Intro 0:00
Welcome to Digital squared, a podcast that explores the implications of living in an increasingly digital world. We're on a mission to inspire our listeners to use technology and data for good. Your host, Tom Andriola is the Vice Chancellor for Information Technology and data and Chief Digital Officer at the University of California at Irvine, join us as Tom and fellow leaders discuss the technological, cultural and societal trends that are shaping our world.
Tom 0:31
On this episode, I'm talking to Phil Komarny, Chief Innovation Officer at Maryville University, and founder, CEO of Lifetrek. We explore how Maryville is reimagining the role of it in higher education by eliminating traditional silos and prioritizing the data strategy. Phil has always been a thought leader around data, thinking of it as the artifact of the human experience. Together, we also discuss the revolutionary impact of AI on educational structures and processes, highlighting innovative projects like the lifegraph and automated transcript processing. I hope you enjoy our talk.
Tom 1:07
Phil, welcome to the podcast.
Phil 1:09
Thanks Tom, thanks for having me.
Tom 1:11
Absolutely so I've known you for several years now, and one of the things that always makes me chuckle what I think about, Phil, is you've always got multiple gigs going on, and right now, I know of at least two, and maybe you're going to share some others with us, but you're the chief innovation officer at Maryville, and then you're also founder, CEO of a relatively new startup. And I'm going to ask you about both of those, but let's start with Maryville, right? What are the big things that you're working with the university leadership on right now.
Phil 1:45
Yeah, that's a great question. Maryville is under is going through a tech modernization we call it. So we've, in the last two years, we've done some really deep understanding of our students journey and understanding the whys of why that journey looks the way it does today. And it's all tech related. Our systems make us work one way, and that's what we've seen at Maryville for a long time, because the student information system there is 47 years old. We were data tell her colleague, illusion, one of their first customers back in the 80s. So that's
Tom. 2:14
Phil, you win.
Phil 2:16
I know I'm amazed, the guy who runs the machine actually is younger than the machine. So this is just an inherent problem in our domain, a bit Tom. So we really focused on that. But instead of thinking about replacing the tech, we wanted to replace the experience, to really think in that first so we did a really qualitative quantitative research study called experience analysis design to understand our students journey from Request for Information a whole way to alumni. So we did that work, and from that, we came up with a data strategy, not a tech strategy, about how we're going to put this back together. And what we realized that is in this world that's AI enabled, and we're just at the beginning of this. So this is for today in the future is the data cannot be in silos anymore, and if it is, it's going to preclude you from a lot of the a lot of the efficiencies and augmentations that you could have within the business and the experiences for your customers over time. If you don't think that way, first, I we think so. We've done that data strategy work for a year, and now we're actually putting technology in place that will help us scale that strategy that allows us to interact with our students across their lifetime, not just when we admit them and graduate them.
Tom 3:30
One of the reasons I love talking to you right is your language speaks to me in terms of what I think the big shift for people is, right? I use some different words. I'll sometimes say, technology is just a data generation engine. The value is in the data. And now that we're all talking about AI, it's you're not going to get any value from Ai unless you have a strong data strategy, right, which is what you've been taking Maryville university through. I gotta ask you about this. You know, I was listening to one of your other podcasts you did recently, and you made this statement, and I'm going to ask you to elaborate it on it for our audience, which is we decided we do not need IT anymore, is what you said in the podcast. That's a direct quote. What does that mean?
Phil 4:15
I was a CIO in my past. I thought I did a pretty good job at it, too. But I think that it's not so much the people or the roles that are being done. It's the foundation of what information technology is turned into. It's basically a control for the business, and usually information technology works in its biggest silo on the campus, that if you can't just really disrupt how technology is driven and delivered, you're never going to make any kind of real change in your business. Because that's what's holding the business together. It's like cement of the silos, is the tech. So that's what I think. We didn't say, we fired the people, we fired the concept, and we created something called collective technology. But that drives our data strategy. It starts with that data. Because when you just mentioned that back a minute ago, Tom, when we reframe what the definition of data was, it's an artifact of a human experience. That's what data is. That came from one of our board members. Her name's Beth Redden. She started in a company called bast.ai who we've been working with pretty closely, but she was chief distinguished scientist from IBM for years, working on cognitive AI there, and when she impressed me with that, that that statement, or that reframing of what data means or what it is, it it really changed my framing, and it really drove me into this. Do we really need IT anymore? If this is the way we think about what data is and how we can come to experiences with with data in a flow state, and how can we create that next experience on top of a really modern tech stack. Everybody was interested in that conversation. Instead of, hey, we got to go out and get a vendor and replace what we currently have, but not think about how we do our work first. So that's really what we did. Was focused on the student, focused on our experience. And then really, how do we want to put that back together to make it better for everybody with AI. So that's high level. That was the way we did it.
Tom 6:10
I love that. So I have to ask you, if you can use the analogy of it has been the cement of the silos, yep. So what if it's not cement? What is the future of it? What replaces cement?
Phil 6:18
That's great. That's a great question. I think what replaces the cement is, instead of having these our departments siloed with this data, it's really letting that data flow through them. I can talk to somebody in our admissions office, and they understand the data and why it's resident, where it's at, and what it means there's I know I've worked at other schools where you'd say, Where's GPA located at? Where's the official GPA in? What system, in what field? I dare you to ask that question. On some campuses, you'd get 17 different answers, because it's true. It really is to them, that's where reality is. But that cement turns into a new way to lead, a new way to bring the university together around the data. So most, most valuable resource universities have, and we use it terribly. We really do. I've worked at Salesforce for a few years and getting outside of education, looking and being in rooms around hospitality and tourism and government relations and banking. They think about data first, I think we think about it last we think about it, cataloging it, not driving an experience that changed dramatically with that redefinition of it. What does it mean? And that made us redefine IT, just because how we were treating the actual resource that we were responsible for changed dramatically with that definition. So it actually changed the way we can think about it and the way we want to interact with our community. So it was trying to change the word, and I think that really helped us culturally inside the university.
