Keynote - The Evolving Role of Data in Software Development
summary
Conference argues that hoovering raw data into lakes sometimes pays off — one team kept speculative logs for a year and later used them to diagnose performance — but risks (breaches, consent, irrevocable backups) push toward datensparsamkeit. Recommends shifting from monolithic lakes to a data mesh, testing ML models for bias and explainability with tools like LIME and emerging differential privacy libraries, and treating schemas as code using Refactoring Databases and event‑sourcing patterns to evolve data safely.
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chapters (11)
- 0:00Introduction and speakers
- 3:50The role of data in software
- 7:01Data and application divide
- 10:41Data lakes and deliberate data
- 16:04Ethics, law, and data consent
- 19:54Integrating data science teams
- 23:32Data literacy and probability
- 26:52ML models and explainability
- 30:20Deliberate modeling practices
- 35:21Refactoring databases
- 38:26Audience Q&A and wrap-up
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