Beyond the Dashboard: Surviving and Thriving in Higher Ed Analytics in the Age of AI

Remember when building a comprehensive KPI dashboard or cobbling school level metrics for a strategic review took weeks of data cleaning, late night pivot tables, and endless stakeholder alignment?

Those days are gone.

With the acceleration of AI and machine learning, anyone across the university can now generate their own descriptive reports, query data pipelines in natural language, and generate visual dashboards in a matter of seconds. For data and planning professionals working within higher education, this is a massive existential shift. If the core value of our teams historically lay in production (ie gathering, formatting, and displaying “what happened”) we are facing a new reality; production has been commoditised.

When the technical act of building a report or tracking a metric is instantaneous and accessible to all, where do data analytics teams position themselves for the mid-term? How do we move past the current wave of AI capability instead of getting swept away by it?

Here is how we need to pivot our careers and our offices to stay indispensable.

Shift from "Dashboard Producers" to "Decision Architects"

When insights are instantly accessible via AI, the bottleneck is no longer accessing data; it is deciding what matters and what to do about it.

AI excels at aggregation and pattern matching, but it completely lacks institutional memory, political nuance, and cultural context. It doesn't know why a specific college operates the way it does, or how a particular department shapes its risk tolerance.

Instead of waiting at the end of the line to visualise outcomes, we need to embed ourselves at the inception of policy and strategy. Our value shifts from building the report to framing the right questions, contextualising the output, and translating raw AI generated numbers into a coherent institutional narrative.

Move from Descriptive to Prescriptive Analytics

Institutional reporting has traditionally been backward looking answering what happened at the university, college, or school level. AI has solved backward looking analytics.

The mid-term opportunity lies in higher order thinking:

• Causal inference and intervention design: Moving beyond predicting an outcome (like applications to offers to acceptances, student attrition or research outputs) to testing and measuring what specific institutional interventions actually fix it. Don’t just admire the problem, test solutions that might work.
• Algorithmic auditing and data governance: As universities increasingly adopt automated systems for everything from resource allocation to student support, someone needs to watch the watchers. Analytics teams are uniquely positioned to act as internal watchdogs, auditing models for bias, drift, and regulatory compliance.

Become Curators of "Ground Truth" and Context

AI is only as good as the context it feeds on. In a complex university ecosystem, public models don’t inherently understand your internal data definitions, nuance, or localised KPIs.

Instead of building static dashboards, our role evolves into maintaining the proprietary context, semantic layers, and secure data pipelines that feed institutional AI tools. We become the master curators of the university's "ground truth."

Senior leaders are now drowning in fast, AI generated metrics, they need trusted guides. Position your team not simply as report builders, but as internal consultants who coach leaders on critical data thinking and protect them from misinterpreting automated noise (hallucinations!).

Embrace Ambiguity and Messy Transformation

AI thrives on structured, historical data. It stumbles on deep ambiguity, novel structural changes, and complex institutional reorganisations.

Higher education is perpetually reshaping itself, whether through shifting government funding models, multi-campus integrations, or massive restructures and mergers. These are messy, qualitative, and deeply political spaces where AI can offer a data point, but it cannot negotiate a consensus or navigate human resistance. That is where human judgment becomes irreplaceable.

The Bottom Line

The commoditisation of dashboarding is not the end for institutional analytics, it is a sign that the days of low value work should be over. By shedding the identity of "report builders" and stepping into the role of strategic sense-makers, decision architects, and governance leads, we can ensure our teams are not just surviving the AI wave, but steering it.

 

Rethinking University KPIs: What Happens When We Stop Treating Students as Sausages?

Universities love their KPIs. Applications, offers, acceptances, enrolments, they form the typical pipeline charts shown at every Council meeting. These numbers matter because, in a revenue-driven system, students are often framed (sometimes quite literally) as inputs to a financial model. I’ve even heard the phrase “sausage factory” used to describe a university suggesting that students simply enter at one end and degrees come out the other.

But is this the only way to think about the purpose of a university? How might alternate conceptions of a university’s purpose encourage us to develop more meaningful indicators than just “how full is the pipeline”?

