Universities already have a data warehouse, and it is already full of meaning: student systems, HR, finance, research grants, space utilisation, learning platforms. The problem is rarely the absence of data. It’s the absence of connection.

When Pilbara shares its models and outputs directly into the university’s Snowflake environment, those outputs stop being reports and start being ingredients. The university can join them, test them, stress them, and—crucially—argue with them using its own evidence. The Pilbara Models are causal Activity-Based Costing Models that mirrors how the institution actually operates, which is an excellent platform for Snowflake Cortex AI. 

Consider course economics. Pilbara models the cost and margin of teaching activities at a granular level: subjects, delivery modes, class sizes, staffing profiles. When that data lives inside the university warehouse, it can be joined to enrolment projections, student progression data, and timetable constraints. Suddenly the question shifts from “Is this course profitable?” to “What happens if enrolments drop by 8% but international load increases?” or “Which subjects become marginal if we change assessment patterns?” These aren’t abstract scenarios; they’re simulations grounded in the university’s own planning assumptions.

Or take workload and staffing decisions. Pilbara data can describe how academic effort is distributed across teaching, research, and service. Joined with HR data—appointment types, contract lengths, leave balances—and learning platform data—assessment volumes, online engagement—it becomes possible to see pressure points forming before they turn into burnout or budget overruns. Faculties can explore whether workload imbalance correlates with attrition, or whether certain teaching models systematically rely on hidden overtime. The insight emerges not because Pilbara tells them, but because the university can see it for itself.

Space and infrastructure planning is another quiet win. Universities hold room bookings, sensor data, timetable data, and capital costs in different systems. Pilbara’s activity-based cost data, when shared into Snowflake, can be joined to actual utilisation patterns. This allows questions like: “Which teaching spaces are expensive but under-used?” or “Are we building capacity for a mode of teaching that’s declining?” Decisions about refurbishment or new builds become evidence-weighted rather than politically negotiated.

Research strategy benefits in subtler ways. Pilbara models can link academic activity to funding and cost structures. When combined with grant income, HDR load, and research outputs already in the warehouse, universities can explore the true cross-subsidies between teaching and research. This enables leadership to test scenarios like shifting internal funding models or supporting emerging disciplines—without relying on heroic assumptions or opaque spreadsheets.

What matters here is not that Snowflake enables sharing—that’s the plumbing. What matters is where the analysis lives. By sharing data into the university’s own Snowflake account, Pilbara avoids creating a parallel truth. There’s no data duplication to manage, no version drift, and no loss of institutional context. Analysts can use familiar tools, governance remains intact, and security boundaries are respected. The university retains control over who can see, combine, and extend the data.

In this model, Pilbara isn’t delivering answers. It’s delivering well-structured, defensible representations of university activity that are designed to be joined, questioned, and evolved. Better decisions emerge because the people closest to the problem can explore the data in context, test assumptions, and build consensus around shared evidence.

That’s the real shift: from “here is your insight” to “here is a shared analytical language for decision-making.”