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Business Analytics

Book Description

Together, Big Data, high-performance computing, and complex environments create unprecedented opportunities for organizations to generate game-changing insights that are based on hard data. Business Analytics: An Introduction explains how to use business analytics to sort through an ever-increasing amount of data and improve the decision-making capabilities of an organization.

Covering the key areas of business analytics, the book explores the concepts, techniques, applications, and emerging trends that professionals across a wide range of industries need to be aware of. Better detection of fraud through visual analytics or better prediction of the likelihood of someone getting an infection while in the hospital are just a few examples of where analytics can play a positive role.

As the field of business analytics continues to emerge rapidly, there is a need for a reliable textbook and reference on the subject. Filling this need, this book is suitable for graduate-level students and undergraduate seniors. It maintains a focus on only the key areas so the material can be covered adequately in a one-semester or one-quarter course. Each chapter includes software-generic exercises, labs, and associated answers to the exercises/labs.

Author Jay Liebowitz recently had an article published in The World Financial Review.

www.worldfinancialreview.com/?p=1904

Table of Contents

  1. Preface
  2. About the Editor
  3. Contributors
  4. Chapter 1 - The Value of Business Analytics
    1. Concepts
      1. Why Business Analytics?
      2. The Changing Role of Insight
        1. The Rise of Big Data
        2. The Advent of High-Performance Computing
        3. The Role of Decision Management
      3. Section Summary
      4. What Is Business Analytics?
      5. Section Summary
      6. What Are the Challenges?
        1. Defining the Value
        2. Communicating the Value
        3. Delivering the Value
        4. Measuring the Value
      7. Section Summary
    2. Techniques and Issues
      1. Defining and Quantifying the Value of Business Analytics
      2. Benefits versus Value
      3. Quantifying the Value of Business Analytics
      4. The Role of Value in Organizational Decision Making
      5. Section Summary
      6. Communicating the Value of Business Analytics
        1. The Analytical Perspective
        2. The Process Perspective
        3. The Personal Perspective
        4. The Strategic Perspective
      7. Section Summary
    3. Emerging Trends
      1. Scalable Value Creation: Operational Analytics and Real-Time Decision Making
      2. Test and Learn: In-Market Experimentation
      3. Assignment
  5. Chapter 2 - Producing Insights from Information through Analytics
    1. Introduction
      1. Goals of Business Analytics
      2. The Need for Business Analytics Is Growing
      3. The ROI of Analytics
      4. Analytics Maturity Model
    2. Types of Analytics and the Benefits They Provide
    3. Typical Business Problems Where Business Analytics Are Applied
      1. Helping Organizations Know Their Customers/Constituents/Citizens
      2. Reputation Management
      3. Improve Operational Efficiency
      4. Financial Management
      5. Manage Waste, Fraud, and Abuse
      6. Proactive Risk Management
      7. Analytics in Education
    4. Examples of Analytics Projects
      1. Descriptive Analytics Example
        1. Case Study: Clark County Family Services Business Intelligence System9
      2. Predictive Analytics Examples
        1. Case Study: Predictive Policing
        2. Case Study: Edelman Winning Project with the General Motors Corporation
      3. Text Analytics Example
        1. Case Study: Seton Healthcare
      4. Prescriptive (Optimization) Analytics Example
        1. Case Study: Edelman Winning Project with the Memorial Sloan–Kettering Cancer Center
      5. Mixed Predictive/Optimization Analytics Example
        1. Case Study: TSA Combines Optimization and Simulation to Schedule Officers at Airport Checkpoints22
    5. The Analytics Process
      1. Before Starting an Analytics Project
        1. Phase 1: Business Understanding
        2. Phase 2: Data Understanding
        3. Phase 3: Data Preparation
        4. Phase 4: Exploration
        5. Phase 5: Modeling
        6. Phase 6: Evaluation
        7. Phase 7: Deployment
        8. Phase 8: Monitoring and Sustainment
    6. Summary
    7. Endnotes
  6. Chapter 3 - Executive/Performance Dashboards
    1. History
    2. Quality Dashboards
    3. What Is a Dashboard?
    4. Reports, Scorecards, and Dashboards
    5. Dashboard Structure
      1. Dashboard Types
      2. Strategic Dashboards
      3. Tactical Dashboards
      4. Operational Dashboards
      5. Analytical Dashboards
      6. Layers of a Dashboard
    6. Indicators
      1. Metrics and Indicators
      2. Types of Indicators
      3. Key Results Indicators
      4. Key Performance Indicators
