From "Me" to "We":
Finding Your Place in Metrics Strategies

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 need to shift your institutional mindset from a traditional research paradigm to a metric strategies paradigm. My guest today is Lee Howell from Florida State University. Lee recently moved into his new role, Senior Director of Administrative Operations and Logistics, from an earlier role as Program Director of Metric Strategies, and prior to that, an institutional research role. He has 20 years of experience with a background in business analytics, budget management, curriculum development, and training and program leadership. He's passionate about leveraging analytics to drive strategic decisions and achieve organizational goals. Lee, thank you for agreeing to be a guest on Data Stakes. I'm so pleased that you're willing to share the information from your recent 2026 AIR Forum presentation entitled "Metric Strategies: Aligning Data, Tactics, and Purpose for IR Success." In your presentation, you begin with a thought-provoking statement: what if the biggest opportunity in higher ed data isn't just producing better data, but helping people find their place in it? So tell us, what do you mean when you say you want your colleagues to find their place in the data?

02:39Well, thank you so much for having me and for the opportunity to share this framework. What I'm really trying to get at is a sense of identity, ownership, and purpose in the data. We're all swimming in data more than ever before, and especially now with AI tools that are sorting, summarizing, categorizing, and extracting information from emails, Teams messages, and dashboards, the line between data and work gets blurrier every day. In institutional research, we often think of data as quantitative — retention rates, graduation rates, rankings. But data is also embedded in the way we communicate, document, track, decide, and evaluate our work. So when I say find your place in the data, I really mean changing our relationship with it. It's not just, here's a number, here's a dashboard, here's a report — it's asking people, where do I sit in this ecosystem? What inputs do I touch? What outputs do I create? How is that work being measured, and who's using it? Does it connect to something larger? People can feel far removed from institutional metrics — they might think, I don't work in IR, I don't work in analytics, I have nothing to do with rankings or strategic plans. But every role takes in inputs, produces outputs, and contributes to some process that's eventually measured, interpreted, and acted on. Helping people find their place means they see that their work does move the needle, even when it doesn't always feel that way in the moment. It shifts data from something that happens to people into something we do with people.

04:40When I came to Cowley College in 2018, one of the things I said was that we were going to move data from "me" to "we." That really hadn't been the framework they'd lived in prior to my hiring. Especially with data like financial aid data, we don't have time to keep up with all of it ourselves — we need our colleagues to understand their place in it, because they provide us with important context. Reporting is based on an intimate knowledge of the data, not just what the number is, but what the number means. I think one of the things that appealed to my colleagues about "me to we" was that they no longer felt like they were just sitting there inputting data every day — they recognized that somebody else saw the value they brought to it.

05:46So you also advocate for what you call a grassroots approach and the need for an institutional mindset. I found it interesting that in your presentation, you didn't call for an IR office mindset shift — the word grassroots makes me think you want data work to move across the boundary lines of roles. So it sounds like you believe that to create organizational impact with data, you have to shift the underlying culture. Is that correct, and what are some specific things that have to shift?

06:16Absolutely, and I think that's really the heart of it. In IR, we're really good at zooming in — we can examine a methodology, reverse-engineer a calculation, find those duplicate records. But sometimes, especially in a central office, we become so focused on the macro-level institutional picture that we forget the daily grind, what that looks like for the people closest to the work. In my current role in IT operations, I see it from a different angle. In IR, I was often working on long-range projects — two, five, ten years — efforts that move slowly but still matter deeply. In operations, the timelines are much tighter and more business-critical. That's reinforced my belief that culture has to shift across the institution, not just inside IR — from data as a central-office product to data as a shared institutional capability. It's a shift from "I have a dashboard that's going to solve that problem" to "I have people who have the tools, training, and context to act confidently with the data." You need better processes, better documentation, redundancy, quality control. It's about respecting that people are busy — the person processing travel reimbursements, the adjunct faculty member teaching many classes, the staff member maintaining the spreadsheet everybody relies on — they may not think of themselves as part of that larger ecosystem, but they really are. Leaders can set goals all day long, but it's the people on the ground who action them. You need good data, which needs good processes, which needs good training. It's all interconnected.

08:18Absolutely — and of course we both know how important data governance and data literacy are, being able to not just look at a dashboard and regurgitate the number, but understand what it means and what the impact is. So you shared a set of five questions you started asking as part of this metric strategies framework. Can you talk about these, with maybe a heavier emphasis on two: who defines the rules, and what can people actually influence?

