>> Live from Las Vegas, it's theCUBE! Covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to theCUBE's live coverage of the AWS Executive Summit here in Las Vegas. I'm your host, Rebecca Knight. We have three guests for this segment. We have Joe Donahue, managing director at Accenture. Hal Stern, AVP, IT Engineering Merck Research Labs. And Derek Seymour, Global Partner Leader Industry Verticals at AWS. Thank you so much for coming on theCUBE. >> Thank you! >> So, we're talking today about a new informatics research platform in the pharmaceutical/medical research industry. Will you paint a picture for us right now, Joe, of what it's like today. Sort of what medical research the time frame we're thinking about, the clunkiness of it all. >> Yeah, so it's a great question Rebecca. Drug discovery today generally takes more than a decade, it costs billions of dollars and has a lot of failures in excess of 90%. So it's not an exact science, we're generating more and more data. And at the same time, just our understanding of human disease biology continues to increase. These metrics haven't really changed. If you look back at the last coupe of decades, it's a 10 year plus process and that much money. So we're looking for ways that we can apply technology to really improve the odds of discovering a new drug that could help patients sooner and faster. >> And that will ultimately save lives. So it's a real social problem, a real problem. Why a platform for this? >> I think if you look at basic research, and you talk about basic blood sciences research, the lingua franca there is chemistry and biology. And we still don't really understand all the aspects, all the mechanisms of action that lead to chronic disease or lead to specific disease that we're interested in. So very, very much research is driven by the scientific method. You formulate a hypothesis based on some data, you run an experiment, you collect the data, you analyze it, and you start over again. So your ability to essentially cycle your data through that discovery process is absolutely critical. The problem is that we buy a lot of applications. And the applications were not designed to be able to interchange data freely. There is no platform to the sense of you have one on your phone, or you have one on your server operating system, where things were designed with a fairly small set of standards that say this is how you share data, this is how you represent it, this is how you access it. Instead we have these very top to bottom integrated applications that, quite honestly, they work together through a variety of copy and paste. Sometimes quite literal copy and paste mechanisms. And our goal in producing a platform is we would like to be able to first separate data from the applications to allow it to flow more freely around the cycle, that basic scientific method. Number two, to now start to allow component substitution. So we'll actually start to encourage more innovation in the space, bring in some of the new players. Make it easier to bring in new ideas is there better ways of analyzing the data or better ways of helping shape and formulate and curate those hypothesis. And finally, there's just a lot of parts of this that are fairly common. They're what we call pre competitive. Everybody has to do them. Everybody has to store data, everybody has to get lab instrument information. Everybody has to be able to go capture assay information. It's very hard to do it better than one of your competitors. So we should just all do it the same way. You see this happen in the cable industry, you see this happen at a variety of other industries where there are industry standards for how you accomplish basic commoditized things, and we haven't really had that. So one of the goals is, let's just sit down and find the first things to commoditize and go drive that economic advantage of being able to buy them as opposed to having to go build them bespoke each time. >> So this pre competitive element is really important. Derek, can you talk a little bit about how this platform in particular operates? >> Certainly. Our goal collectively as partners is to help pharma companies and researchers improve their efficiency and effectiveness in the drug discovery process. So the platform that we built brings together content and service and data from the pharma companies in a way that allows them, the researchers, a greater access to share that information. To do analysis, and to spend their time on researching the data and using their science and less on the work of managing an IT environment. So in that way we can both elevate their work and also take away, what we at AWS, call the undifferentiated heavy lifting of managing an IT environment. >> So you're doing the heavy lifting behind the scenes so that the researchers themselves can do what they do, which is focus on the science. So what have we seen so far? What kind of outcomes are we seeing? Particularly because it is in this pre competitive time. >> Well we've just really started, but we're getting a lot of excitement. Merck obviously is our first client, but our intent is that we'll have other pharmaceutical and biotech companies coming on board. And right now we've effectively started to create this two sided marketplace of pharma and biotech companies on one side and the key technology providers and content providers on the other side. We've effectively created that environment where the technology companies can plug in their secret sauce, you know via standardized APIs and micro services, and then the pharmaceutical and biotech companies can leverage those capabilities as part of this industry standard open platform that we're co creating. And so far we've started that process. The results are really encouraging. And the key thing is, you know really two fold. Get the word out there, we're doing that today here. Talking to other pharmaceutical and biotech companies. As well as not only the established technology providers in this space, but also the new comers. 