IQVIA Holdings Inc. (IQV) Earnings Call Transcript & Summary

Welcome, everyone to the Clean Data Confident Decisions: The Role of Data Governance and Life Sciences Organization Webinar. I'd like to open with a standard disclaimer about forward-looking decisions and statements in this webinar and presentation. So I'm going to leave this on screen for just a few moments for you to have it review. Thank you. We want this to be a very rich and interactive experience. So if you do have a question, please go ahead and put it in the chat box. We've set aside time at the end of the webinar for us to answer as many questions as possible. And any we don't get to, we will follow up with attendees after the webinar. If you can share your thoughts on your channels and if you want to learn more about IQVIA's data governance and stewardship offerings, you can download the fact sheet and the blog from the attachments area. And with that, I'd like to introduce our panel. My name is Claire Goodswen. I'll be your moderator today. I'm part of the IQVIA's information management development and offering development for data governance and stewardship. I lead the global data governance stewardship offering, and I focused on making sure that our offerings are aligned to customer and market needs. And I support our regional centers of excellence. One of those is Phyllis, who leads us is Phyllis, and I'll ask Phyllis to introduce herself.

Thanks, Claire. I'm Phyllis Imparo, I'm the practice leader for the U.S. Data Governance and Stewardship Center of Excellence, been with IQVIA, about 6 years, came out of life sciences where I've spent 27 years, many of those years in a global role supporting a customer and product strategy, implementation of MDMs along with Global Governance and Stewardship.

Hello there. This is Raj here. I'm part of the EMEA team, helping client on the Data Analytics journey across the data product life cycle. I've been in the industry for 22 years with over a decade in life sciences from being different roles from the data engineering side to leading a product owning team in terms of driving NBA and MMM solution. So happy to be here talking about the data governance.

Thank you, Raj. So hello, everyone. My name is Gopi. I'm part of the EMEA business intelligence and information management practice with over 16 years of experience in business strategy and digital strategy consulting, spent over a decade in life sciences and have experience leading several data and digital and tech transformation programs ranging from strategy definitions to making like tactical data management practices, set up and stick in the organizations to technology execution. So happy to be a part of the webinar today.

So you can see our panel. They bring a lot of experience to the table. I want to start off by talking about how data and life sciences has evolved its use and some of the factors that have changed how that data is being governed. So if we go back 10 years ago or to 2015 and before, data use in life sciences really was being used to -- for reporting and a summary of business health. And we saw some divisions or some parts of the business starting to think about that data being used to inform business decisions. And we see different teams in an organization using it to drive outcomes. But in general, at that time, the governance of that data was ad hoc. And then as we move through the years, we saw companies starting to see the value in using the data to drive outcomes. And there were more data sources to contend with globally, and some organizations started to implement an enterprise view of the data. But in many cases, the data management and the governance was still being siloed -- or are still used to being siloed, and there'll be different teams across an organization doing the same thing, but operating separately, sometimes without visibility to each other, and that would create duplicity and inconsistencies. And then in 2020, we had the pandemic and that caused a rapid shift to a decentralized workforce. And that brought resulting in a reactive immediate need to address secure accessibility to data. And so we're just allow business continuity. And then as we move through the pandemic, we saw sort of this great resignation of what people call it the great resignation. Some people call it the big reshuffle. And we saw lots of people moving out from company to company. And that brought new challenges for organizations who were still managing and governing their data in legacy or older methods in spreadsheets on hard drives or share points or even just people, who had that knowledge of their data themselves. And so a lot of deep knowledge about the data that people were using or had started to use to drive their business outcomes was lost as people were moving outside the organization. And so with these 2 factors like the shift to the -- the rapid shift to remote workforce and the knowledge of the data being lost, people started to see a rise in prioritizing and focusing on data governance and processes and people started to see, okay, this is needed to actually do business. So when we look at kind of where we are today, more organizations are understanding the proactive importance of having robust data governance practices in place. There tends to be an understanding that good quality data that's delivered on time is critical to doing business and staying ahead. And governance practices that support quality of that data are prioritized. People want to know what can I do to make sure that my data is good quality. At the same time, that evolving landscape of regulatory compliance and is sort of factoring into like people want to know, who has access to my data, what systems are touching. And so people are looking at governance practices that support that need. And then as we look towards 2025 and beyond, the trends are showing that the industry is just going to keep evolving. Patient journey is going to be at the center of outcomes and driving development and commercialization of health care. And we'll see data norms shifting to streaming, data doesn't seem to be slowing down. And incorporating patient data and generative AI is going to become part of the landscape for many organizations. And while expanding to privacy needs and audit trails to effectively manage compliance is going to be necessary. And so when it comes to governance, we will see technology supported data governance big strategy become key in order to accommodate those factors. And that is where we've seen the shift in priority of governance and as we see it going into the future. So with that introduction describing just some of the factors impacting the shift of governance and the rise in importance in data governance within the life sciences industry, we have a poll just to kind of temperature check where some of the audience is. So we're asking, does your organization have a data governance strategy in place. And I'll leave that up there for a little while just to let everybody have a chance to answer the poll. [Voting]

