In recent years, data-driven teams have faced the challenge of determining which metrics are essential amid an abundance of data, focusing on two main types: infrastructure metrics and product metrics. Infrastructure metrics, tracked using tools like Datadog and New Relic, focus on internal service trends and are stored in time-based event stores, allowing for fast filtering and analysis. In contrast, product metrics, emphasized by platforms such as Moesif and Mixpanel, center around user behavior and experience, linking event data to individual users to uncover trends and issues, such as feature engagement and user retention. These metrics are stored in user-centric data models, which facilitate the analysis of customer experience over time but require more complex data processing. Choosing between time-based and user-centric data models significantly influences the type of analysis possible, with each model having distinct advantages and limitations depending on the analytics goals.