From Compliance to Catalyst: How Parexel’s CIO Builds for Impact

Jonathan Shough, CIO of Parexel, joins us to talk about leading data modernization in one of the world’s most regulated industries. He shares how compliance can be reframed as an enabler, not a blocker—and why it’s critical to deliver value to patients, not just platforms. We get into Parexel’s pragmatic approach to AI adoption, the role of human interaction in digital transformation, and what it really means to modernize data infrastructure without breaking what works.

If you’re balancing transformation with trust—or just trying to give your teams back their Fridays—this one’s for you.

What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.

Chapters

• 0:00 Introducing Jonathan Shuff, Paracel CIO
• 2:30 CIO Evolution: From Support to Strategic Leadership
• 6:00 Human Interactive AI: Turning Sparks into Solutions
• 10:22 Compliance as an Enabler, Not a Barrier
• 17:53 Modernizing Data Infrastructure While Maintaining Legacy Systems
• 30:50 Patient-Guided Approaches and AI Opportunities
• 38:56 CIO Advice for Regulated Industries

Transcript

0:38

Jonathan, how are you doing today? I'm doing great, John. Thanks for having us today. Absolutely. We've been, uh, talking about doing this episode for, uh, a few months now, and I'm, I'm glad we finally have the opportunity to do it. Uh, you're doing a lot of innovative work, uh, and for a, uh, company that's providing just a critical service to, to, to all of us. Uh, you know, here, here in the US and, and, and across the globe. So, really excited to, to get into this, uh, Jonathan, I first I wanna just, uh, ask you, uh, to tell the listeners about yourself. Sure. So, uh, again, Jonathan Shough. I actually am in North Carolina. I have, uh, four children and a beautiful wife who keep me on the straight and narrow and, and, and those four kids ensure that I think I'll be working till I'm 80. Um, I've been in the life sciences industry now for 26 years. Um, and so delivering much needed drugs and therapeutics to patients is something that has become very, very near and dear to my heart. We like to say, uh, in, in this business, if you're not a patient, you certainly know a patient. And if you're not a patient today, you probably will be a patient. And so. To your earlier comment. The work that we do really is a incredible, incredibly rewarding, but it's also instrumental in improving, uh, lives for patients. And we, we really enjoy the work that we do. Yeah. And you know, there's, there's a lot of great topics that will we'll cover today. So about, you know, how you're, how you're innovating, and. Uh, you know, I wanted to understand, you know, how has the role of CIO evolved at Parexel as data has become more central to both operations and this innovation? That's a good question. Um, first and foremost, I think it's evolved quite significantly just in, you know, in this industry, not just within Parexel, but the, the role of CIO really has become more focused on. Looking at one, how do we drive operations, but also how do we drive innovation? And, you know, a, I'm a big proponent that the business really drives our innovation. The business is really where we learn more about how can we drive more innovation, drive more efficiency, give people more time back in their day to do the scientific and, and operational work that that drives products forward. Everything we do really points to how can we improve the value of time, price, and quality for our patients and for our customers. And so I think it's moved from that traditional infrastructure support. Do I have a workstation, do I have software into almost being a cheerleader and, and really focusing again on that operational and innovation. Is gonna drive our business units to really perform better in delivering for, for our patients and our sponsors. I think a lot more focused on strategic leadership around digital transformation. So how do we move from how we've done things over the last 10 years, 20 years, 30 years, to more modernized approaches? How do we drive more data-driven outcomes? Because we can now. Visualize we can access and tap into data now more than we've ever been able to do. And I think also, you know, the advent of that human interactive AI that you and I have talked about where we now can really harness and build on what AI can offer to the everyday user. It's not just a technology tool anymore. It's actually now able to be interacted with by. Uh, a staff, a member of staff, an executive admin, a data manager, a project manager, can all interact with ai. And so we're starting to see that ecosystem evolve and develop. And also I see, you know, driving more standardized approaches, fostering that innovation as I mentioned, and delivering measurable impact. And I think the last thing that that has happened is as a CIO. We're now even more focused on how do we develop our talent? How do we make sure that we're building the talent that's gonna move us into the next generation of delivering pharmaceutical products for patients and our customers? And that's a, that's a big transition for us.'cause again, it's no longer about the back office, it's also about interacting with the business and really driving innovative and creative approaches to improve operations. Yeah, 100%. And you know, as CIO you're, you're building that central brain of the organization, which is really where, you know, the, the, the company gets that, you know, compounding gains, like