'Cause this type of infrastructure, this type of platform, will enable the new innovative companies, the startup companies, to enter a market that traditionally has been very challenging to get into. Because there's so much data, there's so much legacy infrastructure. We're creating a mechanism that pharmaceutical researchers can take advantage of new technologies faster. For example, the latest algorithms on artificial intelligence and machine learning analyze all of this diverse data that's being generated. >> So that's for the startups, and that's sort of the promise of this kind of platform approach. But what about for a Merck, a established player in this. What kinds of things are you feeling and seeing inside the company? >> You think about this efficient frontier of what does is cost us to run the underlying technology systems that are foundational to our science? And you think about it, there are some things we do which are highly commoditized, we want them to be very efficient. And some things we do, which are very highly specialized, they're highly competitive, and it's okay if they're less efficient. You want to invest your money there. And you really want to invest more in things that are going to drive you a unique competitive advantage. And less in the things that are highly commoditized. The example I use frequently is you could go out and buy a barrel of oil, bring it home, refine it in your backyard, make your own gasoline. It's not recommended. It's messy, it really annoys the neighbors. Especially when it goes wrong. And it's not nearly as cost effective or as convenient as driving over to Exxon Mobil and filling up at the pump. If you're in New Jersey, having someone else even pump it for you. That's kind of the environment we're in right now today where we're refining that barrel of oil for every single application we have. So in doing this, we start to establish the base line of really thinking about refactoring our core applications into those things which can be driven by the economics of the commodity platform and those things which are going to give us unique advantage. We will see things I think, like improved adoption of data standards. We're going to see a lower barrier to entry for new applications, for new ideas. We're also going to see a lower barrier to exit. It'll be easier for us to adopt new ideas. Or to change or to substitute components because they really are built as part of a platform. And you see this, you look at, I would say over time things that have sedimented into AWS. It's been a remarkable story of starting with things that were basically resting our faces on a pausics file system and turned all the sudden into a seamless data base. By sedimenting well defined open source projects, we would like to see some of the same thing happen, where some of the core things we have to go do, entity registration, assay data captured, data management. They should be part of the platform. It's really hard to register an entity better than your competitor. What you do with it, how you describe what you're registering, how you capture intellectual property, how it drives your next invention. Completely bespoke, completely highly competitive. I'm going to keep that. But the underlying mechanics of it, to me it's file system stuff, it's data base stuff. We should leverage the economics of our industry. And again, leverage it as technologist ingredient. It's not the top level brand, chemistry and biology are the top level brand, technology's an ingredient brand we should really use the best ingredients we can. >> When you're hearing this conversation so related to life sciences, medical, bio/pharma research, what are sort of the best practices that have emerged, in terms of the way life sciences approaches its platform, and how it can be applied to other industries? >> What we've seen through the early collaboration with Merck and with Accenture is that bringing together these items in a secure environment, multi talent environment, managed by Accenture, run by AWS. We can put those tools in the hands of the researchers. We can provide them with work flow data analytics capabilities, reporting capabilities, to cover the areas that Hal is talking about so that they can elevate the work that they are doing. Over time, we expect to bring in more components. The application, the platform, will become more feature rich as we add additional third parties. And that's a key element in life science is that the science itself, while it may take place in (mumbles), it's a considerable collaboration across a number of research institutes. Both within the pharma and biotech community. Having this infrastructure in place where those companies and the researchers can come together in a secure manner, we're very proud to be supporting of that. >> So Joe, we started this conversation with you describing the state of medical research today, can you describe what you think it will be in 10 years from now as more pharmaceutical companies adopt this platform approach. And we're talking about the Mercks of the world, but then also those hungry start ups that are also. >> Sure, I think we're starting to see that transition actually happen now. And I think it's the recognition and you start to hear it as you hear some of the pharmaceutical CEO's talking about their business and the transformation. They've always talked about the science. They've always talked about the research. Now they're talking about data and informatics and they're realizing being a pharmaceutical company is not just about the science, it's about the data and you have to be as good and as efficient on the informatics and the IT side as you are on the science side. And that's the transition that we're going through right now. In 10 years, where we all hope we should be, is leveraging modern computing architectures. Existing platform technology to let the organizations focus on what's really important. And that's the science and the data that they generate for the benefit potentially of saving patient's lives in the future. >> So not only focusing on their core competencies, but then also that means that drug discovery will be quicker, that failure rates will go down. >> Even a 10 or 20% improvement in failure rates would be incredibly dramatic to the industry. >> And could save millions of lives. And improve lives and outcomes. Great, well thank you all so much for coming on theCUBE. It's been a really fun and interesting conversation. >> Same here, thank you Rebecca. >> Thank you, thank you. >> Thank you. >> I'm Rebecca Knight, we will have more of the AWS Executive Summit and theCUBE's live coverage coming up in just a little bit. (upbeat music)