>>text, you know, consumer opens up their iphone and says, oh my gosh, I love the technology behind my eyes. What's it been like being on the shark tank? You know, filming is fun, hang out, just fun and it's fun to be a celebrity at first your head gets really big and you get a good tables at restaurants who says texas has got a little possess more skin in the game today in charge of his destiny robert Hirschbeck, No stars. Here is CUBA alumni. Yeah, okay. >>Hi. I'm john Ferry, the co founder of silicon angle Media and co host of the cube. I've been in the tech business since I was 19 1st programming on many computers in a large enterprise and then worked at IBM and Hewlett Packard total of nine years in the enterprise brian's jobs from programming, Training, consulting and ultimately as an executive salesperson and then started my first company with 1997 and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology and I love covering you know, emerging technology as trained as a software developer and love business and I love the impact of software and technology to business to me creating technology that starts the company and creates value and jobs is probably the most rewarding things I've ever been involved in. And I bring that energy to the queue because the Cubans were all the ideas are and what the experts are, where the people are and I think what's most exciting about the cube is that we get to talk to people who are making things happen, entrepreneur ceo of companies, venture capitalists, people who are really on a day in and day out basis, building great companies and the technology business is just not a lot of real time live tv coverage and, and the cube is a non linear tv operation. We do everything that the T. V guys on cable don't do. We do longer interviews. We asked tougher questions, we ask sometimes some light questions. We talked about the person and what they feel about. It's not prompted and scripted. It's a conversation authentic And for shows that have the Cube coverage and makes the show buzz. That creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the cube and that is the result. Great content, great conversations and I'm so proud to be part of you with great team. Hi, I'm john ferrier. Thanks for watching the cube. >>Hello and welcome to the cube. We are here live on the ground in the expo floor of a live event. The AWS public sector summit. I'm john for your host of the cube. We're here for the next two days. Wall to wall coverage. I'm here with Sandy carter to kick off the event. Vice president partner as partners on AWS public sector. Great to see you Sandy, >>so great to see you john live and in person, right? >>I'm excited. I'm jumping out of my chair because I did a, I did a twitter periscope yesterday and said a live event and all the comments are, oh my God, an expo floor a real events. Congratulations. >>True. Yeah. We're so excited yesterday. We had our partner day and we sold out the event. It was rock them and pack them and we had to turn people away. So what a great experience. Right, >>Well, I'm excited. People are actually happy. We tried, we tried covering mobile world congress in Barcelona. Still, people were there, people felt good here at same vibe. People are excited to be in person. You get all your partners here. You guys have had had an amazing year. Congratulations. We did a couple awards show with you guys. But I think the big story is the amazon services for the partners. Public sector has been a real game changer. I mean we talked about it before, but again, it continues to happen. What's the update? >>Yeah, well we had, so there's lots of announcements. So let me start out with some really cool growth things because I know you're a big growth guy. So we announced here at the conference yesterday that our government competency program for partners is now the number one industry in AWS for are the competency. That's a huge deal. Government is growing so fast. We saw that during the pandemic, everybody was moving to the cloud and it's just affirmation with the government competency now taking that number one position across AWS. So not across public sector across AWS and then one of our fastest growing areas as well as health care. So we now have an A. T. O. Authority to operate for HIPPA and Hi trust and that's now our fastest growing area with 85% growth. So I love that new news about the growth that we're seeing in public sector and all the energy that's going into the cloud and beyond. >>You know, one of the things that we talked about before and another Cuban of you. But I want to get your reaction now current state of the art now in the moment the pandemic has highlighted the antiquated outdated systems and highlighted help inadequate. They are cloud. You guys have done an amazing job to stand up value quickly now we're in a hybrid world. So you've got hybrid automation ai driving a complete change and it's happening pretty quick. What's the new things that you guys are seeing that's emerging? Obviously a steady state of more growth. But what's the big success programs that you're seeing right now? >>Well, there's a few new programs that we're seeing that have really taken off. So one is called proserve ready. We announced yesterday that it's now G. A. And the U. S. And a media and why that's so important is that our proserve team a lot of times when they're doing contracts, they run out of resources and so they need to tap on the shoulder some partners to come and help them. And the customers told us that they wanted them to be pro served ready so to have that badge of honor if you would that they're using the same template, the same best practices that we use as well. And so we're seeing that as a big value creator for our partners, but also for our customers because now those partners are being trained by us and really helping to be mentored on the job training as they go. Very powerful program. >>Well, one of the things that really impressed by and I've talked to some of your MSP partners on the floor here as they walk by, they see the cube, they're all doing well. They're all happy. They got a spring in their step. And the thing is that this public private partnerships is a real trend we've been talking about for a while. More people in the public sector saying, hey, I want I need a commercial relationship, not the old school, you know, we're public. We have all these rules. There's more collaboration. Can you share your thoughts on how you see that evolving? Because now the partners in the public sector are partnering closer than ever before. >>Yeah, it's really um, I think it's really fascinating because a lot of our new partners are actually commercial partners that are now choosing to add a public sector practice with them. And I think a lot of that is because of these public and private partnerships. So let me give you an example space. So we were at the space symposium our first time ever for a W. S at the space symposium and what we found was there were partners, they're like orbital insight who's bringing data from satellites, There are public sector partner, but that data is being used for insurance companies being used for agriculture being used to impact environment. So I think a lot of those public private partnerships are strengthening as we go through Covid or have like getting alec of it. And we do see a lot of push in that area. >>Talk about health care because health care is again changing radically. We talked to customers all the time. They're like, they have a lot of legacy systems but they can't just throw them away. So cloud native aligns well with health care. >>It does. And in fact, you know, if you think about health care, most health care, they don't build solutions themselves, they depend on partners to build them. So they do the customer doesn't buy and the partner does the build. So it's a great and exciting area for our partners. We just launched a new program called the mission accelerator program. It's in beta and that program is really fascinating because our healthcare partners, our government partners and more now can use these accelerators that maybe isolate a common area like um digital analytics for health care and they can reuse those. So it's pretty, I think it's really exciting today as we think about the potential health care and beyond. >>You know, one of the challenge that I always thought you had that you guys do a good job on, I'd love to get your reaction to now is there's more and more people who want to partner with you than ever before. And sometimes it hasn't always been easy in the old days like to get fed ramp certified or even deal with public sector. If you were a commercial vendor, you guys have done a lot with accelerating certifications. Where are you on that spectrum now, what's next? What's the next wave of partner onboarding or what's the partner trends around the opportunities in public sector? >>Well, one of the new things that we announced, we have tested out in the U. S. You know, that's the amazon way, right, Andy's way, you tested your experiment. If it works, you roll it out, we have a concierge program now to help a lot of those new partners get inundated into public sector. And so it's basically, I'm gonna hold your hand just like at a hotel. I would go up and say, hey, can you direct me to the right restaurant or to the right museum, we do the same thing, we hand hold people through that process. Um, if you don't want to do that, we also have a new program called navigate which is built for brand new partners. And what that enables our partners to do is to kind of be guided through that process. So you are right. We have so many partners now who want to come and grow with us that it's really essential that we provide a great partner, experienced a how to on board. >>Yeah. And the A. P. M. Was the amazon partner network also has a lot of crossover. You see a lot a lot of that going on because the cloud, it's you can do both. >>Absolutely. And I think it's really, you know, we leverage all of the ap in programs that exist today. So for example, there was just a new program that was put out for a growth rebate and that was driven by the A. P. N. And we're leveraging and using that in public sector too. So there's a lot of prosecutes going on to make it easier for our partners to do business with us. >>So I have to ask you on a personal note, I know we've talked about before, your very comfortable the virtual now hybrid space. How's your team doing? How's the structure looks like, what are your goals, what are you excited about? >>Well, I think I have the greatest team ever. So of course I'm excited about our team and we are working in this new hybrid world. So it is a change for everybody uh the other day we had some people in the office and some people calling in virtually so how to manage that, right was really quite interesting. Our goals that we align our whole team around and we talked a little bit about this yesterday are around mission which are the solution areas migration, so getting everything to the cloud and then in the cloud, we talk about modernization, are you gonna use Ai Ml or I O T? And we actually just announced a new program around that to to help out IOT partners to really build and understand that data that's coming in from I O T I D C says that that idea that IOT data has increased by four times uh in the, during the covid period. So there's so many more partners who need help. >>There's a huge shift going on and you know, we always try to explain on the cube. Dave and I talked about a lot and it's re platform with the cloud, which is not just lift and shift you kind of move and then re platform then re factoring your business and there's a nuance there between re platform in which is great. Take advantage of cloud scale. But the re factoring allows for this unique advantage of these high level services. >>That's right >>and this is where people are winning. What's your reaction to that? >>Oh, I completely agree. I think this whole area of modernizing your application, like we have a lot of folks who are doing mainframe migrations and to your point if they just lift what they had in COBOL and they move it to a W S, there's really not a lot of value there, but when they rewrite the code, when they re factor the code, that's where we're seeing tremendous breakthrough momentum with our partner community, you know, Deloitte is one of our top partners with our mainframe migration. They have both our technology and our consulting um, mainframe migration competency there to one of the other things I think you would be interested in is in our session yesterday we just completed some research with r C T O s and we talked about the next mega trends that are coming around Web three dato. And I'm sure you've been hearing a lot about web www dot right? Yeah, >>0.04.0, it's all moving too fast. I mean it's moving >>fast. And so some of the things we talked to our partners about yesterday are like the metaverse that's coming. So you talked about health care yesterday electronic caregiver announced an entire application for virtual caregivers in the metaverse. We talked about Blockchain, you know, and the rise of Blockchain yesterday, we had a whole set of meetings, everybody was talking about Blockchain because now you've got El Salvador Panama Ukraine who have all adopted Bitcoin which is built on the Blockchain. So there are some really exciting things going on in technology and public sector. >>It's a societal shift and I think the confluence of tech user experience data, new, decentralized ways of changing society. You're in the middle of it. >>We are and our partners are in the middle of it and data data, data data, that's what I would say. Everybody is using data. You and I even talked about how you guys are using data. Data is really a hot topic and we we're really trying to help our partners figure out just how to migrate the data to the cloud but also to use that analytics and machine learning on it too. Well, >>thanks for sharing the data here on our opening segment. The insights we will be getting out of the Great Sandy. Great to see you got a couple more interviews with you. Thanks for coming on. I appreciate you And thanks for all your support. You guys are doing great. Your partners are happy you're on a great wave. Congratulations. Thank you, john appreciate more coverage from the queue here. Neither is public sector summit. We'll be right back. Mhm Yeah. >>Mhm. Mhm robert Herjavec. People obviously know you from shark tank

