Dassault Systèmes SE (DSY) Earnings Call Transcript & Summary

Good morning and good afternoon, everybody. Many thanks for joining us at the Morgan Stanley Conference, a virtual event, as you can see. My name is Adam Wood. I actually look after European technology research for Morgan Stanley. And hopefully, we've got a little bit of a different presentation for health care-focused investors today. Just before I go into the main body of the discussion, I need to get the disclaimer out of the way. So please note that this webcast is for Morgan Stanley's clients and appropriate Morgan Stanley employees only. This webcast is not for members of the press. If you're a member of the press, please disconnect and reach out separately. For important disclosures, please see the Morgan Stanley research disclosure website at www.morganstanley.com/research disclosures. If you have any questions, please out -- reach out to your Morgan Stanley sales representative. So with that out the way, let's move on to more interesting things. I'm very pleased to have Rouven Bergmann with me, who is the CFO of Medidata. Rouven, many thanks for joining me.

Thank you, Adam. Great to be here, and hello from New York as well.

Excellent. So as I was alluding to, I'm basically a software analyst by background. So I know a little bit about software. But before Dassault Systèmes acquired Medidata, I have to be honest and say, I didn't know a huge amount about the clinical trial space. And now I've learned a little bit about that. So what I was hoping we could maybe start off with. I guess a lot of people in the audience today will know a lot of our clinical trials and certainly more than I do. And maybe they know a lot about software as well or maybe not. But could you just start off and talk to us a little bit about what Medidata does in terms of the software that you bring to the clinical trial process? Maybe you start off with the core electronic data capture piece. And then once we've done that, we could talk a little bit about how you've been broadening the solution out to do other things, but that would at least help us get us on to the kind of same page for software and health care investors alike.

Cool. Thank you, Adam. And again, Hello from New York. Yes. Let me start off, maybe give a snippet of who Medidata is. I think, it's important how we define us. We are a purpose-driven organization. Our goal has always been, from the beginning, to power smarter treatments and healthier people and to bring novel drugs to patients faster. And this has always propelled us forward. The company was founded in 1999 here in New York and has grown significantly over the last 20 years. We went public in 2009. We have today over 3,000 workforce. That's over 3,000 people globally. We operate global trials across the world. We have almost 6,000 concurrent trials running on our platform right now. So I would say -- we're, to say, that the gold standard as it relates to technology and software to operate clinical trials. We have over 1,600 Life Sciences customers and have grown that installed base, 18% to 20% on average, year-over-year over the last year. So we've been really successful in capturing the market, not only at the top end with the large pharma companies, but also the biotech companies. And one part of our success story has been that we grew with the success of our customers. So I think one part that is really unique about Medidata is that our cloud-based technology, everything powered through our cloud. It's the same technology that our small biotech customers that are running 1 or 2 trials are using as well as the ones that are very sophisticated with a very complex portfolio of trials. So that scalability really has helped us to cover the market in all aspects. Now to your question more directly, Adam, about our portfolio, I always look at it in 3 dimensions. The first dimension is, this is the core data capture platform. It's the -- it is the transactional system on how you operate a clinical trial, manage the data from the enrollment of patients into the trial as well as -- now the lights going out here, I think I have to move. Okay. One second. I hope that doesn't happen again. Sorry. So the core data capture platform, right, which is really from enrollment of patients all the way to randomizing patients, right, because you have to decide, is a patient in receiving standard of care or placebo or receiving the novel treatment. You have to supply drugs to sites. So these clinical trial, the drugs for testing, they are produced in very small batches and need to be supplied. Sometimes, it's biology. So it has very short expiration dates that needs to be managed very, very tightly. We pay sites. We have a payment application to pay and reimburse sites with a budget application so that our sponsors can actually plan and budget clinical trials with sites. We do offer statistical analysis around the progress of trials and the central monitoring, which is very essential when you run a portfolio of trials. And the ability to detect outlines, for example, while the trial is ongoing in real-time, is also a key feature of our core platform. So that's really -- if you want to manage the transaction, I always -- I used to be in more -- in the ERP work. To me, that's like the ERP system. It's like financial and HR is what -- this is the backbone. But then there is a second dimension, which is really a core driver of our growth in the future, where we see the world is shifting from a site-centric approach to a patient-centric approach. So you're trying to -- we are connecting with -- we are enabling our sponsors to connect with patients directly, meaning there are sensors that ingest data at massive scale into our cloud. We use ePRO software. So these are patient-reported outcomes of patients directly enter data into forms that get extracted and uploaded into our cloud. So this is the new concept of virtual trials. So you have a part that's on-site and a part that's virtual and is directly captured. And that part of our business is growing the fastest right now as really the industry is figuring out how can you get front and center to patients. And then the third part is around data analytics and AI, right? I think one part that really defines Medidata is our data strategy and the data assets we have built since the beginning. I said we have almost 5,000 concurrent trials, but we also have 22,000 trials in aggregate since the beginning of Medidata and over 6 million patients. So essentially, what we have in our data cloud is the standard of care of a significant portion of trials that has been conducted by the industry. And now the ability to aggregate this data because we have, for the majority or from our customers' secondary use rights, so we can aggregate the data and can provide unique insights into this -- into clinical trial data. And that has been -- it's a new business, but it's very exciting. We have a lot of interest from top sponsors, but also biotech firms in how they can reuse data and can get access in the hands around data to speed up trials to simulate outcomes, to find the right sites and find the right patients to enroll them faster. So there's a lot of value that we can create through this network effect, right? On the one side, we capture the data, but then we analyze the data that we already have in our cloud, and we provide valuable insights so that you can actually improve in this cycle. And so that's, I would say, the 3 core dimensions of our business today. Medidata alone, right? And now of course, through the acquisition and the combination of Dassault Systèmes, that is now, of course, becoming much richer.

