From instinct to informed decision: launching data in your startup
summary
Provides practical guidance for startups to move from gut (HIPPO) decisions to measurement by building a minimal, production-ready data stack rather than chasing hype. Recommends hiring one senior data-ops, junior data engineers and analysts before hiring data scientists, and using SQL and Python with Airflow, Terraform, a data warehouse, Great Expectations and a catalog (Amundsen/Marquez); also highlights encrypting or hashing PII (example: Tanker) and anticipating governance tradeoffs with data-mesh approaches, citing Doctolib scale (650k appointments/day, 20M monthly users) as context for production needs.
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chapters (5)
- 0:00Introduction
- 2:44Data as business problem
- 12:29What is data mesh
- 23:16Data team profiles
- 35:17Conclusion
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