Sharing outcomes data publicly is a hard call, and if you’re sitting on data you haven’t put out there yet, it’s not because you haven’t thought about it. It’s because you’ve thought about it a lot.
Maybe it’s the fear that a thin response rate for one program makes your whole office look like it’s not doing its job. Maybe it’s picturing the email from a department chair asking why their program’s numbers look worse than the one next door. Maybe it’s the harder thought: a student who chose a major in good faith, and what it means to show them the outcomes weren’t what they hoped. Or the even harder one: what this does to the institution’s reputation, and whether it’s worth the risk.
Those aren’t small worries. They’re the sign of an office that takes its relationship with students and academic partners seriously. Acting on them by staying silent isn’t actually helping anyone, though, and that’s the case we want to make here.
Ask a career services leader why their outcomes data isn’t being put to use, whether it’s sitting in a spreadsheet, stuck waiting on approval from IR or another office, or just not shared publicly, and you’ll hear some version of the same reasons: knowledge rates are too low. Some programs have responses and others don’t. The data they do have doesn’t fully represent actual student outcomes. They don’t want a department or major to look bad.
We get the instinct. We just think it’s the wrong call.
Incomplete data isn’t bad data. It’s early data.
Somewhere along the way, “incomplete” turned into “bad.” But a low response rate doesn’t carry judgment. It’s a fact about where your data collection stands right now, not a verdict on your students or your institution. This isn’t about wrong data, biased data, or misleading data — it’s data that’s honest, even if it’s partial. Some of it will fill in over time. Some programs, especially smaller ones, may never get a large enough response pool to look “complete” by traditional standards, and that’s fine. Either way, what matters is how you use it.
Once you let go of treating early data as something to hide, the real question gets a lot more practical: what’s the best way to use the data you already have, right now, to help students?
Withholding data in the name of fairness isn’t actually fair
Here’s where a lot of well-intentioned institutions talk themselves out of sharing anything at all: “It wouldn’t be fair to show outcomes for some programs and not others.”
If we follow that logic all the way through, it starts to fall apart. If you withhold data for the one program where you do have solid numbers because you don’t have it for every program, you haven’t actually created equity. You’ve created a world where no student gets access to information that could help them make a real decision about their future. That’s not fair. That’s just making sure everyone is equally in the dark.
Waiting for complete data doesn’t fix that. It just delays the point where anyone gets useful information.
The comparison that matters isn’t complete vs. incomplete. It’s something vs. nothing.
The real baseline isn’t some fully populated dataset that doesn’t exist yet. It’s what students get today with no data shared at all: nothing. An empty answer.
Sharing the data you have doesn’t make that baseline worse for the programs you’re leaving out. A program with no data still gives students the same non-answer it always has. But a program with data, even early data, now has something honest to offer a student trying to picture their future. You’re not making things worse for the programs still waiting on more data. You’re just adding value where you’re able to.
Students deserve the information you already have
There’s a bigger stake here than institutional comfort. Students are making real decisions with their time and money: what to major in, what path to pursue, whether the investment they’re making is going to pay off. They can’t factor in information they never see. Withholding data you already have, even early data, isn’t a neutral choice. It’s a choice to let students make those decisions with less than what’s available to help them.
Institutions exist to prepare students for what comes next, and outcomes data is part of how you do that.
And students aren’t the only ones who need it. Academic advisors use outcomes data to guide major and course decisions. Faculty use it to advise students and to show why their program matters. Enrollment teams use it to answer the question every prospective family asks: what happens to graduates of this program? Advancement uses it to make the case to donors. Outcomes data isn’t a career services asset that happens to help students. It’s campus-wide infrastructure and what helps prove the value of a degree at your institution, and career services is often the only office positioned to actually own and maintain it.
Treating it that way changes the calculation. The rest of campus needs this information too, and in a lot of cases, they’re already asking for it.
Precision should match what you actually know
None of this means overstating what your data can tell you. A response from three students in a program shouldn’t be dressed up as a definitive stat. If your numbers come from a small sample, label it as early or partial, or lean on rates and ranges instead of a single hard figure that implies more certainty than you actually have. If you have a strong, representative sample, show it plainly.
That’s not spin. It’s just matching your language and format to the size and shape of the data you actually have, instead of either overstating it or staying silent.
Progress over perfection
We’re not suggesting anyone rush to publish messy or misleading numbers. We’re suggesting a different default: share what’s true and useful today, label what’s still early, and keep building from there. Waiting for a level of completeness that’s realistically years away helps no one in the meantime.
In practice, that looks like:
- Share what you have now. Don’t wait for full coverage across every program before being useful to some students.
- Label early data as early, not hide it. If a specific program’s data is thin, say so, and treat filling that gap as a priority, not a reason to withhold everything.
- Match your format to your confidence. Lean on rates or ranges where your sample is small, and show fuller numbers where it’s strong.
- Treat completeness as ongoing, not a launch requirement. Your data will get better over time. Students shouldn’t have to wait for that before getting anything at all.
Progress over perfection is the only approach that actually gets students what they need.