So that gives us a lot to dig into. But as you're describing it, it's basically a software system in a box that enables you to run the entire process of a clinical trial, being in the cloud, it democratizes it. So large companies can use exactly the same system, the small companies. And then you have this amazing ability to capture and start to use the data to then create a virtuous circle of improvement. That's a great description. So then that brings me at the end of your kind of description, you mentioned Dassault Systèmes. And obviously, Dassault Systèmes acquired Medidata. I guess for a lot of software investors, Dassault Systèmes was traditionally seen -- It was a design software company, but it broadened out into kind of a platform for design, innovation, manufacturing, but traditionally in the automotive, aerospace and in those kind of manufacturing fields. And so a lot of people might be surprised about what they would see in a clinical trial management software company. Could you help us explain a little bit why you were willing to sell to Dassault? What the synergies are? And what are the 2 of you, as you come together, what your vision is for how tech should work in the health care industry on a 3-, 5-, 7-year view?

Yes. I think that's a great question, Adam. And it's -- first off, it's a highly complementary acquisition because Medidata brings the deep expertise in the life sciences and clinical space. As you described, Dassault Systèmes brings unique expertise in simulation and design and manufacturing and supply chain management. And when you think about providing care to patients on a broader scale, right, one of the key challenges is how do you bring -- how you connect the patients to the provider -- to the care provider. In the life sciences world, right, and because there are so many disconnected parts to providing care. And if you can really look at the supply chain end-to-end and can simulate that from the perspective of the patient that's looking to receive dedicated care, right, that there's so much optimization that's possible, right? And there's so much cost that could be safe in the health care system, if you really would have a holistic data model to optimize this. Yes. And so you look at the industry, right? You have the providers. You have the life sciences industry. You have the payers on a broad scale, right? Medidata, traditionally, we are focused on life sciences. But within life sciences, we've been focused on testing, so clinical development. So one you research, you've identified a compound, you need to start testing it with patients through a very complex and regulated process to get the drug approved. And then you need to you need to manufacture it, and then you need to commercialize it, right? So we were -- even though we were the market leader in this very core process, there's a lot of pieces that Medidata was not able to cover in order to really have a holistic conversation around digital transformation of the life sciences industry and solve some of those really important problems. We talk about precision medicine, for example, on the other side, which is it's all about bringing the right treatment to the right patient at the right time. It's a -- if you don't have access to the granular data to identify who the right patient is, and then being able to manufacture this n-of-1, right, which is very specific because it's a targeted treatment. And you need to scale that, right? Traditionally, the life sciences industry has been looking at blockbusters. It was all about scaling it up and then moving it through massive distribution channels. That's not the model today anymore. The model today is to really be targeted, finding the individual patients in the data and then scale it through analytics. You simulate it, you model it and you scale it. And these are the core capabilities that Dassault Systèmes brings in order to really transform the supply chains of life sciences and that we combine with the development expertise of getting through the regulatory compliant process of clinical testing that Medidata brings. So there's so many complementary synergies. We've been almost since the close of the acquisition end of last October, we have now started to have much more conversations with customers combined to see the unique value proposition that we bring, and we see a lot of excitement and interest in really -- and really entering this really broad transformation road map, right, because it's much bigger than what we were able to do before. And so it increases our reach and our relevance in the industry. And I think I end with the following and say, if you look at the combination of Dassault and -- Systèmes and Medidata today, we are the largest technology and software provider for life sciences by IDC before SAP, Oracle and Veeva. So we're the -- we're with the largest customer base. And so it's really up to us to bring these assets strategically together and help the industry to solve these problems.

