(upbeat music) >> Welcome back to Vegas, baby. Lisa Martin here with theCUBE. We are live at Caesar's Forum covering Snowflake Summit 22. This is day two of our wall to wall coverage on theCUBE you won't want to miss. We've got an exciting customer story to talk to you about next with Novartis and Snowflake. Please welcome two guests to theCUBE. Loïc Giraud, Global head digital delivery, Novartis. I hope I got the name right. >> Yes. Hi, thank you. >> I did my best. >> Absolutely. >> Lisa: (laughs) Jesse Cugliotta also joins us. Global Industry Lead, Healthcare and Life Sciences at Snowflake. Welcome with theCUBE, gentlemen. >> Thank you for having us. Good morning. >> So it was great to hear Novartis is a household word now, especially with what's gone on in the last two years. I had a chance to see the Keynote yesterday, heard Novartis mention in terms of a massive outcome that Snowflake is delivering that we're going to get to. But Loic talk to us about Novartis global 500 organization. You rank among the world's top companies investing in R&D, the massive portfolio and you're reaching nearly 800 million patients worldwide. That's huge, but there's been a lot of change in the healthcare and life sciences industry, especially recently. Talk to us about the industry landscape. What are you seeing? >> As you described, Novartis is one of the top life science company in the world. We are number three. We operate in 150 countries, and we have almost 120,000 employees. Our purpose is actually to reimagine medicine for the use of data science and technology and to extend people's life. And we really mean it. I think, as you mentioned, we treat eight or 9 million patient per year with our drugs. We expect to treat more than a billion patients in near time soon. Over the last few years, especially during COVID, our digital transformation help us to accelerate the drug discovery and then the commiseration of our drug to markets. As it was mentioned in the Keynote yesterday, we have actually been able to reduce our time to market. It used to take us up to 12 years and cost around 1.2 billion to discover and commercialize drug. And now we've actually use of technology like Snowflake, we have been able to reduce by two to three years, which ultimately is a benefit for our patients. >> Absolutely. Well, we're talking about life and death situations. Talk about... You mentioned Novartis wants to reimagine medicine. What does that look like? Where is data in that and how is Snowflake an enabler of reimagining medicine? >> So data is core for our asset, is a core of enterprise process. So if you look at our enterprise, we are using data from the research, for drug development, in manufacturing process, and how do we market and sell our product through HCPs and distribute it to reach our patients. If you build through our digital transformation we have created this integrated data ecosystem, where Snowflake is a core component. And through that ecosystem, we are able to identify compounds and cohorts, perform clinical trials, and engage HCPs and HGOs so that can prescribe drugs to serve our patient needs. >> Jesse, let's bring you into the conversation. Snowflake recently launched its healthcare and life sciences data cloud. I believe that was back in March. >> It was. >> Just a couple of months ago. Talk to us about the vertical focus. Talk to us about what this healthcare and life sciences data cloud is aiming to help customers like Novartis achieve. >> Well, as you mentioned there, Snowflake has made a real pivot to kind of focus on the various different industries that we serve in a new way. I think historically, we've been engaged in really, all of the industries across the major sectors where we participate today. But historically we've been often engaging with the office of IT. And there was a recognition as a company that we really need to be able to better speak the language of our customers in with our respective industries. So the entire organization has really made a pivot to start to build that capability internally. That's part of the team that I support here at Snowflake. And with respect to healthcare and life sciences, that means being able to solve some of the challenges that Loic was just speaking about. In particular, we're seeing the industry evolve in a number of ways. You bring up clinical research in the time that it takes to actually bring a drug to market. This is a big one that's really changed a lot over the last couple of years. Some of the reasons are obvious and other ones are somewhat opportunistic. When we looked at what it takes to get a drug to market, there's several stages of clinical research that have to be participated in, and this can often take years. What we saw in the last couple of years, is that all of a sudden, patients didn't want to physically participate in those anymore, because there was fear of potential infection and being in a healthcare facility. So the entire industry realized that it needed to change in terms of way that it would engage with patients in that context. And we're now seeing this concept of decentralized clinical research. And with that, becomes the need to potentially involve many different types of organizations beyond the traditional pharma, their research partners, but we're starting to see organizations like retail pharmacies, like big box retailers, who have either healthcare delivery or pharmaceutical arms actually get involved in the process. And of course, one of the core things that happens here is that everyone needs a better way to collaborate and share data amongst one another. So bringing this back to your original question, this concept of being able to do exactly that is core to the healthcare and the life sciences data cloud. To be able to collaborate and share data amongst those different types of organizations. >> Collaboration and data sharing. It seems to me to be a differentiator for Snowflake, in terms of being able to deliver secure, governed powerful analytics and data sharing to customers, partners to the ecosystem. You mentioned an example of the ecosystem there and how impactful to patients' lives, that collaboration and data sharing can be. >> That's absolutely right. It's something that if you think about all of the major challenges that the industry has had historically, whether it is high costs, whether it are health inequities, whether it is physicians practicing defensive medicine or repeat testing, what's core to each one of these things is kind of the inability to adequate collaborate and share data amongst all of the different players. So the industry has been waiting for the capability or some sort of solution to be able to do this, I think for a long, long time. And this is probably one of the most exciting parts of the conversations that we have with our customers, is when they realize that this is possible. And not only that it's possible within our platform, but that most of the organizations that they work with today are also Snowflake customers. So they realize that everyone's already here. It's just a matter of who else can we work with and how do we get started? >> Join the party. >> Exactly. >> Loic talk to us about Novartis's data journey. I know you guys have been, I believe using Snowflake since 2017 pre pandemic. But you had a largely on-premises infrastructure. Talk to us about the decision of Novartis to go to the cloud, do it securely and why you chose to partner with Snowflake. >> So when we started our journey in 2018, I think the ambition that our CEO, was to transform all enterprise processes for the use of digital tech. And at the core of this digital tech is data foundation. So we started with a large program called Formula One, which aim to integrate all our internal and external data asset into an integrated platform. And for that, I think we've built this multicloud and best upgrade platform, where Snowflake is a core component. And we've been able to integrate almost 1,000 data asset, internal and external for the platform to be able to accelerate the use of data to create insight for our users. In that transformation, we've realized that Snowflake could be a core component because of the scalability and the performance with large dataset. And moreover, when Snowflake started to actually open collaboration for their marketplace, we've been able to integrate new data set that are publicly available at the place that we could not do on ourself, on our own. So that is a core component of what we are trying to do. >> Yeah, and I think that's a great example of really what we're talking about here is that, he's mentioning that they're going out to our marketplace to be able to integrate data more easily with some of the vendors there. And that is kind of this concept of the healthcare and life sciences data cloud realized, where all of a sudden, acquiring and bringing data in and making it ready for analysis becomes much faster, much easier. We continually see more and more vendors coming to us saying, I get it now, I want in. Who else can I work with in this space? So I think that's a perfect example of how this starts to become real for folks. >> Well, it sounds like the marketplace has been an enabler, Loic, of the expansion of use cases. You've grown this beyond drug development. I read that you're developing new products and services for healthcare providers to personalize treatments for patients, which we all are demanding patients. We want that personalized care. But talk about the marketplace as a facilitator of those expanding use cases that Snowflake is powering. >> Yes. That's right. I mean we have currently almost 65 use cases in production and we are in advanced progress for over 200 use cases and they go across all our business sector. So if you look at drug development, we are monitoring our clinical trials using Snowflake. If you look at our omnichannel marketing, we are looking at personalization of information with our HCPs and HGOs using snowflake. If you look at our manufacturing process, we are looking at yet management, freight optimization, inventory, insight. So almost across all the industry sectors that we have, I think we are using the platforms to be able to deliver faster information to our users. >> And that's what we all want. Faster information. I think in the pandemic we learned that access to real time data in every industry wasn't a nice to have. That was a- >> Necessity. >> Absolute necessity. >> Yeah. >> And made the difference for companies that survived and thrived and those that didn't. That's something that we learned. But we also learned that the volume of data just continues to proliferate. Loic, you've been in the industry a couple of decades. What do you see? And you've got, obviously this great foundation now with Snowflake. You've got 65 use cases you said in production. What's the future of the data culture in healthcare and life sciences from your perspective? >> So my perspective. It is time now we give the access to our business technologies to be able to be self-sufficient using digital product. We need to consumerize digital technology so they can be self-sufficient. The amount of problems that we have to solve, and we can now solve with new technology has never been there. And I think where in the past, where in the next few years that you will see an accelerated generation of insight and an accelerated process of medicine by empowering the business technologies to use a technology that like Snowflake and over progress. >> What are your thoughts Loic, of some of the, obviously a lot of news coming out yesterday from Snowflake, we mentioned standing room only in the Keynote. This I believe is north of 10,000 attendees. People are ready to engage in person with Snowflake, but some of the news coming out, what is your perspective? You've been a partner of theirs for a while. What do you see from Snowflake in terms of the news, the volume of customers it's adding, all that good stuff? >> I must say I was blown away yesterday when Frank was talking about the ramp up of customers using Snowflake. But also, and I think in Benoit and Christian, and they talk about the innovation. When you look at native application or you look at hybrid tables, we saw a thing there. And the expansion of the marketplace by monetization application, that is something that is going to accelerate the expansion, not only on the company, but the integration and the utilization of customers. And to Jesse's point, I think that it is key that people collaborate using the platform. I think we want to collaborate with suppliers and providers and they want to collaborate with us. But we want to have a neutral environment where we can do that. And Snowflake can be that environment. >> And do it securely, right? Security is absolutely- >> Of course. I mean that's really table stake for this industry. And I think the point that you just made Loic, is very important, is that, the biggest question that we're often asked by our customers is who else is a customer within this industry that I can collaborate with? I think as Loic here will attest to, one of the challenges within life sciences in particular is that it is a highly regulated industry. It is a highly competitive industry, and folks are very sensitive about referenceability. So about things like logo usage. So to give some ideas here, people often have no idea that we're working with 28 of the top 50 global pharma today, working with seven of the top 12 global medical device companies today. The largest CROs, the largest distributors. So when I say that the party is here, they really are. And that's why we're so excited to have events like these, 'cause people can physically introduce themselves to one another and meet, and actually start to engage in some of these more collaborative discussions that they've been waiting for. >> Jesse, what's been some of the feedback that you've heard the last couple of days on the healthcare and life sciences data cloud? You've obviously finally gotten back to engaging with customers in person. But what are some of the things, feed on this street have said that you've thought, we made the absolute right decision on this pivot? >> Yeah, well I think some of it speaks to the the point I was just speaking about, is that they had no idea that so many of their peers were actually working with Snowflake already and that how mature their implementations have actually been. The other thing that folks are realizing is that, a lot of the technologies that serve this ecosystem, whether they're in the health tech space, whether they're clinical management or commercial engagement or supply chain planning technologies, those companies are also now pivoting to Snowflake, where they're either building a part or the entirety of their platform on top of ours. So it offers this great way to start to collaborate with the ecosystem through some of those capabilities that we spoke about. And that's driving new use cases in commercial, in supply chain, in pharmacovigilance, in clinical operations. >> Well, I think you just sum up beautifully why the theme of this conference is the world of data collaboration. >> Yes, absolutely. >> The potential there, that Snowflake is unleashing to the world is I think is what's captivating to me. That you just scratch on the surface about connecting and facilitating this collaboration and this data sharing in a secure way across industries. Loic, last question for you. Take us home with what is next for Novartis. You've done a tremendous amount of digitalization. 65 use cases in production with Snowflake. What's next for the company? >> See, I think that in next year's to come, open collaboration with the ecosystem, but also personalization. If you look at digital medicine and access to patient's informations, I think this is probably the next revolution that we are entering into. >> Excellent. And of course those demanding patients aren't going to want anything slower or less information. Guys, thank you for joining me on the program talking about the Novartis-Snowflake collaboration. The partnership, the outcomes that you're achieving and how this is really dramatically impacting the lives of hundreds of millions of people. We appreciate your time and your insights. >> Thank you for having us. This was fun. >> My pleasure. >> Thank you. >> For my guests, I'm Lisa Martin. You're watching theCUBE. This is live from Las Vegas, day two of our coverage of Snowflake Summit 22. I'll be right back with my next guest, so stick around. (upbeat music)

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

>>Aziz. Things have changed and transformed with public cloud. So has our approach to the business is really changing the conversation from what your I t requirements to where your business drivers and we've seen the success off educating the customer of the way H P s educated us and articulating the value proposition with an on Prem consumption model >>within advise x long term, the company does see that things were gonna be moving to selling everything as a service. And so the deal that I did with Mohawk Valley was critical in making the next step. >>We were to hospitals that had recently merged. The challenge we were faced with was to combine for Elektronik medical records software packages down to a single product within a one year timeframe. >>Her Mohawk Valley. As we said, you know, Hey, you want to run this on Prem today with these servers and you're gonna spend more for the cloud? Why don't we look at a holistic solution? And if we give you that cloud experience with H. P Green Lake, then we believe that we can help solve your business challenges and do it more cost effectively than you could do in the public cloud >>way were very skeptical of HP Green Lake initially. But then I saw it was so much more licensing, customization and support all bundled at the end of the day. Our CFO wants a predictable cost model and 100% uptime on the system. Green Lake gave us all that The >>implementation process was very successful and actually behind the customer's expectations. >>Our CEO has not stopped breaking about it ever since. He's just so proud of the performance and uptime that we've achieved. >>It was easier than anticipated and we're executing and they're looking to buy other hospitals. And their intention is we're just gonna hold him right in the Green Lake model because it's easy and we know we can execute predictably and with Green Lake, they can build as they grow, not have Tobias. They grow. >>Cova 19 virus hit our organization very hard, but any of the additional features that we needed to activate we were able to do that without any time delays. We just stood up. Any additional resource is we needed, and we were often running just like that >>with regard to remote workers. Additional requirements to the infrastructure, adding beds, patient requests, HP Greenlee provided both the flexibility and agility, so the customer then had basically zero downtime through the pandemic. There's >>never a time when the clinicians don't have access to the tools they need >>to do their job. Mohawk Valley Health System has helped improve patient care, and that means the world to me. >>We've been a partner now with HPD over 35 years. We've seen them grow. We've seen them transform. They're committed to everything as a service and they're backing up with training. They're backing up with investment, and they're backing up with winds with customers. Their vision is clear and it's impressive and we're all in. We believe it's the right strategy.

