Kate Valenti: Hi Rob, thank you so much for taking time to sit down with me today. I know you’ll have lots of insightful things to share, so let’s jump in. You’re a technical architect here at Unicon. You have a history of working with some of our largest customers over the years. How long have you been here and what two or three customers jump out to you as being most impactful to your career?
Rob Nield: I’ve been at Unicon for 15 years. I started as a contractor for six months before coming on full-time. The vast majority of it was spent working with the Cisco Networking Academy. That was probably the most impactful position for my career. It was amazing the breadth of exposure I got on that project. I wasn't in data the entire time. I came on as a software engineer, taking over Dave Mendez's position on that Cisco project. (Dave is now Unicon’s Senior Director of Critical Infrastructure.) Dave interviewed me specifically to take over his portion and to develop some of the additional PHP extensions of a project he was working on.
Kate: What other projects have been impactful in the way that your career has morphed over time?
Rob: Projects with Education Analytics have been amazing, too. The breadth and number of players involved are staggering. With Education Analytics on the Texas Exchange project, we have so many different vendors coming together with different ideas, some we hadn’t seen before. Each facet has been pulled together to become a viable product. It has been amazing. The supporting applications that Education Analytics have developed are really impressive. They have solved many issues we often see when considering a large integration problem.
The other one a stand-out project was our work with San Diego State University (SDSU), which was all Canvas Data several years ago. That project exposed me to the Canvas schema and how the data is structured. I’ve used that knowledge up to today.
Kate: As you mentioned, your work at Cisco wasn't always data and analytics, but I do feel like these days, I associate you with data and analytics. What gets you excited about that space and why is it an area of interest and focus for you?
Rob: Yeah, the funny thing is, especially from an education perspective, we are seeing an evolution happening. We've lived it. When I was at Cisco, we had a warehouse that was still in Oracle. We were still processing tables that were a billion and a half records, which is mind-boggling. When we're talking about big data now, that's trivial. Yes, that's a lot. But is it? It's not that much. To go from that to what we are seeing now, where we kind of went through a phase where everyone was trying to figure out how to consolidate all their data sources, we're past that now. We have most clients with consolidated data. We’re now to the interesting part where we're saying we've pulled all this together, and now, what cool stuff can we do with it? I think that has collided with AI coming in at the same time. It's all been a confluence. We're starting to see real use cases emerge from all of that.
Kate: I'd love to dive into that next because there's a lot of buzz around AI. People are clearly excited about it, but it seems like a lot of its actual value is currently exaggerated. Do you see AI making a difference in the work that you do today? Or in the next couple of years?
Rob: I think AI has made an amazing impact already. Last year was the trial year of introducing these large language models, where most people used them as chatbots. They may have some conversational memory, may help you through some situations, and may help you code a little bit more. Now, we are headed into that next stage, where AI agents are starting to come onto the scene, and they are really advanced. That whole orchestration around AI is pulling meaningful data from sources you know and trust and then feeding that into your tuned large language model to provide meaningful interactions.
Dave and I were just talking about this after we returned from AWS re:Invent. We went from last year saying, yeah, there are use cases for the individual, and now we're starting to see real use cases for people implementing applications that will be useful across the board. We will see AI agents pop up in all sorts of locations this year.
Kate: Are you having clients ask for that specifically, or are you finding that when you're trying to build or create new technical solutions that you're saying here's a perfect place for AI, or both?
Rob: We are seeing AI make its way into education. As usual, the edtech space lags slightly behind other industries. However, things are moving fast now. When I think about The Exchange and their EdVise application, which is ingesting data from an external source and then using QuickSight and Amazon Q on top of it to ask questions about the data, that is a significant step forward from applications we’ve seen in the past. That application has continued to evolve because Q has continued to evolve. Q can now be asked more than simple questions as it understands data shockingly well.
I’m reluctant to say any user can now be an analyst. Historically, that just hasn’t been true. But I think we're getting closer now than ever before. We're also seeing the same approach with classical machine learning. What I mean is that companies like Amazon are making the hard things far more approachable. At AWS re:Invent this year, Amazon has started using terms like “citizen scientists,” meaning someone who understands their data but is not necessarily a data scientist. They're like, “You bring the data, and we'll do the heavy lifting, transform your data, choose a good model using AutoML, help you predict with that model, and help you deploy it.” Most of the time, it functions pretty well. As a data scientist, could you develop a better model? Maybe. But for 90% of the people, probably not.
