Business Intelligence: A Managerial Approach, Global Edition
Námskeið T-707-VGBI Viðskiptagreind V-785 – REK5 - Höfundar: Ramesh Sharda, Dursun Delen, Efraim Turban, David King
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V-785 – REK5
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Annað
- Höfundar: Ramesh Sharda, Dursun Delen, Efraim Turban, David King
- Útgáfa:4
- Útgáfudagur: 2017-09-27
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:Page Fidelity
- ISBN 13: 9781292220567
- Print ISBN: 9781292220543
- ISBN 10: 1292220562
Efnisyfirlit
- Title Page
- Copyright Page
- Brief Contents
- Contents
- Preface
- Acknowledgments
- About the Authors
- Chapter 1: An Overview of Business Intelligence, Analytics, and Data Science
- 1.1. Opening Vignette: Sports Analytics—An Exciting Frontier for Learning and Understanding Applic
- 1.2. Changing Business Environments and Evolving Needs for Decision Support and Analytics
- 1.3. Evolution of Computerized Decision Support to Analytics/Data Science
- 1.4. A Framework for Business Intelligence
- Definitions of BI
- A Brief History of BI
- The Architecture of BI
- The Origins and Drivers of BI
- Application Case 1.1: Sabre Helps Its Clients Through Dashboards and Analytics
- A Multimedia Exercise in Business Intelligence
- Transaction Processing versus Analytic Processing
- Appropriate Planning and Alignment with the Business Strategy
- Real-Time, On-Demand BI Is Attainable
- Developing or Acquiring BI Systems
- Justification and Cost–Benefit Analysis
- Security and Protection of Privacy
- Integration of Systems and Applications
- 1.5. Analytics Overview
- Descriptive Analytics
- Application Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capab
- Application Case 1.3: Siemens Reduces Cost with the Use of Data Visualization
- Predictive Analytics
- Application Case 1.4: Analyzing Athletic Injuries
- Prescriptive Analytics
- Analytics Applied to Different Domains
- Application Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise
- Analytics or Data Science?
- 1.6. Analytics Examples in Selected Domains
- Analytics Applications in Healthcare—Humana Examples
- Analytics in the Retail Value Chain
- 1.7. A Brief Introduction to Big Data Analytics
- What Is Big Data?
- Application Case 1.6: CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Servi
- 1.8. An Overview of the Analytics Ecosystem
- Data Generation Infrastructure Providers
- Data Management Infrastructure Providers
- Data Warehouse Providers
- Middleware Providers
- Data Service Providers
- Analytics-Focused Software Developers
- Application Developers: Industry Specific or General
- Analytics Industry Analysts and Influencers
- Academic Institutions and Certification Agencies
- Regulators and Policy Makers
- Analytics User Organizations
- 1.9. Plan of the Book
- 1.10. Resources, Links, and the Teradata University Network Connection
- Resources and Links
- Vendors, Products, and Demos
- Periodicals
- The Teradata University Network Connection
- The Book’s Web Site
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 2: Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization
- 2.1. Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-D
- 2.2. The Nature of Data
- 2.3. A Simple Taxonomy of Data
- Application Case 2.1: Medical Device Company Ensures Product Quality While Saving Money
- 2.4. The Art and Science of Data Preprocessing
- Application Case 2.2: Improving Student Retention with Data-Driven Analytics
- 2.5. Statistical Modeling for Business Analytics
- Descriptive Statistics for Descriptive Analytics
- Measures of Centrality Tendency (May Also Be Called Measures of Location or Centrality)
- Arithmetic Mean
- Median
- Mode
- Measures of Dispersion (May Also Be Called Measures of Spread Decentrality)
- Range
- Variance
- Standard Deviation
- Mean Absolute Deviation
- Quartiles and Interquartile Range
- Box-and-Whiskers Plot
- The Shape of a Distribution
- Application Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and D
- 2.6 Regression Modeling for Inferential Statistics
- How Do We Develop the Linear Regression Model?
- How Do We Know If the Model Is Good Enough?
- What Are the Most Important Assumptions in Linear Regression?
