Beyond the Technical: Why Campus Culture
Is the Key to Data Success

00:38Thank you for joining me today for another episode of Data Stakes, where I have conversations with professionals who work directly in the institutional research or effectiveness field, or are data-adjacent in their role in higher education. Today's conversation will focus on the importance of campus culture — why it matters, and how to leverage culture as a foundation for successful and sustainable data work. My guest today is Dr. Jason Simon. Jason has over 30 years of leadership experience working in enrollment management, finance and administration, academic affairs, alumni relations, and student services, in higher education data analytics and research roles. His service to the IR and IE community includes current work as a co-instructor for the AIR Leads Leadership Development Curriculum, and as a contributing author on the 2019 statement "Analytics Can Save Higher Education, Really," jointly produced by EDUCAUSE, AIR, and NACUBO. Jason, it's great to see you again. In 2025, you announced that you weren't retiring, but rather rewiring, to become co-founder of SCL Impact LLC — congratulations to you, and to the campuses that are going to benefit from your knowledge. Today we're going to draw from your 2026 AIR Forum presentation, "Analytics Without Culture: A Recipe for Stalled Progress." I think this is something that's really underestimated on campuses — we focus so much on technical ability and data cleanliness, and all of that matters, but let's set the stage: why should our listeners care? Why does culture matter?

02:56I believe we throw a lot of time, energy, and resources toward the technical elements of the challenges we're facing as data practitioners. But it's ironic to me that even institutions with almost unlimited financial resources, large staffs, and robust technology platforms still struggle. That tells me the answer isn't just technical investment. The culture component is probably what's preventing a lot of practitioners — and their executive sponsors, presidents, and trustees — from achieving the level of success they're hoping for. Taking a culture-oriented approach and layering it on top of the technical approach gives leaders a much more robust way to analyze what's really going on in their ecosystem. It also honors the fact that every institution of higher education has a different culture — just because something worked at one institution doesn't mean you can lift and shift it to another and expect the same results. And there's a name for all of this: adaptive challenges. That concept comes from the work of a Harvard professor, Heifetz, who wrote a series of leadership books on the topic. The theory is that technology isn't the root of the challenge — people are. We're all social beings, and we all contribute to a campus culture. Divorcing the human element from our work sets us backwards.

04:36I totally agree — I did my master's in organizational leadership, so this really resonates with me. You included a collection of interesting quotations in your presentation, and I assigned them categories. In the "most controversial" category: "Data is like garbage. You'd better know what you're going to do with it before you collect it" — Mark Twain. And in the "most uncomfortable truth" category: "Data, data everywhere, but not a thought to think" — John Allen Paulos. Why did you choose these quotes, and are they connected to the uncomfortable confrontation that happens when we try to address internal challenges?

05:35Definitely. Take the garbage quote — how many times have we been asked, "I just need the data," or asked for data we as practitioners know doesn't really exist? Sure, I can build you a relational table, a technical connection that gives you access to data you don't have right now. But the underlying question is: for what purpose? From a pro-data-culture perspective, we're trying to enhance data literacy, and part of data literacy is understanding appropriate uses for data resources. It doesn't make sense to boil the ocean and build a massive data lake if the institution culturally isn't even prepared to deal with what we uncover — that's when it becomes like garbage, because we're farming it together without ever understanding the higher purpose. It's a cultural phenomenon: cultures exist where people constantly ask for more and more data as a stalling tactic to avoid making a difficult decision, and it piles up, like piles of garbage. In my presentation I showed the opposite — going from a garbage heap to something more like a recycling company, where like-minded data is sorted into piles with real thought behind which pile goes where and why. And the second quote is just as important: being surrounded by data doesn't mean people know how to understand or use it. How many times have we heard "well, I know a student who said..." or "I met an advisor who thought..."? That doesn't support a pro-culture campus, because it lets individual opinion drive decisions that, from an ecosystem perspective, aren't really appropriate.

07:47That reminds me of guidance we received when my former institution was invited into an Aspen Institute cohort — they wanted us to demonstrate initiatives that had scaled up, not just something that helped 30 or 40 students, even though that mattered, but things that drove widespread cultural shift. It also reminds me of a rule I held to throughout my career, which took some training for my colleagues: when someone emailed requesting a data set, I'd write back — what's your research question? What are you going to do with this data? Who are you going to share it with? At first that was met with a bit of a "what's it to you?" attitude, but my colleagues eventually discovered that conversation was valuable and actually saved time. If I don't understand what you're going to do with the data, how do I know I'm not going to give you garbage?

