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.