The recent EDUCAUSE report — “2026 EDUCAUSE Top 10: Making Connections” — identifies ten pressing technology, data and organisational issues for higher education institutions around the world. (EDUCAUSE Review)
While many of these issues focus on technology and data, a clear theme is emerging: institutions must build connections — across people, functions, data systems, and strategy — to thrive in a time of rapid change.
For universities wrestling with cost pressures, shifting enrollments, complex delivery models and rising demands for transparency, these issues signal strong alignment with the need for sophisticated cost-modelling and financial insight.
Below we pick out several of the key EDUCAUSE issues and show how a robust cost-modelling solution can help institutions respond — not just react.
1. Data Analytics for Operational and Financial Insights (Issue #3)
“Leveraging data analytics to provide insights into spending patterns, enrollment trends, and areas for cost savings and operational efficiencies.”
The Issue
Financial pressures are intensifying: shrinking revenues from traditional sources, increasing scrutiny of return on investment, and rising demands for accountability.
Many institutions are investing in analytics and business intelligence tools, but often still lack the detailed insight into true cost-to-serve of teaching, student services, and academic programs.
Furthermore, cleaning/organising data, aligning spend across functions, and interpreting analytics results remain major hurdles.
How Cost-Modelling Helps
This is the core of our solution specifically addressing this section “New data and analytics capabilities in the form of ERPs, CRMs, and BI platforms (just to name a few) offer opportunities for campus-wide discussions about enrollment trends, student success, financial pain points, and potential opportunities for operational and budgetary efficiencies.”
The Pilbara Insights Model is the connection mechanism of all of these systems and tracing all resourcing (people, facilities, expenditure) through all activities undertaken to the individual subject instance (when, where and how it’s taught) and rolling up to the various Academic Programs. With the easy inclusion of revenue, the institution can readily see, at a very granular level, what’s making money and what’s losing money. Highlighting all of the cross-subsidization that is usually hidden.
2. Building a Data-Centric Culture Across the Institution (Issue #4)
“Expanding and improving data access and unlocking the full potential of data as a strategic asset.”
The Issue
A strong data culture is not just about tools and systems. It’s about shared access, trusted data, aligned definitions, and the ability of users across the institution to interpret and act on insight.
In many institutions, data is siloed: academic departments, administrative units and research centres each use different systems and definitions. That undermines cross-institutional decisions.
To become more agile and connected, universities need solutions that both bring together different data sources and make sense of them for varied stakeholders.
How Cost-Modelling Helps
Pilbara Insights supports the creation of a data-centric culture by:
- Acting as a common framework that ties together cost, activity and revenue data across departments — creating a “single version of the truth” for financial modelling.
- Enabling shared dashboards and reports that different stakeholders (deans, CFOs, program directors) can use with consistent definitions and metrics.
- Facilitating collaborative discussions and shared governance: when cost insights are consistently defined and visible, faculty, academic leaders and administrators can engage in meaningful debates about resource allocation, program viability, growth strategies.
- Encouraging a shift from “I don’t know how much this costs / we have always done it this way” to “Here are the drivers, here’s the cost per student, here’s the margin, here are the scenarios”.
Bonus – Data as a Strategic Asset Whitepaper
The Consortium for Advanced Management – International (CAM-I) a fifty-year old Non-Profit, created the Intelligent Data Quality Management interest group and developed a white paper about the concept of treating data as a strategic asset. Members of the interest group included Boeing, Bank of America, Grant Thornton, USDA, US Army Acquisition Support Center and Definitive Logic. “…if data is an asset, why is it not always treated as such? For example, stores conduct regular audits of their inventory, but an organization will not inventory their data unless a data-related crisis has occurred that spurs the organization into action.” This question was the start of the development of the Data Life Cycle Model and you can download the whitepaper here.
4. From Reactive to Proactive (Issue #8)
“Using data for scenario modeling, forecasting, and prediction to strengthen institutional agility and planning.”
The Issue
Traditionally, higher education has been reactive: responding to enrolment shifts, funding cuts, regulatory changes, cost pressures. The next phase demands forethought.
Institutions seeking agility are moving toward scenario modelling, trend-analysis and “what-if” exploration to avoid being caught off guard.
How Cost-Modelling Helps
The Pilbara Insights model can also support predictive models (5-10 year scenarios) that will support executive decision making by:
- Allowing scenario modelling of key variables: enrolment declines/growth, delivery mode shifts (in-person/hybrid/online), staffing changes, technology adoption, policy/funding changes.
- Demonstrating financial impacts of decisions before they’re locked in: cost per student, program margin, break-even analysis.
- Enabling leadership to evaluate “what if” questions: What if international student enrolment drops by 10 %? What if we shift delivery of Program X to hybrid? What if we reduce tutorial staffing by 5%?
- Making resource allocation a forward-looking strategic exercise rather than an annual reactive scramble.
The standard Pilbara Insights model also supports the newly developed Program Insights tool, that will produce an immediate five-year financial forecast and breakeven calculations of newly proposed Academic Programs.
5. Decision-Maker Data Skills and Literacy (Issue #10)
“Enhancing the value of institutional data by training and equipping decision-makers to use and interpret it properly.”
The Issue
Even with excellent data and analytics infrastructure, institutions are limited by whether decision-makers (deans, directors, budget owners) can interpret data and apply it in decisions. Many are still influenced by intuition, legacy practices or anecdote.
This undermines strategic agility and effective resource allocation.
How Cost-Modelling Helps
A major breakthrough in the use of very large and detailed cost models is AI. In collaboration with Snowflake, we are now developing our own AI interface to the Pilbara Insights model, called Pilbara Intelligence. This is essentially a personal analyst for Higher Ed decision makers, where they can literally talk to the model and ask it questions. The solution is an agentic solution, which means it has a specific agent that will develop SQL Queries, so we get actual numbers and not invented numbers from the model, as well as a large-language model (LLM) that provides a natural language querying and brainstorming assistant. You can read about one of our interactions with our demo model here.
Bringing It All Together
The EDUCAUSE “2026 Top 10” report emphasises that the future of higher education technology and data isn’t just about systems and tools—it’s about people, connections and culture: connecting silos, connecting data with action, and connecting strategic vision with operational reality.
The Pilbara Insights Cost Modelling solution sits squarely at this nexus. By providing transparent, repeatable, activity-based cost insights, it helps institutions build the kind of connected, data-driven, strategic mindset that the EDUCAUSE report identifies as critical.
In a world where budgets are tight, enrolments are shifting, and accountability is rising, having clear cost-to-serve models is no longer optional—it’s a strategic asset.