(gentle music) >> Hey! Welcome back to theCUBE's coverage of WiDS 2023, the eighth Annual Women In Data Science Conference. I'm your host, Lisa Martin. We are at Stanford University, as you know we are every year, having some wonderful conversations with some very inspiring women and men in data science and technical roles. I'm very pleased to introduce Tracy Zhang, my co-host, who is in the Data Journalism program at Stanford. And Tracy and I are pleased to welcome our next guest, Rhonda Crate, Principal Data Scientist at Boeing. Great to have you on the program, Rhonda. >> Tracy: Welcome. >> Hey, thanks for having me. >> Were you always interested in data science or STEM from the time you were young? >> No, actually. I was always interested in archeology and anthropology. >> That's right, we were talking about that, anthropology. Interesting. >> We saw the anthropology background, not even a bachelor's degree, but also a master's degree in anthropology. >> So you were committed for a while. >> I was, I was. I actually started college as a fine arts major, but I always wanted to be an archeologist. So at the last minute, 11 credits in, left to switch to anthropology. And then when I did my master's, I focused a little bit more on quantitative research methods and then I got my Stat Degree. >> Interesting. Talk about some of the data science projects that you're working on. When I think of Boeing, I always think of aircraft. But you are doing a lot of really cool things in IT, data analytics. Talk about some of those intriguing data science projects that you're working on. >> Yeah. So when I first started at Boeing, I worked in information technology and data analytics. And Boeing, at the time, had cored up data science in there. And so we worked as a function across the enterprise working on anything from shared services to user experience in IT products, to airplane programs. So, it has a wide range. I worked on environment health and safety projects for a long time as well. So looking at ergonomics and how people actually put parts onto airplanes, along with things like scheduling and production line, part failures, software testing. Yeah, there's a wide spectrum of things. >> But I think that's so fantastic. We've been talking, Tracy, today about just what we often see at WiDS, which is this breadth of diversity in people's background. You talked about anthropology, archeology, you're doing data science. But also all of the different opportunities that you've had at Boeing. To see so many facets of that organization. I always think that breadth of thought diversity can be hugely impactful. >> Yeah. So I will say my anthropology degree has actually worked to my benefit. I'm a huge proponent of integrating liberal arts and sciences together. And it actually helps me. I'm in the Technical Fellowship program at Boeing, so we have different career paths. So you can go into management, you can be a regular employee, or you can go into the Fellowship program. So right now I'm an Associate Technical Fellow. And part of how I got into the Fellowship program was that diversity in my background, what made me different, what made me stand out on projects. Even applying a human aspect to things like ergonomics, as silly as that sounds, but how does a person actually interact in the space along with, here are the actual measurements coming off of whatever system it is that you're working on. So, I think there's a lot of opportunities, especially in safety as well, which is a big initiative for Boeing right now, as you can imagine. >> Tracy: Yeah, definitely. >> I can't go into too specifics. >> No, 'cause we were like, I think a theme for today that kind of we brought up in in all of our talk is how data is about people, how data is about how people understand the world and how these data can make impact on people's lives. So yeah, I think it's great that you brought this up, and I'm very happy that your anthropology background can tap into that and help in your day-to-day data work too. >> Yeah. And currently, right now, I actually switched over to Strategic Workforce Planning. So it's more how we understand our workforce, how we work towards retaining the talent, how do we get the right talent in our space, and making sure overall that we offer a culture and work environment that is great for our employees to come to. >> That culture is so important. You know, I was looking at some anitab.org stats from 2022 and you know, we always talk about the number of women in technical roles. For a long time it's been hovering around that 25% range. The data from anitab.org showed from '22, it's now 27.6%. So, a little increase. But one of the biggest challenges still, and Tracy and I and our other co-host, Hannah, have been talking about this, is attrition. Attrition more than doubled last year. What are some of the things that Boeing is doing on the retention side, because that is so important especially as, you know, there's this pipeline leakage of women leaving technical roles. Tell us about what Boeing's, how they're invested. >> Yeah, sure. We actually have a publicly available Global Diversity Report that anybody can go and look at and see our statistics for our organization. Right now, off the top of my head, I think we're hovering at about 24% in the US for women in our company. It has been a male majority company for many years. We've invested heavily in increasing the number of women in roles. One interesting thing about this year that came out is that even though with the great resignation and those types of things, the attrition level between men and women were actually pretty close to being equal, which is like the first time in our history. Usually it tends on more women leaving. >> Lisa: That's a good sign. >> Right. >> Yes, that's a good sign. >> And we've actually focused on hiring and bringing in more women and diversity in our company. >> Yeah, some of the stats too from anitab.org talked about the increase, and I have to scroll back and find my notes, the increase in 51% more women being hired in 2022 than 2021 for technical roles. So the data, pun intended, is showing us. I mean, the data is there to show the impact that having females in executive leadership positions make from a revenue perspective. >> Tracy: Definitely. >> Companies are more profitable when there's women at the head, or at least in senior leadership roles. But we're seeing some positive trends, especially in terms of representation of women technologists. One of the things though that I found interesting, and I'm curious to get your thoughts on this, Rhonda, is that the representation of women technologists is growing in all areas, except interns. >> Rhonda: Hmm. >> So I think, we've got to go downstream. You teach, I have to go back to my notes on you, did my due diligence, R programming classes through Boeings Ed Wells program, this is for WSU College of Arts and Sciences, talk about what you teach and how do you think that intern kind of glut could be solved? >> Yeah. So, they're actually two separate programs. So I teach a data analytics course at Washington State University as an Adjunct Professor. And then the Ed Wells program is a SPEEA, which is an Aerospace Union, focused on bringing up more technology and skills to the actual workforce itself. So it's kind of a couple different audiences. One is more seasoned employees, right? The other one is our undergraduates. I teach a Capstone class, so it's a great way to introduce students to what it's actually like to work on an industry project. We partner with Google and Microsoft and Boeing on those. The idea is also that maybe those companies have openings for the students when they're done. Since it's Senior Capstone, there's not a lot of opportunities for internships. But the opportunities to actually get hired increase a little bit. In regards to Boeing, we've actually invested a lot in hiring more women interns. I think the number was 40%, but you'd have to double check. >> Lisa: That's great, that's fantastic. >> Tracy: That's way above average, I think. >> That's a good point. Yeah, it is above average. >> Double check on that. That's all from my memory. >> Is this your first WiDS, or have you been before? >> I did virtually last year. >> Okay. One of the things that I love, I love covering this event every year. theCUBE's been covering it since it's inception in 2015. But it's just the inspiration, the vibe here at Stanford is so positive. WiDS is a movement. It's not an initiative, an organization. There are going to be, I think annually this year, there will be 200 different events. Obviously today we're live on International Women's Day. 60 plus countries, 100,000 plus people involved. So, this is such a positive environment for women and men, because we need everybody, underrepresented minorities, to be able to understand the implication that data has across our lives. If we think about stripping away titles in industries, everybody is a consumer, not everybody, most of mobile devices. And we have this expectation, I was in Barcelona last week at a Mobile World Congress, we have this expectation that we're going to be connected 24/7. I can get whatever I want wherever I am in the world, and that's all data driven. And the average person that isn't involved in data science wouldn't understand that. At the same time, they have expectations that depend on organizations like Boeing being data driven so that they can get that experience that they expect in their consumer lives in any aspect of their lives. And that's one of the things I find so interesting and inspiring about data science. What are some of the things that keep you motivated to continue pursuing this? >> Yeah I will say along those lines, I think it's great to invest in K-12 programs for Data Literacy. I know one of my mentors and directors of the Data Analytics program, Dr. Nairanjana Dasgupta, we're really familiar with each other. So, she runs a WSU program for K-12 Data Literacy. It's also something that we strive for at Boeing, and we have an internal Data Literacy program because, believe it or not, most people are in business. And there's a lot of disconnect between interpreting and understanding data. For me, what kind of drives me to continue data science is that connection between people and data and how we use it to improve our world, which is partly why I work at Boeing too 'cause I feel that they produce products that people need like satellites and airplanes, >> Absolutely. >> and everything. >> Well, it's tangible, it's relatable. We can understand it. Can you do me a quick favor and define data literacy for anyone that might not understand what that means? >> Yeah, so it's just being able to understand elements of data, whether that's a bar chart or even in a sentence, like how to read a statistic and interpret a statistic in a sentence, for example. >> Very cool. >> Yeah. And sounds like Boeing's doing a great job in these programs, and also trying to hire more women. So yeah, I wanted to ask, do you think there's something that Boeing needs to work on? Or where do you see yourself working on say the next five years? >> Yeah, I think as a company, we always think that there's always room for improvement. >> It never, never stops. >> Tracy: Definitely. (laughs) >> I know workforce strategy is an area that they're currently really heavily investing in, along with safety. How do we build safer products for people? How do we help inform the public about things like Covid transmission in airports? For example, we had the Confident Traveler Initiative which was a big push that we had, and we had to be able to inform people about data models around Covid, right? So yeah, I would say our future is more about an investment in our people and in our culture from my perspective >> That's so important. One of the hardest things to change especially for a legacy organization like Boeing, is culture. You know, when I talk with CEO's or CIO's or COO's about what's your company's vision, what's your strategy? Especially those companies that are on that digital journey that have no choice these days. Everybody expects to have a digital experience, whether you're transacting an an Uber ride, you're buying groceries, or you're traveling by air. That culture sounds like Boeing is really focused on that. And that's impressive because that's one of the hardest things to morph and mold, but it's so essential. You know, as we look around the room here at WiDS it's obviously mostly females, but we're talking about women, underrepresented minorities. We're talking about men as well who are mentors and sponsors to us. I'd love to get your advice to your younger self. What would you tell yourself in terms of where you are now to become a leader in the technology field? >> Yeah, I mean, it's kind of an interesting question because I always try to think, live with no regrets to an extent. >> Lisa: I like that. >> But, there's lots of failures along the way. (Tracy laughing) I don't know if I would tell myself anything different because honestly, if I did, I wouldn't be where I am. >> Lisa: Good for you. >> I started out in fine arts, and I didn't end up there. >> That's good. >> Such a good point, yeah. >> We've been talking about that and I find that a lot at events like WiDS, is women have these zigzaggy patterns. I studied biology, I have a master's in molecular biology, I'm in media and marketing. We talked about transportable skills. There's a case I made many years ago when I got into tech about, well in science you learn the art of interpreting esoteric data and creating a story from it. And that's a transportable skill. But I always say, you mentioned failure, I always say failure is not a bad F word. It allows us to kind of zig and zag and learn along the way. And I think that really fosters thought diversity. And in data science, that is one of the things we absolutely need to have is that diversity and thought. You know, we talk about AI models being biased, we need the data and we need the diverse brains to help ensure that the biases are identified, extracted, and removed. Speaking of AI, I've been geeking out with ChatGPT. So, I'm on it yesterday and I ask it, "What's hot in data science?" And I was like, is it going to get that? What's hot? And it did it, it came back with trends. I think if I ask anything, "What's hot?", I should be to Paris Hilton, but I didn't. And so I was geeking out. One of the things I learned recently that I thought was so super cool is the CTO of OpenAI is a woman, Mira Murati, which I didn't know until over the weekend. Because I always think if I had to name top females in tech, who would they be? And I always default to Sheryl Sandberg, Carly Fiorina, Susan Wojcicki running YouTube. Who are some of the people in your history, in your current, that are really inspiring to you? Men, women, indifferent. >> Sure. I think Boeing is one of the companies where you actually do see a lot of women in leadership roles. I think we're one of the top companies with a number of women executives, actually. Susan Doniz, who's our Chief Information Officer, I believe she's actually slotted to speak at a WiDS event come fall. >> Lisa: Cool. >> So that will be exciting. Susan's actually relatively newer to Boeing in some ways. A Boeing time skill is like three years is still kind of new. (laughs) But she's been around for a while and she's done a lot of inspiring things, I think, for women in the organization. She does a lot with Latino communities and things like that as well. For me personally, you know, when I started at Boeing Ahmad Yaghoobi was one of my mentors and my Technical Lead. He came from Iran during a lot of hard times in the 1980s. His brother actually wrote a memoir, (laughs) which is just a fun, interesting fact. >> Tracy: Oh my God! >> Lisa: Wow! >> And so, I kind of gravitate to people that I can learn from that's not in my sphere, that might make me uncomfortable. >> And you probably don't even think about how many people you're influencing along the way. >> No. >> We just keep going and learning from our mentors and probably lose sight of, "I wonder how many people actually admire me?" And I'm sure there are many that admire you, Rhonda, for what you've done, going from anthropology to archeology. You mentioned before we went live you were really interested in photography. Keep going and really gathering all that breadth 'cause it's only making you more inspiring to people like us. >> Exactly. >> We thank you so much for joining us on the program and sharing a little bit about you and what brought you to WiDS. Thank you so much, Rhonda. >> Yeah, thank you. >> Tracy: Thank you so much for being here. >> Lisa: Yeah. >> Alright. >> For our guests, and for Tracy Zhang, this is Lisa Martin live at Stanford University covering the eighth Annual Women In Data Science Conference. Stick around. Next guest will be here in just a second. (gentle 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

(gentle music) >> Welcome to today's session of the AWS Startup Showcase The Next Big Thing in AI, Security, & Life Sciences. Today we're featuring Protegrity for the life sciences track. I'm your host for theCUBE, Natalie Erlich, and now we're joined by our guest, Rick Farnell, the CEO of Protegrity. Thank you so much for being with us. >> Great to be here. Thanks so much Natalie, great to be on theCUBE. >> Yeah, great, and so we're going to talk today about the ransomware game, and how it has changed with kinetic data protection. So, the title of today's video segment makes a bold claim, how are kinetic data and ransomware connected? >> So first off kinetic data, data is in use, it's moving, it's not static, it's no longer sitting still, and your data protection has to adhere to those same standards. And I think if you kind of look at what's happening in the ransomware kind of attacks, there's a couple of different things going on, which is number one, bad actors are getting access to data in the clear, and they're holding that data ransom, and threatening to release that data. So kind of from a Protegrity standpoint, with our protection capabilities, that data would be rendered useless to them in that scenario. So there's lots of ways in which kind of backup data protection, really wonderful opportunities to do both data protection and kind of that backup mixed together really is a wonderful solution to the threat of ransomware. And it's a serious issue and it's not just targeting the most highly regulated industries and customers, we're seeing kind of attacks on pipeline and ferry companies, and really there is no end to where some of these bad actors are really focusing on and the damages can be in the hundreds of millions of dollars and last for years after from a brand reputation. So I think if you look at how data is used today, there's that kind of opposing forces where the business wants to use data at the speed of light to produce more machine learning, and more artificial intelligence, and predict where customers are going to be, and have wonderful services at their fingertips. But at the same time, they really want to protect their data, and sometimes those architectures can be at odds, and at Protegrity, we're really focusing on solving that problem. So free up your data to be used in artificial intelligence and machine learning, while making sure that it is absolutely bulletproof from some of these ransomware attacks. >> Yeah, I mean, you bring a really fascinating point that's really central to your business. Could you tell us more about how you're actually making that data worthless? I mean, that sounds really revolutionary. >> So, it sounds novel, right? To kind of make your data worthless in the wrong hands. And I think from a Protegrity perspective, our kind of policy and protection capability follows the individual piece of data no matter where it lives in the architecture. And we do a ton of work as the world does with Amazon Web Services, so kind of helping customers really blend their hybrid cloud strategies with their on-premise and their use of AWS, is something that we thrive at. So protecting that data, not just at rest or while it's in motion, but it's a continuous protection policy that we can basically preserve the privacy of the data but still keep it unique for use in downstream analytics and machine learning. >> Right, well, traditional security is rather stifling, so how can we fix this, and what are you doing to amend that? >> Well, I think if you look at cybersecurity, and we certainly play a big role in the cybersecurity world but like any industry, there are many layers. And traditional cybersecurity investment has been at the perimeter level, at the network level keeping bad actors out, and once people do get through some of those fences, if your data is not protected at a fine grain level, they have access to it. And I think from our standpoint, yes, we're last line of defense but at the same time, we partner with folks in the cybersecurity industry and with AWS and with others in the backup and recovery to give customers that level of protection, but still allow their kinetic data to be utilized in downstream analytics. >> Right, well, I'd love to hear more about the types of industries that you're helping, and specifically healthcare obviously, a really big subject for the year and probably now for years to come, how is this industry using kinetic protection at the moment? >> So certainly, as you mentioned, some of the most highly regulated industries are our sweet spot. So financial services, insurance, online retail, and healthcare, or any industry that has sensitive data and sensitive customer data, so think first name last name, credit card information, national ID number, social security number blood type, cancer type. That's all sensitive information that you as an organization want to protect. So in the healthcare space, specifically, some of the largest healthcare organizations in the world rely on Protegrity to provide that level of protection, but at the same time, give them the business flexibility to utilize that data. So one of our customers, one of the leaders in online prescriptions, and that is an AWS customer, to allow a wonderful service to be delivered to all of their customers while maintaining protection. If you think about sharing data on your watch with your insurance provider, we have lots of customers that bridge that gap and have that personal data coming in to the insurance companies. All the way to, if in a use case in the future, looking at the pandemic, if you have to prove that you've been vaccinated, we're talking about some sensitive information, so you want to be able to show that information but still have the confidence that it's not going to be used for nefarious purposes. >> Right, and what is next for Protegrity? >> Well, I think continuing on our journey, we've been around for 17 years now, and I think the last couple, there's been an absolute renaissance in fine-grained data protection or that connected data protection, and organizations are recognizing that continuing to protect your perimeter, continuing to protect your firewalls, that's not going to go away anytime soon. Your access points, your points of vulnerability to keep bad actors out, but at the same time, recognizing that the data itself needs to be protected but with that balance of utilizing it downstream for analytic purposes, for machine learning, for artificial intelligence. Keeping the data of hundreds of millions if not billions of people saved, that's what we do. If you were to add up the customers of all of our customers, the largest banks, the largest insurance companies, largest healthcare companies in the world, globally, we're protecting the private data of billions of human beings. And it doesn't just stop there, I think you asked a great question about kind of the industry and yes, insurance, healthcare, retail, where there's a lot of sensitive data that certainly can be a focus point. But in the IOT space, kind of if you think about GPS location or geolocation, if you think about a device, and what it does, and the intelligence that it has, and the decisions that it makes on the fly, protecting data and keeping that safe is not just a personal thing, we're stepping into intellectual property and some of the most valuable assets that companies have, which is their decision-making on how they use data and how they deliver an experience, and I think that's why there's been such a renaissance, if you will, in kind of that fine grain data protection that we