Good afternoon for the ones on the webcast. I'm Francois Bordonado, the Association from the Investor Relations team. From the company, we have, for this 1st Life Sciences Day in New York, then Alice, our Vice Chairman and CEO Pascal Daloz, Dassault Systemes, CFO and Chief Strategy Officer, Thark Sherif and Glenn Duvries, Medidata's founders and co CEOs, Woveren Bergman, Medidata's Chief Operating Officer, Clervio, VP, Life Sciences, and Jason Benedict, Biovia, VP, R And D. I would like to welcome you to Dassault System Life Science Day. At the end of the presentation, as you see in the agenda on the screen, We'll take questions from the audience.
Please note some of the comments we'll make during today's presentation will contain forward looking statements. Which could differ materially from actual results. Please refer to our risk factors in our 2018 document referrals, Let me now introduce Berna Chales, our CEO, and best Chairman.
Thank you, Francois Jose. Good morning to everyone. We are very delighted to have you here for this morning. With a rich program, I have to tell you that, over the last 2 days, I've been learning much more about life science and what people are our customers and partners are doing with BDData. And, it's a good training class for me.
And we want to reveal to you, a little bit more as committed, but what we want to do in the world of life science, because I think this industry, need to change, to say the least in my mind. And I'm ex we are excited, and I am very excited with that perspective. This we have not done a a light study about that that sector. We started 14 years ago. We started really in 2009 with secret projects, and we'll talk to you a little bit more about those in a moment really to understand and socialize with the sector of both life science on health care.
So the first thing I want to, tell you is ADASO system will make bets and we formulate them in a very clear way. And we did that for the last 5 years. I'm answering that more for the the newcomers who necessarily knows us well. The first that we did is to say we were, we're going to unfathom the world by moving drawing system through 3 d designs. It's a little bit like that.
Today, we have built the world's standard in cross many industries, I'm still a lot to do, but there is no question anymore about the fact that this is a must, especially in what we call the fabs here, the industry at large. 10 years later, we did another bet which is represented here, mostly from Google. So it's working, which was to say, well, can we use this to do the digital mock up. We call it the digital mock up of an entire highly complex product coal and airplane. This was done with the, the, with Boeing on the 777.
You have to know, it's a 15,000,000,000 program. 40 countries, 7 years. So I'm not impressed with the numbers in life science. At all because they talk about 1,000,000,000, 1,000,000,000, 1,000,000,000. When I listen, they get they tell me it's very, very high number.
I said, yeah, it is in some way. After they tell me it's very complex, I said, yeah, in some way. And they say, well, by the way, we have a lot of people involved. I say yes. I agree, but I think things can change.
But there is a before on and after 1989, because at that point in time, the entire world industry understood that the digital world could replace physical prototyping in a big way. And it has created an incredible wave of transformation of the world industry. It's established. There are proof points on it. The automaker started to do it slowly.
On the nature of what we do, and I want to mention that now, since the beginning is one. Provide science based infrastructure to manage complexity. Enabled to do highly complex collaborative process management of multidisciplinary knowledge and know how, and use that digital world to life cycle things to add the time to it. So that was 1989. Then we made another bet with another company called Toyota.
And we said, we want to do digitization of your entire production system. You noticed that the Q3, we mentioned that they are, going to the next generation. That was not a minor news. It was small announcement, but big news. And we demonstrated to be aware of that we could do the digital between the virtual twin experience of entire global complex production systems.
On February 9, 2012, we published a two page paper where we said the new equity of the company will address 3 spheres, the fab sphere, the bio sphere and the geosphere, We're putting those tiers away to look at the world in a way where we said the innovation should be at the center of this understanding about the bio world, the fab world on the geoworld material science That was another bet, but I think we are working to talk with all the moves we have done since then. And this was February 9, 20 twelve. And we said, well, focusing on product is not enough. We should put the things upside down. We should create universe where we can formulate experiences, the value of what is used in the economy as opposed to what is sold in the economy.
And that's why we call the platform, the 3DEXPERIENCE platform. Well, you can understand now with the little symbol we have that our goal is to do the virtual twin of an entire human body. That's what we're going to do. We believe we are the reasons Good reason and proof points to make it happen. We have started with our games.
We are, working on human cells. And what you're going to see this morning is related to all the pieces coming together to make this possible. If I was starting on that point, you would say, well, That's the track record from the past, but I believe that those kind of beds are very clear, very precise and I want to go through them quickly. Before I go in the life science by itself, I just to remind you, we're there in June for the 2018 that when we published when Pascal presented the growth plan for the next 5 years. This was almost without to life science world.
There was a little bit of it, but Pascal will come back on it. So this is not new. This is what you have seen in June, We think we can continue double digit growth in, if I shortcut it, the fab spear, the world of the make. Design, simulation, creation, prediction of products and solutions. So that's a baseline for us And what we're going to talk about today is really what is our ambition, midterm and long term for the life science basically a new core for the ecosystem.
Briefly said, We are in the yellow sphere with many industries. We call this fab sphere. We have initiated activities with BioVIA on, of course, Medidata. We position that set of solution in the bio sphere on, as care. On the geosphere, is really related to areas, cities, how you bid the world for citizens, including energy is being put in that that world.
On the numbers here are roughly the GDP numbers in the world economy, Pascal will come on the trends on that standpoint. What I'd say is that the new core is not small. The second thing it says is that the new core from my observation in the last 10 years meeting customers is far behind in understanding how the digital world can help them. These enterprises are document based, great PDFs on digital documents. They are prisoners of office documents, and they don't see the capacity of modeling simulation on data science yet, statistics a bit, but not much more.
So 11 industries, 61 segments, You have 7 of them on the fab side. On the bio side, you have un halved and then you have the ulcer. 3 in the world of what we call geos here. So for those of you who are following us, you are very familiar with that. What I want to mention here is that we have a very precise map of solutions, what we call industry solution experiences, industry solutions is measured by the outcome to the companies.
Developing a car from 17 months to 15 months or 12 months. That's an outcome. And this is a real number. That has happened in the last 15, 20 years. Same for aerospace, Marine And Offshore, Industrial Equipment, etcetera.
So outcome based. We have 3 measures, outcome based performance for teams to do collaboration. We call them process experience. And then making the users champion in what they do on defining their future job on what we call these workforce of the future. With roles.
So we basically deliver solutions, which are based on 3 things: roles, process experience, industry experience. If you want to develop an airplane today, you don't need to go anywhere else, but contact us or system. 9 out of 10 planes in the world, other with Dassault System software, 8 out of 10 cars. I think we are expanding. That's scope.
I'm going to do analogy here. So we have moved from, functionalities. To outcome, process, or all based APIs. That's for what we have done up to now. Now I'm moving to the new core, which is life science on trying to do some analogy.
First, I think it starts on the very strong elements of our equity system, the virtual world, external improve the real world, and I will show you why. That's who was published on February 9 2012. So now we understand and many of the moves we have done are associated to the journey and the growth plan on the value map on those three tiers. When I am asked a question about where is the system going to invest? What we do?
The framework has been established. In 2012, and we usually follow it with high precision. Under our purpose, because we are a purpose driven company, amonized product, nature, and life with the virtual universe. I think it goes a lot of what I've heard yesterday with, the great customers that you have, direct on, on Glenn, built excellent relationship with over the last 20 years since you created your company on Green, improve. We have a common culture.
You have developed your group. I have done the same with my team. So we understand what it is to develop a company from a few people to what it is today. And I think that's was very probably the first criteria for us to decide to come together, but I'm sure you will talk about it in a moment. So 2009, we started a bio intelligence project we said, we are going to put science in this process.
Then we move and do the acceleration. Which is an extremely powerful platform for material science on bioscience, by the way. I present to you something constituted of 28 elements. Myself. You are 28 elements of the Mandalayev table.
Not 29 not 26, only 28 elements of the the Mandalay affair table makes a human. On its 58 elements here. 58 year 28 year. Think about it. People don't think about it that way, but that's an interesting prospect Very interesting.
And by the way, I mentioned mine delays because it's, the 150 years anniversary of the Monday Mondeleev Table this year as it is the 500 years of the deaths of Leonaro da Vinci, my good friend. I am a pupil of Leonardo Dovinci. So acceleris, bioscience, material science, understanding from the molecule how you create new things, whether it's living tissues or new material science or additive layer manufacturing on users. And this is a serious platform which is already integrated. It was a core move for us.
Then in 2018, we revealed the leaving out program, on many users that the team will talk to you about. And now we are in 'nineteen with, Medidata. So what is the play here. We have built a platform, which is experience based. The beauty of experience is when you see an experience, You understand things.
You can capitalize our knowledge and know how. As Einstein said, the ultimate of experience of knowledge is experience. And I will add the ultimate of know how is 3DEXPERIENCE. But in any world of virtual experience help people to understand the phenomenon. But more importantly, if it's presented to a group of people, multiple disciplines that discover it in a different way.
And then the platform as a business model, you are aware of about the marketplace, you are aware of about how we want to do the a zone of production for manufacturing and it's moving toward that direction. So one click away, you can send a digital design and get the physical part those are things that which are happening. So how do we do that? We have invested massively in science. What we call multi discipline science.
And that was for the fab world, the manufacturing world, We are doing the same now for human. And you will see concrete example this morning about how is this being applied Living Heart being one of them on, as you know, SDA loves the Living Heart program because they think it's going to change the world of, surgery, especially vascular surgery, and you will see a few things here. So It's about dermatology, neurology, cardiology. You see here the white spectrum, and we use some of those science based modeling on simulation to serve those things. The interesting news, it works.
We have proof points that it works. So that's what we want to reveal. Now if I look on why we are connected with Medidata, is the way I look at the life science world on the health care system today is there are far expensive as compared to what is the outcome. Those companies have been behaving like very rich companies. It has happened before in other sectors of the industry.
And we have seen how it could change quickly. The way I would re summarize it is the world has been based on small molecules, chemistry, in the farmer. So relatively simpler complexity in the development, gigantic complexity when it's applied to wide population. But since they're coming up upside down, And this is the blockbuster story on all these. And so you need to make money with 1 because you lose so much money with so many that fails.
That's the model today. That's the way I see it, and I don't think I'm so negative. If we were doing airplane this way, we would do 100 airplanes, put them in the air and say this one is flying. I'm going to produce this one. All the other ones that have dropped, have dropped.
Of course, it does not work this way anymore. But the point is This is shifting with biotechs, especially on the coupling of equipment on biologics. It's shifting to higher complexity into development for smaller targets of population, targeted population to an extreme of individual personalized And those companies are used to manage the complexity upfront. They are used to manage the complexity or evaluate the result At the end, not at the beginning. So it's upside down on the process of development research manufacturing will change in a big way.
That's my bet. I'm taking it if you are here, You have to decide if you take it or not, but we are going to make it happen. And if I look at it's even worse when I look at the pa- the stupid patent process, Of course, when you have a simple things, you want to protect it because it's so simple. That's the reality. But if you do very complex things, you don't even need to go to pattern them because in fact, I recommend you don't pattern them, because people will not be able to replicate them anyway.
So things are changing from that standpoint. And then the pattern thing, you know, 20 years, if you take 10 years to, to develop it and, on, on, on, on, do the clinical trial, you have only 10 10 years of life cycle. So it's really a race. And the numbers, those are typical numbers for us. So we know how to work those companies manipulating those cardinal numbers.
And I believe we can help them go and look at the world in a Deepgram way. So here's four things that I will briefly before I conclude tell you about. There are 4 focus in what we do. On the north side is multi discipline collaboration, making sure that all specialists of different disciplines would don't talk to each other, don't understand each other, can finally understand each other with high clarity. And you will see illustration with Claire, on the team, on Jason on that topic.
That's the collaborative process. I visited many companies, very large companies in the life sector, life science sector, I am astonished with the poor environment they have for multi discipline collaboration. It's document based. They don't find the documents they need when they need them. Difficult to read.
Nobody reads them. It's very artisanal. Then on the west side, of course, you have the representation of things on phenomenon. When we started to say that we would be doing a digital airplane, everyone of the top specialists said we will not succeed because it's far too complex. Cyber dynamics is too complex.
The problem is not to be perfect. The problem is to reach a level where a group of people can imagine new solutions just because they see in front of them something which is not far not too far from the real phenomenon.
And that's the power of
Corabatic process. So representation. Of the phenomenon. And then big data analytics, we are big data company for 35 years. I don't know if you have an idea about the volume of data generated by our clients.
They are just gigantic, gigantic volumes of data. Including life cycle of the data they do for the products they do. So we know how to manage very gigantic, large, data structure, highly complex on 80 engineers. And it's not with documents. Which is true representation, what we call modeling on simulation.
And then on the south side, you have the computation between what you think is the representation of the phenomenon with the real phenomenon as it is observed Welcome to Medidata because there is an arm we don't have. Which is all the real data from clinical trial, on confronting this. To the new representation of the world for Life Science is going to be exciting. And this is a video that represents all the process briefly, as I'm going to conclude with timing just been. Before I give the floor to Tarek, and I think you guys need to push maybe once more.
So here is a bigger situation. Corporation, we are from the living grain hole This is the brain. You see a cooperative environment, collaborative environment going on with all the specialists the deep different disciplines. We represent the brains at the level we can represent it. The professors are discussing about this.
They are preparing the process by which they are going to put electrodes in the brain. The problem with the brain is it's moving the head. So it's quite complex. Then you connect this to a mass of course of data, the, individual data profile. Of course, you plan this you connect this with also disease or also problems that might have happened before.
You basically take a system approach.
To what you are going to do on the human. And we take, of course, the scans. We build automatically the 3 d views, like in this case, vascular with, coronary, coronary flows, like, constitution. This can be done in a few minutes. Today, the cycle time for this job is several days.
I've heard about the startup in Boston, trying to do that, and maybe wanted to do an IPO. We need, we need to do, faster because I think our technology is far superior. We can we can, rebuild the vascular in a few minutes. Under the have a very accurate flow in vascular. This is the DNA connection, with, the OSA data, and we connect those elements together And this is a real corporation going on.
In fact, the platform for living brain, I think, I think if I, I think Johnson, the director of research, Cindab. We started together the biointerregions project. We are doing that, and we are starting clinical trial off to get her performance set for the virtual brain right now, as we speak. So you see the process here. So you can understand that if you some of you knows how it works today based on documents and publications, based on big papers of data, on graphs.
This is a new world because you have the world of collaboration the world of passion's journey, the world of modeling and simulation, the world of, real data coming from clinical trializers. So welcome to the new core industry for Dassault System. That's the summary on why we are making this. It has been well prepared And with that, Tyre, join me to tell us how great you have been doing things. And I'm so pleased that, you are here on that.
We are a good dessert.
Me too. We are so excited to be part of the Dessel family. And thank you, Bernard and Harshari from Medidata's co founder and, co CEO. I think just as I thought, you know, after a year of 10 years of being a public company, I thought, Okay. No more analyst presentations and investor presentations.
And then Bernard and Pascal pulled me right back in. But it really is a pleasure to be here and to join, with all of you today. I'm going to give you a little bit of an overview of Medidata, and I think, fill in some of the faces that, Bernard created in terms of talking about what was the rationale and what is it about Medidata? Who are we as an organization and why we're so excited to be joining forces. I think in order to get a sense for us as a company, it's important to understand our mission.
From day 1, we cared about building great technology that would impact patients' lives. And that's something that, underlies our entire culture of our business. The people we attract the customers that we work for, they all care deeply about impacting patients' lives. And I think in a small way, we've been able to do that. And I'll walk you through a little bit of the history of the company.
What brought the 2 companies together was a shared vision And that vision is that there's a big transformation that's happening in the world of life sciences, in the world of the way therapies and drugs are discovered. And we both as organizations have a passion for innovation We care deeply about impacting society. And when we saw that that vision that we have that we could accelerate it together and make it more of a reality sooner, that convinced both sides that we should come together. And I think that's what we're so excited about. Because the words get used What precision medicine means that you're targeting smaller and smaller groups of patients with highly tailored therapies, ultimately getting to the individual as having a tailored therapy just for them.
