Summary
The talk addresses the often misunderstood roles of data scientists and designers, emphasizing the power of their collaboration to create customer delight and actionable insights. Drawing from his experiences at Intuit and a large retailer, the speaker illustrates how data platforms and design thinking come together to improve product experimentation speed, usability, and business outcomes. At Intuit, data scientist and design teams partnered to streamline A/B testing across thousands of developers, revealing the importance of measuring leading indicators like ease of use and time to production rather than just lagging metrics. In retail, a collaboration focused on understanding high-performing store managers led to a simplified, actionable dashboard removing overwhelming data overload and improving conversion by 10%. The third story from eBay shows how cross-functional work with designers and analysts democratized massive data sets, significantly reducing time to insight from weeks to a day. The speaker underscores that fusing data science with design elevates enterprise products and innovations, proposing that data should be the fourth pillar alongside designers, developers, and product managers to foster customer delight and insight-driven decisions.
Key Insights
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Fewer than 1% of global data collected is ever used to generate insights that impact people.
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Data scientists blend skills from statistics, hacking, and entrepreneurship to extract value from big data.
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Rapid experimentation through A/B testing drives innovation but requires platform usability and awareness to scale.
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Measuring leading indicators like time to consumption and platform awareness can better drive adoption than lagging metrics alone.
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Ethnographic field studies with end users (developers, store managers) uncover critical insights missed by pure data analysis.
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Simplifying complex dashboards based on top-performers’ workflows empowers users and improves business outcomes (e.g., 10% sales lift).
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Combining design and data science can democratize complex enterprise data, reducing insight turnaround from weeks to days.
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Data’s role should expand within product teams to become the fourth essential pillar alongside design, product, and development.
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Effective data products require not just analytics but thoughtful user-centered design to shift insights from theory to actionable knowledge.
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Data quality factors like platform uptime and test reliability critically affect the confidence and usage of data-driven features.
Notable Quotes
"Fewer than 1% of the data collected globally is actually ever used to deliver any sort of insights for people."
"A data scientist is part stat geek, part hacker, and part entrepreneur all rolled into one."
"If you want to drive more experiments, the platform has to be easy to use, and people need to actually know it exists."
"What really matters isn’t just how many experiments you run, but how many new features get delivered to customers."
"We found that measuring the lagging indicators wasn’t enough; we needed leading indicators to understand what really drove adoption."
"Instead of overwhelming store managers with 424 pages of data, we focused on what the best-performing stores actually use to run their business."
"Spending time with 300 data analysts helped designers understand their workflow so they could build a product that accelerates insight delivery."
"This data platform became one of the top three services within the company by truly shifting conversations from I think to I know."
"Combining design and data science is how you bring data to life and make it truly meaningful for everyone across an enterprise."
"I hope data becomes the fourth leg of the stool alongside design, development, and product management."
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