A running index of product launches, research releases, and company updates from Clema for institutional research professionals
A general news and updates index for Clema covering product launches, whitepapers, IR benchmark releases, conference appearances, and company news relevant to IR and IE teams in higher ed.
A curated index of Clema's guides, whitepapers, benchmarks, and reading lists for IR and IE professionals
A single hub of every Clema guide, research paper, IR benchmark, conference list, podcast, book, and definitional framework the IR team should bookmark. Updated as new pieces ship.
Peak (Sep, Jan, May), moderate (Oct, Nov, Feb, Mar), and low (Apr, Jun, Jul, Aug, Dec) mapped to the requests, reports, and prep that come with each
A when/lifecycle guide for IR teams: which months are peak, moderate, and low for ad-hoc requests, what reports and accreditations hit each season, and what to prepare ahead of time.
A scenario walkthrough anchored in the whitepaper finding that large IR teams spend up to 1,310 hours per month on ad-hoc requests
How a large IR team (8+ staff) uses Clema's analytics, peer benchmarking, and predictive analytics to scale analyses across schools instead of pulling data one school at a time.
A scenario walkthrough for the institutional intelligence gap, anchored in research that 85% of institutions depend on a single person for institutional knowledge
How an IR office uses Clema's data platform to preserve definitions, sources, and terminology across a key-person departure, anchored in the 85% key-person dependency finding.
A scenario walkthrough for the definitional drift problem, anchored in the whitepaper finding that 73.5% of requests are vague or unclear
How an IR director uses Clema to surface the cohort definition on every answer, so "retention" stops meaning whatever the requester had in mind.
A scenario walkthrough tied to the whitepaper finding that 64.7% of institutions struggle with self-service dashboards
How an IE director uses Clema to keep stakeholders out of the email queue and let them ask dashboards in plain English, anchored in the whitepaper finding on dashboard adoption.
A scenario walkthrough for an IR director running everything solo, anchored in whitepaper findings on small-team peak-season utilization
How a one-person IR office uses Clema to handle IPEDS pulls and ad-hoc dean requests in parallel during peak season, when capacity utilization hits 90%.
What 50+ interviews with IR teams reveal about how much time ad hoc requests actually consume, by team size
Quantified IR capacity benchmarks from 50+ interviews: 40 to 60% of team capacity consumed by ad hoc requests, 2 to 5 clarification cycles per request, and 220 to 3,200 hours annually reclaimable. By team size.
An honest limitations list for IR teams evaluating Clema, and the institutions where a different tool is the better call
Where Clema does not fit today: very small institutions, institutions with no data source to connect, real-time pipeline needs, and bespoke statistical research. Honesty content for IR buyers.
The prompt patterns that return sourced, defensible IPEDS figures in plain English
A procedural guide for asking IPEDS questions in Clema: the prompt structure, the cohort and year disambiguation, the source check, and four worked examples.
A step-by-step walkthrough for constructing a defensible IPEDS peer group in plain English
A procedural guide for building a peer group in Clema by Carnegie class, control, region, or designation, and pulling the same metric for each peer in one conversation.
The exact steps to configure SAML 2.0 or OAuth 2.0 SSO with your institution identity provider
A procedural walkthrough for configuring SSO with Clema using SAML 2.0 or OAuth 2.0, including metadata exchange, attribute mapping, MFA, and the test login that confirms it works.
What happens each week from data connection to stakeholder rollout, with the people and artifacts involved
A week-by-week onboarding checklist for Clema: who does what, what gets signed, what gets connected, and what success looks like at each phase over 2 to 4 weeks.
Five IPEDS questions to ask Clema in your first session, and what each one shows about the federal dataset
A quick start for querying IPEDS in plain English with Clema: enrollment, graduation rates, peer comparison, finance, and faculty figures, with the federal source on every answer.
A starter list of plain-English questions that show what Clema does on federal and institutional data
Five plain-English questions to ask Clema in the first session, what each one demonstrates, and how to interpret the sourced answer.
Published pricing that is free to start, with plans from $99 per month, versus unpublished enterprise pricing that requires a quote
A side-by-side cost comparison of Clema and HelioCampus Theia Analyst for IR and IE teams, covering published pricing, implementation, and what is included.
