TL;DR Self-service analytics enables SaaS teams to pose a business question and receive a reliable, metric-backed answer without waiting on an analyst. In practice, that means: A definition alone isn’t sufficient. Every metric needs a clear owner who updates it as the business evolves. Governance—not tools—is what actually creates self-serve. Most BI initiatives break down at the metric and distribution layer, not at the tooling layer. The real challenge is definitions. What exactly qualifies as churn? Which ARR number belongs in the board deck? Resolve these first. AI is what finally makes self-serve usable for everyone. Natural language queries let anyone ask a business question without hunting for the right dashboard. But the LLM should never be responsible for doing your calculations. The standard to aim for: a decision-maker asks a question, receives a governed answer, and takes action in the same working session. Everything else is just implementation detail. The problem self-service analytics is meant to solve A CEO joins the Monday revenue review and sees two numbers that should match — but don’t. Pipeline coverage shows 2.1x in the board deck and 1.6x in the RevOps dashboard. She asks: “Which one is correct — and why are we arguing about the number instead of the plan?” That moment is exactly what self-service analytics is designed to avoid. Not by flooding everyone with more charts, but by making answers quick, consistent, and defensible. What is self-service analytics? Self-service analytics is an operating model where non-technical business users can pose a business question, receive a trusted, metric-backed answer, and act on it, without…