Author: Chris Atteberry

Higher education has always been in the business of student success. What it has struggled with is knowing, in real terms, what that success actually costs and whether the investments being made are working.

That question has never been more urgent. According to Inside Higher Ed’s 2025 survey, only 28% of Chief Business Officers are highly confident in their institution’s business model. At the same time, affordability remains students’ top concern, and Chief Academic Officers are sounding the alarm about the future of financial aid amid ongoing federal and state funding uncertainty.

Institutions are being asked to do more for students with less certainty about the availability of resources. That is a difficult position to be in, and it becomes nearly impossible when the data needed to make smart decisions is fragmented across systems that don’t talk to each other.

The Silo Problem Nobody Talks About Directly

Ask most institutions whether they track enrollment, financial aid, and retention data, and the answer will almost universally be yes. Ask whether those data sets live in a single, connected view that leaders can act on, and the answer changes quickly.

Enrollment data lives in the SIS. Aid data lives in financial systems. Retention and student success metrics live somewhere else entirely, often managed by a separate team with a separate toolset. Each system is doing its job. The problem is that none of them are talking to each other in a way that surfaces the full picture.

The result is a common and costly gap: institutions are investing in student success without a clear line of sight into which investments are actually moving the needle. Retention initiatives get funded. Aid packages get awarded. Enrollment targets get set. But whether those efforts are connecting, whether a student who received a targeted aid package was more likely to persist, for example, often goes unmeasured simply because the data required to answer that question spans systems that were never designed to work together.

What the Full Picture Actually Reveals

When enrollment, aid, and retention data are unified into a single view, something important happens: patterns that were invisible become actionable. Institutions can begin to see which student populations are most financially vulnerable and most at risk of stopping out, before it happens. They can identify whether aid awards are reaching the students who need them most, or whether gaps exist that are quietly driving attrition. They can connect the dots between enrollment trends, aid utilization, and persistence rates to inform both immediate interventions and longer-term strategies.

This is the difference between spending on student success and investing in it. Spending happens when resources are allocated based on assumptions and historical patterns. Investing happens when decisions are driven by current, connected data that shows what is actually working.

The Cost of Not Connecting the Dots

There is a tendency in higher education to think of data integration as a technology problem, something the IT team will eventually get to. But the real cost is strategic, not technical.

When finance and student success operate from disconnected data, budget conversations happen in a vacuum. Aid decisions are made without full visibility into their downstream impact on retention. Enrollment projections don’t account for the students most likely to leave. And when leadership asks whether the institution’s student success investments are paying off, the honest answer is often: we think so, but we can’t clearly show you.

In an environment where only 28% of CFOs feel confident in their business model, and funding uncertainty is the norm, “we think so” is not a sufficient answer. Boards want evidence. Accreditors want outcomes. Students and families want to know their investment is worth it.

From Siloed Metrics to Strategic Clarity

Connecting enrollment, aid, and retention data isn’t about adding more reports to an already crowded dashboard environment. It’s about replacing disconnected snapshots with a coherent institutional narrative, one that lets leaders ask better questions and get answers while there is still time to act on them.

That means knowing which students are at financial risk right now, not at the end of the term. It means understanding whether current aid strategies are supporting persistence or just reducing short-term attrition. It means being able to walk into a budget conversation with data that connects investment to outcome, rather than defending spending based on intuition.

For institutions navigating the current funding environment, that kind of clarity isn’t a luxury. It’s a strategic necessity.

At Datatelligent, the Fusion Platform is built to do exactly this, bringing together the data that institutions already have across their key systems and making it visible, connected, and actionable in a single environment. When enrollment, aid, and student success data work together, institutions gain a clearer, more complete picture of student success in higher education and stop guessing about what their investments are producing. They start knowing.

And in times like these, knowing makes all the difference.

Higher education has never been short on data. What it has often been short on is the ability to use that data at the speed decisions actually need to be made.

That gap has never been more costly than it is right now. According to Inside Higher Ed’s 2025 survey findings, 50% of Chief Academic Officers report spending more of their time reacting to problems than planning for the future. Meanwhile, 82% of CFOs who expect their institution’s financial health to worsen point directly to uncertainty driven by federal policy and funding as the cause.

The challenge isn’t a lack of awareness. Leaders know what they need to know. The problem is that by the time the data arrives, the moment for action has often already passed.

The Static Report Trap

Most institutions still rely on reporting cycles built for a more predictable world: monthly enrollment snapshots, quarterly budget reviews, annual outcomes reports. These tools made sense when conditions were relatively stable, and decisions could wait.

That world no longer exists.

When federal funding shifts without warning, when enrollment trends break from projections mid-term, when a new state policy lands in the middle of budget planning, institutions need answers now, not at the end of the reporting cycle. Static reports weren’t designed for that kind of pressure. They describe what happened. They don’t help leaders respond to what’s happening.

The result is a familiar and frustrating pattern: by the time leadership has the data they need to act, the decision window has already narrowed or closed entirely.