>>Hello everyone. Thank you, add appreciating the studio. We're here at the cube here in cloud city telco DRS Cloud city. I'm Jeffrey Day Volonte. We're here for the next three days. Wall to wall live coverage. It's a physical event with a virtual program. It's hybrid. We're here with Daniel Royston, the Ceo of telco D. R. And the acting Ceo Toby, which is announced today. Great >>to see you. It's awesome to see you guys. >>Awesome to see how you doing, how you >>Feeling? I'm feeling congratulations. Right. 101 days ago, I didn't even think this doesn't exist. Right. And we got in contact with you guys and we said we knew there was always going to be a big virtual component and we invited you guys and here we are together. It's insane. >>Well we did the preview videos, but we're kind of walking through and document in the early stages. It all came together beautifully spectacular For the folks watching behind us is the most spectacular build out clouds. It's an ecosystem open concept. It feels like the Apple store meets paradise. Of course. We got the cube here in the set and we got the studio with all the command and control of adam there. So I gotta ask you with the connected keynotes going on right now. The connected world. Yeah. It's connected. We all know that everyone knows that what's, what's different now real quick before we get into the program, what's going on? >>Yeah. I think a big part of my messages and advocating it's more than just the network, Right? And I think telcos forever have relied on. That's all it is. That's what it's about. And I'm like, nope, you guys got to start focusing on your subscribers, right? And so the over the top players keep coming in and siphoning away their revenue and it's time for them to start focusing on us, right and making experience great. And I think that's what this is all about. >>So we're gonna get the news but I want to toss it to Katie. The roving reporter is going to give it a detail on how it all came together. So Katie take it away. >>Mhm We're here in Barcelona and so excited to be back in this beautiful city over at the convention center. The team is working hard putting the finishing touches to tell Cody are amazing cloud city booth at MwC Barcelona 2021. I'm sure you know the story of how this all came together as one of the biggest vendors Erickson pulled out of M. W. C. With just over 100 days until the start of the event. When this happened last year, it kicked off a tidal wave of departures and MwC was called off this year. We all wondered if MWC was going to be cancelled again and that's when Daniel Royston Ceo of Telco D. R. And Tito G swooped in and took over the booth all 6000 square meters of it. The plan turn the booth into cloud city, the epicenter of public cloud innovation at MWC crews have been working around the clock. Over 100 and 50 people have been on this construction site for over three weeks with covid testing every day to prevent outbreaks during the build and in 100 days, it's become just that Cloud city has over 30 vendors presenting over 70 demos with 24 private meeting areas. Cloud City Live is a virtual showcase and live broadcast studio featuring 50 guests from cloud Thought leaders around the world. They have telepresence robots for a more personalized experience and the Cloud city quest game with a chance to win more than $100,000 to gain access to live streams of our nightly concerts with rosario flores and rock legend Jon bon Jovi. And don't forget to visit cloud city dot telco D R dot com to join in on the fun Daniel Royston and Nacho Gomez, founder and Ceo of one of the key vendors in the construction of the booth gave us a behind the scenes tour of the booth. >>Nacho. We did it. Yeah, we did. It can't even touch because of Covid. Yeah, but look what we did. But right, 100 days ago I called and I said I'm taking over the Ericsson booth. What did you think? I know you were crazy but just a little bit crazy, realized that you were mortgages than I thought. So at the very, at the very beginning I thought, yeah, she's crazy. But then I couldn't sleep that night. But the next uh then I realized that it was a very good it's a great idea. Yeah super smart. So yeah we're gonna show everyone toward the booth. Yeah let's go. Let's go. Okay So how do we build such an amazing, beautiful building now? So this is we've made building inside a book. So it was very hard to find a glassful of facade. The roof is around 24 tones. Yeah so it's crazy crazy but we made it work and it's totally amazing. Yeah. Do you want to go to tragedy life? Do let's go. Okay so here we are Cloud city live. I know we're producing a whole live streaming tv show. We always knew because of covid that not everyone will be able to come to Mwc as we wanted to make sure that people can learn about the public cloud. So over here we have the keynote stage, we're gonna have awesome speakers talking all throughout M. W. C. People from AWS Microsoft, google vendors companies. So really really great content. And then over there we have the cube interviewing people again 15 minute segments, live streaming but also available on demand. And you can find all of this content on cloud city. Tell Cody are calm and it's available for anyone to you. Well, a lot of content. And what about the roberts? I never get them out. Come on. We remember 100 days ago we were locked down. So we came up with the idea of having robots for the people who cannot attend in person. I know right. We always knew that there was gonna be a big virtual component to MWC this year. So we bought 100 telepresence robots. It's a great way to have a more personal experience inside the boot. Just sign up for one on cloud city dot telco D r dot com and you can control it yourself. Right? So today we have Nikki with us, who's dialing in from the Philippines in Manila? Hello, Nicky. Hi there, how are you? I were great. Can you show us a twirl all gaining on us? Super cool. Yeah, it is. What an experience. So Nikki robots are not the only cool thing we have in cloud city. We also have super awesome concert. We have rosario flores on monday. Who's a latin grammy award winner. We have Jon bon Jovi, Jon bon Jovi on Tuesday, can't be changing telephone that a little bit of rock n roll and that's Tuesday. And on Wednesday we have DJ official, it's going to be a super party. Now if you play our cloud city quest on cloud city telco D R dot com you can participate in a live streaming concert and so I know a lot of people out there have been a lockdown. Haven't been able to be going to concerts. Things from austin texas, which is the live music capital of the world, How to have music. It would be so exciting is gonna be great. I'm getting hungry. Why don't we go to the restaurant? Let's go eat. Let's go. Yeah, Here is our awesome restaurant. I know it's called Cloud nine. Right? It's a place to come and sit down and relax now. Barcelona is known for its great food and I'm a foodie. So we had to have a restaurant. Should we go check out my secret bar? Let's go. Mhm. Yeah, here >>thanks to a R. And thank you Nacho if you're watching this at home, I'm so sorry you can't join us in person. However, let's not forget this is a hybrid event meaning we're bringing all the public cloud action right to you wherever in the world you might be. This includes the Pact cloud city live program. We've partnered with the cube Silicon angle Media's live streaming video studio to make sure that all of the keynotes, panel discussions, demos, case studies interviews and way more are available on demand so you can watch them whenever and wherever you want or you can live stream and enjoy all things cloud city as and when they happen. So for those of you not able to join us in, Barcelona, be sure to log in to cloud city live and catch all the action and don't miss the awesome concert Tuesday night with Jon bon Jovi available for free. If you participate in our cloud city quest game, I'll be here throughout MWc bringing you reports and updates. Stay >>tuned. Yeah. >>Mhm. Okay, we're back here on the cube on the floor at mobile world congress in cloud city telco DRS clouds. They were here with D. R. Of telco, D R. Danielle Rice and great to see you back, we're back. So the keynotes going on connected world, the big news here, I'll see the open shift that's happening is going open. Open ran, it's been a big thing. Open ran alliance. You're starting to see the industry come together around this clear mandate that applications are gonna be cloud native and the public cloud is just coming in like a big wave and people are gonna be driftwood or they'll be surfing the wave. Yeah, this is what's happening. >>Yeah, I think public cloud is an unstoppable megatrend. It's hit every other industry regulated industries like banking, right? Top secret industries like government. They all use the public cloud tells us the last, you know, standing old school industry and it's coming and I don't think we could have had an MWc without talking about open man. That's the other major shift. And so we're bringing both of those ideas here together in cloud city. So >>the big theme is telco transformation. Maybe we could start with the basics like paint a picture of what the telco infrastructure looks like, particularly the data center stuff because they all have big data centers >>because that's >>those are the candidates to go into the cloud explained to the audience. >>Well, do you have a time machine? I think if any of us were in tech in the late 90s and early 2000s, that's what telcos like today. Right. So for people outside of the industry don't know right there mostly still managing their own data centers, they're just sort of adopting virtualization. Some of the more advanced telcos are mostly virtualized public cloud. Is this idea that like this advanced thought and so yeah, I mean things are on premise, things are in silom, things don't use a P. I. S there all integrated with custom code. And so the transformation, we can all see it because we've lived it in other industries. And I'm bringing that to telco and say come along for the ride. It totally works and it's gonna be amazing. >>So it's hardened purpose built infrastructure. Okay. That ultimately parts of that need to go to the public cloud. Right. What parts do you see going first? >>I think all of it. Really. Yeah. And I think when you look at like dish in the W. S. Which was an announcement that came out about two months ago. Right. I mean dish was doing all these are FPs. Everyone knew about it. They were looking for a cloud native software and no one knew what they were. They knew a big part was open man. But their coupling open ran with AWS and deploying their parts of their network onto the public cloud and the whole industry is like wait we thought this was years away, right? Or number two, you're crazy. And I'm saying this is what I've been talking about guys. This is exactly what you can do, leverage the Capex over. Let's see. I think Amazon did $100 billion 2020 right, leverage that Capex for yourself. Get that infinite scalability right? It's going to, well we >>have, we have a saying here in the queue, we just made this up called D. R. That's your initial tucker. The digital revolution and the three Rs reset re platform and re factor. I think the observation we're seeing is that you're coming in with the narrative what everyone's kind of like they're waking up because they have to reset and then re platform with the cloud. But the opportunity is gonna be the re factoring, You're seeing the public cloud, do that already with the Enterprise Enterprises. Already re factoring has done that. Already done that now. Telcos the last area to be innovated by the cloud. >>Yeah, I think there's old school big, we're kind of on a hollowed ground here in the Ericsson booth that I took over, right? They bailed and I kind of made fun of them. I was like, they don't have anything to say, right, They're not going to go to the show. I'm like, this is this is a revolution that's happening in telco and I don't think the big guys are really interested in rewriting their software that frankly makes them billions and billions of dollars of revenue. And I'm like to use the public cloud. All of the software needs to be rewritten needs to be re factored and you've got to start training your teams on how to use it. They don't have any capability. The telcos, in terms of those skills hire the right people, retrain your teams, move your applications, rewrite them. And I think that's what we're talking, this is not a short journey, this is a 10 year journey. So >>let's fast forward to the future a little bit because when I look around cloud city, I see ecosystem everywhere. So as you well know, the telcos have generally done a poor job of attacking adjacent seas. So my question is can they go beyond should they go beyond connectivity or is that going to be the role of the ecosystem? >>Yeah, I think it's time that the telco starts to focus on their subscriber, right? It's been really easy for them to rely on the oligopoly of the network, Right? The network, we live in the United States, we see the 18 T Verizon T mobile five G network, five G network. Like what about us? Right. And it's really easy for the over the top players right, that come in and they're always, telcos are always complaining about being coming dumb pipes and I'm like, you don't focus on the customer, we would rather buy from an Apple and amazon if they provided a mobile service because the customer experience will be better. Right? They need to start focusing on us. They have great businesses but they want to make them better. They need to start focusing on the subscriber, so >>it's a partnership with the ecosystem then for them to go beyond just straight connectivity because you're right, those are the brands that we want to do business >>with. You know, there was a great survey, Peter Atherton who will be talking as a speaker I think um I can't remember when he's talking but he was talking about how there was a survey done, where would you rather get your mobile service from? And it had a couple of big names in telco and then of course the obvious, you know, consumer brands, the ones that we all know and it was like overwhelmingly would rather buy from an amazon or an apple. And I'm like, this is like if you guys don't change, right, if telco doesn't change they keep rolling out 60 and blah blah blah. It's about the network and I don't start making about the subscriber right? Those revenues are going to continue to erode and they just sit there and complain about the O. T. T. Players. Like it's time to fight back. Yeah, I own the subscriber >>relationship. It's a digital revolution and I think This event really encapsulates in my mind this hybrid world here because it's physical events back. It's been since 2019 winter that this event actually happened. >>Well no it was even longer than, well I guess winter it was February of 19, right? And so like you look at ericsson and some of the big names that dropped out of the show, the time they come back, three years will have passed three years, right? This is how you feel your sales funnel is how you connect with your customers right? Tokyo is a very global, you know experience and so you gotta, you gotta get in front of people and you got to talk a >>lot of change to its happened, look at just what public clouds done in 2.5 years. You imagine three years being just >>gone, right? And I think a lot of people back to edition A. W. S. I think the industry was a little bit surprised by that announcement. So I've been telling executives if you were surprised by that, if you think that's, you know, if you don't know how that's gonna work, you need to come to cloud cities, you start meeting all the vendors are here. We have over 30 vendors, 70 demos, right? People who are pushing the technology forward, you need to learn what's going on here. We have several dish vendors here. Come learn about open rand, come learn about public cloud. So >>we're tight on time today, but we're going to have you back and we want to get into the tech, Get it to open, ran a little bit, get into what 5G and beyond and how we're going to take advantage of that and monetize it and what that all means. >>And also we want to hear what's going on the hallways. I know you got a lot of your key noting, you're gonna be a lot of events, the yacht. You've got a lot of briefings, >>yep. Yeah, I've already had two meetings this morning. I shot a video. Um, I met with one of the world's largest groups and I met with a tiny little super app company. Right? So running the gamut, doing everything reporter >>now, we could be like our roaming >>reporter. You know, I love, I love talking to execs and telco getting their perspective on what is public cloud and where are they going, what are they thinking about? And you talked to people who really, really get it and you get people who are just nascent and everywhere in between and I love mwc it's going great. >>Daniel Rose and you are a digital revolution telco DDR. There's amazing. Davis has been fantastic. Again for the folks watching, this is a hybrid events, there's an online component and we're reaching out with our remote interviews to get people brought in and we're shipping this content out to the masses all over the world. It's gonna be really amazing cube coverages here. It's gonna be rocking you guys are doing great. I just want to give you a compliment that you guys just did an amazing job. And of course we've got adam in the studio with the team. So adam, I'm gonna pass it off back to you in the studio

>> I'm Lisa Martin with theCUBE. From all of us here at Silicon Angle Media, we want to wish Metallic a happy first birthday. We were at the Metallic launch last year at Commvault Go 2019. Talked to a lot of folks, have seen what the Metallic team has accomplished in its first 12 months, such as the expansion of the product portfolio, expansion into new geographies and we're looking forward to seeing what Metallic does in it's second year. Happy birthday.

