Web applications generate extensive metadata and user interaction information that is vital for understanding user behavior. However, parsing this data to find the most relevant information for product analytics projects can be challenging due to varying perspectives on what constitutes useful data. To effectively analyze usage data, a comprehensive product analytics strategy covering ingestion, analysis, data identification, verification, and normalization is required. This strategy involves organizing and ingesting all app-emitted usage data upfront, then filtering and querying it to answer key questions. A hierarchical data taxonomy can help separate the data into logical categories and subcategories, while establishing primary keys enables linkage of event data with external reference tables for contextual insights. Verifying and normalizing ingested data is crucial before performing deeper analysis on a larger scale. Effective querying involves identifying useful information from product data consisting of events (user actions) and parameters (metadata associated with events). Visualization tools like Datadog Product Analytics can help identify usage trends, enrich data with context from reference tables, and perform natural language queries to answer granular questions.