Tom 7:51
Yeah, I think it's an it's a critical insight that we got to get this message out more broadly to our industry. In comparison, I think there are two industries that essentially are data industries, but have been laggards in terms of really recognizing it and then leveraging the power of it, higher education, which we're talking about, and the others, healthcare, and what I've seen in this kind of what is a the potential of AI and how is it going to shape the future? Healthcare has really picked up their pace of really understanding. It's really about the data. And then how do we bring the data together, whether you're talking about in terms of personalized health or precision medicine, but it's really about, how do we build this holistic view of the individual, right, tying back to something you mentioned earlier, personalization? And I think you can take some of the labels off, and you can drop higher education in there, and it's almost exactly the same. Obviously, the archetypes, you know, the artifacts of data, are different, and they're generated differently. But I think the concept of it's really a it's a digital representation of an individual that you want to personalize their journey and outcome to that is consistent across both industries. We gotta pick up the pace in education. From my perspective,
Phil 9:07
I agree. I think we're gonna outpace that electronic medical record. I think that the data inside of that industry is sold in tertiary markets for billions and trillions of dollars. If we look at educational data, it's not sold. I think it's just misused, not misused. We do well, we run our businesses and things, but I think now is a way to really engage through it. For instance, at Maryville, we're working on an automated transcript processor with an AI engineer that I brought up. We brought on board a while back, named Craig trim. And Craig, we went months working with vendors in the space, figuring out what, how this works, how that works. Did three or four months with one vendor just to get to an end point that was like unsatisfactory for any of us, but then we came at it differently. All these other companies are looking at transcripts as a template, like, here's the template that this transcript is going to come through. What Craig did was allow us to maybe basically read the paper like a human and just take unstructured data, which is any transcript in any format, and turn it into structured data. And from that structured data, how do we augment or activate that data inside of our processes? So instead of us trying to produce a product or a tool, we want to show how we can get that data in a place that our teams now are like looking out and thinking, Oh, wow, I can do that and that with that. So that's where the magic happens is, once you get everybody in the state that understand data differently, and now we show them, we can take this unstructured data and turn it into structured data instantly. How would that affect your work? And when we look at transcript processing or articulation or transfer articulation, very expensive, very hard to do a blocker for a lot of schools, a blocker for a lot of people. If we can start to allow AI to affect that part of our journey, I think we're all going to see a better result at the end. So that's how we're starting to focus this tech as we go through this deployment of it.
Tom 10:58
Yeah, this is actually where my next question is going to take us, right? If I go back to something we touched on earlier, right? We used to focus on the tech is actually just generating data. AI is just the way that we're generating more value out of the insights that data can give us. What we're finding in the journey, right? Is like everybody wants to talk about AI today, but as you've already talked about it in the Maryville journey AI brings you back to making sure you have a great data strategy. It then forms what technologies you want, not because you need some monolithic system, but because you want to have high quality data that feeds your data strategy. One of the things that we're finding in terms of thinking about it beyond that, back into almost the old model of it's about organizational structure. It's about process redesign, it's about people and skills all that. Really, before we talk about what's a technology solution, we're finding that we're working back into some of those issues that they called the meeting to talk about, AI, but now what we're talking about is the dysfunction of the organizational structure and the process we need to really rethink the process is that, are you finding that's where you're the Maryville conversations are having as well, and are you leading those conversations there?
Phil 12:13
Absolutely, I think AI is a big canary in the coal mine, just like you said, once those start to happen, because you think it's being the conversations happening around something new, like AI, we have to have it. We're hearing in the market, everybody's using this. We're going to be left behind all that BS, instead of, like, really thinking about it deeply, and how it can be, how it can work. But that's what I've been seeing. And we've been, like, I spoke to earlier, engaging our faculty at the beginning of last year, getting everybody in a room with a big mirro board behind us, and having Beth Rudden lead the discussion. She gave a presentation about AI, why not to be afraid of it? What it's going to do, and she started with that quote, it's an artifact of the human experience. That's what data is. So we shouldn't be afraid, if we should treat it that way. And then went through her presentation, but then engaging the faculty in that conversation is really what I think everybody should really take a moment and do not try to say, Hey, this is how we're gonna implement this, and this is the tools you get to use. That's usually how IT functions, or listen to them and go implement them and just support it. We wanted to co-create this with everybody, and Larry listening to everybody's stressors and what they're really interested in, because I think you can't get anybody to innovate without creating a scaffolding for the future like this is what it's going to feel and look like. That's the demonstration we've been doing for the last couple months with you.com and our faculty. Getting them engaged in a tool and protecting them, saying, Look, we're protecting our data with this, and we want to just let you guys be comfortable with it and really helping them understand how to use it, what we could do. And out of that came 20 unbelievable ideas we're about to put in place. But that's co-creation. There's no way I as a tech leader or an innovator could come in and say, Hey, Tom, this is what you're going to do in your classroom. That's that is, like offensive. So we really wanted to just support the data and support what we can do with it, and let them just co-create and dream with us. I think supporting people through that dream state right now, especially with AI and our future, is what every tech leader should be doing, not delivering, just dreaming and being able to support that dream with some understanding about how tech can be instantiated at the campus. But if you come out with this data strategy approach, it really reframes the conversation into something that I think everybody gets engaged in and feels a part of. And I think they have to now, this is getting personal very quickly. This is going to become personal within two years. And Apple is another canary in another coal mine. They're saying personal AI on your phone. First version might not be all that great, but just neither was the app store back in 2010 or 11. So this is a tell that I think universities should really listen to, because this is a real opportunity for us.
