applications of Analytics
4(7)
1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics
11(2)
1.3 Evolution of Computerized Decision Support to Analytics/Data Science
13(2)
1.4 A Framework for Business Intelligence
15(7)
Definitions of BI
16(1)
A Brief History of BI
16(1)
The Architecture of BI
16(1)
The Origins and Drivers of BI
16(3)
Application Case 1.1: Sabre Helps Its Clients through Dashboards and Analytics
18(1)
A Multimedia Exercise in Business Intelligence
19(1)
Transaction Processing versus Analytic Processing
19(1)
Appropriate Planning and Alignment with the Business Strategy
20(1)
Real-Time, On-Demand BI Is Attainable
21(1)
Developing or Acquiring BI Systems
21(1)
Justification and Cost-Benefit Analysis
22(1)
Security and Protection of Privacy
22(1)
Integration of Systems and Applications
22(1)
1.5 Analytics Overview
22(7)
Descriptive Analytics
24(1)
Application Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities
24(1)
Application Case 1.3: Siemens Reduces Cost with the Use of Data Visualization
25(1)
Predictive Analytics
25(1)
Application Case 1.4: Analyzing Athletic Injuries
26(1)
Prescriptive Analytics
26(1)
Analytics Applied to Different Domains
27(1)
Application Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates
27(1)
Analytics or Data Science?
28(1)
1.6 Analytics Examples in Selected Domains
29(6)
Analytics Applications in Healthcare-Humana Examples
29(4)
Analytics in the Retail Value Chain
33(2)
1.7 A Brief Introduction to Big Data Analytics
35(2)
What Is Big Data?
35(2)
Application Case 1.6: CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Service
37(1)
1.8 An Overview of the Analytics Ecosystem
37(9)
Data Generation Infrastructure Providers
39(1)
Data Management Infrastructure Providers
39(1)
Data Warehouse Providers
40(1)
Middleware Providers
40(1)
Data Service Providers
40(1)
Analytics-Focused Software Developers
41(1)
Application Developers: Industry Specific or General
42(1)
Analytics Industry Analysts and Influencers
43(1)
Academic Institutions and Certification Agencies
44(1)
Regulators and Policy Makers
45(1)
Analytics User Organizations
45(1)
1.9 Plan of the Book
46(1)
1.10 Resources, Links, and the Teradata University Network Connection
47(6)
Resources and Links
47(1)
Vendors, Products, and Demos
48(1)
Periodicals
48(1)
The Teradata University Network Connection
48(1)
The Book's Web Site
48(26)
Chapter Highlights
49(1)
Key Terms
49(1)
Questions for Discussion
49(1)
Exercises
50(1)
References
51(2)
Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization 53(74)
2.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing
54(3)
2.2 The Nature of Data
57(4)
2.3 A Simple Taxonomy of Data
61(4)
Application Case 2.1: Medical Device Company Ensures Product Quality While Saving Money
63(2)
2.4 The Art and Science of Data Preprocessing
65(9)
Application Case 2.2: Improving Student Retention with Data-Driven Analytics
68(6)
2.5 Statistical Modeling for Business Analytics
74(12)
Descriptive Statistics for Descriptive Analytics
75(1)
Measures of Centrality Tendency (May Also Be Called Measures of Location or Centrality)
76(1)
Arithmetic Mean
76(1)
Median
77(1)
Mode
77(1)
Measures of Dispersion (May Also Be Called Measures of Spread Decentrality)
77(1)
Range
78(1)
Variance
78(1)
Standard Deviation
78(1)
Mean Absolute Deviation
78(1)
Quartiles and Interquartile Range
78(1)
Box-and-Whiskers Plot
79(1)
The Shape of a Distribution
80(6)
Application Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems
84(2)
2.6 Regression Modeling for Inferential Statistics
86(12)
How Do We Develop the Linear Regression Model?
87(1)
How Do We Know If the Model Is Good Enough?
88(1)
What Are the Most Important Assumptions in Linear Regression?
