Schema is shifting from "nice-to-have for rich results" to "interpretation infrastructure" that reduces ambiguity for machines. This session delves into schema graphs, template-first rollouts, and the QA traps that quietly destroy value (contradictory entities, duplicated markup, injected tags). The focus is pragmatic: which schema work tends to pay off, how to operationalise it across large sites, and how to communicate it internally as data engineering—worthy of investment. To stay focused on things that make a business impact, this session will deliver a prioritised schema roadmap (templates first; highest-intent entities) to help attendees, a QA and governance process to prevent silent schema failures, and help for those in-house (or even for agencies) to create an internal business case framing schema as infrastructure for discovery.

In this talk, she will explain what schema markup is and what it isn’t, and how its role is evolving in Google Search and AI-driven systems. She will share real-world examples from Octopus Energy across three continents, along with a practical schema hygiene framework, common misconceptions, and lessons from scaling structured data at enterprise level.