>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

>> Narrator: From around the globe, it's theCUBE, covering the Upgrade 2020, the NTT Research Summit presented by NTT Research. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in Palo Alto studio for our ongoing coverage of the Upgrade 2020, it's the NTT Research conference. It's our first year covering the event, it's actually the first year for the event inaugural, a year for the events, we're really, really excited to get into this. It's basic research that drives a whole lot of innovation, and we're really excited to have our next guest. He is Kazuhiro Gomi, he is the President and CEO of NTT Research. Kazu, great to see you. >> Hi, good to see you. >> Yeah, so let's jump into it. So this event, like many events was originally scheduled I think for March at Berkeley, clearly COVID came along and you guys had to make some changes. I wonder if you can just share a little bit about your thinking in terms of having this event, getting this great information out, but having to do it in a digital way and kind of rethinking the conference strategy. >> Sure, yeah. So NTT Research, we started our operations about a year ago, July, 2019. and I always wanted to show the world that to give a update of what we have done in the areas of basic and fundamental research. So we plan to do that in March, as you mentioned, however, that the rest of it to some extent history, we needed to cancel the event and then decided to do this time of the year through virtual. Something we learned, however, not everything is bad, by doing this virtual we can certainly reach out to so many peoples around the globe at the same time. So we're taking, I think, trying to get the best out of it. >> Right, right, so you've got a terrific lineup. So let's jump into a little bit. So first thing just about NTT Research, we're all familiar, if you've been around for a little while about Bell Labs, we're fortunate to have Xerox PARC up the street here in Palo Alto, these are kind of famous institutions doing basic research. People probably aren't as familiar at least in the states around NTT basic research. But when you think about real bottom line basic research and how it contributes ultimately, it gets into products, and solutions, and health care, and all kinds of places. How should people think about basic research and its role in ultimately coming to market in products, and services, and all different things. But you're getting way down into the weeds into the really, really basic hardcore technology. >> Sure, yeah, so let me just from my perspective, define the basic research versus some other research and development. For us that the basic research means that we don't necessarily have any like a product roadmap or commercialization roadmap, we just want to look at the fundamental core technology of all things. And from the timescale perspective obviously, not that we're not looking at something new, thing, next year, next six months, that kind of thing. We are looking at five years or sometimes longer than that, potentially 10 years down the road. But you mentioned about the Bell Lab and Xerox PARC. Yeah, well, they used to be such organizations in the United States, however, well, arguably those days have kind of gone, but so that's what's going on in the United States. In Japan, NTT has have done quite a bit of basic research over the years. And so we wanted to, I think because that a lot of the cases that we can talk about the end of the Moore's laws and then the, we are kind of scary time for that. The energy consumptions on ITs We need to make some huge, big, fundamental change has to happen to sustain our long-term development of the ideas and basically for the sake of human beings. >> Right, right. >> So NTT sees that and also we've been doing quite a bit of basic research in Japan. So we recognize this is a time that the let's expand this activities and then by doing, as a part of doing so is open up the research lab in Silicon Valley, where certainly we can really work better, work easier to with that the global talents in this field. So that's how we started this endeavor, like I said, last year. And so far, it's a tremendous progress that we have made, so that's where we are. >> That's great, so just a little bit more specific. So you guys are broken down into three labs as I understand, you've got the Physics, the PHI, which is Physics and Informatics, the CIS lab Cryptography and Information Security, and the MEI lab Medical and Health Informatics, and the conference has really laid out along those same tracks, really day one is a whole lot of stuff, or excuse me, they do to run the Physics and Informatics day. The next day is really Cryptography and Information Security, and then the Medical and Health Informatics. So those are super interesting but very diverse kind of buckets of fundamental research. And you guys are attacking all three of those pillars. >> Yup, so day one, general session, is that we cover the whole, all the topics. And but just that whole general topics. I think some people, those who want to understand what NTT research is all about, joining day one will be a great day to be, to understand more holistic what we are doing. However, given the type of research topic that we are tackling, we need the deep dive conversations, very specific to each topic by the specialist and the experts in each field. Therefore we have a day two, three, and four for a specific topics that we're going to talk about. So that's a configuration of this conference. >> Right, right, and I love. I just have to read a few of the session breakout titles 'cause I think they're just amazing and I always love learning new vocabulary words. Coherent nonlinear dynamics and combinatorial optimization language multipliers, indistinguishability obfuscation from well-founded assumptions, fully deniable communications and computation. I mean, a brief history of the quasi-adaptive NIZKs, which I don't even know what that stands for. (Gomi laughing) Really some interesting topics. But the other thing that jumps out when you go through the sessions is the representation of universities and really the topflight university. So you've got people coming from MIT, CalTech, Stanford, Notre Dame, Michigan, the list goes on and on. Talk to us about the role of academic institutions and how NTT works in conjunction with academic institutions, and how at this basic research level kind of the commercial academic interests align and come together, and work together to really move this basic research down the road. >> Sure, so the working with academic, especially at the top-notch universities are crucial for us. Obviously, that's where the experts in each field of the basic research doing their super activities and we definitely need to get connected, and then we need to accelerate our activities and together with the entities researchers. So that has been kind of one of the number one priority for us to jumpstart and get some going. So as you mentioned, Jeff, that we have a lineup of professors and researchers from each top-notch universities joining to this event and talking at a generous, looking at different sessions. So I'm sure that those who are listening in to those sessions, you will learn well what's going on from the NTT's mind or NTT researchers mind to tackle each problem. But at the same time you will get to hear that top level researchers and professors in each field. So I believe this is going to be a kind of unique, certainly session that to understand what's it's like in a research field of quantum computing, encryptions, and then medical informatics of the world. >> Right. >> So that's, I am sure it's going to be a pretty great lineups. >> Oh, absolutely, a lot of information exchange. And I'm not going to ask you to pick your favorite child 'cause that would be unfair, but what I am going to do is I noticed too that you also write for the Forbes Technology Council members. So you're publishing on Forbes, and one of the articles that you publish relatively recently was about biological digital twins. And this is a topic that I'm really interested in. We used to do a lot of stuff with GE and there was always a lot of conversation about digital twins, for turbines, and motors, and kind of all this big, heavy industrial equipment so that you could get ahead of the curve in terms of anticipating maintenance and basically kind of run simulations of its lifetime. Need concept, now, and that's applied to people in biology, whether that's your heart or maybe it's a bigger system, your cardiovascular system, or the person as a whole. I mean, that just opens up so much interesting opportunities in terms of modeling people and being able to run simulations. If they do things different, I would presume, eat different, walk a little bit more, exercise a little bit more. And you wrote about it, I wonder if you could share kind of your excitement about the potential for digital twins in the medical space. >> Sure, so I think that the benefit is very clear for a lot of people, I would hope that the ones, basically, the computer system can simulate or emulate your own body, not just a generic human body, it's the body for Kazu Gomi at the age of whatever. (Jeff laughing) And so if you get that precise simulation of your body you can do a lot of things. Oh, you, meaning I think a medical professional can do a lot of thing. You can predict what's going to happen to my body in the next year, six months, whatever. Or if I'm feeling sick or whatever the reasons and then the doctor wants to prescribe a few different medicines, but you can really test it out a different kind of medicines, not to you, but to the twin, medical twin then obviously is safer to do some kind of specific medicines or whatever. So anyway, those are the kind of visions that we have. And I have to admit that there's a lot of things, technically we have to overcome, and it will take a lot of years to get there. But I think it's a pretty good goal to define, so we said we did it and I talked with a couple of different experts and I am definitely more convinced that this is a very nice goal to set. However, well, just talking about the goal, just talking about those kinds of futuristic thing, you may just end up with a science fiction. So we need to be more specific, so we have the very researchers are breaking down into different pieces, how to get there, again, it's going to be a pretty long journey, but we're starting from that, they're try to get the digital twin for the cardiovascular system, so basically the create your own heart. Again, the important part is that this model of my heart is very similar to your heart, Jeff, but it's not identical it is somehow different. >> Right, right. >> So we are looking on it and there are certainly some, we're not the only one thinking something like this, there are definitely like-minded researchers in the world. So we are gathered together with those folks and then come up with the exchanging the ideas and coming up with that, the plans, and ideas, that's where we are. But like you said, this is really a exciting goal and exciting project. >> Right, and I like the fact that you consistently in all the background material that I picked up preparing for this today, this focus on tech for good and tech for helping the human species do better down the road. In another topic, in other blog post, you talked about and specifically what are 15 amazing technologies contributing to the greater good and you highlighted cryptography. So there's a lot of interesting conversations around encryption and depending kind of commercialization of quantum computing and how that can break all the existing kind of encryption. And there's going to be this whole renaissance in cryptography, why did you pick that amongst the entire pallet of technologies you can pick from, what's special about cryptography for helping people in the future? >> Okay, so encryption, I think most of the people, just when you hear the study of the encryption, you may think what the goal of these researchers or researches, you may think that you want to make your encryption more robust and more difficult to break. That you can probably imagine that's the type of research that we are doing. >> Jeff: Right. >> And yes, yes, we are doing that, but that's not the only direction that we are working on. Our researchers are working on different kinds of encryptions and basically encryptions controls that you can just reveal, say part of the data being encrypted, or depending upon that kind of attribute of whoever has the key, the information being revealed are slightly different. Those kinds of encryption, well, it's kind of hard to explain verbally, but functional encryption they call is becoming a reality. And I believe those inherit data itself has that protection mechanism, and also controlling who has access to the information is one of the keys to address the current status. Current status, what I mean by that is, that they're more connected world we are going to have, and more information are created through IOT and all that kind of stuff, more sensors out there, I think. So it is great on the one side that we can do a lot of things, but at the same time there's a tons of concerns from the perspective of privacy, and securities, and stuff, and then how to make those things happen together while addressing the concern and the leverage or the benefit you can create super complex accessing systems. But those things, I hate to say that there are some inherently bringing in some vulnerabilities and break at some point, which we don't want to see. >> Right. >> So I think having those securities and privacy mechanism in that the file itself is I think that one of the key to address those issues, again, get the benefit of that they're connected in this, and then while maintaining the privacy and security for the future. >> Right. >> So and then that's, in the end will be the better for everyone and a better society. So I couldn't pick other (Gomi and Jeff laughing) technology but I felt like this is easier for me to explain to a lot of people. So that's mainly the reasons that I went back launching. >> Well, you keep publishing, so I'm sure you'll work your way through most of the technologies over a period of time, but it's really good to hear there's a lot of talk about security not enough about privacy. There's usually the regs and the compliance laws lag, what's kind of happening in the marketplace. So it's good to hear that's really a piece of the conversation because without the privacy the other stuff is not as attractive. And we're seeing all types of issues that are coming up and the regs are catching up. So privacy is a super important piece. But the other thing that is so neat is to be exposed not being an academic, not being in this basic research every day, but have the opportunity to really hear at this level of detail, the amount of work that's being done by big brain smart people to move these basic technologies along, we deal often in kind of higher level applications versus the stuff that's really going on under the cover. So really a great opportunity to learn more and hear from, and probably understand some, understand not all about some of these great, kind of baseline technologies, really good stuff. >> Yup. >> Yeah, so thank-you for inviting us for the first one. And we'll be excited to sit in on some sessions and I'm going to learn. What's that one phrase that I got to learn? The N-I-K-Z-T. NIZKs. (laughs) >> NIZKs. (laughs) >> Yeah, NIZKs, the brief history of quasi-adaptive NI. >> Oh, all right, yeah, yeah. (Gomi and Jeff laughing) >> All right, Kazuhiro, I give you the final word- >> You will find out, yeah. >> You've been working on this thing for over a year, I'm sure you're excited to finally kind of let it out to the world, I wonder if you have any final thoughts you want to share before we send people back off to their sessions. >> Well, let's see, I'm sure if you're watching this video, you are almost there for that actual summit. It's about to start and so hope you enjoy the summit and in a physical, well, I mentioned about the benefit of this virtual, we can reach out to many people, but obviously there's also a flip side of the coin as well. With a physical, we can get more spontaneous conversations and more in-depth discussion, certainly we can do it, perhaps not today. It's more difficult to do it, but yeah, I encourage you to, I think I encouraged my researchers NTT side as well to basic communicate with all of you potentially and hopefully then to have more in-depth, meaningful conversations just starting from here. So just feel comfortable, perhaps just feel comfortable to reach out to me and then all the other NTT folks. And then now, also that the researchers from other organizations, I'm sure they're looking for this type of interactions moving forward as well, yeah. >> Terrific, well, thank-you for that open invitation and you heard it everybody, reach out, and touch base, and communicate, and engage. And it's not quite the same as being physical in the halls, but that you can talk to a whole lot more people. So Kazu, again, thanks for inviting us. Congratulations on the event and really glad to be here covering it. >> Yeah, thank-you very much, Jeff, appreciate it. >> All right, thank-you. He's Kazu, I'm Jeff, we are at the Upgrade 2020, the NTT Research Summit. Thanks for watching, we'll see you next time. (upbeat music)

>>Hello. I'm Professor Toru Suzuki Cherif cardiovascular medicine on associate dean of the College of Life Sciences at the University of Leicester in the United Kingdom, where I'm also director of the Lester Life Sciences accelerator. I'm also honorary consultant cardiologist within our university hospitals. It's part of the national health system NHS Trust. Today, I'd like to talk to you about our Lester Clinical Data Science Initiative. Now brief background on Lester. It's university in hospitals. Lester is in the center of England. The national health system is divided depending on the countries. The United Kingdom, which is comprised of, uh, England, Scotland to the north, whales to the west and Northern Ireland is another part in a different island. But national health system of England is what will be predominantly be discussed. Today has a history of about 70 years now, owing to the fact that we're basically in the center of England. Although this is only about one hour north of London, we have a catchment of about 100 miles, which takes us from the eastern coast of England, bordering with Birmingham to the west north just south of Liverpool, Manchester and just south to the tip of London. We have one of the busiest national health system trust in the United Kingdom, with a catchment about 100 miles and one million patients a year. Our main hospital, the General Hospital, which is actually called the Royal Infirmary, which can has an accident and emergency, which means Emergency Department is that has one of the busiest emergency departments in the nation. I work at Glen Field Hospital, which is one of the main cardiovascular hospitals of the United Kingdom and Europe. Academically, the Medical School of the University of Leicester is ranked 20th in the world on Lee, behind Cambridge, Oxford Imperial College and University College London. For the UK, this is very research. Waited, uh, ranking is Therefore we are very research focused universities as well for the cardiovascular research groups, with it mainly within Glenn Field Hospital, we are ranked as the 29th Independent research institution in the world which places us. A Suffield waited within our group. As you can see those their top ranked this is regardless of cardiology, include institutes like the Broad Institute and Whitehead Institute. Mitt Welcome Trust Sanger, Howard Hughes Medical Institute, Kemble, Cold Spring Harbor and as a hospital we rank within ah in this field in a relatively competitive manner as well. Therefore, we're very research focused. Hospital is well now to give you the unique selling points of Leicester. We're we're the largest and busiest national health system trust in the United Kingdom, but we also have a very large and stable as well as ethnically diverse population. The population ranges often into three generations, which allows us to do a lot of cohort based studies which allows us for the primary and secondary care cohorts, lot of which are well characterized and focused on genomics. In the past. We also have a biomedical research center focusing on chronic diseases, which is funded by the National Institutes of Health Research, which funds clinical research the hospitals of United Kingdom on we also have a very rich regional life science cluster, including med techs and small and medium sized enterprises. Now for this, the bottom line is that I am the director of the letter site left Sciences accelerator, >>which is tasked with industrial engagement in the local national sectors but not excluding the international sectors as well. Broadly, we have academics and clinicians with interest in health care, which includes science and engineering as well as non clinical researchers. And prior to the cove it outbreak, the government announced the £450 million investment into our university hospitals, which I hope will be going forward now to give you a brief background on where the scientific strategy the United Kingdom lies. Three industrial strategy was brought out a za part of the process which involved exiting the European Union, and part of that was the life science sector deal. And among this, as you will see, there were four grand challenges that were put in place a I and data economy, future of mobility, clean growth and aging society and as a medical research institute. A lot of the focus that we have been transitioning with within my group are projects are focused on using data and analytics using artificial intelligence, but also understanding how chronic diseases evolved as part of the aging society, and therefore we will be able to address these grand challenges for the country. Additionally, the national health system also has its long term plans, which we align to. One of those is digitally enabled care and that this hope you're going mainstream over the next 10 years. And to do this, what is envision will be The clinicians will be able to access and interact with patient records and care plants wherever they are with ready access to decision support and artificial intelligence, and that this will enable predictive techniques, which include linking with clinical genomic as well as other data supports, such as image ing a new medical breakthroughs. There has been what's called the Topol Review that discusses the future of health care in the United Kingdom and preparing the health care workforce for the delivery of the digital future, which clearly discusses in the end that we would be using automated image interpretation. Is using artificial intelligence predictive analytics using artificial intelligence as mentioned in the long term plans. That is part of that. We will also be engaging natural language processing speech recognition. I'm reading the genome amusing. Genomic announced this as well. We are in what is called the Midland's. As I mentioned previously, the Midland's comprised the East Midlands, where we are as Lester, other places such as Nottingham. We're here. The West Midland involves Birmingham, and here is ah collective. We are the Midlands. Here we comprise what is called the Midlands engine on the Midland's engine focuses on transport, accelerating innovation, trading with the world as well as the ultra connected region. And therefore our work will also involve connectivity moving forward. And it's part of that. It's part of our health care plans. We hope to also enable total digital connectivity moving forward and that will allow us to embrace digital data as well as collectivity. These three key words will ah Linkous our health care systems for the future. Now, to give you a vision for the future of medicine vision that there will be a very complex data set that we will need to work on, which will involve genomics Phanom ICS image ing which will called, uh oh mix analysis. But this is just meaning that is, uh complex data sets that we need to work on. This will integrate with our clinical data Platforms are bioinformatics, and we'll also get real time information of physiology through interfaces and wearables. Important for this is that we have computing, uh, processes that will now allow this kind of complex data analysis in real time using artificial intelligence and machine learning based applications to allow visualization Analytics, which could be out, put it through various user interfaces to the clinician and others. One of the characteristics of the United Kingdom is that the NHS is that we embrace data and captured data from when most citizens have been born from the cradle toe when they die to the grave. And it's important that we were able to link this data up to understand the journey of that patient. Over time. When they come to hospital, which is secondary care data, we will get disease data when they go to their primary care general practitioner, we will be able to get early check up data is Paula's follow monitoring monitoring, but also social care data. If this could be linked, allow us to understand how aging and deterioration as well as frailty, uh, encompasses thes patients. And to do this, we have many, many numerous data sets available, including clinical letters, blood tests, more advanced tests, which is genetics and imaging, which we can possibly, um, integrate into a patient journey which will allow us to understand the digital journey of that patient. I have called this the digital twin patient cohort to do a digital simulation of patient health journeys using data integration and analytics. This is a technique that has often been used in industrial manufacturing to understand the maintenance and service points for hardware and instruments. But we would be using this to stratify predict diseases. This'll would also be monitored and refined, using wearables and other types of complex data analysis to allow for, in the end, preemptive intervention to allow paradigm shifting. How we undertake medicine at this time, which is more reactive rather than proactive as infrastructure we are presently working on putting together what's it called the Data Safe haven or trusted research environment? One which with in the clinical environment, the university hospitals and curated and data manner, which allows us to enable data mining off the databases or, I should say, the trusted research environment within the clinical environment. Hopefully, we will then be able to anonymous that to allow ah used by academics and possibly also, uh, partnering industry to do further data mining and tool development, which we could then further field test again using our real world data base of patients that will be continually, uh, updating in our system. In the cardiovascular group, we have what's called the bricks cohort, which means biomedical research. Informatics Center for Cardiovascular Science, which was done, started long time even before I joined, uh, in 2010 which has today almost captured about 10,000 patients arm or who come through to Glenn Field Hospital for various treatments or and even those who have not on. We asked for their consent to their blood for genetics, but also for blood tests, uh, genomics testing, but also image ing as well as other consent. Hable medical information s so far there about 10,000 patients and we've been trying to extract and curate their data accordingly. Again, a za reminder of what the strengths of Leicester are. We have one of the largest and busiest trust with the very large, uh, patient cohort Ah, focused dr at the university, which allows for chronic diseases such as heart disease. I just mentioned our efforts on heart disease, uh which are about 10,000 patients ongoing right now. But we would wish thio include further chronic diseases such as diabetes, respiratory diseases, renal disease and further to understand the multi modality between these diseases so that we can understand how they >>interact as well. Finally, I like to talk about the lesser life science accelerator as well. This is a new project that was funded by >>the U started this January for three years. I'm the director for this and all the groups within the College of Life Sciences that are involved with healthcare but also clinical work are involved. And through this we hope to support innovative industrial partnerships and collaborations in the region, a swells nationally and further on into internationally as well. I realized that today is a talked to um, or business and commercial oriented audience. And we would welcome interest from your companies and partners to come to Leicester toe work with us on, uh, clinical health care data and to drive our agenda forward for this so that we can enable innovative research but also product development in partnership with you moving forward. Thank you for your time.

