Unlocking the Secrets of Data Governance with Carly Van Zandt - 201

0:00 Hey there. Welcome, and for those returning, welcome back to How I Met Your Data. I'm Sandy Estrada, here with my co-host, Anjali Bansel. We're your guides through the ever-evolving world of data where the community is as diverse as the stories we share. From hands-on practitioners to the ecosystem supporting them, we bring you insights and conversations that cover everything data-related. Whether you're here for the latest strategies, a dose of inspiration, or just some data banter, you're in the right place. So, grab a coffee, get comfy, and let's dive in. Music.

0:48 Hi, Angeli here. Welcome to Season 2 of How I Met Your Data. After taking a much-needed break, navigating some transitions, stay tuned for more on that in a future episode, and gearing up for this exciting season, we're back and ready to dive in. To kick things off, we're thrilled to have Carly Van Zandt, Senior Director of Data Governance at Fresenius Medical Care, as our guest. Carly brings a fresh and practical approach to data Governance, including her framework for demonstrating the ROI of governance efforts, a topic we know many of you are curious about. Let's jump in.

1:28 Well, welcome, Carly, to our podcast. I'm so happy I was able to have you on. We met at CDAO in Boston, and I ran up to you after your presentation, and I said, Carly, I am so impressed. I need to have you on my podcast. Thank you thank you so carly today we wanted to chat with you and the reason why i wanted you on the podcast was to discuss the incredible work you've been doing for senius related to data governance and more importantly i think is you did something very unique in the data governance space i think you did two really unique things one you created easy to understand roi on the work of governance or bad data, like why, giving everybody the why of the initiative.

2:17 And then two, when you talked about the size of your team, I think everybody in that room gasped because I think you said two. Four, including me. Four, including me. Yeah. And I remember hearing an auditory gasp thinking to myself, yes, that is incredible. That is a feat not many have been able to conquer. So So that's why I ran up to you right after that and said, we need to have you on. I need to hear more. Thank you for being so gracious and for joining us today. You're welcome. I'm happy to be here. I love talking about this stuff, as you can tell. And the more we get the word out, the more people will do the same thing over and over. So.

2:58 That's the dream. So maybe we could start from the beginning because my understanding was you did not start off focused on governance. You had a different focus. So maybe you can walk us through that. Kind of how did you end up with this role? Yeah, absolutely. I actually, in my career itself, I started as a biologist. So I actually started in biology, found out that I was terrible at memorization. So I switched to computer science. Well, not switched. I got my bachelor's. I was too close. I finished in biology. And then I got my master's in computer science because it actually made more sense to me with algorithms. So biology has always been kind of close to my heart and medicine in general.

3:40 So then when I joined Fresenius, I started in clinical research. So I was in the clinical research arm called Fresnova Renal Research. And so while I was doing research, I realized how the data quality directly impacts our research outcomes. And some of the stuff that I was working on in research was enhancing the research data sets with data from our clinical systems. because in Fresenius, we have, you know, we have 200,000 active patients at any point. So you'd think you'd have really accurate, good data, but then we were finding, you know, different data entry patterns, different misinterpretations, you know, you can't really make assumptions.

4:24 And if someone is going to be using this data to submit to the FDA for a new drug, you have to defend exactly what's happening. And so that actually brought up, you know, I was always questioning things. I was the annoying one saying like, well, how come this doesn't look like this? And this doesn't look like this. And so it was actually kind of funny because one of our heads of data analytics, her name is Norma Ofston, who was what we call the data queen. Because I was asking her all these questions, she was like, you would be perfect for data governance because you actually want to know how to find this stuff out. So she stole me into her team of data governance. and I've been there for seven years.

5:05 And then when she retired about two years ago, I kind of, I took over for, for that whole, for the whole group. And it's been a journey, obviously, ever since then. But I think it was really important to see the problems in the clinical data that I was looking at in order to understand what needed to be done. Wow. That does sound like a journey. So you've been part of this prior to ending up owning the team, you were part of the team for seven years. Did I hear that correctly? Yes. I think this is my eighth year. No. It's super curious how that journey has looked for you over seven. Because if you think about data and analytics and just how data has been managed in organizations, especially an organization like Fresenius, right?