>> Announcer: From around the globe, it's theCube with digital coverage of MIT, Chief Data Officer and Information Quality Symposium brought to you by Silicon Angle Media. >> Hi, I'm Stu Miniman and this is the seventh year of theCubes coverage of the MIT, Chief Data Officer and Information Quality Symposium. We love getting to talk to these chief data officers and the people in this ecosystem, the importance of data, driving data-driven cultures, and really happy to welcome to the program, first time guests Eileen Vitrine, Eileen is the Chief Data Officer for the United States Air Force, Eileen, thank you so much for joining us. >> Thank you Stu really excited about being here today. >> All right, so the United States Air Force, I believe had it first CDO office in 2017, you were put in the CDO role in June of 2018. If you could, bring us back, give us how that was formed inside the Air force and how you came to be in that role. >> Well, Stu I like to say that we are a startup organization and a really mature organization, so it's really about culture change and it began by bringing a group of amazing citizen airman reservists back to the Air Force to bring their skills from industry and bring them into the Air Force. So, I like to say that we're a total force because we have active and reservists working with civilians on a daily basis and one of the first things we did in June was we stood up a data lab, that's based in the Jones building on Andrews Air Force Base. And there, we actually take small use cases that have enterprise focus, and we really try to dig deep to try to drive data insights, to inform senior leaders across the department on really important, what I would call enterprise focused challenges, it's pretty exciting. >> Yeah, it's been fascinating when we've dug into this ecosystem, of course while the data itself is very sensitive and I'm sure for the Air Force, there are some very highest level of security, the practices that are done as to how to leverage data, the line between public and private blurs, because you have people that have come from industry that go into government and people that are from government that have leveraged their experiences there. So, if you could give us a little bit of your background and what it is that your charter has been and what you're looking to build out, as you mentioned that culture of change. >> Well, I like to say I began my data leadership journey as an active duty soldier in the army, and I was originally a transportation officer, today we would use the title condition based maintenance, but back then, it was really about running the numbers so that I could optimize my truck fleet on the road each and every day, so that my soldiers were driving safely. Data has always been part of my leadership journey and so I like to say that one of our challenges is really to make sure that data is part of every airmans core DNA, so that they're using the right data at the right level to drive insights, whether it's tactical, operational or strategic. And so it's really about empowering each and every airman, which I think is pretty exciting. >> There's so many pieces of that data, you talk about data quality, there's obviously the data life cycle. I know your presentation that you're given here at the CDO, IQ talks about the data platform that your team has built, could you explain that? What are the key tenants and what maybe differentiates it from what other organizations might have done? >> So, when we first took the challenge to build our data lab, we really wanted to really come up. Our goal was to have a cross domain solution where we could solve data problems at the appropriate classification level. And so we built the VAULT data platform, VAULT stands for visible, accessible, understandable, linked, and trustworthy. And if you look at the DOD data strategy, they will also add the tenants of interoperability and secure. So, the first steps that we have really focused on is making data visible and accessible to airmen, to empower them, to drive insights from available data to solve their problems. So, it's really about that data empowerment, we like to use the hashtag built by airmen because it's really about each and every airman being part of the solution. And I think it's really an exciting time to be in the Air Force because any airman can solve a really hard challenge and it can very quickly wrap it up rapidly, escalate up with great velocity to senior leadership, to be an enterprise solution. >> Is there some basic training that goes on from a data standpoint? For any of those that have lived in data, oftentimes you can get lost in numbers, you have to have context, you need to understand how do I separate good from bad data, or when is data still valid? So, how does someone in the Air Force get some of that beta data competency? >> Well, we have taken a multitenant approach because each and every airman has different needs. So, we have quite a few pathfinders across the Air Force today, to help what I call, upscale our total force. And so I developed a partnership with the Air Force Institute of Technology and they now have a online graduate level data science certificate program. So, individuals studying at AFIT or remotely have the opportunity to really focus on building up their data touchpoints. Just recently, we have been working on a pathfinder to allow our data officers to get their ICCP Federal Data Sector Governance Certificate Program. So, we've been running what I would call short boot camps to prep data officers to be ready for that. And I think the one that I'm most excited about is that this year, this fall, new cadets at the U.S Air Force Academy will be able to have an undergraduate degree in data science and so it's not about a one prong approach, it's about having short courses as well as academe solutions to up skill our total force moving forward. >> Well, information absolutely is such an important differentiator(laughs) in general business and absolutely the military aspects are there. You mentioned the DOD talks about interoperability in their platform, can you speak a little bit to how you make sure that data is secure? Yet, I'm sure there's opportunities for other organizations, for there to be collaboration between them. >> Well, I like to say, that we don't fight alone. So, I work on a daily basis with my peers, Tom Cecila at the Department of Navy and Greg Garcia at the Department of Army, as well as Mr. David Berg in the DOD level. It's really important that we have an integrated approach moving forward and in the DOD we partner with our security experts, so it's not about us doing security individually, it's really about, in the Air Force we use a term called digital air force, and it's about optimizing and building a trusted partnership with our CIO colleagues, as well as our chief management colleagues because it's really about that trusted partnership to make sure that we're working collaboratively across the enterprise and whatever we do in the department, we also have to reach across our services so that we're all working together. >> Eileen, I'm curious if there's been much impact from the global pandemic. When I talk to enterprise companies, that they had to rapidly make sure that while they needed to protect data, when it was in their four walls and maybe for VPN, now everyone is accessing data, much more work from home and the like. I have to imagine some of those security measures you've already taken, but have there anything along those lines or anything else that this shift in where people are, and a little bit more dispersed has impacted your work? >> Well, the story that I like to say is, that this has given us velocity. So, prior to COVID, we built our VAULT data platform as a multitenancy platform that is also cross-domain solution, so it allows people to develop and do their problem solving in an appropriate classification level. And it allows us to connect or pushup if we need to into higher classification levels. The other thing that it has helped us really work smart because we do as much as we can in that unclassified environment and then using our cloud based solution in our gateways, it allows us to bring people in at a very scheduled component so that we maximize, or we optimize their time on site. And so I really think that it's really given us great velocity because it has really allowed people to work on the right problem set, on the right class of patient level at a specific time. And plus the other pieces, we look at what we're doing is that the problem set that we've had has really allowed people to become more data focused. I think that it's personal for folks moving forward, so it has increased understanding in terms of the need for data insights, as we move forward to drive decision making. It's not that data makes the decision, but it's using the insight to make the decision. >> And one of the interesting conversations we've been having about how to get to those data insights is the use of things like machine learning, artificial intelligence, anything you can share about, how you're looking at that journey, where you are along that discovery. >> Well, I love to say that in order to do AI and machine learning, you have to have great volumes of high quality data. And so really step one was visible, accessible data, but we in the Department of the Air Force stood up an accelerator at MIT. And so we have a group of amazing airmen that are actually working with MIT on a daily basis to solve some of those, what I would call opportunities for us to move forward. My office collaborates with them on a consistent basis, because they're doing additional use cases in that academic environment, which I'm pretty excited about because I think it gives us access to some of the smartest minds. >> All right, Eileen also I understand it's your first year doing the event. Unfortunately, we don't get, all come together in Cambridge, walking those hallways and being able to listen to some of those conversations and follow up is something we've very much enjoyed over the years. What excites you about being interact with your peers and participating in the event this year? >> Well, I really think it's about helping each other leverage the amazing lessons learned. I think that if we look collaboratively, both across industry and in the federal sector, there have been amazing lessons learned and it gives us a great forum for us to really share and leverage those lessons learned as we move forward so that we're not hitting the reboot button, but we actually are starting faster. So, it comes back to the velocity component, it all helps us go faster and at a higher quality level and I think that's really exciting. >> So, final question I have for you, we've talked for years about digital transformation, we've really said that having that data strategy and that culture of leveraging data is one of the most critical pieces of having gone through that transformation. For people that are maybe early on their journey, any advice that you'd give them, having worked through a couple of years of this and the experience you've had with your peers. >> I think that the first thing is that you have to really start with a blank slate and really look at the art of the possible. Don't think about what you've always done, think about where you want to go because there are many different paths to get there. And if you look at what the target goal is, it's really about making sure that you do that backward tracking to get to that goal. And the other piece that I tell my colleagues is celebrate the wins. My team of airmen, they are amazing, it's an honor to serve them and the reality is that they are doing great things and sometimes you want more. And it's really important to celebrate the victories because it's a very long journey and we keep moving the goalposts because we're always striving for excellence. >> Absolutely, it is always a journey that we're on, it's not about the destination. Eileen, thank you so much for sharing all that you've learned and glad you could participate. >> Thank you, STU, I appreciate being included today. Have a great day. >> Thanks and thank you for watching theCube. I'm Stu Miniman stay tuned for more from the MIT, CDO IQ event. (lively upbeat music)

>>from around the globe If the cube with digital coverage of M I t. Chief data officer and Information quality symposium brought to you by Silicon Angle Media >>Hello, everyone. This is Day Volonte and welcome back to our continuing coverage of the M I t. Chief Data Officer CDO I Q event Interpol Bhandari is here. He's a leading voice in the CDO community and a longtime Cubillan Interpol. Great to see you. Thanks for coming on for this. Especially >>program. My pleasure. >>So when you you and I first met, you laid out what I thought was, you know, one of the most cogent frameworks to understand what a CDO is job was where the priority should be. And one of those was really understanding how, how, how data contributes to the monetization of station aligning with lines of business, a number of other things. And that was several years ago. A lot of change since then. You know, we've been doing this conference since probably twenty thirteen and back then, you know, Hadoop was coming on strong. A lot of CEOs didn't want to go near the technology that's beginning to change. CDOs and cto Zehr becoming much more aligned at the hip. The reporting organizations have changed. But I love your perspective on what you've observed as changing in the CDO roll over the last half decade or so. >>Well, did you know that I became chief data officer in two thousand six? December two thousand and six And I have done this job four times four major overnight have created of the organization from scratch each time. Now, in December of two thousand six, when I became chief data officer, there were only four. Chief Data Officer, uh, boom and I was the first in health care, and there were three, three others, you know, one of the Internet one and credit guns one and banking. And I think I'm the only one actually left standing still doing this job. That's a good thing or a bad thing. But like, you know, it certainly has allowed me to love the craft and then also scripted down to the level that, you know, I actually do think of it purely as a craft. That is. I know, going into a mutual what I'm gonna do. They were on the central second. No, the interesting things that have unfolded. Obviously, the professions taken off There are literally thousands off chief data officers now, and there are plenty off changes. I think the main change, but the job is it's, I think, a little less daunting in terms off convincing the senior leadership that it's need it because I think the awareness at the CEO level is much, much, much better than what it waas in two thousand six. Across the world. Now, having said that, I think it is still only awareness and don't think that there's really a deep understanding of those levels. And so there's a lot off infusion, which is why you will. You kind of think this is my period. But you saw all these professions take off with C titles, right? Chief Data officer, chief analytics officer, chief digital officer and chief technology officer. See, I off course is being there for a long time. And but I think these newer see positions. They're all very, very related, and they all kind of went to the same need which had to do with enterprise transformation, digital transformation, that enterprises chief digital officer, that's another and and people were all trying to essentially feel the elephants and they could only see part of it at the senior levels, and they came up with which have a role you know, seemed most meaningful to them. But really, all of us are trying to do the same job, which is to accelerate digital transformation in the enterprise. Your comment about you kind of see that the seat eels and sea deals now, uh, partnering up much more than in the past, and I think that's in available the major driving force full. That is, in my view, anyway. It's is artificial intelligence as people try to infuse artificial intelligence. Well, then it's very technical field. Still, it's not something that you know you can just hand over to somebody who has the business jobs, but not the deep technical chops to pull that off. And so, in the case off chief data officers that do have the technical jobs, you'll see them also pretty much heading up the I effort in total and you know, as I do for the IBM case, will be building the Data and AI Enablement internal platform for for IBM. But I think in other cases you you've got Chief date officers who are coming in from a different angle. You know, they built Marghera but the CTO now, because they have to. Otherwise you cannot get a I infused into the organization. >>So there were a lot of other priorities, obviously certainly digital transformation. We've been talking about it for years, but still in many organisations, there was a sense of, well, not on my watch, maybe a sense of complacency or maybe just other priorities. Cove. It obviously has changed that now one hundred percent of the companies that we talked to are really putting this digital transformation on the front burner. So how has that