(upbeat music) >> Hello, and welcome to The Great Cloud Debate. I'm your moderator Rachel Dines. I'm joined by two debaters today Corey Quinn, Cloud Economist at the Duckbill Group and Stu Miniman, Senior Analyst and Host of theCube. Welcome Corey and Stu, this when you can say hello. >> Hey Rachel, great to talk to you. >> And it's better to talk to me. It's always a pleasure to talk to the fine folks over at CloudHealth at by VMware and less of the pleasure to talk to Stu. >> Smack talk is scheduled for later in the agenda gentlemen, so please keep it to a minimum now to keep us on schedule. So here's how today is going to work. I'm going to introduce a debate topic and assign Corey and Stu each to a side. Remember, their assignments are what I decide and they might not actually match their true feelings about a topic, and it definitely does not represent the feelings of their employer or my employer, importantly. Each debater is going to have two minutes to state their opening arguments, then we'll have rebuttals. And each round you the audience gets to vote of who you think is winning. And at the end of the debate, I'll announce the winner. The prize is bragging rights of course, but then also we're having each debater play to win lunch for their local hospital, which is really exciting. So Stu, which hospital are you playing for? >> Yeah, so Rachel, I'm choosing Brigham Women's Hospital. I get a little bit of a home vote for the Boston audience here and was actually my wife's first job out of school. >> Great hospital. Very, very good. All right, Corey, what about you? >> My neighbor winds up being as specialist in infectious diseases as a doctor, and that was always one of those weird things you learn over a cocktail party until this year became incredibly relevant. So I will absolutely be sending the lunch to his department. >> Wonderful! All right. Well, is everyone ready? Any last words? This is your moment for smack talk. >> I think I'll say that for once we can apply it to a specific technology area. Otherwise, it was insulting his appearance and that's too easy. >> All right, let's get going. The first topic is multicloud. Corey, you'll be arguing that companies are better off standardizing on a single cloud. While Stu, you're going to argue the companies are better off with a multicloud strategy. Corey, you're up first, two minutes on the clock and go. >> All right. As a general rule, picking a single provider and going all in leads to the better outcome. Otherwise, you're trying to build every workload to run seamlessly on other providers on a moment's notice. You don't ever actually do it and all you're giving up in return is the ability to leverage whatever your primary cloud provider is letting you build. Now you're suddenly trying to make two differently behaving load balancers work together in the same way, you're using terraform or as I like to call it multicloud formation in the worst of all possible ways. Because now you're having to only really build on one provider, but all the work you're putting in to make that scale to other providers, you might theoretically want to go to at some point, it slows you down, you're never going to be able to move as quickly trying to build for everyone as you are for one particular provider. And I don't care which provider you pick, you probably care which one you pick, I don't care which one. The point is, you've got to pick what's right for your business. And in almost every case, that means start on a single platform. And if you need to migrate down the road years from now, great, that means A you've survived that long, and B you now have the longevity as a business to understand what migrating looks like. Otherwise you're not able to take care of any of the higher level offerings these providers offer that are even slightly differentiated from each other. And even managed database services behave differently. You've got to become a master of all the different ways these things can fail and unfortunate and displeasing ways. It just leaves you in a position where you're not able to specialize, and of course, makes hiring that much harder. Stu, fight me! >> Tough words there. All right, Stu, your turn. Why are companies better off if they go with a multicloud strategy? Got two minutes? >> Yeah, well first of all Corey, I'm really glad that I didn't have to whip out the AWS guidelines, you were not sticking strictly to it and saying that you could not use the words multicloud, cross-cloud, any cloud or every cloud so thank you for saving me that argument. But I want you to kind of come into the real world a little bit. We want access to innovation, we want flexibility, and well, we used to say I would have loved to have a single provider, in the real world we understand that people end up using multiple solutions. If you look at the AI world today, there's not a provider that is a clear leader in every environment that I have. So there's a reason why I might want to use a lot of clouds. Most companies I talked to, Corey, they still have some of their own servers. They're working in a data center, we've seen huge explosion in the service provider world connecting to multiple clouds. So well, a couple of years ago, multicloud was a complete mess. Now, it's only a little bit of a mess, Corey. So absolutely, there's work that we need to do as an industry to make these solutions better. I've been pining for a couple years to say that multicloud needs to be stronger than the sum of its pieces. And we might not yet be there but limiting yourself to a single cloud is reducing your access to innovation, it's reducing your flexibility. And when you start looking at things like edge computing and AI, I'm going to need to access services from multiple providers. So single cloud is a lovely ideal, but in the real world, we understand that teams come with certain skill sets. We end up in many industries, we have mergers and acquisitions. And it's not as easy to just rip out all of your cloud, like you would have 20 years ago, if you said, "Oh, well, they have a phone system or a router "that didn't match what our corporate guidelines is." Cloud is what we're doing. There's lots of solutions out there. And therefore, multicloud is the reality today, and will be the reality going forward for many years to come. >> Strong words from you, Stu. Corey, you've got 60 seconds for rebuttal. I mostly agree with what you just said. I think that having different workloads in different clouds makes an awful lot of sense. Data gravity becomes a bit of a bear. But if you acquire a company that's running on a different cloud than the one that you've picked, you'd be ridiculous to view migrating as anything approaching a strategic priority. Now, this also gets into the question of what is cloud? Our G Suite stuff counts as cloud, but no one really views it in that way. Similarly, when you have an AI specific workload, that's great. As long as it isn't you seriously expensive to move data between providers. That workload doesn't need to live in the same place as your marketing website does. I think that the idea of having a specific cloud provider that you go all in on for every use case, well, at some point that leads to ridiculous things like pretending that Amazon WorkDocs has customers, it does not. But for things that matter to your business and looking at specific workloads, I think that you're going to find a primary provider with secondary workloads here and they're scattered elsewhere to be the strategy that people are getting at when they use the word multicloud badly. >> Time's up for you Corey, Stu we've got time for rebuttal and remember, for those of you in the audience, you can vote at any time and who you think is winning this round. Stu, 60 seconds for a rebuttal. >> Yeah, absolutely Corey. Look, you just gave the Andy Jassy of what multicloud should be 70 to 80% goes to a single provider. And it does make sense we know nobody ever said multicloud equals the same amount in multiple environments but you made a clear case as to why multicloud leveraging multi providers is likely what most companies are going to do. So thank you so much for making a clear case as to why multicloud not equal cloud, across multiple providers is the way to go. So thank you for conceding the victory. >> Last Words, Corey. >> If that's what you took from it Stu, I can't get any closer to it than you have. >> All right, let's move on to the next topic then. The next topic is serverless versus containers which technology is going to be used in, let's say, five to 10 years time? And as a reminder, I'm going to assign each of the debaters these topics, their assignments may or may not match their true feelings about this topic, and they definitely don't represent the topics of my employer, CloudHealth by VMware. Stu, you're going to argue for containers. Corey you're going to argue for start serverless. Stu, you're up first. Two minutes on the clock and go. >> All right, so with all respect to my friends in the serverless community, We need to have a reality check as to how things work. We all know that serverless is a ridiculous name because underneath we do need to worry about all of the infrastructure underneath. So containers today are the de facto building block for cloud native architectures, just as the VM defined the ecosystem for an entire generation of solutions. Containers are the way we build things today. It is the way Google has architected their entire solution and underneath it is often something that's used with serverless. So yes, if you're, building an Alexa service, serverless make what's good for you. But for the vast majority of solutions, I need to have flexibility, I need to understand how things work underneath it. We know in IT that it's great when things work, but we need to understand how to fix them when they break. So containerization gets us to that atomic level, really close to having the same thing as the application. And therefore, we saw the millions of users that deploy Docker, we saw the huge wave of container orchestration led by Kubernetes. And the entire ecosystem and millions of customers are now on board with this way of designing and architecting and breaking down the silos between the infrastructure world and the application developer world. So containers, here to stay growing fast. >> All right, Corey, what do you think? Why is serverless the future? >> I think that you're right in that containers are the way you get from where you were to something that runs effectively in a cloud environment. That is why Google is so strongly behind Kubernetes it helps get the entire industry to write code the way that Google might write code. And that's great. But if you're looking at effectively rewriting something from scratch, or building something that new, the idea of not having to think about infrastructure in the traditional sense of being able to just here, take this code and run it in a given provider that takes whatever it is that you need to do and could loose all these other services together, saves an awful lot of time. As that continues to move up the stack towards the idea of no code or low code. And suddenly, you're now able to build these applications in ways that require just a little bit of code that tie together everything else. We're closer than ever to that old trope of the only code you write is business logic. Serverless gives a much clearer shot of getting there, if you can divorce yourself from the past of legacy workloads. Legacy, of course meaning older than 18 months and makes money. >> Stu, do you have a rebuttal, 60 seconds? >> Yeah. So Corey, we've been talking about this Nirvana in many ways. It's the discussion that we had for paths for over a decade now. I want to be able to write my code once not worry about where it lives, and do all this. But sometimes, there's a reason why we keep trying the same thing over and over again, but never reaching it. So serverless is great for some application If you talked about, okay, if you're some brand new webby thing there and I don't want to have to do this team, that's awesome. I've talked to some wonderful people that don't know anything about coding that have built some cool stuff with serverless. But cool stuff isn't what most business runs on, and therefore containerization is, as you said, it's a bridge to where I need to go, it lives in these cloud environments, and it is the present and it is the future. >> Corey, your response. >> I agree that it's the present, I doubt that it's the future in quite the same way. Right now Kubernetes is really scratching a major itch, which is how all of these companies who are moving to public cloud still I can have their infrastructure teams be able to cosplay as cloud providers themselves. And over time, that becomes simpler and I think on some level, you might even see a convergence of things that are container workloads begin to look a lot more like serverless workloads. Remember, we're aiming at something that is five years away in the context of this question. I think that the serverless and container landscape will look very different. The serverless landscape will be bright and exciting and new, whereas unfortunately the container landscape is going to be represented by people like you Stu. >> Hoarse words from Corey. Stu, any last words or rebuttals? >> Yeah, and look Corey absolutely just like we don't really think about the underlying server or VM, we won't think about the containers you won't think about Kubernetes in the future, but, the question is, which technology will be used in five to 10 years, it'll still be there. It will be the fabric of our lives underneath there for containerization. So, that is what we were talking about. Serverless I think will be useful in pockets of places but will not be the predominant technology, five years from now. >> All right, tough to say who won that one? I'm glad I don't have to decide. I hope everyone out there is voting, last chance to vote on this question before we move on to the next. Next topic is cloud wars. I'm going to give a statement and then I'm going to assign each of you a pro or a con, Google will never be an actual contender in the cloud wars always a far third, we're going to have Corey arguing that Google is never going to be an actual contender. And Stu, you're going to argue that Google is eventually going to overtake the top two AWS and Azure. As a constant reminder, I'm assigning these topics, it's my decision and also they don't match the opinions of me, my employer, or likely Stu or Corey. This is all just for fun and games. But I really want to hear what everyone has to say. So Corey, you're up first two minutes. Why is Google never going to be an actual contender and go. >> The biggest problem Google has in the time of cloud is their ability to forecast longer term on anything that isn't their advertising business, and their ability to talk to human beings long enough to meet people where they are. We're replacing their entire culture is what it's going to take to succeed in the time of cloud and with respect, Thomas Kurian is a spectacular leader internally but look at where he's come from. He spent 22 years at Oracle and now has been transplanted into Google. If we take a look at Satya Nadella's cloud transformation at Microsoft, he was able to pull that off as an insider, after having known intimately every aspect of that company, and he grew organically with it and was perfectly positioned to make that change. You can't instill that kind of culture change by dropping someone externally, on top of an organization and expecting anything to go with this magic one day wake up and everything's going to work out super well. Google has a tremendous amount of strengths, and I don't see that providing common denominator cloud computing services to a number of workloads that from a Google perspective are horrifying, is necessarily in their wheelhouse. It feels like their entire focus on this is well, there's money over there. We should go get some of that too. It comes down to the traditional Google lack of focus. >> Stu, rebuttal? Why do you think Google has a shaft? >> Yeah, so first of all, Corey, I think we'd agree Google is a powerhouse in the world today. My background is networking, when they first came out with with Google Cloud, I said, Google has the best network, second to none in the world. They are ubiquitous today. If you talk about the impact they have on the world, Android phones, you mentioned Kubernetes, everybody uses G Suite maps, YouTube, and the like. That does not mean that they are necessarily going to become the clear leader in cloud but, Corey, they've got really, really smart people. If you're not familiar with that talk to them. They'll tell you how smart they are. And they have built phenomenal solutions, who's going to be able to solve, the challenge every day of, true distributed systems, that a global database that can handle the clock down to the atomic level, Google's the one that does that we've all read the white papers on that. They've set the tone for Hadoop, and various solutions that are all over the place, and their secret weapon is not the advertising, of course, that is a big concern for them, but is that if you talk about, the consumer adoption, everyone uses Google. My kids have all had Chromebooks growing up. It isn't their favorite thing, but they get, indoctrinated with Google technology. And as they go out and leverage technologies in the world, Google is one that is known. Google has the strength of technology and a lot of positioning and partnerships to move them forward. Everybody wants a strong ecosystem in cloud, we don't want a single provider. We already discussed this before, but just from a competitive nature standpoint, if there is a clear counterbalance to AWS, I would say that it is Google, not Microsoft, that is positioned to be that clear and opportune. >> Interesting, very interesting Stu. So your argument is the Gen Zers will of ultimately when they come of age become the big Google proponents. Some strong words that as well but they're the better foil to AWS, Corey rebuttal? >> I think that Stu is one t-shirt change away from a pitch perfect reenactment of Charlie Brown. In this case with Google playing the part of Lucy yanking the football away every time. We've seen it with inbox, Google Reader, Google Maps, API pricing, GKE's pricing for control plane. And when your argument comes down to a suddenly Google is going to change their entire nature and become something that it is as proven as constitutionally incapable of being, namely supporting something that its customers want that it doesn't itself enjoy working on. And to the exclusion of being able to get distracted and focused on other things. Even their own conferences called Next because Google is more interested in what they're shipping than what they're building, than what they're currently shipping. I think that it is a fantasy to pretend that that is somehow going to change without a complete cultural transformation, which again, I don't see the seeds being planted for. >> Some sick burns in there Stu, rebuttal? >> Yeah. So the final word that I'll give you on this is, one of the most important pieces of what we need today. And we need to tomorrow is our data. Now, there are some concerns when we talk about Google and data, but Google also has strong strength in data, understanding data, helping customers leverage data. So while I agree to your points about the cultural shift, they have the opportunity to take the services that they have, and enable customers to be able to take their data to move forward to the wonderful world of AI, cloud, edge computing, and all of those pieces and solve the solution with data. >> Strong words there. All right, that's a tough one. Again, I hope you're all out there voting for who you think won that round. Let's move on to the last round before we start hitting the lightning questions. I put a call out on several channels and social media for people to have questions that they want you to debate. And this one comes from Og-AWS Slack member, Angelo. Angelo asks, "What about IBM Cloud?" Stu you're pro, Corey you're con. Let's have Stu you're up first. The question is, what about IBM Cloud? >> All right, so great question, Angelo. I think when you look at the cloud providers, first of all, you have to understand that they're