Just give it a little bit more longer, because it didn't seem to work at the first and so now it's fully going. Okay. Thank you for those, who participate in the poll. Raj, I'm going to move on to the results.

Well, thank you, Claire. Very interesting results from what I've seen is good to see 30% of the participant says they have fully defined and implemented globally. And I can still see the variation in terms of the other numbers, right? There is a very -- organization where it's implemented, not completely is sort of 12.9%. Interesting. Where is defined, but partially implemented, 27%. I think it's a good point to take. And it will be good to kind of speak to the people, who have said 30% to understand the exchange what are the key success factor to kind of drive that one very interesting. And for those, which is 16%, I think that's where I think the emphasis is what this webinar is about, how do we kind of take a step into the data governance. 16.1% says they don't have any strategy in terms of right now. Interesting results, actually. So if I take these results and try to correlate with Gartner, right, I think everybody knows about the Gartner. Interestingly, if you see, while we -- 18% of the Gartner participants said, they have a mature governance framework. This is 2022. What it also say mentions about the maturity has driven the success in terms of achieving the key outcomes, quite correlating to our poll, we say is 30%, probably it is 6 months, I think different factors, which have influenced that. Look, I think over the years since COVID, there's a lot of data disruption happened. People have embarked on the modern cloud ecosystem. We have digital therapy areas. There's a variables, there's real-world evidence. I think as we progress over from now to the next few years, it's important to look at data governance as a discipline, right, underpinning probably in the data strategy, blueprint, I think that's where I think a lot of emphasis is there. And with all this disruption and the digital initiative, including data-driven analytical journey, which organizes are taking, it's important to kind of look at where we want to be in 3 years from now. Interestingly, just going back to Gartner report, it says that even in 2025, 80% of the organizations who are seeking, they will fail because of the lack of modern approach to data analytics. So this gives an interesting perspective for the organization, who are now embarking on the journey and to those guys, who have mentioned it's not implemented completely or not started to kind of look at this report and see what is it entails for the organization in terms of establishing a data governance framework. I think what we will do is within some of the clients, which we speak about, they have started the data science team. There's a DevOps team. There's also product score team. Fundamentally, most of the team have said there's a basic information missing, for example, the Head of Data Science in one of the client organization said, "Hey, Roger, we can't even access what is the asset there, there's no data catalog available for them to drive analytical journey. So I think some basic stuff is a first step towards establishing the governance framework part. So from our side, what we will do is kind of try to give you a little bit of benchmarking, what are the 4 key pillars, which are important from the strategy and the framework perspective for the data governance goes. Number one, I think defining roles and responsibility with a mandate from the executive response is a very key element. I think one of the things, which we -- most of the organic is looking at is kind of defining and getting the leadership buying. I think it's very important there to kind of have that one. And number two, policy and standard, most of the time, you look at the policy and standard from data lens, okay, classifying the data, giving the frequency of the data, the domain of the data. But the most important factor is what does the standard and policy bring to the business? What is that value we're trying to deliver to the business? That is very important from the step 2, so that we can strive and articulate to the business in the right terminology. Number three, I would say data management covering from the data ingestion to kind of building that framework for curation, building the data quality metrics. We clearly mentioned about the challenges in terms of the duplicity in consistently. Having the clear guardrails is a key one, right, in terms of how we are building that entire ecosystem of data, right? I think -- and also within the commercial world, you have medical team, you have commercial team, you also have a different at country level like Germany is more strict in terms of the security of the data. How do we kind of take that entire zone in the data management, right? Last, but not least, the data architecture, right, is trying to build up an architecture and integrate the right tools, which we talk about in the technology aspect be a noninvasive approach is the key element, right? In one instance, a client started with a modern ecosystem, established the entire data center in U.S. And suddenly, they have realized that some of the European market is a very strict governance policy. So there's a kind of a how do you define the architecture with the right approach with them from a global perspective is a key one. So that's 4 simple key pillars when we have to look at data governance. So it would be interesting to see from Phyllis, my colleague here, Phyllis, is anything which you can shed light among how do we get the executive sponsorship, right, to kind of help the team? Do you want to share something on that side?

