“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.