Tom 14:54
Yeah, so I'm going to ask you to take that one step further. Right. You've been around higher education a long time, you've played different roles, looked at different vantage points, having macro views and micro views. Higher education has a lot of critics out there right now in terms of the value of it, the different options, the price points, the binary nature of you have a degree or not. How do you think if you put that futurist hat on, and I know you can be, what do you think some of the kind of the reformation and the new the new types of options that individuals are going to see coming out of, what the early stages of what you're seeing that these new tools can create?
Phil 15:34
I think these learning companions are a big deal. There's actually, there's a bunch of them are in market already that turn from something that looked like a magic trick into a product within three months. This is there's a pattern there that we're seeing a lot of interest there. So I look back at myself. I'm 57 tomorrow, so I'm looking back at my life, and I'm thinking exactly like that, Tom. If I was 16 right now and this tech was in the market. I never went to college, personally. I learned myself because nobody was teaching the Internet back in 1985 so I just basically started an internet company on my own and learned all that through this. Learned that's why I'm sitting here today talking with you. I think today people have that opportunity. Now they have this very specific companion that can teach them X, Y or Z, and walk along them, right beside them. I don't think that's that's not going to, it can disrupt the hell out of education, and if we don't, if we don't pay attention to that, it should, or we could integrate that into what we do, so we can really make what we do bigger and better and reach everybody, instead of the very privileged few that we do reach with higher education. Maryville speaks to me because they haven't raised tuition in eight years, and they really want to drive the cost down and really change. The reason I'm working at Maryville. Dr Lombardi, their president, said to me, I want to start a revolution. I'm like, that's a big word. We were going to we were going to say that I'm really interested. I'm interested.
Tom 17:03
I was just going to say, I know, Phil enough to go to say, I'm in.
Phil 17:05
Yeah, that's exactly what happened. It was, it took a bit from my wife to say, yeah, you can go do that. Because I did have, I had an amazing role at Salesforce. I love the company, but it came to the point where I really felt that coming out of COVID, where COVID, to me, stood for catalyst of verifiable individual data. COVID. It meant we had to be trusted at the edge, and Salesforce has a CRM that treats a lot of people's data. So what I was doing there was really trying to understand how that type of platform could be changed by having that data resonate with the customer. So coming out of COVID, universities have a huge opportunity right now to do that. And that's what Dr Lombardi in the school really was motivated by and that's why I went and did this work. And we're three years into it. My university was three years, two years, two days ago. Been here for three years. I am fascinated by the amount of work we've done. It's absolutely amazing. I worked at UT for two and a half years with a lot more budget and a lot more people, and got a limited amount of work done compared to what we were able to attain at this 10,000 person school with an aligned vision around data. It's fascinating how fast and far we've moved so far.
Tom 18:11
Yeah, and I hope you get out and evangelize the story, because I think people need to hear the Maryville story and into some of the other let's call it sub-segments that like the cycle off and stay in their own little cocoon. I think more people need to hear this story, because it is transformational, and you all are setting a foundation for quite honestly, I look at Maryville, and I say the type of revolution that Southern New Hampshire University created 20 years ago under Paul LeBlanc, there's now going to be a new generation of that. And I look at the Maryvilles of the world as the places where this is where the seeds are being planted for something that 20 years from now, will look really different and have a tremendous impact on a world that's changing very fast. I think this is great. All right, so I mentioned at the beginning, Phil always has multiple balls being juggled. Tell us about Lifetrek.
Phil 19:00
Wow. What's the other side of the coin. So the other part of me going to Maryville was thinking about, they wanted to start a revolution. So I think you need revolution wear to do that with there's no there wasn't anything in market that could do what we were trying to attain. So we had to build it. And we worked with a company here in Colorado. I live in Colorado too. It's name's burst IQ is the name of the company, and they have a data fabric that's new in market, and Gartner is putting them in a really neat quadrant right now, around Master Data Management, are new ways to do MDMs. So what it does is basically allows us to take our data, the entire university data, not just this system or that system. We're mastering 267 distinct different systems into golden records that can allow us to do modeling, predictive modeling, and everything the business needs to do by also keeping our transactional layer very fresh through Salesforce and a bunch of technology that we're putting in there so that connection with burst and really changing the way we can see your student was the really reason why Lifetrek, or my Lifetrek network, is the name of the company, is starting to exist. We're running some pilots with some some workforce development groups, one from Maryville works. We've been working with a construction company trying to move their their employees into roles that they actually need in the business. What's interesting about Maryville works, they've they have a typical model where they can sell you a course or sell you a subscription systems learning. But really, what our platform allows them to do is see that individual because we understand their life graph from their resume and what they do inside the business, but then they also just define their end point. What do they want to what do they want them to be able to attain skills based what are they going to be able to do? Put them on a learning journey. And at the end of it, they have a demonstration milestone. And through that demonstration milestone, that's where they get their verifiable credential issued, not for learning, from doing. I actually did this, and my employer shows that I can do this. That was motivational to the person way more than any badge for learning and the employer really liked the visibility into actually, these people moving across an individualized journey into this goal. They might all have the same goal, but they all started at different places.