not all playing the same game. We talked about AWS and they are the elephant in the room that moves nimbly as a cheetah. Every other provider plays a little bit of a different game. Google has strength in data. Microsoft, of course, has their, business productivity applications. IBM has a strong legacy. Now, Corey is going to say that they are just legacy and you need to think about them but IBM has strong innovation. They are a player in really what we call chapter two of the cloud. So when we start talking about multicloud, when we start talking about living in many environments, IBM was the first one to partner with VMware for VMware cloud before the mega VMware AWS announcement, there was IBM up on stage and if I remember right, they actually have more VMware customers on IBM Cloud than they do in the AWS cloud. So over my shoulder here, there's of course, the Red Hat $34 billion to bet on that multicloud solution. So as we talk about containerization, and Kubernetes, Red Hat is strongly positioned in open-source, and flexibility. So you really need a company that understands both the infrastructure side and the application side. IBM has database, IBM has infrastructure, IBM has long been the leader in middleware, and therefore IBM has a real chance to be a strong player in this next generation of platforms. Doesn't mean that they're necessarily going to go attack Amazon, they're partnering across the board. So I think you will see a kinder, gentler IBM and they are leveraging open source and Red Hat and I think we've let the dogs out on the IBM solution. >> Indeed. >> So before Corey goes, I feel the need to remind everyone that the views expressed here are not the views of my employer nor myself, nor necessarily of Corey or Stu. I have Corey. >> I haven't even said anything yet. And you're disclaiming what I'm about to say. >> I'm just warning the audience, 'cause I can't wait to hear what you're going to say next. >> Sounds like I have to go for the high score. All right. IBM's best days are behind it. And that is pretty clear. They like to get angry when people talk about how making the jokes about a homogenous looking group of guys in blue suits as being all IBM has to offer. They say that hasn't been true since the '80s. But that was the last time people cared about IBM in any meaningful sense and no one has bothered to update the relevance since then. Now, credit where due, I am seeing an awful lot of promoted tweets from IBM into my timeline, all talking about how amazing their IBM blockchain technology is. And yes, that is absolutely the phrasing of someone who's about to turn it all around and win the game. I don't see it happening. >> Stu, rebuttal? >> Look, Corey, IBM was the company that brought us the UPC code. They understand Mac manufacturing and blockchain actually shows strong presence in supply chain management. So maybe you're not quite aware of some of the industries that IBM is an expert in. So that is one of the big strengths of IBM, they really understand verticals quite well. And, at the IBM things show, I saw a lot in the healthcare world, had very large customers that were leveraging those solutions. So while you might dismiss things when they say, Oh, well, one of the largest telecom providers in India are leveraging OpenStack and you kind of go with them, well, they've got 300 million customers, and they're thrilled with the solution that they're doing with IBM, so it is easy to scoff at them, but IBM is a reliable, trusted provider out there and still very strong financially and by the way, really excited with the new leadership in place there, Arvind Krishna knows product, Jim Whitehurst came from the Red Hat side. So don't be sleeping on IBM. >> Corey, any last words? >> I think that they're subject to massive disruption as soon as they release the AWS 400 mainframe in the cloud. And I think that before we, it's easy to forget this, but before Google was turning off Reader, IBM stopped making the model M buckling spring keyboards. Those things were masterpieces and that was one of the original disappointments that we learned that we can't fall in love with companies, because companies in turn will not love us back. IBM has demonstrated that. Lastly, I think I'm thrilled to be working with IBM is exactly the kind of statement one makes only at gunpoint. >> Hey, Corey, by the way, I think you're spending too much time looking at all titles of AWS services, 'cause you don't know the difference between your mainframe Z series and the AS/400 which of course is heavily pending. >> Also the i series. Oh yes. >> The i series. So you're conflating your system, which still do billions of dollars a year, by the way. >> Oh, absolutely. But that's not we're not seeing new banks launching and then building on top of IBM mainframe technology. I'm not disputing that mainframes were phenomenal. They were, I just don't see them as the future and I don't see a cloud story. >> Only a cloud live your mainframe related smack talk. That's the important thing that we're getting to here. All right, we move-- >> I'm hoping there's an announcement from CloudHealth by VMware that they also will now support mainframe analytics as well as traditional cloud. >> I'll look into that. >> Excellent. >> We're moving on to the lightning rounds. Each debater in this round is only going to get 60 seconds for their opening argument and then 30 seconds for a rebuttal. We're going to hit some really, really big important questions here like this first one, which is who deserves to sit on the Iron Throne at the end of "Game of Thrones?" I've been told that Corey has never seen this TV show so I'm very interested to hear him argue for Sansa. But let's Sansa Stark, let's hear Stu go first with his argument for Jon Snow. Stu one minute on the clock, go. >> All right audience let's hear it from the king of the north first of all. Nothing better than Jon Snow. He made the ultimate sacrifice. He killed his love to save Westeros from clear destruction because Khaleesi had gone mad. So Corey is going to say something like it's time for the women to do this but it was a woman she went mad. She started burning the place down and Jon Snow saved it so it only makes sense that he should have done it. Everyone knows it was a travesty that he was sent back to the Wall, and to just wander the wild. So absolutely Jon Snow vote for King of the North. >> Compelling arguments. Corey, why should Sansa Stark sit on the throne? Never having seen the show I've just heard bits and pieces about it and all involves things like bloody slaughters, for example, the AWS partner Expo right before the keynote is best known as AWS red wedding. We take a look at that across the board and not having seen it, I don't know the answer to this question, but how many of the folks who are in positions of power we're in fact mediocre white dudes and here we have Stu advocating for yet another one. Sure, this is a lightning round of a fun event but yes, we should continue to wind up selecting this mediocre white person has many parallels in terms of power, et cetera, politics, current tech industry as a whole. I think she's right we absolutely should give someone with a look like this a potential opportunity to see what they can do instead. >> Ouch, Stu 30 seconds rebuttal. >> Look, I would just give a call out to the women in the audience and say, don't you want Jon Snow to be king? >> I also think it's quite bold of Corey to say that he looks like Kit Harington. Corey, any last words? >> I think that it sad you think Stu was running for office at this point because he's become everyone's least favorite animal, a panda bear. >> Fire. All right, so on to the next question. This one also very important near and dear to my heart personally, is a hot dog a sandwich. Corey you'll be arguing no, Stu will be arguing yes. I must also add this important disclaimer that these assignments are made by me and might not reflect the actual views of the debaters here so Corey, you're up first. Why is a hot dog not a sandwich? >> Because you'll get punched in the face if you go to a deli of any renown and order a hot dog. That is not what they serve there. They wind up having these famous delicatessen in New York they have different sandwiches named after different celebrities. I shudder to think of the deadly insult that naming a hot dog after a celebrity would be to that not only celebrity in some cases also the hot dog too. If you take a look and you want to get sandwiches for lunch? Sure. What are we having catered for this event? Sandwiches. You show up and you see a hot dog, you're looking around the hot dog to find the rest of the sandwich. Now while it may check all of the boxes for a technical definition of what a sandwich is, as I'm sure Stu will boringly get into, it's not what people expect, there's a matter of checking the actual boxes, and then delivering what customers actually want. It's why you can let your product roadmap be guided by cart by customers or by Gartner but rarely both. >> Wow, that one hurts. Stu, why is the hot dog a sandwich? >> Yeah so like Corey, I'm sorry that you must not have done some decent traveling 'cause I'm glad you brought up the definition because I'm not going to bore you with yes, there's bread and there's meat and there's toppings and everything else like that but there are some phenomenal hot dogs out there. I traveled to Iceland a few years ago, and there's a little hot dog stand out there that's been there for over 40 or 50 years. And it's one of the top 10 culinary experience I put in. And I've been to Michelin star restaurants. You go to Chicago and any local will be absolutely have to try our creation. There are regional hot dogs. There are lots of solutions there and so yeah, of course you don't go to a deli. Of course if you're going to the deli for takeout and you're buying meats, they do sell hot dogs, Corey, it's just not the first thing that you're going to order on the menu. So I think you're underselling the hot dog. Whether you are a child and grew up and like eating nothing more than the mustard or ketchup, wherever you ate on it, or if you're a world traveler, and have tried some of the worst options out there. There are a lot of options for hot dogs so hot dog, sandwich, culinary delight. >> Stu, don't think we didn't hear that pun. I'm not sure if that counts for or against you, but Corey 30 seconds rebuttal. >> In the last question, you were agitating for putting a white guy back in power. Now you're sitting here arguing that, "Oh some of my best friend slash meals or hot dogs." Yeah, I think we see what you're putting down Stu and it's not pretty, it's really not pretty and I think people are just going to start having to ask some very pointed, delicate questions. >> Tough words to hear Stu. Close this out or rebuttal. >> I'm going to take the high road, Rachel and leave that where it stands. >> I think that is smart. All right, next question. Tabs versus spaces. Stu, you're going to argue for tabs, Corey, you're going to argue for spaces just to make this fun. Stu, 60 seconds on the clock, you're up first. Why are tabs the correct approach? >> First of all, my competitor here really isn't into pop culture. So he's probably not familiar with the epic Silicon Valley argument over this discussion. So, Corey, if you could explain the middle of algorithm, we will be quite impressed but since you don't, we'll just have to go with some of the technology first. Looks, developers, we want to make things simple on you. Tabs, they're faster to do they take up less memory. Yes, they aren't quite as particular as using spaces but absolutely, they get the job done and it is important to just, focus on productivity, I believe that the conversation as always, the less code you can write, the better and therefore, if you don't have to focus on exactly how many spaces and you can just simplify with the tabs, you're gona get close enough for most of the job. And it is easier to move forward and focus on the real work rather than some pedantic discussion as to whether one thing is slightly more efficient than the other. >> Great points Stu. Corey, why is your pedantic approach better? >> No one is suggesting you sit there and whack the spacebar four times or eight times you hit the Tab key, but your editor should be reasonably intelligent enough to expand that. At that point, you have now set up a precedent where in other cases, other parts of your codebase you're using spaces because everyone always does. And that winds up in turn, causing a weird dissonance you'll see a bunch of linters throwing issues if you use tabs as a direct result. Now the wrong answer is, of course, and I think Steve will agree with me both in the same line. No one is ever in favor of that. But I also want to argue with Stu over his argument about "Oh, it saves a little bit of space "is the reason one should go with tabs instead." Sorry, that argument said bye bye a long time ago, and that time was the introduction of JavaScript, where it takes many hundreds of Meg's of data to wind up building hello world. Yeah, at that point optimization around small character changes are completely irrelevant. >> Stu, rebuttal? >> Yeah, I didn't know that Corey did not try to defend that he had any idea what Silicon Valley was, or any of the references in there. So Rachel, we might have to avoid any other pop culture references. We know Corey just looks at very specific cloud services and can't have fun with some of the broader themes there. >> You're right my mistake Stu. Corey, any last words? >> It's been suggested that whole middle out seen on the whiteboard was came from a number of conversations I used to have with my co-workers as in people who were sitting in the room with me watching that episode said, Oh my God, I've been in the room while you had this debate with your friend and I will not name here because they at least still strive to remain employable. Yeah, it's, I understand the value in the picking these fights, we could have gone just as easily with vi versus Emacs, AWS versus Azure, or anything else that you really care to pick a fight with. But yeah, this is exactly the kind of pedantic fight that everyone loves to get involved with, which is why I walked a different path and pick other ridiculous arguments. >> Speaking of those ridiculous arguments that brings us to our last debate topic of the day, Corey you are probably best known for your strong feelings about the pronunciation of the acronym for Amazon Machine Image. I will not be saying how I think it is pronounced. We're going to have you argue each. Stu, you're going to argue that the acronym Amazon Machine Image should be pronounced to rhyme with butterfly. Corey, you'll be arguing that it rhymes with mommy. Stu, rhymes with butterfly. Let's hear it, 60 seconds on the clock. >> All right, well, Rachel, first of all, I wish I could go to the videotape because I have clear video evidence from a certain Corey Quinn many times arguing why AMI is the proper way to pronounce this, but it is one of these pedantic arguments, is it GIF or GIF? Sometimes you go back and you say, Okay, well, there's the way that the community did it. And the way that oh wait, the founder said it was a certain way. So the only argument against AMI, Jeff Barr, when he wrote about the history of all of the blogging that he's done from AWS said, I wish when I had launched the service that I pointed out the correct pronunciation, which I won't even deem to talk it because the community has agreed by and large that AMI is the proper way to pronounce it. And boy, the tech industry is rific on this kind of thing. Is it SQL and no SQL and you there's various ways that we butcher these constantly. So AMI, almost everyone agrees and the lead champion for this argument, of course is none other than Corey Quinn. >> Well, unfortunately today Corey needs to argue the opposite. So Corey, why does Amazon Machine Image when pronounce as an acronym rhyme with mommy? >> Because the people who built it at Amazon say that it is and an appeal to authorities generally correct when the folks built this. AWS has said repeatedly that they're willing to be misunderstood for long periods of time. And this is one of those areas in which they have been misunderstood by virtually the entire industry, but they are sticking to their guns and continuing to wind up advocating for AMI as the correct pronunciation. But I'll take it a step further. Let's take a look at the ecosystem companies. Whenever Erica Brescia, who is now the COO and GitHub, but before she wound up there, she was the founder of Bitnami. And whenever I call it Bitn AMI she looks like she is barely successfully restraining herself from punching me right in the mouth for that pronunciation of the company. Clearly, it's Bitnami named after the original source AMI, which is what the proper term pronunciation of the three letter acronym becomes. Fight me Stu. >> Interesting. Interesting argument, Stu 30 seconds, rebuttal. >> Oh, the only thing he can come up with is that, you take the word Bitnami and because it has that we know that things sound very different if you put a prefix or a suffix, if you talk to the Kubernetes founders, Kubernetes should be coop con but the people that run the conference, say it cube con so there are lots of debates between the people that create it and the community. I in general, I'm going to vote with the community most of the time. Corey, last words on this topic 'cause I know you have very strong feelings about it. >> I'm sorry, did Stu just say Kubernetes and its community as bastions of truth when it comes to pronouncing anything correctly? Half of that entire conference is correcting people's pronunciation of Kubernetes, Kubernetes, Kubernetes, Kubernetes and 15 other mispronunciations that they will of course yell at you for but somehow they're right on this one. All right. >> All right, everyone, I hope you've been voting all along for who you think is winning each round, 'cause this has been a tough call. But I would like to say that's a wrap for today. big thank you to our debaters. You've been very good sports, even when I've made you argue for against things that clearly are hurting you deep down inside, we're going to take a quick break and tally all the votes. And we're going to announce a winner up on the Zoom Q and A. So go to the top of your screen, Click on Zoom Q and A to join us and hear the winner announced and also get a couple minutes to chat live with Corey and Stu. Thanks again for attending this session. And thank you again, Corey and Stu. It's been The Great Cloud Debate. All right, so each round I will announce the winner and then we're going to announce the overall winner. Remember that Corey and Stu are playing not just for bragging rights and ownership of all of the internet for the next 24 hours, but also for lunch to be donated to their local hospital. Corey is having lunch donated to the California Pacific Medical Centre. And Stu is having lunch donated to Boston Medical Centre. All right, first up round one multicloud versus monocloud. Stu, you were arguing for multicloud, Corey, you were arguing for one cloud. Stu won that one by 64% of the vote. >> The vendor fix was in. >> Yeah, well, look, CloudHealth started all in AWS by supporting customers across those environments. So and Corey you basically conceded it because we said multicloud does not mean we evenly split things up. So you got to work on those two skills, buddy, 'cause, absolutely you just handed the victory my way. So thank you so much and thank you to the audience for understanding multicloud is where we are today, and unfortunately, it's where we're gonnao be in the future. So as a whole, we're going to try to make it better 'cause it is, as Corey and I both agree, a bit of a mess right now. >> Don't get too cocky. >> One of those days the world is going to catch up with me and realize that ad hominem is not a logical fallacy so much as it is an excellent debating skill. >> Well, yeah, I was going to say, Stu, don't get too cocky because round two serverless versus containers. Stu you argued for containers, Corey you argued for serverless. Corey you won that one with 65, 66 or most percent of the vote. >> You can't fight the future. >> Yeah, and as you know Rachel I'm a big fan of serverless. I've been to the serverless comp, I actually just published