- Logistic Regression
- Application Case 2.4: Predicting NCAA Bowl Game Outcomes
- Time Series Forecasting
- 2.7. Business Reporting
- Application Case 2.5: Flood of Paper Ends at FEMA
- 2.8. Data Visualization
- A Brief History of Data Visualization
- Application Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online
- 2.9. Different Types of Charts and Graphs
- Basic Charts and Graphs
- Specialized Charts and Graphs
- Which Chart or Graph Should You Use?
- 2.10. The Emergence of Visual Analytics
- Visual Analytics
- High-Powered Visual Analytics Environments
- 2.11. Information Dashboards
- Application Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion
- Dashboard Design
- Application Case 2.8: Visual Analytics Helps Energy Supplier Make Better Connections
- What to Look for in a Dashboard
- Best Practices in Dashboard Design
- Benchmark Key Performance Indicators with Industry Standards
- Wrap the Dashboard Metrics with Contextual Metadata
- Validate the Dashboard Design by a Usability Specialist
- Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
- Enrich the Dashboard with Business-User Comments
- Present Information in Three Different Levels
- Pick the Right Visual Construct Using Dashboard Design Principles
- Provide for Guided Analytics
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 3: Descriptive Analytics II: Business Intelligence and Data Warehousing
- 3.1. Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing
- 3.2. Business Intelligence and Data Warehousing
- What Is a Data Warehouse?
- A Historical Perspective to Data Warehousing
- Characteristics of Data Warehousing
- Data Marts
- Operational Data Stores
- Enterprise Data Warehouses (EDW)
- Metadata
- Application Case 3.1: A Better Data Plan: Well- Established TELCOs Leverage Data Warehousing and Ana
- 3.3. Data Warehousing Process
- 3.4. Data Warehousing Architectures
- Alternative Data Warehousing Architectures
- Which Architecture Is the Best?
- 3.5. Data Integration and the Extraction, Transformation, and Load (ETL) Processes
- Data Integration
- Application Case 3.2: BP Lubricants Achieves BIGS Success
- Extraction, Transformation, and Load
- 3.6. Data Warehouse Development
- Application Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery
- Data Warehouse Development Approaches
- Additional Data Warehouse Development Considerations
- Representation of Data in Data Warehouse
- Analysis of Data in Data Warehouse
- OLAP versus OLTP
- OLAP Operations
- 3.7. Data Warehousing Implementation Issues
- Massive Data Warehouses and Scalability
- Application Case 3.4: EDW Helps Connect State Agencies in Michigan
- 3.8. Data Warehouse Administration, Security Issues, and Future Trends
- The Future of Data Warehousing
- 3.9. Business Performance Management
- Closed-Loop BPM Cycle
- Application Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years
- 3.10. Performance Measurement
- Key Performance Indicator (KPI)
- Performance Measurement System
- 3.11. Balanced Scorecards
- The Four Perspectives
- The Meaning of Balance in BSC
- 3.12. Six Sigma as a Performance Measurement System
- The DMAIC Performance Model
- Balanced Scorecard versus Six Sigma
- Effective Performance Measurement
- Application Case 3.6: Expedia.com’s Customer Satisfaction Scorecard
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 4: Predictive Analytics I: Data Mining Process, Methods, and Algorithms
- 4.1. Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fig
- 4.2. Data Mining Concepts and Applications
- Application Case 4.1: Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive
- Definitions, Characteristics, and Benefits
- How Data Mining Works
- Application Case 4.2: Dell Is Staying Agile and Effective with Analytics in the 21st Century
- Data Mining versus Statistics
- 4.3. Data Mining Applications
- Application Case 4.3: Bank Speeds Time to Market with Advanced Analytics
- 4.4. Data Mining Process
- Step 1: Business Understanding
- Step 2: Data Understanding
- Step 3: Data Preparation
- Step 4: Model Building
- Application Case 4.4: Data Mining Helps in Cancer Research
- Step 5: Testing and Evaluation
- Step 6: Deployment
- Other Data Mining Standardized Processes and Methodologies
- 4.5. Data Mining Methods
- Classification
- Estimating the True Accuracy of Classification Models
- Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors Th
- Cluster Analysis for Data Mining
- Association Rule Mining
- 4.6. Data Mining Software Tools
- Application Case 4.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies
- 4.7. Data Mining Privacy Issues, Myths, and Blunders
- Application Case 4.7: Predicting Customer Buying Patterns—The Target Story
- Data Mining Myths and Blunders
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 5: Predictive Analytics II: Text, Web, and Social Media Analytics
