Research management is increasingly driven by data, yet many current re-search information systems (CRIS) remain fragmented and provide limited decision support. This paper introduces a use-case–driven decision model for CRIS that systematically integrates advanced technologies—including data fabric, data mesh, knowledge graphs, predictive analytics, and AI/ML meth-ods—to enable informed, transparent, and explainable decision-making. The proposed framework addresses key challenges such as data heterogeneity, manual processing, and siloed information, offering a scalable, flexible, and user-centric solution for research managers, scientists, and university lead-ers. By combining analytical depth with governance and interpretability, the model provides actionable insights that enhance strategic planning, collabo-ration, and resource allocation in research institutions.