Published pricing that is free to start, with plans from $99 per month, versus custom enterprise pricing that ran $36,000 to $216,000 per year on a published fee schedule
A side-by-side cost comparison of Clema and EAB Edify for IR and IE teams, covering published pricing, implementation fees, and what is included at each tier.
General AI reads the public web and guesses. Higher ed needs AI that reads its data, knows IPEDS, and shows its sources.
Why general-purpose AI assistants fail on higher-ed data questions and what a higher-ed-native AI like Clema gets right: domain knowledge, real data, and sourced answers.
Requests keep climbing, headcount stays flat, and the math no longer works for an IR team of one to three people
The structural mismatch between rising IR data request volume and flat team capacity, and why conversational AI is now the only path that scales without a hiring cycle.
Eleven federal datasets, when they drop, what is current, and what is coming next so your IR team is never caught off guard
A practical release calendar for the 11 federal datasets IR and IE teams depend on: IPEDS, College Scorecard, Pell, loans, cohort default rates, Clery, EADA, PSEO, PPD, and DAPIP.
The data is rarely the problem. The missing layer of meaning on top of it is, and one ordinary term can carry five valid definitions at once.
In a study of 20 IR and IE offices, one term routinely carried several valid definitions at once. Here is why that happens, the eight layers it happens at, and why it breaks reporting even when the data is clean.
In 85% of the institutions we studied, the definitions that run the place live in one person's head. Here is a way to diagnose how fragile yours are.
A diagnostic for institutional research teams: the Institutional Intelligence Gap, the three tiers (55% Large, 40% Moderate, 5% Small), a five-factor self-assessment, and an illustrative cost model for key-person dependency in higher ed.
A six-step framework for moving institutional definitions out of one analyst's head and into governed, survivable infrastructure
Creating data definitions is achievable. Maintenance is where most efforts collapse. Here is a six-step framework, an AI-readiness argument, and tier-by-tier next steps for IR/IE teams.
Most people land in institutional research sideways, from the registrar, the provost office, or a faculty data role. This is the directory of where to actually learn the craft, sorted by what it costs and what you get.
A directory of 25 ways to learn institutional research: graduate certificates, AIR professional development, free IPEDS and PDP training, executive programs, and self-paced courses, with formats, costs, and links.
A graduation rate is a sentence with the ending cut off. Data-informed advocacy is the skill of finishing it for the person in the room.
Data storytelling for IR teams: how to finish the sentence on a number, segment your audience by power and fluency, start with why, and use real federal datasets to turn institutional data into advocacy that drives decisions.
Most institutional data is used to describe the past. Here is how IR leaders flip it forward.
IR teams already sit on years of attendance, GPA, LMS, and aid data. A practical guide to turning that record into forward-looking predictions that change what advisors, deans, and provosts can do this term.
A general-purpose chatbot cannot tell you which students will not return next term. Here is why, and what does.
A plain-English guide for IR leadership on why classical ML (specifically XGBoost) outperforms LLMs on student-level retention and graduation prediction, what the benchmark literature actually shows, and how to read a per-student risk score.
A cost and vendor reality check for IR leaders weighing build, buy, or partner.
A straight comparison between the open-source predictive-analytics stack ($40 to $95 per month) and the major incumbent platforms ($30K to $200K per year). Covers infrastructure cost, licensing, where incumbents are strong and where they leave gaps, and a build-vs-buy framework.
Inside a retention model evaluated on 276,519 unseen student-term records.
A practical look at the features, engineering choices, and timing decisions that drive a real term-to-term retention model. Written for IR leadership who want to understand what the model sees before they hand its output to advisors.
A graduation model evaluated on 36,000 unseen students reached ~0.911 AUC. Here is what it sees that cohort reports do not.
The graduation use case in detail. Features, course-combination effects, plain-English metric reading, the small-group floor that prevents false certainty, and the drift management every long-horizon prediction needs.
Different question, different model family. A guide to time-series forecasting, yield prediction, and program-margin analysis for IR leadership.