provide. >> Yeah, well, what is Protegrity's role now in future proofing businesses against cyber attacks? I mean, you mentioned really the ramifications of that and the impact it can have on businesses, but also on governments. I mean, obviously this is really critical. >> So there's kind of a three-step approach, and this is something that we have certainly kind of felt for a long, long time, and we work on with our customers. One is having that fine-grain data protection. So tokenizing your data so that if someone were to get your data, it's worthless, unless they have the ability to unlock every single individual piece of data. So that's number one, and then that's kind of what Protegrity provides. Number two, having a wonderful backup capability to roll kind of an active-active, AWS being one of the major clouds in the world where we deploy our software regularly and work with our customers, having multi-regions, multi-capabilities for an active-active scenario where if there's something that goes down or happens you can bring that down and bring in a new environment up. And then third is kind of malware detection in the rest of the cyber world to make sure that you rinse kind of your architecture from some of those agents. And I think when you kind of look at it, ransomware, they take data, they encrypt your data, so they force you to give them Bitcoin, or whatnot, or they'll release some of your data. And if that data is rendered useless, that's one huge step in kind of your discussions with these nefarious actors and be like you could release it, but there's nothing there, you're not going to see anything. And then second, if you have a wonderful backup capability where you wind down that environment that has been infiltrated, prove that this new environment is safe, have your production data have rolling and then wind that back up, you're back in business. You don't have to notify your customers, you don't have to deal with the ransomware players. So it's really a three-step process but ultimately it starts with protecting your data and tokenizing your data, and that's something that Protegrity does really, really well. >> So you're basically able to eliminate the financial impact of a breach? >> Honestly, we dramatically reduce the risk of customers being at risk for ransomware attacks 100%. Now, tokenizing data and moving that direction is something that it's not trivial, we are literally replacing production data with a token and then making sure that all downstream applications have the ability to utilize that, and make sure that the analytic systems and machine learning systems, and artificial intelligence applications that are built downstream on that data have the ability to execute, but that is something that from our patent portfolio and what we provide to our customers, again, some of the largest organizations in retail, in financial services, in banking, and in healthcare, we've been doing that for a long time. We're not just saying that we can do this and we're in version one of our product, we've been doing this for years, supporting the largest organizations with a 24 by seven capability. >> Right, and tell us a bit about the competitive landscape, where do you see your offering compared to your competitors? >> So, kind of historically back, let's call it an era ago maybe even before cloud even became a thing, and hybrid cloud, there were a handful of players that could acquire into much larger organizations, those organizations have been dusting off those acquired assets, and we're seeing them come back in. There's some new entrants into our space that have some protection mechanisms, whether it be encryption, or whether it be anonymization, but unless you're doing fine grain tokenization, you're not going to be able to allow that data to participate in the artificial intelligence world. So, we see kind of a range of competition there. And then I'd say probably the biggest competitor, Natalie, is customers not doing tokenization. They're saying, "No, we're okay, we'll continue protecting our firewall, we'll continue protecting our access points, we'll invest a little bit more in maybe some governance, but that fine grain data protection, maybe it's not for us." And that is the big shift that's happening. You look at kind of the beginning of this year with the solar winds attack, and the vulnerability that caused the very large and important organizations found themselves the last few weeks with all the ransomware attacks that are happening on meat processing plants and facilities, shutting down meat production, pipeline, stopping oil and gas and kind of that. So we're seeing a complete shift in the types of organizations and the industries that need to protect their data. It's not just the healthcare organizations, or the banks, or the credit card companies, it is every single industry, every single size company. >> Right, and I got to ask you this questioning, what is your defining contribution to the future of cloud scale? >> Well, ultimately we kind of have a charge here at Protegrity where we feel like we protect the world's most sensitive data. And when we come into work every day, that's what every single employee thinks at Protegrity. We are standing behind billions of individuals who are customers of our customers, and that's a cultural thing for us, and we take that very serious. We have maniacal customer support supporting our biggest customers with a fall of the sun 24 by seven global capability. So that's number one. So, I think our part in this is really helping to educate the world that there is a solution for this ransomware and for some of these things that don't have to happen. Now, naturally with any solution, there's going to be some investment, there's going to be some architecture changes, but with partnerships like AWS, and our partnership with pretty much every data provider, data storage provider, data solution provider in the world, we want to provide fine-grain data protection, any data in any system on any platform. And that's our mission. >> Well, Rick Farnell, this has been really fascinating conversation with you, thank you so much. The CEO of Protegrity, really great to have you on this program for the AWS Startup Showcase, talking about how ransomware game has changed with the kinetic data protection. Really appreciate it. Again, I'm your host Natalie Erlich, thank you again very much for watching. (light music)

(upbeat music) >> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security, & Life Sciences, and in this segment, we feature Sonrai security, of course for the security track I'm your host, Dave Vellante, and today we're joined by Sandy Bird, who's the co-founder and chief technology officer of Sonrai, and Avi Boru, who's the director of cloud engineering at World Fuel Services, and in this discussion, we're going to talk about 22 to two data centers, how World Fuel Services and Sonrai Security actually made it happen securely. Folks, welcome to theCUBE, come on in. >> Thank you. >> So we hear consistent themes from chief information security officers, that many if not most enterprises they struggle today with cloud security, there's confusion with various tools and depressing lack of available talent to attack this problem. So Sandy, I want to start with you, we always love to ask co-founders, why did you start your company? Take us back to that decision. >> Yeah, I think looking at Sonrai Security was interesting in that, it was a time to start over, it was a time to build a native in the cloud, as opposed to having a data center, and be able to use, you know, a vendor of infrastructure, be able to use the latest and greatest technology and really change the way people secure their workloads, what was interesting, you know, when we started the company, I believe that the world was in a more mature space probably in cloud than they were at the time when we were starting it, in that we were really focused around, if we could understand all of the rights and entitlements to data, we could understand data movement, we'd had hope in protecting the data and arriving in cloud, we realized that the maturity of the companies building in cloud, we're not quite there yet, they were really struggling with, you know, the identities models in the cloud, how to actually secure, you know, workloads, server less functions that are ephemeral these types of things, and even just sometimes basic governance problems, and the technology we had built was great at understanding all of the ways that data could be accessed, and we were able to expand that into all the resources of the cloud and it's an exciting space to be in, and it's also, I truly believe we'll be able to actually make cloud environments more secure than what we were doing in enterprise, because again for the first time ever you have full inventory, you have the ability to make controls that apply to the entire infrastructure, it's really an exciting time. >> I mean, I've said many times I feel like security is a do over and the fact that you're coming at it as a data problem and bringing in the cloud that intersection, I think is actually quite exciting. So Avi let's bring you into the conversation, you know, obviously we've seen cloud exploding it's continuing to be a staple of digital business transformations and acceleration especially around identity, so what's your point of view on cloud security, what's different and how does your company approach it? >> Sure, thank you for having me Dave, and just to give you a bit of World Fuel Services, World Fuel Services is a public company, and it's based out of Miami, and we are ranked 91 in the fortune 500 list, so we are spread all across the globe, and as part of our transformation to distress our business, we took over a big challenge to migrate all our global infrastructure from 22 data centers to AWS, that was a massive challenge for us, and we are downright now to 20 data centers, we only have two more to go, and we did this in the last two years, and that was really good for us, but as we've been doing this migration, there was also a strong need for us to build a strong security foundation, because going into the cloud as much as capabilities it gives us to innovate, it also gives us a lot of challenges to deal with from security standpoint, and as part of building the security foundation, we had to tackle some key challenges, one was how do we build our cloud security operating model and how do we up skill our people, the talent that you've been binding it out, and how do we make security a way of working in this new world, and more than choosing a solution we needed a really strong security partner who can help us guide in this journey, help us build the foundations and take us further and mature us in this, and that's where it was really interesting for us to partner with Sonrai, who helped us along the way, develop a foundation and now helping us mature our security platform. >> Avi, what were the technology underpinnings, that enticed you to work with Sonrai? >> Sonrai has lot of unique capabilities but I'll take it out on two key points, right? One, Sonrai has a cloud security posture management which is different from other platforms that are out there because they give you capability for a lot of out of the box frameworks and controls, but in addition to that, every organization has need to build unique specific frameworks, specific controls, they give you that capability, which is massive for enterprises, and the second key thing is, if you look at AWS, it has more than 200 services and every service has its unique capability but one key component they use across all the services, is Identity and Access Management, IAM and Sonrai has a unique perspective of using IAM to track risks and identify the interactions between user and machine identities which was really exciting and new for us, and we felt that was a really good foundation and stepping point to use Sonrai. >> All right, Sandy, we definitely saw the need for a better identity explode, in conjunction with the cloud migrations during the pandemic, it was sort of building and building and then it was accelerated, maybe talk a little bit about how you approach this, and specifically talk about your identity analytics and the graph solution that you guys talk about. >> Yeah, I've been a fan of graph solutions for many years, one of the great benefits in this particular space with identity is that, the cloud models for identity are fairly complex and quite different between AWS, Azure and GCP, however, the way that entitlements work, some identity is granted in entitlement, and that entitlement gives them access to do something, sometimes that's something is to assume another identity, and then do something on that identities behalf, and when you're actually trying to secure these clouds this jumping of identities, which happens a lot in the AWS model, or inheritance which happens a lot in the Azure model where you're given access at one level of the tree and you automatically gain access to things below that if you have that entitlement, those models inside of graph allow us to understand exactly how any given identity when we talk about identity we always think of people, but it's not, of course as you said, sometimes it's a machine, sometimes it's a cloud service, it could be many different things, how does every single one of those identities get access to that given resource? And it's not always as clear as, okay, well, here are the direct identities that can access this resource, it may only be able to be accessed with a single key, but who has access to the key, and what has access to the key, and what's the policy on that key, and if that's set too widely can other maybe nefarious actors get access to that key, and by using the graph, we can tie that whole model together to understand the entire list, of what gets access, I think that's actually what surprises a lot of the identity governance and data governance teams that are not in cloud, you know, when enterprise was very intentional, you configured the database to use the identity provider and the rules that you wanted it to use, and that's all that ever got access to that database. In cloud, there are a lot of configuration knobs and things and depending on how you turn them, you could open up a lot of identities to get access to whatever that resource is, often it's data, but it could be a network, it could be many things. So, the graph allows us to tie all that together, the second part of it is, it really allows us to see, we call them effective permissions, what the effective permission of that identity is, the clouds have done this phenomenal thing in using identities as a control mechanism just like in firewall, like an identity firewall, where they can take permissions away from things based on sets of conditions, so one of the great ways, let's say you didn't want to have any data stored deployed without encryption, you could write a policy at the top of your cloud, that says, anytime a data stores is deployed, if encryption is not there, deny that function. And so what happens is, is you can create this very protective environment using identity controls, but the problem is when you actually go to evaluate your cloud for risk, you may find a scenario where an identity has access as an example, to do something like create an internet gateway, or create a public endpoint, but there's this policy somewhere else, that's taking that away, and you don't want thousands of alerts because of that, you want to actually understand the model and say, look if we understand that this policy is mitigating your risk, then don't show the alert in the first place. And it really helps by putting it in a graph, because we can actually see all of these interconnections, we can see how they're interrelated, and determine the exact effective permissions of any identity and what risks that may have. >> So Avi, I mean, Sandy is really getting to the heart of sort of operationalizing you security in the cloud, and we looked at the compelling aspect of the cloud, and one of them anyway is scale, but people tell us to really take advantage of the cloud, they have to evolve that operating model maybe completely change the operating model, to really take advantage of scale, so my question is how do you operationalize your security practices, what should people think about, in terms of the time it takes to build in automations and bots for things like continuous compliance what can you share in terms of best practice? >> So traditional ways of operating if you look at it is, you identify a security risk, and a ticket is created and teams starts mitigating them. But with so many cloud services and with many solutions, the team start building in the cloud, it becomes too much of an overhead for teams to mitigate all these security risks that keep coming into the backlog, so as we partner with Sonrai in building a foundation, the way we tried to approach it is differently, we said why don't we build this using automatic recommendations, if we know what are the security risks, that we should not be creating in our environment and be noncompliant, how can we mitigate them? And with Sonrai and AWS API capabilities, it's not that hard for us to be a lot of intimidation buds because I didn't find risks, 'cause they have been taken care by Sonrai, the only aspect we need to take care is, how do we mitigate that? So that's the part we chose in building, cloud security operating model, is modeling more than an automated imitations, but as part building that there is always, where everything cannot be remediated automatically, and for these kinds of scenarios, we built a workflow where it still gets funneled to teams, so they can prioritize in their backlog, but other key thing that we did as part of operationalizing is, teams need to use Sonrai as their way of working, teams need to know what and why they should be using Sonrai. So we conduct a lot of training and onboarding and working sessions for teams, so they understand how we use Sonrai, how to consume the data coming out of Sonrai, so they can proactively start acting on how to stay compliant, but yeah, it's been an amazing experience building our foundation though. >> Sandy, I wonder if we can come back to, talking about comparisons with the traditional prevailing security models, I mean, we entering this API economy, as I said before, cloud is a staple of digital business, but you know people have been doing on-prem security for decades, you know, data loss prevention is an entire sub-industry, so what's different about doing it in the cloud, how should we think about that, in terms of whether you know, what responsibilities we have, the technology, what's your perspective on that? >> There's at least five questions in there Dave, so we'll. >> Pick your favorite. >> Yeah, you know, to feed off of what Avi was talking about, you know, he said many times, you know, teams need to solve these issues, teams need to see the issues they're creating, and it's interesting as we move to cloud, we decentralize some of these security functions, and that's actually an important part of the Sonrai solution and how you build a cloud security operating model, there's a set of findings, we'll call them, maybe there are security findings, maybe they're informational findings, that are a fairly low risk, and should be dealt with by the individual teams themselves, but that same team, you know, maybe isn't the person that can sign off on the risk if it's high enough, and if it's not then it needs to be escalated to the next level up to have that risk signed off on. A lot of times in large enterprise for workloads, that was done using unfortunately, you know tickets and systems and, you know, humans actually, you know, filling out some form of a checklist, saying, yes I met this, no I didn't, and we can automate huge numbers of those tests, including distributing them to the teams for the teams to solve themselves, and if they do their job right, there's not even the need for the central security body necessarily to know about the issues because they got solved, but when they don't get solved, that's when rather, you know, escalation to Boston automation or escalating to a centralized team starts to make sense, you kind of said a lot about DLP there as you were doing in cloud and just data security in general, and I do think, you know, cloud has given us this interesting opportunity, that's really upset data security in the old way on its head, you know, we used to do data security by putting agents on systems, or sometimes it was a proxy in front of it but either way that doesn't work well in cloud, when you're consuming platform as a service, you know, Amazon is not going to let you put an agent on their database that they're provisioning for you, and, you know, if you put in your own proxy in front of it you probably just messed up the elastic scalability that was built into the whole thing to begin with. So we needed a different way to look at this, however, we also took away a couple of things, in cloud the application teams themselves generally use fit for purpose data stores, they use the data store that's the best for the workload they're doing, our own workload has many data stores under the covers, it's not one data store, and so because of that, this kind of, you know, the old world of there being a data security team or you know, database optimization team, that you know optimize the database workloads, actually gets distributed as well all back to those teams, and so, we've gained kind of this, you know, fit for purpose smaller sets of data stores that are being used all over, and on top of that, the cloud vendors in many cases have done great things to enable monitoring, you know, part of the reason we were putting agents on database servers, is because the Oracle admin said I can't turn logging on, I don't have a big enough system to do it, it's going to crash the system, well in cloud parts of that go away, you can scale the systems up, you can enable loggings, now you can get that rich data that you wanted when you were an enterprise, and so, you know Sonrai is really kind of taken that model and said, look we can give you the visibility around data movement, we can give you the visibility around all of the entitlements to that data, we can understand, is your data at risk? And then we can profile all that for anomalies, and say, you know, it's kind of odd that the workload that normally connects into this through this automated fashion is now using its access key from a different location, that doesn't make any sense, why is that happening? And so you get kind of strong anomaly detection as well as the governance. So, you know, data security and cloud, if we kind of fast forward a few years, will look very different than it does today, I still believe some of the teams are not quite there yet in cloud, you know, they're still struggling with some of these identity problems we talked about, they still struggle some of them with CSBM problems, and so we have to solve those first obviously before we get to the true data security. But it's interesting that cloud has enabled us with such rich tooling and APIs to actually do it better than what we've done on enterprise. >> A lot of really powerful concepts in there, thank you Sandy. I mean, this notion of decentralizing security functions reminds me when Vogels describes this hyper decentralized distributed system that Amazon is building, and it is clearly a theme, you know, maybe it's bromide, but people talk about shifting left, designing security in, and it's important, not just bolting it on as an afterthought, and so, maybe this next question sort of really relates to the theme of this event, which is all about scale, here's the question Sandy, thinking about your contribution to the future of cloud, obviously you start a company, you want to grow that company, you want to serve customers and grow your revenues et cetera. But what's your defining contribution to the future of cloud scale? >> Look, we want to enable companies to scale faster, we want them to be able to put more workloads in cloud using, you know, the right set of security controls to keep those workloads safe, I know we can actually do this in a way where, you know, we talk about defense in depth for years, right? And usually in enterprise that meant many levels of networks before you got access, now we need to do defense in depth in terms of, you know, actually variety of controls, we can't throw the network control away, it still has to be there, we need an identity control, and it will be the primary control for what we do in cloud, we need a data lock, you know, rather that's through an encryption key policy or whatever it is, so we have multiple different layers of defense in depth, we can use in cloud today, and so it will be a much more secure environment than it was in the future, but we have to, again, so my contribution is hopefully I can help everybody get to that level, because right now we still see way too many breaches with very simple configuration problems that ended up exposing data unintentionally, and that's worrisome. >> You know, it's funny, a lot of people maybe can't relate to that defense in depth, I mean, obviously security people can, but we as individuals who now rely so much on our mobile phones, and things like SMS, and then you start to build in, non SMS, you know, base two factor authentication and you start to build your own personal layers, it's sort of a microcosm of the complexity that you have to think about in the enterprise, but in having tools to automate is critical, and expertise obviously, so let's wrap. Avi give us your final thoughts and key takeaways on building a world-class cloud security. >> I guess the key take of this would be, you know, to choose the right partner, it's not just the solution, another key takeaway is automate your way, because with security in the cloud is different than traditionally how do you do it, and the only fastest way to move is automate yourself away out of it and rely on talent, rely on a lot of young talent that's coming in and all the tools like Sonrai AWS are making it easier to operate in the cloud, so bring up the young talent and up skill the talent and leverage on these tools to be more secure on the cloud. >> Yeah, use automation to solve the big problem of, you know, that talent gap, there is not enough of it out there, and the adversaries they're well-equipped and quite capable. Okay Sandy, please give us your last word. >> Look again, I think a cloud is going to get us to a point where we are more secure than we were on enterprise, we have all of the right tools and controls to do it, we can decentralize the security and make it better, again, I think if anything just to encourage people to really look at a cloud security governance model, right? You can't do this ad hoc, trying to whack-a-mole small issues as they come up, you build it in as an operating model, you automate it and you deal with the exceptions. >> Yeah, I mean, you're very optimistic and I think is for good reason, I just remembered listening to Steven Schmidt a couple of years ago at reinforce, basically saying, look, we feel pretty optimistic about solving this problem, whereas, I have to say every year I look back in the enterprise and on-prem and I know it's getting worse, and so, keep up the good work gents, I really appreciate the time on theCUBE today, thank you. >> Thank you. >> Thank you. >> And thank you for watching theCUBE presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences. I'm Dave Vellante. (upbeat music)