Let’s revisit some alternative ‘ideas of a university’ and use those as lenses to reimagine what we measure.

The University as a Public Good: Beyond Revenue Pipelines

Much of our current KPI thinking comes from a “new public management” mindset where universities are quasi-businesses. Applications predict enrolments, which predict revenue, which predicts sustainability. There is nothing inherently wrong with this view; universities do require money to function.

However, universities historically exist to produce, protect and disseminate forms of knowledge that markets can’t do alone. Once we see the university as a public good, the question shifts:

• Are we attracting the right mix of students for the society we want to build?

This leads to very different indicators:

• The proportion of students entering fields of social need
• Participation of under-represented groups
• The alignment between university programs and societal needs

The pipeline is still there but it becomes a pipeline for public value, not just revenue.

The Ecological View: Students as Co-Contributors, Not Inputs

The idea of the ecological university provides another lens. In this model, the university is part of multiple ecosystems: knowledge, culture, politics, the environment, and the student lifeworld.

Students are not raw material. They are agents participating in an ecology of learning.

If we adopt this lens, enrolment KPIs broaden dramatically:

• How well are we enriching the learning ecosystem?
• How well are we sustaining student capabilities in the long term?

Measures of student wellbeing, belonging, intellectual growth, and capability development become core indicators and not optional extras.

The Democratic University: Students as Citizens-in-Formation

Universities are essential in educating democratic citizens, people who can think critically, debate respectfully, and understand the world beyond their immediate experience.

From this view of a university, student volume is less important than student development.

• What kind of thinkers and citizens are our graduates becoming?

This leads toward indicators like:

• Growth in critical reasoning
• Graduate contributions to communities
• Student exposure to diverse ideas
• Equity of access and equity of outcomes

These metrics might be harder to quantify, but they reflect a deeper purpose than simply “headcount”.

The Entrepreneurial University: Diversifying What We Value

The entrepreneurial university perspective views universities not just as centers for education and research, but as active agents that drive innovation and economic development by collaborating with government and industry

In this world, student numbers are not only about quantity but strategic composition.

• Where do our students enable innovation?
• Which cohorts create new research possibilities, industry relationships, or cultural vitality?

A university focused only on volume misses its own entrepreneurial potential.

So, What Does All This Mean for Student Number KPIs?

If we adopt some of these alternative views of what the purpose of a university is then the traditional student pipeline becomes only one dimension of performance.

A more forward-looking set of indicators might ask:

• Are we cultivating an inclusive and diverse learning community?
• Are our students developing capabilities that matter for society, democracy and culture?
• Are we creating an ecosystem in which students, staff and external partners co-create knowledge and value?
• Are our graduates contributing to the wellbeing of their communities?
• Are we strengthening the intellectual, civic and ecological fabric of our region?

None of these replace enrolment numbers but they do contextualise them. The purpose of a university is not just to convert applicants into revenue, but to sustain a complex ecosystem of knowledge, people and ideas.

Why This Matters Now

At a time when many universities are dealing with mergers, restructures, market volatility and demographic shifts, it’s tempting to remain focussed on the simplest KPIs.

But what if you had a chance to articulate a fresh purpose.

When purpose changes, measurement must change too.

And we must stop treating students as sausages!

The Illusion of Data-Driven Decision Making in Universities

“Data-driven decision making” has become a ubiquitous catchphrase in higher education. Planning and business intelligence (BI) teams often pride themselves on delivering ever-expanding datasets, dashboards, and performance metrics in service of strategic clarity and institutional improvement. Yet despite this flood of data, there’s a persistent disconnect between the volume of information available and the quality or impact of decisions being made.

In practice, universities rarely exhibit the kind of disciplined, data-led decision making they claim to champion. Most planning offices distribute large volumes of raw or lightly formatted data to a wide internal audience, few of whom are in positions of actual decision-making authority. These users may explore the data, form views, or use it in local contexts but they often lack the strategic mandate to act on the insights in any meaningful way. Meanwhile, those with the authority to make high-impact decisions, senior executives and academic leaders, tend to rely far more on heuristics, experience, and intuition than on comprehensive data analysis.