08:40Those two questions are really core to the overall set. When I started Metric Strategies, it was new — there wasn't anything out there about it, it was a concept born out of a meeting and they let me run with it. The full set of questions was: what or who are we being measured against? Who defines the rules? What are the consequences? What investments are already in motion? And what can people actually influence? Who defines the rules is really a methodology question — who decided this metric should be calculated this way? Is it legislative, tied to state policy, from a ranking, or an internal policy we created ourselves — and have we always done it this way? I've always been the person in the room who asks the obvious questions, because either it's not obvious to me, or I want someone to articulate it out loud. If a rule matters enough to shape decisions, funding, and resource allocation, we should be able to explain why it exists in a meeting. The second question, what can people actually influence, matters because some metrics are shaped by forces outside our control — ranking methodologies change, state accountability systems change, federal definitions change. That doesn't mean we're powerless; it means we have to understand the landscape clearly enough to know where influence is possible. For example, if a compliance report goes out monthly and people are measured on timeliness or accuracy, some may feel the data is being used to shame them. The healthier version isn't to induce fear but to provide clarity — if people know what the metric is, how it's calculated, and why it matters, it becomes less of a hammer and more of a tool they can put in their back pocket.

11:40So you created a metric strategies framework synthesis that starts by defining strategy with two questions — why are we doing this, and where are we going — and ends with outcome, the intended change to produce. You used the word outcome rather than output. The first requires proactive, future-thinking skills — was that hard for your colleagues to start doing?

11:54Yes and no. Yes, because it's a mindset shift — it asks people to move beyond their immediate task and show how it connects to larger plans and KPIs, which isn't always natural when people are busy or overloaded. But also no, because many people were already being proactive, already solving problems and improving processes — they just hadn't connected it to the broader context. If your day-to-day work is advising students or processing paperwork, you may not be thinking about performance-based funding or ranking methodologies. Metric strategies helps open that hood a little — it shows people there's an engine here, here's how it works, and maybe we teach how to check the oil, tighten a belt, notice when things aren't working quite right. That's the real point: it starts conversations that give people an open ear into some of the closed-door strategy and metrics discussions that happen with senior leadership, and once people have that context, they connect the dots pretty quickly with how their work fits the bigger picture.

13:04As for outcomes not matching what you envisioned — that's real life. Plans change, goals shift, ownership and priorities change. A new methodology comes out and suddenly something you spent a year on matters far less, while something you were doing quietly in the background turns out to be incredibly important under the new rules. In a large state university, agility can be challenging — these are big systems, they don't turn on a dime. That's where the grassroots mindset helps: if more people understand the goal, the metric, the methodology, they can be more intentional. That's where I introduce frameworks like SMART goals, PDCA (Plan, Do, Check, Act), and DMAIC (Define, Measure, Analyze, Improve, Control) — they give people a structure to recalibrate without losing momentum. When the outcome isn't what you expect, it's not a failure, it's information you use to adapt. Some of the best feedback I've gotten is from people who come back and say, I used what you taught us and it helped me fix this workflow, or automate this process, or get my Excel workbook under control — that's when I know it's working, because it's giving people the tools to free up time for strategy, connection, and collaboration.

14:25That's critical to the culture change we started talking about — when people feel that ownership, they can adapt and be agile, and it improves morale and culture. So it's very apparent you recognize the value colleagues bring, because everyone has a part that connects to the whole. In your framework you have four position-based roles — can you share more about the Visionary, the Analyst, the Tactician, and the Communicator?

14:38Yes, absolutely — this goes right back to finding your place in the data. I borrowed this from the project management world's RACE framework, an ownership model for Responsible, Accountable, Consulted, and Informed, which clarifies who's doing the work, who owns the decision, who needs to be consulted, and who needs to be kept informed. When I put my metric strategies hat on, I retooled the labels a bit. I may have had "metric strategies" in my title, but I wasn't the only person shaping the vision, analyzing data, or entering data — that work is distributed. So I think of it in terms of four roles: the Visionary sets the direction — that could be a president or provost, but you don't have to be the highest-ranking person in the room to be a visionary. The Analyst defines the metric, digs into the data, documents the methodology, finds the patterns, and makes sure what's being produced is trustworthy. The Tactician deploys the intervention — changing processes, advising students, entering data, building workflows. And the Communicator translates the outcomes, helping people understand what happened, what it means, what we learned, and what we should do next. One of the most important things is that no one sticks to a single role forever — we transition between these roles across different projects over time. No one person owns a role; the shared metric sits in the middle and everyone rotates around it as the core reference point. You don't have to do it all — you need to know how your part connects to the whole, and that's where alignment starts to kick in.