5:51 Over that time span, the maturity of how people manage that data has changed. Oh, yeah. Have there been themes over those seven years? Absolutely. So I think like every single data governance group or a group that's working on data stewardship or anyone that's trying to help with data, you will always have, or hopefully, you'll have some false starts. We had a couple of false starts. And one of the things that we started to try and do was take all the categorization and chunking of data domains ourselves. We tried to do that. And then we finally had to realize that we can't do that. It has to be our clinical services, our boots on the ground to explain how they chunk out their areas.

6:37 So we probably false started maybe two times because we were trying to split things in a way that made sense to us, but didn't make sense to the business folks that we then started to work with. So we started to create this beautiful, you know, medication authorization workflow that That we thought was going to solve, you know, the world's problems basically at our company. And it got so complex and confusing to folks that it never went anywhere. So we spent, you know, probably months and months on that. Additionally, we got a tool, which I'm not, I'm not upset that we got a tool, but we got a tool before we got the program up and running.

7:18 So a lot of companies make that mistake as well, because you think a tool is going to solve all your problems when in fact it's just really the enablement of people to help. And we also finally had to accept the fact that people are already doing data governance in their own ways, whether it be spreadsheets, whether it be emails, whether it be writing in a notepad somewhere and acknowledging that and saying, let us help you make it a little bit better. We got a lot more traction than saying, you're not doing anything. The data is crap. Let's start all over because that was very overwhelming for people. So the seven years has had its hiccups. I want to say we really gained more momentum the last two years or so. So we started bottom up.

8:05 Technically, we started top down with one upper management person, which was not enough. One, one in a company of 100,000 employees was not enough because they had a different purpose than all the other management. So then we started from bottom up. Let's do technical. Let's plug in the tool. Let's start with our technical lineage. Look how cool it is. It's cool to us. A clinician doesn't care at all. They don't care at all. So then we got stuck there. And so then we went middle up, which was somewhat successful. When I say middle, I mean the analytics teams. So analytics teams are in between business and purely technical.

8:48 And so we got some traction with them. And when I say traction, I mean, we made the connections to the business. So it was a lot about interpersonal connections and explaining and having our analysts vouch for us saying they know what they're doing. You need to have a conversation. And then we got the green light to really kick off a data governance framework, which is where we landed with getting buy-in from the business and all the upper management groups, not just one. Not just one. Yeah. Across the teams. I guess one question around that, where did you start with that framework where you started to get traction on buy-in?

9:29 Like what was the thing that finally turned the needle for the team? For us, it was business unit focused. So at Fresenius and many other groups, you have groups of people that work on different things. Our company is very large in terms of our business units. Like we have a pharmacy, we have our clinical dialysis services, we have our vascular services, we have a renal pharmaceutical service. So business line was where we got the most traction. Because if we were talking about, you know, patient admissions to someone who works in a pharmacy, that didn't really speak to them. So we would have presentations about one area and everyone else was glassy-eyed and wasn't paying attention.

10:14 Then when we started realizing that we have to speak to the different business units in their own language, we would give examples of pharmacy bad data. We would give examples of clinical services bad data. We give examples of vascular bad data. So speaking to each business unit then as a business unit really helped put it into their perspective. When you say you give examples, were you just kind of assuming you gave examples and the impact of those examples in terms of, hey, we saw this and here's kind the outcomes of why this is not helpful, right? But it was different types of impact, too, because we're speaking to different types of people.

10:58 So physicians, we explained the clinical impact. This patient's not going to get the care they need. That speaks to a physician. When we're talking to the business financial folks, that's when we say, this is how much money you lost because you had this bad piece of data and it caused all these problems. So, and then when we talked to legal, we say, hey, this something, this bad thing happened that if we get audited, we may have to explain, you know, so it's speaking to, giving examples in each layer that, again, speaks to the person you're talking with about what they care about. That's interesting. So did you work backwards then? Did you have a meeting with that group? And I mean, obviously you'd been there seven years or five years, et cetera. So you kind of already knew the individuals that you were trying to sell and what they were caring about at this point, right?

11:49 But that's when our connection to the analysts paid off. Okay. So the analysts know the problems. The analysts know the data issues. The analysts know, hey, this data was down for two months last year, and this happened, this happened, this happened. So making those connections with the people using the data day in and day out was critical to then getting the evidence and the ROIs and the real world examples that we could then present to the management. Oh, it's incredible. It's absolutely incredible. So what happened next? You have these real-world examples. You've got your business folks agreeing that these are the problems that are really kind of impacting them and what that downstream impact looks like. But then what?