The entire industry of life sciences is built around an entirely different model, and that model is around coming up with this model, the infrastructure, all the processes are geared to mass distribution, not to the concept of precision medicine, But the reality of the science, the kinds of improvements that we're starting to see with things like CAR T therapies, are that when you do focus on small groups of patients with specific biomarkers or even on an individual, the results are remarkable. You start saving people's lives. And so the transformation that we together want to enable, and I think we as you hear more of the presentation, and you get to know us a bit more as a combined organization, you'll understand why we are best positioned to help that transformation to happen in life sciences because it is going to happen, but it needs the kind of company that does sew, along with Medidata all the and Biovia and all the other solutions that we have, we need to come together in order to make that transformation a reality. So just, a little bit about, Netidata's, from the very beginning, we were a cloud based company, a subscription based company, And that's something that we're bringing, into the Dassault family.
Give you some stats on the business. Currently, we have about 5,000,000 patients' worth of data. Now those patients are, some of the sickest people in the world. They have rare diseases. They have cancer.
They have all they've gone through clinical trials. And I'll talk a little bit later in the presentation about why that's so important. We're about a 3000 person organization We're global in nature. Clinical trials are run around the world, and, we have been the, the software infrastructure that has run clinical trials in over 100 and 40 countries around the world. An interesting stat in 2018, 13 of the top 15 revenue generating drugs in the world have been developed on Medidata software.
So we are a very key supplier to the life sciences industry, to pharma, to biotech, and to device manufacturers. In total, we've run about 19,000 trials, and I'll give you a little bit of perspective on that later on. And we have about 1400 customer, and that customers, and that number has been growing fairly rapidly. Just to give you a little sense of the business since we began. So we started with a fairly simple idea back in 1999.
And that idea was to harness the power of the Internet to take what was a very manual, slow process and bring the digital world to it. And that in what, what the core of our business was and continues to be something called electronic data capture. It's the idea of bringing data in using the internet helping to manage that data, that was a paper based process back in the 90s and before then, when you were developing new drugs in the clinical development So so the area of drug development that we focus on is when the the work is done in the research lab and you begin to do testing on patients. So that's the clinical trial phase, and and I'll get a little deeper into that. So we saw an opportunity to take something that was a very manual process, very error prone, very lengthy, and very expensive, and harness the power of the Internet and software to make it much more efficient Over time, we saw other areas in the clinical development process that were equally inefficient and manual.
And we started to apply technology to those, and I'm not gonna walk you through all the different solutions that we came up with, but I think you can see that over time, we started to build out our footprint within our customers. And that's very important when Rubin walks you through our business model, you'll understand why we had such great growth over 20 years and why we maintain sort of the strong customer relationships that we have today. Over time, what that led to was us developing a platform that you can view as the system of operations within life sciences. And on the left hand side, what you'll see is all the various inputs of data that are required when you're running a clinical trial. It may be data from the clinician who's running the trial.
It may be sensor data more increasingly from patients today, it may be images, it may be genomic data, it may be consenting to being in a clinical trial or lab data, all the things that our inputs to making a decision on whether you should move forward with developing a drug or not. On the right hand side, you see some of the new data flows that are coming, maybe from EMRs, maybe from social media or claims data, That's the kind of data that you need to enrich the decision making process in clinical development. And obviously, you need advanced analytics to make those decisions. So some of the strategic pillars of our business are to be that operating system at the core of the decision making process for our customers. So we are the technology that they rely on, much like you would rely on the phone service for your communication, we are the core infrastructure and to make the decisions on whether they should move forward and how to move forward with developing new drugs, which, as you know, is critical to their successes, obviously, a very strategic role that we we have.
We surround the technology that we develop with best in class services, we live in a very domain specific vertical where domain expertise is very, very important. And we've spent 20 years building the knowledge to be able to serve our customers effectively because providing technologies not enough, you have to make sure that you deliver the value based on the service that you deliver with that technology. And I think we have a very strong reputation for When we win customers, we never lose them. Our customer attrition rate, we used to report it publicly, was less than 1%, much less than 1%. So our turnover in customers was less than 1% for most of the history of our business.
So we were very sticky, but part of it was part of it was delivering very technology, but part of it was the delivery at self. It's the service people in the frontline who our customers trust. Increasingly, an aspect of drug development that I think is a little bit less appreciated, by broader audiences is that the patients are getting much more involved in drug development. So they are inputs into the development process now. They wear sensors they provide they provide objective data back through diaries that they fill out.
There's more data that allows you to make qualitative decisions but as you're targeting patients in smaller populations. And then and we'll we'll you're gonna hear a lot about this throughout the presentation. I think the obviously, we're in a data business. And as as Bernard pointed out, it may be drug development and the entire process hasn't been as efficient as it should have been. Well, for a process that should be based on data, The analytics have not been very advanced in our industry, and that's something that's changing right now.
The advent of artificial intelligence much more rich data sources, that's bringing about a renaissance in how people think about drug discovery and drug development. And I think we're at a very interesting time coming together to enable more of that to happen. So I would say that, in terms of thinking about the life sciences industry overall, it's probably one of the best times in a century to be in the drug development business. And so it's also a great time to be in the business of providing technology to companies that are developing these drugs. So obviously, it's a huge market, right?
$1,200,000,000,000 or more gets spent on therapies and drugs. And that number has been growing very rapidly. The process of developing drugs is about $100,000,000,000
business plus.
One of the interesting facts about that is technology plays a very small role right now. Think I saw a statistic, a couple of years ago, actually, that said behind the the U. S. Government, pharma was the least advanced in adopting cloud technology. All other industries were further along.
And yet, they spend so much money on developing drugs. And there are a lot of compounds that are in developing 16 1000 different drugs that are currently being developed. And when you look at the and we'll we'll go into some of the issues in a minute. When you look at the number of drugs that actually come to market in a given year, the FDA approves something under 100 drugs a year. So think about that 16,000 drugs being developed, but in any given year, in a good year, it's 100 drugs that are making it to market.
And that tells you something about some of the the the inefficiencies and the problems involved in the drug development process. But before I go into that, I do want to reiterate something. Pharma R and D has very long cycle innovation waves. We came off of 1 in the 2000s, where there had been a series of blockbusters that came out in the 90s that drove growth for the pharma industry And then that cycle ended. There wasn't as much innovation.
There was much more focus on cutting costs on M and A and bringing the industry together and consolidating. Over the last 5 years, the next wave of innovation has started. And these tend to be, call it, 10, 15, 20 year cycles. You're starting to see a lot of innovation coming out of the life sciences industry, that innovation is very good for patients, but it also means there's a lot of opportunity for companies that can help, pharma companies transform, and that's what we get so excited about. So there's a very, very long opportunity that's starting to, to open up in front of us.
Let's look at some of the stats. So you have a 1 in 10 chance of having your drug that you went through multiple years bringing through research to get to a phase 1 study there are 3 phases that you go through before you can commercialize a drug, typically. Sometimes it's a bit more than that, but just to leave it very simple, you have a 1 in 10 chance of getting it right and getting this drug to market. So as, as Bernard said, you know, what could you imagine if you design a plane have, like, a 10% chance that it's gonna fly, that those are not good odds. They spend a lot of money to bring drug to market, $2,600,000,000 is not sustainable because, while it's not as much as an airplane program, there are people who run a lot of these programs, so there's a lot of money going into the development process.
But if you're targeting smaller and smaller groups of patients. It means that revenue on the other side is going to be smaller. If you develop a blockbuster and you can sell it to millions of patients and it drives $10,000,000,000 of revenue, well, then a $2,500,000,000 investment at the front end is easy. But if you know that the maximum revenue you're ever going to get because it's a small group of patients that you've targeted that have the right biomarker profile.
You
maybe you can only spend 2 $50,000,000 or $400,000,000, the industry is not currently equipped to spend less on smaller targeted groups. The timelines are because it you would expect and as it should be. Regulations have gone up, and I think that's been a good thing for the most part, because it means we're bringing safer drugs to market. And I think the the the the one of the other annoying or difficult fact is that even when you get your drug right, even if you've gotten all, you know,
you've you've
managed the risk, you've gotten it to market in a timely way, Only half the drugs that come to market ever achieve the revenue potential that was forecast for them. And there are myriad of reasons having to do with reimbursement, having to do with marketing, etcetera, that may may cause that. So overall, what you're hearing is it's an industry that is, as Bernard likes to say very wealthy, it's cash flow rich. It drives a lot of revenues, it drives a lot of profit profitability, but it has some systemic problems, especially in an era where the underlying science is shifting in a way that they have to transform. And I think that, you know, to us, that means nothing but great opportunity.
And the opportunity comes in a couple of different ways. So our value prop, both singular medidata as a standalone company and now together with Dassault is actually pretty easy. The first one is we're gonna improve productivity So that means we're gonna help you to get your drugs to market faster. We're gonna help you to do it at a lower cost, which, obviously, you have to be able to do. We're gonna help you maximize your ROI.
So you get the return. You get your drugs to market faster. I think one of the other things that we've that we have as a value proposition is using data in a meaningful way to help you make better decisions, to reduce the overall risk when you are bringing a drug to market or when you are bringing a drug out of research into the development process. And ultimately, we want to improve outcomes. We want to make sure that the right drug goes to the right patient at the right time.
Couple
of things about, Medidata specifically that make us unique is I mentioned earlier that we have 5,000,000 patients worth of data. That data is incredibly valuable. About a decade ago, we started to ask our customers for the right to use their data on an anonymized basis, both their operational data and their scientific data. And it's a data it's it's, an amount of data that cannot easily be replicated by anyone else on the globe. We have data that is global in nature.
It comes from every therapeutic area. It's Cross Industry And most importantly, it's very rich and deep. So we know everything about a patient in a clinical trial for the period that they're in that clinical trial. We know every measurement, and you can use that data to drive real meaningful insights and again, I won't go through all of these, but you can help to, to, demonstrate the value of the therapy that you have in development by comparing it to the standard of care. You can make better decisions about where to target which doctors to target or which patients to target in your clinical development process, which means that you'll be able to get your clinical trial up and running faster and done faster, leading cause for delay in clinical trial and higher costs is that you can't accrue enough patients.
We can help you make better decisions about where to find patients. We can help you make better decisions about whether you should proceed with your drug development process or not, and we can help you to explain to regulators and to the payer community and to the providers why your drug that's in development currently is better than the standard of care or anything else out there. And those are very valuable insights that our customers have never been able to get before anywhere else, and they can get them from Medidata today. So I'm gonna ask Reuben Bergman to come up for a minute and just, spend some time explaining our business model to you.
Good morning, everybody, from my side. And, you saw Tarik talking about, our strategic pillar what differentiates us as a company, but I would like to spend some time on this to explain to you what are our strategic components of our business model. How does Medidata actually work? So one of the really important, concepts we have It's the land and expand model, and that's built for long term and durable growth. And there are 5, vectors what I would call it that are very important to understand about our business.
The first one, so it's good to start with customers. You heard about 1400 clients that we have, when we started 20 years ago, we started with the first one. And over time, we saw also the chart that Tarek showed where the curve was going there's an acceleration of customer growth in the last 2 to 3 years. So what's so important with this customer base is that it captures all segments of the market. So we have customers in the top segment, so the largest pharma companies in the world that run the most complex portfolios of clinical trials across many therapeutic areas, med devices, pharma, everything together under one roof but also we are able to cover the biotechs.
The companies have just started and have 1 or 2 trials. And what's really important to understand is that we serve this market with the same standard solution. So our cloud scales across this vector, right? So there are not different sets of metadata version that these companies are using, no, they are starting with that system, and they're scaling their growth with our platform over time. But that's very important to understand.
The ecosystem, we say here, 10 of the top 10 CROs, very important because those, clinical research organizations, they give us access to our biotech market. They serve the industries and operationally running clinical trials. It's very important to work with them. And enable them to be more efficient. And, for us, it's also a very efficient way to get to market, and addressing, the biotech without having to have a direct sales force that covers all these small companies.
The second vector, revenue retention you, Tarek talked about the revenue retention and the record levels of revenue retention that we've been enjoying. So really what I think is important for you to take away from today is that when we have a customer, the customer stays with us. And that's reflective in the numbers. And even if we have a customer, we have a track record of expanding our relationships with these clients. So on average over the last 2 years, we've been able to expand revenue when we renew with a customer that revenue commitment on an annual basis So on an annual basis, like for like by over 25%.
So going through a renewed cycle is for us a source of growth because We can work with our clients to expand our share, wallet our relationships. Our product is very sticky. And you saw also the back the journey of innovation that we have. So we have multiple products that we can offer as part of our renewal cycle. Long term relationships.
Typically, our subscription contracts have a duration of 2 to 5 years, And, then we renew our contracts. And so that's, that's, this gives us a lot of visibility in terms of our revenue model. And they expand with us, over time. And, one point that I don't want to lose mentioning is the differentiated service offerings that we have, which is very important not just from an implementation perspective, but these customers stay with us to have ongoing support services that they that they buy from us. So they really rely on our capabilities to help transform the clinical data management and operations.
For revenue mix, something we are very proud of. We've been, over time, a able to bid a very stable model, about 85 percent of our revenue is cloud subscription based, with a high subscription gross margin, and 15% is services revenue, of which about half of it is recurring support services. Also for those type of offering, we have very good visibility because they go co terminals with our subscription revenue. And, the last point, I think very important and a source of our success. And, I think we, we, as many of our team, are very proud of our pricing model, because the way we've designed it, it is transactional consumption based.
So we essentially what this results in, as our customers are growing and are running more trials, we are able to grow our business and our revenue because how we price our service offerings or our product offerings is based on the number of trials that they have a subscription to use our platform for as well as the number of patients they can enroll, or the number of sites they actually enable to enroll patients. So it's very granular, defined, and it gives an ability as we grow our ecosystem, we grow our revenue. So I think that's 5 very important points to understand. And so when you put those 5 vectors into action, what does it result in? First, we are growing our customer base.
You see that. And, 2013 to today, it's a 3.5 times acceleration in a number of customers. So we are today, Medidator is the standard, the gold standard in life sciences for clinical development, most of the companies are using us across the world, and, that's what's reflected here in the number. But the other important point is. So when we have a customer, we are very focused on expanding our relationship with these clients.
And you see this here on the on the on your left side, that's cohort slide, which is three segments that we chose to kind of want to demonstrate that to you. There's a green one, which is the biggest one. These are customers that we have acquired prior to our IPO. When you look at the revenue we generated with them in 20 team. And a dollar in 2013 equates about $2 in 2019.
So we cover that revenue with these clients over time And it comes because they are using more of our products, but they're also running more trials. And we have done the same thing with clients that we acquired between the IPO in 2013. And for those that joined us from 2014 onwards, we are now 3 3.5 times up. So every time you have a customer, we are able to expand relationships and grow our business. I think that's very important to away as well.
And so how do we
actually execute this land and expand strategy? Our opportunity to grow within our customer base. Bernard talked about the framework that the sole system has built to grow. That is kind of our framework and how we designed our business and our growth strategy is to start sometimes small with our customers. Typically, they start with core data capture, the core data capture platform.
And over time, as we've innovating and investing and make it more seamless to work and expand it to the platform, we have an opportunity to attach more product. So attaching more product gives us an uplift of 2 to 5 times over time. And then when we get to our customers to the that we really are embarking into a transformation journey through data science, analytics and our unique assets that Tarek walked you through we have an ability to go even above that 5 level. And we have a number of customers. We have done this, and I think when we later listen to the panel, Plan and the team will actually show you how this has worked for some of our customers and how successful you've been able to put this strategy into action.
And with that, I hand it back over to Tarek. Thank you so much, Mr. So I
think the big takeaway here is we have an industry that's transport in the midst of or in the beginning of a transformation. And by coming together, I think we can both drive that that transformation, but also create an enormous growth opportunity for our combined organizations because once we're in an account, we tend to grow our revenue profile in that account. I think the the various solutions that Deso brings to the table, as well as those that will be developing over time together, give us an opportunity to really trend to really transform our customers, create a lot of value, but also generate a lot of growth, for our combined businesses. So just want to take a second on our culture because I think it's so essential to understand a bit more about Medidata. The alignment between that social system and Medidata has been so strong.