learning from what you've done in the past, making better decisions in the future. And, you know, traditionally these, these data platforms that CIOs use, you know, there's a, there's a layer of complexity. With, with SQL and being a data analyst and, and, and things like that. And, you know, we were, you were chatting offline and like, like you said, this kind of human interactivity, uh, on the business side that's, that's being enabled by ai. I'd love to hear more about that. Oh, absolutely. I mean, I, I would, would point to the fact that now that we. Some of the interactive AI capabilities we're, we're now taking sparks, if you will, and we're turning those in into to really quick small campfires that allow us with the business to quickly ascertain if, if I just, you know, incrementally thought differently about my process. So for example, at Parexel we, we have, uh, a process that we call the Parexel Precision Pathway. And the idea is. What's the shortest, uh, distance between the point of starting a project or a study as we call it, and finishing and, and everything we can do to compress time to in increase quality and, and also positively impact price. We want to do those incremental activities and, and traditionally it's been very difficult because. You're in, in, in a, a pseudo monolithic environment between the regulatory environment and process and the compliance that's required, that just the inertia of, of complying and ensuring that you're, you're doing things with the rigor that's required. It was hard to move things forward. Now, with, with ai, we can simulate in ways that we've never been able to simulate before. And not only simulate, we can now take that simulation and quickly turn it into active capabilities that the business can leverage to do that thing. That was a spark, you know, just a few months ago. And so we're really seeing, particularly in our office of ai, which we established recently under the, the leadership of our CEO, Peyton, excuse me. And, and we actually are seeing that acceleration and that that ability to take ideas and compress the time that it takes to go from idea in the business to actually looking at real solutions. I'll give you an example. Our, our Paraxel AI assistant, we launched in August, uh, 2023. We now have 18,000 subscribers out of our, call it 22,000 employees. We average about 6,000 6,500 discreet uses per week. We have over a hundred stored queries in our, in our prompt library. We have a center of excellence built around that, that allows those prompts to be reviewed and published in the library so that people can utilize them and, and just in software costs alone or licensing, we're, we're easily seeing a five to $6 million save. On that front, on the production front and, and driving more productivity. We haven't, you know, to be fair, we haven't perfectly articulated what we think they are, but we know they're significant. And just the fact that we've seen over three and a half million prompts go through that system in less than two years is astounding. Yeah. And you know, what you're de, what you're describing is, is operating in, you know, not only, not only enormous scale. But like you said, you're in a regulated industry and, and you know, and, and, and rightly so, and there's different ways of working with data when you're in a highly regulated in industry. So I, I'd love to learn from, from you, you know, what's the path to, to truly like unlocking value from data without compromising on compliance? Yes, and it's a great question, particularly in that sort of accelerated example that I'm, that, that I was referring to because. Still, there's still danger and concern, right? About making the wrong decision. There's concern about are we excluding, uh, potential patients that should be included, et cetera. And so the, I'll call it the bad news to the good news, and I say that a little bit tongue in cheek, but you still have to drive in order to really unlock the value of your data, which you know is what AI is all about, you still have to drive compliance. You still have to drive the regulatory requirements that are key to making sure that, like, in our case, we call it transparent ai, that we're driving a transparent AI process, but I would actually offer that. Part of what we've changed in that discussion is really looking at compliance as an enabler. So instead of thinking about it as, as a, a fence to get through, we actually see it more as a training wheel on a bicycle. Right, that it gives me the ability to stay upright and to make sure that I'm staying on the pathway. And while I'm doing that, I'm learning a lot about the data that I'm utilizing, the patients that we're identifying the therapeutic areas, that we're learning more and more about the process that we're enhancing. But the point is, and why I mentioned the bad, you know, sort of the bad is you still have to drive governance. You still have to have, in fact, I would argue. Even more compliant and, and defined data architecture, you still have to look at how do you monitor and manage the automation that you're driving into the system Because, um, as one of a, a, a partner friend of mine that actually, uh, we work with at SaaS likes to say the. The garbage in is at scale out when you use ai, and so those things continue to be important, so you really can't shortcut them. But because we're using. The approach that we're using with, with a, again, compliance as an enabler and ensuring a tight data