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Learn how to build privacy-enhancing data processing systems for sensitive healthcare data in this 46-minute conference talk from EuroPython 2025. Discover the data architecture and core technologies that enable cross-functional teams of scientists, engineers, and clinicians to collaborate effectively while working with cancer patient data across heterogeneous environments. Explore how to transform raw sensitive data into research-grade datasets while maintaining strict privacy requirements and regulatory compliance. Understand the challenges, failures, and successes encountered when implementing feedback loops between different stakeholders in highly regulated healthcare environments. Examine practical approaches to leveraging domain knowledge, data science, and established technologies to create quality-maintaining tools that accelerate insight generation. Gain insights into shifting insights generation left to empower teams for faster iteration and more impactful outcomes. Master techniques for using the Pythonic data stack to iteratively generate insights from sensitive data while ensuring high data quality standards required for groundbreaking medical research.
