Enterprise decision making increasingly depends on the ability to interpret high-velocity, high-volume, and high-variety data streams in near real time. Traditional decision support systems struggle to scale across organizational boundaries, integrate heterogeneous data sources, and maintain trust among human decision makers. This article presents a scalable enterprise decision support architecture that leverages distributed data platforms to deliver timely, context-aware intelligence while preserving interpretability and operational resilience. The proposed approach integrates event-driven ingestion, distributed analytics, and humancentered decision workflows to support complex enterprise scenarios. Empirical evaluation across simulated enterprise workloads demonstrates improved responsiveness, scalability, and decision quality compared to monolithic and batch-oriented systems