I think in a year of getting to know each other and in now starting to work together, we feel really good about where we are together because as you know, you can have you can have deals that make or acquisitions that make a lot of sense strategically in one way or another, but the thing that has to align for it to work is the culture. There has to be that shared passion. And and I think we see that. You know, we we, our our folks could not be more excited to be part of the Dassault family and the way we've been welcomed in as an organization has really been amazing. Just, you know, corporate social responsibility, very important at Medidata as it is at the system.
We we've we've both done a lot of things proactively internally and, we've gotten external validation around that. One other thing that's quite important. We work in a highly regulated industry as as does, dissoci stem and at least in some of the industries that they're focused on. We have put a lot of time and investment and thought into making sure that we meet all the data privacy or requirements that we have data that's secure us, at least as secure as it can be in today's environment. That's important to our customers.
It's something else that they focus on. And then I wanna leave you with just, a thought here, which is that there I've talked a lot about the opportunity, but there's also a purpose behind it. I think we share this caring for having an impact on society, having a broad impact And as we come together, we can transform an industry, but I think over the longer term, what we really want to do is impact the entire society by how we interact with the overall health care ecosystem, and that is very, very exciting I have to say. With that, I think we're gonna open it up to Q And A. Is that right?
So, that's where we are in in Pascal today.
That's Kuberna, Pascal, Ruben, to join us on the stage. And I'm seeing the first question.
Let me do it.
Great. Thank you. It's Mohammed Moawala at Goldman Sachs. A couple of questions. First, just for Tarik and Rouven on Medidata, Can you just clarify upfront in terms of the data that you hold?
Who owns it? What is the sort of the security arrangements you have in place, is that all fully in the cloud or is some of it set on premise?
So yes, so our customers, our end customers, Pharma own the data as do the sites, obviously, the ones who generated it. We have secondary use rights to it. We anonymize the data. We do hold it in the cloud. But as I said, we take a lot of precautions around the security clinical data is a little bit different from, the data that you typically see around consumers in the sense that it starts off in a de identified way, because most of that data is when when you enroll a patient into a trial, they become a unique identifier rather than having the phone number and the patient name, etcetera.
So it's got multiple layers of the identification in it. There are some use cases where we are collecting data directly from patients now, but, obviously, we're very sensitive to all the the various privacy requirements around that and and the consents that are required. But in a typical clinical trial, you get consent from the patient right up front to use their data.
Okay. And then secondly, I mean, you talked a lot about how much money is a pharma industry spending on drug development, but also how extremely inefficient it is. So it reminds me about banks and their spending on IT. Can you give us a sense of what the IT spend is, among the kind of pharma and biotech companies as a percentage of, of, of your budget if, if you know that? And, you know, as we think about that shift, you know, is that gonna grow in absolute terms?
And within that, if there are any specific shares of wallet, that you are sort of targeting both independently, but now it does so.
Yeah, absolutely. So I'm going to get a little bit on thin ice because the numbers are hard to come by. But, sort of mid single digits is historically the number that's been thrown around for IT related to clinical development. That number's absolutely going up. So, cloud adoption is starting to pick up, and that plays directly into our hands.
The focus on AI and how you can get more value from the data. And that's one of the areas where really, I think the industry has a lot of opportunity going forward because the data was historically used just in from the perspective of, let's run statistical analysis on it. Right? But not not using the rich data and not looking for deeper insights. And that's something that's very it's it's changing.
It's slow right now, but I think it's going to pick up the pace. And so the opportunity for us is to turn, you know, that spend that's in the mid single digits into a double digit spend with Obviously, that's a huge opportunity. But I I'll I'll leave it also if if you wanna add on, anything.
No. I think, When we have developed the plan, the bet is exactly what you said. Today, it's in average between 4 to 5% of spend in the IT related to the total spending. And the goal is at least to reach up to 10% in the next 5 years. This market is really underpenetrated.
And you will see in my presentation that it's highly fragmented with many niche players. Just because the offer is not well structured, there is limits right now of what you can do.
And last one for Bernard, as we think about the platform, we are looking build here. You've obviously got now the data. You've got a lot of the kind of tools within Dassault. Obviously, Veeva is certainly talking about analytics and Veeva is sort of closely aligned with Salesforce have also made some analytic acquisitions. Is there something you need to further augment in terms of your analytic capabilities?
What do you think you have that sort of end to end platform complete now?
I think we saw the due diligence and through our recent discussion with, especially with Ben here. I think we find out that the Medidata as an incredibly powerful data science with Acorn, a great presentation yesterday with customers, and if you add, what is in Medidata, on what we have on the front end side, I believe that we can quickly show the difference as compared to everything that exists today in the industry. We need to showcase and connect. But I think this, should be a very big differentiator. Related to the competitive landscape,
I do, I will do a
lot of analogy with the competitor you mentioned on some of our past competitor in the other sectors, if I may. It's it's very thin. We are very deep. I don't think documents will be win.
As as you bring the 2 companies together, would you envision as you bring the 2 companies together, would you envision, as the processes, products, and systems, and approach come together, that you would be sharing with us over the coming years, the types of long term partnerships you've announced in aerospace, whether it's with Airbus or mining with BHP in the life sciences sector and in your initial strategic thinking, can you sort of share with us what you think that opportunity looks like above and beyond the clients that you already have independently together as the company becomes 1. Thank you.
Yeah. Well, I think there is, if I give you some adjacent inputs to the way I see the decision process evolving, in these industries. 1st, A few years ago, when we initiated the contacts, thanks to Biovia, Acceleries and Biovia, we discovered that in this kind of companies, the decisions are taken at the very relatively low level, at least when it comes to the R, the or manufacturing. Let's speak for clinical trials, but for the site that we know. This is elevating now.
When we meet now with, those companies, the CEO wants to be involved. And it's not, long ago. On on they want to understand. So they are trying to better understand their transformation roadmap. It happened the same way in the other industries we have been serving that you just referred to.
And of course, this is a very critical factor who owns the decision process. Because fragmentation in what they are today is based on the fact that they have left the decisions to the specialist. And you know, the specialties don't care about what the other guys are doing. They just want their nice toolkit for what they do. And this is why the process is broken.
There is no digital continuity in those companies, almost 0. Now they talk about data lake, just thinking that putting things in a big tank will solve the problem, it does not it does not solve the problem because If the data don't understand each other, you do not have the proper same thing. You just have a collection of data and you cannot do anything with it. In short, Yes, we see the evolution of ownership of the decision makers, and I think this is a condition for those long term contract to be set up. I'm convinced that it's going to happen.
Too early to tell you how fast. I'm getting an insider experience from something else I'm doing as a board member of a pharma company. So I'm seeing it from the inside. Which is eye opener, for me, as being a member of the board of Sanofi on, And I think that seems really well from that standpoint.
Just if I may add on, Bruno, I think, What we're starting to see is that the CEOs of some of the pharma, not this isn't that broad yet, and the boards are charging they're basically charging someone in the organization with thinking about building or building a strategy around digital transformation. How long that takes and how quickly it's adopted is a different question, but The conversations has been elevated to the board level. And, there are organizations that that are being more aggressive, some are being less aggressive, but it is conversation that's happening at that level now, whereas in the past, the decision making had always been much lower, you know, head of clinical management, maybe deciding which vendor to use for their infrastructure. Now that's now that's being elevated because these are these are major transformational programs.
Thank you. Good morning, Jeffrey Sauer. A structural question and a practical question, for Bernard and everyone else, The practical question is, have you, develop the product integration roadmap? Can you talk about some of those details for just at the core software architectural level in terms of taking, let's say, DNA from Biovia, or Simuliya or even the manufacturing side of the company and integrating that, in some time frame that you can talk about, with metadata. And then the, the structural question is, Bernard, you've you've talked for years at one of your highest priorities increasing the number of DS users, and you are getting a good customer base, of course, with metadata.
But the number of customers as it was with Exelvers is not particularly large, although they've grown it. So how do you think about the growth dynamics or vectors in the context of a fairly concentrated relatively small number of customers to drive, revenues from the acquisition?
Well, I think on, later on, there would be discussion on, Jen would be well positioned to, to tell you how much he fall in love with the Swedish loans platform. I think that, Medidata is a very powerful platform for King And Drive. It's very well done, cloud based good technology. We have the front end platform or RD on also manufacturing, One lab from BIOLVIA is being extremely well architected now on the discount platform. Jason will show that, this morning.
So I think it's going to be relatively easy to connect on because it's not, rewrite of the code. It's a connection of 2 great platforms. On the number of customers, You know, I think it relates to the previous questions. Who owns the decision in those companies? The reality today, it's very fragmented.
Set of systems that they have. Very disciplined, fragmented. We saw that 30 years ago in other industries. And there are so much so many limits with that. So I think our responsibility is to showcase that the transformation can happen.
It's now, it's possible, and the niche players should be replaced by a consistent, digital pipeline for the industry. So it's up to us to work with customers to do that. But I think
Maybe I should have had a few numbers because I'm not in agreement with what you said. You have close to 5000 pharma today only 1400 has been served by Medidata. Almost 800 by Biovia, and we have an overlap of about 600. So but you still have most of halved to be conquest. It's point number 1.
Every year, you have almost a 1000 startup coming with, active molecules. So they are an active pipeline. And those guys are coming on a regular basis. If you look at the medical devices, we are talking about 50,000 people, customers, sorry. Today, we are reaching 10,000 and not taking into account the hospital, the practitioners, those are the supply chain we want to connect with the platform.
So coming back to your question, I think if you look at the footprint of this sector, is as large as the industry is today?
Yes. Stefan Slowinski from Exane BNP Paribas. A question for Tarek, kind of along the similar lines of the last couple of questions on customers, you talked about the land and expand strategy. You have 12 of the largest 17 pharma companies guess if we focus on the 5 that you don't have, why do you think you haven't been able to win them as customers? What do you think it will take to penetrate those accounts And also just wondering if you have any kind of initial feedback from your clients and customers on the acquisition by Deso CSM.
So let me start with the last question first, and I'll get to the other 2. Their response has been incredibly enthusiastic and positive. A lot of our customers know sole system, and and they trust them and they value their software, they're really happy with us coming together. And in fact, it's it's kind of been an interesting challenge because they're like, okay. When are we gonna see, you know, the the the fruits of this?
They want they wanna get going. Want to see everything come together because I think their view of how the industry needs to transform aligns with with our view and just wanna get going with that. So, it has really been universally positive. And, that's gratifying. So it means we weren't even you know, we we we were we were right when we we made this decision.
I just wanna I I wanna add something as it relates to revenues, by the way, to to what Pascal was saying, which is Our model has always been yes. It's important to add new customers, but the stickiness with the customer and driving increased revenue is how the model has been so successful for us. I would say in any given year, 80% of our revenue, 85% of our revenue. Increase comes from our existing customer base. And so if you're talking about the kinds of numbers that are out there in terms of new customers that we can acquire, And the stickiness that we have at, you know, with no turnover in customers, the negligible turnover, and now a Second, you know, a larger solution set that we can sell into them, and then that doesn't even come for the things that we're going to be developing.
I I've you know, I I suppose I wouldn't be here if I wasn't as enthusiastic. I can't even speak anymore. I got too excited about the idea of what we can do to together.
Is it
low IT spend today? Yeah.
In the low IT spend. So as it relates to the specific customers, probably spent a decade answering this It's it's always kind of been we we have, over time, added more and more of the top 25 pharma to our list of customers. We're gonna get them over time. Some are slow to move. Change is very difficult at pharma, especially when technology is built into a process it can take years to make that decision.
So it works. It's a double edged sword. When you're the incumbent vendor, it's very difficult to displace even if the newcomer has great technology, because you have so much process built around it, change management is so difficult, so expensive takes so long that, you know, and, and it's in a risky part of the business because if you get clinical develop went wrong, you're talking about the future revenue generation. We always point this out, but we were, you know, $1,000,000,000, almost $1,000,000,000 revenue company. That'll give us some credit.
And we were running probably what the future of a $1,000,000,000,000 of software on or of, drug development on on our software. And in in our database. So, you know, it's a you you are core to their future. So they're very slow to change you. I think we have the best technology in the industry.
I think we have the best services folks in the industry and domain knowledge in innovating like hell, and we're gonna be moving even faster. I think over time, those organizations are gonna come to us. Some of it is is entrenchments, any any number of things. I'm I'm not saying we're we're perfect. I'm sure there are places where we could have done a better job of selling into them.
But some of the customers that, we we acquired in the last years like Bristol Myers and Novartis. Novartis took us a decade to have them move. But once they move, they stay with you for decades. So it's worth taking the, you know, it's a long sales cycle, but it's worth it.
Thank you, Stacy Pollard with JP Morgan. So Bernard has provided a vision in life sciences. Tarek, I'd like to see I'd like to ask you in terms of synergies with Dassault in the Life Sciences business, what are what do you think some of the best opportunities are for your business intermingling with Dassault perhaps some specific examples of what you think you'd be doing, which technologies could come together. I know, of course, on the technology side, you've got DSO's data platform. You've got perhaps out scaler as an opportunity, but I'm more interested in the science and specific examples.
Didn't, can I ask you to hold
that question to look through this one part of our presentation? Because we can, we can help you with it visually too. So I'll we'll give you a more in-depth answer than I can give you in a in a, you know, in a short period of time. But what what I would say is that they they are the what The sole system has today and the direction that they're moving is so complimentary to the direction the industry overall is moving. Right now, research is siloed from development, which is which is siloed from commercialization.
Those silos are starting to break down. Not because of some great epiphany, but because the economics of what regulators and what the providers and because of the, the payers, they're pushing pharma to change the way they develop drugs and even conceptualize drugs. That is all going upstream all the way up to research, and that's where that combination of knowledge researchers right now typically don't get any feedback on what's happened in the development process, and even less of what happened in the commercialization process, if we can bring sort of these disjointed data back to the researchers, you're going to get a feedback loop that produces much better drugs, which again, will reduce sort of the timelines that they have for getting drugs out. It will improve the efficiency. But we'll we'll talk a little bit more about that in the next presentation in a few minutes.
Take one last question for this session?
Yes. Thanks very much.
I was wondering if you can comment on trends and vendor consolidation in the clinical trials, IT base as well as do you foresee major share shifts, given, some of your key competitors wanting to gain, gain share of voice with some of the clients that you have.
Sorry. I didn't catch the first part of the question.
You mentioned that
consolidation. Yeah. Yeah. Yeah. So that's an interesting one.
There is some consolidation that happened. We were doing some smaller acquisitions around the core of our space just to bring some additional technology in. Roll up strategy does not work well. In our industry, we we always stayed away from it. And I think what you'd see is that for the most part, it doesn't happen because, the clinical trials are very discreet units.
Unless something's going terribly wrong, nobody wants to move from one technology to another in the middle of it of a clinical trial. It just is a disaster to try to do. We've we've actually been given some trials because using other technologies, been such a disaster. It's not easy to do. We are, I would we are, as Reuben mentioned, and we are the standard in the industry.
We run more than 50% of all clinical trials. There are some com competitors that have tried to come into the space with very, very limited success. On the free. If you're doing things like document management, it's a process. It's a lot easier to do.
If you're dealing with core clinical data, It's the it market share displacement or gains are very difficult to come at. And I think we're a provider that's viewed with a high level of integrity, high level of innovation,
good
services organization, Very few of our customers are unsatisfied with us. I mean, if you walk around the halls outside, we've got a thousand people basically talking about how excited they are to use us in and, you know, to to see the innovation that comes out of it. I don't see major market share shifts happening. I think that we are gonna continue to be very aggressive. We've been taking market share for since we started the business.
And I don't see anything changing that regard.
Maybe one aspect to add, if so actually the need for simplification and consolidation also has been a catalyst where IT departments look to really consolidate and look for partners who can cover the process end to end and really have that vision to move into the future. That's where they come to us. So we have examples where we have customers that move to our platform they were able to get from over 40 Disparated systems to below 20 and further down, right? And they did this because we were their partner. So that is something that of course that's part of our strategy simplification, consolidation, and take it all on.
You do the break? I propose a a very short break, 10 minutes. And if you could be back at 10:30, Sure. That would be fantastic.