governance and defining is it allows us to maintain that alignment. Two, it makes sure that when people are utilizing a tool, whatever that tool might be, that they can trust in the data and that's critically important to us. And then as our, our ecosystem grows. We are building in that continuous adaptation to make sure that inve investment and our compliance native tools continue to remain viable, and we can sort of pre-plan, pre-populate what is it we need to do to sustain competitive advantage. Yeah, and that's, I I, I love how you phrased it as compliance. Was it as an enabler? As an enabler. Yeah. And, and that's so important. I think this applies to lots of industries where, you know, there's, you're really raising the bar of your operations when you say, you know, compliance isn't just something that's slowing us down, but it's also enabling us to, to maintain more rigor around our processes and do things the right way. And ultimately, you're, you're delivering, you know, better, better products to, you know, your, your customers. And I think all. Companies, and even if you're not in a highly regulated industry, should, should figure out like, what's that thing that's, that that should slow you down, right? That ultimately Deli, you know, delivers a better product that was built the right way with the right amount of rigor and right amount of, uh, planning and thought and care to, to the ultimate end customer and, and the internal operations. So, uh, I, yeah, I, I like the way you phrased that and. And of course with, with ai, I mean, one of the things that you, you know, you mentioned this a little bit, uh, was, uh, having the, like the test data sets and like being able to generate synthetic data like at a rate that's, that's never been done before and have that synthetic data, you know, really, um, be a good mimic for like your internal tests. And then that, that kind of helps you sort out some corner cases. Then finally, once you bring in the, the, the real production data, you know, John, that's a, a great point and, and you know, a little, a little pat on the back around Striim, Databricks, uh, Kafka, some of our other partners that we're working with, you know, and I would even point in this case to strain our ability now to get at data. More quickly and, and now that we have in place data, cleaning, data, quality review, data defining, and those aren't in the right order necessarily, but the point is because we now have automated those capabilities as, as we pull data into our lakehouse, leveraging stream actually gives us the ability at scale, at volume and and enterprise grade way. To actually see what's happening with results, what's happening with a site, what's happening with a patient far more, um, near real time or real time than ever. Now, to be fair, real time in our world is an interesting concept because I'll give you an example when a patient goes to screen, uh, or maybe a better example is when a patient shows up for a visit. And let's say they have to have a lab drawn and, and have blood work done. Real time in that context is probably three to four days, right? So when we say near real time or real time, I like to say when necessary, when, when we need access to that data Striim and our, and our architectural approach allow us to get that data. And as soon as it's available, it's gone through the process of ensuring that it's in the right locations. We can actually then deliver it to our scientists, to our operators, to the folks that need to utilize that data to make the next decision and to really understand the impact of the, the, the treatment or the impact of the activity that the, that the patient is engaged in. And, and while that sounds like, oh, well, it's just data and, and moving it quickly is not that important. I would argue it's one of the most important things we do because the faster I can get at the data and, and I'm speaking not as much towards the real time aspect, but more when I can paint a wholesome picture about an activity that a patient has engaged in at a point in time, and I can evolve that with additional and enrich it with additional information. I can make better decisions about how this product is performing. I can make sure that it's safer, quicker, I can, I can identify issues quicker. I can ensure that protocol deviations, which can be problematic. Right? And a protocol deviation as a simple example is, you know, somebody at a site's not taking blood pressure. And the sooner I know that and I could say, whoa, whoa, whoa. You need to take blood pressure.'cause that's a, a critical element in the protocol. All of that is critically important. And I think our ability now to do that, like, like never before, leveraging products like Striim and you know, our access to tooling is just incredible. It, it, it really is changing how we can allow operators to make decisions, not just better. But with more information so that they can be far better, uh, you know, uh, or far more accurate about what they're observing. Yeah. Thank you. We we're, we're always very happy to, to partner with incredible operators, especially those who are delivering a service that's, that's vital to, you know, all of us. You know, with the work you're, you're doing at parexcel. I'd love to drill into that a a bit deeper and, you know, see, like at a high level, you're, I'd, I'd love to understand, you know, the strategic, strategic advantages of using platforms like Databricks and Stream together