Tom 21:14
Sorry, let me ask question there. Phil, so are you saying there is there are learning outcomes that have to be mastered?
Phil 21:16
Oh yeah
Tom 21:17
There's also an element of competencies demonstrated that you're capturing. So there's learning plus mastery that's quote, unquote, captured into this concept of a life graph.
Phil 21:32
That's exactly right. So if we go back to the nursing example, nursing is very structured in what they do, teach and learn. So we have that map, and then we also have our students with their licensure credentials, everything that a nurse does to get into role. Right now, it takes over a month to get that person into role once they graduate. A hospital might you know, employ them, but it takes over a month to verify all their credentials. Right away, this life graph is verifiable. It instantly allows that to be verified. They're instantly on the floor instead of a month later. Difference in the business, common spirit health and some other health providers are looking at us for not just what it can do with the learning Tom, but some of the things in that space, like, how do you pick a preceptor? Ask any nursing school. How do you guys match preceptors with your rising nurses? You know what most of them do. Here's a spreadsheet. Go find your preceptor. So this way think about it, we have 1000s of graduated nurses, 1000s that Maryville loves to have a relationship with. Instead of us reaching out to them through love life relations, we give them their life graph. Put all your credentials here. This is your data. We can help you manage that data. And guess what, when you need some education, we see you. We don't need to admit you or know anything. You just share your data with us, and we can give you exactly what you need. That's where this is moving, and I think we can, I think every university should do so I don't think it's just for a Maryville-like innovation. This is something that, if anybody isn't paying attention, data can be resident with a person, and when that changes, we can really see them a lot clearer and deliver what we do a lot better. So that's why Life Track exists, and we're really focused on really workforce development and nursing education. Third thing we're working on with that is working in Texas with a company called economic mobility solutions. Education, to me, is all about economic mobility at the end of the day, end of the day, love that we get into these networks and meet all these people and have these great relationships, but at the end of the day, they want to get a job and want to feel good part of society. Economic mobility solutions in Texas has been doing this for years, and when I was at Salesforce, I was able to work with Eric band and his team. They're taking Salesforce instances across Texas and putting them in ISDS or different districts, letting that data come into an engine, they can actually motivate these students into Pathways, be it career apprenticeships, community college, whatever. And the state of Texas, will actually pay the schools to see that data. If you can see these students progressing into an apprenticeship. The state two hospitals will actually recoup that will pay the schools for that data or show that progression is happening. So our platform allows that to be seen very clearly. So that's another thing we're really working on, really excited about that, because that's not just Maryville University data that's just seeing that, but something that's going to be from eighth grade to career, and how is that UI going to really persist and change with that person? So I'm really interested in doing that work too. It's going to be a fun relationship working with them.
Tom 24:33
Yeah, we should probably get you to talk to the the people in California who are trying to launch cradle to career. Cradle career model, right? Because that type of scaffolding underneath is the type of thing that they're going to need, beyond just trying to integrate learning outcome related data and some socioeconomic data, really, some type of scaffolding that looks at kind of skill and competency building and trajectory into certain types of careers and jobs within careers. That's, that's very cool. Okay, one last question to wrap up. And this is the question that I'm asking every one of our guests in season three, because it's just so appropriate, and I think the answers are always very interesting, which is, AI is the topic of, certainly the day, maybe the month, maybe the year. What's the coolest thing you've seen done with AI in the last 30 days?
Phil 25:23
Oh, wow, that's a great question. Last 30 days. That's too much. Paragon AI is the one that blew my mind. Young lady, think she's like, 18, created a new spreadsheet. Basically, instead of me being a research assistant thinking about what data I'm going to go capture I'll send you a video when we hang up to them, because you have to see this. It's that impressive. But really it's a communicative interface that lets somebody interact with data in a much different way, but literally turns into a research assistant like that. She just basically says chat window, I'm doing some research about some startups in this space. Can you help me and I want to know some basic information about all the companies. So with that prompt, it generated all the column headings from like startup founder, name, LinkedIn, everything you can think of, and then you hit enrich. And what it does, it goes through every one of those cells and writes a prompt for each cell and then fills it in with a query. It's absolutely fascinating. In minutes, you can populate a database full of, like, actual data that's close to 98% accurate, which is, that's a that's when I saw it, I was like, that's magic. It's one of those magic things, just like I talked about magic tricks earlier, about these social learning companions, stuff this cycle, and you've been in the market as long as I have, like seeing things come out that look absolutely amazing, and then they get commoditized within a year, within months now. So somebody has a great idea 50 ways to do it just happened. And that's what really is, why the other reasons that Maryville hasn't really made some big investments in AI, it's more about investing in our data strategy, in our way we connect our data through things so we can partake in we have six or seven different relationships, working with different vendors to understand different ways to deploy this, not one way, all the ways. And we'll be, I think we'll get to a better result for our students. That way is really letting our data help us instead of hinder us because of the way it's situated.
Tom 27:26
Yeah, that's amazing. That's amazing. Thank you for sharing that example with us. Phil, as always, I love talking to you. The combination of the vision, the content and the delivery. I always just so enjoy. So I want to say thanks for joining us on the podcast and sharing some of what you're doing with our listeners.
Phil 27:46
You make me blush. That's crazy. I really thank you for having me on. This has been fun.
Tom 27:50
All right, and hopefully we'll see you at a conference somewhere soon, telling the but I think both the Maryville story, but I also love to hear you talking more about the LifeTrek story, because it's also a very timely topic in our society.
Phil 27:59
I really appreciate it. I think so too, and I hope see you at A conference someday soon.
Tom 28:02
All right, take care. See you.