- 5.1. Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson
- 5.2. Text Analytics and Text Mining Overview
- Application Case 5.1: Insurance Group Strengthens Risk Management with Text Mining Solution
- 5.3. Natural Language Processing (NLP)
- Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and A
- 5.4. Text Mining Applications
- Marketing Applications
- Security Applications
- Application Case 5.3: Mining for Lies
- Biomedical Applications
- Academic Applications
- Application Case 5.4: Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Reth
- 5.5. Text Mining Process
- Task 1: Establish the Corpus
- Task 2: Create the Term–Document Matrix
- Task 3: Extract the Knowledge
- Application Case 5.5: Research Literature Survey with Text Mining
- 5.6. Sentiment Analysis
- Application Case 5.6: Creating a Unique Digital Experience to Capture the Moments That Matter at Wim
- Sentiment Analysis Applications
- Sentiment Analysis Process
- Methods for Polarity Identification
- Using a Lexicon
- Using a Collection of Training Documents
- Identifying Semantic Orientation of Sentences and Phrases
- Identifying Semantic Orientation of Documents
- 5.7. Web Mining Overview
- Web Content and Web Structure Mining
- 5.8. Search Engines
- Anatomy of a Search Engine
- 1. Development Cycle
- 2. Response Cycle
- Search Engine Optimization
- Methods for Search Engine Optimization
- Application Case 5.7: Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sa
- 5.9. Web Usage Mining (Web Analytics)
- Web Analytics Technologies
- Web Analytics Metrics
- Web Site Usability
- Traffic Sources
- Visitor Profiles
- Conversion Statistics
- 5.10. Social Analytics
- Social Network Analysis
- Social Network Analysis Metrics
- Application Case 5.8: Tito’s Vodka Establishes Brand Loyalty with an Authentic Social Strategy
- Connections
- Distributions
- Segmentation
- Social Media Analytics
- How Do People Use Social Media?
- Measuring the Social Media Impact
- Best Practices in Social Media Analytics
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 6: Prescriptive Analytics: Optimization and Simulation
- 6.1. Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal S
- 6.2. Model-Based Decision Making
- Prescriptive Analytics Model Examples
- Application Case 6.1: Optimal Transport for ExxonMobil Downstream through a DSS
- Identification of the Problem and Environmental Analysis
- Model Categories
- Application Case 6.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions
- 6.3. Structure of Mathematical Models for Decision Support
- The Components of Decision Support Mathematical Models
- The Structure of Mathematical Models
- 6.4. Certainty, Uncertainty, and Risk
- Decision Making under Certainty
- Decision Making under Uncertainty
- Decision Making under Risk (Risk Analysis)
- 6.5. Decision Modeling with Spreadsheets
- Application Case 6.3: Primary Schools in Slovenia Use Interactive and Automated Scheduling Systems t
- Application Case 6.4: Spreadsheet Helps Optimize Production Planning in Chilean Swine Companies
- Application Case 6.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Rout
- 6.6 Mathematical Programming Optimization
- Application Case 6.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Cent
- Linear Programming Model
- Modeling in LP: An Example
- Implementation
- 6.7. Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
- Multiple Goals
- Sensitivity Analysis
- What-If Analysis
- Goal Seeking
- 6.8. Decision Analysis with Decision Tables and Decision Trees
- Decision Tables
- Decision Trees
- 6.9. Introduction to Simulation
- Major Characteristics of Simulation
- Application Case 6.7: Syngenta Uses Monte Carlo Simulation Models to Increase Soybean Crop Productio
- Advantages of Simulation
- Disadvantages of Simulation
- The Methodology of Simulation
- Simulation Types
- Monte Carlo Simulation
- Discrete Event Simulation
- Application Case 6.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation
- 6.10. Visual Interactive Simulation
- Conventional Simulation Inadequacies
- Visual Interactive Simulation
- Visual Interactive Models and DSS
- Simulation Software
- Application Case 6.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Asses
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 7: Big Data Concepts and Tools
- 7.1. Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods
- 7.2. Definition of Big Data
- The “V”s That Define Big Data
- Application Case 7.1: Alternative Data for Market Analysis or Forecasts
- 7.3. Fundamentals of Big Data Analytics
- Business Problems Addressed by Big Data Analytics
- Application Case 7.2: Top Five Investment Bank Achieves Single Source of the Truth
- 7.4. Big Data Technologies
- MapReduce
- Why Use MapReduce?