What “Agile Data” Actually Means

Agile data isn’t a buzzword. It’s a practical shift in how institutions think about information and when it’s available.

An agile data environment is one where:

• Data is unified across systems, so enrollment, finance, and student success aren’t living in separate silos that have to be manually reconciled before anyone can answer a question.
• Dashboards reflect near-real-time conditions, not last month’s snapshot.
• Leaders can stress-test scenarios against actual institutional data, rather than relying on projections built from incomplete or stale inputs.
• Follow-up questions don’t trigger another round of data preparation. The infrastructure is already in place to answer them.

The difference between a reactive institution and a proactive one often comes down to whether the data is ready when the question is asked.

From Reporting Cycles to Real-Time Strategy

Consider a common scenario: enrollment numbers begin to soften two weeks into a term. In a traditional reporting environment, that signal might not surface clearly until the end-of-term report is built and reviewed. By then, the options for intervention have narrowed considerably.

In an agile data environment, the same signal appears on a live dashboard. Leaders can see it, discuss it, and act on it while there is still time to make a meaningful difference, whether that means targeting outreach to specific student populations, adjusting course offerings, or reallocating support resources.

This is what it means to move from descriptive to prescriptive analytics. Not just knowing what happened, but being positioned to ask what to do next.

The Leadership Time Problem

The IHE findings also point to something else worth naming directly: leaders are stretched. Presidents report that the demands of the role can no longer realistically be handled by one person. CAOs cite burnout as a top driver of institutional turnover. Both groups are being asked to do more strategic work with less time and fewer resources.

Agile data helps solve that problem in a way that additional staff cannot. When dashboards surface the right information proactively, and when near-real-time visibility replaces manual report requests, leaders spend less time chasing numbers and more time making decisions. That shift, from data preparation to strategic thinking, is where the real return on investment lives.

Building the Foundation for Agile Data

Agile data doesn’t happen by accident. It requires a foundation, specifically a unified data environment where institutional systems are integrated, data flows are automated, and the logic behind every metric is consistent and trustworthy.

Without that foundation, even the best dashboard tools will underdeliver. Leaders will still find themselves waiting on data, debating whose numbers are right, and making decisions from an incomplete picture.

At Datatelligent, the Fusion Platform is built around this principle. By unifying data from across an institution’s key systems and delivering pre-built, higher-ed-specific analytic solutions, we help institutions stop having to rebuild reports from scratch every time a new question arises. The data is ready. The answers are accessible. And when conditions change, institutions can pivot without waiting for the next reporting cycle to catch up.

In a higher education environment defined by uncertainty, the ability to act on current, reliable data isn’t a competitive advantage. It’s a survival skill.

If you ask Institutional Research (IR) or higher education analytics teams how they spend their time, the answer is often the same, just phrased differently:

• “We’re constantly pulling data.”
• “We spend more time cleaning than analyzing.”
• “By the time the report is done, the question has already changed.”

This isn’t a skills problem. It’s not a motivation problem. And it’s certainly not because higher education lacks data.

It’s a Data Wrangling problem.

The Hidden Cost of Manual Data Wrangling

On most campuses, answering even a straightforward question requires a familiar sequence of manual data wrangling tasks:

1. Pull data from multiple systems
2. Normalize formats and definitions
3. Reconcile inconsistencies
4. Validate results
5. Rebuild the same logic again next month

This work is necessary but it’s also repetitive, fragile, and time-consuming. It quietly consumes the time and attention of some of the most analytically capable people on campus.

The result is that highly trained Institutional Research professionals become data custodians, keeping systems running, reports updated, and requests fulfilled, rather than strategic partners helping leadership think ahead.

Why Weak Data Governance Stalls Strategic Planning

When data governance is an afterthought and preparation dominates the workflow, institutions fall into a reactive rhythm:

• Reports are built on fixed schedules
• Questions are answered after decisions are already in motion
• Follow-up questions trigger another round of manual work

Even when leadership wants to plan more proactively, the reporting cycle becomes a constraint. Not because people aren’t asking the right questions, but because the governance frameworks aren’t in place to make data ready when they need it.

This creates a frustrating gap between what leaders want (insight, context, options) and what teams can deliver (static snapshots built after the fact).

Automating Data Flows to Empower Institutional Research

Reducing the data wrangling burden doesn’t start with better dashboards or more tools. It starts earlier, with how data moves.

When data flows are automated and backed by strong data governance standards:

• Data arrives consistently, not manually
• Logic is defined once, not recreated repeatedly
• Updates happen continuously, not on request

This significantly shifts the role of Institutional Research teams. Instead of spending their time preparing data, they can spend it interpreting, contextualizing, and advising.

Moving from Descriptive to Prescriptive Analytics

That’s the difference between answering: “What happened last term?”

and helping leaders ask: “What’s changing, and what should we do next?”

The Real Outcome: Time for Student Success & Strategy

The most valuable outcome of reducing the data wrangling burden isn’t faster reports. It’s time.