That's really interesting. And it's interesting is because when we talk to manufacturing companies and consumer companies, they talk about moving to lot size of one, the ability to personalize products and then manufacture them for individual customers. And actually, that's exactly what you're trying to solve in the pharma industry. So the problem has actually become quite similar between the 2 sides.

Yes. Exactly.

Very interesting. So you've talked about that presence in the industry and being the biggest company there and this big platform. How broad do you think your coverage needs to be of the industry? So I mean, for example, you mentioned Veeva that has been very successful delivering CRM into the pharma industry. Would that be something that you'd look into you? Do want to cover everything? Or is there a kind of limit to how far you want to go in the coverage of the market?

No, that's a really good question, Adam. And of course, for us, it's less about asking ourselves what Veeva does. It's more about asking us what is -- what do our customers need? It would always be a terrible idea to replicate something that a very successful company has already done. So we are looking in ways to -- I talked about how important data is for the life sciences industry. And our significant expertise in clinical development and the data that we've collected along the clinical development life cycle and process. And the other side, Veeva traditionally has been more on the commercial side of pharma, but not really on the data side as we are. And so we've made -- in 2018, we really looked strategically at where we are positioned as Medidata and how we are going to expand our relevance from the clinical development into the commercial space. And the way we've seen the industry really figuring out this problem and how to accelerate time to market in drugs because that's ultimately what you are trying to solve for, right? It's not about, okay, I develop in CRM system, but what's the problem that we're going to try to go after? The problem that we're trying to go after is how do we help our customers to accelerate growth? How do we help them to gain faster return on their investment? We've already done this for the research and development side. And now we need to transition that into the commercial side. So our commercial data analytics platform where -- with the investment we made when we acquired SHYFT Analytics in Boston in 2018 enabled us to really expand our platform and capabilities to ingest real-world data, merge them with the clinical trial data to help our customers to identify patients post drug approval. Now the lights are going on again. So this post-drug approval life cycle is very critical because when you look at -- not every pharma company has large sales forces, right, that they can send into the field. Many biotech firms are really constrained with their sales force, and they need to be really smart with the data or to find where -- to see where the patients are. Also rare disease companies, there is a scarcity of patients. But there's a lot of value in developing these drugs. But ethical value as well, right, not only a commercial value. It's an ethical value to respond to these needs that patients have. So you help -- you have to help to find these patients in the data. And that's what this real-world data platform has enabled us to do. So when we really think about the -- our role in commercial analytics and in the commercial life cycle, it's around analytics, understanding the data, identifying where the patients are, that are in need of these drugs, identifying the sites and the doctors and inform the commercial strategy to go there, right? It's -- that's the path we are on today. We have over 20 customers using already this application and platform. The Acorn AI business that we have founded in 2019 has taken that under their wins to further expand use cases and the relevancy. So we feel very excited, but it's at the same point in time, super early, but it's a fast-growing market, and it's so natural to combine the clinical side with the commercial side and manufacturing. So that's how we really think about the expansion and the relevance of our business to solve these at the ultimate problem for the industry.

