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:
- Pull data from multiple systems
- Normalize formats and definitions
- Reconcile inconsistencies
- Validate results
- 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