89(1)
Logistic Regression
90(6)
Application Case 2.4: Predicting NCAA Bowl Game Outcomes
91(5)
Time Series Forecasting
96(2)
2.7 Business Reporting
98(3)
Application Case 2.5: Flood of Paper Ends at FEMA
100(1)
2.8 Data Visualization
101(5)
A Brief History of Data Visualization
101(5)
Application Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online
103(3)
2.9 Different Types of Charts and Graphs
106(4)
Basic Charts and Graphs
106(1)
Specialized Charts and Graphs
107(1)
Which Chart or Graph Should You Use?
108(2)
2.10 The Emergence of Visual Analytics
110(7)
Visual Analytics
112(1)
High-Powered Visual Analytics Environments
112(5)
2.11 Information Dashboards
117(10)
Application Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion
118(1)
Dashboard Design
119(2)
Application Case 2.8: Visual Analytics Helps Energy Supplier Make Better Connections
119(2)
What to Look for in a Dashboard
121(1)
Best Practices in Dashboard Design
121(1)
Benchmark Key Performance Indicators with Industry Standards
121(1)
Wrap the Dashboard Metrics with Contextual Metadata
121(1)
Validate the Dashboard Design by a Usability Specialist
122(1)
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
122(1)
Enrich Dashboard with Business-User Comments
122(1)
Present Information in Three Different Levels
122(1)
Pick the Right Visual Construct Using Dashboard Design Principles
122(1)
Provide for Guided Analytics
122(1)
Chapter Highlights
123(1)
Key Terms
123(1)
Questions for Discussion
124(1)
Exercises
124(2)
References
126(1)
Chapter 3 Descriptive Analytics II: Business Intelligence and Data Warehousing 127(62)
3.1 Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing
128(2)
3.2 Business Intelligence and Data Warehousing
130(7)
What Is a Data Warehouse?
131(1)
A Historical Perspective to Data Warehousing
132(1)
Characteristics of Data Warehousing
133(1)
Data Marts
134(1)
Operational Data Stores
135(1)
Enterprise Data Warehouses (EDW)
135(1)
Metadata
135(4)
Application Case 3.1: A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry
135(2)
3.3 Data Warehousing Process
137(2)
3.4 Data Warehousing Architectures
139(6)
Alternative Data Warehousing Architectures
142(2)
Which Architecture Is the Best?
144(1)
3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes
145(5)
Data Integration
146(2)
Application Case 3.2: BP Lubricants Achieves BIGS Success
146(2)
Extraction, Transformation, and Load
148(2)
3.6 Data Warehouse Development
150(10)
Application Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery
151(2)
Data Warehouse Development Approaches
153(3)
Additional Data Warehouse Development Considerations
156(1)
Representation of Data in Data Warehouse
156(2)
Analysis of Data in Data Warehouse
158(1)
OLAP versus OLTP
158(1)
OLAP Operations
159(1)
3.7 Data Warehousing Implementation Issues
160(4)
Massive Data Warehouses and Scalability
162(2)
Application Case 3.4: EDW Helps Connect State Agencies in Michigan
163(1)
3.8 Data Warehouse Administration, Security Issues, and Future Trends
164(6)
The Future of Data Warehousing
165(5)
3.9 Business Performance Management
170(5)
Closed-Loop BPM Cycle
171(4)
Application Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years
173(2)
3.10 Performance Measurement
175(2)
Key Performance Indicator (KPI)
175(1)
Performance Measurement System
176(1)
3.11 Balanced Scorecards
177(2)
The Four Perspectives
177(2)
The Meaning of Balance in BSC
179(1)
3.12 Six Sigma as a Performance Measurement System
179(10)
The DMAIC Performance Model
180(1)
Balanced Scorecard versus Six Sigma
180(1)
Effective Performance Measurement
181(12)
Application Case 3.6: Expedia.com's Customer Satisfaction Scorecard
182(1)
Chapter Highlights
183(1)
Key Terms
184(1)
Questions for Discussion
184(1)
Exercises
185(2)
References
187(2)
Chapter 4 Predictive Analytics I: Data Mining Process, Methods, and Algorithms 189(58)