an excellent interview with Liberty Mutual and what they're doing with serverless. So love the future, it's got a lot of maturity to deliver on the promise that it has today but containers isn't going anyway or either so. >> So, you're not sad that you lost that one. Got it, good concession speech. Next one up was cloud wars specifically Google. is Google a real contender in the clouds? Stu, you were arguing yes they are. Corey, you were arguing no they aren't. Corey also won this round was 72% of the votes. >> Yeah, it's one of those things where at some point, it's sort of embarrassing if you miss a six inch pot. So it's nice that that didn't happen in this case. >> Yeah, so Corey, is this the last week that we have any competitors to AWS? Is that what we're saying? And we all accept our new overlords. Thank you so much, Corey. >> Well I hope not, my God, I don't know what to be an Amazonian monoculture anymore than I do anyone else. Competition makes all of us better. But again, we're seeing a lot of anti competitive behaviour. For example, took until this year for Microsoft to finally make calculator uninstallable and I trust concerned took a long time to work its way of course. >> Yeah, and Corey, I think everyone is listening to what you've been saying about what Google's doing with Google Meet and forcing that us when we make our pieces there. So definitely there's some things that Google culture, we'd love them to clean up. And that's one of the things that's really held back Google's enterprise budget is that advertised advertising driven culture. So we will see. We are working hand-- >> That was already opted out of Hangouts, how do we fix it? We call it something else that they haven't opted out of yet. >> Hey, but Corey, I know you're looking forward to at least two months of weekly Google live stuff starting this summer. So we'll have a lot of time to talk about google. >> Let's not kid ourselves they're going to cancel it halfway through. (Stu laughs) >> Boys, I thought we didn't have any more smack talk left in you but clearly you do. So, all right, moving on. Next slide. This is the last question that we did in the main part of the debate. IBM Cloud. What about IBM Cloud was the question, Stu, you were pro, Corey you were con. Corey, you won this one again with 62% of the vote and for the main. >> It wasn't just me, IBM Cloud also won. The problem is that competition was oxymoron of the day. >> I don't know Rachel, I thought this one had a real shot as to putting where IBM fits. I thought we had a good discussion there. It seemed like some of the early voting was going my way but it just went otherwise. >> It did. We had some last minute swings in these polls. They were going one direction they rapidly swung another it's a fickle crowd today. So right now we've got Corey with three points Stu with one but really the lightning round anyone's game. They got very close here. The next question, lightning round question one, was "Game of Thrones" who deserves to sit on the Iron Throne? Stu was arguing for Jon Snow, Corey was arguing for Sansa Stark also Corey has never seen Game of Thrones. This was shockingly close with Stu at 51.5% of the vote took the crown on this King of the North Stu. >> Well, I'm thrilled and excited that King of the North pulled things out because it would have been just a complete embarrassment if I lost to Corey on this question. >> It would. >> It was the right answer, and as you said, he had no idea what he's talking about, which, unfortunately is how he is on most of the rest of it. You just don't realize that he doesn't know what he's talking about. 'Cause he uses all those fast words and discussion points. >> Well, thank you for saying the quiet part out loud. Now, I am completely crestfallen as to the results of this question about a thing I've never seen and could not possibly care less about not going in my favor. I will someday managed to get over this. >> I'm glad you can really pull yourself together and keep on going with life, Corey it's inspiring. All right, next question. Was the lightning round question two is a hot dog a sandwich? Stu, you were arguing yes. Corey, you were arguing no. Corey landslide, you won this 75% of the vote. >> It all comes down to customer expectations. >> Yeah. >> Just disappointment. Disappointment. >> All right, next question tabs versus spaces. Another very close one. Stu, what were you arguing for Stu? >> I was voting tabs. >> Tabs, yeah. And Corey, you were arguing spaces. This did not turn out the way I expected. So Stu you lost this by slim margin Corey 53% of the vote. You won with spaces. >> Yep. And I use spaces in my day to day life. So that's a position I can actually believe in. >> See, I thought I was giving you the opposite point of view there. I mistook you for the correct answer, in my opinion, which is tabs. >> Well, it is funnier to stalk me on Twitter and look what I have to there than on GitHub where I just completely commit different kinds of atrocities. So I don't blame you. >> Caught that pun there. All right, the last rounds. Speaking of atrocities, AMI, Amazon Machine Image is it pronounced AMI or AMI? >> I better not have won this one. >> So Stu you were arguing that this is pronounced AMI rhymes with butterfly. Corey, you were arguing that it's pronounced AMI like mommy. Any guesses under who won this? >> It better be Stu. >> It was a 50, 50 split complete tie. So no points to anyone. >> For your complete and utterly failed on this because I should have won in a landslide. My entire argument was based on every discussion you've had on this. So, Corey I think they're just voting for you. So I'm really surprised-- >> I think at this point it shows I'm such a skilled debater that I could have also probably brought you to a standstill taking the position that gravity doesn't exist. >> You're a master of few things, Corey. Usually it's when you were dressed up nicely and I think they like the t-shirt. It's a nice t-shirt but not how we're usually hiding behind the attire. >> Truly >> Well. >> Clothes don't always make a demand. >> Gentlemen, I would like to say overall our winner today with five points is Corey. Congratulations, Corey. >> Thank you very much. It's always a pleasure to mop the floor with you Stu. >> Actually I was going to ask Stu to give the acceptance speech for you, Corey and, Corey, if you could give a few words of concession, >> Oh, that's a different direction. Stu, we'll start with you, I suppose. >> Yeah, well, thank you to the audience. Obviously, you voted for me without really understanding that I don't know what I'm talking about. I'm a loudmouth on Twitter. I just create a bunch of arguments out there. I'm influential for reasons I don't really understand. But once again, thank you for your votes so much. >> Yeah, it's always unfortunate to wind up losing a discussion with someone and you wouldn't consider it losing 'cause most of the time, my entire shtick is that I sit around and talk to people who know what they're talking about. And I look smart just by osmosis sitting next to them. Video has been rough on me. So I was sort of hoping that I'd be able to parlay that into something approaching a victory. But sadly, that hasn't worked out quite so well. This is just yet another production brought to you by theCube which shut down my original idea of calling it a bunch of squares. (Rachael laughs) >> All right, well, on that note, I would like to say thank you both Stu and Corey. I think we can close out officially the debate, but we can all stick around for a couple more minutes in case any fans have questions for either of them or want to get them-- >> Find us a real life? Yeah. >> Yeah, have a quick Zoom fight. So thanks, everyone, for attending. And thank you Stu, thank you Corey. This has been The Great Cloud Debate.

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to again sunset legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, This is John Furrier here inside theCUBE studios we serve 85 of the Fortune 100, over half the global 2000. for an update with Informatica's digital conference. The reason why customers come back and speak on our behalf Take a look at it, check it out online. and literally thousands of customers speak on our behalf, Join the community. Be part of those thousands of customers that they have, it's humbling, is because we have the best and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. intelligent data platform in the market. I'm John Furrier, thanks for watching. And we also understand our customers aren't buying software. (soft music) They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight.

>> Narrator: From around the globe it's theCUBE with digital coverage of DockerCon Live 2020. Brought to you by Docker and its ecosystem partners. >> Hi, I'm Stu Miniman and welcome to theCUBE's coverage of DockerCon Live 2020. Really excited to be part of this online event. We've been involved with DockerCon for a long time, of course one of my favorite things is always to be able to talk to the practitioners. Of course we remember for years, Docker exploded onto the marketplace, millions of people downloaded it, using it. So joining me is Hui Xue, who is a Principal Deputy Director of Medical Signal Processing at the National Heart, Lung, and Blood Institute, which is part of the National Institute of Health. Hui, thank you so much for joining us. >> Thank you for inviting me. >> So let's start. Of course, the name of your institute, very specific. I think anyone in the United States knows the NIH. Tell us a little bit about your role there and kind of the scope of what your team covers. >> So I'm basically a researcher and developer of the medical imaging technology. We are the heart, lung and the blood, so we work and focus on imaging the heart. So what we exactly do is to develop the new and novel imaging technology and deploy them to the front of our clinical library, which Docker played an essential role in the process. So, yeah, that's what we do at NHLBI. >> Okay, excellent. So research, you know, of course in the medical field with the global pandemic gets a lot of attention. So you keyed it up there. Let's understand, where does containerization and Docker specifically play into the work that your team is doing? >> So, maybe I'd like to give an example which will suffice. So for example, we're working on the magnetic resonance imaging, MRI. Many of us may may already have been scanned. So we're using MRI to image the heart. What Docker plays, is Docker allow us to deploy our imaging technology to the clinical hospital. So we have a global deployment around 40 hospitals, a bit more, around the world. If we are for example develop a new AI-based image analysis for the heart image, what we do with Docker is we can put our model and software into the Docker so that our collaboration sites, they will pull the software that contains the latest technology, then use them for the patients, of course under the research agreement at NIH. Because Docker is so efficient, available globally, we can actually implement a continuous integration and testing, update the framework based on Docker. Then our collaborators would have the latest technology instead of, you know, in the traditional medical imaging in general, the iteration of technology is pretty slow. But with all this latest technology, and such like container Docker come into the field. It's actually relatively new. In the past two to three years, all these paradigm is, it's changing, certainly very exciting to us. It give us the flexibility we never had before to reach our customers, to reach other people in the world to help them. They also help us so that's a very good experience to have. >> Yeah that's pretty powerful what you're talking about there rather than you know, we install some equipment, who knows how often things get updated, how do you make sure to synchronize between different locations. Obviously the medical field highly regulated and being a government agency, talk a little bit about how you make sure you have the right version control, security is in place, how do all of those things sort out? >> Yes, that's an essential question. So firstly I want to clarify one thing. So it's not NIH who endorse Docker, it's us as researchers. We practiced Docker too and we trust its performance. This container technology is efficient, it's globally available and it's very secure. So all the communication between the container and the imaging equipment is encrypted. We also have all the paperwork it saved to set up to allow us to provide technology to our clinician. When they post the latest software, every version they put up into the Docker went through an automated integration test system. So every time they make a change, the newer version of software runs through a rigorous test, something like 200 gigabytes of data runs through and checked everything is still working. So the basic principle is we don't allow any version of the software to be delivered to customer without testing Docker. Let's say this container technology in general actually is 100% automating all this process, which actually give us a lot of freedom so we have a rather very small team here at NIH. Many people are actually very impressed by how many customer we support within this so small team. So the key reason is because we have a strongly utilized container technology, so its automation is unparalleled, certainly much better than anything I had before using this container technology. So that's actually the key to maintain the quality and the continuous service to our customers. >> Yeah, absolutely. Automation is something we've been talking about in the industry for a long time but if we implement it properly it can have a huge impact. Can you bring us inside a little bit, you know, what tools are you doing? How is that automation set up and managed? And how that fits into the Docker environment. >> So I kind of describe to be more specific. So we are using a continuous testing framework. There are several apps to be using a specific one to build on, which is an open source Python tool, rather small actually. What it can do is, this tool will set up at the service, then this service will watch for example our GitHub repo. Whenever I make a change or someone in the team makes a change for example, fix a bug, add a new feature, or maybe update a new AI model, we push the edge of the GitHub then there's a continuous building system that will notice, it will trigger the integration test run all inside Docker environment. So this is the key. What container technology offers is that we can have 100% reproducible runtime environment for our customers as the software provider, because in our particular use case we don't set up customer with the uniform hardware so they bought their own server around the world, so everyone may have slightly different hardware. We don't want that to get into our software experience. So Docker actually offers us the 100% control of the runtime environment which is very essential if we want to deliver a consistent medical imaging experience because most applications actually it's rather computational intensive, so they don't want something to run for like one minute in one site and maybe three minutes at another site. So what Docker place is that Docker will run all the integration tests. If everything pass then they pack the Docker image then send to the Docker Hub. Then all our collaborators around the world have new image then they will coordinate with them so they will find a proper time to update then they have the newer technology in time. So that's why Docker is such a useful tool for us. >> Yeah, absolutely. Okay, containerization in Docker really transformed the way a lot of those computational solutions happen. I'm wondering if you can explain a little bit more the stack that you're using if people that might not have looked at solutions for a couple of years think oh it's containers, it's dateless architectures, I'm not sure how it fits into my other network environment. Can you tell us what are you doing for the storage in the network? >> So we actually have a rather vertical integration in this medical imaging application, so we build our own service as the software, its backbone is C++ for the higher computational efficiency. There's lots of Python because these days AI model essential. What Docker provides, as I mentioned, uniform always this runtime environment so we have a fixed GCC version then if we want to go into that detail. Specific version of numerical library, certain versions of Python, will be using PyTorch a lot. So that's our AI backbone. Another way of using Docker is actually we deploy the same container into the Microsoft Azure cloud. That's another ability I found out about Docker, so we never need to change anything in our software development process, but the same container I give you must work everywhere on the cloud, on site, for our customers. This actually reduces the development cost, also improve our efficiency a lot. Another important aspect is this actually will improve customers', how do they say it, customer acceptance a lot because they go to one customer, tell them the software you are running is actually running on 30 other sites exactly the same up to the let's say heights there, so it's bit by bit consistent. This actually help us convince many people. Every time when I describe this process I think most people accept the idea. They actually appreciate the way how we deliver software to them because we always can falling back. So yes, here is another aspect. So we have many Docker images that's in the Docker Hub, so if one deployment fails, they can easily falling back. That's actually very important for medical imaging applications that fail because hospitals need to maintain their continuous level of service. So even we want to avoid this completely but yes occasionally, very occasionally, there will be some function not working or some new test case never covered before, then we give them an magnet then, falling back, that's actually also our policy and offered by the container technology. >> Yeah, absolutely. You brought up, many have said that the container is that atomic unit of building block and that portability around any platform environment. What about container orchestration? How are you managing these environments you talked about in the public cloud or in different environments? What are you doing for container orchestration? >> Actually our set-up might be the simplest case. So we basically have a private Docker repo which we paid, actually the Institute has paid. We have something like 50 or 100 private repos, then for every repo we have one specific Docker setup with different software versions of different, for example some image is for PyTorch another for TensorFlow depending on our application. Maybe some customer has the requirement to have rather small Docker image size then they have some trimmed down version of image. In this process, because it's still in a small number like 20, 30 active repo, we are actually managing it semi-automatically so we have the service running to push and pull, and loading back images but we actually configured this process here at the Institute whenever we feel we have something new to offer to the customer. Regarding managing this Docker image, it's actually another aspect for the medical image. So at the customer side, we had a lot of discussion with them for whether we want to set up a continuous automated app, but in the end they decided, they said they'd better have customers involved. Better have some people. So we were finally stopped there by, we noticed customer, there are something new to update then they will decide when to update, how to test. So this is another aspect. Even we have a very high level of confirmation using the container technology, we found it's not 100%. In some site, it's still better have human supervision to help because if the goal is to maintain 100% continuous service then in the end they need some experts on the field to test and verify. So that's how they are in the current stage of deployment of this Docker image. We found it's rather light-weight so even with a few people at NIH in our team, they can manage a rather large network globally, so it's really exciting for us. >> Excellent. Great. I guess final question, give us a little bit of a road