(upbeat music) >> Narrator: From around the globe. It's The CUBE, with digital coverage of AWS public sector online, (upbeat music) brought to you by, Amazon Web Services. >> Everyone welcome back to The CUBE virtual coverage, of AWS public sector summit online. I'm John Furrier, your host of The CUBE. Normally we're in person, out on Asia-Pacific, and all the different events related to public sector. But this year we have to do it remote, and we're going to do the remote virtual CUBE, with Data Virtual Public Sector Online Summit. And we have two great guests here, about Digital Earth Africa project, Clive Charlton. Head of Solutions Architecture, Sub-Saharan Africa with AWS, Clive thanks for coming on, and Aditya Agrawal founder of D4DInsights, and also the advisor for the Digital Earth Africa project with AWS. So gentlemen, thank you for coming on. Appreciate you coming on remotely. >> Thanks for having us. >> Thank you for having us, John. >> So Clive take us through real quickly. Just take a minute to describe what is the Digital Earth Africa Project. What are the problems, that you're aiming to solve? >> Well, we're really aiming to provide, actionable data to governments, and organization around Africa, by providing satellite imagery, in an easy to use format, and doing that on the cloud, that serves countries throughout Africa. >> And just from a cloud perspective, give us a quick taste of what's going on, just with the tech, it's on Amazon. You got a little satellite action. Is there ground station involved? Give us a little bit more color around, you know, what's the scope of the project. >> Yeah, so, historically speaking you'd have to process satellite imagery down link it, and then do some heavy heavy lifting, around the processing of the data. Digital Earth Africa was built, from the experiences from Digital Earth Australia, originally developed by a Geo-sciences Australia and they use container services for Kubernetes's called Elastic Kubernetes Service to spin up virtual machines, which we are required to process the raw satellite imagery, into a format called a Cloud Optimized GeoTIFF. This format is used to store very large volumes of data in a format that's really easy to query. So, organizations can just use NHTTP get range request. Just a query part of the file, that they're interested in, which means, the results are served much, much quicker, from much, much better overall experience, under the hood, the store where the data is stored in the Amazon Simple Storage Service, which is S3, and the Metadata Index in a Relational Database Service, that runs the Open Data CUBE Library, which is allows Digital Earth Africa, to store this data in both space and time. >> It's interesting. I just did a, some interviews last week, on a symposium on space and cybersecurity, and we were talking about , the impact of satellites and GPS and just the overall infrastructure shift. And it's just another part of the edge of the network. Aditya, I want to get your thoughts on this, and your reaction to the Digital Earth, cause you're an advisor. Let's zoom out. What's the impact of people's lives? Give us a quick overview, of how you see it playing out because, explaining to someone, who doesn't know anything about the project, like, okay what is it about, and how does it actually impact people? >> Sure. So, you know, as, as Clive mentioned, I mean there's, there's definitely a, a digital infrastructure behind Digital Earth Africa, in a way that it's going to be able to serve free and open satellite data. And often the, the issue around satellite data, especially within the context of Africa, and other parts of the world is that there's a level of capacity that's required, in order to be able to use that data. But there's also all kinds of access issues, because, traditionally satellite data is heavy. There's the old model of being able to download the data and then being able to do something with it. And then often about 80% of the time, that you spend on satellite data is spent, just pre processing the data, before you can actually, do any of the fun analysis around it, that really sort of impacts the kinds of decisions and actions that you're looking for. And so that's why Digital Earth Africa. And that's why this partnership, with Amazon is a fantastic partnership, because it really allows us, to be able, to scale the approach across the entire continent, make it easy for that data to be accessed and make it easier for people to be able to use that data. The way that Digital Earth Africa is being operationalized, is that we're not just looking at it, from the perspective of, let's put another infrastructure into Africa. We want this program, and it is a program, that we want institutionalized within Africa itself. One that leverages expertise across the continent, and one that brings in organizations across the continent to really sort of take the leadership and ownership of this program as it moves forward. The idea of it is that, once you're able to have this information, being able to address issues like food security, climate change, coastal resilience, land degradation where illegal mining is, where is the water? We want to be able to do that, in a way that it's really looking at what are the national development priorities within the countries themselves, and how does it also then support regional and global frameworks like Africa's Agenda 2063 and the sustainable development goals. >> No doubt in my mind, obviously, is that huge benefits to these kinds of technologies. I want to also just ask you, as a follow up is a huge space race going on, right now, explosion of availability of satellite data. And again, more satellites going up, There's more congestion, more contention. Again, we had a big event on that cybersecurity, and the congestion issue, but, you know, satellite data was power everyone here in the United States, you want an Uber, you want Google Maps you've got your everywhere with GPS, without it, we'd be kind of like (laughing), wondering what's going on. How do we even vote these days? So certainly an impact, but there's a huge surge of availability, of the use of satellite data. How do you explain this? And what are some of the challenges, from the data side that's coming, from the Digital Earth Africa project that you guys hope to resolve? >> Sure. I mean, that's a great question. I mean, I think at one level, when you're looking at the space race right now, satellites are becoming cheaper. They're becoming more efficient. There's increased technology now, on the types of sensors that you can deploy. There's companies like Planet, that are really revolutionizing how even small countries are able to deploy their own satellites, and the constellation that they're putting forward, in terms of the frequency by which, you're able to get data, for any given part of the earth on a daily basis, coupled with that. And you know, this is really sort of in climbs per view, but the cloud computing capabilities, and overall computing power that you have today, then what you had 10 years, 15 years ago is so vastly different. What used to take weeks to do before, for any kind of analysis on satellite data, which is heavy data now takes, you know, minutes or hours to do. So when you put all that together, again, you know, I think it really speaks, to the power of this partnership with Amazon and really, what that means, for how this data is going to be delivered to Africa, because it really allows for the scalability, for anything that happens through Digital Earth Africa. And so, for example, one of the approaches, that we're taking us, we identify what the priorities, and needs are at the country level. Let's say that it's a land degradation, there's often common issues across countries. And so when we can take one particular issue, tested with additional countries, and then we can scale it across the whole continent because the infrastructure is there for the whole continent. >> Yeah. That's a great point. So many storylines here. We'll get to climb in a second on sustainability. And I want to talk about the Open Data Platform. Obviously, open data, having data is one thing, but now train data, and having more trusted data becomes a huge issue. Again, I want to dig into that for a second, but, Clive, I want to ask you, first, what region are we in? I mean, is this, you guys actually have a great, first of all, we've been covering the region expansion from Bahrain all the way, as moves around the world, probably soon in space. There'll be a region Amazon space station region probably, someday in the future but, what region are you running the project out of? Can you, and why is it important? Can you share the update on the regional piece? >> Well, we're very pleased, that Digital Earth Africa, is using the new Africa region in Cape Town, in South Africa, which was launched in April of this year. It's one of 24 regions around the world and we have another three new regions announced, what this means for users of Digital Earth Africa is, they're able to use region closest to them, which gives them the best user experience. It's the, it's the quickest connection for them. But more importantly, we also wanted to use, an African solution, for African people and using the Africa region in Cape Town, really aligned with that thinking. >> So, localization on the data, latency, all that stuff is kind of within the region, within country here. Right? >> That's right, Yeah >> And why is that important? Is there any other benefits? Why should someone care? Obviously, this failover option, I mean, in any other countries to go to, but why is having something, in that region important for this project? >> Well, it comes down to latency for the, for the users. So, being as close to the data, as possible is, is really important, for the user experience. Especially when you're looking at large data sets, and big queries. You don't want to be, you don't want to be waiting a long lag time, for that query to go backwards and forwards, between the user and the region. So, having the data, in the Africa region in Cape Town is important. >> So it's about the region, I love when these new regions rollout from Amazon, Cause obviously it's this huge buildup CapEx, in this huge data center servers and everything. Sustainability is a huge part of the story. How does the sustainability piece fit into the, the data initiative supported in Africa? Can you share some updates on that? >> Well, this, this project is also closely aligned with the, Amazon Sustainability Data Initiative, which looks to accelerate sustainability research. and innovation, really by minimizing the cost, and the time required to acquire, and analyze large sustainability datasets. So the initiative supports innovators, and researchers with the data and tools, and, and technical experience, that they need to move sustainability, to the next level. These are public datasets and publicly available to anyone. In addition, to that, the initiative provides cloud grants to those who are interested in exploring, exploring the use of AWS technology and scalable infrastructure, to serve sustainability challenges, of this nature. >> Aditya, I want to hear your thoughts, on this comment that Clive made around latency, and certainly having a region there has great benefits. You don't need to hop on that. Everyone knows I'm a big fan of the regional model, but it brings up the issue, of what's going on in the country, from an infrastructure standpoint, a lot of mobility, a lot of edge computing. I can almost imagine that. So, so how do you see that evolving, from a business standpoint, from a project standpoint data standpoint, can you comment and react to that edge, edge angle? >> Yeah, I mean, I think, I think that, the value of an open data infrastructure, is that, you want to use that infrastructure, to create a whole data ecosystem type of an approach. And so, from the perspective of being able. to make this data readily accessible, making it efficiently accessible, and really being able to bring industry, into that ecosystem, because of what we really want as we, as the program matures, is for this program, to then also instigate the development of new businesses, entrepreneurship, really get the young people across Africa, which has the largest proportion of young people, anywhere in the world, to be engaged around what you can do, with satellite data, and the types of businesses that can be developed around it. And, so, by having all of our data reside in Cape Town on the continent there's obviously technical benefits, to that in terms of, being able to apply the data, and create new businesses. There's also a, a perception in the fact that, the data that Digital Earth Africa is serving, is in Africa and residing in Africa which does have, which does go a long way. >> Yeah. And that's a huge value. And I can just imagine the creativity cloud, if you can comment on this open data platform idea, because some of the commentary that we've been having on The CUBE here, and all around the world is data's great. We all know we're living with a lot of data, you starting to see that, the commoditization and horizontal scalability of data, is one thing, but to put it into software defined environments, whether, it's an entrepreneur coding up an app, or doing something to share some transparency, around some initiatives going on within the region or on the continent, it's about trusted data. It's about sharing algorithms. AI is also a consumer of data, machines consume data. So, it's not just the technology data, is part of this new normal. What's this Open Data Platform, And how does that translate into value in your opinion? >> I, yeah. And you know, when, when data is shared on, on AWS anyone can analyze it and build services on top of it, using a broad range of compute and data to data analytics products, you know, things like Amazon EC2, or Lambda, which is all serverless compute, to things like Amazon Elastic MapReduce, for complex extract and transformation processes, but sharing data in the cloud, lets users, spend more time on the data analysis, rather than, than the data acquisition. And researchers can analyze data shared on AWS, without needing to pay to store their own copy, which is what the Open Data Platform provides. You only have to pay for the compute that you use and you don't need to purchase storage, to start a new project. So the registry of the open data on AWS, makes it easy to find those datasets, but, by making them publicly available through AWS services. And when you share, share your data on AWS, you make it available, to a large and growing community of developers, and startups, and enterprises, all around the world. And you know, and we've been talking particularly around, around Africa. >> Yeah. So it's an open source model, basically, it's free. You don't, it doesn't cost you anything probably, just started maybe down the road, if it gets heavy, maybe to charging but the most part easy for scientists to use and then you're leveraging it into the open, contributing back. Is that right? >> Yep. That's right. To me getting, getting researchers, and startups, and organizations growing quickly, without having to worry about the data acquisition, they can just get going and start building. >> I want to get back to Aditya, on this skill gap issue, because you brought up something that, I thought was really cool. People are going to start building apps. I'm going to start to see more innovation. What are the needs out there? Because we're seeing a huge onboarding of new talent, young talent, people rescaling from existing jobs, certainly COVID accelerated, people looking for more different kinds of work. I'm sure there's a lot of (laughing) demand to, to do some innovative things. The question I always get, and want to get your reaction is, what are the skills needed to, to get involved, to one contribute, but also benefit from it, whether it's the data satellite, data or just how to get involved skill-wise >> Sure. >> Yes. >> Yeah. So most recently we've created a six week training course. That's really kind of taken users from understanding, the basics of Earth Observation Data, to how to work, with Python, to how to create their own Jupyter notebooks, and their own Use cases. And so there's a, there's a wide sort of range of skill sets, that are required depending on who you are because, effectively, what we want to be able to do is get everyone from, kind of the technical user, that might have some remote sensing background to the developer, to the policy maker, and decision maker, to understand the value of this infrastructure, whether you're the one who's actually analyzing the data. If you're the one who's developing new applications, or you're taking that information from a managerial or policy level discussion to actually deliver the action and sort of impact that you're looking for. And so, you know, in, in that regard, we're working with ITC in the Netherlands and again, with institutions across Africa, that already have a mandate, and expertise in this particular area, to create a holistic capacity development program, that will address all of those different factors. >> So I guess the follow up question I want to have is, how do you ensure the priorities of Africa are addressed, as part of this program? >> Yeah, so, we are, we've created a governance model, that really is both top down, and bottom up. At the bottom up level, We have a technical advisory committee, that has over 15 institutions, many of which are based across Africa, that really have a good understanding of the needs, the priorities, and the mandate for how to work with countries. And at the top down level, we're developing a governing board, that will be inclusive, of the key continental level institutions, that really provide the political buy-in, the sustainability of the program, and really provide overall guidance. And within that, we're also creating an operational models, such that these institutions, that do have the capacity to support the program, they're actually the ones, who are also going to be supporting, the implementation of the program itself. >> And there's been some United Nations, sustained development projects all kinds of government involvement, around making sure certain things would happen, within the country. Can you just share, some of the highlights, or some of the key initiatives, that are going on, that you're supporting, to make it a better, better world? >> Yeah. So this is, this program is very closely aligned to a sustainable development agenda. And so looking after, looking developing methods, that really address, the sustainable development goals as one facet, in Africa, there's another program looking overall, overall national development priorities and sustainability called the Agenda 2063. And really like, I think what it really comes down to this, this wouldn't be happening, without the country level involvement themselves. So, this started with five countries, originally, Senegal, Ghana, Kenya, Tanzania, and the government of Kenya itself, has really been, a kind of a founding partner for, how Digital Earth Africa and it's predecessor of Africa Regional Data Cube, came to be. And so without high level support, and political buying within those governments, I mean, it's really because of that. That's why we're, we're where we are. >> I need you to thank you for coming on and sharing that insight. Clive will give you the final word, for the folks watching Digital Earth Africa, processes, petabytes of data. I mean the satellite data as well, huge, you mentioned it's a new region. You're running Kubernetes, Elastic Kubernetes Service, making containers easy to use, pay as you go. So you get cutting edge, take the one minute to, to share why this region's cutting edge. Does it have the scale of other regions? What should they know about AWS, in Cape Town, for Africa's new region? Take a minute to, to put plugin. >> Yeah, thank you for that, John. So all regions are built in the, in the same way, all around the world. So they're built for redundancy and reliability. They typically have a minimum of three, what we call Availability Zones. And each one is a contains a, a cluster of, of data centers, and all interconnected with fast fiber. So, you know, you can survive, you know, a failure with with no impact to your services. And the Cape Town region is built in exactly the same the same way, we have most of the services available in the, in the Cape Town region, like most other regions. So, as a user of AWS, you, you can have the confidence that, You can deploy your services and workloads, into AWS and run it in the same in the same way, with the same kind of speed, and the same kind of support, and infrastructure that's backing any region, anywhere else in the world. >> Well great. Thanks for that plug, Aditya, thank you for your insight. And again, innovation follows cloud computing, whether you're building on top of it as a startup a government or enterprise, or the big society better, in this case, the Digital Earth Africa project. Great. A great story. Thank you for sharing. I appreciate it. >> Thank you for having us. >> Thank you for having us, John >> I'm John Furrier with, The CUBE, virtual remote, not in person this year. I hope to see you next time in person. Thanks for watching. (upbeat music) (upbeat music decreases)

>>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.

