The Analytics Factory: Building the Machine That Understands Your Business

AI can build software faster. But that does not mean it builds better software.

The software industry’s answer is not better prompts. It's the software factory: a system built around better blueprints. Define what good looks like once, and AI can build toward it, test its work, and improve over time. The blueprint becomes the asset. The code becomes an output.

Data is next, but the modern data stack was built for an older model of analytics. It resembles an early assembly line: fixed stages, manual handoffs, fragmented context, and weeks of cycle time. It was built for questions you can anticipate, not for exploration that moves freely across the business.

The next model is the analytics factory. Its foundation is ontology as code: a single, version-controlled definition of the business that storage, pipelines, metrics, dashboards, playbooks, and agent workflows can all build from. Change the definition once, and the factory retools around it.

This talk explores what an analytics factory looks like in production: how the ontology becomes the blueprint AI agents work against, why verification rather than generation is the real bottleneck in AI analytics, and what it takes to move beyond a stack built for predetermined questions.