map as to, you've already talked about leveraging AI in there, the various pieces, what are you looking for from Docker in the ecosystem, and your solution for the rest of the year? >> I would say the future definitely is on the cloud. One major direction we are trying to push is to go the clinical hospital, linking and use the cloud in building as a routine. So in current status, some of sites, hospital may be very conservative, they are afraid of the security, the connection, all kinds of issues related to cloud. But this scenario is changing rapidly, especially container technology contributes a lot on the cloud. So it makes the whole thing so easy, so reliable. So our next push is to move in lots of the application into the cloud only. So the model will be, for example, we have new AI applications. It may be only available on the cloud. If some customer is waiting to use them they will have to be willing to connect to the cloud and maybe sending data there and receive, for example, the AI apps from our running Docker image in the cloud, but what we need to do is to make the Docker building even more efficiency. Make the computation 100% stable so we can utilize the huge computational power in the cloud. Also the price, so the key here is the price. So if we have one setup in the cloud, a data center for example, we currently maintain two data centers one across Europe, another is in United States. So if we have one data center and 50 hospitals using it every day, then we need the numbers. The average price for one patient comes to a few dollars per patient. So if we consider this medical health care system the costs, the ideal costs of using cloud computing can be truly trivial, but what we can offer to patients and doctor has never happened. The computation you can bring to us is something they never saw before and they never experienced. So I believe that's the future, it's not, the old model is everyone has his own computational server, then maintaining that, it costs a lot of work. Even doctor make the software aspects much easier, but the hardware, someone still need to set-up them. But using cloud will change all of. So I think the next future is definitely to wholly utilize the cloud with the container technology. >> Excellent. Well, we thank you so much. I know everyone appreciates the work your team's doing and absolutely if things can be done to allow scalability and lower cost per patient that would be a huge benefit. Thank you so much for joining us. >> Thank you. >> All right, stay tuned for lots more coverage from theCUBE at DockerCon Live 2020. I'm Stu Miniman and thank you for watching theCUBE. (gentle music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to gain some legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, we serve 85 of the Fortune 100, over half the global 2000. The reason why customers come back and speak on our behalf and literally thousands of customers speak on our behalf, it's humbling, is because we have the best intelligent data platform in the market. And we also understand our customers aren't buying software. They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight. One final personal question I want to ask you. I've been following you guys for a long time, and we've had you on theCUBE many times. You've been a seasoned veteran in the industry. You've seen cycles of innovation. You've seen the ups and downs over the years. You've been on boards, you've been a leader, a senior leader. What do you talk about with your friends and peers when you look at this current inflection point? As there's the candid conversations are happening, it's really an opportunity, but also there are serious challenges. As a leader, how should leaders be thinking about getting through this? What's your personal view? You've seen many cycles. You've see many waves. This wave coming is going to be big. This change is certainly going to create an uptick, we believe, exponentially a step function transformation. What's your view? What are some of the conversations that you're having with your friends, peers around what to do? >> Yeah, so I think in any situation like the one that we're in, it's important first and foremost to take care of the employees, take care of the customers, take care of the short term needs. That's critical. And yet at the same time in parallel, to be thinking longer term because there is an opportunity when you go through a situation like this to regroup and to think about, what will be the key markets that come back the fastest? What will be your differentiation, your company's differentiation so that you come out of this when the market does start to rebound and really thriving. So it's always this constant balance of how you deal with the short-term and the realities that we're in because people are making some tough decisions. And yet at the same time, make sure that you're very clear on your long-term strategy so that you can come out of this swinging. >> Great advice. That's a masterclass right there. Thank you for sharing that. Of course, check out Informatica's CLAIREview event. Of course, the digital events are always online. Check them out. Tracey, thanks for your time and thanks for that insight and update, appreciate it. >> Yeah, great to be here, John. Look forward to seeing you in person soon. >> Okay, take care. This is John Furrier here inside theCUBE studios for an update with Informatica's digital conference. Take a look at it, check it out online. Join the community. Be part of those thousands of customers that they have, and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. I'm John Furrier, thanks for watching. (soft music)

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service, is >> welcome back here to New York City. You're watching the Cube, the worldwide leader in Enterprise Tech cover jumps to minimum. My co host for today is Cory Quinn and happy to welcome to the program. A first time guest on the program, says Heart O. B. Who is a senior leader of global business development with Healthcare Life. Scientists know this group and AWS thanks so much for joining us. All right, so you know, we love digging into some of the verticals here in New York City. Of course, it's been a lot of time on the financial service is peas we actually had, Ah, another one of our teams out of the eight of us. Imagine show going on yesterday in Seattle with a lot of the education pieces. So healthcare, life sciences in genomics, little bit of tech involved in those groups, a lot of change going on in that world. So give us a thumbnail if you would as toe what what's happening in your >> world so well just from a scope one of you Health care includes life set paid on provider Life sciences is far more by attacking its most medical device and then genomics and what we're seeing in those spaces. Let's start with health care. It's such a broad thing, will just sort of back to back and forth in health care itself. What we're sort of seeing their customs ask us to focus on and to help them do falls into three categories. First, is a lot of customers ask us to help them personalized the consumer health journey. You and I, all of us, are so accustomed to that frictionless experiences we have elsewhere and in health care. There's a lot more friction. And so we're getting a lot of enquiries and request for us to help them transform that experience. Make it frictionless. So an example That would be if you're familiar with Doc. Doc started here in New York. Actually, when you want a book, an appointment, Doc, Doc, you can normally, if you go online, I have to put information for insurance. You type it all. Then it's full of friction. Have to put all the fields in. They use one of our A I service's image recognition, and you simply hold up your card to the camera and it able to pull your in transporation, determine eligibility and look the right appointment for you. So that's an example of removing friction for the consumer of the health consume over the patient as they're trying to go to that health care and excessive category one frictionless experiences using AWS to support it with a i service is category, too. We're getting a lot of interest for us to help health systems predict patient health events. So anything of value base care the way you actually are able to change the cost. Quality Curve is predicting events, not just dealing with math and so using a i Am L service is on top of data to predict and forecast events is a big part of one example would be with sooner where they moved, they're healthy and 10 platform, which is a launch to a patient record platform onto AWS. About 223,000,000 individuals that are on that platform Men we did a study with him where way consume about 210,000 individual patient data and created a machine learning model this is published where you can predict congestive heart failure 15 months in advance of it actually occurring. So when you look at that, that prediction are forecasting that sort of one of the powers of this princess. What category number two is predicting health events, and then the last one I'd be remiss in leaving out is that you probably have heard a lot of discussion on physician and a clinician. Burnout to the frustrations of the nurses or doctors and Muslims have the heart of that is not having the right information the right time to take care of the right patient. Data liquidity and in Trop ability is a huge challenge, and a lot of our customers are asking us to help solve those problems with them. You know it hims. This year we announced, together with change Healthcare Change Healthcare said they want to provide free and troubling to the country on AWS, with the platform supporting that. So those are sort of three categories. Personalize the consumer health journey. Predicting patient health events and promoting intra ability is sort of the signals that we're seeing in areas that were actively supporting our customers and sort of elevating the human condition. >> It's very easy to look at the regulation around things like health care and say, Oh, that gets in the way and its onerous and we're not gonna deal with it or it should be faster. I don't think anyone actively wants that. We like the fact that our hospitals were safe, that health care is regulated and in some of the ways that it is at least. But I saw an artifact of that means that more than many other areas of what AWS does is your subject to regulatory speed of Sloane. A speed of feature announcement, as opposed to being able to do it as fast technology allows relatively easy example of this was a few years back. In order to run, get eight of us to sign a B A. For hip, a certification, you have to run dedicated tendency instances and will not changed about a year and 1/2 2 years ago or even longer. Depending it's it all starts to run together after a time, but once people learn something, they don't tend to go back and validate whether it's still true. How do you just find that communicating to your customers about things that were not possible yesterday now are, >> yeah, when you look at hip eligibility. So as you know, a devious is about over 100 him eligible service's, which means that these are so this is that so compliance that you start their compliance, Remember, is an outcome, not a future. So compliance is a combination of people process platform, and we bring the platform that's hip eligible, and our customers bring the people in process, if you will, to use that platform, which then becomes complying with regulatory requirements. And so you're absolutely right. There's a diffusion of sort of understanding of eligibility, a platform, and then they worked with customers have to do in order as a shared responsibility to do it. That diffusion is sometimes slower. In fact, there's sometimes misinformation. So we always see it work with our customers and that shared, responsive model so that they can meet their requirements as they come to the cloud. And we can bring platforms that are eligible for hip. They can actually carry out the work clothes they need to. So it's it's that money, you know, the way I think of it is. This when you think of compliance, is that if if I were to build for you a deadbolt for your door and I can tell you that this complies boasted of things, but you put the key under the mat way might not be complying with security and regular requirements for our house. So it's a share responsible. I'll make the platform be eligible and compliant, and so that collective does daytime and dusting. People are saying that there is a flat from this eligible, and then they have to also, in their response to work to the people in process potion to make the totality of it comply with the requirements for regulatory for healthcare regulatory requirements. >> Some of the interesting conversations I've had in the last few years in health care in the industry is collaborations that are going on, you know, how do we share data while still maintaining all of the regulations that are involved? Where does that leave us get involved? There >> should. That's a fact. There is a data sharing part of that did a liquidity story that we talked about earlier in terms of instability. I'll give an example of where AWS actually actively working in that space. You may be familiar with a service we launched last November at Reinvent called Amazon Campion Medical and Campion Medical. What it does is it looks at a medical note and can extract key information. So if you think back to in high school, when you used to read a book in highlighting yellow key concepts that you wanted to remember for an exam Amazon Carmen Medical Same thing exactly, can lift key elements and goes from a text blob, too discrete data that has relationship ontology and that allows data sharing where you where you need to. But then there's one of the piece, so that's when you're allowed to disclose there's one of me. Sometimes you and I want to work on something, but we want to actually read act the patient information that allows data sharing as well. So Amazon coming medical also allows you to read, act. Think of when a new challenge shows that federally protected doctor that's blacked out Amazon com for American also remove patient identifying information. So if you and I want to collaborate on research project, you have a set of data that you wanna anonima de identify. I have data information of I D identified. To put it together, I can use Amazon com Medical Read Act All the patient information Make it d identified. You can do the same. And now we can combine the three of us that information to build models, to look a research and to do data sharing. So whether you have full authority to to share patient information and use the ontological portion of it, or whether you want to do the identifying matter, Amazon competent medical helps you do that. >> What's impressive and incredible is that whether we like it or not, there's something a little special about health care where I can decide I'm not going to be on the Internet. Social media things all stop tweeting. Most people would thank me for that, or I can opt out of ride sharing and only take taxis, for example. But we're all sooner or later going to be customers of the health care industry, and as a result, this is some of that effects, all of us, whether we want to acknowledge that or not. I mean, where some of us are still young enough to believe that we have this immortality streak going on. So far, so good. But it becomes clear that this is the sort of thing where the ultimate customer is all of us. As you take a look at that, does that inform how AWS is approaching this entire sector? >> Absolutely. In fact, I'd like to think that a W brought a physician toe lead sector because they understood that in addition to our customer obsession that we see through the customer to the individual and that we want to elevate the human condition we wanted obsess over our customers success so that we can affect positive action on the lives of individuals everywhere. To me, that is a turn. The reason I joined it of U. S s. So that's it. Certainly practice of healthcare Life's I said on genomic Seti ws has been around for about six years. A doubIe s double that. And so actually it's a mature practice and our understanding of our customers definitely includes that core flame that it's about people and each of us come with a special story. In fact, you know the people that work in the U. S. Healthcare life, science team people that have been to the bedside there, people that have been adventure that I worked in the farm industry, healthcare, population, health. They all are there because of that thing you just said. Certainly I'm there because that on the entire practice of self life sciences is keenly aware of looking through the customers to the >> individual pieces. All right, how much? You know, mix, you know, definitely an area where compute storage are critically important than we've seen. Dramatic change. You know, in the last 5 to 10 years, anything specific you could share on that >> Genomics genomex is an area where you need incredible computer storage on. In our case, for example, alumina, which is one of our customers, runs about 85% of all gene sequencing on the planet is in aws customer stores. All that data on AWS. So when you look at genomex, real power of genomics is the fact that enables precision diagnostics. And so when you look at one of our customers, Grail Grail, that uses genomic fragments in the blood that may be coming from cancer and actually sequences that fragment and then on AWS will use the power of the computer to do machine learning on that Gino Mexicans from to determine if you might have one of those 1 10 to 12 cancers that they're currently screening for. And so when you talk to a position health, it really can't be done without position diagnostics, which depends on genomex, which really is an example of that. It runs on AWS because we bring compute and storage essentially infinite power. To do that you want, For example, you know the first whole genome sequence took 14 years. And how many billions of dollars Children's Hospital Philadelphia now does 1000 whole genome sequences in two hours and 20 minutes on AWS, they spike up 20,000 see few cores, do that desi and then moved back down. Genomics. The field that literally can't be. My humble opinion can't be done outside the cloud. It just the mechanics of needed. The storage and compute power is one that is born in the cloud on AWS has those examples that I shared with you. >> It's absolutely fantastic and emerging space, and it's it's interesting to watch that despite the fact there is a regulatory burden that everything was gonna dispute that and the gravity of what it does. I'm not left with sense that feature enhancement and development and velocity of releases is slower somehow in health care than it is across the entire rest of the stack. Is that an accurate assessment, or is there a bit of a drag effect on that? >> Do you mean in the health care customers are on AWS speaking >> on AWS aside, citizen customers are going to be customers. Love them. We >> do aws. You know, we obviously innovation is a rowdy and we release gosh everything. About 2011 we released 80 front service than features and jumped 1015 where it was like 702 jumped 2018. Where was 1957 features? That's like a 25 fold. Our pace of innovation is not going to slow down. It's going to continue. It's in our blood in our d. N. A. We in fact, hire people that are just not satisfied. The status quo on want to innovate and change things. Just, you know, innovation is the beginning of the end of the story, so, no, I don't have to spend any slowdown. In fact, when you add machine learning models on machine learning service that we're putting in? I only see it. An even faster hockey stick of the service is that we're gonna bring out. And I want you to come to reinvent where we're going to announce the mall and you you will be there and see that. All >> right, well, on that note thank you so much for giving us the update on healthcare Life Sciences in genomics. Absolutely. Want to see the continued growth and innovation in that? >> My pleasure. Thank you for having a show. All >> right. For Cory, Queen of Stupid Men. The Cube's coverage never stops either. We, of course, will be at eight of us reinvent this fall as well as many other shows. So, as always, thanks for watching the cue.

>> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering Red Hat Summit 2019. Brought to you by Red Hat. >> Welcome back here on theCUBE, joined by Stu Miniman, I'm John Walls, as we wrap up our coverage here of the Red Hat Summit here in 2019. We've been here in Boston all week, three days, Stu, of really fascinating programming on one hand, the keynotes showing quite a diverse ecosystem that Red Hat has certainly built, and we've seen that array of guests reflected as well here, on theCUBE. And you leave with a pretty distinct impression about the vast reach, you might say, of Red Hat, and how they diversified their offerings and their services. >> Yeah, so, John, as we've talked about, this is the sixth year we've had theCUBE here. It's my fifth year doing it and I'll be honest, I've worked with Red Hat for 19 years, but the first year I came, it was like, all right, you know, I know lots of Linux people, I've worked with Linux people, but, you know, I'm not in there in the terminal and doing all this stuff, so it took me a little while to get used to. Today, I know not only a lot more people in Red Hat and the ecosystem, but where the ecosystem is matured and where the portfolio is grown. There's been some acquisitions on the Red Hat side. There's a certain pending acquisition that is kind of a big deal that we talked about this week. But Red Hat's position in this IT marketplace, especially in the hybrid and multi-cloud world, has been fun to watch and really enjoyed digging in it with you this week and, John Walls, I'll turn the camera to you because- >> I don't like this. (laughing) >> It was your first time on the program. Yeah, you know- >> I like asking you the questions. >> But we have to do this, you know, three days of Walls to Miniman coverage. So let's get the Walls perspective. >> John: All right. >> On your take. You've been to many shows. >> John: Yeah, no, I think that what's interesting about what I've seen here at Red Hat is this willingness to adapt to the marketplace, at least that's the impression I got, is that there are a lot of command and control models about this is the way it's going to be, and this is what we're going to give you, and you're gonna have to take it and like it. And Red Hat's just on the other end of that spectrum, right? It's very much a company that's built on an open source philosophy. And it's been more of what has the marketplace wanted? What have you needed? And now how can we work with you to build it and make it functional? And now we're gonna just offer it to a lot of people, and we're gonna make a lot of money doing that. And so, I think to me, that's at least what I got talking to Jim Whitehurst, you know about his philosophy and where he's taken this company, and has made it obviously a very attractive entity, IBM certainly thinks so to the tune of 34 billion. But you see that. >> Yeah, it's, you know, some companies say, oh well, you know, it's the leadership from the top. Well, Jim's philosophy though, it is The Open Organization. Highly recommend the book, it was a great read. We've talked to him about the program, but very much it's 12, 13 thousand people at the company. They're very much opinionated, they go in there, they have discussions. It's not like, well okay, one person pass this down. It's we're gonna debate and argue and fight. Doesn't mean we come to a full consensus, but open source at the core is what they do, and therefore, the community drives a lot of it. They contribute it all back up-stream, but, you know, we know what Red Hat's doing. It's fascinating to talk to Jim about, yeah you know, on the days where I'm thinking half glass empty, it's, you know, wow, we're not yet quite four billion dollars of the company, and look what an impact they had. They did a study with IDC and said, ten trillion dollars of the economy that they touch through RHEL, but on the half empty, on the half full days, they're having a huge impact outside. He said 34 billion dollars that IBM's paying is actually a bargain- >> It's a great deal! (laughing) >> for where they're going. But big announcements. RHEL 8, which had been almost five years in the works there. Some good advancements there. But the highlight for me this week really was OpenShift. We've been watching OpenShift since the early days, really pre-Kubernetes. It had a good vision and gained adoption in the marketplace, and was the open source choice for what we called Paths back then. But, when Kubernetes came around, it really helped solidify where OpenShift was going. It is the delivery mechanism for containerization and that container cluster management and Red Hat has a leadership position in that space. I think that almost every customer that we talked to this week, John, OpenShift was the underpinning. >> John: Absolutely. >> You would expect that RHEL's underneath there, but OpenShift as the lever for digital transformation. And that was something that I really enjoyed talking to. DBS Bank from Singapore, and Delta, and UPS. It was, we talked about their actual transformation journeys, both the technology and the organizational standpoint, and OpenShift really was the lever to give them that push. >> You know, another thing, I know you've been looking at this and watching this for many many years. There's certainly the evolution of open source, but we talked to Chris Wright earlier, and he was talking about the pace of change and how it really is incremental. And yet, if you're on the outside looking in, and you think, gosh, technology is just changing so fast, it's so crazy, it's so disruptive, but to hear it from Chris, not so. You don't go A to Z, you go A to B to C to D to D point one. (laughing) It takes time. And there's a patience almost and a cadence that has this slow revolution that I'm a little surprised at. I sense they, or got a sense of, you know, a much more rapid change of pace and that's not how the people on the inside see it. >> Yeah. Couple of comment back at that. Number one is we know how much rapid change there is going because if you looked at the Linux kernel or what's happening with Kubernetes and the open source, there's so much change going on there. There's the data point thrown out there that, you know, I forget, that 75% or 95% of all the data in the world was created in the last two years. Yet, only 2% of that is really usable and searchable and things like that. That's a lot of change. And the code base of Linux in the last two years, a third of the code is completely overhauled. This is technology that has been around for decades. But if you look at it, if you think about a company, one of the challenges that we had is if they're making those incremental change, and slowly looking at them, a lot of people from the outside would be like, oh, Red Hat, yeah that's that little Linux company, you know, that I'm familiar with and it runs on lots of places there. When we came in six years ago, there was a big push by Red Hat to say, "We're much more than Linux." They have their three pillars that we spent a lot of time through from the infrastructure layer to the cloud native to automation and management. Lots of shows I go to, AnsiballZ all over the place. We talked about OpenShift 4 is something that seems to be resonating. Red Hat takes a leadership position, not just in the communities and the foundations, but working with their customers to be a more trusted and deeper partner in what they're doing with digital transformation. There might have been little changes, but, you know, this is not the Red Hat that people would think of two years or five years ago because a large percentage of Red Hat has changed. One last nugget from Chris Wright there, is, you know, he spent a lot of time talking about AI. And some of these companies go buzzwords in these environments, but, you know, but he hit a nice cogent message with the punchline is machines enhance human intelligence because these are really complex systems, distributed architectures, and we know that the people just can't keep up with all of the change, and the scope, and the scale that they need to handle. So software should be able to be helping me get my arms around it, as well as where it can automate and even take actions, as long as we're careful about how we do it. >> John: Sure. There's another, point at least, I want to pick your brain about, is really the power of presence. The fact that we have the Microsoft CEO on the stage. Everybody thought, well (mumbles) But we heard it from guest after guest after guest this week, saying how cool was that? How impressive was that? How monumental was that? And, you know, it's great to have that kind of opportunity, but the power of Nadella's presence here, it's unmistakable in the message that has sent to this community. >> Yeah, you know, John, you could probably do a case study talking about culture and the power of culture because, I talked about Red Hat's not the Red Hat that you know. Well, the Satya Nadella led Microsoft is a very different Microsoft than before he was on board. Not only are they making great strides in, you know, we talk about SaaS and public cloud and the like, but from a partnership standpoint, Microsoft of old, you know, Linux and Red Hat were the enemy and you know, Windows was the solution and they were gonna bake everything into it. Well, Microsoft partnered with many more companies. Partnerships and ecosystem, a key message this week. We talked about Microsoft with Red Hat, but, you know, announcement today was, surprised me a little bit, but when we think about it, not too much. OpenShift supported on VMware environments, so, you know, VMware has in that family of Dell, there's competitive solutions against OpenShift and, you know, so, and virtualization. You know, Red Hat has, you know, RHV, the Red Hat Virtualization. >> John: Right, right, right. >> The old day of the lines in the swim lanes, as one of our guests talked about, really are there. Customers are living in a heterogeneous, multi-cloud world and the customers are gonna go and say, "You need to work together, before you're not gonna be there." >> Azure. Right, also we have Azure compatibility going on here. >> Stu: Yeah, deep, not just some tested, but deep integration. I can go to Azure and buy OpenShift. I mean that, the, to say it's in the, you know, not just in the marketplace, but a deep integration. And yeah, there was a little poke, if our audience caught it, from Paul Cormier. And said, you know, Microsoft really understands enterprise. That's why they're working tightly with us. Uh, there's a certain other large cloud provider that created Kubernetes, that has their own solution, that maybe doesn't understand enterprise as much and aren't working as closely with Red Hat as they might. So we'll see what response there is from them out there. Always, you know, we always love on theCUBE to, you know, the horse is on the track and where they're racing, but, you know, more and more all of our worlds are cross-pollinating. You know, the AI and AI Ops stuff. The software ecosystems because software does have this unifying factor that the API economy, and having all these things work together, more and more. If you don't, customers will go look for solutions that do provide the full end to end solution stuff they're looking for. >> All right, so we're, I've got a couple in mind as far as guests we've had on the show. And we saw them in action on the keynotes stage too. Anybody that jumps out at you, just like, wow, that was cool, that was, not that we, we love all of our children, right? (laughing) But every once in awhile, there's a story or two that does stand out. >> Yeah, so, it is so tough, you know. I loved, you know, the stories. John, I'm sure I'm going to ask you, you know, Mr. B and what he's doing with the children. >> John: Right, Franklin Middle School. >> And the hospitals with Dr. Ellen and the end of the brains. You know, those tech for good are phenomenal. For me, you know, the CIOs that we had on our first day of program. Delta was great and going through transformation, but, you know, our first guest that we had on, was DBS Bank in Singapore and- >> John: David Gledhill. >> He was so articulate and has such a good story about, I took outsourced environments. I didn't just bring it into my environment, say okay, IT can do it a little bit better, and I'll respond to business. No, no, we're going to total restructure the company. Not we're a software company. We're a technology company, and we're gonna learn from the Googles of the world and the like. And he said, We want to be considered there, you know, what was his term there? It was like, you know, bank less, uh, live more and bank less. I mean, what- >> Joyful banking, that was another of his. >> Joyful banking. You don't think of a financial institution as, you know, we want you to think less of the bank. You know, that's just a powerful statement. Total reorganization and, as we mentioned, of course, OpenShift, one of those levers underneath helping them to do that. >> Yeah, you mentioned Dr. Ellen Grant, Boston Children's Hospital, I think about that. She's in fetal neuroimaging and a Professor of Radiology at Harvard Medical School. The work they're doing in terms of diagnostics through imaging is spectacular. I thought about Robin Goldstone at the Livermore Laboratory, about our nuclear weapon monitoring and efficacy of our monitoring. >> Lawrence Livermore. So good. And John, talk about the diversity of our guests. We had expats from four different countries, phenomenal accents. A wonderful slate of brilliant women on the program. From the customer side, some of the award winners that you interviewed. The executives on the program. You know, Stefanie Chiras, always great, and Denise who were up on the keynotes stage. Denise with her 3D printed, new Red Hat logo earrings. Yeah, it was an, um- >> And a couple of old Yanks (laughing). Well, I enjoyed it, Stu. As always, great working with you, and we thank you for being with us as well. For now, we're gonna say so long. We're gonna see you at the next Red Hat Summit, I'm sure, 2020 in San Francisco. Might be a, I guess a slightly different company, but it might be the same old Red Hat too, but they're going to have 34 billion dollars behind them at that point and probably riding pretty high. That will do it for our CUBE coverage here from Boston. Thanks for much for joining us. For Stu Miniman, and our entire crew, have a good day. (funky music)

>> live from Boston, Massachusetts. It's the you covering your red hat. Some twenty nineteen rots. You buy bread hat. >> Well, good afternoon. Welcome back here on the Cube as we continue our coverage of the Red Hat Summit and you know, every once in a while you come across one of these fascinating topics. It's what's doing I get so excited about when we do the Cube interviews is that you never know where >> you're >> going to go, the direction you're going to take. And I think this next interview has been a fit into one of those wow interviews for you at home. Along was to minimum. I am John Walls, and we're joined by Dr Ellen Grant, who was the director of the fetal neo NATO Neuroimaging and Developmental Science Center of Boston Children's Hospital. So far, so good, right? And the professor, Radiology and pediatrics at the Harvard Medical School's Dr Grant. Thank you for joining us here on the Cube and Dr Rudolph Pienaar, who is the technical director at the F n N D. S. C. And an instructor of radiology at the Harvard Medical School. So Dr Rudolph Pienaar, thank you for joining us as well. Thank you very much. All right. Good. So we're talking about what? The Chris Project, which was technically based. Project Boston Children's Hospital. I'm going to let you take from their doctor Grant. If you would just talk about the genesis of this program, the project, what its goal, wass And now how it's been carried out. And then we'LL bring in Dr PNR after that. So if you would place >> sure, it's so The goal of the Chris Project was to bring innovated imaging, announces to the bedside to the front end where clinicians are not like high are working all the time but aren't sophisticated enough or don't have enough memory to remember how to do, you know, line code in Lenox. So this is where initially started when I was reading clinical studies and I wanted to run a complex analysis, but there was no way to do it easily. I'd have tio call up someone to log into a different computer, bring the images over again lots of conflict steps to run that analysis, and even to do any of these analysis, you have to download the program set up your environment again. Many many steps, said someone. As a physician, I would rather deal with the interpretation and understanding the meaning of those images. Then all that infrastructure steps to bring it together. So that was the genesis of Chris's trying to have a simple Windows point and click way for a physician such as myself, to be able to rapidly do something interesting and then able to show it to a clinician in a conference or in the at the bedside >> and who's working on it, then, I mean, who was supplying what kind of manpower, If you will root off of the project >> kind of in the beginning, I would say maybe one way to characterize it is that we wanted to bring this research software, which lives mostly online, ex onto a Windows world, right? So the people developing that software researchers or computational researchers who do a lot of amazing stuff with image processing. But those tools just never make it really from the research lab outside of that. And one of the reasons is because someone like Ellen might not ever want to fire paternal and typing these commands. So people working on it are all this huge population of researchers making these tools on what we try to do. What I try to help with, How do we get those tools really easily usable in excess of one and, you know, to make a difference? Obviously. So that was a genesis. I was kind of need that we had in the beginning, so it started out, really, as a bunch of scrips, shell scripts, you slight a type of couple stuff, but not so many things on gradually, with time, we try to move to the Web, and then it began to grow and then kind of from the Web stretching to the cloud. And that's kind of the trajectory in the natural. As each step moved along, more and more people kind of came in to play. >> Dr Grant, I think back, you know, I work for a very large storage company and member object storage was going to transform because we have the giant files. We need to be able to store them and manage them and hold them up. But let's talk about the patient side of things. What does this really mean? You know, we had a talk about order of magnitude that cloud can make things faster and easier. But what? What does this mean to patient care? Quality service? >> Well, I think what it means or the goal for patient care is really getting to specialized medicine or individualized medicine on to be able to not just rely on my memory as to what a normal or abnormal images or the patients I may have seen just in my institution. But can we pull together all the knowledge across multiple institutions throughout the country and use more rigorous data announces to support my memory? So I want to have these big bridal in front lobes that air there, the cloud that helped me remember things into tidies connections and not have to remind just rely on my visual gestalt memory, which is obviously going to have some flaws in it. So and