Project Details

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Data Governance Platform for Campus Facility Digital Twins

Company: Cocoziello Institute of Real Estate Innovation

Major(s):
Primary: CMPSC
Secondary: IE

Non-Disclosure Agreement: NO

Intellectual Property: NO

Universities and other large organizations are building "digital twins" of their buildings and campuses — live connected digital models that pull together data from building models, geographic information systems, sensors, maintenance records, space management systems, security cameras, and more to improve operations, sustainability, and emergency planning and response. But as these systems grow, a challenging question keeps coming up: who should be allowed to access what data, when, and for what purpose? Most organizations know who the lead owner is for each data source, but there's rarely a structured data governance process to request, review, and approve access across systems. Today, that usually means emailing the right office and waiting. This project asks a student team to design and build a working prototype of a data governance process and platform: a system where users can request access to specific data, have that request routed through a clear approval workflow, and have every decision logged and auditable. The team will define the different types of users (facilities staff, researchers, security, outside partners, public), what data each should be allowed to see, and build software that runs that approval process automatically instead of relying on manual requests. Good data governance isn't just about restricting access — it's also what makes new capabilities possible. When data from separate systems can be securely combined, it opens the door to applying artificial intelligence across those combined sources: for example, spotting maintenance issues before they happen, predicting occupancy and energy use, or flagging unusual patterns that a human monitoring one system at a time would miss. Part of the project is showing how the data governance platform doesn't just control access, but actively enables this kind of cross-system analysis and AI use. The team will work with a realistic example facility — think a museum, stadium, hospital, or airport — and demonstrate the system handling multiple users requesting different kinds of access for different digital twin use cases, with the platform granting, denying, and logging each request appropriately. Expected outcome: A clearly defined data governance process — covering access requests, approvals, and audit logging — demonstrated through a working software prototype that shows how that governance can safely enable AI-driven insights across combined data sources.