So it's also interesting that you said you came from an ERP background in the past. And I've been having discussions with software investors for over 20 years around suites versus best-of-breed solutions. And what you have described is a suite of products that can solve a big problem for a broad range of life sciences companies. Could you talk a little bit? You obviously face best-of-breed competitors in individual niches on that platform. Could you talk a little bit about what you think the benefits are of running suite versus best-of-breed? And would you generally describe those point -- those solutions that you have in each area as best-of-breed or other areas where you need to build out and improve or maybe even acquire to fill spaces?

Yes. Great question, Adam. I think specifically, when you look at a regulated market, right, I think it's even more complicated to work with point solutions because the regulated market requires you to have is -- it -- an audit trail of your data, right? And if you have your data are in different systems, you have to reconcile and rebuild your audit trail. It's very expensive. And then you think of the wealth of the data that you have captured and the information that you can draw from it into a database, that's very valuable over a period of time, right, also forward-looking. So when you think about your data model and you have it in different parts, I think it's very hard to overcome these inefficiencies. So we have seen the market actually turning from point solutions to an integrated unified platform. When you -- when I outlined our offering at the beginning, the 3 dimensions, I started off with the platform. That's where we try to unification, and we see that there is significant advantages in terms of simplification, Adam, right? It's really you want to have a unified platform that simplifies the process end-to-end. You want to be able to have one data model because, otherwise, you have to clean your data. You have to have data operations. You need people to actually do this for you. This doesn't have to be if you really work in one system. And then having also the core transactional or -- and the analytics part in one system is also creating a lot of efficiencies. So I think our strategy has always been the platform approach. And I think it's a journey because the capabilities of the platform continue to evolve, you've never finished, right? And our argument is with -- against point solutions is you have -- really have to look at your end-to-end process and the outcome that you're trying to achieve, and we can deliver that out of the box. And when you have point solutions, in order to connect them, you also have a lot of manual steps in between. I think one of the parts that we are very excited about is besides the point -- competing against point solution where we believe we are superior, we also are seeing the need to digitize a lot of manual tasks and spreadsheets that are still existing. Silos that still need to be connected. And the best way to do it is to not have best-of-breed systems running different silos is actually build a system of record across the enterprise. That enables the different parts of the company to work effectively together.

And maybe just in terms of the competition you face. I mean, we've mentioned the best-of-breed. Is that primarily who you're up against and who you'll compete within the market where it's a point? It's not someone else who has a vision as broad as yours and platform as broad as yours.

Absolutely right, Adam. I think when we win also big enterprise deals, it's about the broad vision, right? It's -- there's always been a perspective that people say EDC. So the electronic data capture part, our core database, and has over the years, spoken to a lot of analysts and investors, where there's always this hypothesis that the EDC system is commoditized, right, but the opposite is the case. If I see the data, I know our ASPs, we've been really successful in keeping very healthy ASPs and continuously increasing the value of our platform and our core data capture platform because of all these -- the integrations and the unification across the platform we offer, right? It's the -- it's a network effect, right, of all the other pieces that you get, right? It's like I buy an Apple PC because it's so deeply integrated and so easy to use, right? It's -- and I'm willing to pay more for it. There are so many examples that are speaking for this strategy and see that how it works. And yes, most of it, it is -- there are still a lot of RFPs, to your point, right? Where the industry goes out on very pointed solutions and problems. And it could very well be that for a single point solution, there might be alternatives, right, that you can consider, right? It's a market and there are different players. But at the end of the day, if you broaden the conversation on how you need to connect all the parts to deliver the outcome, I think that's where we -- most of the time win. And we have a number of cases where, I think, big RFPs resulted in that and customers came back to us and said, "Yes, we're not just looking for capturing data, we are looking for the ability to analyze the data. We are looking for ways to harmonize all the different systems that we have." And now of course, that equation becomes much more powerful because of all the pieces that we cannot connect from Dassault Systèmes as well.

It makes perfect sense. That's very helpful. And one of the things that's interesting, you -- we've talked a lot about data and AI and the ability to use that and the value it brings. So really 2 questions that I'd like to ask on that topic. Just first of all, on the data itself, how easy is it for you legally to use the data that sits in your platform? Does it belong to those companies that you serve?

Yes.

Does it -- you -- how can you take in and use that value? And then I've got a follow-up after that.