Books & Videos

Table of Contents

  1. Chapter 1 What Do We Mean by Data-Driven?

    1. Data Collection

    2. Data Access

    3. Reporting

    4. Alerting

    5. From Reporting and Alerting to Analysis

    6. Hallmarks of Data-Drivenness

    7. Analytics Maturity

    8. Overview

  2. Chapter 2 Data Quality

    1. Facets of Data Quality

    2. Dirty Data

    3. Data Provenance

    4. Data Quality Is a Shared Responsibility

  3. Chapter 3 Data Collection

    1. Collect All the Things

    2. Prioritizing Data Sources

    3. Connecting the Dots

    4. Data Collection

    5. Purchasing Data

    6. Data Retention

  4. Chapter 4 The Analyst Organization

    1. Types of Analysts

    2. Analytics Is a Team Sport

    3. Skills and Qualities

    4. Just One More Tool

  5. Chapter 5 Data Analysis

    1. What Is Analysis?

    2. Types of Analysis

  6. Chapter 6 Metric Design

    1. Metric Design

    2. Key Performance Indicators

  7. Chapter 7 Storytelling with Data

    1. Storytelling

    2. First Steps

    3. Sell, Sell, Sell!

    4. Data Visualization

    5. Delivery

    6. Summary

  8. Chapter 8 A/B Testing

    1. Why A/B Test?

    2. How To: Best Practices in A/B Testing

    3. Other Approaches

    4. Cultural Implications

  9. Chapter 9 Decision Making

    1. How Are Decisions Made?

    2. What Makes Decision Making Hard?

    3. Solutions

    4. Conclusion

  10. Chapter 10 Data-Driven Culture

    1. Open, Trusting Culture

    2. Broad Data Literacy

    3. Goals-First Culture

    4. Inquisitive, Questioning Culture

    5. Iterative, Learning Culture

    6. Anti-HiPPO Culture

    7. Data Leadership

  11. Chapter 11 The Data-Driven C-Suite

    1. Chief Data Officer

    2. Chief Analytics Officer

    3. Conclusion

  12. Chapter 12 Privacy, Ethics, and Risk

    1. Respect Privacy

    2. Practice Empathy

    3. Data Quality

    4. Security

    5. Enforcement

    6. Conclusions

  13. Chapter 13 Conclusion

  14. Appendix On the Unreasonable Effectiveness of Data: Why Is More Data Better?

    1. Nearest Neighbor Type Problems

    2. Relative Frequency Problems

    3. Estimating Univariate Distribution Problems

    4. Multivariate Problems

  15. Appendix Vision Statement

    1. Value

    2. Activation