Kate: I won't make a joke here about how I wish my Amazon Alexa had any of those smarts! This is exciting for the space, because I do feel like the AI hype can cause people to be very skeptical about its applications in a real sense. So, it is nice to see patterns emerging where you're actually seeing value coming out of new releases.
Rob: Yes, to some degree, I think we all got a little burned on the big data promises. It was going to revolutionize everything, big data, big data which for many, those outcomes were just not revolutionary. Many organizations we work with have gathered all their data from sources now, but continue to have no clue what to do with it. It is a common theme. We've certainly changed our tune on ROI for big data as well. Flashforward to now and we're starting to think about the ROI of AI first. We are talking about, how long until we get some sort of return from our investment? That alone is a big lesson learned.
Kate: Exactly. It's a good turn. At least it's now going to start to pay off.
Shifting gears to your personal interests, I haven't done any formal research around this, but in my experience the most talented technical minds tend to be polymaths. I know that you have a number of varied interests outside of your day job. I'm always fascinated to hear what you are learning and investigating and leading your family through. I can see your beautiful musical instruments behind you on the wall there. Tell us about a few of your favorite hobbies outside of the technology world.
Rob: Sure. Well, if you've been on calls with me, I kind of have a shtick that I often say because people usually comment on the ukulele, but I always say that while I like the ukulele, I love the banjo, which I've played for a crazy number of years since I was 12. The ukulele was a COVID thing. As soon as COVID started, I set my kids down and I said, “this is going to be a once-in-a-lifetime opportunity. What do you want to learn?” My oldest said, “I want to learn to play ukulele.” And I said, “We should do that.” We got a couple of ukuleles, and we started to play. Then we joined a group and played with them once a month. It's been fun. It's something we’ve continued forward. Over the years we’ve gotten my daughter an electric ukulele. She takes it very seriously, which is great.
During that same time, I also picked up the harmonica. I promise my kids will need therapy someday because of how bad I was. We couldn't go anywhere that first full year, and they only heard me play in this office for an entire year.
About 25 years ago, I said I wanted to get my ham radio license. I have this list of stuff I call unfinished business. So, I decided I needed to get my ham radio license during COVID. I started to study and got my amateur extra, which is the highest level. I had friends with an amateur extra and while the privileges were not that much more that the previous level (general), I could not allow them to be above me.
Kate: That moment right there, where you sat your kids down and asked what they wanted to learn - that moment sets you apart. You took the pandemic as an opportunity to learn something new. I think that summarizes you so well as an individual, that you could see that opportunity for you and your family, and you took it! That’s really incredible.
Okay, last question for you: what’s your recommendation for one book, one blog, and one podcast?
Rob: My favorite book of all time, which I read once a year is Fooled by Randomness by Nassim Nicholas Taleb. Even though it feels like a finance book, that's only a small part of it. It's really about the philosophy of randomness and how it affects us in many ways. When things go well, we assume it's not randomness, and when things go poorly, we say, “Oh, there's nothing I could do”, randomness. The book explores the psychology of randomness and how it affects us in many facets. I just really enjoy it. And every year I read it, I think I'm just in a different spot as I grow older, as the world changes, and I think I get something different out of it every year.
Kate: Awesome. I'm going to pick it up. How about a blog?
Rob: There's a blog called Data Science Central. It's popular. They often have really good nuggets. I tend to see some new patterns starting to emerge through there. I like to keep an eye on that space.
Kate: Finally, a podcast.
Rob: Linda Feng (VP Architecture, D2L) turned me on to the Data Engineer podcast. I loved it. As weird as it is, that is one podcast that I continue to use in consulting engagements. We were looking at observability at one point. I had heard of three or four different observability applications that we could investigate immediately, which we did. During that project, we went through a lot of them and presented them with many options. That's an area where we could probably do a lot better, understanding what is out there and understanding where to plug in, where it makes more sense to buy than to build.
One more podcast not related to work. There is a podcast called ChooseFi. Josh Corbin (now retired Unicon Architect) turned me on to that. And that was his mantra for a long time. They also wrote a book, Choose FI: Your Blueprint to Financial Independence. This year, I realized that there was some financial fine tuning I needed to do. Certain additional retirement accounts were available through my wife, as a public servant, that I was unaware of and found through that book. The book and the podcast are both excellent.
Kate: Awesome! Thank you so much, Rob. It's a pleasure as always.
Rob: Thank you.