A practical guide to the predictive questions Provosts and CFOs own. Enrollment forecasting (ARIMA, Prophet, LSTM), yield prediction, program margin and viability, IPEDS-based screening, and the honest limits of each.
A 16-section atlas of the "Do No Harm" earnings-rule docket. Who showed up, what they argued, what they fear, what they want, and what is most likely to land before February 2027.
A deep-dive atlas of every public comment on the Department of Education's "Do No Harm" / STATS earnings-accountability proposal. Counts, themes, campaigns, pull-quotes, emotional register, and the implementation changes the sector is asking for before the first February 2027 results.
October 1, 2026 is the last FVT/GE submission. Four moves to make before the first Earnings Premium results in February 2027.
October 1, 2026 is the last FVT/GE submission under the old regime. Here is exactly what carries over to STATS, what does not, and the four things IR teams should do between now and the first Earnings Premium results.
The clean line from statute to STATS to the 2-of-3 eligibility math, with a worked example
The One Big Beautiful Bill created a new earnings-accountability regime for higher ed. Here is the line from OBBB to STATS to the Earnings Premium formula, and the 2-of-3 eligibility math, in plain English, with a worked example.
A program-level playbook for the eighteen months between PPD:2026 and ED's first official results
ED publishes the first official Earnings Premium results in February 2027. Here's how to map your program-level exposure now, score, stress-test the two-of-three-years window, and plan substitution paths, before the results land in public.
The institutional-level STATS trigger most administrators haven't priced in
STATS introduced an institutional-level consequence that didn't exist under FVT/GE: if more than 50% of an institution's Title IV funds or recipients sit in failing programs, the entire institution goes on provisional status. Here's how to model your exposure before the cabinet asks.
The IR analyst's working view of the PPD:2026 dataset, and where the time actually goes
PPD:2026 looks like a single dataset. On disk it's six Excel files joined on opeid6 + credlev + cip4, with mixed-vintage dollars, statistical-noise privacy suppression, and a CIP-4 key that doesn't match the CIP-6 the STATS test will use. Here's where the analyst hours actually go, and how to skip them.
Three names, one dataset, and why they keep getting confused
STATS, FVT/GE, and PPD all describe pieces of the same federal accountability regime under OBBBA. Here's a plain-English explainer of what each term means and how they fit together.
An analysis of 18 university data request forms reveals three dominant intake formats, threefold complexity variation, and the design gaps that create hidden work for IR teams
35% of universities have no formal intake process, and 77% of IR offices require extensive clarification before analysis begins. Analysis of 18 university data request forms reveals what the best systems do differently.
Scale vs. Speed: How Dual Enrollment Differs Across Sectors Using IPEDS 2023–2024 Data
IPEDS 2023–2024 data reveals four-year institutions growing 3x faster in dual enrollment than community colleges, while community colleges carry 32.4% structural reliance. A full side-by-side benchmark.
Four Core Benchmarking Domains and Three Benchmarking Types from 50 IR Director Interviews
Discover what IR teams actually benchmark most often, from student outcomes and faculty metrics to aspirational peer lists, based on evidence from 50 institutional research interviews.
Why IR Teams Lose Capacity Before Analysis Even Begins
Research shows 80% of IR data requests arrive missing critical elements. Learn how Request Intelligence (the quality of requests at intake) determines whether your team spends time analyzing or clarifying.
Data Request Guidelines for Streamlining Self-Service Dashboards
Discover proven strategies from IR leaders to streamline data requests, implement self-service dashboards effectively, and reclaim up to 60% of your team's capacity for strategic work.
14 conferences worth your time — from the national flagship to regional gatherings and data-focused events
A guide to the 14 conferences IR professionals should consider in 2026, from AIR Forum to regional and data-focused events.
Newsletters, listservs, and peer communities that IR professionals actually use to stay sharp
A resource guide built from 50+ interviews with IR professionals: the newsletters they read, the communities they trust, and where peer-to-peer learning happens.
The podcasts, books, and training programs IR professionals recommend for going deeper
Part 2 of our resource guide: the podcasts IR pros listen to on their commute, the books on their desk, and the training programs they recommend.