Welcome to Digital squared, a podcast that explores the implications of living in an increasingly digital world. We're on a mission to inspire our listeners to use technology and data for good. Your host, Tom Andriola is the Vice Chancellor for Information Technology and data and Chief Digital Officer at the University of California at Irvine, join us as Tom and fellow leaders discuss the technological, cultural and societal trends that are shaping our world.
Tom 0:31
On this episode, I'm talking to Phil Komarny, Chief Innovation Officer at Maryville University, and founder, CEO of Lifetrek. We explore how Maryville is reimagining the role of it in higher education by eliminating traditional silos and prioritizing the data strategy. Phil has always been a thought leader around data, thinking of it as the artifact of the human experience. Together, we also discuss the revolutionary impact of AI on educational structures and processes, highlighting innovative projects like the lifegraph and automated transcript processing. I hope you enjoy our talk.
Tom 1:07
Phil, welcome to the podcast.
Phil 1:09
Thanks Tom, thanks for having me.
Tom 1:11
Absolutely so I've known you for several years now, and one of the things that always makes me chuckle what I think about, Phil, is you've always got multiple gigs going on, and right now, I know of at least two, and maybe you're going to share some others with us, but you're the chief innovation officer at Maryville, and then you're also founder, CEO of a relatively new startup. And I'm going to ask you about both of those, but let's start with Maryville, right? What are the big things that you're working with the university leadership on right now.
Phil 1:45
Yeah, that's a great question. Maryville is under is going through a tech modernization we call it. So we've, in the last two years, we've done some really deep understanding of our students journey and understanding the whys of why that journey looks the way it does today. And it's all tech related. Our systems make us work one way, and that's what we've seen at Maryville for a long time, because the student information system there is 47 years old. We were data tell her colleague, illusion, one of their first customers back in the 80s. So that's
Tom. 2:14
Phil, you win.
Phil 2:16
I know I'm amazed, the guy who runs the machine actually is younger than the machine. So this is just an inherent problem in our domain, a bit Tom. So we really focused on that. But instead of thinking about replacing the tech, we wanted to replace the experience, to really think in that first so we did a really qualitative quantitative research study called experience analysis design to understand our students journey from Request for Information a whole way to alumni. So we did that work, and from that, we came up with a data strategy, not a tech strategy, about how we're going to put this back together. And what we realized that is in this world that's AI enabled, and we're just at the beginning of this. So this is for today in the future is the data cannot be in silos anymore, and if it is, it's going to preclude you from a lot of the a lot of the efficiencies and augmentations that you could have within the business and the experiences for your customers over time. If you don't think that way, first, I we think so. We've done that data strategy work for a year, and now we're actually putting technology in place that will help us scale that strategy that allows us to interact with our students across their lifetime, not just when we admit them and graduate them.
Tom 3:30
One of the reasons I love talking to you right is your language speaks to me in terms of what I think the big shift for people is, right? I use some different words. I'll sometimes say, technology is just a data generation engine. The value is in the data. And now that we're all talking about AI, it's you're not going to get any value from Ai unless you have a strong data strategy, right, which is what you've been taking Maryville university through. I gotta ask you about this. You know, I was listening to one of your other podcasts you did recently, and you made this statement, and I'm going to ask you to elaborate it on it for our audience, which is we decided we do not need IT anymore, is what you said in the podcast. That's a direct quote. What does that mean?
Phil 4:15
I was a CIO in my past. I thought I did a pretty good job at it, too. But I think that it's not so much the people or the roles that are being done. It's the foundation of what information technology is turned into. It's basically a control for the business, and usually information technology works in its biggest silo on the campus, that if you can't just really disrupt how technology is driven and delivered, you're never going to make any kind of real change in your business. Because that's what's holding the business together. It's like cement of the silos, is the tech. So that's what I think. We didn't say, we fired the people, we fired the concept, and we created something called collective technology. But that drives our data strategy. It starts with that data. Because when you just mentioned that back a minute ago, Tom, when we reframe what the definition of data was, it's an artifact of a human experience. That's what data is. That came from one of our board members. Her name's Beth Redden. She started in a company called bast.ai who we've been working with pretty closely, but she was chief distinguished scientist from IBM for years, working on cognitive AI there, and when she impressed me with that, that that statement, or that reframing of what data means or what it is, it it really changed my framing, and it really drove me into this. Do we really need IT anymore? If this is the way we think about what data is and how we can come to experiences with with data in a flow state, and how can we create that next experience on top of a really modern tech stack. Everybody was interested in that conversation. Instead of, hey, we got to go out and get a vendor and replace what we currently have, but not think about how we do our work first. So that's really what we did. Was focused on the student, focused on our experience. And then really, how do we want to put that back together to make it better for everybody with AI. So that's high level. That was the way we did it.
Tom 6:10
I love that. So I have to ask you, if you can use the analogy of it has been the cement of the silos, yep. So what if it's not cement? What is the future of it? What replaces cement?
Phil 6:18
That's great. That's a great question. I think what replaces the cement is, instead of having these our departments siloed with this data, it's really letting that data flow through them. I can talk to somebody in our admissions office, and they understand the data and why it's resident, where it's at, and what it means there's I know I've worked at other schools where you'd say, Where's GPA located at? Where's the official GPA in? What system, in what field? I dare you to ask that question. On some campuses, you'd get 17 different answers, because it's true. It really is to them, that's where reality is. But that cement turns into a new way to lead, a new way to bring the university together around the data. So most, most valuable resource universities have, and we use it terribly. We really do. I've worked at Salesforce for a few years and getting outside of education, looking and being in rooms around hospitality and tourism and government relations and banking. They think about data first, I think we think about it last we think about it, cataloging it, not driving an experience that changed dramatically with that redefinition of it. What does it mean? And that made us redefine IT, just because how we were treating the actual resource that we were responsible for changed dramatically with that definition. So it actually changed the way we can think about it and the way we want to interact with our community. So it was trying to change the word, and I think that really helped us culturally inside the university.