      5. Other Indicators—RI and PI
      6. The Magic Number of Indicators
      7. Data Organization and Terminology
      8. Summarizing Data to Provide Maximum Value to the Audience
      9. Implementing Performance Indicators
    7. Effective Dashboard Design
      1. Memory Types and Information Processing
      2. Gestalt Principles of Visual Perception
      3. Data-to-Ink Ratio
      4. Dashboard Media
        1. Text
        2. Icons
        3. Organizers
        4. Graphs
        5. Dashboard Real Estate
    8. Dashboard Trends
    9. Summary
  7. Chapter 4 - Data Mining: Helping to Make Sense of Big Data
    1. Introduction
    2. Data Mining
    3. The Tools of Data Mining
    4. Statistical Forecasting and Data Mining
    5. Terminology in Data Mining: Speak Like a Data Miner
    6. A Data Mining Example: k-Nearest-Neighbor and R.A. Fisher
    7. Churning: A Business Example
    8. Text Analytics
    9. Summary
  8. Chapter 5 - Big Data Analytics for Business Intelligence
    1. Discussion of the Concepts, Techniques, Issues, Applications, and Trends
      1. Big Data and Big Data Analytics
      2. Data Science and the Data Scientist
    2. Big Data for Business Intelligence: Real-World Examples
    3. Big Data Technologies
      1. Cloud Computing
      2. Big Data Analytics Software and Services
        1. NoSQL
        2. MapReduce
        3. Hadoop Implementation of MapReduce
        4. Hadoop Ecosystem
    4. Applications
      1. Targeted Online Advertising
      2. Recommendation Systems
    5. Future of Big Data
    6. Summary of the Key Points
    7. References
  9. Chapter 6 - Text Mining Fundamentals
    1. Introduction
    2. Concepts
      1. Text Mining Metrics
        1. Corpus Collection Metrics
        2. Corpus Creation Metrics
        3. Tagging Metrics
      2. Data Selection: Conversion and Filtering
        1. Optical Character Recognition (OCR)
        2. Speech Recognition
        3. Decryption
      3. Information Retrieval
        1. Regular Expressions
        2. Named Entities
        3. Relations
      4. Tagging Techniques
      5. Clustering
      6. Document Categorization
      7. Word or Tag Clouds
    3. Applications
      1. Sentiment Analysis/Opinion Mining
      2. Fraud Detection
      3. Plagiarism
      4. Rumor Categorization
      5. Contradiction Detection
      6. Text Mining in Legal Information Systems
    4. Trends
      1. NLP + Machine Learning + Distant Supervision
      2. Crowdsourcing for Creation of Annotated Corpora
      3. Cloud Computing for Application Hosting
    5. Further Readings
      1. Books/Chapters/Proceedings
      2. Scientific Papers
      3. Web Sources
  10. Chapter 7 - Neural Network Fundamentals
    1. Introduction
    2. Mathematics Primer
    3. Neural Network Models
      1. Perceptron
      2. Multi-Layer Perceptron
      3. Function Approximation Using Neural Networks
      4. Radial Basis Networks
      5. Lattice Neural Networks
    4. Discussion and Conclusions
    5. References
  11. Chapter 8 - Measuring Success in Social Media: An Information Strategy in a Data Obese World
    1. Introduction
    2. Goal-Based Reporting
    3. Start with Website Tracking
      1. Google Analytics
      2. Link Shorteners
      3. Real-Time Analytics
      4. Visitor Visualization
    4. Focus on Measurement
      1. Common Terms
      2. Facebook
      3. Twitter
      4. HTML Email
      5. Key Performance Indicators (KPIs)
    5. Aggregation Measurement Tools
    6. Influence
    7. Conclusion
  12. Chapter 9 - The Legal and Privacy Implications of Data Mining
    1. Introduction
    2. Why Data Mining Raises New Concerns
    3. An Overview of Privacy Law
      1. Protection from Government Intrusion
      2. Privacy and the Federal Constitution
      3. The Fourth Amendment
      4. Digital Searches and Seizures
      5. Legislation Restricting Government Surveillance
    4. National Security
    5. Government and Private Sector Data Collection Overlap/Shared Uses
    6. Protection from Private Intrusion
      1. Common Law/Tort Protection of Privacy
    7. Principles Governing Federal Data Mining Regulation
      1. Fair Information Practice Principles (FIPPs)
        1. 1. Notice
        2. 2. Choice/Consent
        3. 3. Access/Participation
        4. 4. Integrity/Security
        5. 5. Enforcement/Redress
      2. Mobile Application Privacy Procedures
      3. Do Not Track Campaign
    8. Regulation of Private Sector Information
      1. Notice of Data and Consent to Data Collection
        1. Special Protections
        2. Financial Information
        3. Medical Information
        4. Student Information
        5. Information Collected from Children
      2. Data Analysis and Personal Identification
        1. Data Dissemination
        2. Data Security
        3. International Approaches
    9. Conclusion
    10. References
  13. Chapter 10 - Epilogue: Parting Thoughts about Business Analytics
    1. Business Analytics and Decision Making
    2. The Years Ahead