changed the role of CDO? Has it just been interpolate an acceleration of that reality, or has it also somewhat altered the swim lanes? >>I think I think it's It's It's Bolt actually, so I have a way of looking at this in my mind, the CDO role. But if you look at it from a business perspective, they're looking for three things. The CEO is looking for three things from the CDO. One is you know this person is going to help with the revenue off the company by enabling the production of new products, new products of resulting in new revenue and so forth. That's kind of one aspect of the monetization. Another aspect is the CEO is going to help with the efficiency within the organization by making data a lot more accessible, as well as enabling insights that reduce into and cycle time for major processes. And so that's another way that they have monitor. And the last one is a risk reduction that they're going to reduce the risk, you know, as regulations. And as you have cybersecurity exposure on incidents that you know just keep keep accelerating as well. You're gonna have to also step in and help with that. So every CDO, the way their senior leadership looks at them is some mix off three. And in some cases, one has given more importance than the other, and so far, but that's how they are essentially looking at it now. I think what digital transformation has done is it's managed to accelerate, accelerate all three off these outcomes because you need to attend to all three as you move forward. But I think that the individual balance that's struck for individuals reveals really depends on their ah, their company, their situation, who their peers are, who is actually leading the transformation and so >>forth, you know, in the value pie. A lot of the early activity around CDO sort of emanated from the quality portions of the organization. It was sort of a compliance waited roll, not necessarily when you started your own journey here. Obviously been focused on monetization how data contributes to that. But But you saw that generally, organizations, even if they didn't have a CDO, they had this sort of back office alliance thing that has totally changed the the in the value equation. It's really much more about insights, as you mentioned. So one of the big changes we've seen in the organization is that data pipeline you mentioned and and cycle time. And I'd like to dig into that a little bit because you and I have talked about this. This is one of the ways that a chief data officer and the related organizations can add the most value reduction in that cycle time. That's really where the business value comes from. So I wonder if we could talk about that a little bit and how that the constituents in the stakeholders in that in that life cycle across that data pipeline have changed. >>That's a very good question. Very insightful questions. So if you look at ah, company like idea, you know, my role in totally within IBM is to enable Ibn itself to become an AI enterprise. So infuse a on into all our major business processes. You know, things like our supply chain lead to cash well, process, you know, our finance processes like accounts receivable and procurement that soulful every major process that you can think off is using Watson mouth. So that's the That's the That's the vision that's essentially what we've implemented. And that's how we are using that now as a showcase for clients and customers. One of the things that be realized is the data and Ai enablement spots off business. You know, the work that I do also has processes. Now that's the pipeline you refer to. You know, we're setting up the data pipeline. We're setting up the machine learning pipeline, deep learning blank like we're always setting up these pipelines, And so now you have the opportunity to actually turn the so called EI ladder on its head because the Islander has to do with a first You collected data, then you curated. You make sure that it's high quality, etcetera, etcetera, fit for EI. And then eventually you get to applying, you know, ai and then infusing it into business processes. And so far, But once you recognize that the very first the earliest creases of work with the data those themselves are essentially processes. You can infuse AI into those processes, and that's what's made the cycle time reduction. And although things that I'm talking about possible because it just makes it much, much easier for somebody to then implement ai within a lot enterprise, I mean, AI requires specialized knowledge. There are pieces of a I like deep learning, but there are, you know, typically a company's gonna have, like a handful of people who even understand what that is, how to apply it. You know how models drift when they need to be refreshed, etcetera, etcetera, and so that's difficult. You can't possibly expect every business process, every business area to have that expertise, and so you've then got to rely on some core group which is going to enable them to do so. But that group can't do it manually because I get otherwise. That doesn't scale again. So then you come down to these pipelines and you've got to actually infuse AI into these data and ai enablement processes so that it becomes much, much easier to scale across another. >>Some of the CEOs, maybe they don't have the reporting structure that you do, or or maybe it's more of a far flung organization. Not that IBM is not far flung, but they may not have the ability to sort of inject AI. Maybe they can advocate for it. Do you see that as a challenge for some CEOs? And how do they so to get through that, what's what's the way in which they should be working with their constituents across the organization to successfully infuse ai? >>Yeah, that's it's. In fact, you get a very good point. I mean, when I joined IBM, one of the first observations I made and I in fact made it to a senior leadership, is that I didn't think that from a business standpoint, people really understood what a I met. So when we talked about a cognitive enterprise on the I enterprise a zaydi em. You know, our clients don't really understand what that meant, which is why it became really important to enable IBM itself to be any I enterprise. You know that. That's my data strategy. Your you kind of alluded to the fact that I have this approach. There are these five steps, while the very first step is to come up with the data strategy that enables a business strategy that the company's on. And in my case, it was, Hey, I'm going to enable the company because it wants to become a cloud and cognitive company. I'm going to enable that. And so we essentially are data strategy became one off making IBM. It's something I enterprise, but the reason for doing that the reason why that was so important was because then we could use it as a showcase for clients and customers. And so But I'm talking with our clients and customers. That's my role. I'm really the only role I'm playing is what I call an experiential selling there. I'm saying, Forget about you know, the fact that we're selling this particular product or that particular product that you got GPU servers. We've got you know what's an open scale or whatever? It doesn't really matter. Why don't you come and see what we've done internally at scale? And then we'll also lay out for you all the different pain points that we have to work through using our products so that you can kind of make the same case when you when you when you apply it internally and same common with regard to the benefit, you know the cycle, time reduction, some of the cycle time reductions that we've seen in my process is itself, you know, like this. Think about metadata business metadata generating that is so difficult. And it's again, something that's critical if you want to scale your data because you know you can't really have a good catalogue of data if you don't have good business, meditate. Eso. Anybody looking at what's in your catalog won't understand what it is. They won't be able to use it etcetera. And so we've essentially automated business metadata generation using AI and the cycle time reduction that was like ninety five percent, you know, haven't actually argue. It's more than that, because in the past, most people would not. For many many data sets, the pragmatic approach would be. Don't even bother with the business matter data. Then it becomes just put somewhere in the are, you know, data architecture somewhere in your data leg or whatever, you have data warehouse, and then it becomes the data swamp because nobody understands it now with regard to our experience applying AI, infusing it across all our major business processes are average cycle time reduction is seventy percent, so just a tremendous amount of gains are there. But to your point, unless you're able to point to some application at scale within the enterprise, you know that's meaningful for the enterprise, Which is kind of what the what the role I play in terms of bringing it forward to our clients and customers. It's harder to argue. I'll make a case or investment into A I would then be enterprise without actually being able to point to those types of use cases that have been scaled where you can demonstrate the value. So that's extremely important part of the equation. To make sure that that happens on a regular basis with our clients and customers, I will say that you know your point is vomited a lot off. Our clients and customers come back and say, Tell me when they're having a conversation. I was having a conversation just last week with major major financial service of all nations, and I got the same point saying, If you're coming out of regulation, how do I convince my leadership about the value of a I and you know, I basically responded. He asked me about the scale use cases You can show that. But perhaps the biggest point that you can make as a CDO after the senior readership is can we afford to be left up? That is the I think the biggest, you know, point that the leadership has to appreciate. Can you afford to be left up? >>I want to come back to this notion of seventy percent on average, the cycle time reduction. That's astounding. And I want to make sure people understand the potential impacts. And, I would say suspected many CEOs, if not most understand sort of system thinking. It's obviously something that you're big on but often times within organisations. You might see them trying to optimize one little portion of the data lifecycle and you know having. Okay, hey, celebrate that success. But unless you can take that systems view and reduce that overall cycle time, that's really where the business value is. And I guess my we're real question around. This is Every organization has some kind of Northstar, many about profit, and you can increase revenue are cut costs, and you can do that with data. It might be saving lives, but ultimately to drive this data culture, you've got to get people thinking about getting insights that help you with that North Star, that mission of the company, but then taking a systems view and that's seventy percent cycle time reduction is just the enormous business value that that drives, I think, sometimes gets lost on people. And these air telephone numbers in the business case aren't >>yes, No, absolutely. It's, you know, there's just a tremendous amount of potential on, and it's it's not an easy, easy thing to do by any means. So we've been always very transparent about the Dave. As you know, we put forward this this blueprint right, the cognitive enterprise blueprint, how you get to it, and I kind of have these four major pillars for the blueprint. There's obviously does this data and you're getting the data ready for the consummation that you want to do but also things like training data sets. How do you kind of run hundreds of thousands of experiments on a regular basis, which kind of review to the other pillar, which is techology? But then the last two pillars are business process, change and the culture organizational culture, you know, managing organizational considerations, that culture. If you don't keep all four in lockstep, the transformation is usually not successful at an end to end level, then it becomes much more what you pointed out, which is you have kind of point solutions and the role, you know, the CEO role doesn't make the kind of strategic impact that otherwise it could do so and this also comes back to some of the only appointee of you to do. If you think about how do you keep those four pillars and lock sync? It means you've gotta have the data leader. You also gotta have the technology, and in some cases they might be the same people. Hey, just for the moment, sake of argument, let's say they're all different people and many, many times. They are so the data leader of the technology of you and the operations leaders because the other ones own the business processes as well as the organizational years. You know, they've got it all worked together to make it an effective conservation. And so the organization structure that you talked about that in some cases my peers may not have that. You know, that's that. That is true. If the if the senior leadership is not thinking overall digital transformation, it's going to be difficult for them to them go out that >>you've also seen that culturally, historically, when it comes to data and analytics, a lot of times that the lines of business you know their their first response is to attack the quality of the data because the data may not support their agenda. So there's this idea of a data culture on, and I want to ask you how self serve fits into that. I mean, to the degree that the business feels as though they actually have some kind of ownership in the data, and it's largely, you know, their responsibility as opposed to a lot of the finger pointing that has historically gone on. Whether it's been decision support or enterprise data, warehousing or even, you know, Data Lakes. They've sort of failed toe live up to that. That promise, particularly from a cultural standpoint, it and so I wonder, How have you guys done in that regard? How did you get there? Many Any other observations you could make in that regard? >>Yeah. So, you know, I think culture is probably the hardest nut to crack all of those four pillars that I back up and you've got You've got to address that, Uh, not, you know, not just stop down, but also bottom up as well. As you know, period. Appear I'll give you some some examples based on our experience, that idea. So the way my organization is set up is there is a obviously a technology on the other. People who are doing all the data engineering were kind of laying out the foundational technical elements or the transformation. You know, the the AI enabled one be planning networks, and so so that are those people. And then there is another senior leader who reports directly to me, and his organization is all around adoptions. He's responsible for essentially taking what's available in the technology and then working with the business areas to move forward and make this make and infuse. A. I do the processes that the business and he is looking. It's done in a bottom upwards, deliberately set up, designed it to be bottom up. So what I mean by that is the team on my side is fully empowered to move forward. Why did they find a like minded team on the other side and go ahead and do it? They don't have to come back for funding they don't have, You know, they just go ahead and do it. They're basically empowered to do that. And that particular set up enabled enabled us in a couple of years to have one hundred thousand internal users on our Central data and AI enabled platform. And when I mean hundred thousand users, I mean users who were using it on a monthly basis. We company, you know, So if you haven't used it in a month, we won't come. So there it's over one hundred thousand, even very rapidly to that. That's kind of the enterprise wide storm. That's kind of the bottom up direction. The top down direction Waas the strategic element that I talked with you about what I said, Hey, be our data strategy is going to be to create, make IBM itself into any I enterprise and then use that as a showcase for plants and customers That kind of and be reiterated back. And I worked the senior leadership on that view all the time talking to customers, the central and our senior leaders. And so that's kind of the air cover to do this, you know, that mix gives you, gives you that possibility. I think from a peer to peer standpoint, but you get to these lot scale and to end processes, and that there, a couple of ways I worked that one way is we've kind of looked at our enterprise data and said, Okay, therefore, major pillars off data that we want to go after data, tomato plants, data about our offerings, data about financial data, that s and then our work full student and then within that there are obviously some pillars, like some sales data that comes in and, you know, been workforce. You could have contractors. Was his employees a center But I think for the moment, about these four major pillars off data. And so let me map that to end to end large business processes within the company. You know, the really large ones, like Enterprise Performance Management, into a or lead to cash generation into and risk insides across our