09:17That positions the IR function as an ally in the faculty member's research, not just IR's own research priorities. We did something similar — we'd ask for the research question, specific details on the cohorts they were interested in, follow-up questions on a form, so we weren't doing 35 rounds of ad hoc back-and-forth before figuring out what someone actually wanted. From a culture perspective, that also created real opportunities to partner with our Office of Sponsored Research — their VP would bring us in to train faculty researchers. It wasn't just about not letting people ask for data the day before a grant was due; it positioned the IR office as an ally with that faculty member, and that blossomed into a cohort of people who went to bat for the data function at the institution. That's what we're talking about: the soft skills IR needs to embrace. We're all trained in a practitioner-based model — the person who trained you was probably trained by their boss in IR. There's no master's degree solely focused on institutional research; maybe a course here or there. It really is institutional culture. I wish we had more opportunities to dig into conversations like this one, because it's the differentiator — it boosts an individual's performance, makes an impact on students, and sets people up to be viewed as experts in their field.

11:22So I know you want to circle back to the importance of adaptive leadership for data professionals, and I loved this statement from your presentation: "Almost every adaptive challenge is directly linked to campus culture." Let's talk about what an adaptive challenge is, as opposed to a technical challenge, and why you think adaptive leadership skills are the key to changing culture.

11:50Technical challenges are typically focused on a problem — what's the problem we're trying to fix? Adaptive challenges work best when we're trying to understand what we need to learn — that's the first clue: is this a learning issue? Second, technical problems often don't require people to lose something, whereas an adaptive challenge requires a loss — maybe in autonomy, in individual decision-making, or just in coming to terms with being wrong about something. That's a big differentiator. Adaptive challenges also aren't easy to see on the surface; they're usually inextricably linked to culture — norms, symbols, values, beliefs, whether the campus allows data to be used for good or weaponized as a punishing tactic. That's all part of adaptive challenges and culture. You can have the best strategic roadmap for a data maturity process or a new data warehouse, but the adaptive challenge is: what's the relationship like between IR and IT? Between the people doing data quality and cleansing work and the data modeling team building table structure? Those are adaptive, not technical — they come down to competing priorities, timelines, and purposes. If institutional researchers really want to be effective data culture leaders, they have to embrace the hardest parts of the job: the relationships and the people — and not just the people you already like and who like you. The hard part is building allyships across your institution with people who may have been your harshest critics — maybe you need to take them to lunch and say, "you said this in a meeting, and I want to unpack that with you so I can better understand where you're coming from." Notice that's not "I felt offended" — it's "I want to learn." That's the adaptive piece: adaptive leaders want to continually learn, not just the facts, but the people, the beliefs, the feelings. I'd encourage your listeners to read up on adaptive leadership — I believe heavily in reflective praxis, looking backwards to inform the future, and as I dug into adaptive leadership, a lot of things became clear: why someone behaved a certain way, or why something fell off the rails and we had to put it back on track. It's a valuable exercise to think beyond just the technical toolbox.

15:06So what this means is we have to embrace a side of ourselves that makes us uncomfortable, because this takes real emotional intelligence — and let's face it, a lot of colleagues signed on to a data role expecting to stay behind a closed door, not necessarily to deal with a lot of people. But we're seeing more of that expectation shift as campuses move toward institutional effectiveness roles. One thing that was really underestimated early in my career was how much fear plays a part in strategic planning. During my first strategic plan, a president told me, "I noticed you've only invited people you already get along with" — and named two people I really didn't want in the room. His response to my hesitation was, "this will stretch you, you'll learn to know them better, and if you're successful, they might become new allies." Those early meetings, revising the mission statement, were uncomfortable, but it was so worth it. Another time, as an interim administrator working with faculty on outcomes assessment, I sat and listened to a faculty member yell at me for ten or fifteen minutes. When it was done, I didn't tell him how it upset me — I said, "I understand, I'm sorry." He'd been through objectives, then outcomes, then something else, then outcomes again, and he was tired.

17:22And you weren't the target — you were just the outlet for his frustration, and that's part of culture too. My example: when I first joined my previous employer, we were still using a paper fact book — 364 pages, no joke. By the time I left, we had a full machine-learning data science capability; we went from paper to dashboards. When we introduced that program, I didn't just drop a new tool on the institution — I went around and talked with all our deans. One dean told me, "it doesn't matter what you bring, I don't have the mental space to learn another system right now." That was an important lesson: we had those conversations before we even announced the tool, so by the time I left, that same dean would have taken the hill with me if the system had gone away, because she'd seen the value and felt like a participant in creating it. From a culture perspective, we very intentionally introduced concierge-style training to a select group of people who could talk to their peers and create positive messaging — a force multiplier. A lot of our IR shops are one-person shops expected to do so much; by building these allyships, your message gets carried more broadly because it's not just you out there preaching.