12:37 How did that conversation then transition into, let's govern your data? Yeah. It's never a fast process. So again, with a team of four, including me, you have to put a lot of the most of the onus on the business units themselves to govern their own data. So for a period of time, you have to work a little bit like a project manager. And I know that's not fun and sexy. But in the beginning, showing people this is how you do it. It's not as bad as you think it is. But let us help you get started. Keeping them on task a little bit is really helpful. Because otherwise, you know it's going to go into the background of all their other work.

13:20 So the now what is in part of our data governance framework, we had our steering committee, which is what we presented to the upper management that were like, oh my gosh, wow, we do have a problem. And then they nominated what we call the actual regular committee, which is the groups of folks that are at like a middle management layer usually, but they're not actually programming in the data, but they know what's going on in their different business units. And then they nominated the work groups. So we worked with this middle group of folks to then actually, council, that was the word. So this was the council, the Data Governance Council then nominated the folks in the areas that they needed work on.

14:06 So they would identify, we're having a problem with this data. Here are the folks in the work group that can help implement and document and do the work. So then we would have regular meeting cadences. So the steering committee was quarterly. They obviously don't want to meet that often. And we only met if there was something to talk about. The council was monthly. Again, if there was something to talk about, sometimes we would split up the councils into business units if we didn't make progress in one business unit, but we did in another. And then the work groups, it really was up to them. We tried to even do those weekly or biweekly. and that was kind of keeping them on task but, what you have to do is work with a problem that they're currently having.

14:52 So they're already dealing with it. They're already having issues with it. So adding on this additional work was actually benefiting them a lot quicker. So they saw their own ROI, even if it wasn't in dollars, faster because it was something they identified. That is actually a key win right there, what you just said, because it's always like the carrot, the stick, the carrot, the stick, and nobody knows how to create a carrot and doesn't want to use the stick, right? In terms of getting that working group working. Because my next question was going to be, how did you get them to show up to a meeting? Because most people just say, I'm too busy. But you answered that by telling us that it sounds like you used the steering group, the middle management group, to figure out what the challenges are that they wanted to address and then brought that back down to the working group that obviously was trying to address it for that individual or group of individuals within that business line and then use that as the immediate focus for that group. That's genius.

15:51 Exactly. Well, every group has problems. Maybe there was one group. We started with like 10 different business lines. Maybe there was one that was like, well, we can't identify anything that you could help with immediately. So we were like, all right, we'll give you your space. When you want to come back to us, come back to us. We can't, we couldn't force anyone to do this work. But like I said, like that was one group out of about 10 and the rest of them, they had a list, laundry list of issues. So I don't know any group in any company that cannot say these are the issues we're struggling with. Even if it's not a specific data issue, usually you can trace it back to a data issue. Right. Yeah. I love that because it's, it's, you immediately know the impact you're making. Everybody is on the same page.

16:37 And it isn't this build it and they will come fiasco that I see so many organizations. That's where some of the false starts were obviously like, build the technical lineage and everyone's going to be so excited. What is that? What is that? Like they say. Your poor, your poor little anical nerd heart. Your, your nerd heart was, was hurting. They say they need it. I need to see visibility and traceability. Okay. I'll give it to you. never looks at it. That's not what I wanted. Accurate data. Yeah. They look at it to check the box to say, yep, I've got that. But the usability is relatively low. So Carly, I love this approach. And it's one that we try to evangelize, right? It's like really, let's stop the bleeding.

17:23 What's your biggest problem today? Let's bring all hands together to start focusing But I think one of the things that we tend to run into or I've seen happen at organizations is, we get some wins, right? But then continuing to drive the program stalls because it feels like we've addressed the challenge. We've moved past it. So now what, right? So having that ongoing program for, you know, now the two years subsequent to those conversations, how do you keep that going? Well, first of all, we had such a large chunk of ramp up that we still are working on a lot of the stuff that have been initial, I mean, people gave us, like I said, laundry lists. It's not just one problem.

18:12 So, but the ones that you really, I don't think you ever get to a state of fully governed. I really think that's kind of a pie in the sky. That's not real. Maybe it's just because we have such a huge company, but I feel like there's always room for improvement of our data. I mean, our company has been around for, I think at least 25, if not 30 years. So that much historical information, that much data that we've gathered over time, we're still doing kind of backfills of that. But I would say that if you get to a point where it kind of slows down a bit, there's different flavors. So we start gently, if that makes sense. So when we start out with a group, we start gently.