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this a CUBE conversation. >> Hello, and welcome to this CUBE conversation, I'm John Furrier, host of theCUBE here, in our Palo Alto, California studios, here with our quarantine crew. We're getting all the remote interviews during this time of COVID-19. We've got two great remote guests here, Rachini Moosavi who's the Executive Director of Analytical Services and Data Governance at UNC Healthcare, and Sonya Jordan, Enterprise Analytics Manager of Data Governance at UNC Health. Welcome to theCUBE, thanks for coming on. >> Thank you. >> Thanks for having us. >> So, I'm super excited. University of North Carolina, my daughter will be a freshman this year, and she is coming, so hopefully she won't have to visit UNC Health, but looking forward to having more visits down there, it's a great place. So, thanks for coming on, really appreciate it. Okay, so the conversation today is going to be about how data and how analytics are helping solve problems, and ultimately, in your case, serve the community, and this is a super important conversation. So, before we get started, talk about UNC Health, what's going on there, how you guys organize, how big is it, what are some of the challenges that you have? >> SO UNC Health is comprised of about 12 different entities within our hospital system. We have physician groups as well as hospitals, and we serve, we're spread throughout all of North Carolina, and so we serve the patients of North Carolina, and that is our primary focus and responsibility for our mission. As part of the offices Sonya and I are in, we are in the Enterprise Analytics and Data Sciences Office that serves all of those entities and so we are centrally located in the triangle area of North Carolina, which is pretty central to the state, and we serve all of our entities equally from our Analytics and Data Governance needs. >> John: You guys got a different customer base, obviously you've got the clinical support, and you got the business applications, you got to be agile, that's what it's all about today, you don't need to rely on IT support. How do you guys do that? What's the framework? How do you guys tackle that problem of being agile, having the data be available, and you got two different customers, you got all the compliance issues with clinical, I can only imagine all the regulations involved, and you've got the business applications. How do you handle those? >> Yeah, so for us in the roles that we are in, we are fully responsible for more of the data and analytics needs of the organization, and so we provide services that truly are balanced across our clinician group, so we have physicians, and nurses, and all of the other ancillary clinical staff that we support, as well as the operational needs as well, so revenue cycle, finance, pharmacy, any of those groups that are required in order to run a healthcare system. So, we balance our time amongst all of those and for the work that we take on and how we continuously support them is really based on governance at the end of the day. How we make decisions around what the priorities are and what needs to happen next, and requires the best insights, is really how we focus on what work we do next. As for the applications that we build, in our office, we truly only build analytical applications or products like visualizations within Tableau as well as we support data governance platforms and services and so we provide some of the tools that enable our end users to be able to interact with the information that we're providing around analytics and insights, at the end of the day. >> Sonya, what's your job? Your title is Analytics Manager of Data Governance, obviously that sounds broad but governance is obviously required in all things. What is your job, what is your day-to-day roles like? What's your focus? >> Well, my day-to-day operations is first around building a data governance program. I try to work with identifying customers who we can start partnering with so that we can start getting documentation and utilizing a lot of the programs that we currently have, such as certification, so when we talk about initiatives, this is one of the initiatives that we use to partner with our stakeholders in order to start bringing visibilities to the various assets, such as metrics, or universes that we want to certify, or dashboards, algorithm, just various lists of different types of assets that we certify that we like to partner with the customers in order for them to start documenting within the tools, so that we can bring visibility to what's available, really focusing on data literacy, helping people to understand what assets are available, not only what assets are available, but who owns them, and who own the asset, and what can they do with it, making sure that we have great documentation in order to be able to leverage literacy as well. >> So, I can only imagine with how much volume you guys are dealing from a data standpoint, and the diversity, that the data warehouse must be massive, or it must be architected in a way that it can be agile because the needs, of the diverse needs. Can you guys share your thoughts on how you guys look on the data warehouse challenge and opportunity, and what you guys are currently doing? >> Well, so- >> Yeah you go ahead, Rachini. >> Go ahead, Sonya. >> Well, last year we implemented a tool, an enterprise warehouse, basically behind a tool that we implemented, and that was an opportunity for Data Governance to really lay some foundation and really bring visibility to the work that we could provide for the enterprise. We were able to embed into probably about six or seven of the 13 initiatives, I was actually within that project, and with that we were able to develop our stewardship committee, our data governance council, and because Rachini managed Data Solutions, our data solution manager was able to really help with the architect and integration of the tools. >> Rachini, your thoughts on running the data warehouse, because you've got to have flexibility for new types of data sources. How do you look at that? >> So, as Sonya just mentioned, we upgraded our data warehouse platform just recently because of these evolving needs, and like a lot of healthcare providers out there, a lot of them are either one or the other EMRs that are top in the market. With our EMR, they provide their own data warehouse, so you have to factor almost the impact of what they bring to the table in with an addition to all of those other sources of data that you're trying to co-mingle and bring together into the same data warehouse, and so for us, it was time for us to evolve our data warehouse. We ended up deciding on trying to create a virtual data warehouse, and in doing so, with virtualization, we had to upgrade our platform, which is what created that opportunity that Sonya was mentioning. And by moving to this new platform we are now able to bring all of that into one space and it's enabled us to think about how does the community of analysts interact with the data? How do we make that available to them in a secure way? In a way that they can take advantage of reusable master data files that could be our source of truth within our data warehouse, while also being able to have the flexibility to build what they need in their own functional spaces so that they can get the wealth of information that they need out of the same source and it's available to everyone. >> Okay, so I got to ask the question, and I was trying to get the good stuff out first, but let's get at the reality of COVID-19. You got pre-COVID-19 pandemic, we're kind of in the middle of it, and people are looking at strategies to come out of it, obviously the world will be changed, higher with a lot of virtualization, virtual meetings, and virtual workforce, but the data still needs to be, the business still needs to run, but data will be changing different sources, how are you guys responding to that crisis because you're going to be leaned on heavily for more and more support? >> Yeah it's been non-stop since March (laughs). So, I'm going to tell you about the reporting aspects of it, and then I'd love to turn it over to Sonya to tell you about some of the great things that we've actually been able to do to it and enhance our data governance program by not wasting this terrible event and this opportunity that's come up. So, with COVID, when it kicked off back in March, we actually formed a war room to address the needs around reporting analytics and just insights that our executives needed, and so in doing so, we created within the first week, our first weekend actually, our first dashboard, and within the next two weeks we had about eight or nine other dashboards that were available. And we continuously add to that. Information is so critical to our executives, to our clinicians, to be able to know how to address the evolving needs of COVID-19 and how we need to respond. We literally, and I'm not even exaggerating, at this very moment we have probably, let's see, I think it's seven different forecasts that we're trying to build all at the same time to try and help us prepare for this new recovery, this sort of ramp up efforts, so to your point, it started off as we're shutting down so that we can flatten the curve, but now as we try to also reopen at the same time while we're still meeting the needs of our COVID patients, there's this balancing act that we're trying to keep up with and so analytics is playing a critical factor in doing that. >> Sonya, your thoughts. First of all, congratulations, and action is what defines the players from the pretenders in my mind, you're seeing that play out, so congratulations for taking great action, I know you're working hard. Sonya, your thoughts, COVID, it's putting a lot of pressure? It highlights the weaknesses and strengths of what's kind of out there, what's your thoughts? >> Well, it just requires a great deal of collaboration and making sure that you're documenting metrics in a way where you're factoring true definition because at the end of the day, this information can go into a dashboard that's going to be visualized across the organization, I think what COVID has done was really enhanced the need and the understanding of why data governance is important and also it has allowed us to create a lot of standardization, where we we're standardizing a lot of processes that we currently had in correct place but just enhancing them. >> You know, not to go on a tangent, but I will, it's funny how the reality has kind of pulled back, exposed a lot of things, whether it's the remote work situation, people are VPNing, not under provision with the IT side. On the data side, everyone now understands the quality of the data. I mean, I got my kids talking progression analysis, "Oh, the curves are all wrong," I mean people are now seeing the science behind the data and they're looking at graphs all the time, you guys are in the visualization piece, this really highlights the need of data as a story, because there's an impact, and two, quality data. And if you don't have the data, the story isn't being told and then misinformation comes out of it, and this is actually playing out in real time, so it's not like it's just a use case for the most analytics but this again highlights the value of proposition of what you guys do. What's your personal thoughts on all this because this really is playing out globally. >> Yeah, it's been amazing how much information is out there. So, we have been extremely blessed at times but also burdened at times by that amount of information. So, there's the data that's going through our healthcare system that we're trying to manage and wrangle and do that data storytelling so that people can drive those insights to very effective decisions. But there's also all of this external data that we're trying to be able to leverage as well. And this is where the whole sharing of information can sometimes become really hard to try and get ahead of, we leverage the Johns Hopkins data for some time, but even that, too, can have some hiccups in terms of what's available. We try to use our State Department of Health and Human Services data and they just about updated their website and how information was being shared every other week and it was making it impossible for us to ingest that into our dashboards that we were providing, and so there's really great opportunities but also risks in some of the information that we're pulling. >> Sonya, what's your thoughts? I was just having a conversation this morning with the Chief of Analytics and Insight from NOA which is the National Oceanic Administration, about weather data and forecasting weather, and they've got this community model where they're trying to get the edges to kind of come in, this teases out a template. You guys have multiple locations. As you get more democratized in the connection points, whether it's third-party data, having a system managing that is hard, and again, this is a new trend that's emerging, this community connection points, where I think you guys might also might be a template, and your multiple locations, what's your general thoughts on that because the data's coming in, it's now connected in, whether it's first-party to the healthcare system or third-party. >> Yeah, well we have been leveraging our data governance tool to try to get that centralized location, making sure that we obtain the documentations. Due to COVID, everything is moving very fast, so it requires us to really sit down and capture the information and when you don't have enough resources in order to do that, it's easy to miss some very important information, so really trying to encourage people to understand the reason why we have data governance tools in order for them to leverage, in order to capture the documentation in a way that it can tell the story about the data, but most of all, to be able to capture it in a way so that if that person happened to leave the organization, we're not spending a lot of time trying to figure out how was this information created, how was this dashboard designed, where are the requirements, where are the specifications, where are the key elements, where does that information live, and making sure we capture that up front. >> So, guys, you guys are using Informatica, how are they helping you? Obviously, they have a system they're getting some great feedback on, how are you using Informatica, how is it going, and how has that enabled you guys to be successful? >> Yeah, so we decided on Informatica after doing a really thorough vetting of all of the other vendors in the industry that could provide us these services. We've really loved the capabilities that we've been able to provide to our customers at this point. It's evolving, I think, for us, the ability to partner with a group like Prominence, to be able to really leverage the capabilities of Informatica and then be really super, super hyper focused on providing data literacy back to our end users and making that the full intent of what we're doing within data governance has really enabled us to take the tools and make it something that's specific to UNC Health and the needs that our end users are verbalizing and provide that to them in a very positive way. >> Sonya, they talk about this master catalog, and I've talked to the CEO of Informatica and all their leaders, governance is a big part of it, and I've always said, I've always kind of had a hard time, I'm an entrepreneur, I like to innovate, move fast, break things, which is kind of not the way you work in the data world, you don't want to be breaking anything, so how do you balance governance and compliance with innovation? This has been a key topic and I know that you guys are using their enterprise data catolog. Is that helping? How does that fit in, is that part of it? >> Well, yeah, so during our COVID initiatives and building these telos dashboards, these visualizations and forecast models for executive leaders, we were able to document and EMPower you, which we rebranded Axon to EMPower, we were able to document a lot of our dashboards, which is a data set, and pretty much document attributes and show lineage from EMPower to EDC, so that users would know exactly when they start looking at the visualization not only what does this information mean, but they're also able to see what other sources that that information impacts as well as the data lineage, where did the information come from in EDC. >> So I got to ask the question to kind of wrap things up, has Informatica helped you guys out now that you're in this crisis? Obviously you've implemented before, now that you're in the middle of it, have you seen any things that jumped out at you that's been helpful, and are there areas that need to be worked on so that you guys continue to fight the good fight, come out of this thing stronger than before you came in? >> Yeah, there is a lot of new information, what we consider as "aha" moments that we've been learning about, and how EMPower, yes there's definitely a learning curve because we implemented EDC and EMPower last year doing our warehouse implementation, and so there's a lot of work that still needs to be done, but based on where we were the first of the year, I can say we have evolved tremendously due to a lot of the pandemic issues that arised, and we're looking to really evolve even greater, and pilot across the entire organization so that they can start leveraging these tools for their needs. >> Rachini you got any thoughts on your end on what's worked, what you see improvements coming, anything to share? >> Yeah, so we're excited about some of the new capabilities like the marketplace for example that's available in Axon, we're looking forward to being able to take advantage of some of these great new aspects of the tool so that we can really focus more on providing those insights back to our end users. I think for us, during COVID, it's really been about how do we take advantage of the immediate needs that are surfacing. How do we build all of these dashboards in record-breaking time but also make sure that folks understand exactly what's being represented within those dashboards, and so being able to provide that through our Informatica tools and service it back to our end users, almost in a seamless way like it's built into our dashboards, has been a really critical factor for us, and feeling like we can provide that level of transparency, and so I think that's where as we evolve that we would look for more opportunities, too. How do we make it simple for people to get that immediate answers to their questions, of what does the information need without it feeling like they're going elsewhere for the information. >> Rachini, thank you so much for your insight, Sonya as well, thanks for the insight, and stay safe. Sonya, behind you, I was pointing out, that's your artwork, you painted that picture. >> Yes. >> Looks beautiful. >> Yes, I did. >> You got two jobs, you're an artist, and you're doing data governance. >> Yes, I am, and I enjoy painting, that's how I relax (laughs). >> Looks great, get that on the market soon, get that on the marketplace, let's get that going. Appreciate the time, thank you so much for the insights, and stay safe and again, congratulations on the hard work you're doing, I know there's still a lot more to do, thanks for your time, appreciate it. >> Thank you. >> Thank you. >> It's theCUBE conversation, I'm John Furrier at the Palo Alto studios, for the remote interviews with Informatica, I'm John Furrier, thanks for watching. (upbeat music)

