An introduction to event data modeling

Event data modeling is a critical component of the Snowplow data pipeline, enabling companies to effectively utilize event data across their organizations to inform decision-making for various teams such as marketing and product management. This process involves applying business logic to aggregate atomic, event-level data into modeled data that is easier to query, providing insights into complex user behaviors and business processes. Event data modeling is not straightforward due to the need to interpret sequences of events and apply evolving business logic, which results in mutable data models that adapt to new insights or changes in understanding. The modeled data is typically aggregated into higher-order entities like workflows, sessions, or user journeys, allowing businesses to analyze patterns and impacts of events across different dimensions. Despite its complexity, event data modeling produces datasets that are simpler and more efficient to analyze, making it an essential practice for businesses aiming to leverage their data effectively.