Dmitri Mendeleev’s approach to the periodic table serves as a metaphor for designing data platforms that adapt to future needs. Mendeleev intentionally left gaps for undiscovered elements, emphasizing that a robust framework should focus on potential rather than just current data. Modern data platforms should similarly anticipate future demands, leaving space for new consumption patterns and decision-making processes. The article outlines a structured framework to project data needs by analyzing consumer behavior, inquiry modes, and decision tiers. The framework consists of a three-dimensional "Cuboid of Future Needs." Each axis defines a different aspect of data consumption: the consumer spectrum ranges from human decision-makers to autonomous machines, the inquiry mode shifts from hypothesis-driven to exploratory, and the decision tier spans strategic to operational contexts. By plotting analytics use cases within this cuboid, organizations can identify existing demand patterns and pinpoint gaps that indicate future opportunities. This predictive mapping helps organizations avoid becoming obsolete as their data platforms evolve. A key insight is that data platforms often become irrelevant when they are designed solely for current workflows. As contexts change, rigid structures can hinder adaptability. Therefore, it's essential to focus on structural completeness that can accommodate new technologies and decision models. The article highlights the importance of understanding where consumption is today, where it's emerging, and where it must go, offering a roadmap for future-proofing data investments. The ultimate goal is to translate this predictive framework into actionable architecture that supports evolving data needs.