It’s October. The leaves are changing, and the Pumpkin Spice Latte (PSL) has made its annual, triumphant return to menus around the world.
Full disclosure: I don’t drink Pumpkin Spice Lattes. I like my coffee to taste like coffee, preferably without enough sugar to qualify as a dessert. But I am unapologetically an autumn girl, and I fully support PSL season, even if I’m not personally contributing to its sales figures.
And speaking of figures, let’s talk about something considerably less festive: institutional data.
Grande, Venti, or Whatever You Want to Call Me
Our fearless leader, Susan, once received a coffee cup labeled “Susane,” which made us all laugh and instantly became part of our EduCred Services lore. “Susane” (pronounced sue-sane) now describes the place where Susan, our organized and occasionally perfection-driven leader, meets the slightly insane overwhelm of it all. It’s that special blend of competence and barely contained exasperation that emerges during accreditation deadlines and those delightful moments when institutional data refuses to cooperate. Sometimes you’re Susan. Sometimes you’re Susane. And if you’ve worked in higher education long enough, you’ve probably been both before lunch.
Fortunately, a misspelled name on a coffee cup is usually harmless. You laugh and move on. After all, if the barista gets the order right, does it really matter what name is written on the cup? But imagine ordering a grande Pumpkin Spice Latte and receiving a tall iced caramel macchiato. You check your receipt, which says venti cappuccino. Meanwhile, the barista insists you ordered a chai tea.
Now we have a problem.
Higher education institutions sometimes find themselves in a similar situation when preparing annual reports, accreditation self-studies, Integrated Postsecondary Education Data System (IPEDS) submissions, and institutional effectiveness reports.
The institution reports 1,200 students in its annual report, 1,350 in its accreditation self-study, and 1,175 in its internal enrollment dashboard.
Which number is correct? Possibly all three. Or possibly none of them.
The answer depends on how the institution defined the population being measured and when it did the counting. Fall enrollment, unduplicated annual headcount, and active enrollment on a particular date are not necessarily the same thing. But if nobody can explain the differences, the institution has a credibility problem. And unlike a misspelled name on a coffee cup, this one isn’t particularly Instagram-worthy.
Same Ingredients, Different Recipes
One reason the PSL has become such an enduring seasonal phenomenon is consistency. Customers know what they’re ordering. The recipe doesn’t change dramatically depending on which barista is working or which Starbucks location they’re visiting. Institutional data should operate with the same consistency.
Consider graduation rates. Your institution’s outcomes assessment report might calculate the percentage of students who completed their programs during a particular reporting period. Your IPEDS submission might use a federally prescribed cohort and methodology. Your accreditor might require another defined calculation. These numbers may legitimately differ because they measure different populations or use different methodologies. That’s fine. The trouble starts when nobody knows why they differ.
If your institutional effectiveness plan reports a 65% graduation rate, your self-study reports 58%, and your website advertises 72%, you should be prepared to explain what each number represents.
And preferably before an accreditation evaluator asks.
Institutions need clearly defined data sources, reporting periods, calculation methodologies, and responsible personnel. When everyone uses the same definitions and understands the reporting requirements, the institution can explain its results confidently.
Think of it as a standardized recipe. Without one, somebody is eventually going to put six pumps of pumpkin syrup in a drink that calls for two. And nobody wants that. Well, almost nobody. (Ahem, Emily.)
Too Many Baristas Behind the Counter?
Here’s where things get complicated. Institutional data rarely comes from a single person or department. Admissions tracks applicants and enrollment. The Registrar maintains academic records. Academic leadership monitors course completion and student achievement. Student Services tracks retention and engagement. Finance monitors revenue and sustainability. Meanwhile, the person responsible for preparing the accreditation self-study is trying to pull all those numbers together into one coherent institutional narrative.
Sound familiar?
It’s not unusual for different departments to maintain separate spreadsheets and dashboards. These tools may work perfectly well for their intended purposes. Problems arise when the institution assumes everyone is measuring the same thing.