18:54 We say, all right, start with just a business glossary. Tell us the words that you know. There's much more advanced data governance things out there like approval workflows, policies, you know, SOPs, procedures, there's so much more to it that if you just throw them into the deep end, they'll never make it. So there's really never an official stall. You just need to make sure that you bring them to the next step. Like, great, you've done this very baby step. Let's take the next baby step when you're ready. That's fantastic. You just sounded like my daughter's kindergarten teacher. Alex, tell me what words you know.

19:36 I hate to say it, but really, that's what you have to do. You have to make people realize that it's in their best interest. And I mean, the carrot, but you also, you know, we also had, you know, a data governance winner of the month. It was like the star of the month. And I mean, we didn't do anything super fancy except like a nice little email. But it really, it helps tell the story, like the kindergartner story. And not to say that people act like kindergartners, but people want recognition and praise for the work that they're doing. This is not sexy, show you how beautiful AI, if the word AI doesn't exist in this space right now.

20:18 But, you know, this is not something you're going to present to the management board. Because it's not something that they care about. But giving them recognition for the hard work they're doing and showing how it is benefiting is what they need to have this snowball effect to keep going to the point where we actually got it as part of the JDs for some clinical folks to maintain, you know, good data stewardship. Going back to the point you made earlier, where you were able to document the impact. Let's take that a step further, because the thing that I was so impressed with was the full ROI, not just the impact, but the cost and the benefit aspect of it.

21:03 Can you walk us through, like, at what point in this entire journey did you do that? Was it, you know, the way you got the buy-in at the executive level? Is that what won it? And what is the methodology there that you can share with our listeners? Because I'm sure many people would get something out of that. Yeah, it was what we started with when we went back to the management. So you remember we had one person buy-in, but then we needed to get a much larger group buy-in. That's what we started with. And the way that we did that was, again, with our connection with the analysts, we identified problems. And so we kind of thought of, all right, so what problems can we trace back to finances?

21:43 Everyone says this, but it's really true. Any data issue that you have, you can get back to dollars, whether that be, you know, technical debt in data storage, whether that's time and effort, which is what we really focused on, whether that's like when you get dirty data, what are the repercussions? So we picked a couple of examples, especially ones that just happened that were kind of fresh in our mind, like, hey, this data was not flowing for, I don't know, a month, and nobody really knew it. And so this, what we did was we figured out, all right, we talked to the analysts who was impacted by this. And they, you know, people were like, yeah, we were impacted.

22:24 And we said, okay, what was impacted? Oh, this algorithm, this predictive model. And then we said, all right, where does that predictive model go? Well, it goes to the nurses. So then we went, all right, let's talk to our head of nursing. So we went to our nursing department and said, all right, if this data is bad. What happens? What happens in the clinics? What happens to the nurses? And they said, the nurse then has to go use this algorithm that's like 10 pages long and figure it out themselves. So then we said, how much time does that take a nurse? And they were like, it takes about 10 to 15 minutes to do that rather than having it right in front of them. Okay. So then we went back into our data and said, how many people in this time period, how many patients were impacted by this. We got that number.

23:12 Then we went a step further just to cover all of our bases. We went to HR and said, talk about our average reimbursement for our nurses across the country so we can actually put, I know we went really far because we wanted to make sure we were very solid in that. So you can see you, we really, you just keep digging. You keep trying to figure out and get back to the dollars. So time is money, but then you have to figure out how much money is the time and then you have to have a hard numbers of these are the patients that were impacted. We were very lucky to that we were able to do that because we had such good connections with the analysts. Right. You can see how kind of like building that middle tier is important because those people can tell you the dirty secrets.

23:56 I'm curious how long did that take like that example that you just went through like how long did it take for the team to get from beginning to end of that? I think that took about a month. The hardest part was setting up the meetings and trying to explain what we wanted. A lot of times, the biggest hurdle that people run into is explaining what you're trying to do in the verbiage that the person you're talking to understands. A nurse is going to hear, oh, the data didn't flow from point A to point B, and they're like, how does that impact me? But if you say, hey, this specific spot in the EMR was not correct for two months, how did that impact you? Then they get that. So it's translating the issue into the different areas it's impacting, scheduling the meetings, as we all know, those get tough.

