The Scenario
A large research university (32,000 students) has an IR office with 12 analysts covering 9 schools. Each school asks for enrollment, retention, graduation, finance, and faculty figures every term. The team is not understaffed in headcount; it is understaffed in throughput, because every school-level request is a one-off that pulls an analyst out of strategic work. During peak season, large teams hit 85% maximum utilization, the same ceiling small teams hit at 90%.
The Whitepaper Finding
The data request workflows whitepaper found that large IR teams (8+ staff) spend an average of 969.8 hours per month on ad-hoc requests, with a maximum of 1,310 hours. That is the equivalent of 60 to 80 FTE-weeks per month consumed by repeat question answering. The whitepaper estimates large teams can reclaim 2,133 to 3,200 hours annually, or 267 to 400 days, with AI-enabled workflows. The primary challenge for large teams is not resource scarcity; it is scale and complexity, managing breadth across schools.
How Clema fits the large-team workflow
Natural-language analytics across the warehouse
Instead of an analyst pulling data for one school at a time, the IR director uses Analytics to ask: "Compare fall-to-fall retention across all nine schools for the latest cohort, with the IPEDS GRS definition applied." Clema returns the comparison table, school by school, with the warehouse source shown. One query, nine schools.
Peer benchmarking per school
Each school at a research university has different peers. With Peer Benchmarking, the IR director builds peer groups by Carnegie class, control, and discipline (business, engineering, education) and pulls the same metric for each peer set in one conversation. The federal source and IPEDS year are shown on every figure for board memos.
Predictive analytics across schools
With Predictive Analytics, the IR director runs enrollment forecasts, retention models, and graduation predictions for each school in plain English. The same question does not get re-run nine times; Clema segments by school and returns the forecasts together, with the model drivers shown so deans can act.
Widgets drop the answer where deans already work
With Widgets, the IR director embeds the school-specific answer in the dean's Power BI dashboard or IR portal page. Deans ask in plain English inside their own tool, and the IR analyst stops being the human go-between for school-by-school queries.
What This Scenario Shows
For large IR teams, the bottleneck is not headcount; it is throughput across breadth. The whitepaper found large teams spend up to 1,310 hours per month on ad-hoc requests because each school-level query is a one-off, even when the underlying metric is the same.
Clema addresses the root cause by making analytical breadth queryable. The IR director asks for the metric across all schools at once, segments by peer group, runs forecasts across schools in plain English, and drops the answer where deans already work. The 2,133 to 3,200 reclaimable hours the whitepaper estimates for large teams is what moves back to strategic analysis when the team stops pulling data one school at a time.
See analytics across your schools
Book a demo with the large-team breadth scenario built for your institution, or start a 14-day free trial on a sample source.
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