full supply chain and to and things like that. And we've kind of tied these four major data pillars to those major into and processes Well, well, yes, that there's a mechanism they're obviously in terms off facilitating, and to some extent one might argue, even forcing some interaction between teams that are the way they talk. But it also brings me and my peers much closer together when you set it up that way. And that means, you know, people from the HR side people from the operation side, the data side technology side, all coming together to really move things forward. So all three tracks being hit very, very hard to move the culture fall. >>Am I also correct that you have, uh, chief data officers that reporting to you whether it's a matrix or direct within the division's? Is that right? >>Yeah, so? So I mean, you know, for in terms off our structure, as you know, way our global company, we're also far flung company. We have many different products in business units and so forth. And so, uh, one of the things that I realized early on waas we are going to need data officers, each of those business units and the business units. There's obviously the enterprise objective. And, you know, you could think of the enterprise objectives in terms of some examples based on what I said in the past, which is so enterprise objective would be We've gotta have a data foundation by essentially making data along these four pillars. I talked about clients offerings, etcetera, you know, very accessible self service. You have mentioned south, so thank you. This is where the South seven speaks. Comes it right. So you can you can get at that data quickly and appropriately, right? You want to make sure that the access control, all that stuff is designed out and you're able to change your policies and you'd swap manual. But, you know, those things got implemented very rapidly and quickly. And so you've got you've got that piece off off the off the puzzle due to go after. And then I think the other aspect off off. This is, though, when you recognize that every business unit also has its own objectives and they are looking at some of those things somewhat differently. So I'll give you an example. We've got data any our product units. Now, those CEOs right there, concern is going to be a lot more around the products themselves And how were monetizing those box and so they're not per se concerned with, You know, how you reduce the enter and cycle time off IBM in total supply chain so that this is my point. So they but they're gonna have substantial considerations and objectives that they want to accomplish. And so I recognize that early on, and we came up with this notion off a data officer council and I helped staff the council s. So this is why that's the Matrix to reporting that we talked about. But I selected some of the key Blair's that we have in those units, and I also made sure they were funded by the unit. So they report into the units because their paycheck is actually determined. Pilot unit and which makes them than aligned with the objectives off the unit, but also obviously part of my central approach so that I can disseminate it out to the organization. It comes in very, very handy when you are trying to do things across the company as well. So when we you know GDP our way, we have to get the company ready for Judy PR, I would say that this mechanism became a key key aspect of what enabled us to move forward and do it rapidly. Trouble them >>be because you had the structure that perhaps the lines of business weren't. Maybe is concerned about GDP are, but you had to be concerned with it overall. And this allowed you to sort of hiding their importance, >>right? Because think of in the case of Jeannie PR, they have to be a company wide policy and implementation, right? And if he did not have that structure already in place, it would have made it that much harder. Do you get that uniformity and consistency across the company, right, You know, So you will have to in the weapon that structure, but we already have it because way said Hey, this is around for data. We're gonna have these types of considerations that they are. And so we have this thing regular. You know, this man network that meat meets regularly every month, actually, and you know, when things like GDP are much more frequently than that, >>right? So that makes sense. We're out of time. But I wonder if we could just close if you could address the M I t CDO audience that probably this is the largest audience, Believe or not, now that it's that's virtual definitely expanded the audience, but it's still a very elite group. And the reason why I was so pleased that you agreed to do this is because you've got one of the more complex organizations out there and you've succeeded. And, ah, a lot of the hard, hard work. So what? What message would you leave the M I t CDO audience Interpol? >>So I would say that you know, it's it's this particular professional. Receiving a profession is, uh, if I have to pick one trait of let me pick two traits, I think what is your A change agent? So you have to be really comfortable with change things are going to change, the organization is going to look to you to make those changes. And so that's what aspect off your job, you know, may or may not be part of me immediately. But the those particular set of skills and characteristics and something that you know, one has to, uh one has to develop or time, And I think the other thing I would say is it's a continuous looming jaw. So you continue sexism and things keep changing around you and changing rapidly. And, you know, if you just even think just in terms off the subject areas, I mean this Syria today you've got to understand technology. Obviously, you've gotta understand data you've got to understand in a I and data science. You've got to understand cybersecurity. You've gotta understand the regulatory framework, and you've got to keep all that in mind, and you've got to distill it down to certain trends. That's that's happening, right? I mean, so this is an example of that is that there's a trend towards more regulation around privacy and also in terms off individual ownership of data, which is very different from what's before the that's kind of weather. Bucket's going and so you've got to be on top off all those things. And so the you know, the characteristic of being a continual learner, I think is a is a key aspect off this job. One other thing I would add. And this is All Star Coleman nineteen, you know, prik over nineteen in terms of those four pillars that we talked about, you know, which had to do with the data technology, business process and organization and culture. From a CDO perspective, the data and technology will obviously from consent, I would say most covert nineteen most the civil unrest. And so far, you know, the other two aspects are going to be critical as we move forward. And so the people aspect of the job has never bean, you know, more important down it's today, right? That's something that I find myself regularly doing the stalking at all levels of the organization, one on a one, which is something that we never really did before. But now we find time to do it so obviously is doable. I don't think it's just it's a change that's here to stay, and it ships >>well to your to your point about change if you were in your comfort zone before twenty twenty two things years certainly taking you out of it into Parliament. All right, thanks so much for coming back in. The Cuban addressing the M I t CDO audience really appreciate it. >>Thank you for having me. That my pleasant >>You're very welcome. And thank you for watching everybody. This is Dave a lot. They will be right back after this short >>break. You're watching the queue.

>>from the >>Cube Studios in Palo Alto in Boston, connecting with thought leaders around the globe. >>These are cloud native insights. Hi, I'm stupid, man. And welcome to Episode one of Cloud Native Insights. So this is a new program brought to you by Silicon Angle Media's The Cube. I am your host stew minimum, and we're going to be digging in to cloud native and, of course, cloud native like cloud before kind of a generic term. If you look at it online, there's a lot of buzzwords. There's a lot of jargon out there, and so we want to help. Understand what? This is what This isn't on And really happy to welcome back to the program to help me kick it off you piss car. He is an industry analyst. His company is T l A Tech. You. Thanks so much for joining us. >>Thanks, Dave. Glad we're >>all right. And one of the reasons I wanted you to help me kick this off. Not only have you been on the Cube, you know your background. I met you when you were the cto of a service provider over there in Europe, where you're Netherlands based. You were did strategy for a very large ah, supermarket chain also. And you've been on the program that shows like docker con in the past. You work in the cloud native space you've done consulting for. Some of the companies will be talking about today. But you help me kick this off a little bit. When you heard here the term cloud native. Does that mean anything to you? Did that mean anything back in your previous roles? You know, help us tee that up. >>So, you know, it kind of gives off a certain direction and where people are going. Right. Um so to me, Cloud native is more about the way you use cloud, not necessarily about the cloud services themselves. So, you know, for instance, I'll take the example of the supermarket. They had a big e commerce presence. And so we were come getting them to a place where they could, in smaller teams, deploy software in a faster, more often and in a safer way so that teams could work independently of each other, work on, you know, adding business value, whatever that may be for any kind of different company. That's a cloud native to me, Connie means using that to the fullest extent, using those services available to you in a way organizationally and culturally. That makes sense to you, you know, Go wherever you need to go. Be that release every hour or, you know, transform your s AP environment to something that is more nimble, more flexible, literally more agile. So what cloud native means so many things to so many people? Because it's immediately is not directly about the technology, but how you actually use it. >>Um, and u Pua and I are in, you know, strong agreement on this thing. One is you've noticed we haven't said kubernetes yet. We haven't talked about containers because cloud native is not about the tooling. We're, you know, strong participants in you know, the CN CF activities. The Cloud Native Computing Foundation, cube con and cloud native is a huge show. Great momentum one. We're big fans of too often people would conflate and they'd say, Oh, cloud native equals. I'm doing containers and I've, you know, deployed kubernetes one of the challenges out there. You talk about companies, you know? Well, you know, I had a cloud first initiative and I'm using multi cloud and all this stuff. It's like, Well, are you actually leveraging these capabilities, or did I shove things in something I'd railed about for the last couple of years? You talk about repatriation, and repatriation is often I went to go do cloud. I didn't really understand what I was doing. I didn't understand how to leverage that stuff. And I crawled back to what I was doing before because I knew how to do that. Well, so, you know, I think you said it really well. Cloud native means I'm taking advantage of the services. I'm doing things in a much more modern way. The thing I've loved talking to practitioners and one of things I want to do on this program absolutely is talk to practitioners is how have you gone through things organizationally, there are lots of things right now. Talk about like, thin ops. And, of course, all the spin off from Dev Ops and Dev SEC ops. And, like, how are we breaking through silos? How we're modernizing our environments, how we're taking advantage of new ways of doing things and new services. So yeah, I guess you You know, there are some really cool tools out there. Those are awesome things. But, you know, I love your viewpoint. Your perspective on often people in tech are like, Hey, I have this really cool new tool that I can use, you know? Can I take advantage of that? You know, do I do things in a new way, or do I just kind of take my old way and just make things maybe a little incrementally better? Hopefully with some new tooling. >>Oh, yeah. I mean, I totally agree. Um, you know, tooling is cool. Let me let me start by saying that I You know, I'm an engineer by heart, so I love tinkering with new new stuff. So I love communities I love. Um, you know that a new terra form released, for instance, I love seeing competition in the container orchestration space. I love driving into K native server lists. You know, all those technologies I like, But it is a matter of, you know, what can you do with them, right. So, for instance, has she corporate line of mine? I work on their hashtag off. Even they offer kind of Ah, not necessarily an alternative, but kind of adjacent approach to you what the CNC F is doing, and even in those cases, and I'm up specifically calling out Hashi Corp. But I'm kind of giving. The broader overview is, um, it doesn't actually matter what to use, Even though it'll help me. It'll make me happy just to play around with them. But those new tools have to mean something. They have to solve a particular problem. You have either in speed of delivery or consistency of delivery or quality of service, the thing you are building for your customers. So it has to mean something. So back in the day when I started out in engineering 15 years ago, a lot of the engineering loss for the sake of engineering just because, you know you could create a piece of infrastructure a little faster, but there was no actual business value to be out there. That's a lot of the engineering kind of was stuck inside of its own realm, or as what you see now is, if you can use terraform and actually get all of you know the potential out of you, it allow you to release offer more quickly because you're able to stand up infrastructure for that software more quickly. And so you know, we've kind of shifted from back in the in the attic or in the basement doing I t. Stuff that no one really understands. The one kind of perceives the business value of it into the realm of okay, If we can deploy this faster or we don't even need to use a server, we can use server lists. Then we have an advantage in the marketplace. You know, whatever marketplace that is, whatever application we're talking about. And so that's the difference to me. And that was that. You know, that's what CN CF is doing to me. That is what has she Corpus is helping build. That is what you know. A lot of companies that built, for instance, a managed kubernetes service. But from nine spectral crowd, all those kinds of companies, they will help, you know, a given customer to speed up their delivery, to not care about the underlying infrastructure anymore. And that's what this is all about to me. And that is what cloud native means use it in a way that I don't actually have to do the toil off the engineering anymore. There's loads of smart people working for, you know, the Big Three cloud vendors. There's loads of people working for those manage service providers, but he's used them so that you can speed up your delivery, create better software created faster, make customers happy. >>Yeah, it's a lot to unpack there. I want to talk a little bit about that landscape, right When you talk about, you know, cloud native, maybe a little compare contrast I think about, you know, the wave of Dev ops and for often people like, you know, Dev Ops. You know, that's a cultural movement. But there's also tooling that I could buy to help me along that weighs automation, you know, going agile methodology. See, I CD are all things that you're like. Well, is this part of Dev Ops, isn't it? There's lots of companies out there that we saw rows rode that wave of Dev ops. And if you talk about cloud native, you know the first thing you know, you start with the cloud providers. So when I hear you talking about, how do we get rid of things that we don't need to worry about? Well, for years, we heard Amazon Web services talk about getting rid of undifferentiated heavy lifting. And it's something that we're huge fans off you talk about. What is the business outcome? It's not. Hey, I went from, you know, a stand alone server to I did virtualized environments. And now I'm looking container ization or serverless. What can I get rid of? How do I take advantage of native services and all of those cloud platforms? One of the huge values there is, it isn't Hey, I deployed this and maybe it's a little bit cheaper and maybe a little better. But there's that that is really the center of where innovation is happening not only from the platform providers they're setting themselves, but from that ecosystem. And I guess I'll put it out there. One of the things I would like to see from Cloud Native should be that I should be able to take care of take advantage of innovation wherever it is. So Cloud Native does not mean it must live in the public cloud. It does not necessarily