Dr. Bill Rand joined the Institute for Advanced Analytics as Director with a background that spans computer science, philosophy of science, artificial intelligence, and applied analytics. I sat down with Dr. Rand to learn more about his journey, what drew him to the field, and his vision for preparing future data scientists in an era where AI is evolving rapidly.

Jarrel: To let readers get to know you on a personal level, what are some things you like to do outside of work? What helps you recharge?

Dr. Rand: There are three things I try to do every day: ten minutes of stretching, ten minutes of meditation, and at least ten minutes of exercise. I usually do more than that, but I always try to hit those three. I run fairly regularly, I actually just finished a half marathon, and over my life I’ve done a number of marathons. I’m trying to complete the World Major Marathon list. I’ve done Chicago, New York, Berlin, and Tokyo, and I still need London and Boston.

When I’m not running, I do CrossFit and I also like to build Legos. I see it almost as another form of meditation. I put on some vinyl and build sets, usually with my daughters in the evenings. I also coach my daughter’s middle school cross-country team, which has been a lot of fun.

Jarrel: Shifting to your career, could you share some of your background and what led you to direct the IAA? And what originally drew you to analytics and data science?

Dr. Rand: My undergraduate degree was in computer science, but I also picked up a second degree in philosophy, specifically philosophy of science. When I went to grad school in AI, I realized that I wasn’t really interested in improving algorithms or focusing on precision/recall metrics. I was more interested in whether these tools could help solve problems that hadn’t been solved.

Back then, applying AI wasn’t really considered computer science the way it is now. Today we see things like self-driving cars or unmanned aerial vehicles using algorithms developed elsewhere and applying them to new sensors and data. That applied mindset got me into complex systems theory for a while, which eventually led me to Northwestern. A faculty member there, Brian Uzzi, a sociologist, felt that what I was doing was more applicable to business schools. He encouraged me to apply, and eventually I ended up teaching in marketing, where I was doing analytics and data science before those terms were widely used.

I spent about twenty years applying those tools in business schools, first at Maryland and then elsewhere. I had known about the IAA for a long time and admired the program. So when my dean asked if I’d be interested in taking on the directorship here, it felt like a natural fit. Given my background in AI and applied analytics, it seemed like a place where I could help take the program to the next level.

Jarrel: Now that you’re leading the Institute, how do you see the IAA evolving over the next few years, especially with the rapid changes in AI?

Dr. Rand: Before I accepted the position, I talked with the provost and vice provost about my vision. The first thing I wanted to focus on was preparing students to be what I call AI-ready. That means not just knowing how to use AI in their analytics, but also how to use AI as a collaborator, as a teammate. AI can help generate code, help with statistical analysis, and in a way that makes you more efficient. And we’re already doing a curriculum review to integrate AI more deeply.

The second thing was business acumen. The practicum is amazing, it gives students deep knowledge in one domain, but 95% of our students go into business analytics roles. If a student does their practicum in marketing, they learn a lot of marketing, but maybe not operations or finance. I want to increase exposure to those other areas so students have the vocabulary to operate across business functions.

The third thing I mentioned was better integration with the rest of NC State. The IAA was created intentionally outside the standard university structure because of its interdisciplinary nature. But the university has changed, there are more analytics offerings across campus now. I want to strengthen our connections, both in curriculum and in visibility.

And I’ve added a fourth priority now: critical thinking. Alumni often say students need stronger critical-thinking skills, but “critical thinking” is vague. One way to teach it is to use frameworks like CRISP-DM, the Double Diamond, design science, or the INFORMS analytics process. Embedding those frameworks across the practicum and assessments gives students a structured way to approach new problems.

Jarrel: What skills do you feel will be most important for future data science professionals as the field evolves?

Dr. Rand: We’ve already mentioned working with AI as a teammate—that’s a big one. Another is understanding the language of business. Even knowing basic vocabulary from different functional areas helps you frame problems better.

Of course, critical thinking is key. But I also think interpersonal and social-emotional skills will become more important. AI isn’t good at understanding how humans relate to each other. A data scientist needs to think about how their work will be perceived—say, a pricing policy that might affect different customer groups. How will customers talk to each other about it? How will it be received? Thinking about the human side is going to be really important.

Jarrel: From your perspective, what are some interesting or challenging trends in AI right now?

Dr. Rand: Agentic AI is one that a lot of people are talking about. The idea is that you give an AI a big task and it spawns smaller AIs to handle different parts of it. I think this could work in certain business processes. But the broader idea – that an AI will book an entire trip for you by spawning agents that buy flights, pick hotels, handle payments – runs into problems. As soon as an AI system has to interact with external systems, it has to prove its identity, log in, handle credentials, avoid bot detection. The promise is interesting, but execution is another story.

In education, I think AI will be transformative. I really like Sal Khan’s vision of personalized AI tutors who guide students but don’t just give answers. Tools like NotebookLM are steps in that direction. Students can already take class materials, upload them, and generate a personalized study aid. I think there’s a lot of potential there, and the question is how we make these tools available in the right way.

Jarrel: For students transitioning from other fields, what advice do you have for making the most out of the MSA program?

Dr. Rand: The program teaches you the core skills, but I think you’d be doing yourself a disservice if you only did what’s required. Take the skills and apply them elsewhere, Kaggle competitions, hackathons, Data-for-Good projects. Practice applying models outside the classroom examples.

And if you’re preparing to enter the program, refresh your Python and your statistics. Even if it’s been a few years since you took stats, brushing up will help. It’s like what I tell my cross-country runners – we run with them three days a week, but if they run four, they get better faster. The same idea applies here.

Jarrel: What kind of impact do you hope IAA graduates have in the analytics world?

Dr. Rand: Personally, I hope graduates help people make better decisions using data. There’s that old “HIPPO” analogy – the highest-paid person’s opinion – but good analytics brings decision-making back to evidence. In a polarized world, grounding conversations in data can help people find common ground. I think that’s incredibly useful.

Jarrel: Lastly, is there a personal lesson or insight that guides your work today?

Dr. Rand: There’s an old Latin phrase: Fortis fortuna adiuvat – “Fortune favors the brave.” I studied Latin and Greek for five years in high school, so it stuck with me. For me, it means being open to opportunities, even unexpected ones. Many of the good things in my career have come from following that idea.

Closing Reflection

Talking with Dr. Rand gave me a clearer picture of how the IAA is positioning itself for the future, especially when it comes to AI, critical thinking, and the human side of analytics. I appreciated how candid he was about the challenges and opportunities ahead. Hopefully, this column offers insight you can apply to your own journey into the data analytics field, and leaves you just as inspired as I was after conducting this interview.

Thank you for reading, and good luck in your journeys!