Absolutely, Raj. From past experience, I can't stress enough the importance of executive sponsorship and driving a governance program down into an organization. That sponsorship, along with a business strategy, right, for how you want to leverage the data and how you want to manage that data in a structured approach is critical in order to deliver success in a governance framework. I will tell you having a global responsibility in a previous job as well as working with all of the regions to establish master data management solutions along with governance and stewardship. The top-down approach, right, with a core strategy that's defined and socialized into the company was very successful. Everywhere where we try to drive it from the bottom up you really get obviously change, right? People are resistant to change. And coming into an organization and basically implementing a structured approach for who has decision rights around the data means change, right, to that company. And so you will get a lot of resistance from a bottom-up approach. And I will say probably 9 times out of 10 when you're trying to push that bolder uphill, you're not going to be successful. So it's really, really critical that the strategy is defined, the executive sponsor -- you understand their strategy and you align your governance framework to their strategy, and you help them to socialize that and build the advocacy they need in order to drive the program within their company. And if that's done, I will tell you, I worked across regions and 1 specific market within the Asia Pac region, which had a very strong leadership, very strong strategy was really on board with aligning to the framework that we were implementing, drove it into his organization and was extremely successful in driving not only his business strategy, but also compliance adherence. There was total benefits across his commercial operation organization, because of the leadership that he drove with this program into the organization. And it was interesting to see when we met about a year after he implemented this program, we met globally with all the other regions. And when they presented what they were doing in their market with the understanding of that customer across their entire ecosystem, and how they were digitally being able to market and do next best action type of activities. All of the other regions were like shaking their head, yes, we need to do that too, right? So it really proved to everyone globally the importance of that framework in driving business success and business outcomes.

Okay. Fantastic. So that is a wonderful segue actually, Phyllis, you just lined it out for me perfectly, because we've talked about getting that data strategy in place, but we do want to move on and talk about how to implement that data management into day-to-day operations. So we do have another poll here, and I'm going to move it on to the next slide, because it seems to take a few moments to get the poll up and running. But we want to talk about how embedded data management is into day-to-day operations. So really, we're looking to see whether it's implemented into your systems and your processes. So we'll see how the audience can respond to that. [Voting]

Thank you, everyone, right. Just give it a few more movements. Okay. I'm going to move on here? And hand you over to Gopi, who is going to talk about the results we've seen here.