mean that I'm going, you know, full bore, multi cloud everywhere. I've had some great debates with Corey Quinn, on the Cube Online and the like, because if you look at customer environments today, you know, yes, they absolutely have their data centers. They're leveraging, typically more than one public cloud. SAS is a big part of the picture and then edge computing and pulls everything away into a much more distributed architecture. So, you know, I'm glad you brought up. You know, Hashi, a company you're working with really interesting. And if you talk about cloud native, it's there. They're not trying to get people to, oh, use multiple clouds because it's good for us. It's they. Hey, the reality is that you're probably using multiple clouds, and whether it's one cloud or many clouds or even in your data center, we have a set of tools that we can offer you. So you know, Hashi, you mentioned, you know, terra form vault. You know, the various tooling is that they have open source, you know, big play in this environment, both under the CN CF umbrella and beyond. Give us a little bit as to, you know, where are the interesting places where you see either vendors and technology today, or opportunity to make these solutions better for users. >>So that's an interesting question, because I literally don't know where to begin. The spectrum is so so broad, it's all start off with a joke on this, right? You cannot buy that helps. But the vendors were sure try and sell it to you. So it's kind of where you know, the battle is is raging on its getting foothold into an organization. Um, and you see that? You know, you see companies like, how is she doing that? Um, they started out with open source tooling that kind of move into the enterprise realm. Um, you solve the issues that enterprises usually have, and that's what the club defenders will trying to you as although you know, the kind of kick start you with a free service and then move you up into their their stack. And that's you know, that's where Cloud native is kind of risky because the landscape is so fragmented, it is really hard to figure out. Okay, this tool, it actually solves my use case versus this one doesn't. But again, it's in the ecosystem in this ecosystem already, so let's let's still use it just because it's easier. Um, but it does boil the disk a lot of the discussion down into. Basically, it's a friction. How much effort does it take to start using something? Because that's where and that's basically the issues enterprises are trying to solve. It's around friction, and it used to be friction around, you know, buying servers and then kind of being stuck with him for 4 to 5 years. But now it is the vendor lock in where people in organizations have to make tough decisions. You know, what ecosystems am I going to buy into it? It's It's also where a lot of the multi cloud marketing comes from on the way down to get you into a specific ecosystem on your end companies kind of filling that gap, helping you manage that complexity and how she corpus is one of those examples in my book that help you manage that multi cloud ah challenge. So but yeah, But it is all part of that discussion around friction. >>Yeah, and I guess I would start if you say, as you said, it is such a broad spectrum out there. If you look in the developer tooling marketplace is, there's lots of people that have, you know, landscapes out there. So CN cf even has a great landscape. And you know, things like Security, you no matter wherever I am and everywhere that I am. And there's a lot of effort to try to make sure that I can have something that spans across the environment. Of course, Security, you know, huge issue in general. And right now, Cohen, 19. The global pandemic coming on has been, you know, putting a spotlight on it even more. We know shared responsibility models where security needs to be. Data is at the center of what we're talking about when we've been talking for years about companies going through their transformation, I hadn't talked about, you know, digital transformation. What that means is, at the end of the day, you need to be data driven. So there's lots of companies, you know, big movement and things like ml ops. How can I actually harness my data? I said one of the things I think we got out of the whole big data wave. It was that bit flip from, Oh my God, their data everywhere. And maybe that's a challenge for me. It now becomes an opportunity and often times somewhere that I can have new value or even new business models that we can create around data. So, you know, data security on and everyone is modernizing. So, you know, worry a bit that there is sometimes, you know, cloud native washing. You know, just like everything else. It's, you know, cloud enabled. You know, ai ready from an infrastructure standpoint, you know, how much are you actually leveraging Cloud native? The bar, we always said, is, you know, if you're putting something in your data center, how does that compare against what I could get if I'm doing aws azure or Google type of environment? So I have seen good progress over the last couple of years in what we used to call it Private Cloud. And now it's more Ah, hybrid environment or multi cloud. And it looks and acts and is managed much more like the public cloud at a lot of that. Is that driver for developers? So you know Palmer, you know, developers, developers, developers, you know, absolutely. He was right as to how important that is. And one of the things I've been a little bit hardened at is it used to be. You talked about the enterprise and while the developers were off in the corner and, you know, we need to think about them and help enable them. But now, like the Dev Ops movement, we're trying to break down those silos. You know, developers are much more in the workflow. When I look at tools out there not only get hub, you know, you talked about Hashi, you know, get lab answerable and others. Often they have ways to have nothing to developers. The product owners and others all get visibility into it. Because if you can get, you know, people in the organization all accessing the same work stream the way that they need to have it there. There's goodness there. So I guess final question I have for you is you know, what advice do we have for practitioners themselves? Often, the question is, how do I get from where I've been? So where I'm going, This whole discussion of Cloud native is you know, we spent more than a decade talking about cloud, and it was often the kind of where in the movement and the like So what? I want to tee up with cloud native is discussion, really for the next decade. And you know, if I'm, you know, a c i o If I'm in, i t how do I make sure that I'm ready for these next opportunities while still managing? You know what I have in my own environment. >>So that kind of circles back to where we started this discussion, right? Cloud native and Dev ops and a couple of those methodologies they're not actually about the tooling. They are about what to do with them. Can you leverage them to achieve a goal? And so my biggest advice is Look for that goal. First, have something toward towards because if you have a problem, the solution will present itself. Um, and I'm not saying go look for a problem. The problems, they're already It's a matter of, um, you know, articulating that problem in a way that your developers will actually understand what to do. And then they will go and find the tools that are needed to solve that particular problem. And so we turn this around in a sense that so finally, we are at a point where we can have business problems. Actually, solved by I t in a way that doesn't require, you know, millions of upfront investment or, you know, consultants from an outside company. Your developers are now able to start solving those problems, and it will maybe take a while. They may need some outside help Teoh to figure some stuff out, But the point is, we can now use you know, these cloud resource is these cloud native services in such a small, practical way that we can actually start solving these business problems in a real way. >>Yeah, you actually, earlier this year I've done a series of interviews getting ready for this type of environment. You know, one of the areas I spent a bunch of time trying to dig in. And to be frank, understand has been server lists. So, you know, people very excited about server lists. You know, one of the dynamics always is, You know, everything we're talking about with containers and kubernetes driving them to think about that. I always looked as container ization was kind of moving up the stack in making infrastructure easier. The work for applications, but something like serverless it comes, top down. It's it's more of not the tooling, but how do I build those applications in those environments and not need to think at least as much about the infrastructure? So server lists Absolutely something we will cover, you know, containers, kubernetes what I'm looking for. Always love practitioners love to somebody. You you've been, you know, in that end, user it before startups. Absolutely. We'll be talking to as well as other people you know, in the ecosystem that you want to help, have discussions, have debates. You know, we don't have, you know, a strong. You know, this is the agenda that we have for cloud native, but I really want to help facilitate the dialogue. So I'll give you a final word here. Anything You know, what's exciting you these days when you talk to your peers out there, you know, in general, you know, it can be some tools, even though we understand tools are only a piece of it or any other final tips that you have in this market >>space. Well, I want to kind of go go forward on on your statement earlier about server lists without calling, You know, any specific serverless technology out there specifically, but you're looking at those technologies you'll see, But we're now able to solve those business problems. Um, without actually even needing I t right. So no code low code platforms are very adjacent to you to do serverless movement. Um, and that's where you know, that's what really excites me of this at this point, simply because, you know, we no longer need actual hardcore engineering as a trait Teoh use i t to move the needle forward. And that's what I love about the cloud native movement that it used to be hard. And it's getting simpler in a way also more complex in a way. What we're paying someone else Teoh to solve those issues. So I'm excited to see where you know, no code low code survivalism those the kinds of technologies will take us in the next decade. >>Absolutely wonderful. When you have technology that makes it more globally accessible There, obviously, you know, large generational shifts happening in the workforce. You Thank you so much for joining us, >>actually, Sue. >>All right. And I guess the final call to action really is We are looking for those guests out there, so, you know, practitioners, startups people that have a strong viewpoint. You can reach out to me. My emails just stew Stu at silicon angle dot com where you can hit me up on the twitters. I'm just at stew on there. Also. Eso thank you so much for joining us. Planning to do these in General Weekly cadence. You'll find the articles that go along with these on silicon angle dot com. Of course. All the video on the cube dot net I'm stew minimum in and love to hear more about your cloud Native insights >>Yeah, yeah, yeah, yeah, yeah

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Hi! And welcome to the Cube. I'm your host, Sonia, to guard. And we're live at Stanford University covering Woods Women in Data Science Conference The fifth annual one And joining us today is Daphne Koller, who is the co founder who sorry is the CEO and founder of In Citro that Daphne. Welcome to the Cube. >>Nice to be here, Sonia. Thank you for having me. So >>tell us a little bit about in Citro how you how you got founded and more about your >>role. So I've been working in the intersection of machine learning and biology and health for quite a while, and it was always a bit of an interesting journey and that the data sets were quite small and limited. We're now in a different world where there's tools that are allowing us to create massive biological data sense that I think can help us solve really significant societal problems. And one of those problems that I think is really important is drug discovery and development, where despite many important advancements, the costs just keep going up and up and up. And the question is, can we use machine learning to solve that problem >>better? And you talk about this more in your keynote, so give us a few highlights of what you talked about. So in the last, you can think of >>drug discovery development in the last 50 to 70 years as being a bit of a glass half full glass, half empty. The glass half full is the fact that there's diseases that used to be a death sentence or of sentenced, a lifelong of pain and suffering that >>are now >>addressed by some of the modern day medicines. And I think that's absolutely amazing. The >>other side of >>it is that the cost of developing new drugs has been growing exponentially and what's come to be known as the Rooms law being the inverse of Moore's law, which is the one we're all familiar with because the number of drugs approved per 1,000,000,000 U. S. Dollars just keeps going down exponentially. So the question is, can we change that curve? >>And you talked in your keynote about the interdisciplinary culture to tell us more about that? I think in >>order to address some of the critical problems that we're facing. One needs to really build a culture of people who work together at from different disciplines, each bringing their own insights and their own ideas into the mix. So and in Citro, we actually have a company. That's half life scientists, many of whom are producing data for the purpose of driving machine learning models and the other Halford machine learning people in data scientists who are working on those. But it's not a handoff where one group produces that they then the other one consumes and interpreted. But really, they start from the very beginning to understand. What are the problems that one could solve together? How do you design the experiment? How do you build the model and how do you derive insights from that that can help us make better medicines for people? >>And, um, I also wanted to ask you the you co founded coursera, so tell us a little bit more about that platform. So I found that >>coursera as a result of work that I've been doing at Stanford, working on how technology can make education better and more accessible. This was a project that I did here, number of my colleagues as well. And at some point in the fall of 2011 there was an experiment of Let's take some of the content that we've been we've been developing within within Stanford and put it out there for people to just benefit from, and we didn't know what would happen. Would it be a few 1000 people, but within a matter of weeks with minimal advertising Other than one New York Times article that went viral, we had 100,000 people in each of those courses. And that was a moment in time where, you know, we looked at it at this and said, Can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world? And so I ended up taking a what was supposed to be to really absence from Stanford to go and co found coursera, and I thought I'd go back after two years, but the But at the end of that two year period, the there was just so much more to be done and so much more impact that we could bring to people all over the world, people of both genders, people of different social economic status, every single country around the world. We just felt like this was something that I couldn't not dio. >>And how did you Why did you decide to go from an educational platform to then going into machine learning and biomedicine? >>So I've been doing Corsair for about five years in 2016 and the company was on a great trajectory. But it's primarily >>a >>a content company, and around me, machine learning was transforming the world, and I wanted to come back and be part of that. And when I looked around, I saw machine learning being applied to e commerce and the natural language and to self driving cars. But there really wasn't a lot of impact being made on the life science area. I wanted to be part of making that happen, partly because I felt like coming back to your earlier comment that in order to really have that impact, you need to have someone who speaks both languages. And while there's a new generation of researchers who are bilingual in biology and machine learning, there's still a small group in there, very few of those in kind of my age cohort and I thought that I would be able to have a real impact by bullying company in the space. >>So it sounds like your background is pretty varied. What advice would you give to women who are just starting college now who may be interested in the similar field? Would you tell them they have to major in math? Or or do you think that maybe, like there's some other majors that may be influential as well? I think >>there is a lot of ways to get into data science. Math is one of them. But there's also statistics or