The problem
Universities sit on enormous spatial datasets and can't see any of it. Room schedules, class sizes, department allocations, building utilisation, student movement between classes — all real, all captured, all stranded in spreadsheets and floor plans that nobody cross-references.
Architects and planners were being asked to make decisions about renovations and new buildings without a clear picture of how space was actually used. Difficult to turn fragmented data into design proposals when the data won't sit in one place.
Two projects, same underlying problem.
Dookie University — spatial relationships dashboard
A comprehensive Power BI dashboard visualising the organisational and spatial breakdown of the campus: departments, classes, room types, and distribution across multiple buildings.
I worked as Product Owner and Visual Analytics Lead, sitting as the liaison between the strategy, architecture, and data teams — three groups that each held part of the picture and none of which spoke the same language.
What it did:
- Consolidated data across schools and departments — number of classes, class types, teaching staff per area
- Drill-down views from school level to class level
- Data exports formatted for the architectural and strategic planning teams
- Designed and built an interface that surfaced architectural-planning and decision-making concepts in operational, real-world terms
The outcome: architects and planners working from a shared source of truth for planning decisions. Reduced reliance on hard-coded assumptions. Decisions about renovations and growth grounded in usage data, not anecdote.
RMIT University — 3D campus connectivity
A 3D data visualisation tool for campus connectivity — built to optimise student movement and timetable scheduling across a dense urban campus.
I held project oversight and the systems lead role, working as Product Manager for the spatial data systems and collaborating between academic departments and the tech build.
The tool turned scheduling and movement data into a spatial model planners could read at a glance — which buildings flood at which hour, where the bottlenecks form, how a timetable change ripples across the campus.
What I learned
The hard part of data visualisation in a spatial context is not the chart. It's the translation. Strategy teams talk in growth targets, architects talk in floor plans, data teams talk in tables. A useful dashboard is one that lets all three look at the same screen and agree on what they're seeing.
Product ownership here meant being the translator first and the builder second.
Stack: Power BI · spatial data modelling · 3D visualisation. Engagement type: product ownership and visual analytics lead. Academic and institutional planning projects.