19:17So important. You listed six key takeaways that lead to what you call "culture in action." Can you speak to three of them: making sense of your institutional culture, deciphering leaders' words and actions, and responding to change?

19:40I'd lump those together under one rubric: we can no longer afford to be the fish in the fish tank who doesn't see the water — we have to look more critically at the ecosystems we work in. Even just a half hour a month, being reflective, maybe as a team discussion, asking: what's happening on our campus right now, from a data perspective, that we need to be paying attention to? We first have to be true to our own perspective, then look at the campus community and ask bigger questions — why do new initiatives fail? What is being said versus what is being intended, since what's said isn't always the actual intent? I always encouraged my team to attend or watch our president's State of the Union address, so we'd hear the same institutional priorities I was hearing, and then we'd have a facilitated conversation about reading between the lines and how it would impact our data ecosystem. And building on the last point — having those uncomfortable conversations with people who might offer a different perspective on your work. I get the risk; I get that it doesn't feel comfortable, because if we ask the question and they give us the answer, that might mean more work. But I'd rather have the difficult conversation and understand the expectations than be judged against a standard I don't even understand. Taken together, it creates the need for IR practitioners to run a different race than the horse with blinders on, looking only one direction. If every strategic plan initiative is tied back to data, and we're the data team, then we're inextricably involved in the institution's success — even if our leaders don't fully recognize that. It's adding tools to an already technical toolbox that let you recognize the need for functional subject-matter expertise, including human relationships, not just technical expertise.

22:39So totally agree. Let's flash back to a regular survey AIR does, where you report on your function and role on campus. Early in my career I didn't report to the president, but at my first institution, a new president came in and said exactly what you've described: "we involve data in everything — why isn't Debbie on the administrative council?" Listeners, if you don't sit in a place where you feel you can effectively move culture because of your role, maybe it's time to talk to your dean, provost, or president about making your role more effective — you can only function within the fence around your field. If your pasture isn't very big, you can't wander into other areas; people would tell me, "you work in student services, why are you making suggestions to academic affairs?" But I got lucky and moved to academic affairs, working for a dean who helped expand my role. She legitimized data on our campus, and that's what eventually led to becoming an administrator with even greater reach to affect culture.

24:20That's a challenging one — I'd build in fifteen minutes before and after any meetings in the main administrative building. It might not be a best practice for everyone, and it drove my team a little crazy, but I'd pop into offices, building relationships over time, not on day one. Those hallway conversations, bumping into people — it might look unintentional, but there was real strategy behind it, because it gave me a touch point with people who weren't in the conversations I was having, and let them help me spread the case for why the data function matters.

25:26You ended your AIR Forum presentation by challenging attendees to embrace one of nine "data-forward behaviors" as a catalyst for culture change. Let's share our favorites. I'll go first: "Behave with confidence — make data a non-negotiable in decision-making." That's been a drumbeat throughout my twenty years in higher ed; it's even the impetus for this podcast's name, Data Stakes. Data deserves a seat at the table. IR and IE professionals shouldn't be viewed as just the person who does the external reporting — "oh, Debbie does IPEDS" — but as an important administrative asset guiding meaningful change. Without data, where does the conversation even begin? Between data and context, you have to have allies — you have to have conversations.

26:45Let's be honest — higher ed isn't exactly in a well-perceived public state right now, and that might be generous. One of the criticisms of our field is the disconnect between outcomes-driven behavior and what makes sense — how many times have we heard "let's be more like a business"? We're not a business, we're higher ed. We have elements that mirror corporate ecosystems, but this isn't new — higher education goes back to sub-Saharan African institutions and medieval European universities. My perspective is that we as practitioners need to be comfortable calling out things that aren't true when we're in the room — not taking the moral high ground silently, but asking, "what does the data actually tell us about this?" By the time I left my last institution, several VPs told me the one thing they could always rely on from me was the truth — the facts, without hyperbole or sugarcoating. I took that as the ultimate compliment, because you can't misuse data if you're afraid to even tell people what it is. It takes courage.

28:37Yes — it takes courage to work in data. It's not for everyone.