physics. And I would say that especially for the field that I'm currently in, which is at the intersection of machine learning data science on the one hand, and biology and health on the other one can, um, get there from biology or medicine as well. But what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in, because that foundation is a really strong one. There is ah lot of people out there who are basically lightweight consumers of data science, and they don't really understand how the methods that they're deploying, how they work and that limits thumb in their ability to advance the field and come up with new methods that are better suited, perhaps, of the problems of their tackling. So I think it's totally fine. And in fact, there's a lot of value to coming into data science from fields other than now third computer science. But I think taking courses in those fields, even while you're majoring in whatever field you're interested in, is going to make you a much better person who lives at that intersection. >>And how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies >>that are very strongly R and D, focused like like in Citro and others? Having a technical co founder is absolutely essential because it's fine to have and understanding of whatever the user needs and so on and come from the business side of it. And a lot of companies have a business co founder. But not understanding what the technology can actually do is highly limiting because you end up hallucinating. Oh, if we could only do this and that would be great. But you can't and people end up often times making ridiculous promises about what's technology will or will not do because they just don't understand where the land mines sit. And, um, and where you're going to hit reels, obstacles in the path. So I think it's really important to have a strong technical foundation in these companies. >>And that being said, Where do you see in Teacher in the future? And how do you see it solving, Say, Nash, that you talked about in your keynote. >>So we hope that in Citro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up. In the old approach of that is very much a bespoke scientific um, analysis of the biology of different diseases and then going after targets are ways of dealing with the disease that are driven by human intuition. Where I think we have the opportunity to go today is to build a very data driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't. And so hopefully we'll be able to overtime create enough data and applying machine learning to address key bottlenecks in the drug discovery development process that we can bring better drugs to people, and we can do it faster and hopefully it much lower cost. >>That's great. And you also mention in your keynote that you think the 20 twenties is like a digital biology era, so tell us more about that. So I think if >>you look, if you take a historical perspective on science and think back, you realize that there's periods in history where one discipline has made a tremendous amount of progress in relatively short amount of time because of a new technology or a new way of looking at things in the 18 seventies, that discipline was chemistry with the understanding of the periodic table, and that you actually couldn't turn lead into gold in the 19 hundreds. That was physics with understanding the connection between matter and energy in between space and time. In the 19 fifties that was computing where silicon chips were suddenly able to perform calculations that up until that point, only people have been able to >>dio. And then in 19 nineties, >>there was an interesting bifurcation. One was three era of data, which is related to computing but also involves elements, statistics and optimization of neuroscience. And the other one was quantitative biology. In which file do you move from a descriptive signs of taxonomy izing phenomenon to really probing and measuring biology in a very detailed on high throughput way, using techniques like micro arrays that measure the activity of 20,000 genes at once, or the human genome sequencing of the human genome and many others. But >>these two fields kind of >>evolved in parallel, and what I think is coming now, 30 years later, is the convergence of those two fields into one field that I like to think of a digital biology where we are able using the tools that have and continue to be developed, measure biology, an entirely new levels of detail, of fidelity of scale. We can use the techniques of machine learning and data signs to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do. And that will have implications and bio materials in energy and the environment in agriculture. And I think also in human health. And it's a incredibly exciting space toe to be in right now, because just so much is happening in the opportunities to make a difference and make the world a better place or just so large. >>That sounds awesome. Stephanie. Thank you for your insight. And thanks for being on the Cube. Thank you. I'm Sonia. Taqueria. Thanks for watching. Stay tuned for more. Okay? Great. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in Data Science 2020. Brought to you by Silicon Angle Media >>and welcome to the Cube. I'm your host Sonia category, and we're live at Stanford University, covering the fifth annual Woods Women in Data Science conference. Joining us today is Tilapia Williams, who's the associate professor of mathematics at Harvey Mudd College and host of Nova Wonders at PBS to leave a welcome to the Cappy to be here. Thanks for having me. So you have a lot of rules. So let's first tell us about being an associate professor at Harvey Mudd. >>Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun in the math department, but I'm a statistician by training, so I teach a lot of courses and statistics and data science and things like that. >>Very cool. And you're also a host of API s show called Novo Wonders. >>Yeah, that came about a couple of years ago. Folks at PBS reached out they had seen my Ted talk, and they said, Hey, it looks like you could be fund host of this science documentary shows So, Nova Wonders, is a six episode Siri's. It kind of takes viewers on a journey of what the cutting edge questions and science are. Um, so I got to host the show with a couple other co host and really think about like, you know, what are what are the animals saying? And so we've got some really fun episodes to do. What's the universe made of? Was one of them what's living inside of us. That was definitely a gross win. Todo figure out all the different micro organisms that live inside our body. So, yeah, it's been funded in hopes that show as well. >>And you talk about data science and AI and all that stuff on >>Yeah. Oh, yeah, yeah, one of the episodes. Can we build a Brain was dealt with a lot of data, big data and artificial intelligence, and you know, how good can we get? How good can computers get and really sort of compared to what we see in the movies? We're a long way away from that, but it seems like you know we're getting better every year, building technology that is truly intelligent, >>and you gave a talk today about mining for your own personal data. So give us some highlights from your talk. Yeah, >>so that talks sort of stemmed out of the Ted talk that I gave on owning your body's data. And it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions on DSO ways that you can use, like your temperature data or your heart rate. Dina. Or what is data say over time? What does it say about your body's health and really challenging the audience to get excited about looking at that data? We have so many devices that collect data automatically for us, and often we don't pause on enough to actually look at that historical data. And so that was what the talk was about today, like, here's what you can find when you actually sit down and look at that data. >>What's the most important data you think people should be collecting about themselves? >>Well, definitely not. Your weight is. I don't >>want to know what that >>is. Um, it depends, you know, I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body's fertile. When you're ovulating, it can. So that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar. You have high blood pressure or high blood sugar. Those things become really critical to keep an eye on. And, um, and I really encourage people whatever data they take, too, the active in the understanding of an interpretation of the data. It's not like if you take this data, you'll be healthy radio. You live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. So >>absolutely put putting people in charge of their >>own bodies. That's >>right. >>And actually speaking about that in your Ted talk, you mentioned how you were. Your doctor told you to have a C section and you looked at the data and he said, No, I'm gonna have this baby naturally. So tell us more about that. >>Yes, you should always listen to your medical pressures. But in this case, I will say that it was It was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that, like, I have a PhD in statistics and I know data, he was really kind of objectively with the on call doctor at the time, looking at the data >>and talking about it. >>And this doctor was this is his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person would have only looked at this one chart and was it was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say, like, let me tell you more about you know, my body and this is pregnancy number three like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, Here's my advice. We just need to do this. I'm like, >>Oh, >>you know, and so is gently as possible. I tried to really share that data. Um, and then it got to the point where it was sort of like either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, Well, to sign the waiver that cost quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so, yeah, >>I think >>that at the time, >>But, >>you know, with that caveat that you should listen to what, your doctors >>Yeah. I mean, there's really interesting, like, what's the boundary between, Like what the numbers tell you and what professional >>tells me Because I don't have an MD. Right. And so, you know, I'm cautious not to overstep that, but I felt like in that case, the doctor wasn't really even considering the data that I was bringing. Um, I was we were actually induced with our first son, but again, that was more of a conversation, more of a dialogue. Here's what's happening here is what we're concerned about and the data to really back it up. And so I felt like in that case, like Yeah, I'm happy to go with your suggestion, but I could number three. It was just like, No, this isn't really >>great. Um, so you also wrote a book called Power In Numbers. The Rebel Women of Mathematics. So what inspired you to write this book? And what do you hope readers take away from it? >>A couple different things. I remember when I saw the movie hidden figures. And, um, I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fun connection toe, you know, having worked at NASA. And, um, when this movie came out and I'm sitting there watching it and I'm, like ball in just crying, like I didn't know that there were black women who worked at NASA like, before me, you know, um and so it felt it felt it was just so transformative for me to see these stories just sort of unfold. And I thought, like, Well, why didn't I learn about these women growing up? Like imagine, Had I known about Katherine Johnsons of the world? Maybe that would have really inspired Not just me, but, you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say, like, there have been women who've done mathematics, um, and statistics and data science for years, and they're women who are doing it now. So a lot of the about 1/3 of the book are women who were still here and, like, active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some things like, Oh my gosh, >>women in math >>It is really like a very picturesque book of showing this beautiful images of the women and their mathematics and their work. And yes, I'm really proud of it. >>That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African American women, especially who are part of this industry. So what advice would you give to those women who who feel like they don't belong. >>Yeah, well, a they really do belong. Um, and I think it's also incumbent of people in the industry to sort of recognize ways that they could be advocate for women, and especially for women of color, because often it takes someone who's already at the table to invite other people to the table. And I can't just walk up like move over, get out the way I'm here now. But really being thoughtful about who's not representative, how do we get those voices here? And so I think the onus is often mawr on. People who occupy those spaces are ready to think about how they can be more intentional in bringing diversity in other spaces >>and going back to your talk a little bit. Um uh, how how should people use their data? >>Yeah, so I mean, I think, um, the ways that we've used our data, um, have been to change our lifestyle practices. And so, for example, when I first got a Fitbit, um, it wasn't really that I was like, Oh, I have a goal. It was just like I want something to keep track of my steps And then I look at him and I feel like, Oh, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, Okay, let me see if I hit 10 stuff, you know, 10,000 >>steps in a day or >>and so, in some ways, having the data allows you to set goals. Some people come in knowing, like, I've got this goal. I want to hit it. But for me, it was just sort of like, um and so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a gold up, restore it, and it also gives me that historical information to see like, Oh, this is how far I've come. Like I can't stop there, you know, >>that's a great social impact. >>That's right. Yeah, absolutely. >>and, um, Do you think that so in terms of, like, a security and privacy point of view, like if you're recording all your personal data on these devices, how do you navigate that? >>Yeah, that's a tough one. I mean, because you are giving up that data privacy. Um, I usually make sure that the data that I'm allowing access to this sort of data that I wouldn't care if it got published on the cover of you know, the New York Times. Maybe I wouldn't want everyone to see what my weight is, but, um, and so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data as opposed to, say, my DNA. Like I'm not going to do a DNA test. You know, I don't want my data to be mapped it out there for the world. Um, but I think that that's increasingly become a concern because people are giving access to of their information to different companies. It's not clear how companies would use that information, so if they're using my data to build a product will make a product better. You know we don't see any world from that way. We don't have the benefit of it, but they have access to our data. And so I think in terms of data, privacy and data ethics, there's a huge conversation to have around that. We're only kind >>of at the beginning of understanding what that is. Yeah, >>well, thank you so much for being on the Cube. Really having you here. Thank you. Thanks. So I'm Sonia to Gary. Thanks so much for watching the cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah, and welcome to the Cube. I'm your host Sonia Category and we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is new Sha Ajami, who's the director of urban water policy for Stanford. You should welcome to the Cube. Thank you for having me. Absolutely. So tell us a little bit about your role. So >>I directed around water policy program at Stanford. We focused on building solutions for resilient cities to try to use data science and also the mathematical models to better understand how water use is changing and how we can build a future cities and infrastructure to address the needs of the people in the US, in California and across the world. >>That's great. And you're gonna give a talk today about how to build water security using big data. So give us a preview of your talk. >>Sure. So the 20th century water infrastructure model was very much of a >>top down model, >>so we built solutions or infrastructure to bring water to people, but people were not part of the loop. They were not the way that they behaved their decision making process. What they used, how they use it wasn't necessarily part of the process and the assume. There's enough water out there to bring water to people, and they can do whatever they want with it. So what we're trying to do is you want to change this paradigm and try to make it more bottom up at to engage people's decision making process and the uncertainty associated with that as part of the infrastructure planning process. Until I'll be talking, I'll talk a little bit about that. >>And where is the most water usage coming from? So, >>interestingly enough, in developed world, especially in the in the western United States, 50% of our water is used outdoors for grass and outdoor spacing, which we don't necessarily are dependent on. Our lives depend on it. I'll talk about the statistics and my talk, but