24:43 And then trying to, you know, every single person we met with then gave us another person to talk to. Yeah. So that was the hardest part. It's incredible. I really do love that story because I feel that a lot of people just stop with, well, there's a repercussion. We can't have that happen again, right? But we put it in real dollars and everyone mobilizes because that impacts the executives all the way down. Everybody mobilizes on that. So that's pretty cool. You can also Google a lot. So for example, Another example we looked at, so our company in healthcare, and not just our company, in healthcare in general, there's a lot of reimbursement.

25:24 There's a lot of Center for Medicaid Services, their National Health, the NHSN, National Health Safety Network, a lot of things that are critical. And if something goes wrong, there are fines. So you can look up previous fines and like why they were fine. And then that's also, you know, a little bit of a this is what could happen. So that is not you can't always just say what could happen. But also, you don't need your own. If you don't have access to your specific data, like we did in the previous example, you can still find examples that could relate to you, that could have a possible impact. It's a great call out, particularly if like you find that, okay, if this happens, this could be the possible impact and doing a little research to see if there's any oversight in that data element and the pathway there so that you can kind of make a case as well.

26:18 You know, this is loosey-goosey. We've been lucky. I've had a couple of clients where that has been the case. Exactly. Yeah. Yeah. So Carly, I was going to say, so for other organizations that may be listening to this methodology and are kind of thinking of implementing something similar for their own organizations. What are some of the key steps that you would really recommend they prioritize? I would say make a connection with the analysts. Talk to them. Have conversations with the analysts because they are the ones that can explain what they struggle with. Like what issues do the analysts have? That's where we started. Another way you can start is you can start with the business units as well and ask them what issues they're having.

27:01 And their issues may not be data-related, but again, like I mentioned, you can usually kind of explain that metadata is still data. So we start with our business units. So let's put the analysts aside for the moment. With our business units, we start with the business glossary. We start with a report catalog because most business units do their own slight reporting. It doesn't always live in IT. I think everyone knows what Excel sheets they kind of do manipulations on. And then what do they maintain in an Excel sheet that's difficult to maintain, that they need to know when something changes and they need a little bit of an audit trail.

27:42 So the business glossary was the biggest hit because, I mean, turnover, how many new people do you get that have no clue what an abbreviation means? You know, these basic things. I think those are the biggest, those are the first steps. Particularly in a large organization where we have that kind of turnover, ramp up is always pretty slow for those reasons or issues come up or wasting time. Creating a report that nobody understands because you're using the wrong terminology. You know, those kind of things that end up happening when a new hire comes on. Yep. Yeah, that makes sense. You mentioned, you know, at the start of this, we discussed the two things that wowed me. One was the ROI, but the other one was the size of your team.

28:25 I think you spoke to this, though. You definitely spoke to this, right? Because it's not necessarily the fact that you went off and hired governance people to do governance work. That's really not what that was about. It was about enabling the business with a methodology and a process and a reason to do governance work. So you said you had a team of four. I'm curious, how much of the business do you support? Like how many different teams are you supporting? How many working groups is your team supporting? And what do the four individuals that you have on your team do? We have, I want to say we have about 12 business units. We currently covering care delivery, which is our patient type services. Like I mentioned, pharmacy and vascular and dialysis.

29:12 U.S. We have an international piece as well. And actually, that's kind of what I'm, I have a slightly new role that's called clinical data integrity and stewardship. And they didn't, sometimes the word data governance scares people, as many, many people know. And I'm going to be starting to look at more global data, which is kind of a new thing that I'm very excited about. But back to my staff, we focus on one area, which has about 12 different business units. The working groups and keeping everybody in line is kind of not in line, but actually helping people navigate their own governance is kind of a full-time job. So really one person is dedicated to running the framework, making sure they assist these groups with, we use a metadata management software, Calibra, assisting these users to get their information in Calibra, teaching them how to use it, creating training materials, really enabling the business folks to do their own governance.

30:14 So that's really a one full FTE. And like I said, it's not always the sexy, fun work, but it's what gets the snowball. And that person also managed a JIRA board and is the person that kind of like communicates the most with the work groups and the council. We have another person that helps with triaging and translating between the business and the data folks, because that's always a challenge is understanding that communication between the two. Because you'll have the business saying there's a problem and the technical people saying, no, there isn't a problem, but there is a data discrepancy.

30:58 So we have a person that helps bring bridges, those gaps like, oh, well, maybe you don't see it on the back end, but in the front of it looks like this and we think it's blah, blah, blah. So we kind of have a translator. That sounds strange, but all of the people that we have, we actually stole from other areas of the company that had been with the company for a period of time and understood our systems. And then the last person that we have is a more architecture type person. And he focuses a lot on what's the best, architecture for not only our metadata management software, but when you're creating different governance processes.