(upbeat music) >> Hey welcome back everybody. Jeff Frick here with theCUBE. I wish I could give you my best John Miller impersonation but I'm just not that good. But we are at Oracle Park, home of the San Francisco Giants. We haven't really done a show here since 2014, so we're excited to be back. Pretty unique event, it's called Sports Tech Tokyo World Demo Day. About 25 companies representing about 100 different companies really demonstrating a bunch of cool technology that's used for sports as well as beyond sports, so we're excited to have one of the companies here who's demoing their software today, or their solution I should say. It's Albert Ng, he's the founder and CEO of Misapplied Sciences. Albert, great to see you. >> Great to see you, thank you for having me. >> So Misapplied Sciences. Now I want to hear about the debates on that name. So how did that come about? >> Yeah, so I used to work part time for Microsoft, at Microsoft Research, and one of the groups I worked for was called the Applied Sciences group. And so it was a little bit of a spin on that and it conveys the way that we come up with innovations at our company. We're a little bit more whimsical as a company that we take technologies that weren't intended for the ways that we apply them and so we misapply those technologies to create new innovations. >> Okay, so you're here today, you're showing a demo. So what is it? What is your technology all about? And what is the application in sports, and then we'll talk about beyond sports. >> Yeah, so Misapplied Sciences, we came up with a new display technology. Think like LED video wall, digital signage, that sort of display. But what's unique about our displays, is you can have a crowd of people, all looking at the same display at the same time, yet every single person sees something completely different. You don't need to have any special glasses or anything like that. You look at your displays with your naked eyes, except everyone gets their own personalized experience. >> Interesting. So how is that achieved? Obviously, we've all been on airplanes and we know privacy filters that people put on laptops so we know there's definitely some changes based on angle. Is it based on the angles that you're watching it? How do you accomplish that and is it completely different, or I just see a little bit of difference here, there, and in other places? >> Sure, so at the risk of sounding a little too technical, it's in the pixel technology that we developed itself. So each of our pixels can control the color of light that it sends in many different directions. So one time a single pixel can emit green light towards you, whereas red light towards the person sitting right next to you, so you perceive green, whereas the person right next to you perceives red at the same time. We can do that at a massive scale. So our pixels can control the color of light that they send between tens of thousands, up to a million different angles. So using our software, our processors on our back end, we can control what each of our pixels looks like from up to a million different angles. >> So how does it have an edge between a million points of a compass? That's got to be, obviously it's your secret sauce, but what's going on in layman's terms? >> Yeah, so it's a very sophisticated technology. It's a full stack technology, as we call it. So it's everything from new optics to new high performance computing. We had to develop our own custom processor to drive this. Computer vision, software user interfaces, everything. And so this is an innovation we can up with after four and a half years in stealth mode. So we started the company in late 2014, and we were all the way completely in stealth mode until middle of last year. So about four years just hardcore doing the development work, because the technology's very sophisticated. And I know when I say this, it does sound a little impossible, a little bit like science fiction, so we knew that. So now we have our first product coming on the market, our first public installation later next year and it's going to be really exciting. >> Great. So, obviously you're not going to have a million different feeds, 'cuz you have to have a different feed I would imagine, for each different view, 'cuz you designate this is the view from point A. This is the view from point B. Use feed A, use feed B. I assume you use something like that 'cuz obviously the controller's a big piece of the display. >> Exactly, so a lot of the technology underneath the hood is to reduce the calculations, or the rendering required from a normal computer, so you can actually drive our big displays that can control hundreds of different views using a normal PC, just using our platform. >> So what's the application. You know obviously it's cute and it's fun and I told you it's a dog, no it's a cat as you said, but what are some of the applications that you see in sports? What are you going to do in your first demo that you're putting out? >> Yeah, so what the technology enables is finally having personalized experiences when in a public environment, like a stadium, like an airport, like a shopping mall. So let me give an airport example. So imagine you go up to the giant flight board and instead of a list of a hundred flights, you see only your own flight information in big letters so you can see it from 50 feet away. You can have arrows that light your path towards your particular gate. The displays could let you know exactly how many minutes you have to board, and suggest places for you to eat and shop that are convenient for you. So the environment can be tailored just for you and you're not looking down at a smart phone, you're not wearing any special glasses to see everything that you want to see. So that ability to personalize a venue stretch, is to every single public venue, even in the stadium here, imagine the stadium knowing whether you're a home team fan or away team fan or your fantasy players. You can see it all on the jumbotron or any of the displays that are in the interstitial areas. We can have the entire stadium come alive just for you and personalize it. >> Except you're not going to have 10,000 different feeds, so is there going to be some subset of infinite that people are driving in terms of the content side? >> Mhmm. >> So on your first one, you're first installation, what's that installation going to be all about? >> The first installation is going to be at an airport, I can't see right now publicly where it's going to be or when it's going to be or what partner. But the idea is to be able to have a giant flight board that you only see your own flight information, navigating you to your particular gate. You know when you're at an airport, or any other public venue like a stadium, a lot of times you feel like cow in a herd, right? And it's not tailored for you in any way. You don't have as good of an experience. So we can personalize that for you. >> All right, Misapplied Sciences. Oh I'll come down and take a look at the booth a little bit later. And thanks for taking a few minutes. Good luck on the adventure. I look forward to watching it unfold. >> Appreciate it, thank you so much. >> All right, he's Albert I'm Jeff. You're watching theCUBE. We're at Oracle Park, on the shores of McCovey Cove. Thanks for watching, we'll see ya next time. >> Thank you. (upbeat music)

(upbeat music) >> Welcome back, everybody, Jeff Frick here with theCube. We are at Oracle Park, formerly AT&T Park, recently named Oracle Park. Right on the shores of McCovey Cove, in downtown San Francisco. We haven't been here since Sport's Data, I think 2014. I can't believe it's been five years. So maybe now the Giants' situation will turn as we make a run for the pennant. We're here at a really interesting event, it's called Sports Tech Tokyo World Demo Day. And we're here with kind of the master of ceremonies, if you will, he's Mike Proman, the Managing Director of Scrum Ventures. Mike, great to see you. >> Great to be here. Thanks again for the time. >> Absolutely. So what is this day all about? Give us the low down. >> Yeah so, start up frenzy, right? Sports tech community's just in it's infancy right now. There's a lot of fragmentation though, in this world. And how do we best connect start ups to best-in-class companies, right? Japanese companies, there's a lot of excitement in Japan right now. We have Rugby World Cup coming up next month, we have the Olympics next year. How do we enable the start up community to realize those opportunities from a partnership perspective? So, we set out on this journey about a year ago. Bringing together companies of all different stages, all different geographic regions, and all different areas of focus within sports tech. And our job was to connect them to opportunities in Japan. What we kind of uncovered along the journey right, is that this is a community. And that we're building a platform here that transcends Asia, right. We want to help this community, and whether it's connecting them with the venture audience, or otherwise, we feel this is a great reflection of innovation coming in to this industry. >> Now you took kind of an interesting tact. You've called them, before we turned the cameras on, kind of a cohort, kind of an incubator, not really an incubator. So how is this thing structured, how do people get involved? What are some of the benefits of being part of this group versus out there slogging it on your own? >> Well, absolutely, and I think everyone's first reaction is, oh, this is just another accelerator, right? And we've really made a point of not identifying ourselves as an accelerator, for a variety of reasons. Number one, it's a stage-agnostic cohorts, right. So a lot of the companies that are representative here today, the 159 in our cohort, they've raised 10, 20, 30, $40 million. In many respects, they're all grows up, right. They don't need a quote unquote, a traditional accelerator. But our reality is, everybody needs acceleration. And particularly in Asia, Japan in particular, right? You need allies, you need advocates, you need facilitators. And people who are going to help revenue optimization, as well as just breaking the door in some cases. There's a lot of high profile content coming to that region, and if we can help people, it all comes back to us, long term. >> Right, right. And then the other piece, obviously, is the investment piece. 'Cause you work with a number of Japanese investment firms, so that's really kind of part of the, you know, we're sitting in San Franscisco, the event's called Tokyo, the Olympics are a year way, and you're from the Mid-West. So, you're kind of bringing it all together here in San Franscisco. >> You know, sport is the great unifier, right. So this is a great opportunity for us to speak to other industries, and bring the venture community into this conversation. Because, as you know, it's about top-line growth for a lot of these startups, but in many cases, they need capital to be able to accelerate into that growth. And so, you know, it's a very exciting time, and we're here to help support everybody. Our DNA, we're investors, right. We're a venture capital firm. But at the end of the day, what ends up happening is, these companies needs advocacy and connections, and that's what we're here to provide. >> Right, so, you said 100 plus companies in cohort. So, there's a lot of things going on in sports tech, but what are some of the really oddball ones that you're seeing a little further out than maybe most people aren't thinking about. >> Yeah, you know, the trends to me that I'm really excited about personally, are those opportunities that transcend the industry, right. Where is there opportunity for us to democratize things, from just a lead athletes, right, into things that you and I both need. So look at athlete performance. Look at recovery health, as an industry focus, right. Hydration, you look at mental health, sleep health, dietary health, you know. Players of the Giants, they need that, right? But you and I need that too. So where are those technologies that are innovators or thought leaders and leading the way in those spaces? The nice thing about Sports Tech Tokyo is we focus in athlete performance, stadium experience, and fan engagement, right. And there are 13 sub-categories, so it's a very broad based cohort, a lot of different areas of expertise. But bringing them all together is what's most rewarding. >> What's your favorite piece of it? I mean, it's hard to pick your favorite kid, but a couple of interesting companies in the portfolio that you'd like to highlight. >> Everyone's always saying, oh, you put me on the spot. No, absolutely not, Jeff. But in reality, my background is, I've been an entrepreneur for 10 plus years before this. And I've worked with brands like Coca Cola, and the NBA. What excites me most-- >> So we framed you up with a Coke bottle, by the way. >> Thank you very much. That was a nice product placement there. The nice thing is, I'm seeing technology today that didn't fundamentally exist a year or two ago. So I could tell you my favorite right now, in 2 weeks that might be entirely different, right. You're going to meet somebody from Misapplied Sciences, and they are doing some of the most breakthrough, cutting edge tech that, it's mind boggling, in terms of what they can do. And what's great about a company like Misapplied, is that they're doing it in sports, but they're also doing it in retail, and other high-dense environments. And so to me, those are the winners in this cohort. The ones that can transcend sport, and add value to so many other places. >> Right, so, before I let you go, you got a busy day ahead. What's the run of the day, what should people expect who are coming through the gates here at Oracle today? >> Well I said this is not your traditional accelerator. Well, this is not your traditional demo day, by any means, right. Traditionally, demo day is a bunch of company pitches, and then there's maybe some conversation afterwards. To us, this is a celebration of a broader cohort, right. Our 100 plus mentors that make up the Sports Tech Tokyo community. And we wanted to celebrate those individuals, right. The 100 mentors, the 400 plus attendees we have here today. So, think of it as an extended cocktail party, right. We want people to connect, and connect at scale. And so that's the back half of the day. The front half of the day is more content oriented. We have a lot of industry experts, again, common theme is transcending the vertical. Looking at opportunities to bring the venture community into the conversation. >> All right, well Mike, good luck and have a great and very busy day. >> Yeah, thank you so much. Appreciate it Jeff. >> He's Mike, I'm Jeff, you're watching theCube. We're at Oracle Park in San Francisco on the shores of McCovey Cove, thanks for watching. We'll see you next time. (upbeat digital music)

>> Live from Washington, DC, it's theCUBE, covering AWS Public Sector Summit. Brought to you by Amazon Web Services. >> Welcome back everyone to theCUBE's live coverage of the AWS Public Sector Summit, here in our nation's capital. I'm your host, Rebecca Knight, co-hosting with John Furrier. We have two guests for this segment, we have George Gagne, he is the Chief Information Officer at Defense POW/MIA Accounting Agency. Welcome, George. And we have Christopher McDermott, who is the CDO of the POW/MIA Accounting Agency. Welcome, Chris. >> Thank you. >> Thank you both so much for coming on the show. >> Thank you. >> So, I want to start with you George, why don't you tell our viewers a little bit about the POW/MIA Accounting Agency. >> Sure, so the mission has been around for decades actually. In 2015, Secretary of Defense, Hagel, looked at the accounting community as a whole and for efficiency gains made decision to consolidate some of the accounting community into a single organization. And they took the former JPAC, which was a direct reporting unit to PACOM out of Hawaii, which was the operational arm of the accounting community, responsible for research, investigation, recovery and identification. They took that organization, they looked at the policy portion of the organization, which is here in Crystal City, DPMO and then they took another part of the organization, our Life Sciences Support Equipment laboratory in Dayton, Ohio, and consolidated that to make the defense POW/MIA Accounting Agency, Under the Office of Secretary Defense for Policy. So that was step one. Our mission is the fullest possible accounting of missing U.S. personnel to their families and to our nation. That's our mission, we have approximately 82,000 Americans missing from our past conflicts, our service members from World War II, Korea War, Korea, Vietnam and the Cold War. When you look at the demographics of that, we have approximately 1,600 still missing from the Vietnam conflict. We have just over a 100 still missing from the Cold War conflict. We have approximately 7,700 still missing from the Korean War and the remainder of are from World War II. So, you know, one of the challenges when our organization was first formed, was we had three different organizations all had different reporting chains, they had their own cultures, disparate cultures, disparate systems, disparate processes, and step one of that was to get everybody on the same backbone and the same network. Step two to that, was to look at all those on-prem legacy systems that we had across our environment and look at the consolidation of that. And because our organization is so geographically dispersed, I just mentioned three, we also have a laboratory in Offutt, Nebraska. We have detachments in Southeast Asia, Thailand, Vietnam, Laos, and we have a detachment in Germany. And we're highly mobile. We conduct about, this year we're planned to do 84 missions around the world, 34 countries. And those missions last 30 to 45 day increments. So highly mobile, very globally diverse organization. So when we looked at that environment obviously we knew the first step after we got everybody on one network was to look to cloud architectures and models in order to be able to communicate, coordinate, and collaborate, so we developed a case management system that consist of a business intelligence software along with some enterprise content software coupled with some forensics software for our laboratory staff that make up what we call our case management system that cloud hosted. >> So business challenges, the consolidation, the reset or set-up for the mission, but then the data types, it's a different kind of data problem to work, to achieve the outcomes you're looking for. Christopher, talk about that dynamic because, >> Sure. >> You know, there are historical different types of data. >> That's right. And a lot of our data started as IBM punchcards or it started from, you know, paper files. When I started the work, we were still looking things up on microfiche and microfilm, so we've been working on an aggressive program to get all that kind of data digitized, but then we have to make it accessible. And we had, you know as George was saying, multiple different organizations doing similar work. So you had a lot of duplication of the same information, but kept in different structures, searchable in different pathways. So we have to bring all of that together and make and make it accessible, so that the government can all be on the same page. Because again, as George said, there's a large number of cases that we potentially can work on, but we have to be able to triage that down to the ones that have the best opportunity for us to use our current methods to solve. So rather than look for all 82,000 at once, we want to be able to navigate through that data and find the cases that have the most likelihood of success. >> So where do you even begin? What's the data that you're looking at? What have you seen has had the best indicators for success, of finding those people who are prisoners of war or missing in action? >> Well, you know, for some degrees as George was saying, our missions has been going on for decades. So, you know, a lot of the files that we're working from today were created at the time of the incidents. For the Vietnam cases, we have a lot of continuity. So we're still working on the leads that the strongest out of that set. And we still send multiple teams a year into Vietnam and Laos, Cambodia. And that's where, you know, you try to build upon the previous investigations, but that's also where if those investigations were done in the '70s or the '80s we have to then surface what's actionable out of that information, which pathways have we trod that didn't pay off. So a lot of it is, What can we reanalyze today? What new techniques can we bring? Can we bring in, you know, remote sensing data? Can we bring GIS applications to analyze where's the best scenario for resolving these cases after all this time? >> I mean, it's interesting one of the things we hear from the Amazon, we've done so many interviews with Amazon executives, we've kind of know their messaging. So here's one of them, "Eliminate the undifferentiated heavy lifting." You hear that a lot right. So there might be a lot of that here and then Teresa had a slide up today talking about COBOL and mainframe, talk about punch cards >> Absolutely. >> So you have a lot of data that's different types older data. So it's a true digitization project that you got to enable as well as other complexity. >> Absolutely, when the agency was formed in 2015 we really begin the process of an information modernization effort across the organization. Because like I said, these were legacy on-prem systems that were their systems' of record that had specific ways and didn't really have the ability to share the data, collaborate, coordinate, and communicate. So, it was a heavy lift across the board getting everyone on one backbone. But then going through an agency information modernization evolution, if you will, that we're still working our way through, because we're so mobilely diversified as well, our field communications capability and reach back and into the cloud and being able to access that data from geographical locations around the world, whether it's in the Himalayas, whether it's in Vietnam, whether it's in Papua New Guinea, wherever we may be. Not just our fixed locations. >> George and Christopher, if you each could comment for our audience, I would love to get this on record as you guys are really doing a great modernization project. Talk about, if you each could talk about key learnings and it could be from scar tissue. It could be from pain and suffering to an epiphany or some breakthrough. What was some of the key learnings as you when through the modernization? Could you share some from a CIO perspective and from a CDO perspective? >> Well, I'll give you a couple takeaways of what I thought I think we did well and some areas I thought that we could have done better. And for us as we looked at building our case management system, I think step one of defining our problem statement, it was years in planning before we actually took steps to actually start building out our infrastructure in the Amazon Cloud, or our applications. But building and defining that problem statement, we took some time to really take a look at that, because of the different in cultures from the disparate organizations and our processes and so on and so forth. Defining that problem statement was critical to our success and moving forward. I'd say one of the areas that I say that we could have done better is probably associated with communication and stakeholder buy-in. Because we are so geographically dispersed and highly mobile, getting the word out to everybody and all those geographically locations and all those time zones with our workforce that's out in the field a lot at 30 to 45 days at a time, three or four missions a year, sometimes more. It certainly made it difficult to get part of that get that messaging out with some of that stakeholder buy-in. And I think probably moving forward and we still deal regarding challenges is data hygiene. And that's for us, something else we did really well was we established this CDO role within our organization, because it's no longer about the systems that are used to process and store the data. It's really about the data. And who better to know the data but our data owners, not custodians and our chief data officer and our data governance council that was established. >> Christopher you're learnings, takeaways? >> What we're trying to build upon is, you define your problem statement, but the pathway there is you have to get results in front of the end users. You have get them to the people who are doing the work, so you can keep guiding it toward the solution actually meets all the needs, as well as build something that can innovate continuously over time. Because the technology space is changing so quickly and dynamically that the more we can surface our problem set, the more help we can to help find ways to navigate through that. >> So one of the things you said is that you're using data to look at the past. Whereas, so many of the guests we're talking today and so many of the people here at this summit are talking about using data to predict the future. Are you able to look your data sets from the past and then also sort of say, And then this is how we can prevent more POW. Are you using, are you thinking at all, are you looking at the future at all with you data? >> I mean, certainly especially from our laboratory science perspective, we have have probably the most advanced human identification capability in the world. >> Right. >> And recovery. And so all of those lessons really go a long ways to what what information needs to be accessible and actionable for us to be able to, recover individuals in those circumstances and make those identifications as quickly as possible. At the same time