I just have one comment. If you know Pascal, he cannot smile. Right? Seriously, he got, he got an accident 2 days ago. So I speak for him.
We suspect a competitor wanted to clap the door on his, we cannot find the competitor yet. So if he's not smiling, it's because he has tense stitches on his, on his, a lip. Leaves. But, are you okay?
I'm okay. I'm very good.
So just for you to know.
10:30. Thank you. Ladies and gentlemen, may I ask you to be back to your seats in the room?
Everyone. Can you hear me well? Yes? So it's my great pleasure to introduce the next session morning. So I will share the stage with, Linda Briss, who is the Medidata Kusio, a co founder and David Benedict, who is the Vice President for Baidullah R&D, I am Carabio, vice president for Afghanistan's industry, and we are safe to be with you today to present maybe data combined with sets of system joint value proposition.
So as you heard from Bernard earlier today, we have a disease at Dassault System, which is that the virtual work extent and improve the real world. What does this mean for life sciences? Well, of course, we will still need real therapeutic totri of real patients, but we're convinced that this therapeutic can be optimized, thanks to the virtual world, thanks to modeling and simulation. Now as many of you know already and they're not covered that earlier today, it is a major challenge to develop a new drug, moving from early stage discovery all the way to commercialization. It can take up an average 10 years, $2,600,000,000 and it is a major challenge for vaccines.
This company is to move to accelerate time to marches. Why? Because they to address patient unmet needs faster and they also want to increase the duration of market exclusivity. Down that pack of course, there is a scientific challenge moving from 1000 to 100 of 1000 compounds to a single substance, but also when this substance has been discovered, you want to validate it and it becomes a challenge if you want to move towards precision medicine, what are the right population that you want to treat, which clinical trials should you run? And also, after that, you want to manufacture these products in several forms, dosages, packaging, depending on the market that countries.
So obviously, there is a massive challenge in terms of clients, but there is also one of managing complexity. Now if you look at top 20 companies annual reports and industry analyst assessment, what are the internal and external forces that threaten it for change in bio in a life cancer companies. Well, we identify 5 of them that I will briefly describe now. The first one is personalized health. What we mean by that is that licenses companies aim at developing a holistic approach to care, leverage human data, genomics, but also behavior and the environment together with technological breakthroughs such as IoT, AI, to achieve precision system.
So Tarek already covered that and Donna as well, but it's very important for us. I will insist a bit more. This is not about developing the treatment for each and every individual, but here it's about considering individual differences over the course of prevention, diagnosis and treatment. The second challenge is knowledge capitalization. This is a for transformation that we know well at Dassault System.
It's because it's well shared across industries. But now importantly, over the past decade, life sciences companies have grown a lot by merger and acquisition and and you have a witness that's happened. And as a result, they are often divided by into numerous isolated divisions. And to manage this, most of the companies have organized complex metrics based organizations to an communication and data change, but much more is required. Knowledge capitalization is requiring connecting people systems and data in the ecosystem across the entire innovation continuum.
The first need for changes to total quality here it's about ensuring quality and achieving regulatory compliance. The goal is to create a compliance framework for innovation, ensuring that embed quality and regulatory best practices early in the development cycle and entrance for stability of the product throughout cycle. The first need for transformation is to achieve development and manufacturing excellence. And here, what we can say, why is that important? Well, if you want to move towards personalized product and if you want to drive the custom, you want to have adaptive and predictive development and manufacturing.
But does it mean for clinical trials? Well, to you want to have smart clinical trials design, and you want have agility in the way you run clinical operations. And then moving down to manufacturing, the companies want to leverage, the technologies coming from what we call industry response to be able to manage production processes in real time, while continuous scale up and tech transfer should ensure that the product manufactured as it has been designed and as registered. And the 5th need for transformation is about reinventing the value chain. As Tarek already mentioned, life sciences companies have understood that they need to shift their focus from the product to the patient and to the outcome for the patients.
And they have to move away from a one time fits all approach to supply chain. Therefore, they are sticking to the rate value to payers, to regulators, to patients, to physicians, and that pushes them to reinvent their business models and has an impact the entire value chain. Now experience. So what is our joint value proposition to meet these challenges? Well, joining forces allows us create the first end to end scientific and business platform going from early research and discovery all the way to commercial station, including preclinical development, clinical testing and manufacturing.
Jason and Glenn are going to take you along all of these stages and highlight our game changing values and synergies along the innovation continuum. But if I summarize briefly, in research and discovery, We unified predictive science and experimental results to accelerate therapeutic innovation design. With one lab, we try Form, laboratory compliance, efficiency and collaboration. In the field of clinical testing, we come with a broad range of solutions and acting data management and clinical operation to accelerate value, reduce risk and optimize patient outcomes. With license to cure, we connect quality across the biopharma enterprise and provide data driven approach to regulatory processes.
With made secure, we have organization, Remagine Engineering, the operations and planning to achieve manufacturing excellence with a special focus on biological processes. And within the field of digitalization, we help our customers demonstrate real world value to payers, regulators, stations and institutions. Now before turning to let me make an important point. We are confident that Medidata's core assets in patient data. You have heard a little bit of that already.
So coming from clinical trials, but also reward evidence and advanced analytics, we're strongly combined with the power of modeling and simulation. To catalyze the next generation of patients inclusive therapeutics. And that is the perfect illustration of our belief, which is that the virtual ward extend and improve the real world. With that, let me turn to Jason who is going to take you through the innovation continuum.
Thank you, Claire.
I'm going to start with a little demonstration of some of the capabilities in design to cure. All of our industry solution experiences in life science, we refer to them as V plus R virtual plus real. This complements the virtual world extending the real world. We start by characterizing, what we wanna do on our drug discovery program. We we refer to that as a target product file.
And from there, we're able to structure the drug discovery and development process, ask questions along the continuum, look at analytics and take decisions You can do this through the 3 d experience platform and the social context, also using the ideation funnel here, for example, to exchange with your colleagues. Not only is it social, but there is deep science here. This is not a a thin platform, but a very deep scientific platform that adds social on top of the science. And this helps us break down the traditional barriers where scientists hoard their information to themselves. We can take that information and publish it into a knowledge graph.
We heard about knowledge graphs earlier. This is a knowledge graph for biological research data, bringing together multiple disciplines into a solution that we call the living map. And on the living map, it's not just a knowledge model. You can simulate. And with that simulation, we can find new drug targets or multiple targets for a drug candidate, helping us improve how we target a therapeutic for a patient population.
And once we have that target, in the 3DEXPERIENCE platform, we're able to exploit multiple modalities of drug design and development from small molecule biologics, and now with emerging technologies and immunotherapies, cellular therapies, CAR T, CAR T therapies. All of those modalities can be addressed, connected, and designed, and we use the word design very purposefully here. Because we're making this an intentional design, process, all of that knowledge, all of that capability to know how it can be expressed through the 3 d experience platform here. In a common data model, in a common user experience, that combines us together. Now at its best, we're able to combine the power of modeling and simulation and knowledge based models expressed here through our AI based drug design and development ability.
What you see here is a solution we call generative therapeutics design. Our first target is small molecule, and we're gonna extend that to multiple modalities as well. And with that, you see multiple modalities are coming out of research. This is putting a lot of pressure on the drug development organization to change their game too. So all of the value that you see here are derived from real value engagements with our customers.
We are looking to shave years off of the drug development design and development processes we're looking to help target the right therapy at the right target, and increase the time to market so that we can cure patients faster. Now picking that up in drug development, this is an area that's undergoing a lot of digital transformation right now, and and the potential gains are huge. So I'm going to take you on a small tour of our 1 lab industry solution experience. The first thing is we start to see a decomposition of the traditional barriers between discovery and development. This has to happen because of the new modalities that are emerging.
And with key solutions that we have here, like scientific intelligence, we're able to connect the dots This is not a a simple search engine. This is a a knowledge based data lake solution with specific scientific capability built in, searching the chemistry, searching the biology, aware of the biology and able to connect the dots from all that information that we created in discovery and now also in development. And we need to develop procedures. We need to develop the drug process. With 1 lab industry solution experience, we've been able to replace multiple disparate solution this comes back to the n-one question.
Do we see a consolidation? Yes. We need to have a consolidation because the knowledge is in too many We see multiple ELNs, multiple limb systems, and we're able to replace those with 1 lab. One lab is not just a replacement these systems, it is better. It is fully digital.
The methods and the drug development process that we're designing here are supported by an ISA 88, ISA 95 structure, and that eliminates the technology transfer between the different silos. What we're starting to see is our customers are adopting 1 lab, not only in process development, but also in quality control and manufacturing. And as they make that adoption in both places, they're able to see the synergy between them where they no longer have this recoding barrier for the technology moving from process development into quality control. And so with that, we're able to eliminate further systems. We're able to realize efficiencies, and we're able to get better quality out of the system.
So we see a big, land and expand opportunity there, just like Medidata has seen with their solutions. Okay? Here, we're completing a a study. This is a drug formulation and stability study. You can see that one lab is connected to the 3 d experience platform.
That we can capitalize the drug development knowledge into the scientific intelligence capability, along with the drug design capability, and all the way up to, the clinical batch release. But that's not all we can do. We can also develop the device. The drug delivery device. And here you see a small vignette, from our IASO demonstrator.
You
can go look at IASO. We produce this. It's real. Biologics, we see an increase in the Biologics investment. These things are hard to deliver to the patient in a comfortable form, right?
Their viscous, they need to be injected or absorbed. With IASA, we were able to design a device that the patient can wear at home or on the road. And it slowly diffuses, the the large molecule therapy, to them. They don't have to get an injection. They don't have to get an IV bag.
So overall, you're able to create a better molecule to target their disease. You're able to create a better delivery experience for that disease all in one platform. And with that clinical batch release, we need to test it.
And I'm gonna hand them
my good friend, Glenn, to tell you about that.
Thanks, Jason.
So Now we've got the right target. We've got the right drug. How do we give it to the right patient at the right time? And that is where we come in with many days approach to being the system of operations for a clinical trial. We need to bring multiple modalities of data together about patients in the real world and match it up to our virtual expectations that we have established prior to these tests.
What you see here is an illustration of all of those types of data going into the Medidata platform And let me give you a sense of what we then do with them. Running a clinical trial is an extraordinary complicated business process. It is dependent on doctors and nurses operationally that are not part of the pharmaceutical company or the medical device company that is running that study. You saw earlier today how unpredictable timing is in clinical trials. It's because of this dependency in many cases between the planned experiment and what is actually happening from a patient availability perspective in the real world.
On the Medidata platform, we help people plan and execute and then analyze every step that is necessary along the spectrum of executing a clinical trial. And we do that in a way that provides context to data both in the real world and Apropos to one of the questions about product synergies and roadmap actually gives us a context for looking at that data in a more sophisticated way in the virtual world. So I'll give you an example of patient data. In this case, it's coming from an iPad that the patient using to enter data. But this process that I'm illustrating is exactly the same, whether the source of data is a nurse recording something in a specific application for a clinical trial, whether it's a piece of data that's coming from an EMR record, whether it's a piece of genomic data.
It goes in and gets categorized from a clinical data management perspective. If you're not an expert on life sciences, which is okay, Claire used two terms, which are incredibly important, clinical data management, which is what we're looking at here, and clinical operations. Those are illustrated as Ray BDC and CTMS on this chart. It used to be 2 separate systems. And you heard Rubin talking about our clients, liking the fact that we bring this together at Medidata.
The data manager is looking at the quality of data and the clinical operations team here is looking at any operational issues, any flags that are on that data to make sure that it's all collected in a compliant matter that's going to meet regulatory expectations. We bring that together in our platform for every type of data, and we also give it data context. So in terms of our platform, and the Dassault System 3 d experience platform, we actually think about semantic layers, how we categorize data, in very compatible ways. You just saw Jason talking about that at a molecular level. Well, that's what we do at a clinical level.
So we can have that data available for reporting from a scientific perspective or from an operational perspective. Operationally, this is terrific. We actually can help our clients do clinical trials in a more efficient way than they're doing them with disparate systems and older processes. And that is one of the things that we have, a 1000 clients in this building in dozens of tracks simultaneously going through, learning about all the different benefits that you get from our platform in today's world. But the world of clinical trials and the world of life sciences, as you heard Claire talking about, is also changing.
People are realizing that they need to be and putting it into this complex giant market, which had its own challenges or changing. That was a population by problem. It was an epidemiological problem. But today, as we start thinking about treating individual patients, as we think about precision medicine, as we think about individualized therapies, it's now a problem that we need to think about on a very personal level. And that has implications for clinical trials.
So one of the things that we think about NetI data is not a light switch, but a dimmer, a dial. As patients are being thought of in a more central way as individuals, not just as parts of a large denominator We need to start to be able to shift the way we think about clinical trials from that population basis or from something that you would do in a clinic to something that is done in a home. And so part of what Medidata is trying to do is not just facilitate that in the clinical trial context, but also set ourselves up to operate in a world where patients are the center of a therapy instead of the physician who is prescribing it being the center of that therapy. And I just want to take an opportunity to also answer a question about users. So one of the announcements that we made yesterday at Medidata was that we are extending what we do in terms of individual apps for patients to actually create a central platform location for patients who are participating in clinical trials to come and participate in of those clinical trials, how they learn about them, how they're consented, how they provide data, how they get data back so they can understand what's happening with them And in fact, the life sciences industry hasn't done a great job of thinking about the fact that patients are not just central to one clinical trial, But we should think about a patient in the context of every clinical trial that they go into.
A lot of cancer patients go into a clinical trial for one company and then wind up in another trial with another company, and we are not thinking of, as an industry around how to create that transition. That's what Mediadata is doing now with our patient platform. And I think one of the exciting opportunities, and I'll come back to this when we get to the commercial part of the spiral that we were showing. Is that that can extend into a platform for patients to be connected to the therapeutic companies that are developing the things that are helping them, in a much more holistic and commercial way. So that means that the 1,000,000 Medidata users that we're adding to the 3DEXPERIENCE platform as of today will actually turn into not just clinical trial physicians, but physicians who are treating patients in the real world and the patients themselves.
So that goes from 1,000,000 to tens of 1,000,000. I think honestly, literally to, as we incorporate patients into the platform more, billions. That brings me to analytics. When you start to think about these numbers and in some cases, the numbers are incredibly small. Of that.
And in some cases, the numbers are incredibly large. There is an opportunity, as Tara highlighted. For the life sciences industry, to do a better job of thinking which you will probably never see these layers on again. Well, you'll probably see these 3 layers, but it's missing a really important layer here, which is the virtual layer. But what you will see, again, from a product perspective, is how, if you look at the virtual world and the real world, all of these steps along the process of what Jason was illustrating, what we're talking about with clinical trials and what we will now talk about in the future of commercial, actually come together with beautiful parallel tracks.
You've got a data fabric, again, inclusive of research data, On the BIOVIA side, inclusive of all the patient data on the Medidata side, illustrated at the bottom. At the top, you've got the life cycle of all the activities that are being done today in terms of running clinical trials. And in the middle, Acorn is our brand name within Netidata for our most advanced analytics, some of the the most rocket science type things that we're doing. And I want to give you an example of a couple of them to illustrate what I mean about this analytic future and large and small sets of data. So as we think about the world of clinical development changing, as we go from these small molecules going into big complex markets, to large molecules and very specific therapies that are targeted for very small groups of patients who we can categorize and understand very well We need to think about a reality, which is that the smaller and smaller sets of patients who get more and more precise therapies may not always provide enough statistical evidence for a regulator, for a prescription writer, for a patient, for a payer to make decisions around.
To what we've embarked on with Medidata and with our unique data assets, as Tarik and Rubin, we're talking about, is the ability to take a single patient and reuse their data over and over again. And I know not everybody is a life scientist in the room. This is a survival graph. Everybody at the beginning on this one corner is still okay. And as you go down, you see people are either having their tumor progress or they're having a cardiac event.