31:40 And he is more, once you get the metadata and the data that you need for governance, what is the best architecture way to make sure that that is a seamless flow? He's the one that worked on our reference data management. Also, he's incredibly good at, you know, training documentation of the tool itself and the like approval processes of the data. So it's not just kind of wild west. So it's then he is putting the controls around what these groups are starting to enter. That's great. I heard enablement, somebody who's focused on enabling others and, you know, within Calibra, within the platform that you've selected.

32:21 There's also the translator who handles triage and communications and then the architect. Did I miss one? Me. Wow. Okay. All right. I do a bit of all of it, to be honest. I have the technical knowledge to do the programming and the tool. I have the relationships to do the translation. And I'm also, I run the steering committee, which is the highest level. So my hands in all of it, honestly, I wouldn't say I'm one, I'm the puppet master. That's kidding. And we call, I always call my team member unicorns because they have to speak multiple languages. They have to speak the technical, they speak the business, they have to understand the flow within our company. So those are the kinds of people that you want on your team.

33:02 A curiosity I have is, you mentioned that all these individuals were in other parts of the organization. How did you convince them to do this? Because data governance is not sexy. Nobody, you say data governance, people will literally run away from you. My wedding personality. I don't know if I had your emails or Slack messages anymore today. Like, how did you convince that? Well, one of them really enjoyed... Processes and procedures. Like that was her, I know, right? She's, again, she was like a unicorn in that regard. So she was the one that everyone would go to in our technical part to be like, well, what's the standard of blah, blah, blah.

33:48 So, you know, there's a combination of why people choose to move departments. And part of it is like, I don't see growth in the place that I am. And the way that our department was set up is we were set up outside of IT, which gave us a lot more flexibility. And I think that appealed to some of these folks that were in IT that were kind of restrained by their processes and procedures. And it was more limited to think out. They couldn't think outside the box. They did think outside the box, but they couldn't implement some of the stuff that they wanted to implement. So she was all excited. She's the one took over the data governance framework. She came over. The second one was an analyst.

34:29 So she was part of the analytical group and actually led a group of analysts. She was so frustrated with the problems with our data that she was like, it would save me time to join this group and fix the data instead of doing all this disaster. So she's the translator. So she was the one who also loved to dig and try to understand data issues. When you understand data issues, you also understand a lot of what's happening in the patient realm, in the clinic realm. And it's kind of, it's very interesting to her. And then the last person just, he really liked me and was like, oh, let's go come join your group. He was the person that helped us analyze and look at the data governance software and the metadata management tool.

35:18 And he also was in IT and felt restrained that he couldn't do as much documentation as he wanted, which I thought was really fascinating. I know. See, you have to find these unicorns. I know. And he wanted to do things that took a little bit longer, but were going to help later on. And the pressure in IT is do, do, do, get it done, get it done, get it done. So we found those people by talking to them. And just like my mentor stole me because I would ask the questions. I asked the questions and then you kind of pick the people out that ask the questions because you know that they care and they want to understand it. Well, I think we have two more questions for you.

36:02 I'm just fascinated right now, by the way. I know. I'm like, where are these unicorns? I've spent 20 years trying to find my people. So I think people, I think Mr. Rogers said when there's an accident or something bad happens, look for the helpers. Yep. Look for those that, and that's similar in this scenario. Look for those that ask the questions and that care about the data quality. Those are the ones that are going to be your unicorns. Yeah, absolutely. I love that. I absolutely love that. So you mentioned that you don't report and you're outside of IT. What area are you in then? We are in our global medical office.

36:42 So we're more clinically focused. And I think it doesn't have to be in some type of medical office that could also live in the business of the area that you're governing. This is just the way that our company was structured and set up in terms of like who gets the headcount and he doesn't and but living outside of it i think is really important or if you're in it having the kind of autonomy to say this is how we're going to do it is really important and another thing is we could help without telling someone we're going to charge for our time and effort. That is also really important.

37:24 Yeah, I agree with that. I think it's also, I mean, IT always gets a bad rap and I always feel horrible. I mean, I obviously worked in IT for a number of years in the middle of me being a consultant, but I will say that, yeah, they always get a bad rap and it's not necessarily the individual's faults that work in there. Absolutely not. A lot of people want to do the right thing and help that there's just so much to do, right? But I think having it outside of IT also helps in terms of having those honest conversations about what's happening and no one's being defensive because it really is what is the right thing for the business, right? So you're put in that position, which is fantastic.