28:44It's not. When I'm consulting with IR teams and running staff retreats, these topics always come up. Part of my goal at this stage of my career is to keep giving back to higher ed by educating practitioners to be force multipliers at their own institutions — I don't just want to contribute at one institution anymore, I want to help the whole field. As a first-generation student who broke four generations of poverty, higher ed was a differentiator for me — I'm on a completely different life trajectory because of the role it played. So for me it's a social and ethical calling. We're so quick to talk about graduation rates, retention rates, enrollment counts, and grade distributions that we forget the individual impact on a student's life. I think our field could do a better job sharing that — sometimes taking a hill and planting a flag to talk about the uncomfortable data a situation reveals.

30:07So which one of the nine was your pick?

30:12That's like asking a parent to pick a favorite child — I tried to write them so each hit a different aspect of culture, but if I had to choose: "behave as a champion, promote data literacy across all roles." Data literacy means helping people understand the sources of data, how it's gathered, collected, and used, the use cases for it, and — the piece we often miss — the return on investment of leveraging that data. What good happened, and how do we measure it? Data literacy meets the individual data consumer where they are; it doesn't assume anything. At my last institution we built entry-level courses, secondary courses, and a short video training through our HR platform, aimed at helping people ask better questions of the data. It was remarkable watching the ad hoc requests change — the super-simple questions stopped coming in, and the really thorny, sticky ones started, which told us people were no longer data-agnostic, they were becoming data-literate. That should be our goal — nobody wants a frontline analyst handling the same simple request every single day; that's how you burn out your staff, and why they leave. Behaving as a data champion, in every sense of the word, profoundly changes the discussions you're part of, the campus culture, and — hopefully — student outcomes. If not us, then who?

32:45What you're also describing is a shift in ownership, so data doesn't just belong to the IR office. When I went to Cowley, one of the first things I shared was moving data from "me" to "we" — intimidating at first for colleagues who don't work in data, but with patience and the right relationships, you start to see the shift. Like your example — I had a faculty member call me after we'd used the same Tableau dashboards for academic program review for two or three cycles, and she said, "I've been thinking, our program is different from all the others, could we add this?" — and before I could even ask why, she explained her reasoning. I was so proud; that was a huge moment.

33:52That acknowledges you've built real competency in someone whose main line of work isn't data.

33:59And not only that, she felt comfortable — she probably worried about sounding uninformed, but she still called and asked, and it was a terrific addition I hadn't thought of, because I don't teach her subject. Culture, culture, culture. Data can be perfect and clean, and you can win a prize for it, but if you want sustainability, if you want to shift at the organizational level, if you want to impact students at scale, you have to have the culture.

34:37And I want people to be upset, offended, and angry if something you built that provided value is suddenly gone — that's the hallmark of success. Apathy doesn't help us; we need people engaged. My close second pick, tying directly into what you just said, is "behave as a connector, break down data silos." That's another area where we have to look in the mirror as a field — we do a lot with faculty data but hardly anything with staff HR data, or real finance data: general ledger, commitment control, cost margins. We need to really unpack research data too, because IR has historically been pigeonholed within academic affairs, and if we're going to advocate for data on our campus, we need to be in conversations that aren't comfortable for us. Even if IR doesn't end up doing the financial analysis itself, partnering with the people who do creates a shared vision for how data can elevate institutional outcomes. And AIR does an annual survey of presidents — I'd always laugh when they'd say data-informed decision-making was "very important," and then the next question, about where their financial priorities and spending were going, showed data and technology falling to the bottom. We have a real opportunity as IR practitioners to help influence those decisions and show our value beyond just delivering the ad hoc report every Monday morning. We have to open our practice up to include a culture view and adaptive leadership that elevates the role from purely technical to technical and functional.

36:58So thanks again for being a guest — I've attended your AIR Forum sessions in the past, and I think our listeners will agree: your dedication to meaningful data work, grounded in integrity and focused on student success, comes through clearly.

37:30Thank you for the opportunity to talk with your listeners — this is something I'm passionate about, and something I want to help more people unpack in their own skill sets. When you add these components to your toolbox, you're not pigeonholed in the same job for your entire career — it opens up opportunities, lets you advance professionally, and do more with your role. Who wouldn't want to be part of those conversations?

38:02Agreed. Listeners, thanks for joining us for another episode of Data Stakes. Data Stakes is sponsored by the Data Analytics Alliance for Higher Education — visit our website to learn more about our upcoming quarterly meetings. We're several hundred strong, we meet four times a year, and in a few weeks we'll have more meetings focused on AI. If you have questions about today's conversation, don't hesitate to reach out to me at dphelps@datatelligent.ai. See you next time.