the cases that we're working on are the hardest ones. >> Right. >> The ones that are still left. But each success that we have teaches us something that can then be applied going forward. >> What is the human side of your job? Because here you are, these two wonky data number crunchers and yet, you are these are people who died fighting for their country. How do you manage those two, really two important parts of your job and how do you think about that? >> Yeah, I will say that it does amp up the emotional quotient of our agency and everybody really feels passionately about all the work that they do. About 10 times a year our agency meets with family members of the missing at different locations around the country. And those are really powerful reminders of why we're doing this. And you do get a lot of gratitude, but at the same time each case that's waiting still that's the one that matters to them. And you see that in the passion our agency brings to the data questions and quickly they want us to progress. It's never fast enough. There's always another case to pursue. So that definitely adds a lot to it, but it is very meaningful when we can help tell that story. And even for a case where we may never have the answers, being able to say, "This is what the government knows about your case and these are efforts that have been undertaken to this point." >> The fact there's an effort going on is really a wonderful thing for everybody involved. Good outcomes coming out from that. But interesting angle as a techy, IT, former IT techy back in the day in the '80s, '90s, I can't help but marvel at your perspective on your project because you're historians in a way too. You've got type punch cards, you know you got, I never used punch cards. >> Put them in a museum. >> I was the first generation post punch cards, but you have a historical view of IT state of the art at the time of the data you're working with. You have to make that data actionable in an outcome scenario workload work-stream for today. >> Yeah, another example we have is we're reclaiming chest X-rays that they did for induction when guys were which would screen for tuberculosis when they came into service. We're able to use those X-rays now for comparison with the remains that are recovered from the field. >> So you guys are really digging into history of IT. >> Yeah. >> So I'd love to get your perspective. To me, I marvel and I've always been critical of Washington's slowness with respect to cloud, but seeing you catch up now with the tailwinds here with cloud and Amazon and now Microsoft coming in with AI. You kind of see the visibility that leads to value. As you look back at the industry of federal, state, and local governments in public over the years, what's your view of the current state of union of modernization, because it seems to be a renaissance? >> Yeah, I would say the analogy I would give you it's same as that of the industrial revolutions went through in the early 20th century, but it's more about the technology revolution that we're going through now. That's how I'd probably characterize it. If I were to look back and tell my children's children about, hey, the advent of technology and that progression of where we're at. Cloud architecture certainly take down geographical barriers that before were problems for us. Now we're able to overcome those. We can't overcome the timezone barriers, but certainly the geographical barriers of separation of an organization with cloud computing has certainly changed. >> Do you see your peers within the government sector, other agencies, kind of catching wind of this going, Wow, I could really change the game. And will it be a step function into your kind of mind as you kind of have to project kind of forward where we are. Is it going to a small improvement, a step function? What do you guys see? What's the sentiment around town? >> I'm from Hawaii, so Chris probably has a better perspective of that with some of our sister organizations here in town. But, I would say there's more and more organizations that are adopting cloud architectures. It's my understanding very few organizations now are co-located in one facility and one location, right. Take a look at telework today, cost of doing business, remote accessibility regardless of where you're at. So, I'd say it's a force multiplier by far for any line of business, whether it's public sector, federal government or whatever. It's certainly enhanced our capabilities and it's a force multiplier for us. >> And I think that's where the expectation increasingly is that the data should be available and I should be able to act on it wherever I am whenever the the opportunity arises. And that's where the more we can democratize our ability to get that data out to our partners to our teams in the field, the faster those answers can come through. And the faster we can make decisions based upon the information we have, not just the process that we follow. >> And it feeds the creativity and the work product of the actors involved. Getting the data out there versus hoarding it, wall guarding it, asylumming it. >> Right, yeah. You know, becoming the lone expert on this sack of paper in the filing cabinet, doesn't have as much power as getting that data accessible to a much broader squad and everyone can contribute. >> We're doing our part. >> That's right, it's open sourcing it right here. >> To your point, death by PowerPoint. I'm sure you've heard that before. Well business intelligence software now by the click of a button reduces the level of effort for man-power and resources to put together slide decks. Where in business intelligence software can reach out to those structured data platforms and pull out the data that you want at the click of a button and build those presentations for you on the fly. Think about, I mean, if that's our force multiplier in advances in technology of. I think the biggest thing is we understand as humans how to exploit and leverage the technologies and the capabilities. Because I still don't think we fully grasp the potential of technology and how it can be leveraged to empower us. >> That's great insight and really respect what you guys do. Love your mission. Thanks for sharing. >> Yeah, thanks so much for coming on the show. >> Thank you for having us. >> I'm Rebecca Knight for John Ferrer. We will have much more coming up tomorrow on the AWS Public Sector Summit here in Washington, DC. (upbeat music)

from Boston Massachusetts it's the cube covering wtg transform 2018 brought to you by Winslow technology group welcome back I'm Stu minimun and this is the cube coverage of wdg transform 2018 I'm happy to welcome back to the program probably an interesting who's come all the way from Boston University he said three blocks away about three blocks why yes all right Brian's the director of College of Arts and Sciences information technology great to see you again thank you all right back so good news is we spoke it was just about a year ago it was August last year it's June this year I'm sure nothing's changed in your environment you know students never change technology never changes there's a little bit of change on your end a little bit a little bit last year we'd spoke of quite a bit about hyperconvergence and what's that's gonna mean in terms of Education and how we deliver that and what the experience could be like for these students and I think at this point we're satisfied with everything that Nutanix has brought to us we've deployed VDI and a couple of large deployments for whole bunch of classes so we decided to reassess and reevaluate work what we're doing this year and now we move on to application development that's great so we get many ways they say you need to modernize your platform and then once you do that we can look at what the long haul 210 which is really at the application side right exactly once we knew what we had what we could possibly do with it we decided to move forward and figure out what else can we change and we had a lot of legacy applications for the business and so this past year we hired a developer who's focusing solely on docker izing our applications so we're deploying docker and a whole bunch of applications within the college and then we're going to be doing kubernetes deployment later this year ok and let's be clear where does this live you know is this on the Nutanix platform is it in you know service riders public clouds where does this span because kubernetes can live in all of those environments in the containerized stuff at Casa and currently it's all contained within a handful of VMs within our Nutanix server environment ok we're planning on looking at calm and use using natural blueprints to deploy kubernetes and docker down the road ok so I've got the Nutanix platform what hypervisor am i using HP ok so using the HP using which of courses Newt annexes comes on on the platform and then you know in the VMS you're using containers we are um have you looked at bare metal um you know because that's one of the discussions is like well if I'm doing containers you know do I just do that on Linux on bare metal or do I do it virtual is a virtualized and there's there's pluses and minuses for each of those we did a few of the pluses that my sis had means really enjoy is when our developer is going to go crazy and do new things we can make snapshot so if he happens to do something to the environment we can restore it in ten minutes and I think as far as my developer is concerned he doesn't want to have to rebuild the environment every time he makes a mistake he's had a few close calls so far and having HP and the ability to snapshot restore it's been awesome for him okay what insight can you give us about what you know what sort of applications are they building and you said Dockers in two minute Kruger burn Eddie's you know are they building their own stack are they leveraging you know how are they getting to that state well we're taking some business apps that were focusing on both student and faculty applications dealing with various components of each and he's pulling them apart to figure out what components go into the docker containers what do we have to still reside in VMs for security and long-term use and try to figure out how to reimagine the application stack to move forward we're starting to look at reusing components that he's developing and I'm hoping that we have a lot of pieces that we can do that with so we have a lot of applications to rewrite okay and just to drill in a little bit because I've got we've got a team of the cube that's gonna be at docker con next week I've been go to the kubernetes show for a while so when you say docker are you using just the free containers which is now called mobi or using the dr. CEO as part of that I actually can't tell you that because that's miss all my developers work I did so they're using docker as you said it's like the Kleenex and do you know from kubernetes standpoint have they just built their own do you have a distribution or a platform that you just do Tanic we just downloaded the distro from kubernetes instead of a small cluster himself we're going to be looking at using calm to do a deployment on their channels natively okay really interesting stuff what what is you know you talked a bit about you know you can give a little bit of stability and recovery and things like that for your developers to be able to play in that sandbox is what gives us a little bit of the roadmap as to you know how long do they play with this and then you know how does this roll out for the university so we're looking at probably a three to six month development cycle on a lot of new applications right now part of my developers job is to try to figure out how this environments going to work my sis admins are deeply engaged with him but since most of doctrine kubernetes is developed with faced he has to do most of the legwork and figure out how it's all gonna work and so we're hoping to leverage Nutanix to have multiple environments all with the same back-end so we have dev tests and production all in the same hardware but different pieces of actually physical clusters that'll be separated so he doesn't mess around the production all too much but set up a baseline that we can use to short that development cycle even further yeah one of the things we always look at is right you've got your developers doing their thing how does that fit with the operation side is it DevOps even I interviewed Solomon hikes last year that was the founder of docker and he said actually it was an operation mindset that I had when I created this container format how are you seeing it's actually great you're all working together you're you're in discussion there do you have a DevOps rollout and what you're doing or you do you keep it separate I still keep them somewhat separate but my administrators are writing a little bit more code and scripting than they used to and I think in general that's going to be the in the entire industry where you can't just look at and have your developer do everything in docker and not understand how it works Brian talk to us about your partners for doing this how involved are the likes of Nutanix and Winslet technology and you know in Dell in this discussion of the containers agent and your developers Nutanix we've been utilizing a lot of documentation and we're gonna be leveraging them a lot when we start to look at com Winslow's we haven't really talked to them about it to be honest we probably should because they might have some ideas and other partners we can talk to Dell in it there's really just a hardware to run everything on that's stable we don't have to worry about it I'm so happy with that yeah that's not in any you know oh I don't need to worry about them there's certain pieces we always look at and I'd love your feedback on this if you know when we virtualized first and now even when we containerize how much don't I need to worry about the infrastructure I mean remember back you know 15 years ago it's like oh I'll virtualized that well have you checked the BIOS because the BIOS might not work and the server could break things the OS could cause problem you know virtualization relatively stable these days how are you finding the container stuff it's really interesting and very very unique to virtualize a virtualized environment even further it's it's kind of mind-blowing just I've been doing this for twenty years and this is much further than I've ever expected the industry to go oh yeah just wait and it's you go even further than kubernetes it's like wait is it on top of underneath or side by side with the technologies you're doing from a Cooper nettie standpoint you said today it's all in the note annex what's the value of kubernetes for you is it just kind of the cluster orchestration of containers or you know are you is its portability a piece even part of the concern that you look at there oh it's it's mostly from portability part of the applications that we're looking at down the road are going to be vertical applications especially some student facing ones and certain times of the year we're gonna have to go from maybe a hundred people logged in to several thousand at the same time so we're hoping to stand up something that we can easily move to a cloud provider and still work the same way that we're expecting it to and so I think kubernetes along with the orchestration internally on-prem it's gonna be a huge benefit for us to know the environment it's gonna be exactly the same when we move it to Amazon or Google or adder all right so so Brian you're still kind of in the thick of it here but from what you've learned so far any any learnings or things that you'd recommend to your peers that oh wait if I could turn back the clock three months I might have adjusted or pointed things in a different direction yes yeah well when our developer started he focused more on getting an application up and running before starting to learn docker I would encourage anybody that's just starting down the road get your developer learning doctor and kubernetes first because they might want to rewrite what they're doing in the application okay well Brian this has been fascinating want to give you the final word is that you look out through the rest of the year so it's a lot you know so far since last time we talked but by the time we come around next year you'll be all serverless and you know deploying things off side the globe I'm assuming but I have no idea if you told me your ago that we're gonna be doing what we're doing now I wouldn't believe you it's it's a fantastic journey it's it's amazing what we learn every day all right well Brian appreciate you sharing some of the learnings as we go it's one of the reasons we come to events like this I know yourself to talk to your peers here what's going out thank you for moving forward with thank you all right plus more coverage here at wtg transform 2018 I'm Stu minimun and thanks for watching the Q

>> Narrator: Live from the Bill Graham Auditorium in San Francisco. It's the Cube. Covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. >> Welcome back to Pure Storage Accelerate 2018. I'm Lisa Martin with the Cube. I'm with Dave Vellante. We are here in San Francisco at the Bill Graham Civic Auditorium which is why we're sporting some concert t-shirts. >> Who. >> The Who and the Clong. >> Roger. Roger Delchi. >> Roger. We are here with the CIO of the David Geffen School of Medicine at UCLA, Pure customer, Ben Nathan. Ben, welcome to the Cube. Thanks for having me. So, talk to us about the shool of medicine at UCLA. You are the CIO there, you've been there for about three years. Give us a little bit of the 10,000 foot view of what your organization looks like to support the school of medicine. >> Sure. We're about 170 people. We have changed a lot over the last three years. So, when I got to UCLA there was 25 separate IT organizations, all smaller groups, operating in each individual department. And, they had built their own sets of managed infrastructure, distributed throughout every closet, nook and cranny in the school. We've consolidated all that under one set of service lines, one organization, and that's including consolidating all the systems and applications as well. So, we've brought all those together and now we're additionally running IT for three more health sciences schools at UCLA, nursing, dentistry, and school of public health, Fielding School of Public Health. Like a lot of CIOs, you serve many masters. You got the administration, you got the students, right. You've got the broader constituency. The community, UCLA. Where do you start? What's the quote on quote customer experience that you're trying to achieve? That's a great way to put it. There's really sort of four pillars that we try to serve. The patient being first and foremost. So, for us, everything is built around a great patient experience. And, that means that when we're educating students it's so they can be great providers of patient care. When we're doing research, When we're doing that research in an effort to eradicate disease et cetera. And, when we're doing community outreach it's also around improving health and peoples lives, so, in IT, we try to stay very connected to those missions. I think it's a large part of what drives people to be a part of an organization that's healthcare or that's a provider. That mission is really, really important. So, yes. We're serving all four of those things at once. >> So, you had lots of silos, lots of data, that's all continuing to grow but, this is data that literally life and death decisions can be made on this. Talk to us about the volumes of data, all the different sources that are generating data. People, sensors, things and how did you make this decision to consolidate leveraging Pure Storage as that foundation? >> Yeah, there's and incredible amount of work going on at UCLA. Particularly in their research education and patient care spaces. We had every brand of server in storage that you've never heard of. Things bought at lowest, bitter methods but, the technical data that we had incurred as part of that was enormous. Right, it's unsustainable. It's unsupportable. It's insecure-able. When I got there and we started to think about how do we deal with all of this? We knew we had an opportunity to green field an infrastructure and consolidate everything onto it. That was the first, that was started us down the road that led us to Pure as one of our major storage vendors. I had worked with them before but, they won on their merits, right? We do these very rigorous RFP processes when we buy things. The thing that really, I think, got them the the victory is us is that the deduplication of data got us to something like an eight to one ratio of virtual to physical. So, we get a lot of virtual servers running on relatively small amount of storage. And, that it's encrypted you know, sort of the time, right? There's not like a switch you might flip or something a vendor says they'll do but it >> Always on. >> doesn't really do, it is always on. And, it's critical for us. We're really building a far more secure and manageable set of services and so all the vendors we work with meet that criteria. >> So, is as a CIO, I would imagine you don't want to wake up every day and think of storage. With all due respect to our friends at Pure. >> That's true. >> So, has bringing it in for infrastructure in, like Pure, that prides itself on simplicity, allowed you to do the things that you really want to do and need to do for your organization? >> Yeah. I'll give you a two part answer. I mean one is simply, I think, it's operationally a really great service. I think that it's well designed, and run, and managed. And, we get great use of out it. I think the thing that makes it so that I don't have to think about it is actually, the business model that they have. So, the fact that I know that it's not going to really obsolete on its own, as long as you're like in the support model, you're upgrading the system every few years, changes, you know the, model for me, 'cause I don't have to think about these new, massive capitalization efforts, it's more of a predictable operational costs and that helps me sleep well because I know what we look like over the next few years and I can explain that to my financial organization. >> Just a follow up on that, a large incumbent storage supplier or system vendor might say, "Well, we can make that transparent to you. We can use our financial services to hide that complexity or make a cloud-like rental experience or you know, play financial games to hide that. Why does that not suffice for you? >> Well, I think, first and foremost we sort of want to run our financials on our own and