Tom 7:51
Yeah, I think it's an it's a critical insight that we got to get this message out more broadly to our industry. In comparison, I think there are two industries that essentially are data industries, but have been laggards in terms of really recognizing it and then leveraging the power of it, higher education, which we're talking about, and the others, healthcare, and what I've seen in this kind of what is a the potential of AI and how is it going to shape the future? Healthcare has really picked up their pace of really understanding. It's really about the data. And then how do we bring the data together, whether you're talking about in terms of personalized health or precision medicine, but it's really about, how do we build this holistic view of the individual, right, tying back to something you mentioned earlier, personalization? And I think you can take some of the labels off, and you can drop higher education in there, and it's almost exactly the same. Obviously, the archetypes, you know, the artifacts of data, are different, and they're generated differently. But I think the concept of it's really a it's a digital representation of an individual that you want to personalize their journey and outcome to that is consistent across both industries. We gotta pick up the pace in education. From my perspective,
Phil 9:07
I agree. I think we're gonna outpace that electronic medical record. I think that the data inside of that industry is sold in tertiary markets for billions and trillions of dollars. If we look at educational data, it's not sold. I think it's just misused, not misused. We do well, we run our businesses and things, but I think now is a way to really engage through it. For instance, at Maryville, we're working on an automated transcript processor with an AI engineer that I brought up. We brought on board a while back, named Craig trim. And Craig, we went months working with vendors in the space, figuring out what, how this works, how that works. Did three or four months with one vendor just to get to an end point that was like unsatisfactory for any of us, but then we came at it differently. All these other companies are looking at transcripts as a template, like, here's the template that this transcript is going to come through. What Craig did was allow us to maybe basically read the paper like a human and just take unstructured data, which is any transcript in any format, and turn it into structured data. And from that structured data, how do we augment or activate that data inside of our processes? So instead of us trying to produce a product or a tool, we want to show how we can get that data in a place that our teams now are like looking out and thinking, Oh, wow, I can do that and that with that. So that's where the magic happens is, once you get everybody in the state that understand data differently, and now we show them, we can take this unstructured data and turn it into structured data instantly. How would that affect your work? And when we look at transcript processing or articulation or transfer articulation, very expensive, very hard to do a blocker for a lot of schools, a blocker for a lot of people. If we can start to allow AI to affect that part of our journey, I think we're all going to see a better result at the end. So that's how we're starting to focus this tech as we go through this deployment of it.
Tom 10:58
Yeah, this is actually where my next question is going to take us, right? If I go back to something we touched on earlier, right? We used to focus on the tech is actually just generating data. AI is just the way that we're generating more value out of the insights that data can give us. What we're finding in the journey, right? Is like everybody wants to talk about AI today, but as you've already talked about it in the Maryville journey AI brings you back to making sure you have a great data strategy. It then forms what technologies you want, not because you need some monolithic system, but because you want to have high quality data that feeds your data strategy. One of the things that we're finding in terms of thinking about it beyond that, back into almost the old model of it's about organizational structure. It's about process redesign, it's about people and skills all that. Really, before we talk about what's a technology solution, we're finding that we're working back into some of those issues that they called the meeting to talk about, AI, but now what we're talking about is the dysfunction of the organizational structure and the process we need to really rethink the process is that, are you finding that's where you're the Maryville conversations are having as well, and are you leading those conversations there?
Phil 12:13
Absolutely, I think AI is a big canary in the coal mine, just like you said, once those start to happen, because you think it's being the conversations happening around something new, like AI, we have to have it. We're hearing in the market, everybody's using this. We're going to be left behind all that BS, instead of, like, really thinking about it deeply, and how it can be, how it can work. But that's what I've been seeing. And we've been, like, I spoke to earlier, engaging our faculty at the beginning of last year, getting everybody in a room with a big mirro board behind us, and having Beth Rudden lead the discussion. She gave a presentation about AI, why not to be afraid of it? What it's going to do, and she started with that quote, it's an artifact of the human experience. That's what data is. So we shouldn't be afraid, if we should treat it that way. And then went through her presentation, but then engaging the faculty in that conversation is really what I think everybody should really take a moment and do not try to say, Hey, this is how we're gonna implement this, and this is the tools you get to use. That's usually how IT functions, or listen to them and go implement them and just support it. We wanted to co-create this with everybody, and Larry listening to everybody's stressors and what they're really interested in, because I think you can't get anybody to innovate without creating a scaffolding for the future like this is what it's going to feel and look like. That's the demonstration we've been doing for the last couple months with you.com and our faculty. Getting them engaged in a tool and protecting them, saying, Look, we're protecting our data with this, and we want to just let you guys be comfortable with it and really helping them understand how to use it, what we could do. And out of that came 20 unbelievable ideas we're about to put in place. But that's co-creation. There's no way I as a tech leader or an innovator could come in and say, Hey, Tom, this is what you're going to do in your classroom. That's that is, like offensive. So we really wanted to just support the data and support what we can do with it, and let them just co-create and dream with us. I think supporting people through that dream state right now, especially with AI and our future, is what every tech leader should be doing, not delivering, just dreaming and being able to support that dream with some understanding about how tech can be instantiated at the campus. But if you come out with this data strategy approach, it really reframes the conversation into something that I think everybody gets engaged in and feels a part of. And I think they have to now, this is getting personal very quickly. This is going to become personal within two years. And Apple is another canary in another coal mine. They're saying personal AI on your phone. First version might not be all that great, but just neither was the app store back in 2010 or 11. So this is a tell that I think universities should really listen to, because this is a real opportunity for us.
