Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics

Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics

by Nathaniel Lin
Released December 2014
Publisher(s): Pearson
ISBN: 9780133481549

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Bridge the gap between analytics and execution, and actually translate analytics into better business decision-making! Now that you've collected data and crunched numbers, Applied Business Analytics reveals how to fully apply the information and knowledge you've gleaned from quants and tech teams. Nathaniel Lin explains why "analytics value chains" often break due to organizational and cultural issues, and offers "in the trenches" guidance for overcoming these obstacles. You'll discover why a special breed of "analytics deciders" is indispensable for any organization that seeks to compete on analytics… how to become one of those deciders… and how to identify, foster, support, empower, and reward others to join you.

Lin draws on actual cases and examples from his own experience, augmenting them with hands-on examples and exercises to integrate analytics at all levels: from top-level business questions to low-level technical details. Along the way, you'll learn how to bring together analytics team members with widely diverse goals, knowledge, and backgrounds. Coverage includes:  

  • How analytical and conventional decision making differ — and the challenging implications

  • How to determine who your analytics deciders are, and ought to be

  • Proven best practices for actually applying analytics to decision-making

  • How to optimize your use of analytics as an analyst, manager, executive, or C-level officer

    • Applied Business Analytics will be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics, including Chief Analytics Officers, Chief Data Officers, Chief Scientists, Chief Marketing Officers, Chief Risk Officers, Chief Strategy Officers, VPs of Analytics and/or Big Data, data scientists, business strategists, and line of business executives. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program, including candidates for INFORMS certification.

