This article explores how to anticipate and design data platforms that remain relevant over time. It introduces a framework for projecting data needs based on consumer behavior, inquiry modes, and decision-making tiers, emphasizing the importance of leaving gaps for future requirements. It also discusses the role of data products in adapting to changing business environments.

This article explores a shift in data modeling from rigid orthodoxies to a more pragmatic approach. It emphasizes starting with simple structures, adding complexity only when necessary, and leveraging semantic clarity for flexibility across different modeling techniques.