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In today’s era of big data, higher education professionals have unprecedented access to information about students, institutions and academic programs. But access alone isn’t enough. With so much data at our fingertips, the challenge is to know what matters, how to interpret it and how to turn it into actionable insights.
Over the past decade, a quiet but essential shift has occurred in discussing data in higher education. Increasingly, institutions are moving from “data-driven” to “data-informed.” Though subtle, this distinction matters. Being data-driven suggests that decisions are made solely based on what the data say. Being data-informed acknowledges the critical role of interpretation, context and professional judgment in decision-making. After all, data doesn’t speak for itself—it must be examined, evaluated and translated into meaning.
Data Analytics Tools and Decision-Making
My research partner and I recently conducted a quality improvement study involving nearly 100 presidents of independent colleges and universities to explore how data analytics tools are currently used in institutional decision-making. We sought to understand how leaders engage with data, which tools they value most and how those tools influence organizational decisions.
Our findings revealed that the primary users of data analytics tools across campuses were institutional presidents, chief financial officers and institutional research offices. These leaders gravitated most toward financial and enrollment data, with 62% and 63% identifying these as their most critical data sources. Given the reliance on tuition revenue and enrollment stability in the business models of many independent institutions, this focus isn’t surprising.
The Risk of an Executive Echo Chamber
When data analysis is concentrated among senior leaders, it can create a closed information loop. In this executive echo chamber, the same voices interpret and act on data, often without input from professionals deeper in the organization.
This structure overlooks valuable perspectives from frontline staff, mid-level managers and other subject-matter experts who operate closer to the student experience and academic processes. Their contextual knowledge can provide essential insights that enhance the understanding and interpretation of institutional data.
By keeping analytics in the hands of only a few, institutions risk missing opportunities to build learning organizations—campuses where insights are shared, examined collaboratively and translated into better decisions and outcomes.
Expanding the Feedback Loop
To break this cycle, institutions must build a culture that democratizes data and encourages broad participation in its interpretation. Creating more open data feedback loops requires intentional structural and cultural changes.
“When data stays confined to senior leaders, valuable insights are lost. By involving voices across campus, colleges can turn information into collaboration and move from isolated decisions toward shared learning that strengthens both strategy and student outcomes”
First, senior leaders must be willing to share key data sets with trusted professionals across departments and invite them to engage with that data. This doesn’t mean giving everyone access to everything—it means creating purposeful opportunities for individuals to contribute their expertise to the interpretation process while prioritizing data security.
Second, institutions should ask these professionals to offer feedback and context that enrich decision-making. For example, an admissions officer may recognize enrollment trends not captured in top-level dashboards, or a faculty advisor might identify patterns in course registration that speak to evolving student needs.
Finally, colleges and universities can formalize the process by developing internal “data consultant” roles within divisions. These individuals can serve as bridges between data producers and decision-makers, offering localized insights and helping integrate data into day-to-day practices. This shared responsibility helps build organizational maturity in the use of analytics.
Building a Learning Organization
Mature data organizations move beyond using data only for reporting. They engage with analytics in three distinct ways: descriptively (understanding what has happened), predictively (projecting what might happen) and prescriptively (guiding what should be done). Many institutions already use predictive modeling through partnerships with technology vendors to forecast enrollment or financial trends. However, prescriptive analytics—where data suggests specific actions— requires a much deeper contextual understanding.
In all three scenarios, the more diverse the voices at the table, the better the institution can interpret and act on data. Leaders must ensure that data-informed decisions are technically sound, contextually appropriate and aligned with institutional values.
The Path Forward
To build a truly data-informed culture, colleges and universities should take the following steps:
1. Share selectively and strategically. Distribute data among key professionals within divisions who can provide meaningful analysis, preferably behind a log-in-protected wall to maximize data security.
2. Solicit feedback regularly. Encourage ongoing dialogue about the data's meaning and how it should inform decisions.
3. Formalize roles. Create designated data consultants or liaison roles to support cross-functional collaboration and shared responsibility.
4. Encourage context over compliance. Promote a culture where data use is not just about hitting targets but understanding challenges and improving processes.
Higher education is too complex for data interpretation to remain the sole province of senior leaders. By widening the circle of those who engage with institutional data, colleges and universities can move from reactive decision-making to strategic learning—building campuses that collect data and grow from it.
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