Tom 14:54
Yeah, so I'm going to ask you to take that one step further. Right. You've been around higher education a long time, you've played different roles, looked at different vantage points, having macro views and micro views. Higher education has a lot of critics out there right now in terms of the value of it, the different options, the price points, the binary nature of you have a degree or not. How do you think if you put that futurist hat on, and I know you can be, what do you think some of the kind of the reformation and the new the new types of options that individuals are going to see coming out of, what the early stages of what you're seeing that these new tools can create?
Phil 15:34
I think these learning companions are a big deal. There's actually, there's a bunch of them are in market already that turn from something that looked like a magic trick into a product within three months. This is there's a pattern there that we're seeing a lot of interest there. So I look back at myself. I'm 57 tomorrow, so I'm looking back at my life, and I'm thinking exactly like that, Tom. If I was 16 right now and this tech was in the market. I never went to college, personally. I learned myself because nobody was teaching the Internet back in 1985 so I just basically started an internet company on my own and learned all that through this. Learned that's why I'm sitting here today talking with you. I think today people have that opportunity. Now they have this very specific companion that can teach them X, Y or Z, and walk along them, right beside them. I don't think that's that's not going to, it can disrupt the hell out of education, and if we don't, if we don't pay attention to that, it should, or we could integrate that into what we do, so we can really make what we do bigger and better and reach everybody, instead of the very privileged few that we do reach with higher education. Maryville speaks to me because they haven't raised tuition in eight years, and they really want to drive the cost down and really change. The reason I'm working at Maryville. Dr Lombardi, their president, said to me, I want to start a revolution. I'm like, that's a big word. We were going to we were going to say that I'm really interested. I'm interested.
Tom 17:03
I was just going to say, I know, Phil enough to go to say, I'm in.
Phil 17:05
Yeah, that's exactly what happened. It was, it took a bit from my wife to say, yeah, you can go do that. Because I did have, I had an amazing role at Salesforce. I love the company, but it came to the point where I really felt that coming out of COVID, where COVID, to me, stood for catalyst of verifiable individual data. COVID. It meant we had to be trusted at the edge, and Salesforce has a CRM that treats a lot of people's data. So what I was doing there was really trying to understand how that type of platform could be changed by having that data resonate with the customer. So coming out of COVID, universities have a huge opportunity right now to do that. And that's what Dr Lombardi in the school really was motivated by and that's why I went and did this work. And we're three years into it. My university was three years, two years, two days ago. Been here for three years. I am fascinated by the amount of work we've done. It's absolutely amazing. I worked at UT for two and a half years with a lot more budget and a lot more people, and got a limited amount of work done compared to what we were able to attain at this 10,000 person school with an aligned vision around data. It's fascinating how fast and far we've moved so far.
Tom 18:11
Yeah, and I hope you get out and evangelize the story, because I think people need to hear the Maryville story and into some of the other let's call it sub-segments that like the cycle off and stay in their own little cocoon. I think more people need to hear this story, because it is transformational, and you all are setting a foundation for quite honestly, I look at Maryville, and I say the type of revolution that Southern New Hampshire University created 20 years ago under Paul LeBlanc, there's now going to be a new generation of that. And I look at the Maryvilles of the world as the places where this is where the seeds are being planted for something that 20 years from now, will look really different and have a tremendous impact on a world that's changing very fast. I think this is great. All right, so I mentioned at the beginning, Phil always has multiple balls being juggled. Tell us about Lifetrek.
Phil 19:00
Wow. What's the other side of the coin. So the other part of me going to Maryville was thinking about, they wanted to start a revolution. So I think you need revolution wear to do that with there's no there wasn't anything in market that could do what we were trying to attain. So we had to build it. And we worked with a company here in Colorado. I live in Colorado too. It's name's burst IQ is the name of the company, and they have a data fabric that's new in market, and Gartner is putting them in a really neat quadrant right now, around Master Data Management, are new ways to do MDMs. So what it does is basically allows us to take our data, the entire university data, not just this system or that system. We're mastering 267 distinct different systems into golden records that can allow us to do modeling, predictive modeling, and everything the business needs to do by also keeping our transactional layer very fresh through Salesforce and a bunch of technology that we're putting in there so that connection with burst and really changing the way we can see your student was the really reason why Lifetrek, or my Lifetrek network, is the name of the company, is starting to exist. We're running some pilots with some some workforce development groups, one from Maryville works. We've been working with a construction company trying to move their their employees into roles that they actually need in the business. What's interesting about Maryville works, they've they have a typical model where they can sell you a course or sell you a subscription systems learning. But really, what our platform allows them to do is see that individual because we understand their life graph from their resume and what they do inside the business, but then they also just define their end point. What do they want to what do they want them to be able to attain skills based what are they going to be able to do? Put them on a learning journey. And at the end of it, they have a demonstration milestone. And through that demonstration milestone, that's where they get their verifiable credential issued, not for learning, from doing. I actually did this, and my employer shows that I can do this. That was motivational to the person way more than any badge for learning and the employer really liked the visibility into actually, these people moving across an individualized journey into this goal. They might all have the same goal, but they all started at different places.