and, and how that's sort of helping you create that, that enterprise, uh, source of truth. You know, it, it, it sort of goes back or it does go back to integration and I think. Being able to leverage for, you know, specific to your question, streaming Databricks, that integration and and analysis capability at unprecedented speed and scale allow us to streamline the other activities that we need to do related to that data to make sure that it's in the right position when, uh, an operator needs access to it. And, you know, we go back. 10 months ago, 11 months ago, I had a team of, you know, a couple of folks that spent a lot of time monitoring reports, getting reports back on, you know, what's the status of a particular piece of, of information and what does that, where does that data need to go? And I've had a problem and so now I need to rerun it. And how do I do that and how do I keep it in sync with the rest of the database? Those, those issues really have disappeared, leveraging stream and Databricks and, and it really therefore gives us the ability to really look at investing time in some of those AI powered solutions that many of us are talking about, whether we're building them ourselves or we're acquiring them through partners and, and in our world that, that, you know, real time capability at quality. At scale also allow us to think really in, in real terms about how we accelerate the overall drug development process. And importantly that there's such a tight integration between those capabilities that it, it allows us to continue down this unified sort of data ecosystem road where. You know, just in a couple years past, we were having to build integrations and, and monitor those integrations and, you know, internally really manage the, the, the data infrastructure that connected together to make sure that data delivered. Where it needed to deliver at the right time. And so, you know, it's really unbelievable the leaps and bounds that we've made leveraging, again, Striim and Databricks, and it, it's really driven down our cost and increased our operational efficiency as well. The, I, I should probably add, and I should probably add, sorry, I didn't think about this. I mentioned early on about, you know, developing resources. The other thing it does. And this is important in our world. Uh, and I'll give you a little aside, it is not uncommon for companies like ours, pharma, biotech, CROs, smaller organizations that deal with, with clinical development projects. Um, and I have some myself to have hundreds of, I'll just call 'em databases, right? Hundreds of databases running. On platforms sort of sitting in suspense, just because I'm required to tacking onto the, the balance between modernization between, uh, of your data infrastructure and keeping the legacy systems stable, like, like through this modernization. How do you balance the people skills, like the actual people building these pipelines between the modern and prove modern technologies that you're bringing in and the proven. Data technologies that have been around for, for decades? That's a great question, John, because as you well know, we, we have an environment and, and it's true of our customers. It's, it's true of our competitors, where due to safety, due to regulatory requirements, due to historical record keeping and long term safety follow through. There are many times when you might have legacy environments that that will continue in the, in the environment for up to 10 years in some cases. Um, and so one, it also, you know, as we think about how we do that modernization and we drive down that incremental path, we also have to think about how do we, how do we focus the, the teams that have, that have been more legacy, right. Getting them exposed to the new tools and technology while at the same time allowing us to maintain, excuse me, that legacy infrastructure. And, and so we also are looking at how do we bring in those new technologies? How do we think about more modern environments like r and Python and, and the things that we're doing with open source to ensure that we have those new skills developing. And one of the things that we are also doing is, is we're making sure that. We don't, we don't lock anybody in a box. We wanna make sure that all of our teams, both on the business and on the data and technology front have access to those tools. So a, we can continue to keep those legacy environments running and, and maintain them, and ensuring that the data can be captured and, and reviewed as necessary, but also exposing them. To new tools, new patterns, new capabilities that, that allow them to take advantage of what AI can do, what new automations and hyper automations might be available. Uh, and also experiment. Right. Be innovative. Yeah. I, I, you know, working with a lot of, uh, CIOs and, and chief data officers, there's always this balance between modernizing data infrastructure. And doing things like AI and, and real time analytics and then keeping those kind of proven production systems that have been, you know, there for decades, stable. And, you know, no one wants to, to shut off a mainframe or a database that's been running for 20 years. I mean, yeah, people will raise their hand and say, you just broke some process that's gonna take us months to repair. Right. So how do you, how do you balance this? That's, that's a man, that's a great question. And, and, and we are an industry by design. And let's be clear, uh, we, we sort of jokingly