Super. Thanks, Claire. It generally is only quite interesting in order to see the split. I mean, especially the fully embedded part like 20% of the respondents like I have mentioned that the data governance practices, the data management practices are fully embedded in the ecosystem. I would be happy to also understand like how they have done it and what has worked best and where the challenges still lie. But generally speaking, it is like interesting to see the range of implementations across the organization, but I see the maximum number of organizations are still in the A, C and D category, right? And this is what like I see personally from my experience as well. So most organizations either have partially embedded data management in their landscape or they have just started the journey. But we all know that in order to truly become like a digitally driven or an insights like driven organization, data management as foundation is key, right? So one of the facts that also need to be acknowledged in this aspect is there are going to be always global and regional in-market differences. I mean we cannot like escape that, especially in life sciences. This is going to be the case because of the various local differences in the market expectations, right? And as Phyllis and Raj already mentioned, the leadership sponsor is important, but the buck doesn't stop there. So we saw that around 30% of the organizations really have the data strategy and framework in place and 20% of the respondents have said they have the data management practices, but it is always a challenge to keep it consistently evolving and stick, right? So that is where the data governance operating model comes into a picture -- comes into the picture, because you need cross-functional teams to ensure the data value, the risks are properly managed. The synergies are properly leveraged and so on. Now just wanted to like take an example of a customer that we now closely work with a global generics player. So they did have properly defined data strategy in place. They even had a data policy signed up by the senior leadership properly electrical down within the organization. However, the big challenge for them was now, well, I have this nice concept what do I do with this, right? So this is where essentially, we collaborated to establish a tactical flow of things where the various enterprise like functions could come together to make sure data management like is done in the right way. Now the data management office was set up with key influencers and data voices or data leaders from the different parts of the organization. And like Raj already mentioned, like the defined roles and responsibilities were clearly like implemented in a sense that the right teams were -- or the people were identified and nominated. So -- and since data management is not just an IT activity, it is like a combination of all the enterprise and all the functions coming and collaborating together, the data owners, the data SMEs and the data stewards, all the necessary people to kind of keep the lights on where properly like selected and nominate. Now -- well, this is like usually done. And so far, it is like a bit of a straightforward exercise, right? So how do you move along the path to kind of progress further? And this is where it is important to do the implementation and the setup in vapes. I'll come to that like briefly. However, what the senior management like smartly did was like they kind of set up like some soft KPIs to the assigned like data management roles, because one thing is like having the people and the KPIs properly like set up. But the other thing is keeping them all incentivized and making sure it is adopted as part of the day-to-day routine, right? So the bottom line is it's a collective effort and the data quality of this organization slowly, but steadily started improving, and there were certain like measures through these KPIs to really see how the progress was made. Now the biggest barrier that we faced during the whole process was quantifying the data benefits, because even though the senior sponsorship was there, the leadership was still not used to the data management like investments as such. So they took a conservative approach in the beginning, meaning some of the investments were questioned and value cases that were identified were not always approved.

That's really interesting. You say that Gopi actually, because that leads on to my next stat, like we actually had a slide in here about that, because what we see typically hearing about this story in this particular use case is interesting, because we have seen that the average cost of data remediation is going to increase as we move downstream. And so to hear it firsthand from you in the story where people are not seeing the benefit is interesting, because there is a tangible cost to actually not putting something in place.

Yes. Precisely, Claire. I mean, this is where the 1:10:100 scenario paves kind of a hybrid approach to value our business cases, right? I mean, let me just take a practical example, right? So I mean, let's say, a sales rep is having a discussion with an HCP. And the HCP is really interested in the content of another specific product in his or her specialty. And then wants to hear more. And by the way, the HCP also notifies the sales rep that well, I would like to receive this information, but I have a new e-mail address. Now the sales rep and the CRM captures everything, but then fails to update the e-mail address. So an automated email is sent to the old address. So that way, there is like the poor data quality or the data management aspect as such is leading to the HCP not receiving the right information. Now the HCP is like still very interested, and he or she reaches out registers on the web portal. Then if there are not proper master data management practices or data stewardship practices in place, a new record is created because of the new e-mail. So as you can see, there is a chain reaction here. So that's what we exactly mean by 1:10:100, right? So if -- the most organizations that I see are taking reactive measures, but rather in being proactive, would save a lot of effort and cost right from the beginning. So now coming back to...

Yes. I mean I was -- I mean, even if you look at it, I mean I'm more of a tech person. So if you look at it on the tech side of that as well, like an engineer kind of like pulling pipes for data like kind of reengineering of pipes data like that, somebody will look at that and be like, well, that's a lot of costs. Do you know what I mean? So having the right data coming in is better than having to retrain or redo all your data models, because you have the wrong data in them, which is better than having all your reporting incorrect, because you -- and then like reporting incorrectly, or using the wrong data downstream and having to take corrective action after you use the wrong information, right? Like -- so it's kind of like -- it does multiply the impact and the spread of that gets bigger as you go downstream from that point.