Tom 21:14
Sorry, let me ask question there. Phil, so are you saying there is there are learning outcomes that have to be mastered?
Phil 21:16
Oh yeah
Tom 21:17
There's also an element of competencies demonstrated that you're capturing. So there's learning plus mastery that's quote, unquote, captured into this concept of a life graph.
Phil 21:32
That's exactly right. So if we go back to the nursing example, nursing is very structured in what they do, teach and learn. So we have that map, and then we also have our students with their licensure credentials, everything that a nurse does to get into role. Right now, it takes over a month to get that person into role once they graduate. A hospital might you know, employ them, but it takes over a month to verify all their credentials. Right away, this life graph is verifiable. It instantly allows that to be verified. They're instantly on the floor instead of a month later. Difference in the business, common spirit health and some other health providers are looking at us for not just what it can do with the learning Tom, but some of the things in that space, like, how do you pick a preceptor? Ask any nursing school. How do you guys match preceptors with your rising nurses? You know what most of them do. Here's a spreadsheet. Go find your preceptor. So this way think about it, we have 1000s of graduated nurses, 1000s that Maryville loves to have a relationship with. Instead of us reaching out to them through love life relations, we give them their life graph. Put all your credentials here. This is your data. We can help you manage that data. And guess what, when you need some education, we see you. We don't need to admit you or know anything. You just share your data with us, and we can give you exactly what you need. That's where this is moving, and I think we can, I think every university should do so I don't think it's just for a Maryville-like innovation. This is something that, if anybody isn't paying attention, data can be resident with a person, and when that changes, we can really see them a lot clearer and deliver what we do a lot better. So that's why Life Track exists, and we're really focused on really workforce development and nursing education. Third thing we're working on with that is working in Texas with a company called economic mobility solutions. Education, to me, is all about economic mobility at the end of the day, end of the day, love that we get into these networks and meet all these people and have these great relationships, but at the end of the day, they want to get a job and want to feel good part of society. Economic mobility solutions in Texas has been doing this for years, and when I was at Salesforce, I was able to work with Eric band and his team. They're taking Salesforce instances across Texas and putting them in ISDS or different districts, letting that data come into an engine, they can actually motivate these students into Pathways, be it career apprenticeships, community college, whatever. And the state of Texas, will actually pay the schools to see that data. If you can see these students progressing into an apprenticeship. The state two hospitals will actually recoup that will pay the schools for that data or show that progression is happening. So our platform allows that to be seen very clearly. So that's another thing we're really working on, really excited about that, because that's not just Maryville University data that's just seeing that, but something that's going to be from eighth grade to career, and how is that UI going to really persist and change with that person? So I'm really interested in doing that work too. It's going to be a fun relationship working with them.
Tom 24:33
Yeah, we should probably get you to talk to the the people in California who are trying to launch cradle to career. Cradle career model, right? Because that type of scaffolding underneath is the type of thing that they're going to need, beyond just trying to integrate learning outcome related data and some socioeconomic data, really, some type of scaffolding that looks at kind of skill and competency building and trajectory into certain types of careers and jobs within careers. That's, that's very cool. Okay, one last question to wrap up. And this is the question that I'm asking every one of our guests in season three, because it's just so appropriate, and I think the answers are always very interesting, which is, AI is the topic of, certainly the day, maybe the month, maybe the year. What's the coolest thing you've seen done with AI in the last 30 days?
Phil 25:23
Oh, wow, that's a great question. Last 30 days. That's too much. Paragon AI is the one that blew my mind. Young lady, think she's like, 18, created a new spreadsheet. Basically, instead of me being a research assistant thinking about what data I'm going to go capture I'll send you a video when we hang up to them, because you have to see this. It's that impressive. But really it's a communicative interface that lets somebody interact with data in a much different way, but literally turns into a research assistant like that. She just basically says chat window, I'm doing some research about some startups in this space. Can you help me and I want to know some basic information about all the companies. So with that prompt, it generated all the column headings from like startup founder, name, LinkedIn, everything you can think of, and then you hit enrich. And what it does, it goes through every one of those cells and writes a prompt for each cell and then fills it in with a query. It's absolutely fascinating. In minutes, you can populate a database full of, like, actual data that's close to 98% accurate, which is, that's a that's when I saw it, I was like, that's magic. It's one of those magic things, just like I talked about magic tricks earlier, about these social learning companions, stuff this cycle, and you've been in the market as long as I have, like seeing things come out that look absolutely amazing, and then they get commoditized within a year, within months now. So somebody has a great idea 50 ways to do it just happened. And that's what really is, why the other reasons that Maryville hasn't really made some big investments in AI, it's more about investing in our data strategy, in our way we connect our data through things so we can partake in we have six or seven different relationships, working with different vendors to understand different ways to deploy this, not one way, all the ways. And we'll be, I think we'll get to a better result for our students. That way is really letting our data help us instead of hinder us because of the way it's situated.
Tom 27:26
Yeah, that's amazing. That's amazing. Thank you for sharing that example with us. Phil, as always, I love talking to you. The combination of the vision, the content and the delivery. I always just so enjoy. So I want to say thanks for joining us on the podcast and sharing some of what you're doing with our listeners.
Phil 27:46
You make me blush. That's crazy. I really thank you for having me on. This has been fun.
Tom 27:50
All right, and hopefully we'll see you at a conference somewhere soon, telling the but I think both the Maryville story, but I also love to hear you talking more about the LifeTrek story, because it's also a very timely topic in our society.
Phil 27:59
I really appreciate it. I think so too, and I hope see you at A conference someday soon.
Tom 28:02
All right, take care. See you.