if I've never seen a specific disorder, say, for example, at my institution, if they've seen it at other institutions who run these comparisons all of sudden, I made be aware of a new treatment that otherwise I may not have known about >> All right, so one of my understanding is this is tied into the mass open cloud which I've had the pleasure of talking on the program at another show back here in Boston. Talk about a little bit about you know how this is enable I mean massive amounts of data you need to make sure you get that. You know the right data and it's valuable information and to the right people, and it gets updated all the time, so give us a little bit of the inner workings. >> Exactly. So thie inner workings, That's it can be a pretty big story, but kind of the short >> story time Theo Short >> story is that if we can get data in one place, and not just from one institution, from many places, that we can start to do things that are not really possible otherwise so, that's kind of the grand vision. So we're moving along those steps on the mass Open cloud for us makes perfect sense because it's there's a academic linked to Boston University. And then there's thie, Red Hat, being one of the academic sponsors as well in that for this kind of synergy that came together really almost perfectly at the right time, as the cloud was developing as where that was moving in it as we were trying to move to the cloud. It just began to link all together. And that's very much how we got there at the moment on what we're trying to do, which is get data so that we can cause medicine. Really, it's amazing to me. In some ways there's all these amazing devices, but computational e medicine lag so far behind the rest of the industry. There's so little integration. There's so little advanced processing going on. There's so much you can do with so little effort, you could do so much. So that's part of the >> vision as well. So help me out here a little bit, Yeah, I mean, maybe it before and after. Let's look at the situation may be clinically speaking here, where a finding or a revelation that you developed is now possible where it wasn't before and kind of what those consequences might have been. And then maybe, how the result has changed now. So maybe that would help paint up a practical picture of what we're talking about. >> I could use one example we're working on, but we haven't got fully to the clouds. All of these things are in their infancy because we still have to deal with the encryption part, which is a work in progress. But for example, we have mind our clinical databases to get examples of normal images and using that I can run comparisons of a case. It comes up to say whether this looks normal or abnormal sweat flags. The condition is to whether it's normal or abnormal, and that helps when there's trainees are people, not is experienced in reading those kinds of images. So again we're at the very beginnings of this. It's one set of pictures. There's many sets of pictures that we get, so there's a long road to get to fully female type are characterized anyone brain. But we're starting at the beginning those steps to very to digitally characterize each brain so we can then start to run. Comparisons against large libraries of other normals are large libraries of genetic disorders and start to match them up. And >> this is insecure. You working in fetal neural imaging as well. So you're saying you could take a an image of ah baby in a mother's womb and many hundreds thousands, whatever it is and you developed this basically a catalogue of what a healthy brain might look like. And now you're offering an opportunity to take a image here on early May of twenty nineteen. And compared to that catalogue, look and determine whether might be anabel normality that otherwise could have been spotted before. >> Correct and put a number to that in terms of a similarity value our probability values so that it's not just Mia's a collision, say Well, I think it's a little abnormal because it is hard to interpret that in terms of how severe is the spectrum of normal. How how? Sure you. So we put all these dated together. We can start to get more predictive value because we couldn't follow more kids and understand if it's that a a sima that too similar what's most likely disorder? What's the best treatment? So it gives you better FINA typing of the disorders that appear early and fetal life, some of which are linked to we think he treated, say, for example, with upcoming gene therapies and other nutritional intervention so we could do this characterization early on. We hope we can identify early therapies that our target to targeted to the abnormalities we detect. >> So intervene well ahead of time. Absolutely. >> I don't know. The other thing is, I mean Ellen has often times said how many images she looks at in the day on other radiologist, and it's it's amazing. It's she said, the number hundred thousand one point so you can imagine the human fatigue, right? So it Matt, imagine if you could do a quick pre processing on just flag ones that really are abnormal by you know they could be grossly abnormal. But at least let's get those on the top of the queue when you can look at it when you are much more able to, you know, think, think, think these things through. So there's one good reason of having these things sitting on an automated system. Stay out of the cloud over it might be >> Where are we with the roll out of this? This and kind of expansion toe, maybe other partners. >> So a lot of stuff has been happening over the last year. I mean, the the entire platform is still, I would say, somewhat prototypical, but we have a ll the pipelines kind of connected, so data can flow from a place like the hospital flowed to the cloud. Of course, this is all you know, protected and encrypted on the cloud weaken Do kind of weaken. Do any analysis we want to do Provided the analysis already exists, we can get the results back. Two definition we have the interface is the weapon to faces built their growing. So you can at this point, almost run the entire system without ever touching a command line. A year ago, it was partially there. A year ago, you had to use a command line. Now you don't have to. Next year will be even more streamlined. So this is the way it's moving right now and was great for me personally. About the cloud as well is that it's not just here in Boston where you, Khun benefit from using these technologies, you know, for the price of a cellphone on DH cell signal. You can use this kind of technology anywhere. You could be in the bush in Africa for argument's sake, and you can have access to these libraries of databases imaging that might exist. You, khun compare Images are collected wherever it might be just for the price of connecting to the Internet. >> You just need a broadband connection >> just right. Just exactly. >> Sometimes when you think about again about you know, we've talked about mobile technology five g coming on as it is here in the U. S. Rural health care leveling that and Third World, I was thinking more along the lines of here in the States and with some memories that just don't have access to the kind of, like, obviously platinum carry you get here in the Boston area. But all those possibilities would exist or could exist based on the findings that you're getting right now with Chris Project. So >> where does the Chris project go from here? >> Well, what we'd like to do is get more hospitals on board, uh, thinking pediatrics, we have a lot of challenge because there are so many different rare disorders that it's hard to study any one of them from one hospital. So we have to work together. There's been some effort to bring together some genetic databases, but we really need to being also the imaging bait databases together. So hopefully we can start to get a consortium of some of the pediatric hospitals working together. We need that also because normal for normal, you need to know the gender, the age, the thie ethnicity. You know, so many demographics that are nice to characterize what normal is. So if we all work together, we can also get a better idea of what is normal. What is normal variants. And there's a lot of other projects that are funded by N. H. Building up some of those databases as well, too. But we could put him into all into one place where we can actually now query on that. Then we could start to really do precision medicine. >> And the other thing, which we definitely are working on and I want to do, is build a community of developers around this platform because, you know, there's no way our team can write all of these tools. No, no, no, we want to. But we want everyone else who wants to make these tools very easily hop onto this platform. And that's very important to us because it's so much easier to develop to christen it just about the Amazon. There's almost no comparison. How much easier >> we'Ll Definitely theme, we hear echoing throughout Red Hat summit here is that Does that tie into, like, the open shift community? Or, you know, what is the intersection with red hat? >> It definitely does, because this is kind of the age of continue ization, which makes so many things so much easier on DH. This platform that we've developed is all about container ization. So we want to have medical by medical or any kind of scientific developers get onto that container ization idea because when they do that and it's not that hard to do. But when you do that, then suddenly you can have your your analysis run almost anywhere. >> And that's an important part in medicine, because I run the same analysis on different computers, get different results. So the container ization concept, I think, is something that we've been after, which is a reproduce ability that anybody can run it along there, use the same container we know we're going. Same result. And that is >> critical. Yes, especially with what you're doing right, you have to have that one hundred percent certainty. Yep. Standardisation goes along, Ray. Sort of fascinating stuff. Thank you both for joining us. And good luck. You're an exciting phase, that's for sure. And we wish you all the best going forward here. Thank you so much. Thank you both. Back with more from Boston. You're watching Red Hat Summit coverage live here on the Q t.

>> Announcer: Live from San Francisco, it's theCUBE. Covering Google Cloud Next '19. Brought to you by Google Cloud and its ecosystem partners. >> Hey, welcome back everyone, and we're here at theCUBE coverage in San Francisco for Google Next 2019, I'm John Furrier, Dave Vellante, our next guest is Menaka Shroff global marketing head for emerging business at Google. Welcome to theCUBE, thanks for joining us. >> Thank you. Thank you for having me. >> So define emerging business, what is it within the Google Cloud? Just take a minute to explain what the business is. >> Yeah. Emerging business team is a group of marketers basically focused on products that help build a better Google story, so products like Chrome Browser, Chromebooks, Drive and especially Cloud Identity. All of these form the team of portfolio products that my team manages. >> And so they go to market, is it product development, both, or just? >> It's predominately marketing and go to market, yeah. >> What are some of the things that you're talking about here at the event? What's some news that you have, you guys got some news? >> Yeah, so one of the patterns we're seeing is this trend of cloud workers, where these are employees that spend almost four hours a day using SaaS applications using the browser as you just mentioned, that you do as well. And we're seeing-- >> Eight hours a day, 15 hours a day, yeah! >> Yes, excellent. And so, we're seeing this pattern actually, not only with digital natives but also with frontline, you know, back of the office front of the office where they're sort of skipping the traditional PC era and moving straight to a clouds based model. And so today we're actually announcing our Chrome Browser Cloud Management. So it's one central place to manage your browser deployments across, you know, a segmented workforce that's using Windows or Mac or Linux, and Chromebooks. and what you can do is have them obviously manage the Chrome Browser extensions and all of the deployment, but also have this IT collaborating and delegation within the same console. So of course if you're using G Suite, it's all in the same console, it's very easily available. >> And so this kind of brings back to conscious, we've been hearing the themes here, besides this is customer focused, it is end to end developer. So, life cycle from coding to deploying and running. So you run it on a Chromebook, or a Chrome Browser, you can have software at the endpoint for security, and integration, right? >> Exactly. So, what's great about being here is you see that full stack approach in how we want to make it available for our customers starting all the way from infrastructure to end user computing apps that people are using, all with that security layer and mindset. Obviously Chromebooks are known to be cloud based devices, historically popular with students, as you had just mentioned, as well. But we're seeing really good trends happening even with personal computing and in enterprise, because of the security model that runs through how cloud is architected, especially at Google. >> What're some of the conversations you're having here at the show, with customers and partners? What's the main driver? >> Yeah, it's really phenomenal because Chromebooks are actually 100% partner driven so we're already very partner-centric from that point of view, but, some of the customer conversations we're hearing, I'll mention three customers that I just talked to. SoulCycle, they have 94 locations with 500 endpoints deployed, and they're using this as their retail experience. That customer UX mindset with their Chromebooks, again, they're very cloud native. We have Starbucks that is using the Chrome Browser management capabilities across all of their stores, again thinking about extension management, but centralizing it all in one panel for all their locations. And then, very interesting, we have one medical hospital. They're using Chromebooks for their paramedics. Obviously we want paramedics to have the best technology available while they're doing their important job, saving lives. But they're doing this in a way where we want to enable them to do the right outcome which is, good patient experience. These are all things we're seeing in the variety of SMBs to IT, to, small businesses in variety of verticals, across geographies. Japan, India, all of that, in one place at Next, which is exciting. >> So very specific vertical use cases that you just mentioned, it's also this sort of general business usage, it's the old thin client story, right? Now, mobile becomes somewhat of a challenge for folks, but, I mean, I've written blog posts on my mobile. Yeah, we live, like I said, on Google Docs, and Google Sheets but, >> Absolutely. >> so, what are some of the things you're hearing, first of all, is that a tailwind for you? Is that a trend that you guys are leaning in to? And what are some of the things that your clients are asking for there? >> Yeah, so, phenomenal example. I think what we're seeing is the seamless application usage across different locations but also across different form factors. So what I do on my mobile, I want to be able to do on my tablet, on my phone, in a way that I interact in the same way, with the right context in mind. And we want to make that available. We definitely see that at Google because we are, after all, the biggest cloud native company if you think about that, and we operate in that model. But we're seeing this trend, actually with legacy companies which is, a new thing that is a good discovery for us and we obviously want to offer the best technology for our customers, we are definitely seeing a little bit of that happen as well. >> And Drive is part of your swim lane as well? >> Yes. >> I suppose, so, I mean one of the things I see a lot of people do is they'll take every document on their desktop, or their laptop, and put it up into the cloud. So they always have access to it. >> Yeah, I think Drive is phenomenal because not only does it serve the traditional ECM or the content management solution space, I mean, Drive has over a billion users now, so it's very worldwide known. But also it has the editors and the, you know, Google Docs, Google Sheets, as part of the solution mix too, so. Really when you offer that up along with the Chromebook it becomes a very powerful solution in combination for any cloud native employee. >> Well you've created, you got a tiger by the tail, 'cause it's so easy to create a Doc now, it's easier than spitting up a VM. >> Menaka: Well, I mean students are growing up with this as well, right? So we're seeing that. >> So what do you, are you getting a lot of requests to simplify the management of all those Docs, and what is Google doing in that regard? >> Yeah, I think ease of management, ease of deployment, ease of end user computing is always on our mind and we're always striving to do a great job, trying to make sure it doesn't take very long for anyone in IT to set up, whether it's their Drive instance or whether it's their Chromebooks we want to make it incredibly easy. And we are seeing this happen today, actually we have grab and go devices here, where you could take a Chromebook, log in and all your personalization kicks in within two minutes of you logging in, and then you shift a user, or give it to him and it doesn't require any reconfiguration. It sort of cleans out on its own, and has all of the other personalization set up. So we're thinking constantly about how do we do this for IT? So a five person team, actually I had a customer that has a five person team managing 4000 endpoints with just a small IT staff. And they want to be able to do interesting creative things not just manage end user devices, so we really are thinking hard about how do we do this in a way that's easy. >> Take the heavy lifting off the customer. >> Yeah exactly. We absolutely want to do that, even for end user, it should feel seamless. >> Menaka, great to to hear all the traction, love the end to end Chrome Browser, final question for you, what's new for you guys? What's going on under your business? What's your marketing plan? What are some of the exciting things that you're doing? >> Yeah we're just following the success we're seeing with our customers as you had mentioned earlier, we're seeing that with frontline, we're seeing that with healthcare, retail, those are all opportunities that we see, leaning in and supporting our customers in their journey to the cloud. And we see ours as a starting spot for that. >> Awesome, well congratulations. >> We'll have to look at getting some Chromebooks for theCUBE with a CUBE sticker. >> Yes! >> Can you make some custom Chromebooks for us? >> Custom, I don't, custom stickers. >> How about a custom browser? >> Custom stickers, browser is your personal, you can customize your browser as much as you like. >> John: We got stickers for you here. >> Oh, thank you! >> John: Love Chrome Browser, love the extensions, >> We'll take them. >> Programmability end to end, congratulations. Thanks for coming on. >> Thank you very much. >> Appreciate it. CUBE coverage here at San Francisco live, it's theCUBE covering Google Next 2019, stay with us for more after this short break. (electronic music)