Exactly.

Just to chime in a little bit, to Gopi and Claire, right, to quantify, right? That governance framework and helping you manage your data. What we've seen with a lot of companies is, obviously, without that governance approach, you have siloed activities, right? So groups within a company are creating the same report over and over and over, right, or they're defining KPIs. So you can really quantify where they're purchasing data, the same data over and over again. You can really quantify the success rate of a governance program by the money that the company can save in really managing that in a more structured approach, getting rid of that extra overhead in cost, right, around what they're doing is...

Well said, Phyllis. I mean, this is exactly what like the kind of approach this client organization I was talking about took, because they established a data governance council, which would centrally manage the data portfolio, be it like acquisition of new data assets or any kind of data initiatives, right? So they are centrally like accountable for it, and they are having an eye on it. But -- that doesn't mean that, well, they are the complete decision makers to what the organization does going forward with the data, right? So what they did in a very nice way was anyone from the organization using the 1:10:100 approach, who has a nice use case or a data-driven insights case with them ranging from a simple BI dashboard, like you mentioned, Phyllis files or an automated tender management use case or be it any kind of a digital health topic, could approach them. And then they had a setup criteria that they requested information on and they could get the funding. And normally, when such kind of fundings happen, like there is a notion within some of the client organizations as well. I mean if I kind of get the funding now, it will be my responsibility to deliver it, do I have the bandwidth for that and those kind of stuff, right? So this is where the senior leadership, together with the data governance council smartly made sure that, well, the person who is bringing the use case, yes, absolutely, it's there, brainchild, but that doesn't mean that they are the only ones, who will be held accountable for the success. So it's again a collective effort. So this is what the make sure of. So -- now one size doesn't fit all, but the takeaway is that a stable operating model, a collaboration practice is going to make data governance take as such. Yes.

So that's -- I mean this goes back -- Phyllis brought this up right in the -- when we were talking about strategy, about trying to get adoption in the organization. So this would be great to kind of give more information about how to make it stick, right? So if you can talk about this, bring this up, it'd be great to talk -- brings some color or some depth to this, Gopi.

Absolutely, Claire. Happy to. I mean, I earlier mentioned that when it comes to establishing a data governance operating model, it is better to do in waves, right? The simple reason is that the organizations in most cases have so many initiatives going on with people pull left and right with limited capacities and it makes obviously sense to start with the most business-critical and value-generating areas and stakeholder groups for us, right? So what we have seen to work very well in order to make the data governance operating model stick and sustain is a 4-step approach we see here. So first of all, starting with establishing the baseline. I mean there's no rocket science for sure. I mean, value workshops and interviews and the stakeholder mapping and really understanding what the change would mean. I mean, -- it's not like, again, every function within an organization has the same issue, right? I mean if you look at it -- if we look at a financing function, they will have absolutely different challenges compared to a product supply organization or a commercial unit. So it is really critical to really understand the change impact as such. And then the second aspect what we look at is defining proper like adoption strategy. I mean, when I say like a proper adoption strategy, what's key here is like what's in it for me, right? Because the stakeholders might need different things. And if it is not properly defined, if it is not resonating with them, then obviously, the data management practice is not going to work, right? So this is what the key step in the whole 4-step approaches. And the third aspect is more around building the excitement itself, because it has to be fun to work with data management. It shouldn't be actually an overhead because everybody benefits at the end of the day if the foundation is strong, right? Because there going to be multiple technology investments. And Raj, you mentioned like infrastructure modernizations and all that. But in order for all those to work, the foundation needs to work well. So what we now like normally see that works quite well is to kind of identify champions within the individual functions, who act as ambassadors and who kind of explains and ensures that the teams are staying motivated and really understand the value coming out of it. And last, but not least, none of the approaches our investments are meaningful, if there is no way to kind of measure the impact, right? So here's where I mentioned like soft KPIs to really like in a quantitative or qualitative manner to look at the impact that the data management is creating is key. And last, but not least, like continuous improvement because, again, data landscape of every organization is evolving. There are no like -- there's a huge volume of like both external data, transactional data and like critical decision-making data that the organizations are generating. So what I would like to mention as a last aspect here is that order for such setups to be sustainable, right? So I was once interviewing a group Chief Data Officer and asked him what a true success means to him in his role. And he said that it is basically when he's not needed anymore by the organization. And nobody even questions why data management is needed. So I absolutely loved it. And I mean like I second that kind of thought process, and that should be the way forward.