Time to:

• Explore trends instead of just summarizing them
• Ask better questions
• Connect data across enrollment, finance, and Student Success
• Support planning rather than chasing deadlines

In an environment where institutions are being asked to do more with fewer resources, freeing up analytical capacity isn’t a luxury; it’s a necessity.

A Practical Path: Data Flows as the Foundation for Data Governance

Reducing the burden doesn’t require ripping out systems or rebuilding everything at once. On many campuses, the biggest gains come from improving how data flows between existing systems.

This is where the right infrastructure makes the difference. At Datatelligent, Data Flows play a foundational role in the Fusion Platform. By automating and standardizing extraction, transformation, and delivery, Data Flows help institutions enforce data governance while eliminating repetitive preparation.

When data flows are dependable, Institutional Research teams spend less time wrangling inputs and more time partnering with leaders on interpretation, planning, and decision-making. That shift, from preparation to perspective, is what enables analytics teams to operate strategically rather than reactively

Higher education is entering a new era of data-informed decision-making. As institutions look to leverage artificial intelligence (AI), the success of these initiatives depends on a robust foundation built on a modern data infrastructure. Central to that foundation is the data lake. 

Why Data Lakes Matter 

Unlike traditional databases and data warehouses, which require structured inputs, data lakes allow the storage of all data types—structured and unstructured—without forcing early decisions about format or structure. This makes data lakes ideal for institutions facing increasing data complexity and scale. 

As noted in EdTech Magazine, data lakes collect all types of data “without imposing a structure until the query is taking place,” giving data scientists the flexibility to shape data as needed for advanced analytics. Institutions can consolidate student information systems, learning management (LMS) platforms, customer relation management (CRM) systems, and more—breaking down data silos and gaining a holistic view of the student journey. 

By adopting a more flexible data approach, institutions can more easily explore and understand their data as new questions emerge, without being constrained by rigid reporting formats. This enables deeper insights and quicker responses to changing needs. 

Building the Foundation for AI 

The flexibility and scalability of data lakes make them the perfect launchpad for deploying AI and machine learning tools. With clean, accessible data, colleges can: 

• Predict student retention risk using machine learning models trained on behavioral and academic trends 

• Tailor learning paths and student services through AI-powered personalization 

• Forecast enrollment and operational trends to improve institutional planning 

A study published in Scientific Reports highlighted how combining academic and behavioral data from over 50,000 students enabled machine learning models to predict first-semester attrition with up to 88% accuracy—showing the true potential of applying machine learning using student data. 

These models also demonstrate the advantage of incorporating more than just grades. By leveraging student interactions with learning platforms, campus support services, and peer communities, institutions can more accurately identify risk factors and intervene earlier. 

Real-World Application 

Examples like Crown College show the value of predictive analytics in action. By building a data-informed retention program, they raised freshman fall-to-spring retention from 84% to 89% over four years. Their success was driven by clear, actionable insights grounded in high-quality data—achievable only with the proper infrastructure. 

Similarly, Cowley College partnered with Datatelligent to implement a unified, Snowflake-based data lake and campus-wide dashboards. With access to real-time information, Cowley’s staff could spot emerging trends, reduce manual reporting tasks, and focus more energy on strategic action. 

Other institutions are using data lakes to power dashboards that monitor student engagement, automate compliance reporting, and benchmark performance against institutional goals. Feeding these systems with raw and diverse data ensures their analytics are grounded in a comprehensive, real-world picture. 

AI in Higher Education: Opportunities and Challenges 

A recent Datatelligent Industry Survey found that while many institutions are enthusiastic about AI in higher education, most are in the early stages. 61% of colleges lacked formal AI policies or had not started developing them. Still, forward-thinking campuses are beginning to invest in talent, ethical data governance, and pilot AI projects grounded in data lakes. 

However, barriers remain, including limited staff capacity, concerns about student data privacy, and uncertainty about how best to deploy AI responsibly. These aren’t technical hurdles alone—they’re cultural and operational, requiring cross-campus alignment and leadership support. 

Setting the Stage for Scalable AI 

For institutions to scale AI responsibly, they must begin with a clear strategy for collecting, governing, and using data. This involves: 

• Defining data quality standards for consistent data collection 

• Training staff in ethical AI and data interpretation 

• Creating feedback loops to evaluate model effectiveness and adjust practices 

Just as importantly, institutions must cultivate a data informed culture that values transparency, inquiry, and evidence-based decision-making. This cultural shift often starts with small wins, such as a KPI dashboard that saves time or a pilot model that improves advising outcomes. These successes build momentum creating further student success. 

Final Thought 

AI has the power to transform how colleges and universities use student data, but only if they are ready. Investing in a well architected data lake is not just a technology upgrade, it is a strategic investment. With the right foundation, institutions can turn abundant data into meaningful insights, leading to more personalized, responsive, and effective educational experiences. 

Need help setting the foundation? Datatelligent partners with higher education institutions to implement data lake strategies designed for real-world impact.