This is not a failure of character or competence. It is a well-documented feature of executive decision-making. Nobel laureate Herbert Simon’s concept of bounded rationality describes how decision makers, faced with limited time, incomplete information, and cognitive overload, opt for “satisficing”, a strategy of choosing a solution that is good enough, rather than optimal. Gerd Gigerenzer’s research further reinforces that in complex or uncertain environments, heuristics, simple, experience-based rules, are not just common but often effective. In fact, they are often superior to overly data-intensive approaches that falter in the face of ambiguity.

In the university context, empirical studies support this reality. Research by Kahneman and Lovallo, and later by Kahneman, Sibony and Lovallo, reveals that senior managers frequently fall back on pattern recognition and intuitive judgement, especially in high-stakes or uncertain decisions. Data, when used, typically plays a secondary role: it may validate a gut decision, lend legitimacy to a choice already made, or gently nudge a leader in a particular direction. Rarely does it drive the decision outright.

This presents a challenge for BI and planning professionals. The current model of data democratisation through widespread reporting assumes that more access equals better decisions. But if most data recipients are not decision makers, and if senior leaders prefer heuristics anyway, this approach may lead to diffusion without impact. Worse, it risks data fatigue and confusion, as users interpret numbers in inconsistent or unproductive ways.

A more effective model is not more data, but smarter data. This means synthesising and analysing complex datasets to produce timely, targeted insights, delivered directly to those with the authority to act. It requires planning professionals to move beyond reporting and become trusted strategic advisers, translating information into relevance, and aligning it with institutional priorities and decision cycles.

In short, the promise of data-driven decision making will not be realised by expanding access alone. It requires understanding how decisions are actually made and designing our data practices to meet decision makers where they are, not where we wish they were.

A Global Top 100 Debut: Why Adelaide University’s QS Ranking Is a Milestone Worth Celebrating

Adelaide University, the soon-to-be-launched institution born from the merger of the University of South Australia and the University of Adelaide, has made its debut on the global stage with a QS World University Ranking of 82. This result is nothing short of remarkable and represents a strong vindication of the university’s ambitious goal to be ranked among the world’s top 100 institutions.

When the merger was first proposed, many critics dismissed the idea, suggesting that combining the two institutions would weaken rather than strengthen South Australia’s university sector. Detractors argued that UniSA would dilute the quality of the more prestigious University of Adelaide. Even now, with the ink barely dry on the merger legislation, some commentators continue to talk down the achievement. A recent AFR piece labelled the result “lacklustre”, a surprising take given that fewer than 100 of the world's approximately 26,000 universities ever break into the top 100.

Others question how the new university could be ranked at all before it officially opens its doors on January 1, 2026. The answer is simple: global ranking agencies evaluate institutional identity, not bricks and mortar. They assess research output, reputation, academic strength, and global engagement, all of which are already active, measurable, and very real in the merged institution.

Rather than nit-pick or diminish the achievement, this moment deserves recognition. A debut at 82 places Adelaide University in elite company, and sends a clear message: the merger hasn’t weakened the institutions; it has elevated them.

The goal was to create a world-class university for South Australia. That goal is already being realised. Let’s celebrate that.

Want Growth? Build a University

The article titled "The Economic Impact of Universities: Evidence from Across the Globe" by Anna Valero and John Van Reenen, published in the Economics of Education Review, explores the relationship between the presence of universities and regional economic growth. Utilising a comprehensive dataset encompassing nearly 15,000 universities across approximately 1,500 regions in 78 countries, the study examines data from 1950 to 2010 to assess how the number of universities influences GDP per capita.

Key Findings:

• Positive Correlation with Economic Growth: The study finds that an increase in the number of universities within a region is positively associated with higher future GDP per capita. Specifically, a 10% increase in universities per capita correlates with a 0.4% rise in future GDP per capita.
• Spillover Effects: The economic benefits of universities extend beyond their immediate regions, positively impacting neighbouring areas within the same country.
• Mechanisms of Impact: The presence of universities contributes to economic growth not merely through direct expenditures but also by enhancing human capital and fostering innovation.
• Influence on Democratic Attitudes: Regions with a historical presence of universities tend to exhibit stronger pro-democratic attitudes, suggesting a broader societal impact.

