Leveraging data to generate insights in product analytics platforms like Mixpanel requires more than just data collection; it necessitates an optimized and accessible data structure for effective analysis. The article emphasizes the importance of structuring and naming data to enhance its accessibility and democratization, allowing analysts of varying expertise to unlock the full potential of Mixpanel's querying engine. By reorganizing events and utilizing properties effectively, the process of answering analytical questions becomes more straightforward and less error-prone, reducing the complexity and risk associated with data analysis. This optimization not only democratizes data by making it intuitive and self-explanatory for non-technical users but also empowers various team members, including designers, marketers, and sales teams, to participate in data-driven decision-making. Ultimately, fostering a well-structured data-collection layer is a critical first step in the data-driven product development process, maximizing the potential to derive meaningful insights that can drive business growth and improve customer experiences.