4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime
190(3)
4.2 Data Mining Concepts and Applications
193(10)
Application Case 4.1: Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining
194(2)
Definitions, Characteristics, and Benefits
196(1)
How Data Mining Works
197(6)
Application Case 4.2: Dell Is Staying Agile and Effective with Analytics in the 2Ist Century
198(5)
Data Mining versus Statistics
203(1)
4.3 Data Mining Applications
203(3)
Application Case 4.3: Predictive Analytic and Data Mining Help Stop Terrorist Funding
205(1)
4.4 Data Mining Process
206(9)
Step I: Business Understanding
207(1)
Step 2: Data Understanding
208(1)
Step 3: Data Preparation
208(1)
Step 4: Model Building
209(3)
Application Case 4.4: Data Mining Helps in Cancer Research
209(3)
Step 5: Testing and Evaluation
212(1)
Step 6: Deployment
212(1)
Other Data Mining Standardized Processes and Methodologies
212(3)
4.5 Data Mining Methods
215(16)
Classification
215(1)
Estimating the True Accuracy of Classification Models
216(9)
Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People's Healthcare Decisions
223(2)
Cluster Analysis for Data Mining
225(2)
Association Rule Mining
227(4)
4.6 Data Mining Software Tools
231(6)
Application Case 4.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies
233(4)
4.7 Data Mining Privacy Issues, Myths, and Blunders
237(10)
Application Case 4.7: Predicting Customer Buying Patterns-The Target Story
238(1)
Data Mining Myths and Blunders
238(23)
Chapter Highlights
241(1)
Key Terms
242(1)
Questions for Discussion
242(1)
Exercises
243(2)
References
245(2)
Chapter 5 Predictive Analytics II: Text, Web, and Social Media Analytics 247(72)
5.1 Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson
248(3)
5.2 Text Analytics and Text Mining Overview
251(4)
Application Case 5.1: Insurance Group Strengthens Risk Management with Text Mining Solution
254(1)
5.3 Natural Language Processing (NLP)
255(6)
Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World
257(4)
5.4 Text Mining Applications
261(7)
Marketing Applications
261(1)
Security Applications
261(3)
Application Case 5.3: Mining for Lies
262(2)
Biomedical Applications
264(2)
Academic Applications
266(2)
Application Case 5.4: Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Rethink Its Redesign
266(2)
5.5 Text Mining Process
268(8)
Task 1: Establish the Corpus
269(1)
Task 2: Create the Term-Document Matrix
269(2)
Task 3: Extract the Knowledge
271(5)
Application Case 5.5: Research Literature Survey with Text Mining
273(3)
5.6 Sentiment Analysis
276(11)
Application Case 5.6: Creating a Unique Digital Experience to Capture the Moments That Matter at Wimbledon
277(3)
Sentiment Analysis Applications
280(2)
Sentiment Analysis Process
282(2)
Methods for Polarity Identification
284(1)
Using a Lexicon
284(1)
Using a Collection of Training Documents
285(1)
Identifying Semantic Orientation of Sentences and Phrases
286(1)
Identifying Semantic Orientation of Documents
286(1)
5.7 Web Mining Overview
287(4)
Web Content and Web Structure Mining
289(2)
5.8 Search Engines
291(7)
Anatomy of a Search Engine
292(2)
1 Development Cycle
292(1)
2 Response Cycle
293(1)
Search Engine Optimization
294(1)
Methods for Search Engine Optimization
295(3)
Application Case 5.7: Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sales Increase
297(1)
5.9 Web Usage Mining (Web Analytics)
298(6)
Web Analytics Technologies
299(1)
Web Analytics Metrics
300(1)
Web Site Usability
300(1)
Traffic Sources
301(1)
Visitor Profiles
302(1)
Conversion Statistics
302(2)
5.10 Social Analytics
304(15)
Social Network Analysis
304(1)
Social Network Analysis Metrics
305(3)
Application Case 5.8: Tito's Vodka Establishes Brand Loyalty with an Authentic Social Strategy
305(3)
Connections
308(1)
Distributions
308(1)
Segmentation
309(1)
Social Media Analytics
309(1)
How Do People Use Social Media?