This research underscores the multifaceted role of universities in promoting economic development and societal progress, highlighting their significance beyond education.

https://doi.org/10.1016/j.econedurev.2018.09.001

Beyond the Numbers: Reclaiming Academic Purpose from Performative Pressures

 

In today’s data-driven universities, research performance is often equated with metrics: citations, publications, grant income. But are we losing sight of what truly matters in academia?

Recent reflections from Elsevier’s Research MetricsGuidebook and a compelling paper by Visser et al. (Journal of Education Policy, 2024) point to a growing concern: the rise of performativity. That is, the pressure for academics to continuously prove their value through measurable outputs, often at the expense of deeper scholarly and educational contributions.

This performative culture distorts academic behaviour. Researchers may prioritise what is countable over what is meaningful. Critical activities such as teaching, mentoring, peer review, and community engagement can be undervalued simply because they are less visible

However,  metrics can still play a constructive role if used responsibly. The Elsevier guide promotes two simple but powerful rules: use more than one metric, and always pair metrics with expert judgment. This triangulation helps avoid simplistic rankings and ensures context-sensitive assessment.

A responsible metrics culture reframes the use of metrics but doesn't reject measurement. It acknowledges disciplinary diversity, career stages, and the rich variety of academic contributions. It supports, rather than distorts, academic integrity.

To shift the culture, institutions must lead: redesign evaluation processes, train staff in interpreting metrics critically, and celebrate contributions that metrics alone can’t capture.

Metrics should serve academic purpose; not replace it.

 

What a University Council Really Needs to Know About Research: Clarity Beyond the Metrics

The Strategic Role of Research

University research is a source of both pride and complexity. It underpins our global rankings, attracts funding, enables industry partnerships, and drives innovation and societal impact. Yet for many members of university Council, research can feel like a "black box"; full of acronyms, shifting benchmarks, and dense performance tables.

While governance bodies are not expected to be immersed in operational detail, they do need to understand the high-level performance, risks, and opportunities within research to fulfil their strategic oversight role. This short piece offers a clear view of what matters most.

The Problem: Too Many Numbers, Not Enough Insight

Research reporting to Council is often technical and fragmented. Data might include ERA results, HERDC income, grant success rates, citation metrics, rankings data, and individual initiatives, but rarely a coherent picture.

Without synthesis or trend context, it becomes hard to tell: Are we improving? Where are we strong? What should we be concerned about?

What Council Really Needs to Know

To support good governance and strategic stewardship, Council needs clear, contextual answers to five key questions:

Is our research activity growing or shrinking?

Look at trends in external research income, research-active FTE, and publication volume. Growth indicates momentum; flatlining may indicate capacity or competitiveness risks.

Is our research quality competitive?

Use field-weighted citation impact (FWCI) or citations per paper compared to sector benchmarks. Context is key, where do we stand among peers or international standards?

Are we building capability for the future?

Consider the proportion of early-career researchers, pipeline of grant applications, or internal schemes for research development. Long-term health depends on today’s investments.

How aligned is our research to strategy?

Are we publishing and attracting grants in strategic priority areas? Are we working with industry or partners in mission-aligned fields?

Are we positioned for policy and funding changes?

Anticipate the impact of ERA's replacement, the shift toward impact and translation, and potential changes to funding schemes.

From Metrics to Meaning: How to Shift the Conversation

Rather than loading Council papers with every available KPI, consider a more strategic approach:

• Use trends, not snapshots
• Provide benchmarks or context, not just figures
• Focus on signals, not noise: where performance is moving
• Link metrics to mission: how does this support our university's strategy?

A compact dashboard with visual trends and commentary can be more effective than dense tables.

Conclusion: Clarity Builds Confidence

Research is a long-term, high-stakes endeavour. Giving Council the right insights, without overwhelming detail, builds trust, improves decision-making, and strengthens advocacy beyond the university.

It’s not about oversimplifying. It’s about focusing on what truly matters.