refer to those types of things tongue in cheek, but we maintain those, those historical reference points because safety is imperative, right? Nonetheless. Maintaining that sort of modernized data infrastructure and legacy. It's a journey and it's a battle and, and what we've, what we've really tried to do, and, and this really takes partnership with our CFO, with our CEO, with our chief business officer, our head, what we call our OEDI team and our business units to recognize, one, we have to maintain modernized systems. As much as we can. At the same time, we have to be cognizant that we also can't break the business and, and go through these multidimensional, you know, multi-phase, um, programs of activity just to upgrade a system. And so what, what we really started to do, it's look at it more as a modernization journey. And so. As we implement new technology, particularly as it interacts with the user or the user interacts with it, one, it's a journey, not an event. So we want to focus more on, um, not incrementalism, but, but let's do things in a way that are easily digestible too. Our, our, uh, uh, engineering pattern has to be, integration is essential, and so. No longer are we allowing, if I can use that word, products and services that do not tightly integrate with our ecosystem. So if you've got a, a service that you know is gonna hang the moon and, and grow revenues, we need to figure out a way to get it integrated. And if we can't, then, then one, is it an acquisition target? Okay. And two are, are there other options that we could build to find, find that could fill that gap? We also are therefore big on governance and compliance. Again, as I mentioned earlier, we're, we're, we're not making that a barrier, but really an enabler and ensuring that as we work closely with our legal partners, our regulatory, our quality partners, that we understand the changing landscape. So as we think about that journey. The path that we're on to maintain that modernized infrastructure or service that we're, we're always, you know, maybe like a V1 in lock step behind where the regulations, where the quality requirements are, because we don't want to be in front of them for sure, but we also don't wanna be too far behind that we could get caught out. Yeah, I mean, if, if, if you have the, the, the strategy and you know, they have the, you know, uh, the, the pill you need to swallow, you know, for, for lack of a better phrase. Yeah. You know, just wrap it up by saying, Hey, you know, I'll, I'll, I'll help fix your problems, uh, in a few months. A few quarters right? That's right. That's where the, that's where the partnership starts, and. I think the, the, the great thing about, you know, CIOs who have that, uh, intuitive awareness of what the business needs, uh, and can find out like the right ways to, to, to sort of phase out the deliverables, uh, within your strategy. Like for instance, you're, you're modernizing your data infrastructure. That's not something that happens overnight and it's really hard to tell people in the business. You just gotta wait till I, till I modernize our data infrastructure. Right? No, no one, no one wants to hear that. And I mean, people understand and, you know, understand the value of that, but, you know, so, so it's definitely part of the, the, the whole balance. Yes, it is. And I'll tell you the other thing that's, that's really critical on that front, and we've really tried to focus on this the last few years at Parexel. We, IE the data and technology organization and our, and our technologists in the business, we have to deliver, meaning we, one of the things that is sort of the, the worst thing you could do as a data and technology leader, whether you're the CIO CTO, you know, et cetera, is these perpetual projects that never seem to complete or. Only always getting to 70 or 80% and never getting to the, the, you know, the end. Being able to close things in a way that positively impact your user community. That there's a lot of what I like to tell my team in emotional bank account that comes with that. And so the better we can plan with our business, what success is, what it looks like when we expect it, and then deliver that. And, and by the way, it doesn't always mean you're, you're not gonna change dates or you're not gonna run a little hot on budget or, or you're gonna have to change things. But if you can have that tight collaboration and open, transparent, honest communication with, with each other, and you start from the position that, that we are all looking to achieve, um, something really positive for our business. There's not much you can do better than that in order to be successful. And, and that is for Parexel to be successful, for our business, for our operators, for our tech people, our data people, for our patients, for our customers. And I think that that is a critical skill. And in fact, you know, our CEO Peyton is super focused on that and, and I think. Without that leadership and without developing that culture of we're all looking to really achieve positive outcomes for our patient, for our customers and ourselves, um, you won't be successful. So that's a, that is a critical, it's a critical attribute of the work that we do. So another, we, we talked about this a little bit, but. You know, really just to, to drill down on ai, which is reshaping many industries. Like where do you see the biggest opportunities? And, you know, we talked about compliance as an enabler, right? How