310(1)
Measuring the Social Media Impact
311(1)
Best Practices in Social Media Analytics
311(11)
Chapter Highlights
313(1)
Key Terms
314(1)
Questions for Discussion
315(1)
Exercises
315(1)
References
316(3)
Chapter 6 Prescriptive Analytics: Optimization and Simulation 319(50)
6.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts
320(2)
6.2 Model-Based Decision Making
322(6)
Prescriptive Analytics Model Examples
322(2)
Application Case 6.1: Optimal Transport for ExxonMobil Downstream through a DSS
323(1)
Identification of the Problem and Environmental Analysis
324(1)
Model Categories
324(4)
Application Case 6.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
325(3)
6.3 Structure of Mathematical Models for Decision Support
328(2)
The Components of Decision Support Mathematical Models
328(1)
The Structure of Mathematical Models
329(1)
6.4 Certainty, Uncertainty, and Risk
330(1)
Decision Making under Certainty
330(1)
Decision Making under Uncertainty
331(1)
Decision Making under Risk (Risk Analysis)
331(1)
6.5 Decision Modeling with Spreadsheets
331(5)
Application Case 6.3: American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes
332(1)
Application Case 6.4: Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families
333(1)
Application Case 6.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes
334(2)
6.6 Mathematical Programming Optimization
336(10)
Application Case 6.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians
337(1)
Linear Programming Model
338(1)
Modeling in LP: An Example
339(5)
Implementation
344(2)
6.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
346(3)
Multiple Goals
346(1)
Sensitivity Analysis
347(1)
What-If Analysis
348(1)
Goal Seeking
348(1)
6.8 Decision Analysis with Decision Tables and Decision Trees
349(3)
Decision Tables
350(1)
Decision Trees
351(1)
6.9 Introduction to Simulation
352(7)
Major Characteristics of Simulation
352(2)
Application Case 6.7: Simulating Effects of Hepatitis B Interventions
353(1)
Advantages of Simulation
354(1)
Disadvantages of Simulation
355(1)
The Methodology of Simulation
355(1)
Simulation Types
356(1)
Monte Carlo Simulation
357(1)
Discrete Event Simulation
358(1)
Application Case 6.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation
358(1)
6.10 Visual Interactive Simulation
359(10)
Conventional Simulation Inadequacies
359(1)
Visual Interactive Simulation
359(1)
Visual Interactive Models and DSS
360(1)
Simulation Software
360(13)
Application Case 6.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment
361(3)
Chapter Highlights
364(1)
Key Terms
364(1)
Questions for Discussion
365(1)
Exercises
365(2)
References
367(2)
Chapter 7 Big Data Concepts and Tools 369(48)
7.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
370(3)
7.2 Definition of Big Data
373(5)
The "V"s That Define Big Data
374(4)
Application Case 7.1: Alternative Data for Market Analysis or Forecasts
377(1)
7.3 Fundamentals of Big Data Analytics
378(5)
Business Problems Addressed by Big Data Analytics
381(2)
Application Case 7.2: Top Five Investment Bank Achieves Single Source of the Truth
382(1)
7.4 Big Data Technologies
383(10)
MapReduce
383(2)
Why Use MapReduce?
385(1)
Hadoop
385(1)
How Does Hadoop Work?