Some endpoint could be death is happening in the study. And what we want to show is that the curve for the new therapy looks shallower than the curve for an existing therapy. Well, usually, patients are used once and once only in these analysis They appear in one of those 2 curves in one submission to the FDA. But by categorizing the data on our platform as we have, we're able to use patients in multiple curves, in multiple pieces of evidence generation. And in fact, if you think about the virtual world supplement to what we do here, The obvious, I think, not simple idea, this is incredibly complicated simulation we have to do, but is to actually add something to this, which is inclusive of a virtual twin in terms of being able to predict from a simulation perspective and other survival curve.
This is work that we are doing not on paper, not theoretically. We at Medidata present this at scientific conferences. We work with groups like friends of cancer research. We are at the at the FDA presenting this, and we are working with clients to use this kind of evidence generation for the submission of new drug packages. So again, there was the question about competitive differentiation.
I think that we're making the rubber meet the road in ways that, other people are putting on press releases about. I'll give you another example. This was a big data example. Let's reuse the millions of patients on our platform. I'll give you, hopefully, an incredibly compelling small data example, and it also fits perfectly into everything that Jason was presenting.
So, yesterday, we had a presentation, from a physician. His name is David Faganbaum, and, not only is he a physician scientist, he also suffers from a rare disease. To disease called Castleman Disease. And, I guess for, 20% of the people with Castleman Disease, the good news is there's a drug called ciltuximab, which can actually keep the disease under control. It's autoimmune.
You wind up going into auto organ failure in most patients die a couple of years after diagnosis. So for 1 out of 5 of those patients, this on market drug is a great answer. Nobody knows or knew how to figure out what patients would be that 1 out of 5. And so what we were able to do is by taking data in the exact same process that you saw. Remember we started with data from that iPad?
Well, imagine the other pads for data from clinics For data about the patient's proteomic profiles, so what genes are on versus off, we were able to take data from multiple clinical trials not on thousands or millions of patients on fewer than 100. Bring that data together, stack it up and do an analysis on it in a way that allowed us to identify, not how 1 out of 5 patients, so a less than 20% chance of being right about this drug working to a 69% chance of being able to identify patients for whom that drug would work. This is literal precision medicine, doing a better job of finding the right treatment for the right patient at the right time. Okay. So you may not be a life scientist, but if you're a financial analyst, you're probably good at math.
You're probably sitting there saying, well, what about the other 4 patients who are not helping? That is where the connectivity between what Jason was talking about and what we have at Medidata comes into play. One of the exciting things that David was also presenting about yesterday is if you start to look at the commonality of the patients who aren't helped by celtuximab, which is related to, looking at interleukin-six, a particular protein. Well, there happens to be some other commonality There's another target called mTOR, and there happened to be a drug already on the market that was used in kidney transplantation that can actually suppress that particular protein. And that drug is now keeping David alive and has created a whole path for development of new therapies for patients with Castleman disease.
So again, in example, one, I was showing you how with data reuse and big data, we could make things, that created more evidence per patient who is enrolled in the real world by creating what effectively is a new virtual representation of that data. And as this example, we're figuring out how to take these theoretical views of pathways and molecules that can then be applied to real world scenarios to create a effective, incredibly valuable medications and rare disease, which is regarded as the hardest part of life sciences to get involved in and move the needle on. So with that, now that we've got something that we can give to the right patient at the right time,
I'll go back to Jason.
All right.
And we have to deal with some regulatory and quality affairs, I think, as well. So this is what license to Cure does. I'm gonna start with the new stuff. Okay? This has traditionally been a document dominated world, It's an arts and crafts project.
Filing a CMC report can take 7000 hours of labor. It's risky. You get audited. You have to show all the, evidence. What you see here is a combination of content and data driven automation.
The world of regulatory, the world of quality is gonna shift. On the regulatory side, we're able to, through the semantic data layer, grab the data that we need with full traceability, and automate large sections, if not all of, these critical assets the companies have to produce. So you can imagine the savings of going from 7000 hours per filing per year per product, down to maybe 100 hours, okay? It's reasonable we can achieve it. The objects that show up in these are not simple charts.
They're they're live business objects, life cycle managed. You can click through. You can see the audit trail. You can go all the way back to the source them. So when the auditor shows up, you go to the artifact in question, and you can look at the entire provenance.
It reduces the risk. For our for our customers. This is where we're going. Everything's going to be automated, data driven, scientific in nature. Okay?
On the quality topic, if we do our job well in process development, we're doing quality by design. The risk in the quality phase goes down. We still need to have a quality system. You'll see it in a minute. We have both the quality, enterprise quality management, system, and we have the enterprise document management system.
But I started with the next generation up front. We're putting all this into building a Symantec data fabric so that we can leverage it to replace these thin line arts and crafts projects with real data driven evidence. It also enables us to do new things like continuous submission, okay? We can do this for both the drug and the device. This is what licensure will allow us to do.
Okay. And then we need to manufacture it. So just a few facts here for license secure. Again, these are from real customer value engineering engagements. Into manufacturing.
Again, in process development today, most companies are in this phase where they develop the process it's a little bit ad hoc. They don't have a process model that they're actually building toward. They're building a body of knowledge in a document again. Then they have to go to manufacturing, and they have to recode this for their manufacturing execution system, we can do better. How do we do better?
When we're developing the process, we're able to put the knowledge into a model. Instead of building a document, you're building a model of the of the development process of the manufacturing process, both the device and then actually the plant. And you can see here you can navigate on the plant. You can do virtual commissioning. This is a huge, huge value to our customers.
You can simulate, large or all elements of the plants. You can see the ergonomics of the plant. So how is the, assembly of the device going to work? Okay? And then you can plan your production, and you can optimize your production.
You can optimize the clinical supply. You can optimize the production supply. You can even optimize the delivery, the optimal delivery to the marketplace, which are get through in a little bit. Okay. You can go in and you can run your day to day operations in the 3 d experience platform and manufacturing, okay.
This is developed, in partnership with multiple brands. So, Bernard referred to the power of the brands. This is developed in combination with Bellemia brand. Biovia brings, to the table, a deep understanding of bio processing and the ability to monitor in in real time to control strategy and to keep your, process running, save you batches. Here, you can also manage your bill of materials against the manufacturing process.
To make sure that you're compliant, and you're able to transfer the technology from development into manufacturing digitally, not through documentation. Again, this is a huge savings, both in time, air, and optimization of the process. And, now that we've produced a product, We have to go to market, I think.
So we've got a treatment that works. We've got all the regulatory documentation to prove and we're ready to actually get it to that right patient at the right time. So what next? And this is where, to to just put a personal spin on it. I I love Lego.
And as as Rubin and Tara and I got to know the Desosystem team, and the platform, it was amazing how these pieces fit together. So we can now take the other side of the way Medidata thought of the world, before a week ago from Monday. And that is not just the clinical trial data, but the real world data, data from practices around the world, data from EMRs, data around prescriptions, data around reimbursements, and we bring those into our platform in a way that allows our customers to make commercial decisions. Now commercial decisions span a couple different things today. It is certainly the traditional view of what markets should I go into?
What countries should I launch my drug in? How should I deploy my sales force? But increasingly, the way we think about commercialization of therapeutics, apropos to what I was talking about in terms of patients, is in a very much more patient centric way. That's not just because the therapeutics are more specific themselves. That's because we also have a commercial need in life sciences in a value based care, in a value based contracting environment to actually show that this therapeutic was deployed effectively and produced the kind of therapeutic value, had the level of efficacy and safety that we expected, when it was given to a patient for a payment to be made back or to not give a rebate.
What you see here is data coming into the Medidata platform from real world data sources. It's data that we can put into our semantic layer and actually treat the same way we would think about clinical research data and assemble it into reports that show safety, efficacy, value, commercial success, as well as scientific success both at a population level and at an individual patient level. And this is actually an example of a project where, for robotic surgeries, this is a medical device example, we are not just showing the outcomes of a particular case, a particular surgery for a particular patient, but looking at from a physician's perspective, from the surgeon's perspective, how well they are doing, as well as providing a dashboard back to the life sciences company, they can see at a population level how well the treatment is working. This is the final step in going from the initial concept of a therapy, all the way to, as we're showing here, it's successful delivery. With that,
I will hand it back to you.
Thank you very much. Let me wrap up the session by focusing on the key benefits that we provide to our customers? Well, importantly, so we are basing this KPI guidance on value engagement that we have been conducting with our customers and we are benchmarking this against publication policy. Importantly, the value we bring to a customer concerns post the top line and the bottom line. As far as the top line is concerned, we help our customers accelerate time to market by 20 to 35 months.
And at the same time, we help them increase the probability of success by 30%, which is that in a given period of time, they can bring more drugs to, target unmet medical needs to margins. Now as far as the bottom is concerned, we have our customers drive the cost down by 5 to 10% and at the same time, we help them improve quality by reducing the risk by about 25 first sentence. Importantly, we cannot achieve these results with our journey forces together because as we have shown you today, you need an integrated platform connect people ideas and data. And the 3DEXPERIENCE platform is the catalyst and enabler to make this happen. Thank you for your attention.
Thank you very much.
Karen Jason, please, Glenn, stay on on stage. Marisa, and Jason, can you join us on stage?
Okay. So ladies and gentlemen, this is Jason Raines and Marissa Coe, 2 Medidata clients. Long term clients, people who, we know very well. And so, actually want to, I'll let you guys introduce yourselves and then we'll just kind of talk about our histories working together. Other case you want
to go for?
Sure. Bob Jason Raines, I'm the Vice President of Data And Digital Technologies at Pellus Pharmaceuticals. Been in the industry about 25 years now, and I cut my teeth as a research coordinator years years ago at an obesity clinic in the East Texas and, fell in love with research and worked my way into, the CRO landscape and then quickly into pharma. And I've been hitting up functions And the data management and the data science space for, I guess, 12 or 13 years now, and, been partners with, metadata for, going back since 2007.
We'll get back to that.
Hi. My name is Marisa. Can you hear me? Yes. My name is Marisa school.
I've been in the tentatively 34 years, and, I currently run the and D business insights and analytics at, at VMS. And what that does is, what that means is use analytics for, 3 purposes one is to, accelerate the asset strategy. And, most of those curves that Glenn showed My team in collaboration with, the clinical scientists and vice the decision to actually, run so that we can develop the right the clinical trial analytics, who actually uses that information on clinical trial design to figure out where, countries and sites we should, we should, run the clinical trial. And then I run the, clinical, the real world data team that takes all of that that Glenn was showing and, and understands the use of real world data to actually help reimbursement and value access teams as well as HUR teams and regulatory teams to actually prove the value of our medicines. To payers, regulators, health care physicians, and patients overall.
So thank you both for being here. And, and actually, Jason has worked at some extremely large companies as well, but we have an interesting contrast. We have a relatively small versus a very large political company, somebody who has come to the, the business from the more transactional side versus Marissa from the analytics side. And I hope that what you'll take away is this. We're all now in the middle in a very interesting place, but let's start with the transactional side.
So Jason and I met about 10 years ago, and as you heard from Tarek, the origin of Medidata was doing this thing called electronic data cap making sure that we were replacing paper, in clinical trials. And so maybe tell us a little bit about your Medidata experience.
So when I became the head of data management at Alcon, this was back in 2007, 2008 time frame. We were using a very old way of capturing clinical data from the sites, the investigators. So this method was basically they would write this information into the medical chart. Then they would have to transcribe it into a piece of papers, a 3 party NCR paper. This would then be sent into the pharmaceutical companies, and it was hand entered in the clinical databases.
Double data entry to make sure we've got quality and there are no mistakes between source of capture at the site. So the point at which we actually had it in the clinical database, we can actually do the analytics on determine if we have a safe and effective product. So this system was fraught with quality issues and it was very slow. So electronic data capture was actually created, and I think metadata product was was a very viable solution to transform the industry back then and get us away from this legacy and, slow and poor quality processes. So we implemented metadata ADC, we did some piloting at first and, proved out the concept.
We had some change management I think anytime you have a remarkable change in the industry like this, you have some, some generational type of, pushback. You know, I think a lot of innovation is related to generations changing. And, there was some pushback that we didn't believe that this actually worked as a regulated industry and how we're going to prove that, that, things that are going into the metadata cloud are safe and that they're not going to do something bad with the data. There was a lot of these questions at the table. And so Eventually, we implemented and, we really did some remarkable things in terms of the speed at which we could execute clinical trials and we can make decisions.
By months, if not, halfs of years, faster clinical trials, we were actually able to decrease the total cost of delivery of clinical trials by, in some cases, in some of our areas, up to 20%. And the quality was actually improved. We had and when we get to quality faster. So in terms of quick win, fail, different types of clinical trials, we could actually decide to kill a project faster and accelerate projects that we believe were actually working faster, and this ultimately benefits patients. And I can attest to, multiple through, through Alcon, Novartis, and Biogen now at APELUS.
I have confidence that I have a partner and have a capability that enables me to help patients faster. There's no question.
So Alcon, Novartis, Biogen PeLLis, you're a 4 time implementer of the Medidata platform. That's correct. I think that I think you're definitely on the upper end of that curve. So thank
you. That's correct.
So, so, Jason and I met in, in, I think, a software evaluation originally, for Medidata. Marissa and I, I think, were sitting together at a medical conference because everybody thought we were crazy, nobody wanted to talk to us.
That's right.
Maybe tell us about our history.
You have a Medidata.
Yeah. So so we will go before BMS, I worked for Amgen for a number of years. And, and, that's when our kind of path first crossed. And, and, back in early 2000, we were not only changing our ADC system, but also implementing a very sophisticated rear wheel risk based monitoring environment, which means when you think about, how we conduct clinical trials, for the FDA. All that data that we collect, all the data that the sites collect from patients and so on and so forth, in back in the days and probably, is still today, has to be checked that everything is complete, that there are no deviations from the actual, data that is expected from a protocol.
And all of that was done, by hand, by, by folks that actually looked at the patient charts and the data that we collect, through many ways and actually make sure that all the data was was there was clean, to be able to do the analytics to send to the FDA. Well, today with technology and with, AI, all of that could be done in almost no time constantly, shaving months and months and months between the end of a clinical trial. And when we have the data, what we call the database lock, the data lock, so then biostatistics can proceed with the analysis. As you can imagine, not doing that correctly could yield to a full, false submission, which for us is probably one of the worst nightmare. So that's kind of the, the one implementation with with metadata.
The second interaction that Glenn and I had was almost like, a, a geeky date kind of thing. We were in Florida at one of the metadata conferences. And, when I retired from Pharma, but the first time, one of the things that I did is, I joined in a group of Avolandiers that actually had the the thought that we could do a clinical, clinical trial, and we created Mitris. So, so I run clinical trial sites, and one of my biggest passion was how do we make this very easy for patients, especially in the informed consent, because I saw firsthand what giving a consent to a, a patient was like, working with a patient to try to helping them understand the 45 patients, sometimes patients, pages, sometimes 60 pages, but an informed consent entails. And, and, you know, I said to, to, Glenn, have you ever worked with Mitra's or heard of mitra's, and is it possible to add the informed consent to, the metadata platform and continue to create a seamless platform, within all the steps of the clinical trials.
And so I'm thrilled to see that part of Medidata. And very recently, we finished a project together where we, we use the metadata, the data housed in the systems as well as the sophistication of the analytics team, the Acorn AI team, to actually save a clinical trial that was, around astray, that for those investors that are here, represented significant value to the top line, And we could have we could not have done it had we not been had it not been for the Medidata platform and the Acorna AI team. So So,
so, thank you. To connect the dots, that patient platform, the CEO of Mitris, which we wound up acquiring, was the person, Anthony Costello, our SVP of patient, who made the announcement about our patient platform yesterday. So That was a great conversation we had. I can ask both of you, but I'll start based on what you just said Marissa. So, There's the processes around running a clinical trial today.
There's the extension of platforms with things like econsent and thinking about the patient. There's this idea of using data to do a better job of or when we have to rescue a clinical trial. What is your vision? Let's keep it to clinical trials from the moment of platform of the future? What does the future look like in your perfect world?
Well, to me, and I keep it I think I would extend it a little bit beyond clinical trial because folks in life science, including the way in which most large pharmaceutical companies work. Is with the traditional approach of drug development is linear. Drug development is anything but linear. And, and what what I see the potential for, the clinical trial of the future, if you wish, is you really need to start in the discovery space and the translational space. Because most of where we select the patients, we stratify the patients, we decide what might be the right drug for the right patient.
