A Data-Driven Framework for Sustainable Resource Allocation in Social Systems: A Case Study of Saudi Arabia’s Nonprofit Sector

The principles of system efficiency and resource optimization, focused on optimizing resource flow and minimizing waste, can be effectively applied to social systems. This study presents a data-driven resource allocation framework for Saudi Arabia’s burgeoning nonprofit sector, demonstrating how data analytics can enhance social sustainability. Utilizing a dataset of 1197 organizations from a national fundraising platform, we applied K-Means clustering based on governance, financial reporting quality, administrative costs, and fundraising compliance. An elbow test confirmed a two-cluster solution, effectively segmenting the sector into high-performing organizations (Cluster 1: high governance, high compliance, low overhead) and those requiring intervention (Cluster 0: lower governance, low compliance, high overhead). The results demonstrate that a significant portion of sector resources are currently consumed by administrative overhead in less efficient organizations. We translate these findings into an optimized allocation framework that channels funds towards high-impact entities while prescribing targeted capacity-building for lagging groups. This approach directly supports the social development pillars of Saudi Vision 2030 by maximizing the societal return on investment and reducing systemic waste, showcasing how data-driven management techniques can foster a more robust and sustainable social ecosystem.