Five dimensions for data production strategy — whether you're running a traditional data company or building data operations at an AI lab.
Who do we produce for, and what decisions do they make?
Define target groups, map their use cases, and understand the workflows that data must serve. The supreme discipline of data production.
→ Articles 1, 2, 3What should we produce, and what should we stop?
Evaluate data types across strategic, financial, and production attractiveness. Manage the portfolio as a disciplined allocation, not a backlog.
→ Article 4How do we organize and produce at scale?
Team structure, production means, methods, AI-enabled tooling, content steering. Building the factory and running it.
→ Articles 5, 6, 7How do we measure quality and stay close to customers?
Quality frameworks, feedback loops, customer signal collection. Why you can't QA your way out of a production design problem.
→ Articles 8, 9What do we sell, and how do we get it to market?
Data vs. insights vs. software. Products, pricing, packaging, and the Sales collaboration.
→ Articles 10, 11