Come really from a, an ecosystem that starts with, creating the drug, taking the drug to the clinical process then into the market. And then identifying that for some patients that drug doesn't work. And for the patients that the drug does work, we are thrilled, but most more often than not, especially in oncology, for 75 of the patients, that drug doesn't work. So we take that information, and we have to go back to the discovery, back in the translational medicine, and back into what are the patient characteristics? What is the phenotypic and genotypic characteristics that may uncover why the drug doesn't work.
And that really is going back to the early stages of translational medicine and discovery, to figure out, is there anything else that we get changed in a molecular structure, a change in a biomarker, a change or a new biomarker that we can pursue. And that starts the process all over again. With clinical trials and so on and so forth. So the idea of a, a truly connected platform that allows me to understand right?
At the
end of the day, what is that particular patient population? How do I identify it? How do we put it in a clinical trial? How do we ask we identify where those patients are. And here, we absolutely need the head, the help of the providers who actually have the patients.
So the idea of linking
to the
real world
data and having a direct access to those patients, to those physicians who have the patients so we can alert them, that a drug or a clinical trial might be beneficial to the patient and actually them in a seamless way, enroll that patient in a clinical trial without necessarily having to, to jump through hoops for informed consent, patient identification, the site survey, and a whole host of things that today with technology could be done seamlessly. The pharmaceutical companies still are, working in some of them in the 1950s. So what I love about what Medidata is doing is is forcing all of us, to actually run faster because they're 10 years ahead of any pharmaceutical company in terms of how they're thinking about drug development. That drug discovery and development and commercialization in a seamless unison way versus in the way, in the fracture way that we think of body.
It's a fractured way. We work together, Jason and I on when it was really a best of breed idea of let's take as a life sides as a company, all these different systems and time together, and that's how we'll be successful. And we kind of went into the platform. Or what are your thoughts about the future of
trials? Well, think that was extremely well said. I think that, when I think about the future of clinical development, it's it's going to be the accumulation of a lot of different disparate data assets that are coming from a lot of different places. And in some cases, I think when we think about best of breed and we think about a holistic suite of products, I think that you're on the right path to actually provide a platform by which we can integrate all these different data assets and hit it with advanced analytics to find the needle in the haystack, because precision medicine is truly about a patient's response that is really unique to them only. And historically, it's all been about a brute force kind of approach and you test a lot of people, and and you have, as an average, maybe, and not a a significant signal to say that I have a safe and effective drug.
But if I hit nation. I really, 10 years ago, there was sort of a best of breed approach because the majority of the technology and the technology companies wasn't really there. So perhaps I had to pick this 1 and I had to pick this 1 and pick this 1 and make them work. But today, There's not a lot of companies that actually provide this holistic suite of products that actually enables clinical development. Metadata, in my opinion, is the leading company that can partner with, pharma and biotech actually deliver all of these data assets in a way that actually accelerates clinical development and precision medicine.
And I can defend that, all of my experiences with other companies and and the processes by which I actually have to implement in order to get this to work. So the questions often are to me that, Jason, how were you able to get a lot of these metrics that you were able to obtain with metadata? And I answer it usually with, with an analogy of the, of cars or racing. And I wanna pick the best car. But everyone else can maybe pick the best car to actually get the fastest record, if you will, or to win the race.
So metadata is the car, but I actually need professional services. I need people that know how to drive that car. And can teach me how to drive that car in a way that makes me win, right? And winning for me is faster and, and, precision medicine that are getting this to the patients. So I also have to have a humility and be open to thinking differently about this as well.
And this is what I have gotten from metadata from the years, Clint. I can come to you. I've come to you dozens of times, and I said, look, I have this problem. I'll give you my opinion is what you say, but I know who you need to talk to. So this is about a partnership too.
So it's the technical solution, which I believe is a better car than what else out there in terms of the competition, because it is a holistic integrated suite from consent to lock, post lock management of the data in an efficient way in a compliant way, but also get an enormous amount of confidence experience within metadata that I can leverage to help me drive the car better. So what happens in the future is just expanding, I think, that capability And integrating data in a more seamless way from electronic medical records, claims, digital. The digital footprint is going to continue to go up. I think patients are going to be empowered with digital. They're going to be measuring their own symptoms.
There are same non invasive hemoglobin apps, there's all sorts of just ex this is exploding. In a clinical setting, this needs to be connected through your ECOA solutions, etcetera, you have the capabilities to really actually push us more in that direction. And, with the data sciences and advanced analytics, we can find the patients that needed therapy. So So
you were on a panel yesterday, Marissa, on data science analytics, and you're talking about success, but getting success. Guessing off point where we could discuss that?
Yeah. Sure. Again, What I told you yesterday, which is, you're forcing most of us to think differently to think more now in in a more integrated way, which is very difficult in an industry that has done this that has run-in the same way for years years years. And is fragmented by nature. The concept of, at BMS 3 years ago, we decided to, consolidate analytics for commercial, for R And D, for GPS or global production supply.
And, and, you know, one of the toughest thing was to really bring all of these disciplines together, like real world data and clinical trial analytics and asset strategy, and start making sense of, how do we deploy analytics to make really faster, decisions? And, you know, there was A lot of resistance because that's not the way we think. That's, you know, typically in, in R and D, we extremely good at understanding the science behind the data, but not necessarily in applying kind of the business concept using the science to apply or apply to business concepts and, and, and make decisions. And I think the early successes that we have like accelerating a clinical trial, greatly accelerating clinical trial with with data or using real world data to actually, help it pay or under can, that, that our drug, although it appears a little bit more expensive, it actually causes, a lot less adverse events and a lot less hospitalizations that are very, very costly. So all of that data and insights that we've generated, and went to, whether it be to support better reimbursement or regulatory decisions, whether to accelerate a clinical trial or using analytics to actually bypass the competition, in a trial design and and filing early, all of those, are now expected.
And, and the idea of partnering with a company like metadata where all that wealth of information apply with, or combined with a very sophisticated analytics team and, and, in Acorn AI, It, it only can make us even more successful. So, part of the success was when we started 3 years ago, my team was about 50, 55 folks. And then, after that, with the integration of as of Jean, we will be about 160 people in doing analytics for R&D.
Just a question about the the project that we were talking about in terms of, of, of, a rescue study, not specifically that project, but it was one where we looked at data from outside the walls of your company. Is that a trend that clearly we believe is going to continue? But how do you think
about that? We have to. I mean, we A, we do not have enough information to actually make the decisions that we make. That's first of all, the second is In order to, for us to make certain determinations and certain and build models that actually are trustworthy to regulators or payers and so on and so forth, we need 1,000,000 and 1,000,000 and 1,000,000 of data points. And that will not come from the Four walls of a pharmaceutical company.
So we're constantly partnering with, others who have data and one of the things that, that attracts me the most, there's so many things that attract me about what the salt and metadata are doing. But, the idea of using utilizing and deploying real world data in a certain analysis has allowed us to identify those patient population for for whom the drugs are not beneficial. And, and we tried to build models for how do we stratify those patients even more to understand the molecular dynamics of those patients, of the, that patient cohort. The reality is in order for those for those models to actually have any validity in clinical practice or even for the regulatory we need to validate those. And typically, the most trustworthy way to validate, reward their model is with clinical data because that's kind of the standard.
So the only company that I know of that has the ability to kind of bridge those worlds is, is metadata. So I'm, you know, thrilled on that standpoint, on the other standpoint, because I I have been a many data plan for years. But, you know, talking listening to Bernard today and and what the bring to the table. One of the most difficult things in, in drug development, that's we haven't quite figured it out yet, is, our manufacturing folks need to figure out what, how much production they need very, very early, even 2 years before we even know the dose of the drugs that we're developing. And why is it important?
Well, as the former credit of finance, R And D, I know that, how much it costs to add build a manufacturing facility and, or create production plan if, and if you're wrong in certain assumptions about dosing, you might be spending $5,000,000,000, for, you know, sometimes you need, like, half of that So, yeah, really early in the in the, in the drug development in translational medicine, we do clinical studies to actually, find what is the appropriate dose to, use with the patient. But by that time, it's way too late to tell clinical manufacturing how much we need. Using, the data that Dassault has relates to manufacturing production with the data that metadata has as it relates to all the trials, the early trials for those findings, Today, we could potentially simulate how much drug might we need, and, and, you know, help the our clinical manufacturing folks actually, estimate what kind of lot and what kind of manufacturing you're going to need.
So Okay. So as we now supersize our view, I think in a good way in terms of the systems and the data and the connectivity, We want to take a shot at what the structural changes of a life sciences company will be. We used to have our little departments for data management and clin ops would you think the companies that you're the company that you're at or the companies that you'll be at in the future are going to look like and how that will be different than they are today, Jason?
That's a good question. So I think so the evolution, I think, will require different competencies. So there will be a different I think pool within the organization. I think there'll be talent that, will be, ingrained and competent within data sciences, for sure, to the use case that was just mentioned regarding the ability to actually look at what is needed from a production manufacturing perspective and being able to take all information and leverage that in such a way that you can create these simulations and predictions, we need support technically for that. And just to be able to understand it, I think, So I think that's one thing.
The other is, from an operational aspect, I think that you can actually federate and decentralized to some degree, some of these things, which actually may help out because the data can be,
what's the
word? Democratized in such a way that you could share it freely and in a confident way. So it's an empower organizations to have access to it so that they were more informed about what's going on, either in front of them or behind them. And this creates a culture of accountability within the organization, which I think is, is going to be important. So I think those types of operational changes will come naturally as the technology and that these, data science competencies start to evolve, In the problem statement, statements will shift
to
some degree as a result of that. Remember, lots
of times that we actually introduced new KPIs into projects that we're on together. And that the introduction of that visibility and transparency was what changed some
of the behaviors, right? Exactly. I think, we quote a bunch of different authors around this, but when you measure it, people change their behaviors based on just the fact that it's being measured. And one of the benefits of metadata I think is not only the scientific data being managed, but also the operational data that's being exposed related to site performance. The quality of the data that's coming out of the system And you change your behaviors and you become very competitive.
So if you actually have the industry, if you have over 50% in the clinical trials for the last 10 years, I can actually benchmark my performance relative to my competition or others. And just exposing that to the leadership of the company, you were naturally competitive. We want to be better Right. So that alone, I think, helps, even with the sites. If you expose it to the sites and the investigator sees that he's the slowest of the underperforming relative to his peers, his or her peers, then their performance naturally changes.
Actually, I'm curious, about both of your opinions about this that a patient is equally, part of that equation. Like, one of the things, and again, it was part of your motivation around Mitra's and informed consent. Putting things in front of the patient will actually make the patient behave differently. That could have real therapeutic impact impact on the outcomes of the therapies you're developing, right?
So to me, going back to your question, I don't think we will see massive changes in organizational structure. I think companies have structures just make sense of the chaos of drug development, right? But one of the things at least you know, at Bristol Myers Squibb, one of the things that we have noticed is, you know, through the, our digital health effort, which was a company wide effort where, leaders from many functions actually came together to figure out what are the questions that we need to resolve and how do we go about resolving them? And, and then out of that came, the operating model that now allows a translational medicine person with the biostatistician with the Trailers and with the analytics person actually to work together to, solve those problems. And, and that mindset of there is no single group that can actually solve the problem.
You know, you need you need a village to raise a child. I think you need a village to raise one of our challenge, our our child, or children who that is a, a drug that we need to, to get to market. So to me, a, the talent that we're bringing into the company is one, they have a very different approach to, how they work and an expectation that they will be part of an ecosystem, not part of an organizational structure, chain.
More of a more of a collaborative change than a difference in the way, actually, Jason was talking about before people communicate doing science at certain scales and with certain models, you need to have the communications integrated into the actual innovative platform. So, a lot of what Medidata has done over the years, has, come from our clients, not from us, And actually, Jason and Marissa have, as you've already heard, been involved in many of those conversations. So I like a little pressure. I didn't tell them I was going to ask them this question. But, now you've seen a bunch of things.
Do you have any requests for what you'd like us to work on next of Medidata?
Well, how much how many hours do we have?
I I
have the rest of my life.
I'll take you up on the offer.
5 minutes. 5 minutes now,
but we'll keep working on it.
Well, to me, is, again, in the spirit of precision medicine. Precision medicine requires not only data and analytics and, and the physicians, but precision medicine requires that, we convince the physician that, in algorithm could be the solution to a diagnosis and a treatment. And for that, to convince the physician or to convince the regulators, that's that, that is the case. We're going to need is to validate all the model, all the simulation, all the models that we put forth. As a as a treatment pattern.
One of the things that, that I was talking to, Glenn about it is, in order to do that, we need to now bring the power of real world data, the power of clinical data, combined with our kind of regulatory experts and our biostatisticians and start really developing tools that that will be deployed to, physicians to ascertain and to decide which patient requires which drug. With, a lot of degree of confidence that that in validation, that that, is actually the right treatment pattern. Because of 2 things. One is the, the, the patient privacy laws will suggest that they there's a clause that says the right to an explanation. And, physicians that don't understand am the black box of any AI model.
And so on and so forth, we'll never use a, an AI driven decision to actually, tell the patient what or even, what treatment you they, they need to pursue. In therapeutics. I think we're going to have to work together across the IO between the real world data space and the clinical space. To actually validate those algorithms and convince the regulators that we could do, we can stratify patients and decide treatment algorithms through AI.
Right. Patrick and I are on that one.
To that point, I think the FDA and others other agencies are really thinking a lot about the black box and the AI. And so they're creating guidances and they're modernizing their thinking about how to actually create softwares and medical device and algorithm as is a treatment, recommender, if you will, or a therapeutic, recommender. But I think one of the gaps that I would like to see metadata this will work on would be, I think one of the issues we have, I think, with patients is that they don't really control their medical record. So how can we technically solve that problem as an industry would be something I'd love to see you guys work with because you have the clinical data. You have access to real world data through claims and pharmacy and different mechanisms of extracting this from the insured and MRs, etcetera.
But the patient, if they want to really subscribe to research, how do they get their retrospective pool? Do they get their genome and all their phenotype and all the treatments in the the polypharma that probably exists that actually can help with stratification can help with actually marrying that up with the clinical studies, to do this profiling that's really needed to decide which patients are going to be more susceptible to a certain treatment or an adverse event pattern. So that to me might be something that you can actually supplement or augment your suite of products with that, that enables a patient of the opportunity to just go in and say, look, I want all my information to be housed in my medical record. I want to participate in clinical trials, have everything under my control and in one environment. And just imagine the power of that if it gets big, too far behind biotech actually exploit.
That to me is something that would be exciting to think about.
It's actually kind of interesting set of things together. You've got the black box, even giving people the patient the visibility of what the inputs to that black box are, is it improvement of what we have probably pre requisite to getting people to understand trust and and have that validation.
Yeah. And if I can have one more wish, when I close my ice and dream, now that, that, you guys partnered with, a European company, right, as much as we have availability of data in the U. S, we have very, very little as it relates to, you know, access to patient in, in Europe. And I was talking to the European commissioner not long ago, and, and she said to me, Melissa, we have shown that we can reduce by 50% the time to disease diagnosis from 5 to 1 point 2 to 2.5 years, just by using data. And yet, only 9% of our patients, have access to EMRs, and most of them limited access to EMR.
So to me, that in there's a lot of, momentum in Europe to drive Digital Europe to drive the standardization and interoperability of platforms. I think what they need is really a somebody who understands how to do it. There is a lot of momentum and there is a lot of investments. In this area by the European Commissioner, we're talking about 1,000,000,000, which is not a minor undertaking. I think we need, solutions for our European counterparts.
All right. So it's on the list. Jason Marissa, thank you so much, because our very busy. It means a lot to us that you take the time. So hopefully, you'll all think this is worth me
and join me in thanking
Thanks a lot, Marisa, and Jason Pascal and Ruben may I ask you to go on the stage And you will have a Q And A session, after Pascal and Reuben presentation. Okay.
