Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data

Book description

The Definitive Guide to Enterprise-Level Analytics Strategy, Technology, Implementation, and Management

Organizations are capturing exponentially larger amounts of data than ever, and now they have to figure out what to do with it. Using analytics, you can harness this data, discover hidden patterns, and use this knowledge to act meaningfully for competitive advantage. Suddenly, you can go beyond understanding “how, when, and where” events have occurred, to understand why – and use this knowledge to reshape the future. Now, analytics pioneer Tom Davenport and the world-renowned experts at the International Institute for Analytics (IIA) have brought together the latest techniques, best practices, and research on analytics in a single primer for maximizing the value of enterprise data. Enterprise Analytics is today’s definitive guide to analytics strategy, planning, organization, implementation, and usage. It covers everything from building better analytics organizations to gathering data; implementing predictive analytics to linking analysis with organizational performance. The authors offer specific insights for optimizing supply chains, online services, marketing, fraud detection, and many other business functions. They support their powerful techniques with many real-world examples, including chapter-length case studies from healthcare, retail, and financial services. Enterprise Analytics will be an invaluable resource for every business and technical professional who wants to make better data-driven decisions: operations, supply chain, and product managers; product, financial, and marketing analysts; CIOs and other IT leaders; data, web, and data warehouse specialists, and many others.

Table of contents

  1. Title Page
  2. Copyright Page
  3. Contents at a Glance
  4. Contents
  5. Foreword and Acknowledgments
  6. About the Authors
  7. Introduction: The New World of Enterprise Analytics
    1. The Rise of Analytics
    2. Enterprise Analytics
    3. The Rise of “Big Data”
    4. IIA and the Research for This Book
    5. The Structure of This Book
  8. Part I. Overview of Analytics and Their Value
    1. 1. What Do We Talk About When We Talk About Analytics?
      1. Why We Needed a New Term: Issues with Traditional Business Intelligence
      2. Three Types of Analytics
      3. Where Does Data Mining Fit In?
      4. Business Analytics Versus Other Types
      5. Web Analytics
      6. Big-Data Analytics
      7. Conclusion
    2. 2. The Return on Investments in Analytics
      1. Traditional ROI Analysis
      2. The Teradata Method for Evaluating Analytics Investments
      3. An Example of Calculating the Value 1
      4. Analytics ROI at Freescale Semiconductor
  9. Part II. Application of Analytics
    1. 3. Leveraging Proprietary Data for Analytical Advantage
      1. Issues with Managing Proprietary Data and Analytics
      2. Lessons Learned from Payments Data
      3. Endnote
    2. 4. Analytics on Web Data: The Original Big Data
      1. Web Data Overview
      2. What Web Data Reveals
      3. Web Data in Action
      4. Wrap-Up
    3. 5. The Analytics of Online Engagement
      1. The Definition of Engagement
      2. A Model to Measure Online Engagement
      3. The Value of Engagement Scores
      4. Engagement Analytics at PBS
      5. Engagement Analytics at Philly.com
    4. 6. The Path to “Next Best Offers” for Retail Customers
      1. Analytics and the Path to Effective Next Best Offers
      2. Offer Strategy Design
      3. Know Your Customer
      4. Know Your Offers
      5. Know the Purchase Context
      6. Analytics and Execution: Deciding on and Making the Offer
      7. Learning from and Adapting NBOs
  10. Part III. Technologies for Analytics
    1. 7. Applying Analytics at Production Scale
      1. Decisions Involve Actions
      2. Time to Business Impact
      3. Business Decisions in Operation
      4. Compliance Issues
      5. Data Considerations
      6. Example of Analytics at Production Scale: YouSee
      7. Lessons Learned from Other Successful Companies
      8. Endnote
    2. 8. Predictive Analytics in the Cloud
      1. Business Solutions Focus
      2. Five Key Opportunities
      3. The State of the Market
      4. Pros and Cons
      5. Adopting Cloud-Based Predictive Analytics
      6. Endnote
    3. 9. Analytical Technology and the Business User
      1. Separate but Unequal
      2. Staged Data
      3. Multipurpose
      4. Generally Complex
      5. Premises- and Product-Based
      6. Industry-Generic
      7. Exclusively Quantitative
      8. Business Unit-Driven
      9. Specialized Vendors
      10. Problems with the Current Model
      11. Changes Emerging in Analytical Technology
      12. Creating the Analytical Apps of the Future
      13. Summary
    4. 10. Linking Decisions and Analytics for Organizational Performance 1
      1. A Study of Decisions and Analytics
      2. Linking Decisions and Analytics
      3. A Process for Connecting Decisions and Information
      4. Looking Ahead in Decision Management
      5. Endnotes
  11. Part IV. The Human Side of Analytics
    1. 11. Organizing Analysts
      1. Why Organization Matters
      2. General Goals of Organizational Structure
      3. Goals of a Particular Analytics Organization
      4. Basic Models for Organizing Analysts
      5. Coordination Approaches
      6. What Model Fits Your Business?
      7. How Bold Can You Be?
      8. Triangulating on Your Model and Coordination Mechanisms
      9. Analytical Leadership and the Chief Analytics Officer
      10. To Where Should Analytical Functions Report?
      11. Building an Analytical Ecosystem
      12. Developing the Analytical Organization Over Time
      13. The Bottom Line
      14. Endnotes
    2. 12. Engaging Analytical Talent
      1. Four Breeds of Analytical Talent
      2. Engaging Analysts
      3. Arm Analysts with Critical Information About the Business
      4. Define Roles and Expectations
      5. Feed Analysts’ Love of New Techniques, Tools, and Technologies
      6. Employ More Centralized Analytical Organization Structures
    3. 13. Governance for Analytics
      1. Guiding Principles
      2. Elements of Governance
      3. You Know You’re Succeeding When...
    4. 14. Building a Global Analytical Capability
      1. Widespread Geographic Variation
      2. Central Coordination, Centralized Organization
      3. A Strong Center of Excellence
      4. A Coordinated “Division of Labor” Approach
      5. Other Global Analytics Trends
      6. Endnotes
  12. Part V. Case Studies in the Use of Analytics
    1. 15. Partners HealthCare System
      1. Centralized Data and Systems at Partners
      2. Managing Clinical Informatics and Knowledge at Partners
      3. High-Performance Medicine at Partners
      4. New Analytical Challenges for Partners
      5. Centralized Business Analytics at Partners
      6. Hospital-Specific Analytical Activities: Massachusetts General Hospital
      7. Hospital-Specific Analytical Activities: Brigham & Women’s Hospital
      8. Endnotes
    2. 16. Analytics in the HR Function at Sears Holdings Corporation
      1. What We Do
      2. Who Make Good HR Analysts
      3. Our Recipe for Maximum Value
      4. Key Lessons Learned
    3. 17. Commercial Analytics Culture and Relationships at Merck
      1. Decision-Maker Partnerships
      2. Reasons for the Group’s Success
      3. Embedding Analyses into Tools
      4. Future Directions for Commercial Analytics and Decision Sciences
    4. 18. Descriptive Analytics for the Supply Chain at Bernard Chaus, Inc.
      1. The Need for Supply Chain Visibility
      2. Analytics Strengthened Alignment Between Chaus’s IT and Business Units
  13. Index

Product information

  • Title: Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data
  • Author(s): Thomas H. Davenport
  • Release date: September 2012
  • Publisher(s): Pearson
  • ISBN: 9780133039498