we're pretty anxious about having anyone else in the middle of all that. Number two is it seems to me different in terms of Pure having built that model from the ground up as part of their service offerings. So, I don't think we see that with too many other vendors and I think that obviously there's far less technical than what I had in the previous design but it can still add up if you're not careful about whatever, what server mechanism you have in place, et cetera. >> But, it eliminates the forklift upgrade, right. Even with those financial incentives or tricks, you still got to forklift it and it's a disruption to your operation. >> Yeah, and I'm sure that's true, yeah. >> So, when you guys were back a year and a half or so, maybe two years ago, looking at this consolidation, where were your thoughts in terms of beyond consolidation and looking at being able to harness the power of AI, for example, we heard a lot of AI today already and this need for legacy infrastructures are insufficient to support that. Was that also part of your plan, was not simply to consolidate and bring your (speaks very rapidly) environment unto Pure source but also to leverage a modern platform that can allow you to harness the power of AI? >> Yeah. That was sort of the later phase bonus period that we're starting to enter now. So, after we sort of consolidate and secure everything, now, we can actually do far more interesting things that would've been much more difficult before. And, in terms of Pure, when we had set out to do this we imagined doing a lot of our analytics and AI machine learning kind of cloud only and we tried that. We're doing a lot of really great things in the cloud but not all of it is makes sense in that environment. Either from a cost perspective or from a capabilities perspective. Particularly with what Pure has been announcing lately, I think there's a really good opportunity for us to build high performance computing clusters in our on premise environment that leverage Pure as a potential storage back end. And that's where our really interesting data goes. We can do the analytics or the AI machine learning on the data that's in our electronic medical record or in our genomics workflows or things like that can all flow through a service like that and there's some interesting discoveries that ought to come from it. >> There's a lot of talk at this event about artificial intelligence, machine intelligence, how do you see AI in health care, generally? And specifically, how you're going to apply it? Is it helping doctors with diagnosis? Is it maybe maintaining better compliance? Or, talk about that a little. >> I think there's two things that I can think of off the top of my head. The first is decision support. So this is helping physicians when they're working directly with patients there's only, there's so many systems, so many data sets, so many way to analyze, and yet getting it all in front of them in some kind of real time way so that they can use it effectively is tricky. So, AI, machine learning, have a chance to help us funnel that into something that's immediately useful in the moment. And then the other thing that we're seeing is that most of the research on genomics and the outcomes that have resulted in changes to clinical care are around individualized mutations in a single nucleotide so there's, those are I guess, quote, relatively easy for a researcher to pick out. There's a letter here that is normally a different letter. But, there are other scenarios where there's not a direct easy tie from a single mutation to an outcome. so, like in autism or diabetes, we're not sure what the genetic components are but we think that with AI machine learning, those things will start to identify patterns in genomic sequences that humans aren't finding with their typical approaches and so, we're really excited to see our genomic platforms built up to a point where they have sequences in them to do that sort of analysis and you need big compute, fast storage to do that kind of thing. >> How is it going to help the big compute, fast storage, this modern infrastructure, help whether its genomics or clinicians be able to sort through masses amounts of data to try to find those needles in the haystack 'cause I think the staff this morning that Charlie Jean and Carla mentioned was that half a percent of data in the world is analyzed. So, how would that under the hood infrastructure going to help facilitate your smart folks getting those needles in the haystack just to start really making big impacts? >> UCLA has an incredible faculty, like brilliant researchers, and sometimes what I've found since I've gotten there, the only ingredient that's missing is the platform where they can do some of this stuff. So, some of them are incredibly enterprising, they've built their own platforms for their own analysis. Others we work with they have a lot of data sets they don't have a place to put them where they can properly interrelate them and do, apply their algorithms at scale. So, we've run into people that are trying to do these massive analysis on a laptop or a little computer or whatever it just fails, right? Or it runs forever. So, giving them, providing a way to have the infrastructure that they can run these things is really the ingredient that we're trying to add and so, that's about storage and compute, et cetera. >> How do you see the role of the CIO evolving? We hear a lot of people on the Cube and these conferences talk about digital transformation and the digital CIO, how much of that is permeating your organization and what do you think it means to the CIO world going forward? >> I wish I knew the real answer to that question. I don't know, time will tell. But, I think that certainly we're trying to follow the trends that we see more broadly which is there's a job of keeping the lights on of operations. And you're not really, you shouldn't have a seat at any other table and so those things are quite excellent. >> Table stakes. >> Yeah. Right. Exactly, table stakes. Security, all that stuff. Once, you've got that, you know, my belief is you need to deeply understand the business and find your way into helping to solve problems for it and so, you know, our realm, a lot of that these days is how do we understand the student journey from prior to, from when they maybe want to apply all the way 'til when they go out and become a resident and then a physician. There's a ton of data that's gathered along that way. We got to ask a lot of questions we don't have easy answers to but, if we put the data together properly, we start to, right? On the research side, same sort of idea, right? Where the more we know about the particular clinical outcomes they're trying to achieve or even just basic science research that they're looking into, the better that we can better micro target a solution to them. Whether it's a on prem, private cloud, or public cloud, either one of those can be harnessed for really specific workloads and I think when we start to do that, we've enabled our faculty to do things that have been tougher for them to do before. Once, we understand the business in those ways I think we really start to have an impact at the strategic level, the organization. >> You've got this centralized services model that was a strategic initiative that you put in place. You've got the foundation there that's going to allow you to start opening up other opportunities. I'm curious, in the UCLA system, maybe the UC system, are there other organizations or schools that are looking at what you're doing as a model to maybe replicate across the system? >> I think there's I don't know about a model. I think there's certainly efforts among some to find, to centralize at least some services because of economies to scale or security or all the normal things. With the anticipated, and then anticipating that that could ultimately provide more value once the baseline stuff is out of the way. UC is vast and varied system so there's a lot of amazing things going on in different realms and we're I think, doing more than ever working together and trying to find common solutions to problems. So, we'll see whose model works out. >> Well, Ben. Thanks so much for stopping by the Cube and sharing the impact that your making at the UCLA School of Medicine, leveraging storage and all the different capabilities that that is generating. We thank you for your time. >> Thanks so much for having me. >> We want to thank you for watching the Cube. I'm Lisa Martin with Dave Vellante. We are live at Pure Accelerate 2018 in San Francisco. Stick around, we'll be right back with our next guest.

>> Narrator: Live from Las Vegas, it's the Cube, covering VMWorld 2017. Brought to you by VMWare and its ecosystem partners. (bright music) >> Hi, I'm Stu Miniman with the Cube, here with my guest host, Justin Warren. Happy to have a returning Cube alum, but in a different role then we had. It's been a few years. Tal Klein, who is the author of The Punch Escrow. >> Au-tor, please. No, I'm just kidding. (laughing) Tal, thanks so much for joining us. It's great for you to be able to find time to hang out with the tech geeks rather than all the Hollywood people that you've been with recently. (laughing) >> You guys are more interesting. (laughing) >> Well thank you for saying that. So last time we interviewed you, you were working for a sizable tech company. You were talking about things like, you know, virtualization, everything like that. Your Twitter handle's VirtualTal. So how does a guy like that become not only an author but an author that's been optioned for a movie, which those of us that, you know, are geeks and everything are looking at, as a matter of fact, Pac Elsiger this morning said, "we are seeing science fiction become science fact." >> That's right. >> Stu: So tell us a little of the journey. >> Yeah, cool, I hope you read the book. (laughing) I don't know, the journey is really about marketing, right? Cause a lot of times when we talk about virtual, like, in fact last time I was on the Cube, we were talking about the idea that desktops could be virtual. Cause back then it was still this, you know, almost hypothetical notion, like could desktops be virtual, and so today, you know, so much of our life is virtual. So much of the things that we do are not actually direct. I was watching this great video by Apple's new augmented reality product, where you sit in the restaurant and you look at it with your iPad, and it's your plate, and you can just shift the menu items, and you see the menu items on your plate in the context of the restaurant and your seat and the person you're sitting across from. So I think the future is now. >> Yeah, it reminds of, you know, the movie Wall-E, the animated one. We're all going to be sitting in chairs with our devices or Ready Player One, you know, very popular sci-fi book that's being done by Speilberg, I believe. >> Yes, yeah, very exciting. >> Tell us a little bit about your book, you know, we talked, when I was younger and used to read a lot of sci-fi, it was like, what stuff had they done 50 years ago that now's reality, and what stuff had they predicted, like, you know, we're going to go away from currency and go digital currency, and it's like we're almost there. But we still don't have flying cars. >> Yeah, we're, I mean, the main problem with flying cars is that we need pilots. And I think actually we're very close to flying cars, cause once we have self-driving vehicles and we no longer need to worry about it being a person behind the joystick, then we're in really good shape. That's really the issue, you know, the problem with flying cars is that we are so incompetent at driving and or flying. That's not our core competency, so let's just put things that do understand how to make those things happen and eliminate us from the equation. >> Everything is a people problem. >> Yeah, so when I wrote the book, Punch Escrow, Punch Escrow, (laughing) when I wrote the book, I really thought about all the things that I read growing up in science fiction, you know, things like teleportation, things like nanotechnology, things like digital currency, you know, how do we make those, how do we present those in a viable way that doesn't seem too science fictiony. Like one of the things I really get when people read the book is it feels really near-future, even though it's set like 100 plus years in the future, all the concepts in it feel very pragmatic or within reach, you know? >> Yeah, absolutely. It's interesting, we look at, you know, what things happen in a couple of years and what things take a long time. So artificial intelligence, machine learning, it's not like these are new concepts, you know? I read a great book by, you know, it was Isaacson, The Innovators. You go back to like Aida Lovelace, and the idea of what a machine or computer would be able to do. So 100 years from now, what's real, what's not real? We still all have jobs or something? >> We have jobs but different. Remember, I don't know if you're a historian, but back in the industrial age, there was a whole bunch of people screaming doom and gloom. In fact, if we go way back to the age of the Luddites, who just hated machines of any kind. I think that in general, we don't like, you know, we're scared of change. So I do think a lot of the jobs that exist today are going to be done by machines or code. That doesn't mean the jobs are going away. It means jobs are changing. A lot of the jobs that people have today didn't exist in the industrial age. So I think that we have to accept that we are going to be pragmatic enough to accept the fact that humans will continue to evolve as the infrastructure powering our world evolves, you know? We talk about living in the age of the quantified self, right? There's a whole bunch that we don't understand how to do yet. For example, I can think of a whole industry that tethers my FitBit to my nutrition. You know, like there's so much opportunity that for us to say, oh that's going to be the end of jobs, or the end of innovation or the end of capitalism, is insane. I think this just ushers in a whole new age of opportunity. And that's me, I'm just an optimist that way, you know. >> So the Luddites did famously try to destroy the machines. But the thing is, the Luddites weren't wrong. They did lose their jobs. So what about the people whose jobs are replaced, as you say net new, there's a net new number of jobs. But specific individuals, like people who manufacture cars for example, lose their jobs because a robot can do that job safer and better and faster than a human can do it. So what do we do with those humans? Because how do we get people to have new jobs and retrain themselves? >> I address some of these notions in the book. For example, one of the weird things that we're suffering from is the lack of welders in society today, cause welding has become this weird thing that we don't think we need people for, so people don't really get trained up in it because, you know, machines do a lot of welding but there's actually specialty welding that machines can't do. So I think the people who are really good at the things that they do will continue to have careers. I think their careers will become more niche. Therefore they'll be able to create, to demand a higher wage for it because almost like a carpenter, you know, a specialist carpenter will be able to earn a much higher wage today by having fewer customers who want really custom carpentry versus things that can be carved up by a machine. So I think what we end up seeing is that it's not that those jobs go away. It's they become more specialized. People still want Rolls Royces. People still want McLarens. Those are not done by machines. Those are hand-made, you know? >> That's an interesting point, so the value of something being hand-made becomes, instead of it being a worse product, it's actually- >> Tal: That's a big concept in the book. >> Oh okay, right. >> A big concept in the book is that we place a lot of value on the uniqueness of an object. And that parlays in multiple ways. So one of the examples that I use in the book is the value of a Big Mac actually coming from McDonald's. Like, you can make a Big Mac. We know the recipe for a Big Mac. But there is a weird sort of nacent value to getting a Big Mac from McDonald's. It's something in our brain that clicks that tethers it to an originality. Diamonds, another really good example. Or you know, we know there's synthetic diamonds. We still want the ones that get mined in the cave. Why? We don't know. Right, they're just special. >> Because De Beers still has really good marketing. (laughing) >> So I think there's- >> That's interesting, so the concept of uniqueness, which again comes to scarcity and so on. As an author, someone who is no doubt, signed a lot of his book, that means that that book is unique because it's signed by the author, unlike something which is mass produced and there is hopefully thousands and thousands of copies that you sell. >> Going into this, I actually thought about that a lot. And that's why I've created like multiple editions of the book. So like the first 500 people who pre-ordered it, they get like a special edition of the book that's like stamped and all this kind of stuff. I even used different pens. (laughs) I appreciate that because I'm also a collector. I collect music, I collect books. And you know, so I see those aspects in myself. So I know what I value about them, you know? >> And the crossover between music and books is interesting. So as someone who has a musical background, I know that there's a lot of musicians who'll come out with special editions, and you know, because this is an age where we can download it. You can download the book. Do you think there is something, is there something that is intrinsic to having a physical object in a virtual world? >> I think to our generation, yes. I'm not so sure about millennials, when they grow up. But there are, for example, I'm going to see U2 next week, I'm very lucky to see that. But part of the U2 buying experience, to get access to the presale, you need to be part of their fan club. To be a part of their fan club, you need to get, you get like a whole bunch of limited edition posters, limited edition vinyl, and all this kind of stuff. So there's an experience. It's no longer just about going to see U2 at a concert. There's like the entire package of you being a special U2 fan. And they surround it with uniqueness. It's not necessarily limited, but there's an enhanced experience that can't just be, it's not just about you having a ticket to a single concert. >> Justin: Yeah, okay. >> I'm curious, the genre, if you'd call it, is hard science fiction. >> Yes. >> The challenge with that is, you know, what is an extension of what we're doing, and what is fiction? And people probably poke at that. Have you had any interesting experience, things like that? I mean, I've listened to a lot of stuff like Andy Weir, like let the community give feedback before he created the final The Martian. (laughing) But so yeah, what's it like, cause we can, the geeks can be really harsh. >> Yes, I've learned from my Reddit experience that, so what's really funny about it is the first draft of this novel was hard as nails. It was crazy. And my publisher read it, and it would have made all the hard science fiction guys super happy. My publisher read it, he was like, you've written a really great hard science fiction book, and all five people who read it are going to love it. (laughing) You know, but like, I came here with my buddy Danny. He couldn't even get through the first three pages of it. He's like, he wanted to read it. So part of working through the editorial process is saying, look, I care a lot about the science because one of my deep goals is to write a STEM-oriented book that gets people excited about technology and present the future as not a dystopian place. And so I wanted the science to be there and have a sort of gravity to the narrative. But yeah, it's tough. I worked with a physicist, a biologist, a geneticist, an anthropologist, and a lawyer. (laughs) Just to try to figure out, how do we carve out, you know, what does the future look like, what does the evolution of each individual sciences, we talked about the mosquitoes, right? You know, we're already doing a lot of crazy stuff with mosquitoes. We're modifying them so that the males mate with females that carry the Zika virus, you know, give birth to offspring that never reach maturity. I mean, this is just crazy, it's science fiction. And now that they're working on modifying female mosquitoes into vaccine carriers instead of disease carriers. I mean, this is science fiction, right? Like who believes this stuff? It's crazy. >> Christopher is amazing. >> Yeah, I've loved, there's been a bunch of movies recently that have kind of helped to educate on STEM some, you know, Martian got a lot of people excited, you know, Hidden Figures, the one that I could being my kids that are teenagers now into it and they get excited, oh, science is great. So the movie, how much will you be involved? You know, what can you share about that experience, too, so far? >> It's been, it's very surreal. That's the word is use to describe it, the honest, god's honest truth, I mean. I've been very lucky in that my representation in Hollywood is this rock-solid guy called Howie Sanders. And he's this bigger-than-life Hollywood agent guy. He's hooked me up, we've made a lot of business decisions that we're focused less on the money and more on the team, which is nice to be, like when you're in your 40s and you're more financially settled, you're not in the kind of situation where you might be in your 20s and just going to sign the first deal that people give you. So we really focused on hooking up with like the director, James Bovin is, you know, he's the guy who co-created Flight of the Concords. He did the Muppets movie, you know, Alice Through the Looking Glass. Really professional guy but also really understands the tone of the book, which is like humorous, you know, kind of sarcastic. It's not just about the technology. It's also about the characters. Same thing with the production team. The two producers, Mandeville Productions, I was just talking to Todd Lieberman, and we're talking about just what is augmented reality, like how does it look like on the screen? So I'm not- >> It's not going to look like Blade Runner is what I'm hearing. >> (laughs) I don't know. It's going to look real. I imagine, I don't know, they're going to make whatever movie they're going to make, but their perspective, one of the things we talked about is keeping the movie very grounded. Like you know, one of the big questions they ask first going