So good morning to all of you. Good afternoon for the one being attending the sessions through the webcast. It's my pleasure to conclude these sessions, and I hope you saw many examples read illustrations of what I shared with you, almost since June, since the announcement of the acquisitions. So before I'm going to make a quick disclaimer, not a forward looking statement disclaimer because it's due to my articulation. And please do not tell me at the end of my session that it's almost the same than usual.
I'm not sure I will take it positively. So, what I want to do, today is ready to, focus almost on the three questions you raised since we announced the acquisition of BD Data. Does not mean that what you have seen today will not contribute for the growth for the next 10 to 20 years, but I want to focus on the your 2018 to your 2023 plan. That's the purpose of my presentations. And the three questions I want to address are the following.
The first one is, why you made such a big investment in the health care? I remember doing the roadshow at the time while the bones, and many of you asked these questions, and I will give you some answer. The second question is it's a it's a domain per se with a lot of players and we are not sure we understand the competitive landscape and how you are planning to differentiate yourself. I hope you have seen some concrete things today, but I will come back on this. And the last point will be finally, what will be the contribution of Medidata to the year 2018 to 13 plan, 23, sorry.
And for that, I will share the presentation with Ruben. Because Rubin is a former CFO of Medidata, but now he's the COO. So is a guy being accountable to make the post merger plan a reality and a success. So that's the reason why I want him to be on stage with me because it's a way for him to be committed. I'm not be the only one.
Okay? So this is for the introductions. So let's start for the first questions. Why are we investing so much money in health care? You know, we are a long term company.
And being a long term means many things. It means that we had to prepare the growth at least 10 years in advance. The proof of that is not only we are sharing the 20 years vision with you, and this is what they are now did this morning, We are, committing our plan on the 5 years periods, but we have also to see it this way. And with I took the framework, the purpose, you have seen it, Bella presented it, I put the percentage of the GDP, And you see that the traditional manufacturing industry represent 20%. The what we call infrastructure and territory close to 50%.
So to a certain extent, you could ask me the questions why you are not reinforcing the position you have in the manufacturing industry, oh, why you are not trying to expand your footprint in infrastructure and territories? And the answer is here. If you look at the gross because at the end, the only purpose is to fulfill the gross, the organic growth on the long term. The GDP growth for the health care is twice than the other. At least this is what we are expecting for the next 20 years.
And the reason to believe are the following. There are 2. Today, only half of the population have access to the health services on the health. So 7,000,000,000 people on this 3.5. It took for many countries more than 40 years for them to be able to cover 100% of the populations.
This is what's happened mainly in Europe and in the U. S. More recently, you have a country like Spain or positives, Korea, it took 20 years. And if you look at the last trend, China, for example, it's only 10 years for them to have to almost have access to a coverage of 100%. So in all the nations, they took the commitment that in year 2013, 100% of the worldwide population will get access to the basics health care services.
So this is at a high level, a big trend. The second trend is the following. There is a strong correlations between the life expectancy adverse with the spending. And you'll notice on this graph, you have few bubbles. So in average, it's around $4000 per capita.
And the life expense I see is adversities exceeding eighty years old. You have an exceptions, the US, They spend almost twice. And the life expectancy is a little bit lower. So it means that health is not only the health care and how you treat it, but it's also how you behave, how you take care of yourself. So I do not want you to restrict the definition of health care to only the pharma and the main device sector.
It's much more broader than that. And we are engaged to bring also this on the long term. And then you have a bunch of countries where usually the coverage is not at 100% of the populations, and we see this acceleration trends. So if you combine those two things, We had good reasons to believe that the GDP growth for the next 20 years will be twice the traditional manufacturing sectors of the infrastructure and territories. That's the basic reasons why we took these decisions now to invest to fulfill the organic growth for the next 20 years.
Because there is no way you can, you know, give access to the health services to the worldwide populations the same way we did for half of the populations. The economic equations need to change. I think Tarik highlighted in a very good manner in his presentation when he told you that there is a new innovation cycle starting. And I think this is the reason why we have the legit the legitimacy to be in this space. Because you could also questions why, guys, what would be your value added to come to this And against, we are convinced that the innovation cycle in this industry is science based.
And everything we do since day 1 is based on the fact that we put science into an IT system. So that's the reasons. The second reason is because you have seen this number. The result of inefficiency. This industry cannot continue to spend as much and the health services has to be affordable for the worldwide populations.
So the economic equations needs to change. And this is where we play a role with this. Against it's everything you have stated. I'm just summarizing just for you to be sure you have the story. So What we are bringing is unique because not only it's a way to cover all the different discipline to foster innovation cycle, And you have seen it in actions with Jason, Glenn as well as Claire.
And you also have listen carefully the testimony of the 2 customers. But it's because, as Bernard said, we have a different way to promote this value. The first one is through the solutions, we are maximizing the outcome and the outcome is at stake for this industry. The second thing is, if you want this to be master with a high level of quality, with a lot of efficiency, with reducing the time cycle, sorry, you need to also foster the collaborations between all the different disciplines and we have defined the processes to make it happen. And last but not least, with all the roles we have to cover all the different disciplines.
We know how to equip all the people being involved in this innovation cycle. And everything is relying on one single platform. And again, remember, this platform is unique because it's the only way the only platform being able to manage the knowledge and the know how in the collaborative manner, combining the 2 approach one which is a data science and the other one which is the power of the imagination through modeling and simulations. That's the core of what we do. If you look at the landscape and I briefly discuss it through the Q And A, it's highly fragmented.
And none of the competitors mentioned in the slides could do what we are doing. Let's take, I mean, several categories. If you look at the highly specialized one, especially as a guiding upstream, They have the domain expertise, but they do not have the platform, they do not have the go to market, they do not have the full industry expertise. So to a certain extent, they are very good point solutions, but as many customers say during these, events, you know, their knowledge and know how is stuck in one single systems. And there is no way you can flow this across the buyer of the organizations.
Then you take the big one, the one you know, because they cover many, many different industries. They have an IT platform, but from fish pickings, they do not have industry expertise This is probably the reason why Medidata won many market share against one of the big names mentioned into it. It's also true for the manufacturing parts. This industry is science based And if you don't know how to mix the science with the industry expertise, you are not in the game. And last but not least, you have a newcomer You heard the name, twice, at least.
If you look at the reality, they are, doing twothree of their revenue with CRM systems. Salesforce Automations. This is where the revenue is coming from. The revenue is not coming from the product lifecycle or the drug lifecycle management. Even if they claim that they have an ambition.
And if we zoom, they only have a point, which is quality and compliance management. And through the demo you have seen from Jasons, I hope you understood through this demo that the game will go over to be document based. Because we have to be data driven and generate automatically all the reports for the regulators. That's the way we are planning to do the change. That's the way we want to become changer.
So my point is no one in town can replicate what we have. If you sum up the industry expertise, the platform, the knowledge and the domain expertise, and this will combine with 2 teams willing to be together. And Tarek has stated very clearly that the culture is the same, the DNA is the same and the will to transform this industry is here. So the third question is related to the contribution of our 5 years plan. So the first point is this 1.
You remember, we committed to double the addressable market over the time, going from USD 16,000,000,000 to USD 32,000,000,000 with 3DEXPERIENCE platform, we announced it in year 2012. Where are we? So today, you will see there is animation on the slides, sorry, Yes, thanks. It's a USD 38,000,000,000 addressable markets, of which 8,000,000,000 is coming from the health care. And against this market sizing, it's a pure software revenue.
It's the complications, the addition of all the existing revenue coming from all the point solutions you have seen previously. This does not take into account that you still have homegrown systems running in many, many pharmaceutical company. So my point is this quantifications of the market is probably the minimum quantifications. The second message is the growth expected is 10% growth. On an annual basis.
And the main drivers for now is on the slides. Is the growing pipelines in terms of drugs. Today, it's more than 16,000 new drugs in the pipelines, It's almost twice compared to your 2001. And you see an inflection point because in your 2013, the trend changed dramatically, which is a proof that there is a new innovation cycles going on. And this is a proof also that the complex city of developing a new drugs is higher because you need more options in your pipeline to make it happen.
That's what is driven the market now, knowing that my belief, and I think it's also the belief the BD Data teams. The coverage of this market is not well done. You only have few company being served. And the penetration of all the solution is quite limited. It's almost a third.
So if you combine all those levers, you know, I'm pretty convinced that the 10% growth will be sustainable over the next 10 years. Now how do we split these markets? And again, it's for you, just to have a sense, So you have the 3 big existing markets, the lab, 1 lab, you have seen it with JSON demonstrations, the clinical trials and also the production side with what we call the make to cure. Does not mean that the users are small. It means that the users are not well deployed.
That's the point. Okay. That's for the market growth. Now from a market share standpoint, where are we? With the combination of Medidata And Dassault System, we are number 1, with 9% market share.
Almost twice than OHAACLE and SNP and slightly ahead compared to Veeva. But I just want to remind you that in VIVA's numbers 2 third of the revenue is coming from the CRM. It's not coming from the market we want to tackle. That's very important for you to understand. The second message is if you assume that the market growth will be at 10%.
And we will continue to gain at least half a point in terms of market share per year. We will be able to continue to grow at more than 15%. Now how this combination will work effectively? Here is the way we want to do it. Maybe data is becoming a business unit as part of Dassault System, and you have seen the management team with Tarek, Glenn, and the Rubin.
They are the leaders, and they will continue to run this business and continue to grow this business leveraging the rest of what we do. The life science industry is becoming the 2nd largest industry for us, slightly behind the auto sectors, but bigger than the aerospace sectors. And I remember you pushing me, punching me, I should say. It's not because I have stitches in my mouth, but punching me about that we had too much dependency on the auto sectors. A certain extent, you have an answer now that we are balancing the revenue and the footprint across multiple industries.
And last but not least, we will combine the go to market of Viovia with Medidata because there is so much company we are not serving. I gave already the numbers, but if you think about it, we are touching only a third of the market. And we are touching a third with almost a third of the portfolio we have. That's the point. So if we structure the go to market and it would be a dedicated one, does not mean we want to leverage the rest of what we do.
It has to be dedicated because coming back to the question we had during the Q and A, it's a different way to engage. It's a different way because they are at different stage of maturity. We need to be able to start in a different manner compared to other industries. So that's the reasons. The second reason is because you need to have high degree of industry expertise across the field ops operations.
And it's not easy to build such a team. And I think if you combine the 2 team, we have, by far, the largest field operations to serve this market in a proper way. And in additions, BD Data and I and Dassault System, we started to build the complete ecosystems. And you have seen during a Tykes presentations, the CROs, company like Pikesell of IQVIA. And I remember some of you during the roadshow telling me, but they are your competitors.
And I say no, they are our partners. Why so? Because you know that we have an indirect model for the for the traditional manufacturing industries. SOLIDWORKS is promote and market through an indirect model. We could do the same and the name, but the vowels are not the vowels.
There are a different nature of partner and the CROs is a good example. They could become our partner to at least expand the reach of this market and serve them in a proper way with against a high level degree of expertise and maybe some new business model because we could be much more linked to outcome what will be produced with our software. Ruben, I think it's the time for you to come because I think you have a few things to say to the crowd. Thank you. Thanks, Pascal.
Yes, Pascal, as you already introduced, I think, what's top of mind for you is to understand how does our growth plan going to look like, for the next 5 years? And Before I go through the components, what I want to start with first, we think that this is a very attractive plan for investors. And secondly, we believe we are very confident that this is achievable for us. So the 13% to 15% is
what we are
targeting, now for the next 5 years, every year. And there are a few things I will this is a very simplistic summary but I think it is a good framework for you to think about how the, what the different levers and vectors of growth are going to be for us. The first one, our core business. You heard a lot from Glenn earlier today in the product presentations and our really strong presence in, electronic data capture and how we serve the life sciences industry and operate clinical trials. The same point in time, Pascal just walked you through, the overall market economics, right?
So it's a very strong growing market. So and we have a very good track record of taking market share from the competition, Pascal alluded to this. So we feel very confident that we continue over the next 5 years to take market share from the competition. Number 1, number 2, that there's continuous growth in the market. There will be more clinical trials started because That's how the industry, as Marissa said, that's how they're changing.
There will be more precision medicine, more trials. This would be more opportunities for us to monetize. And then the 3rd point That's why I was so particular earlier in the morning in my presentation was around our pricing model. Because the way we've defined our pricing model, we can we are capturing at the level of clinical trial metrics, the pricing. So the way we are not contracting with our customers, they don't think that they have an all you can eat type arrangement.
They don't have that. As when they start more trials, we will be able to charge more. So that will be the reason why we can monetize this growth with our core capability. So we believe that 6 points of the 13% to 15% over time will come from our core product offering, that's also what we have shown over time. So we have a good track record of at least delivering these growth numbers.
And then
again, you saw the capabilities that we have built, through continuous innovation. We serve really from the submission of, from the enrollment of patients all the way to the submission of data to the authorities, we cover all aspects of the platform. So we have a lot of products and capabilities to attach to increase our share of wallet, and that adds another four points, we believe, conservatively. So if we put that together for our core competencies, that's about 10 points of growth that we will add over the years. By continuing to do what we do.
I will also mention one additional point. I think Pascal alluded quickly to this as well as Marissa mentioned the difference between the U. S. And the European market. When you look at Medidata, We are very focused on the U.
S. Market. We have about 75% of our revenue is U. S. Based.
We will be part of a European headquarter company, and I think our presence in the market will be much stronger. And that gives us better access to clients and will also strengthen our core business. So I think these are all arguments to believe that the ten points are realistic. For data and analytics, you see this is really one of the big opportunities that we have to help transform the industry and conservatively, we're assuming this to have very marginal contribution to our plan, but of course, we are much bolder in the way we we pursue the opportunity. So I I leave it there.
Professional services, we talk a lot about the importance of it. They are an enabler for transformation. The domain knowledge is very critical, and it's differentiating us in the market. So that will continue to add growth to our company as we grow. So this is also nothing changing compared to what we've been doing.
And then, I think we also wanted to make sure that, you know, as a combined company, we have to really be, thoughtful around, we address our synergies in the market and how we build the framework to go after a much, much bigger opportunity as we hurt our customers need to transform and we can only do this together. So bringing these capabilities together should at least add two points of growth over time, and you see we don't consider this to be immediate, but over time, to contribute marginally to our growth. And of course, it with the goal to be much bolder and drive more growth from that. So that's the top line picture. Now let me transition to the bottom line picture.
And here, also the same time frame, the the next 5 years, we, Our plan is to achieve 27 percent, profitability. That is about, 2 percentage points increase year over year or 200 basis points year over year increase. We believe that we have a real opportunity to improve we have done this as a stand alone company. We always set about 80 to 100 basis points organic margin improvements year over year. So we have a track record of driving operating leverage in our core business.
And that's one thing that we will continue to do as well. You see this here, operating leverage, we estimate that to continue with about the contribution of 6 points to this plan. So That's about half of the 200 basis points per year that we will achieve a little bit more, but this is, of course, of framework and I think maybe conceptually to add to this, there are multiple ways for us to achieve these numbers, of course, right? It's not just one way there are multiple levers that I'm introducing here. And of course, now we have an opportunity to really go after synergies, and accelerate that path to increasing profitability by combining the organization, and for us, when we think about a number of areas in R&D, we we've been becoming, as a cloud company, more and more exposed to some of the increasing cost trends, that infrastructure as a service company companies like AWS has introduced to us.
We now have an opportunity to reverse this trend by leveraging, vessel systems, out scale capabilities over time and really become more efficient in providing our services cloud power to our customers everywhere in the world, right? That's so important because That's also where the regulation is changing. We have to be closer to our customers. And this would otherwise to be, as a stand alone company, massive investments we would have to take. Resources and engineering resources.
As we are continuing to grow our portfolio, we have a real opportunity to optimize our resource mix and location mix. This is something that would have been an opportunity. As a standalone company, we would have to do, but now We are having an infrastructure that we can work with and can tap into. So that, that's, gives us an advantage. So these are just two very high level points.
And then G and A synergies, as every one of you would expect, are going to contribute to that. I give you a couple of sound bytes. We would have much higher buying power in the future. We are, going to be, working with an organization that already has a large presence across the globe, right? We have 16 offices today.