understanding the risk factor of ai, where does that sit for, for Par XL and and in your clients? You know, that's a great question and, and I think what, what, what, what I hear a lot in, in our business. First off, and, and you've heard me say patient many times, patients and customers, we, we are, we are patients first in everything that we do. And, you know, we think patients. First, it's quality, respect, empowerment, and accountability. And, and one of the things that we're really starting to, to see and, and think more about with our operators is what are the patient guided approaches that we may begin to see? Think about it, I, I'm not wearing my Apple Watch today, but you've got Apple watches, you've got, I do have a lumen device. You have lumen, you have, uh, you know, ultra human ring or aura ring. You have all this patient reported outcomes, capability that there you go. That have existed. And, and I think what we're starting to, to see our, our medical teams and our, and our project teams in the, in the launch. Community think about is how can we, how can we harness that, that patient data in a way that we can start to develop better patient guided approaches? And that's important because one of the things in clinical development that's difficult is this idea of patient burden. And if you can imagine, um, you know, looking at a protocol. That, that engages the patient in, in a number of visits. And as part of their visit, they're going to get, you know, uh, a liver punch biopsy. Right. How many of those you think they're gonna do? They keep coming back. Not many, right. Or, you know, they, they have to, they have to have spinal fluid drawn and, and I'm using those sort of. They're not even outliers, but, but examples that make people cringe'cause they're real examples. But also, if we can learn more'cause of the patient reported capabilities that we have now, could we actually drive protocols? Because the therapeutic areas are, you know, we're focused in more complex areas, particularly oncology and CNS. Could we learn better? How to learn from the patients or learn from the potential patients. In that case that guide us different to make different decisions about the protocols. Also, I think it, it will in AI will substantively enhance the, you know, the term you hear a lot data-driven decisions. If you think back just five or 10 years ago, data-driven decisions were about analytics. Well, we're now beyond analytics. We're now about knowledge. And the thing that the advent of large and small language models and generative AI and inference and you know, the things that can be harnessed under that umbrella of AI can really change those data-driven decisions from responsive to proactive. And, and I think that's gonna be a massive game changer for all of us. Then there's the obvious operational efficiency. If I can, you know, one of the things that we've deployed is an AI driven monitoring visit report. For example, if I can give that clinical monitor back a day of travel, it's less expensive for my customer. It's less, you know, cumbersome and tiring for the monitor. The monitor can spend more, more time doing the work they need to do as a monitor, not as a, you know, data entry person. As I like to say, if I can give a monitor back Friday every week, they're also gonna remain an employee, right? Because they can now go do things that they've not been able to do. Because I will tell people, you've never done a CRA ride along. You really don't know what travel is because it's, it's a back breaker. It's out on Sunday. Back on a, a Thursday night, if you're lucky, sometimes Friday morning. And then you gotta catch up on all the paperwork. And so, you know, if we can, if we can give people back time or, you know, said differently, quality of life, we're gonna, we're gonna maintain that employee population, we're gonna improve the results that we see from them. And, and that's all good for the patient and, and better for our customer. Yeah. These, these efficiencies in the, in the, in the operations. People under underestimate how big of an impact that makes to the end customer. In your case, you know, the patients, right? And the quality of life of the people who are getting services around these events in their life that are, that, that can be traumatic in some ways, right? Um, uh, and, and you know, the, the, and people have family members who go through this and ultimately you want this all to be streamlined and efficient. You don't want them to. You don't want the care providers to, to not have data and slow things down and, and make it more of a, you know, uh, just, just bog it down with manual office processes. So, absolutely. How do you imagine, you know, Databricks and Striim helping you achieve the AI vision? Well, I think by and large it, it's the a, the integration that's, that's developed as part of the tooling. To, it's our ability now to to quickly harness multi sources of data at speeds and, and at and at reliability that, that we struggled with in the past. Um, and I think it's also, uh, an at scale discussion. What we are seeing through the use of Striim and Databricks show us that one, not only can we address. In ordinance amounts of data, we, we can also ensure that they're compliant, that they're consistent, that we're efficient, that we're driving the best costs for turn in the CPU as as we can. And, and that's important the more AI grows. And so I think there's really not many examples that, that I