16:17Listeners, I wanted you to know that we're going to be able to post Lee's slide deck — Lee, will that be okay with you? Because we're almost at the end of our 30 minutes, and you might be thinking, wow, this was a lot of detailed information, and I want to see the whole picture — the slide deck will be available, along with some contact information. So let's talk about how positioning data work as metric strategy can help a campus better communicate the value of higher education.

16:23I'm so glad you asked, because it ties into my own doctoral research on value in higher education. Those of us who work in higher ed have both a privilege and a responsibility — we serve students, families, and communities, often regionally or globally, and because many of us live where we work, we're also the face of the institution in everyday life. When we talk about the value of higher education, we have to move beyond abstract claims and connect the dots more clearly. People are asking, what do I get out of this degree, what's the return on investment, what is this university doing for my community that private industry can't? Metric strategies helps connect the technical work to the measurement of human value. If we can explain how student success metrics connect to more graduates, how research metrics connect to innovation, how alumni outcomes connect to community impact, we're better able to tell the story of institutional value. We have to be the champions of that story, grounded in an evidence-informed way where we understand the numbers and what they represent.

17:21You're so right — and I also think the public wants to know what we're struggling with and what we're doing about it, that kind of honest appraisal: our retention numbers are rising, but they're not where we want them to be, and here's what we're doing and who's involved. At the community college level, a significant portion of our funding comes from taxes, so exactly what you said — I can't tell you the number of times in Walmart I made the mistake of wearing a college t-shirt and had people stop me to ask about things they'd heard or read. I wanted all my colleagues to be able to answer with enthusiasm what they're actually doing for students and the community.

18:00I was really happy to see a favorite quote of mine in your slide deck, from Rear Admiral Grace Hopper: "The only phrase I've ever disliked is, we've always done it that way. I always tell young people, go ahead and do it — you can always apologize later." I think we're both on that wavelength — why, how long have you done it this way, does it really serve us, why should we continue, and can it be made better? This statement must have some significance for you — can you share that?

18:18I love that quote so much, because I've heard some version of it in every role I've ever had — "we've always done it this way." I kick off a lot of my trainings with this quote because it helps people shift their mindset. To be fair, sometimes there's a good reason a process exists — you dig into the history and discover a legal reason, a compliance reason, or an audit. But "we've always done it this way" is never enough to satisfy me. It resonates with metric strategies because so much of the work starts by questioning inherited processes — why are we tracking this metric this way, why is this workbook so complicated, why is this form still in the workflow? I've seen processes that started as temporary workarounds become permanent policy just because no one stopped to ask why — a system goes down, someone creates a temporary form, the system comes back online, but the form never leaves the workflow and becomes part of policy, and years later people are still struggling through it. Grace Hopper's quote is about curiosity, courage, and taking action. You should understand a process before you blow it up, but if something's broken, inefficient, or no longer serving its purpose, we should be willing to challenge it — be the person in the room who asks the obvious questions, because it may unravel a deeper rationale for why things exist, and you can use that to inform a better way of doing things.

19:25One final question. Now that you've established a strategy for reorienting data work toward meaningful change, and you've moved into an information-technology-focused role — does artificial intelligence fit somewhere in metric strategies?

19:31So much — this geeks me out probably more than anything. I think AI fits everywhere in metric strategies, but we have to approach it with the right mindset — what's the purpose, what's the methodology, what's the context. At FSU we're in a strong place because we have a strong data foundation and data maturity we've been developing for years — we've invested in semantic layers, data catalog definitions, governance, and training. All of that matters because AI is only useful if the quality of context and information behind it is good. I see AI as another tool in the metric strategies toolkit — it can help summarize complex information, extract themes, and draft communications, helping people move from the seed of an idea to something more developed. But I don't think AI will ever replace the person behind the work, because the person behind the AI is more valuable than what the AI produces — it needs good input, and good input needs context, training, and awareness. I'm working with a colleague in IR to develop a best-practices AI training around ethics, tool selection, and prompt engineering that we'll push out on campus. It's aligned with this same culture of awareness — where do I fit, and how do I use my position to improve the institution as a whole.

20:56It was great having you as a guest. You're such a perfect example of the generous spirit within the higher education data community — by sharing your expertise with others, you help all of us grow. Some of my colleagues at Datatelligent are always amazed at how willing everyone is at AIR Forum to stop and generously talk about what they're doing. No one's afraid you'll steal their ideas — actually, that's how you got roped into this podcast, you made the mistake of stopping to talk to Steve and me a little too long! To our listeners, I hope you enjoyed this today, and I hope you'll come back to our website and look for the slide deck we're preparing for you. Thank you again 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 meeting. If you have questions about today's conversation, don't hesitate to reach out to me at dphelps@datatelligent.ai. Until next time, have a great day.