High Signal: Data Science | Career | AI

Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson

Jan 30, 2025

Eric Colson, former Chief Algorithms Officer at Stitch Fix and VP of Data Science at Netflix, discusses why 90% of data science initiatives fail. He emphasizes the need to treat data scientists as strategic drivers rather than mere service providers. Colson highlights the power of cognitive repertoires in problem-solving and advocates for a culture of experimentation, where trial and error leads to innovation. He also shares insights on restructuring data teams to transform them from cost centers into revenue generators, enhancing business value through collaboration.

01:09:40

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Episode guests

Eric Colson

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Episode notes

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Quick takeaways

Empowering data scientists as strategic drivers rather than support functions unlocks their potential for business innovation and growth.

Emphasizing trial and error in data initiatives fosters a culture of experimentation, leading to more significant achievements despite typical failures.

Decoupling algorithm development from application interfaces allows data scientists to prioritize impactful ideas and enhance their contributions to overall business strategy.

Deep dives

The Role of Data Scientists in Organizations

Data scientists are often perceived as a support function rather than essential contributors in organizations. This role limits their potential as they primarily respond to requests from business teams, which can undervalue their insights and ideas. It is essential for organizations to recognize the dual flow of ideas, where data scientists should be allowed to contribute concepts that drive business strategy. Encouraging collaboration will not only harness their full capabilities but also unlock significant opportunities that may otherwise be overlooked.

Intro

4min

Unlocking the Collaborative Potential of Data Scientists

4min

Unlocking Business Potential with Cognitive Repertoires in Data Science

5min

Navigating Data Science: From Simplicity to Complexity

14min

The Power of Experimentation in Data Science

26min

Transforming Data Science into a Revenue Generator

14min

Proactive Collaboration and the Evolving Role of Data Science

2min

In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as a service function to empowering them as strategic drivers of business impact.

Key topics from the conversation include:

Data Science as a Strategic Function: Why many companies limit their data teams to answering business requests instead of leveraging their ideas for competitive advantage.
Beyond Skills—The Power of Cognitive Repertoires: How data scientists' unique ways of framing problems can lead to breakthrough innovations.
Trial and Error as a Competitive Advantage: Why most experiments fail—but scaling experimentation is the key to big wins.
Decoupling Algorithms from Applications: How separating data science from engineering enables rapid iteration and direct business impact.
Shifting from Cost Center to Revenue Generator: Practical steps for structuring data teams to drive measurable value and long-term success.

💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities.

You can find more on our website: https://high-signal.delphina.ai/

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High Signal: Data Science | Career | AI

Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

The Role of Data Scientists in Organizations

Emerging Centrality of Data Science

Leveraging Ideas from Data Scientists

Embracing Trial and Error in Experimentation

Creating Spaces for Autonomous Data Science

Remember Everything You Learn from Podcasts