into it is before we even had any sort of movie discussions is like is this more of like a Looper, Gattica, or District Nine, or is it more like The Fifth Element, you know, I mean, is it like, do you want it to be this sort of grounded movie that feels authentic and real and near future or do you want this to be like completely alien and weird and out of it. And the story is more grounded. So I think a lot, hopefully what we display on the screen will not feel that far away from reality. >> Okay, yeah. >> You do marketing in your day job. >> I do. >> I'm curious as you look at this, kind of the balance of educating, reaching a broad audience, you have passion for STEM, what's your thoughts around that? Is it, I worry there's so much general, like television or things like that, when I see the science stuff, it like makes me groan. Because you know, it's like I don't understand that. >> I am the worst, because I got a security background too, so that's the one I get scrambled on. The war, I mean, like. >> Wait, thank goodness I updated my firewall settings because I saved the world from terrorists. >> Hang on, we're breaking through the first firewall. Now we're through the second firewall. (laughing) Now we're going through the third firewall, like 15 firewalls. And let me upload the virus, like all that stuff. It's difficult for me. I think that, you know, hopefully, there's also a group in Hollywood called the Hollywood Science and Entertainment Exchange. And they're a group of scientists who work with film makers on, you know, reigning things in. And film makers don't usually take all their advice, i.e. Interstellar, (laughing) but you know, I think (laughing) in many cases there's some really good ideas that come to play into it that hopefully bring up, like I think Jarvis for example, in Iron Man or the Avengers is a really cool implementation of what the future of AI systems might be like. And I know they used the Hollywood Science Exchange to figure out how is that going to work? And I think the marketing aspect is, you know, the reason I came up with the idea for this book is because my CEO of a company I used to work for, he had this whole conversation about teleportation, like teleportation was impossible. And he's like, it's not because the science, yes, the science is a problem right now, but we'll get over it. The main issue is that nobody would ever step foot into a device that vaporizes them and then printed them out somewhere else. And I said, well that's great, cause that's a marketing problem. (laughing) >> Yeah, you're dead every time you do it. But it's the same you, I can't tell the difference. >> Well, you say you're dead, I'm saying you're just moving. (laughing) >> Artificial intelligence, you know, kind of a big gap between the hype to where we need to go. What's your thoughts on that space in general? >> I think that we have, it's a great question because I feel like that's a term that gets thrown around a lot, and I think as a result it's becoming watered down. So you've this sort of artificial intelligence that comes with like, you know, Google building an app that can beat the world's best Go player, which is a really, really difficult puzzle. The problem is, that app can do one thing, and that's play Go. You put in it a chess game, and it's like I don't know what's going on. >> It's a very specialized kind of intelligence, yeah. >> Now with Open AI, you know, they just had some pretty interesting implementations where they actually played video games with a real live competition and won. Again, you know, but without the smack talk, which really I think would add a lot. Now you got to get an AI to smack talk. So I think the problem is we haven't figured out a really good way of creating a general purpose AI. And there's a lot of parallels to the evolution of computing in general because if you look at how computers were before we had general purpose operating systems like Unix, every computer was built to do a very, very specific function, and that's kind of what AI is right now. So we're still waiting to have a sort of general purpose AI that can do a lot of specialized activities. >> Even most robots are still very single-purpose today. >> That's the fundamental problem. But you're seeing the Cambridge guys are working on sort of the bipedal robot that can do lots of things. And Siri's getting better, Cortana's getting better, Watson's getting better, but we're not there. We still need to find a really good way of integrating deep knowledge with general purpose conversational AI. Cause that's really what you need to like, Stu, what do you need? Here, let me give it to you, you know? >> Do you draw a distinction between AI that's able to simply sort of react as a fairly complex machine or something that can create new things and add something? >> That's in the book as well. So the fundamental thing that I don't think we get around even in the future is giving computers the ability to actually come up with new ideas. There's actually a career, the main job of the protagonist in the book, his job is a salter. And his job is to salt AI algorithms to introduce entropy so they can come up with new ideas. >> Okay, interesting. >> So based off the sort of chaos theory. >> Like chaos monkey, right? >> Yeah. And that's really what you're trying to do is like, okay, react to things that are happening because you can't just come up with them on their own. There's a whole, I don't want to bore you, but there's a whole bunch of stuff in the book about how that works. >> It's like hand-carving ideas that are then mass produced by machines. >> Yeah, I don't know if you guys are going to have Simon Crosby on here, he's kind of like an expert on that. He was the Dean of Kings College, which is where Turing came from. So he really knows a lot about that. He's got a lot of strong ideas about it. But I learned a lot from him in that regard. There's a lot of like, the snarky spirit of Simon Crosby lives on in my book somewhere. But he's just funny cause he's, coming from that field, he immediately sees a lot of BS right off the bat, whenever anybody's presenting. He's got like the ability to just cut through it. Because he understands what it would actually take to make that happen, you know? So I tried to preserve some of that in the book. >> That is refreshing in the tech industry. >> So Tal, I need to let you, you know, wrap this up. Give us a plug for the book, tell us, when are we going to be able to see this on the big screen? >> I don't know about the big screen, but the Punch Escrow is now available. You can get it on Amazon, Barnes and Noble, anywhere books are sold. It's been optioned by Lionsgate. The director attached to it is James Bovin, production team is Mandeville Productions. I'm very excited about it. Go check it out. It's a pretty quick read, reads like a technothriller. It's not too hard. And it's fun for the whole family. I think one of the coolest things about it is that the feedback I've been getting has been that it really is appealing to everybody. I've got mother-in-laws reading it, you know, it's pretty cool. Initially I sold it, my initial audience is like us, but it's kind of cool, like, Stu will finish the book, he'll give it to, you know, wife, daughter, anything, and they're really digging it. So it's kind of fun. >> Justin: Thanks a lot. >> Tal Klein, really appreciate you coming. Congratulations on the book, we look forward to the movie. Maybe, you know, we'll get the Cube involved down the road. (laughing) >> And we're giving away 75 copies of it here at Lakeside booth, if you guys want to come. >> Tal Klein, author of The Punch Escrow, also CMO of Lakeside, who is here in the thing. But yeah, (laughing) a lot of stuff. Justin and I will be back with more coverage here from VMWorld 2017. You're watching the Cube. (bright music)

>> Narrator: From the Computer History Museum in the heart of Silicon Valley it's the Cube, covering food I.T., fork to farm, brought to you by Western Digital. >> Hey, welcome back, everybody. Jeffrey here with The Cube. We're at the Computer History Museum in Mountain View, California, at the Food I.T. show. About 350 people from academe, from food producers, somebody came all the way from New Zealand for this show. A lot of tech, big companies and start-ups talking about applying IT to food, everything from ag to consumption to your home kitchen to what do you do with the scraps that we all throw away. We're excited now to get to the "Big Brain" segment. We've got our Ph.D.s on here. We're excited to have Doctor Glenda Humiston. She's the V.P. of agriculture and natural resources for the University of California. Welcome. And also, Doctor Helene Dillard. She's the dean of the College of Agricultural and Environmental Sciences at UC Davis. Welcome. >> Thank you. >> So first off, we were talking a little bit before we turned the cameras on. Neither of you have been to this event before. Just kind of your impressions of the event in general? >> Glenda: I love seeing the mix of the folks here as you were saying in your intro. There's quite a diverse array of people, and I personally believe that's what's really going to help us find solutions moving forward, that cross-pollination. >> Helene: And I've enjoyed it, just seeing all the different people that are here, but then the interaction with the audience was very uniquely done, and I just think that's a real big positive for the show. >> So you guys were on a panel earlier today, and I thought one of the really interesting topics that came up on that panel was, what is good tech? You know, everybody wants it all, but unfortunately there's no free lunch, right? Something we all learned as kids. There's always a trade-off, and so people want perfect, organic, this-free, that-free, cage-free, at the same time they want it to look beautiful, be economical and delivered to their door on Amazon Prime within two hours. So it's interesting when we think of the trade-offs that we have to make in the food industry to kind of hit all these pieces, or can we hit all these pieces or how does stuff get prioritized? >> Well I think that for us, it's going to be a balance, and trying to figure out how do you provide the needs for all these different audiences and all the different things that they want and I don't think one farmer can do it for all these different groups that have different demands on what they're looking for. And some of the tradeoffs could be, as we go away from pesticides and from other things, we might have more blemishes. And those are still edible pieces of fruit and vegetables, it's just that maybe it's curly, maybe the carrot's not straight, you know, maybe it's forked, but it's still very edible. And so I think that we have to do a lot more to help educate consumers, help people understand that it doesn't have to look perfect to give you perfect nutrition. >> Right, right. >> Glenda: Yeah, yeah, Helene is absolutely right. Some of it's just education, but some of it's also us finding the new technology that is acceptable to the public. Part of the problem is we sometimes have researchers working on their own, trying to find the best solution to a problem and we're not socializing that with the public as we're moving forward. So then all of a sudden, here's this new type of technology and they're like, where did this come from? What does it mean to me? Do I need to worry about it? And that's one reason--we talked earlier on the panel too, about the need to really engage more of our citizens in the scientific process itself, and really start dealing with that scientific illiteracy that's out there. >> Because there was a lot of talk about transparency in the conversation-- >> Yes. >> Earlier today about what is transparency. Cause you always think about people complaining about genetically modified foods. Well what is genetically modified? Well, all you have to do is look at the picture of the first apple ever, and it was a tiny little nasty-looking thing that nobody would want to eat compared to what we see at the grocery store today. A different type of genetic modification, but still, you don't plant the ugly one, and you plant the ones that are bigger and have more fruit. Guess what, the next round has more fruit. So it does seem like a big education problem. >> It is, and yet, for the average human being out there, all you have to do is look at a chihuahua next to a Saint Bernard. None of that was done with a genetically modified technology and yet people just--they forget that we've been doing this for thousands of years. >> Jeffrey: Right, right. You talked about, Glenda, the VINE earlier on in the panel. What is the VINE? What's the VINE all about? >> Well, it's brand new. It's still getting rolled out. In fact, we announced it today. It's the Verde Innovation Network for Entrepreneurship. You know, you've got to think of a clever way to get that acronym in there >> Which comes first, the chicken or the egg? >> Basically it's our intent from University of California to catalyze regional innovation and entrepreneurship ecosystems. Part of what's driving that is we've got a fairly good amount of resources scattered around the state, even in some of our rural areas, on small business development centers, our community colleges, our county cooperative extension offices, and a host of other resources including lately, the last several years, incubators, accelerators, maker's labs. But they don't talk to each other, they don't work together. So we're trying to go in, region by region, and catalyze a coalition so that we can make sure that our innovators, our inventors out there, are able to go from idea to commercialization with all the support they need. Via just basic legal advice, on should they be patenting something. Access to people to discuss finances, access to people that can help them with business plans. Opportunities to partner with the University in joint research projects. Whatever it takes, make sure that for anybody in California they can access that kind of support. >> That's interesting. Obviously at Haas, and at Stanford, not far from here, you know, a lot of the technologies of such companies come out of, you know, kind of an entrepreneurial spin with a business-focused grad and often a tech grad in a tech world. You know, ton of stuff at Berkeley on that, but >> Yeah, but those folks this is really for ag >> are in urban areas >> If you're in a large urban area or you're near a major campus you've probably got access to most of that. If you're in agriculture, natural resources, and in particular, our more remote, rural communities, you typically have no access, or very little. >> Right. So biggest question is, Helene, so you're at Davis, right, obviously known as one of the top agricultural-focused schools certainly in the UC system, if not in the world. I mean, how is the role of academic institutions evolving in this space, as we move forward? >> I would say it's evolving in that we're getting more entrepreneurship on campus. So professors are being encouraged to look at what they're working on and see if there's patent potential for this. And also, we have a group on UC Davis campus called Innovation Access, but looking at how can they access this population of people with money and, you know, the startups to help them bring their thing to market? So that's becoming-- that's a very different campus than years ago. I think the other thing is, we're also encouraging our students to look at innovation. And so we have a competition called the Big Bang, and students participate in that. They do Hag-a-thon, they do all these kinds of things that we tend to think that only the adults are doing those but now the students are doing them as well. And so we're trying to push that entrepreneurship spirit out onto all of our campus, onto everyone on the campus. >> And I do want to emphasize that this isn't just for our students or our faculty. One of the key focuses of the VINE is all of our external partners, too. Just the farmers, the landowners, the average citizens we're working with out there. If they've got a great idea, we'd like to help them. >> Jeffrey: And what's nice about tech is, you know, tech is a vehicle you can change the world without having a big company. And I would imagine that ag is kind of-- big ag rolled up a lot of the smaller, midsize things, and there probably didn't feel like there was an opportunity that you could have this huge impact. But as we know, sitting across the street from Google, that via software and technology, you can have a huge impact far beyond the size and scope of your company. And I would imagine that this is a theme that you guys are playing off of pretty aggressively. >> Absolutely. I think that there are people on campus that are looking for small farm answers and mechanization as well as large farm answers. We have people that are working overseas in developing countries with really, really small farm answers. We have people that are working with the Driscolls and partnering up with some of these other big companies. >> We talked a little bit before we went on air about kind of the challenges of an academic institution, with some of the resources and scale. These are big, complicated problems. I mean, obviously water is kind of the elephant in the room at this conference, and it's not being talked about specifically I think they've got other water shows. Just drive up and down the valley by Turlock and Merced and you can see the signs. We want the water for the farms, not for the salmon in the streams, so where do the--the environmental impacts. So these are big, hairy problems. These are not simple solutions. So it does take a lot of the systems approach to think through, what are the tradeoffs of a free lunch? >> It really does take a systems approach, and that's one thing here in California, we're doing some very innovative work on. A great example that both UC Davis, my division, and other parts of the UC system are working on is Central Valley AgPlus Food and Beverage Manufacturing Consortium, which is 28 counties, the central valley and up into the Sierra. And what's exciting about it is, it is taking that holistic approach. It's looking at bringing around the table the folks from research and development, workforce, trained workforce, adequate infrastructure, financing, access to capital, supply chain infrastructure, and having them actually work together to decide what's needed, and leverage each other's resources. And I think that offers a lot of possibility moving forward. >> And I would say that at least in our college, and I would call the whole UC Davis, there's a lot of integration of that whole agriculture environmental space. So we've been working with the rice farmers on when can you flood the rice fields so that there's landing places for the migrating birds? Cause this is the Pacific flyway. And can we grow baby salmonids in that ricewater and then put them back in the bay? And they figured out a way to do that, and have it actually be like a fish hatchery, only even better, because we're not feeding them little tiny pellets, they're actually eating real food, (laughs) whole foods. >> And how has an evolution changed from, again, this is no different than anyplace else, an old school intuition, the way we've always done it versus really a more data driven, scientific approach where people are starting to realize there's a lot of data out there, we've got all this cool technology with the sensors and the cloud and edge computing and drones and a whole lot of ways to collect data in ways that we couldn't do before and analyze it in ways that we couldn't do before to start to change behavior, and be more data-driven as opposed to more intuition driven. >> I would say that what we're seeing is as this data starts to come in precision gets better. And so now that we understand that this corner of the field needs more water than the other side, we don't have to flood the whole thing all at once. You can start on the dry side and work over to the other side. So I think the precision is getting much, much better. And so with that precision comes water efficiency, chemical efficiency, so to me it's just getting better every time. >> And frankly, we're just at the beginning of that. We're just starting to really use drones extensively to gather that type of data. New ways of using satellite imagery, new way of using soil sensors. But one of the problems, one of the big challenges we have, back to infrastructure, is in many parts of your agricultural areas, access to the internet. That pipeline, broadband. If you've got thousand of sensors zapping information back you can fill up that pipeline pretty fast. It becomes a problem. >> Jeffrey: That pesky soft underbelly of the cloud, right? You've got to be connected. Well, we're out of time, unfortunately. I want to give you the last word for people that aren't as familiar with this, basically, myself included, what would you like to share with people that could kind of raise their awareness of what's happening with technology and agriculture? >> Well, I guess that I would start out saying not to be afraid of it, and to look at the technology that has come. Remember when we had the rotary dial phone? My son doesn't even know what that is! (laughs) >> Jeffrey: Mom, why do you say dial them up? >> Yeah, why do you say dial people up? So I think, looking at your rotary phone, now, looking at your smart phone, which has more computing power than your first Macintosh. It's very--the world is changing, and so why do we expect agriculture to stay in the 1800s mindset? It's moving too, and it's growing too, and it's getting better just like that iPhone that you have in your hand. >> I think I would add that to that, back to the citizen science, I would love people out there, anybody, average citizens young or old to know that there's opportunities for them to engage. If they're concerned about the science or the technology come work with us! We have over twenty thousand volunteers in our programs right now. We will happily take more. And they will have a chance to see, up close and personal, what this technology is and what it can do for them. >> Alright. Well that's great advice. We're going to leave it there, and Dr. Humiston, Dr. Dillard, thank you for taking a few moments out of your day. I'm Jeffrey. You're watching the Cube. We're at the Computer History Museum. Food IT. Learning all about the IT transformation in the agriculture industry. Also to the kitchen, your kitchen, the kitchen of the local restaurant and all the stuff that can happen with those scraps that we throw away at the end of the day. Thanks for watching, and we'll be right back after this short break. (electronic music)