would choose differently. Because the, the combo of, of Striim and Databricks is just an accelerant to the work that we do. At the end of the day, we're delivering, again, a, a product that the better we can do with time, you know, reduce time, the better we can, can, can drive quality, so greater quality and at a better price. That's a winning combination and certainly. Architecture, including Striim and, and Databricks, allow us to do that at, at one reasonable rate and also at at very high capacity. And that, that's a perfect combination with the onslaught of data and, and results that we have in our business. We're always happy to to to partner. And, and, and we, and we do love how you think about this at, you know, uh, you know, at a patient level. And, you know, and that's, that's ultimately core, core to the business is, you know, the, the experience of the patient and then the, the, the, the labs that are, you know, running these, these tests. Uh, and this, it's all related, right? Like these are all just little cells that make up the, the, the body of a company. And it's. It's great to see how you work and how you think about this. It's, it's, it is really inspiring. And I wanted to ask you, what advice would you give to other CIOs trying to lead modernization and transformation in, in compliance heavy industries and also just industries that, that operate at, that, the type of scale that Parexel sells at. Just in, in industries like ours, um, that are, that are heavy on the compliance and regulatory front. So, you know, many of, many, many industries that, that you guys deal in. Um, it's interesting 'cause we have to navigate a, a uniquely complex environment when you're balancing. A global capability. And I mean, if you can imagine, you've got different regulatory requirements depending on where we're operating, you know, in, in one of hundred countries around the globe on a daily basis. But I'd go back to one, don't make those things a barrier. Make, make them part of the journey. Make, make the, the requirements that you have. For quality compliance regulation, actually a skill that you hone and it, it becomes an enabler. As I mentioned earlier, it's, it's like the training was on a bicycle, not a fence to jump over. That's the first thing, and I think coming to terms with that, I, I think it's critically important. Two. What I mentioned earlier, you know, for example, our partnership with our OEDI team who really acts as a proxy into our business and, and we've done that to make sure that we are as tightly coupled inside the business as practical so that when we're driving towards solutions to, to address those sparks I mentioned earlier. That we really understand the why and, and one of the things that, that I would stress to, to folks looking to do what I do is back to that getting the right people in your organization and thinking about what's the next iteration of those staff that I often say to those folks, we are not an it, we're not a IT organization inside of Parexel. And we call ourselves data and technology as a way to, to not get stuck in that term. And you may ask, well, why is that? We're, we're a clinical development enabling grid. We are enabling clinical development. And it just so happens that we deal with the data and technology. And so that business alignment really requires deep understanding and, you know. We haven't done it post COVID as much, but we, you know, one of the things that, that we used to do quite frequently is, is what we call ridealongs, right? And we're gonna start that up here probably in the next couple of months. Um, it, I've mentioned COVID because it, it, it changed where people could go, right? There wasn't a lot of people spending time face to face with one another, but that business alignment is critical. And also we talked about it earlier, modernization, you've got to. Build a plan with your legal team, your commercial team.'cause even contracts need to refer to it. Your financial partners and, and you know, the overall operational leaders that we have to build in those changes that are gonna allow us to maintain that. I like to use the n minus one modernization, right? We're not always gonna be at the latest version, but we cannot afford to be multi versions behind.. And then I would also go back to what I said earlier around, you still gotta do the hard yards. To quote a really close friend and and mentor of mine, you gotta do the hard yards. It is about data definitions. It's about data governance. It's about ensuring you have. Business ownership of the data, and, and you can, you drive that governance process. Otherwise, you're, you're gonna fall afoul of data quality and, and as our tools with the advent of AI and products like Striim and others, you cannot afford for your data to be in the wrong place or not at the, where it needs to be at the right time or the wrong data to, to be available when, when you know lives depend on it. Absolutely. Uh, Jonathan Shough, CIO at Parexel. Thank you so much for joining today's episode of What's New in Data. And thank you to the listeners for tuning in. This was super special, a lot of great, uh, advice here and just, uh, learnings that, that a lot of people can, can take with them. So thanks again, Jonathan, for joining. Thank you for having us and I, I hope that our discussion, uh, impacts somebody in a positive way and I really appreciate you giving us the opportunity to present today. Thank you very much. I've enjoyed it.