- Hadoop
- How Does Hadoop Work?
- Hadoop Technical Components
- Hadoop: The Pros and Cons
- NoSQL
- Application Case 7.3: eBay’s Big Data Solution
- Application Case 7.4: Understanding Quality and Reliability of Healthcare Support Information on Twi
- 7.5. Big Data and Data Warehousing
- Use Cases for Hadoop
- Use Cases for Data Warehousing
- The Gray Areas (Any One of the Two Would Do the Job)
- Coexistence of Hadoop and Data Warehouse
- 7.6. Big Data Vendors and Platforms
- IBM InfoSphere BigInsights
- Application Case 7.5: Using Social Media for Nowcasting the Flu Activity
- Teradata Aster
- Application Case 7.6: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse
- 7.7. Big Data and Stream Analytics
- Stream Analytics versus Perpetual Analytics
- Critical Event Processing
- Data Stream Mining
- 7.8. Applications of Stream Analytics
- e-Commerce
- Telecommunications
- Application Case 7.7: Salesforce Is Using Streaming Data to Enhance Customer Value
- Law Enforcement and Cybersecurity
- Power Industry
- Financial Services
- Health Sciences
- Government
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics
- 8.1. Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures
- 8.2. Internet of Things
- Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans
- Application Case 8.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets
- IoT Technology Infrastructure
- RFID Sensors
- Fog Computing
- IoT Platforms
- Application Case 8.3: Pitney Bowes Collaborates with General Electric IoT Platform to Optimize Produ
- IoT Start-Up Ecosystem
- Managerial Considerations in the Internet of Things
- 8.3. Cloud Computing and Business Analytics
- Data as a Service (DaaS)
- Software as a Service (SaaS)
- Platform as a Service (PaaS)
- Infrastructure as a Service (IaaS)
- Essential Technologies for Cloud Computing
- Cloud Deployment Models
- Major Cloud Platform Providers in Analytics
- Analytics as a Service (AaaS)
- Representative Analytics as a Service Offerings
- Illustrative Analytics Applications Employing the Cloud Infrastructure
- MD Anderson Cancer Center Utilizes Cognitive Computing Capabilities of IBM Watson to Give Better Tre
- Public School Education in Tacoma, Washington, Uses Microsoft Azure Machine Learning to Predict Scho
- Dartmouth-Hitchcock Medical Center Provides Personalized Proactive Healthcare Using Microsoft Cortan
- Mankind Pharma Uses IBM Cloud Infrastructure to Reduce Application Implementation Time by 98%
- Gulf Air Uses Big Data to Get Deeper Customer Insight
- Chime Enhances Customer Experience Using Snowflake
- 8.4. Location-Based Analytics for Organizations
- Geospatial Analytics
- Application Case 8.4: Indian Police Departments Use Geospatial Analytics to Fight Crime
- Application Case 8.5: Starbucks Exploits GIS and Analytics to Grow Worldwide
- Real-Time Location Intelligence
- Application Case 8.6: Quiznos Targets Customers for Its Sandwiches
- Analytics Applications for Consumers
- 8.5. Issues of Legality, Privacy, and Ethics
- Legal Issues
- Privacy
- Collecting Information about Individuals
- Mobile User Privacy
- Homeland Security and Individual Privacy
- Recent Technology Issues in Privacy and Analytics
- Who Owns Our Private Data?
- Ethics in Decision Making and Support
- 8.6. Impacts of Analytics in Organizations: An Overview
- New Organizational Units
- Redesign of an Organization through the Use of Analytics
- Analytics Impact on Managers’ Activities, Performance, and Job Satisfaction
- Industrial Restructuring
- Automation’s Impact on Jobs
- Unintended Effects of Analytics
- 8.7. Data Scientist as a Profession
- Where Do Data Scientists Come From?
- Chapter Highlights
- Key Terms
- Questions for Discussion
- Exercises
- References
- Glossary
- Index
- Back Cover
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- Gerð : 208
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- Útgáfuár : 2017
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