How to Fix Annotation Bottlenecks: Ontology-Level Insights with Encord Analytics

Building production AI at scale requires more than just collecting data. You need a high-quality, efficient data development pipeline. But when labels keep getting rejected or annotation slows down, how do you identify the real problem? In this demo, Jagan, Technical Deployment Strategist, shows how to use Encord Analytics to uncover ontology-level insights across your annotation projects. You’ll learn how to: - Identify which labels are rejected most often - Detect annotation bottlenecks (e.g., truck vs car confusion) - Determine whether issues come from annotators, ontology definitions, or the data itself - Use analytics to fix root causes, not just symptoms 👉 Learn more about Encord: https://encord.com