Introduction
Each step cuts a different kind of waste. Intake cuts the back-and-forth before work starts. Centralizing cuts the hunt across systems, and automation catches the request you have already answered three times this year.
The waste is large. In our data request workflows whitepaper, based on 50+ interviews with IR and IE professionals across 19 states, 91% of institutions reported problems with how requests are managed, and ad hoc requests took 40 to 60% of team capacity. That is the share of an office's week that never reaches retention studies, program review, or planning.
The three steps below are in the order most offices can take them, cheapest first.
Step 1: Standardize intake
Most of the time a request costs is spent before anyone touches data. In the interviews, 73.5% of institutions named vague or incomplete requests as a bottleneck, and a typical ad hoc request went through 2 to 5 rounds of clarification before an analyst could start. Each round means another email and another wait.
Standardizing intake means every request arrives through one channel and answers the same questions up front:
- Who is asking, and what decision the number will inform
- The population and term (fall census or end of term, first-time or all undergraduates)
- The definition to use, named from your data dictionary rather than described in a sentence
- The deadline, and whether a range or an older figure would do
- Where the answer is going: a board deck, an accreditation report, a grant, or a dean's own planning
One channel also makes the queue visible, which is what lets an office prioritize instead of answering whoever emailed last. For field-by-field form design, see data request form design.
Step 2: Centralize data sources
Once a request is clear, the next cost is finding the data. Campus data lives in separate systems that were never designed to answer questions together:
- Student information system (SIS): enrollment, registration, grades, completions, and the census snapshot behind IPEDS
- Learning management system (LMS): course activity and engagement data
- Customer relationship management (CRM): prospects, applicants, and admissions funnel data
- Financial aid: awards, Pell status, and the inputs behind net price
In the whitepaper interviews, 82.4% of institutions described data spread across multiple systems as a problem. The whitepaper's conclusion is that offices are not short of tools; they are short of integration between them.
Centralizing does not require a new warehouse project. It requires one governed place where the SIS, LMS, CRM and financial aid data can be joined under agreed definitions: an existing warehouse, an operational data store, or a single connected tool. The definitions matter as much as the plumbing. If "retention" means one thing in the SIS extract and another in the dashboard, centralizing only moves the argument. The six-step framework for governing data definitions covers that side.
Step 3: Automate repetitive reporting
The last step goes after volume. A large share of ad hoc requests are variations on questions the office has already answered: headcount by program, retention by cohort, a peer comparison for a board meeting. Each one gets rebuilt by hand because the earlier answer is buried in someone's inbox.
Automating repetitive reporting means three things:
- Schedule the recurring reports (census snapshots, term enrollment, retention updates) so they publish without a request
- Keep every fulfilled request, with its query and definition, so the next variation starts from the last answer
- Let requesters ask repeat questions in plain language and get the governed answer with its source, while analysts review anything new or sensitive
An AI assistant helps most at this step. Clema's data request management takes requests as a conversation, asks the clarifying questions before an analyst sees them, and answers repeat questions from your data with sources attached. It connects to systems such as Banner, Colleague, PeopleSoft, Workday, Canvas and Blackboard, so it works from the data you already have, with no data migration.
What to expect
The whitepaper estimates that AI-enabled request workflows can give an office back 220 to 3,200 hours a year, depending on team size. Small offices of 1 to 3 staff sit at the low end in hours (220 to 330 a year), but in peak season ad hoc requests can take up to 90% of their capacity. The IR capacity benchmarks post breaks those figures out by team size for a budget case.
Start with intake. It costs nothing but agreement on a form and a channel, and it shortens every request that follows.
Read the research behind the three steps
The full findings from 50+ interviews with IR and IE professionals across 19 states, including capacity by team size.
Get the whitepaper