385(1)
Hadoop Technical Components
386(1)
Hadoop: The Pros and Cons
387(2)
NoSQL
389(4)
Application Case 7.3: eBay's Big Data Solution
390(2)
Application Case 7.4: Understanding Quality and Reliability of Healthcare Support Information on Twitter
392(1)
7.5 Big Data and Data Warehousing
393(4)
Use Cases for Hadoop
393(1)
Use Cases for Data Warehousing
394(1)
The Gray Areas (Any One of the Two Would Do the Job)
395(1)
Coexistence of Hadoop and Data Warehouse
396(1)
7.6 Big Data Vendors and Platforms
397(9)
IBM InfoSphere Biglnsights
398(3)
Application Case 7.5: Using Social Media for Nowcasting the Flu Activity
400(1)
Teradata Aster
401(5)
Application Case 7.6: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
402(4)
7.7 Big Data and Stream Analytics
406(3)
Stream Analytics versus Perpetual Analytics
408(1)
Critical Event Processing
408(1)
Data Stream Mining
408(1)
7.8 Applications of Stream Analytics
409(8)
e-Commerce
409(1)
Telecommunications
409(2)
Application Case 7.7: Salesforce Is Using Streaming Data to Enhance Customer Value
410(1)
Law Enforcement and Cybersecurity
411(1)
Power Industry
411(1)
Financial Services
411(1)
Health Sciences
411(1)
Government
412(7)
Chapter Highlights
412(1)
Key Terms
413(1)
Questions for Discussion
413(1)
Exercises
413(1)
References
414(3)
Chapter 8 Future Trends, Privacy and Managerial Considerations in Analytics 417(50)
8.1 Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures
418(1)
8.2 Internet of Things
419(10)
Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans
420(1)
Application Case 8.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets
421(1)
loT Technology Infrastructure
422(1)
RFID Sensors
422(3)
Fog Computing
425(1)
loT Platforms
426(1)
Application Case 8.3: Pitney Bowes Collaborates with General Electric loT Platform to Optimize Production
426(1)
loT Start-Up Ecosystem
427(1)
Managerial Considerations in the Internet of Things
428(1)
8.3 Cloud Computing and Business Analytics
429(12)
Data as a Service (DaaS)
431(1)
Software as a Service (SaaS)
432(1)
Platform as a Service (PaaS)
432(1)
Infrastructure as a Service (laaS)
432(1)
Essential Technologies for Cloud Computing
433(1)
Cloud Deployment Models
433(1)
Major Cloud Platform Providers in Analytics
434(1)
Analytics as a Service (AaaS)
435(1)
Representative Analytics as a Service Offerings
435(1)
Illustrative Analytics Applications Employing the Cloud Infrastructure
436(5)
MD Anderson Cancer Center Utilizes Cognitive Computing Capabilities of IBM Watson to Give Better Treatment to Cancer Patients
436(1)
Public School Education in Tacoma, Washington, Uses Microsoft Azure Machine Learning to Predict School Dropouts
437(1)
Dartmouth-Hitchcock Medical Center Provides Personalized Proactive Healthcare Using Microsoft Cortana Analytics Suite
438(1)
Mankind Pharma Uses IBM Cloud Infrastructure to Reduce Application Implementation Time by 98%
438(1)
Gulf Air Uses Big Data to Get Deeper Customer Insight
439(1)
Chime Enhances Customer Experience Using Snowflake
440(1)
8.4 Location-Based Analytics for Organizations
441(7)
Geospatial Analytics
441(4)
Application Case 8.4: Great Clips Employs Spatial Analytics to Shave Time in Location Decisions
443(1)
Application Case 8.5: Starbucks Exploits GIS and Analytics to Grow Worldwide
444(1)
Real-Time Location Intelligence
445(1)
Application Case 8.6: Quiznos Targets Customers for Its Sandwiches
446(1)
Analytics Applications for Consumers
446(2)
8.5 Issues of Legality, Privacy, and Ethics
448(5)
Legal Issues
448(1)
Privacy
449(1)
Collecting Information about Individuals
449(1)
Mobile User Privacy
450(1)
Homeland Security and Individual Privacy
450(1)
Recent Technology Issues in Privacy and Analytics
451(1)
Who Owns Our Private Data?
452(1)
Ethics in Decision Making and Support
452(1)
8.6 Impacts of Analytics in Organizations: An Overview
453(6)
New Organizational Units
454(1)
Redesign of an Organization through the Use of Analytics
455(1)
Analytics Impact on Managers' Activities, Performance, and Job Satisfaction
455(1)
Industrial Restructuring
456(1)
Automation's Impact on Jobs
457(1)
Unintended Effects of Analytics
458(1)
8.7 Data Scientist as a Profession
459(8)
Where Do Data Scientists Come From?