    Table of contents

    1. About This eBook
    2. Title Page
    3. Copyright Page
    4. Dedication Page
    5. Contents
    6. Foreword
    7. Acknowledgments
    8. About the Author
    9. Preface
      1. Why Another Book on Analytics?
      2. How This Book Is Organized
      3. After Reading and Working Through This Book
    10. 1. Introduction
      1. Raw Data, the New Oil
        1. Data Big and Small Is Not New
        2. Definition of Analytics
      2. Top 10 Business Questions for Analytics
        1. Financial Management
        2. Customer Management
        3. HR Management
        4. Internal Operations
      3. Vital Lessons Learned
        1. Use Analytics
        2. Reasons Why Analytics Are Not Used
      4. Linking Analytics to Business
        1. Business Analytics Value Chain
        2. Integrated Approach
        3. Hands-on Exercises
        4. Reasons for Using KNIME Workflows
      5. Conclusion
    11. 2. Know Your Ingredients—Data Big and Small
      1. Garbage in, Garbage out
      2. Data or Big Data
        1. Definition of Big Data
      3. Data Types
        1. Company Data
        2. Individual Consumer Data
        3. Sensor Data
        4. Syndicated Data
      4. Data Formats
        1. Structured, Poorly Structured, and Unstructured Data
      5. Conclusion
    12. 3. Data Management—Integration, Data Quality, and Governance
      1. Data Integration
      2. Data Quality
      3. Data Security and Data Privacy
        1. Data Security
        2. Data Privacy
      4. Data Governance
      5. Data Preparation
      6. Data Manipulation
        1. Types of Data
        2. Categorize Numerical Variables
        3. Dummy Variables
        4. Missing Values
        5. Data Normalization
        6. Data Partitions
      7. Exploratory Data Analysis
        1. Multidimensional Cube
        2. Slicing
        3. Dicing
        4. Drilling Down/Up
        5. Pivoting
      8. Visualization of Data Patterns and Trends
        1. Popularity of BI Visualization
        2. Selecting a BI Visualization Tool
        3. Beyond BI Visualizations
      9. Conclusion
    13. 4. Handle the Tools: Analytics Methodology and Tools
      1. Getting Familiar with the Tools
      2. Master Chefs Who Can’t Cook
      3. Types of Analytics
        1. Descriptive and Diagnostic Tools: BI Visualization and Reporting
        2. Advanced Analytics Tools: Prediction, Optimization, and Knowledge Discovery
        3. A Unified View of BI Analysis, Advanced Analytics, and Visualization
        4. Two Ways of Knowledge Discovery
        5. KNIME Advanced Analytics Platform
      4. Types of Advanced Analytics and Applications
        1. Analytics Modeling Tools by Functions
        2. Modeling Likelihood
        3. Modeling Groupings
        4. Supervised Learning
        5. Unsupervised Learning
        6. Value Prediction
        7. Other Models
      5. Conclusion
    14. 5. Analytics Decision-Making Process and the Analytics Deciders
      1. Time to Take Off the Mittens
      2. Overview of the Business Analytics Process (BAP)
      3. Analytics Rapid Prototyping
        1. Analytics Sandbox for Instant Business Insights
        2. Analytics IT Sandbox Database
      4. People and the Decision Blinders
        1. Risks of Crossing the Chasms
      5. The Medici Effect
      6. Analytics Deciders
        1. How to Find Analytics Deciders
        2. Becoming an Analytics Decider
      7. Conclusion
    15. 6. Business Processes and Analytics
      1. Overview of Process Families
        1. Enterprise Resource Planning
        2. Customer Relationship Management
        3. Product Lifecycle Management
      2. Shortcomings of Operational Systems
      3. Embedding Advanced Analytics into Operational Systems
        1. Example 1: Forecast
        2. Example 2: Improving Salesforce Decisions
        3. Example 3: Engineers Get Instant Feedback on Their Design Choices
      4. Conclusion
    16. 7. Identifying Business Opportunities by Recognizing Patterns
      1. Patterns of Group Behavior
      2. Importance of Pattern Recognition in Business
        1. Group Patterns by Clustering and Decision Trees
        2. Three Ways of Grouping
      3. Recognize Purchase Patterns: Association Analysis
        1. Association Rules
        2. Business Case
      4. Patterns over Time: Time Series Predictions
        1. Time Series Models
      5. Conclusion
    17. 8. Knowing the Unknowable
      1. Unknowable Events
      2. Unknowable in Business
        1. Poor or Inadequate Data
        2. Data with Limited Views
        3. Business Case
        4. Predicting Individual Customer Behaviors in Real-Time
      3. Lever Settings and Causality in Business
        1. Start with a High Baseline
        2. Causality with Control Groups
      4. Conclusion
    18. 9. Demonstration of Business Analytics Workflows: Analytics Enterprise
      1. A Case for Illustration
      2. Top Questions for Analytics Applications
        1. Financial Management
        2. Human Resources
        3. Internal Operations
      3. Conclusion
    19. 10. Demonstration of Business Analytics Workflows—Analytics CRM
      1. Questions About Customers
      2. Know the Customers
      3. Actionable Customer Insights
      4. Social and Mobile CRM Issues
      5. CRM Knowledge Management
      6. Conclusion
    20. 11. Analytics Competencies and Ecosystem
      1. Analytics Maturity Levels
      2. Analytics Organizational Structure
        1. The Centralized Model
        2. The Consulting Model
        3. The Decentralized Model
        4. The Center of Excellence Model
        5. Reporting Structures
      3. Roles and Responsibilities
        1. Analytics Roles
        2. Business Strategy and Leadership Roles
        3. Data and Quantitative Roles
      4. Analytics Ecosystem
        1. The In-House IT Function
        2. External Analytics Advisory and Consulting Resources
      5. Analytics Talent Management
      6. Conclusion
    21. 12. Conclusions and Now What?
      1. Analytics Is Not a Fad
      2. Acquire Rich and Effective Data
      3. Start with EDA and BI Analysis
      4. Gain Firsthand Analytics Experience
      5. Become an Analytics Decider and Recruit Others
      6. Empower Enterprise Business Processes with Analytics
      7. Recognize Patterns with Analytics
      8. Know the Unknowable
      9. Imbue Business Processes with Analytics
      10. Acquire Analytics Competencies and Establish Ecosystem
      11. Epilogue
    22. A. KNIME Basics
      1. Data Preparation
        1. Types of Variable Values
        2. Dummy Variables
        3. Missing Values
      2. Data Normalization
        1. Data Partitions
      3. Exploratory Data Analysis (EDA)
        1. Multi-Dimensional Cube
        2. Slicing
        3. Dicing
        4. Drilling Down or Up
        5. Pivoting
    23. Index

    Product information

    • Title: Applied Business Analytics: Integrating Business Process, Big Data, and Advanced Analytics
    • Author(s): Nathaniel Lin
    • Release date: December 2014
    • Publisher(s): Pearson
    • ISBN: 9780133481549