I think Industrial Systems has 180 offices. We don't need to invest into real estate, right? We are going to be part of a Tassault System, who's already present, and we can mitigate a lot of those, reduce a lot of investments in the G And A area. Yeah, so that's the high level outline, and I hand it back over back to Pascal and
Thank you, Robert. Stay with me because the last slide is the result of, but I'll just just explained is the contributions to the €6 EPS target for 2023. And you remember, we slice the €6 EPS in different pieces. The one coming from the adoption of the 3DEXPERIENCE platform by the largest surveys we have, second one is the continuum of the industry diversifications. And we had this KPI or I would say target objectives to have 80¢ coming from acquisitions and new business model.
And among the 80¢, 70¢ will come from, you know, what Horvan just presented to you, assuming the plan, which is almost doubling the review of metadata in the next 5 years and gaining almost 10 points, a bit margin over 5 years. The rest of the assumptions stay the same. I mean, I didn't change the currency, exchange rate, the tax rate is the same. So when you're going to do your model, you can basically rely on the same assumptions? There is only 1, 2 things.
You remember, now we have debt and the financial interest and contribution will not be the same. So clearly, we expect to have a deleverage over the investment cycle. So to come back to one times EBITDA, net debt over EBITDA ratio in the time of the investment cycle. So today, after the transaction, we had 2.5 And last but not least, you know, there is the EBIT margin of Dassault System including, Medidata will not be below 30%. So you have my commitments that we will stay at 30% plus.
And I think what Rubin shared with you. It's not only a realistic plan, but we know how to do it. And we have been able to demonstrate on both sides that when the, when it's time put in place the right set of actions, we know how to do it. That's it. I think it's time now to take some questions.
And to do so, maybe we'll ask, all the different presenters to come on stage for the second part of the session.
Thank you, J. Please Howard, Griffin Securities. Two questions. First, I'd like to return to the subject of integration, specifically product or technical integration. We saw some examples earlier of the life sciences industry solutions you've already introduced, by DS.
And those were the culmination of what I think were, one of your most important internal initiatives for cross brand, integrations across DELMIA, Somalia, but those did take time. And so the question is, how are you thinking for the intermediate term of the initial batch of co packaging, co integration between metadata and what we just saw on the life sciences side from DS and who in fact would manage that? Is that moving or who's going to manage that process? Secondly, we've heard the term, outcomes quite a bit today as in patient outcomes, for example, What is the potential for, employing outcomes based pricing model and or marketplaces to any or all of, life sciences development, commercialization, and or even patient therapeutics.
Glenn, you take the first one. Thank
you.
So, I actually as you heard from Marissa earlier, a lot of the projects that we do that are innovative, they're using use cases with clients. So I that there will be projects where we will collaborate with the other teams that of certain use cases. Obviously, it's going to be use case dependent, but that's where we'll bring the elements of data together and the elements of the ontologies together. So I don't know if I'm going to scratch the edge on, well, what day is something going to be done, but that's because we're in the process of identifying those exact use cases in those first points. That will then proliferate into more and more platform integration.
Maybe I could add a few things. So from a timing standpoint, The first phase is against, as Rouven say, is to leverage the cloud capability we have. So the number one priority for 2020 is to substitute progressively, you know, the cloud, the infrastructure with 3ds health care, point number 1. Point number 2, we have 2 platform, the 2 platform needs to be connected. And we have a strategy so called power buy to make it happen.
So we will develop the power by strategy in 2020. It will be probably on the market in 2021. And then we will start to develop as Claire and Glenn and Jason demonstrated some, I would say, combined products. Again, we will start the development in 2020, but you notice that in the revenue synergies, Reuben was highlighting the most of the impact will be 22, 23. So this is, in terms of sequence, what we are planning to do.
And we expect to have the completeness, I would say before 5 years, right? My friend? Just a second question related to the outcomes maybe for you.
Yeah. Happy to answer. So, customers are at times quite interested in in risk based contracting, one kind of outcomes based contracting. What we seem typically happened in the past is that when we go down that path, as you start to think about what the up side or the downside for them, but the upside for us is they come back to a much more traditional contracting model. The one space where I see that there's a divergence early days is with Acorn because we see that, some of the smaller biotechs who are so tied to outcomes.
And there's a lot of potential variability. They wanna either get to the finish of a study so that they can submit to the FDA or have a monetization event, maybe more funding or sell the company, they're willing to put themselves put more of the contract at risk. And quite frankly, we do have an interest in doing that, but it's going to be, I would say over the next 5 years relatively small part of our overall strategy. We're certainly open to it, but there's not, you know, the the inter there's not that much industry momentum behind it.
Claire, you maybe want to add a few things?
Yes, sure. So, in the past, I've been involved with pricing and reimbursement at the Ministry of Health in France, and I've you know, growing interest in outcome based payment. And so what I want to say is, even if we remain in a traditional, pricing model, there is already a lot we can do to help, life sciences companies moving towards this outcome based contracts because they need to be able to demonstrate outcome to pay your and all the data science that we do are going to have them do that. But then if you think about the 3 Dayforce platform, there is really two sides of time, right? There is the platform as a system of operations, and we could already help our licenses customers move towards outcome based pricing, but now we should think about this different platform as a business model, there is much more we can do to connect the extended value network and here you might want to think about more innovative way to price outcome based payments being one of this way.
Market estimates weren't out there for Medidata all the way to 2023, but there was already some pretty bullish expectations for 2021. If we extrapolate those clearly reconciles with your helpful presentations around what you're expecting for the business. Not with any of your comments around the product roadmap and how that comes together later, given the compelling logic we've heard today and the encouraging responses that you spoke to among customers, is there not opportunity for near term synergies purely from the coming together of the 2 sales organizations. Link to that, I wanted just to ask you for a bit more detail around the go to market. You mentioned maybe an opportunity later to, pursue the indirect channel.
And at the moment, what's the sales process like for metadata? I presume it's a very complex and bold sales cycle. You know, there some overlap and benefit there to the coming together of the businesses? Thank you.
Let me take the second question. That's okay. So our our go to market model, It it's a mixed model. So, let me explain. So we have a direct model where we sell to pharma, life sciences, med device, and we have a seal, a Salesforce field Salesforce, as you would imagine, geographically based.
And they sell the breadth of our solutions. There is an indirect channel, but it's not in in the sense of the Dassault System indirect channel. There are CROs and some SIs who also are our partners in sales. So, we we view them as our indirect channel, but they are really implementers. And, so the CRO industry does has multiple functions for pharmaceutical companies.
But as it relates to clinical development, they are they are outsourced, data management They are outsourced monitoring, so going to the, clinical sites and making sure that everything's happening. And then there are some other functions that that they provide Historically, we've worked with them as a as a channel partner. So what they would do is they would COVID, but say there's a study that's being started and the CRO wants to do the implementation work. They would cope with Medidata's EDC product, for instance, And, when we won, it would be either on both of our paper or on their paper, and we would be a pass through, always a pass
through to
the customer. That relationship with the CROs has changed over time. It's become much more strategic where they are consuming our product, folks, to make their own operations more efficient and they're committing to, our to use our technology and then reselling it as they resell their services to the pharmaceutical companies. And that's sort of so that's the model that we've had. We continue to see the CROs play an increasingly larger role in our sales efforts, but we will have, you know, maybe I'd say at this point about 70% plus of our revenue is coming from our direct channel And that's gonna continue to be a a significant, effort from on our part.
So in fact, as we come together as organizations, the breadth of our the the the the breath of what we can do on a global basis from a direct perspective actually increases because of some of the the, as Rubin mentioned, the presence of the SOS system in Europe, just stronger than we are there, and also they have a much longer, larger presence Asia Pacific, then we do even though we have a certain amount of revenue that comes from there, China, for example, it's a very high growth market for us, but I think we can accelerate there.
To complement what you just said, the CRO is only a category of partner for the research. But you know, you have the equivalents for the manufacturing side. So clearly, it's an entire new ecosystem we are able to build. Expand the footprint and the reach. So please keep this in mind because it's only the starting point.
The sole things coming back to the questions, what can we do in 2020 from a go to market standpoint? You have to be realistic. The two teams will be together for the first time. And I do not want to take the risk to default them. Because, you know, it has to be well prepared.
So the plan is the following. We are working on the name list of accounts. So it will be a limiting number of accounts and we will approach them together with a combined value proposal. For the rest, We will continue in 2020 to promote independently, but it's only for 2020. Great.
Thank you.
As Carlos, on the financial plan, I was surprised that the G and A optimization is sort of limited to two points. Given, I think, Medidata's G and A to sales ratio is actually quite, quite high. I think it's in the double digits. Are there additional investments you're sort of baking in? And then also, when it comes to sales and marketing, is there any optimization there or even acceleration of spend baked in the plan?
And then lastly, on cash flow, I know that the cash conversion of Medidata relative to Dassault is quite different. Can you shed some light on any plans to improve cash conversion?
Okay. So I will start with the last one, and maybe the G and A, you don't have what you want, Ruben, feel free. So the cash flow is to do the math. You have a revenue growing between 13% to 15% and you have a leverage of almost two points per year. So automatically, the cash flow is almost triple over the time of the 5, the next 5 years.
So going from a little bit less than 100,000,000 to a little bit less than 300,000,000. This is a plan. And I'm sensitive to that because, as you know, we have debt, and we took the commitment to deleverage rapidly. And you remember, for the 1st 9 months, we have generated 1,000,000,000. So you could expect, as to continue to be on the same trend for the next few years.
And half of the cash flow will be used to reinforce the debt. That's the plan. On the sales and marketing, could we accelerate and spend more? Yes. But before, you know, I think Bernard stated very clearly, the customer needs to be ready also.
And this market is shifting now. So I do not want to be in the positions whereby we overinvest sales and marketing and discover that the is taking more times than expected to ramp up the sales. You almost blamed me for the last 5 years because I was too pushy on the 3DEXPERIENCE platform. And now you start to see the light with big deals coming. So I want to be almost in the same positions, manage it collectively because we are on the same boat.
And we will decide together how far we go from an investment standpoint on the sales and marketing side. But right now, I think There is so much we can leverage by having our salespeople promoting the combined solutions that it's probably the first priority for us. On G And A?
Yes, and maybe before I come to G And A, one maybe clarifying aspect to cash conversion on the Medidata side is Cash conversion is always also a function of the timing of invoicing. I think our invoicing timing is very different to the way that our systems currently operating it. And of course, it remains for us to see if we are going to adopt this and change, but we typically invoice quarterly in advance. We have some some contracts where we also invoice in a rear based on a consumption basis, so that delays cash generation. But that's just to keep in mind.
We have a track record of increasing cash flow over time. So it's never been an issue for us. On the G and A side, I think to answer it very simply, no, there are no incremental wind plan, that would be a headwind for us to be able to get more leverage out of G And A. In more detail terms, I mentioned buying power. I think we have to still quantify what that is going to look like.
We want it to be prudent. Secondly, we will be, as many data, we haven't taken this step yet to think about global share services. So we pretty much operate with a template operating out of the United States, which will give us an opportunity to tap into certain infrastructure of Tassault systems that I think will give us definitely incremental leverage. I don't see that we would have to invest into G And A to continue to grow our business as we have been had to do in the past. And so all of these factors will contribute.
And last but not least, this story is not about, cost saving. It's about growth. And there is something maybe that is doing we do not have yet your full experience. It's a pure SAS business model. So all the G and A have dedicated to support the pure SaaS model, and I think it's our common interest, with the rest of what we do to leverage this expertise And to some extent, diffuse, put the right leaders at the right place, and we want to capitalize on the current organizations to make it happen.
Just a follow-up question on share based compensation,
and
how you that to evolve over time on the metadata side. I think there was a 9 point difference between non IFRS or non GAAP and GAAP margins you expect that delta to remain the same over the next 4 years?
You want to take this one?
Frank? End it correctly.
No, there are two questions, I think. You have the accounting treatment? So clearly, we will apply the rules of what we do currently. So it will be it will be part of the non IFRS. And then there is related to the policy, I guess, Are we continuing to do the same things and what we do compared to because we have a different practice on the way we compensate people and the way we do association, we associate the people.
But there is one common thesis, if I may. We are rewarding the success. So if we stick to the plan, I just highlight to you and this woman, The practice will be almost the same than what you did.
I think we have a track record of attracting very good people and retaining them. So our regrettable attrition rate globally for a software company was under 4%, which is highly unusual. And that's because of the benefits package we have. It's because of our paid practices, etcetera. Obviously, we can't be a special snowflake in the sosystem.
We have to be part of the family. That's what we signed up for. But I think there are there is definitely room for us to to do continue the practices and benefits that we have in a way that makes sense within the larger family that we're now part of. So we'll continue to I think the big focus is hire the best talent and execute well. And I think that's the focus for for everybody, up on the statement now.
We will take one last question.
Yes. It's Charlie Brennan here from Credit Suisse. Just three really quick ones. I'm conscious of time, so it will be quick. Firstly, just to follow-up on that share based payment comment.
Can you just help us from a modeling perspective? What's your expectation of the 2020 charge for DASO in shared based payments, including metadata. Secondly, we've heard about Altscale a few times as a source of synergies. I can understand how this solves a short term utilization, a problem, but over a 5 year, view, can you just remind us why you're competing against the hyperscalers? Quite a few of the software companies are going the other way and embracing them And lastly, from a software company business model, I don't often hear companies talk about wanting more services Most people want to embrace the partners, whether it's a cognizant or an extension or can you just remind us of the business model?
What's the margin profile of services for you? And why not just give it to the airlines?
You want to take the services piece?
Yes, let me address the services fees. So, actually, services in in the business that we're we're in are are atypical for most SaaS companies. As you know, the the model for a lot of SaaS companies that they effectively give their services away. They have 0 or negative margins on it. We drive between 35% 40% gross margins on our services, they are an integral part of how the software is delivered and maintained by our customers.
And they value it. We have customers who many years ago, over a decade ago, implemented Medidata's core EDC platform and who maintain services relationships with us over that entire time. So if you look at about half of our services revenue, it's repeat business. It comes every single year from our customers, in fact, it's grown over time. So it's not a commodity service.
Yes. It can be outsourced. CROs can be an outsourcing partner. Some of the SIs can be, but we bring I think, a depth of knowledge and understanding that our customers value, and they're willing to pay for that. And it drives a very, I think, a very profitable very well margin business for us.
We continue to see that growing. In fact, as we look at what's happening with Acorn AI, we see that, being able to deliver the technology is very important, but having the right service wrapper so that you get the value from the technology is something that our customers are looking at. That's not gonna go away overnight.
For the stock based compensations planned for 2020, my recommendation is wait fab next year. Because we are here to discuss, the long term. It's a capital market day. It's not, you know, an annual objective definitions meeting. So clearly, I will give you the details at that time.
So last question was related to the sales synergy, right? It's maybe something. Maybe you can take this one over because you have the guy been negotiating with that.
Yeah. Yeah.
I think what's underestimated and underappreciated is you create quite some dependency. And, having the ability to offer a, highly scalable and a service that, is is regional and not just not just global, but also local to where your customers are is extremely critical. I think it's going to be a key differentiator. For us, is, you can look at us as a small company and a big company, right? Our spend with AWS is somewhere between $10,000,000 $20,000,000.
That's how far I can call it precise I can be. But I also have to say that it is very little compared in terms of business volume. When you look at our overall business and customers that we serve, it's only a small part of it, what goes through AWS. So when you think about how we are going to scale this out into the future One of the concerns that we would have is how much money would we have to invest into an into an AWS in order to bring all of many data to these hyper scale companies. And of course, they're all coming and want our business.
I think having options and trust and being able to, leverage them is a significant advantage.
Well, I think my conclusion is we have the best team to serve the market. Really, I do think this is the most important criteria. And, I think with the best team, we can truly become game changer for an industry where we think the needs are there. In every countries. And the evolution on the timing is right, Lloyd.
So that's what we're trying to communicate to you today. And I hope that you have seen things that you have never seen anywhere else, in a real demonstration. On everything you have seen, are not prototypes. They are real existing solutions as we speak. One thing we are going to offend to do it.
Congratulations to all of you. And thank you very much for participating to this event.