You are previewing Delivering Business Analytics: Practical Guidelines for Best Practice.
O'Reilly logo
Delivering Business Analytics: Practical Guidelines for Best Practice

Book Description

AVOID THE MISTAKES THAT OTHERS MAKE - LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS

This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes.

Delivering Business Analytics also outlines the Data Scientist's Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue's solution; and how these solutions can optimally create organizational value.

With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on:

  • The economic principles behind how business analytics leads to competitive differentiation

  • The elements which define best practice

  • The Data Scientist's Code, fifteen management principles that when followed help teams move towards best practice

  • Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making

Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists.

Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.

Table of Contents

  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Part One: Business Analytics Best Practices
    1. Chapter 1: Business Analytics: A Definition
      1. What Is Business Analytics?
      2. Core Concepts and Definitions
      3. Note
    2. Chapter 2: The Competitive Advantage of Business Analytics
      1. Advantages of Business Analytics
      2. Challenges of Business Analytics
      3. Establishing Best Practices
      4. Notes
  9. Part Two: The Data Scientist’s Code
    1. Chapter 3: Designing the Approach
      1. Think about Competencies, Not Functions
      2. Drive Outcomes, Not Insight
      3. Automate Everything Non-Value-Added
      4. Start Flexible, Become Structured
      5. Eliminate Bottlenecks
      6. Notes
    2. Chapter 4: Creating Assets
      1. Design Your Platform for Use, Not Purity
      2. Always Have a Plan B
      3. Know What You Are Worth
      4. Own Your Intellectual Property
      5. Minimize Custom Development
    3. Chapter 5: Managing Information and Making Decisions
      1. Understand Your Data
      2. It’s Better to Have Too Much Data Than Too Little
      3. Keep Things Simple
      4. Function Should Dictate Form
      5. Watch the Dynamic, Not Just the Static
      6. Note
  10. Part Three: Practical Solutions: People and Process
    1. Chapter 6: Driving Operational Outcomes
      1. Augmenting Operational Systems
      2. Breaking Bottlenecks
      3. Optimizing Monitoring Processes
      4. Encouraging Innovation
      5. Notes
    2. Chapter 7: Analytical Process Management
      1. Coping with Information Overload
      2. Keeping Everyone Aligned
      3. Allocating Responsibilities
      4. Opening the Platform
  11. Part Four: Practical Solutions: Systems and Assets
    1. Chapter 8: Computational Architectures
      1. Moving Beyond the Spreadsheet
      2. Scaling Past the PC
      3. Staying Mobile and Connected
      4. Smoothing Growth with the Cloud
      5. Notes
    2. Chapter 9: Asset Management
      1. Moving to Operational Analytics
      2. Measuring Value
      3. Measuring Performance
      4. Measuring Effort
      5. Note
  12. Part Five: Practical Solutions: Data and Decision Making
    1. Chapter 10: Information Management
      1. Creating the Data Architecture
      2. Understanding the Data Value Chain
      3. Creating Data-Management Processes
      4. Capturing the Right Data
      5. Notes
    2. Chapter 11: Decision-Making Structures
      1. Linking Analytics to Value
      2. Reducing Time to Recommendation
      3. Enabling Real-Time Scoring
      4. Blending Rules with Models
  13. Appendix: The Cheat Sheets
  14. Glossary
  15. Further Reading
  16. About the Author
  17. Index