I love that quote actually. One of the things that I really like hearing you say, Gopi actually, is it keeps coming back, and I think keeps through everything that everyone is saying is focused on business goals, right? Like that it shouldn't just be an overhead, because I think a lot of people see data governance sometimes as bureaucracy, but you guys keep bringing it back to this business goals, focused on what's needed to drive business. And that, I think, is really important about bringing it back to that element. Raj, do you have anything to add based on your experience and things that you know from your work?

Yes, yes, particularly working with some of the EMEA clients, I think very well articulated, Claire, right? The business value came number one. Number two is how do you softly measure as part of that? And then the most important is motivated community of data governance team. That's the key elements, which we have also seen in some of the clients we are working. But most of the commercial world, right, they are driving digital data-driven initiatives with this next best action or omnichannel orchestration, trying to personalize the messages to the reps -- from the reps to the HCPs to other stakeholders. So out of all this, the most important is the data and the true part of that its how good the data is governed and the quality of the data, right? So this underpins the entire story about if you want to fast track or accelerating the data-driven journey, we need to go back in these fundamentals and make it strong, right? What I've seen in the operating model, 2 scenarios. Scenario one where -- because most of the big pharma companies have the call center at a regional level. So what we have seen is the European team, establishing a small governance council, trying to establish the framework, the quality checks, and they are collaborating with other regions like U.S. and EM region. So that itself become what Gopi was talking about, semi-formal, noninvasive data governance concept. And that's the scenario one. That's how we have seen some of the clients are working. And that's why you can see the 30% uplift in terms of established process. And the second scenario, what I've seen is there is a very good emphasis from the executive leadership. They have got Chief Data Officer in the team, along with that, there's a data -- Head of Data Governance, and that person takes the responsibility of driving the entire MDM needs as well as establishing the governance. And in a more flexible way. So it depends on the organization, honestly, Claire, in terms of what works well. I think these 2 scenarios, which I've laid out, I think it's working well based on the organization which they are. I think -- but very, in fact, business value is the key one from my side as a message, how do you take the data governance and articulating the business value for the business community.

I mean one thing I would like to add is like absolutely, there is no silver bullet, right, like Raj mentioned. And what needs to be taken into account, which is usually kind of an all like put on a back burner is the organization's culture. You might have like amazing frameworks strategy and all those aspects in place. You might even know what needs to be done, but then how we know like is where many organizations get stuck, right? So this is like one of the aspects that need to be mapped out and discussed like upfront, not after like defining the nice concepts and like doing several investments. So that is something I wanted to call out.

Yes. okay. So really interesting stuff from Raj and Gopi on the data governance operating model. And just tying it back to some of the things we said in the beginning and hearing sort of threads tied through about systems and implementing and access management. One of the things that sort of who has access to data, what systems it touches, that's one of the key things that a robust data governance strategy should address, right? And I have a business review survey in their assessment found that more than 70% of employees had access to some data that they shouldn't. And so knowing what data your company has, what systems it touches, who's accessing it is becoming a much higher priority within organizations. And some companies are adding platforms and technologies layering on that to manage and control that. And so aligned to that, we have another poll. And next Paul is to ask if you have a data catalog or a data intelligence platform as part of your data governance strategy. And so I'm going to open the poll. We'll give it some time to allow people to answer this one. [Voting]

I'm very interested to see the result of this one clear.

Yes, me too. It is something that we're seeing become more popular, so it'll be interesting.