For example, Admissions may report the number of students who enrolled during the year, while the Registrar reports the number who were actively enrolled on a specific date. Both numbers may be accurate, but they are not interchangeable. And if those differences aren’t documented, someone may inadvertently use the wrong number in a report.
Institutional data accuracy is an institutional responsibility, and it belongs to far more people than the IT department.
That means departments need to communicate, agree on definitions, reconcile discrepancies, and establish who validates the information before publishing or submitting it. After all, even the most talented barista can’t make the right drink if the order ticket is wrong.
A Little Quality Control Before Serving
Accreditation evaluators understand that numbers can differ from one institutional document to the next. What they expect is accurate information that the institution can support.
And when the numbers don’t align, questions follow.
Why does the institution report one completion rate in its outcomes assessment plan and another in its self-study? Why does the student enrollment figure differ from the number reported to IPEDS? Why do the annual report and institutional effectiveness plan appear to use different definitions of retention?
Sometimes there’s a perfectly reasonable explanation. Sometimes the data was pulled on different dates. Sometimes a calculation was revised. And sometimes someone copied last year’s numbers into this year’s report and forgot to update them. (We’ve all encountered a version of that last one. No judgment. Well, maybe a little.)
The important thing is to identify and resolve these issues before documents are finalized.
A few practical steps can make a significant difference:
- Define your ingredients. Establish consistent definitions for key institutional metrics, including enrollment, retention, graduation, completion, and student achievement. Document any differences required by specific reporting agencies.
- Check the recipe. Identify the authoritative data source, reporting period, and calculation methodology for each metric. Make sure the institution can reproduce the results.
- Assign the baristas. Designate responsibility for collecting, reviewing, validating, and approving institutional data. Someone should know where the numbers came from and be accountable for their accuracy.
- Taste before serving. Compare data across institutional reports before submission or publication. If numbers differ, determine whether the discrepancy is legitimate, document the explanation, and correct errors.
- Keep the receipts. Maintain supporting documentation, including source reports, calculation worksheets, cohort definitions, and explanations of adjustments. You don’t want to reconstruct three years of graduation data two weeks before an accreditation visit.
None of these steps is particularly glamorous. Neither is calibrating an espresso machine. But these behind-the-scenes processes make a consistent, reliable product possible.
Bigger Than Getting the Numbers Right
Here’s the bigger issue: institutions collect data for far more than reporting requirements.
Accurate data helps institutions understand what’s working, identify areas for improvement, allocate resources, and make decisions that affect students.
If your retention data is inaccurate, how can you determine whether your student support initiatives are effective? If completion rates are calculated inconsistently, how can you evaluate whether changes to your curriculum or academic policies are producing meaningful improvements?
And if institutional leadership makes decisions based on numbers nobody has verified, those decisions may address problems that don’t exist while overlooking ones that do. That’s a much bigger concern than an uncomfortable conversation with an accreditation evaluator.
Reliable data is foundational to institutional effectiveness. Institutions cannot meaningfully assess performance or make informed decisions without confidence in the information they use.
Data integrity means knowing what the numbers actually mean and having the confidence to act on them. Sometimes the data tells us exactly what we hoped to hear. Sometimes it reveals something we’d rather not know. Either way, an accurate picture of institutional performance is better than a beautifully formatted report full of questionable numbers.
One Last Sip
As I settle into my favorite season, I’ll happily enjoy the changing leaves and the annual return of all things pumpkin spice. I’ll leave the sweet coffee drinks to those who appreciate them.
But I’ll keep appreciating what Starbucks and higher education institutions should share: consistency and delivering what was promised. Because whether you’re preparing a Pumpkin Spice Latte or an accreditation self-study, the details matter. A misspelled name on a coffee cup might make someone laugh. A graduation rate that changes depending on which report you’re reading? Not so much.
So before submitting that next self-study or IPEDS survey, take another look at your numbers. Make sure you know what they represent and why they might differ from other reports.
And remember: A little pumpkin spice can cover a lot of things. Inaccurate institutional data shouldn’t be one of them.