Addie Beach and Shah Ahmed discuss the challenges of parsing metadata and user interaction information to understand user behavior, particularly when combining various analytical lenses. They advocate for a subtractive model that involves ingesting all usage data upfront and filtering it afterwards, enabling flexibility in adapting to changing project goals or realizing the need for new data. Organizing usage data into a hierarchical taxonomy helps analysts link and query data more easily, while verifying and normalizing data ensures accuracy and consistency. To effectively query product data, identifying useful events and parameters is crucial, considering both user interactions and metadata. The authors suggest using Datadog Product Analytics to visualize and query product usage data, providing various visualizations and enabling natural language queries. By collecting, normalizing, and verifying data, analysts can build accurate insights that ground UX decisions in concrete trends.