Metrics are essential for experimentation as they help evaluate ideas and guide future directions. Teams collect diverse metrics, but integrating data from various sources can be challenging due to silos. There are two main types of experimental data: event stream data, which provides real-time insights and tracks application performance, and transactional data, which is used for long-term analyses and business metrics. Designing experiments involves setting goal metrics, which tie closely to hypotheses, and driver and guardrail metrics, which are derived from event stream data for immediate insights. Teams also use transactional data for audience selection and post-experiment analysis. Datadog integrates event stream and transactional data into a single platform, facilitating robust experiments and analyses by offering real-time observability and precise business insights. This integration enables cross-team collaboration and efficient decision-making, with Datadog providing tools to visualize and manage experimental data effectively, reducing silos and increasing confidence in results.