Observability extends beyond the traditional "three pillars" of logging, metrics, and tracing, and should be understood as a data analytics problem, requiring insights rather than just raw data. Dotan Horovits argues that observability is about enabling humans to ask and answer questions about systems, advocating for a shift from reactive monitoring to a proactive data analyst approach. This involves collecting and correlating data from diverse sources, using structured and standardized formats, and integrating new data types like events and profiling. OpenTelemetry is highlighted as a valuable tool for overcoming data silos by providing a vendor-agnostic framework for telemetry data. The discussion emphasizes the critical importance of structured data and consistent conventions to enhance system observability, along with the need for a unified query and visualization approach amidst a fragmented market. Organizations are encouraged to evaluate the value of their data to optimize its usefulness for real-time troubleshooting and effective observability, while ongoing community efforts aim to make observability more accessible.