459(8)
Chapter Highlights
462(1)
Key Terms
463(1)
Questions for Discussion
463(1)
Exercises
463(1)
References
464(3)
Glossary 467(8)
Index 475
Ramesh Sharda (M.B.A., Ph.D., University of WisconsinMadison)  is the Vice Dean  for Research and Graduate  Programs,  Watson/ConocoPhillips Chair and a Regents Professor of Management  Science  and  Information  Systems  in  the  Spears  School  of Business  at Oklahoma  State University. He cofounded and  directed  OSUs PhD in Business  for the Executives  Program.  About  200 papers  describing  his research  have  been  published in major journals, including Operations Research, Management Science, Information Systems Research, Decision Support Systems, and  Journal of MIS. He cofounded the  AIS SIG on Decision  Support  Systems and  Knowledge  Management  (SIGDSS). Dr. Sharda serves  on several editorial boards, including those of Decision Sciences Journal, Decision Support Systems, and ACM Data Base. He has authored and edited several textbooks  and research books  and serves as the co-editor  of several book  series (Integrated  Series in Information Systems, Operations Research/Computer Science Interfaces, and Annals of Information Systems)  with  Springer.  He  is  also  currently  serving  as  the  executive  director  of  the Teradata  University Network.  His current  research  interests  are in decision  support  sys- tems, business  analytics, and technologies for managing  information  overload.







Dursun  Delen (Ph.D.,  Oklahoma   State  University)  is  the  Spears  Endowed   Chair  in Business  Administration,  Patterson   Foundation  Endowed   Chair  in  Business  Analytics, Director of Research for the Center for Health Systems Innovation,  and Regents Professor of Management  Science  and  Information  Systems  in  the  Spears  School  of Business  at Oklahoma  State University (OSU). Prior to his academic  career, he worked  for a privately owned  research  and  consultancy  company, Knowledge  Based  Systems Inc., in College Station, Texas, as a research  scientist  for five years,  during  which  he  led  a number  of decision  support  and other  information  systemsrelated research  projects funded  by sev- eral federal  agencies  including  Department of Defense  (DoD), National Aeronautics  and Space  Administration  (NASA), National  Institute  for  Standards  and  Technology  (NIST), Ballistic Missile Defense  Organization (BMDO), and  Department of Energy  (DOE). Dr. Delen has published more than 100 peer reviewed  articles, some of which have appeared in major journals like Decision Sciences, Decision Support Systems, Communications of the ACM, Computers and  Operations Research, Computers in Industry,  Journal of Production Operations  Management, Artificial  Intelligence  in  Medicine,  International  Journal  of Medical Informatics, Expert Systems with Applications, and IEEE Wireless Communications. He recently authored/co-authored seven textbooks  in the broad  areas of business  analyt- ics, data  mining,  text mining,  business  intelligence  and  decision  support  systems.  He is often  invited  to national  and  international  conferences for keynote  addresses on  topics related  to data/text mining,  business  analytics, decision  support  systems,  business  intel- ligence and knowledge management. He served as the general cochair for the Fourth International Conference on Network Computing and Advanced Information Management (September  24, 2008, in Soul, South Korea) and regularly chairs tracks and mini-tracks at various  information  systems and analytics conferences. He is currently  serving as editor- in-chief, senior editor, associate editor or editorial board member for more than a dozen academic  journals. His research  and teaching  interests  are in data and text mining, busi- ness analytics, decision  support  systems, knowledge management, business  intelligence, and enterprise modeling.







Efraim Turban (M.B.A., Ph.D., University of California, Berkeley) is a visiting scholar at the Pacific Institute for Information System Management, University of Hawaii. Prior to this, he was on the staff of several universities, including City University of Hong Kong, Lehigh University, Florida International University, California State University, Long Beach; Eastern Illinois University, and the University of Southern California. Dr. Turban is the author of more than 100 refereed papers published in leading journals, such as "Management Science", "MIS Quarterly", and "Decision Support System". He is also the author of 20 books, including "Electronic Commerce: A Managerial Perspective" and "Information Technology for Management". He is also a consultant to major corporations worldwide. Dr. Turban's current areas of interest are Web-based decision support systems, social commerce, and collaborative decision making.