Designing a data model that reflects your user journey

The text explores the intricacies of designing data models to effectively serve different internal teams and business needs, emphasizing the importance of structuring models to reflect specific user interactions, cycles, and entities. It critiques the traditional web analytics model, which organizes data into page views, sessions, and users, by highlighting its limitations, such as its inability to capture diverse interactions and varying user journeys across platforms like mobile and web. The discussion extends into practical applications, showcasing how different business models, like eCommerce and subscription services, require tailored data models to answer distinct questions from teams such as marketing, inventory, product, and content. By examining examples from these sectors, the text illustrates how data models must be adapted to provide actionable insights, whether for optimizing marketing strategies, understanding product popularity, or reducing user churn. Additionally, it addresses technical considerations in building data models, such as dataset size, data warehouse architecture, and end-use cases, while warning against the pitfalls of creating overly complex models. Ultimately, the goal is to construct data models that are not only valuable and reusable but also aligned with the organization’s strategic objectives, ensuring the data serves its intended purpose effectively.