Why 90% of Data Science Fails — And How to Fix It — With Eric Colson

Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—explains why most companies fail to fully leverage their data science teams. Drawing on his experience leading data functions at top tech companies, he shares how organizations can move beyond treating data science as a support function and instead empower data scientists to drive strategic impact through experimentation, iteration, and algorithmic decision-making.

Unlocking Data Scientist Potential: From Support Role to Core Business Impact with Eric Colson

In this episode, Eric Colson, a data science and machine learning advisor, shares his invaluable insights on how companies can fully leverage the potential of their data science teams. Colson delves into the frequent pitfalls of treating data scientists as support functions and outlines strategies for organizations to unlock the true value of data scientists' ideas. He discusses the importance of bi-directional flow of ideas, the benefits of decoupling algorithms from engineering, and the necessity of adopting a trial and error approach to experimentation. Colson also elaborates on the structural, organizational, and cultural changes required to transform data science from a supporting role into a core function driving business impact, emphasizing collaboration and accountability. This conversation is a treasure trove of strategic and tactical advice for leaders aiming to harness data science for significant revenue and retention improvements.

00:00 Data Scientist Value Left on the Table
00:28 Meet Eric Colson: DS/ML Advisor, ex-StitchFix, Netflix
01:34 The Evolving Role of Data Science
03:40 Unlocking the Potential of Data Scientists
04:10 Common Pitfalls in Data Science Utilization
07:59 The Importance of Cognitive Repertoires
09:54 Case Study: Stitch Fix's Algorithm Transformation
12:55 Leveraging Data Scientists' Unique Insights
21:30 Balancing Short-Term and Long-Term Goals
27:41 The Role of Experimentation in DS & ML
36:40 The Importance of Evidence in Data Science
37:59 Exploration and Trial Phases in DS & ML
38:29 Challenges in A/B Testing for Non-Algorithm Functions
39:36 Optionality in Data Science Experiments
45:13 Scaling Experimentation and Mitigating Risks
54:16 Organizational Changes for Data Science Impact
01:05:10 Final Thoughts and Takeaways