38:04 I know Anjali is dying to ask about AI. I am. I am. So I know you touched on AI a little bit, saying it really isn't something that's in your realm right now. But are you starting to think about either AI for governance or governance for AI? Yes. AI governance is... It inherits data governance. So you need data governance to do AI governance. And then AI governance obviously is newer for a lot of folks. And one of the reasons why, like I mentioned earlier, I'm going more into the global space is because EMEA and other areas of the world are coming up with AI rules and regulations.

38:49 So in order to meet those rules and regulations, you have to meet the AI governance standards they put in place, which then you need the data governance to enable the AI governance. So we're working on understanding what is needed in the EMEA space, which then we would kind of apply to the US space because we know US usually follows suit after a period of time. And that's actually really helpful because you have to think about fines. And if you don't have this in place, what's going to happen? And the challenge The challenge with AI governance right now is AI can't be left alone yet, especially in the healthcare space. You need a second set of eyes on things. You need review.

39:32 So if you, a lot of people, like you have technical folks developing some type of AI model, and then you have a physician that looks at it and tears it apart. So we have the challenge of not just doing the data governance part of the AI governance, but the AI governance part with how do you incorporate it in healthcare and make an impact to the patients without getting hallucinations. So I think there's two parts of AI governance. I'm helping more with the data governance piece, and I'm part of the bigger conversation of AI governance, which then has to bring in the experts of the field to review the models themselves, not the technical piece, but the clinical and the outcomes and say, yeah, okay, this makes sense, or this is ridiculous.

40:22 It's not worth it. Stop altogether. Mm-hmm. Yeah. Love that. Yeah. Yeah. And that's what we're seeing so many of our clients, especially at EMEA, start to struggle with where they're saying, you know, we jumped in the AI pool, but now we got to make sure we don't get fined for doing it. So they're really looking at how do we take some of the work that's been done foundationally on improving or at least governing their data and applying that frictionlessly to AI governance as well. So big topic, I think, that's going to continue to explode over the next two to five years.

41:02 And unfortunately, because it's not frictionless, you're going to need members of multiple areas of your company. And it's going to have to be a little bit of a brute force method until there's a better understanding of it. So it's critical to include members of the different areas that are impacted by whatever AI you're creating. That's critical. The end user, the person in the middle that has to run the model, the person in the beginning that's developing the model. And that's what I've been finding companies are not doing. They're not including everyone because they're like, oh, we don't want to keep having meetings. But because this space is so new, you can't leave anyone out yet.

41:44 Not that you would in the future, but it's not streamlined enough yet to do that. Yeah. Yeah. That's such a great point. You can't set it and forget it. You absolutely cannot. Not yet. Not as to, you have to know what data you've poured into the model, make sure that that makes sense. You have to ensure that the model, you know, stays compliant throughout its usage and you need to understand the ultimate impact to, in your case, your patient, right. And others, it's consumers. But I, I, I find it interesting that organizations are just like, Oh, oh, I don't want to have another meeting, but wait a minute. This AI capability is actually accelerating a lot of what you're going to be doing as a business, and it probably is worth your time to make sure that it is accurate and complete and working in a way that impacts your business positively, not negatively in the long run.

42:37 I'm waiting to see hopefully people just do the right thing moving forward because they have real reasons to do so beyond just, oh, somebody's telling me to. It's more actually there's financial impact here. That should be interesting. I like, I am just amazed that I got to meet you, Carly. And I'm so happy that our worlds collided a little bit there at CDAO. We're probably going to have you back like in a year, like next season, just so that we can hear what's going on with the AI stuff that you're doing as well, because I'm sure it's going to be phenomenal. Yeah, I'll talk about all the world data now. I even went to Germany two weeks ago for my first lesson on our global data, which is a whole problem in and of itself. I mean, you think about all the different countries.

43:20 You know, I just had to deal with one country's regulations. Oh, boy. So in a year, I'll probably have more gray. That's awesome. All right, Carly. Well, thank you so much for being on. It's been an absolute pleasure chatting with you again and hearing your story again. And I absolutely learned so much and I'm sure our listeners did as well. So I appreciate that. You're welcome. Thanks for having me.