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Chapter Summary: Data Strategy

Key Takeaways

  1. Data moats have shifted from volume to systems: The defensible advantage comes from data generated through product usage—not datasets you own. Harvey won with LexisNexis partnerships and workflow integration. IBM lost despite $4 billion in healthcare data acquisitions. The moat test: can a competitor buy, scrape, or partner for this data? If yes, your moat is measured in weeks.

  2. Flywheels require network learning, not just feedback loops: A true flywheel improves value for all users, not just the one generating data. Duolingo's Birdbrain updates difficulty estimates in real-time for all learners—driving 59% DAU growth. Klarna's AI handled 80% of inquiries but plateaued because customer service complexity doesn't compound. The test: if you delete one user's data, would other users notice?

  3. Start simple, add complexity when measured: Notion manages 200 billion blocks on sharded PostgreSQL. Vector databases grew 377% in 2024, but don't add one until you need embeddings. The pattern: PostgreSQL first, cache when latency is measured, document store when schema flexibility blocks iteration, vector when AI features require it. If you can't name the bottleneck each component solves, you have resume-driven architecture.

  4. Privacy accelerates flywheels, not constrains them: GDPR fines hit EUR 1.2 billion in 2024 (up 38%). EU AI Act high-risk deadline is August 2, 2026. But privacy-first companies like Mistral win enterprise contracts specifically because of compliance positioning. When users trust you, they share more valuable data. That's not idealism—that's unit economics.

  5. Build the flywheel after product-market fit: 43% of AI startups fail because they build products nobody wanted. 95% of enterprise pilots never reach production. The 92% startup failure rate within 18 months isn't from lack of innovation—it's from building infrastructure before validating demand. Validate manually, then automate.

Next: AI-Augmented Operations and GTM

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