grass is the biggest club you're going in the US while you're not really needing it for food consumption and also uses four times more water >>than than >>corn, which is which is a lot of water. And in California alone, if you just think about some of the spaces that we have grass or green spaces, we have our doors in the in. The in the malls are institutional buildings or different outdoor spaces. We have some of that water. If we can save, it can provide water for about a 1,000,000 or two million people a year. So that's a lot of water that we can be able to we can save and use, or you are actually a repurpose for needs that you really half. >>So does that also boil down to like people of watering their own lawns? Or is the problem for a much bigger grass message? >>Actually, interestingly enough, that's only 10% of that water out the water use. The rest of it is actually the residential water use, which is what you and I, the grass you and I have in our backyard and watering it so that water is even more than that amount that I mentioned. So we use a lot of water outdoors and again. Some of these green spaces are important for community building for making sure everybody has access to green spaces and people. Kids can play soccer or play outdoors, but really our individual lawns and outdoor spaces. If there are not really a native you know landscaping, it's not something that views enough to justify the amount of water you use for that purpose. >>So taking longer showers and all the stuff is very minimal compared to no, not >>at all. Sure, those are also very, very important. That's another 50% of our water. They're using that urban areas. It is important to be mindful the baby wash dishes. Maybe take shower the baby brush rt. They're not wasting water while you're doing that. And a lot of other individual decisions that we make that can impact water use on a daily basis. >>Right, So So tell us a little bit more about right now in California, We just had a dry February was the 1st 150 years, and you know, this is a huge issue for cities, agriculture and for potential wildfires. So tell us about your opinion about that. So, >>um, the 20th century's infrastructure model I mentioned at the beginning One of the flaws in that system is that it assumes that we will have enough snow in the mountains that would melt during the spring and summer time and would provide us water. The problem is, climate change has really, really impacted that assumption, and now you're not getting as much snow, which is comes back to the fact that this February we have not received any snow. We're still in the winter and we have spring weather and we don't really have much snow on the mountain. Which means that's going to impact the amount of water we have for summer and spring time this year. We had a great last year. We got enough water in our reservoirs, which means that you can potentially make it through. But then you have consecutive years that are dry and they don't receive a lot of water precipitation in form of snow or rain. That will become a very problematic issue to meet future water demands in California. >>And do you think this issue is along with not having enough rainfall, but also about how we store water, or do you think there should be a change in that policy? >>Sure, I think that it definitely has something also in the way we store water and be definitely you're in the 21st century. We have different problems and challenges. It's good to think about alternative ways off a storing water, including using groundwater sources. Groundwater as a way off, storing excess water or moving water around faster and making sure we use every drop of water that falls on the ground and also protecting our water supplies from contamination or pollution. >>And you see it's ever going to desalination or to get clean water. So, interestingly >>enough, I think desalination definitely has worth in other parts of the world, and then they have. Then you have smaller population or you have already tapped out of all the other options that are available to you. Desalination is expensive. Solution costs a lot of money to build this infrastructure and also again depends on you know, this centralized approach that we will build something and provide resources to people from from that location. So it's very costly to build this kind of solutions. I think for for California we still have plenty of water that we can save and repurpose, I would say, and also we still can do recycling and reuse. We can capture our stone water and reuse it, so there's so many other, cheaper, more accessible options available before you go ahead and build a desalination plants >>and you're gonna be talking about sustainable water resource management. So tell us a little bit more about that, too. So the thing with >>water mismanagement and occasionally I use also the word like building resilient water. Future is all about diversifying our water supply and being mindful of how they use our water, every drop of water that use its degraded on. It needs to be cleaned up and put back in the environment, so it always starts from the bottom. The more you save, the less impact you have on the environment. The second thing is you want to make sure every trouble wanted have used. We can use it as many times possible and not make it not not. Take it, use it, lose its right away, but actually be able to use it multiple times for different purposes. Another point that's very important, as actually majority of the water they've used on a daily basis is it doesn't need to be extremely clean drinking water quality. For example, if you tell someone that you're flushing down our toilets. Drinkable water would surprise you that we would spend this much time and resources and money and energy to clean that water to flush it down the toilet video using it. So So basically rethinking the way we built this infrastructure model is very important, being able to tailor water to the needs that we have and also being mindful of Have you use that resource? >>So is your research focus mainly on California or the local community? We actually >>are solutions that we built on our California focus. Actually, we try to build solutions that can be easily applied to different places. Having said that, because you're working from the bottom up, wavy approach water from the bottom up, you need to have a local collaboration and local perspective to bring to their to this picture on. A lot of our collaborators have been so far in California, we have had data from them. We were able to sort of demonstrate some of the assumptions we had in California. But we work actually all over the world. We have collaborators in Europe in Asia and they're all trying to do the same thing that we dio on. You're trying to sort of collaborate with them on some of the projects in other parts of the world. >>That's awesome. So going forward, what do you hope to see with sustainable water management? So, to >>be honest with you, I would often we think about technology as a way that would solve all our problems and move us out of the challenges we have. I would say technology is great, but we need to really rethink the way we manager resource is on the institutions that we have on there. We manage our data and information that we have. And I really hope that became revolutionized that part of the water sector and disrupt that part because as we disrupt this institutional part >>on the >>system, provide more system level thinking to the water sector, I'm hoping that that would change the way we manage our water and then actually opens up space for some of these technologies to come into play as >>we go forward. That's awesome. So before we leave here, you're originally from Tehran. Um and and now you're in this data science industry. What would you say to a kid who's abroad, who wants to maybe move here and have a career in data science? >>I would say Study hard, Don't let anything to disk or do you know we're all equal? Our brains are all made the same way. Doesn't matter what's on the surface. So, um so I and encourage all the girls study hard and not get discouraged and fail as many times as you can, because failing is an opportunity to become more resilient and learn how to grow. And, um and I have, and I really hope to see more girls and women in this in these engineering and stem fields, to be more active on, become more prominent. >>Have you seen a large growth within the past few years? Definitely, >>the conversation is definitely there, and there are a lot more women, and I love how Margot and her team are sort of trying to highlight the number of people who are out there. And working on these issues because that demonstrates that the field wasn't necessarily empty was just not not highlighted as much. So for sure, it's very encouraging to see how much growth you have seen over the years for sure >>you shed. Thank you so much. It's really inspiring all the work you do. Thank you for having me. So no, Absolutely nice to meet you. I'm Senator Gary. Thanks for watching the Cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. Yeah. >>Hi. And welcome to the Cube. I'm your host Sonia Category. And we're live at Stanford University for the fifth annual Woods Women in Data Science Conference. Joining us today is none. Ho, the director of data Science at Intuit None. Welcome to the Cube. >>Thank you for having me here, so yeah, >>so tell us a little bit about your role at Intuit. So I leave the >>applied Machine Learning teams for our QuickBooks product lines and also for our customer success organization within my team. We do applied machine learning. So what? We specialize in building machine learning products and delivering them into our products for >>our users. Great. Today. Today you're giving a talk. You talked about how organizations want to achieve greater flexibility, speed and cost efficiencies on. And you're giving it a technical vision. Talk today about data science in the cloud world. So what should data scientists know about data science in a cloud world? >>Well, I'll just give you a little bit of a preview into my talk later because I don't want to spoil anything. Yeah, but I think one of the most important things being a data scientist in a cloud world is that you have to fundamentally change the way you work a lot of a start on our laptops or a server and do our work. But when you move to the cloud, it's like all bets are off. All the limiters are off. And so how do you fully take advantage of that? How do you change your workflow? What are some of the things that are available to you that you may not know about? And in addition to that, some some things that you have to rewire in your brain to operate in this new environment. And I'm going to share some experiences that I learned firsthand and also from my team in into its cloud migration over the past six years. >>That's great. Excited to hear that on DSO you were getting into it into it has sponsored Woods for many years now. Last year we spoke with could be the San Juan from Intuit. So tell us about this Intuit's sponsorship. Yeah, >>so into it. We are a champion of gender diversity and also all sorts of diversity. And when we first learned about which we said, We need to be a champion of the women in data science conference because for me personally, often times when I'm in a room, um, going over technical details I'm often the only woman and not just I'm often the only woman executive and so part of the sponsorship is to create this community of women, very technical women in this field, to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science. >>And so Intuit has always been really great for embracing diversity. Tell us a little bit about about bad experience, about being part of Intuit and also about the tech women part. Yeah, >>so one of the things that into it that I really appreciate is we have employees groups around specific interests, and one of those employees groups is tech women at Intuit and Tech women at Intuit. The goal is to create a community of women who can provide coaching, mentorship, technical development, leadership development and I think one of the unique things about it is that it's not just focused on the technical development side, but on helping women develop into leadership positions. For me, When I first started out, there were very few women in executive positions in our field and data science is a brand new field, and so it takes time to get there. Now that I'm on the other side, one of the things that I want to do is be able to give back and coach the next generation. And so the tech women at Intuit Group allows me to do that through a very strong mentorship program that matches me and early career mentees across multiple different fields so that I can provide that coaching in that leadership development >>and speaking about like diversity. In the opening address, we heard that diversity creates perspectives, and it also takes away bias. So why gender diversity is so important into it, and how does it help take away that bias? Yeah, >>so one of the important things that I think a lot of people don't realize is when you go and you build your products, you bring in a lot of biases and how you build the product and ultimately the people who use your products are the general population for us. We serve consumer, small businesses and self employed. And if you take a look at the diversity of our customers, it mirrors the general population. And so when you think about building products, you need to bring in those diverse perspectives so you could build the best products possible because of people who are using those products come from a diverse background as well, >>right? And so now at Intuit like instead of going from a desktop based application, we're at a cloud based application, which is a big part of your talk. How do you use data Teoh for a B testing and why is it important? >>Yeah, a B testing That is a personal passion of mine, actually, because as a scientist, what we like to do is run a lot of experiments and say, Okay, what is the best thing out there so that ultimately, when you ship a new product or feature, you send the best thing possible that's verified by data, and you know exactly how users are going to react to it. When we were on desktop, they made it incredibly difficult because those were back in the days. And I don't know if you remember those put back in the days when you had a floppy disk, right or even a CD ROM's. That's how we shipped our products. And so all the changes that you wanted to make had to be contained. In the end, you really only ship it once per year. So if there's any type of testing that we did, we're bringing our users and have them use our products a little bit and then say Okay, we know exactly what we need to dio ship that out. So you only get one chance now that we're in the cloud. What that allows us to do is to test continuously via a B, testing every new feature that comes out. We have a champion Challenger model, and we can say Okay, the new version that we're shipping out is this much better than the previous one. We know it performs in this way, and then we got to make the decision. Is this the best thing to do for a customer? And so you turn what was once a one time process, a one time change management process. So one that's distributed throughout the entire year and at any one time we're running hundreds of tests to make sure that we're shipping exactly the best things for our customers. >>That's awesome. Um, so, um, what advice would you give to the next generation of women who are interested in stem but maybe feel like, Oh, I might be the only woman. I don't know if I should do this. Yeah, I think that the biggest >>thing for me was finding men's ownership, and initially, when I was very early career and even when I was doing my graduate studies for me, a mentor with someone who was in my field. But when I first joined into it, an executive in another group who is a female, said, Hey, I'd like to take your side, provide you some feedback, and this is some coaching I want to give you, And that was when I realized you don't actually need to have that person be in your field to actually guide you through to the next up. And so, for women who are going through their journey and early on, I recommend finding a mentor who is at a stage where you want to go, regardless of which field there in, because everybody has diverse perspectives and things that they can teach you as you go along. >>And how do you think Woods is helping women feel like they can do data science and be a part of the community? Yeah, I think >>what you'll see in the program today is a huge diversity of our speakers, our Panelists through all different stages of their career and all different fields. And so what we get to see is not only the time baseline of women who are in their PhDs all the way to very, very well established women. The provost of Stanford University was here today, which is amazing to see someone at the very top of the career who's been around the block. But the other thing is also the diversity and fields. When you think about data science, a lot of us think about just the tech industry. But you see it in healthcare. You see it in academia and there's a scene that wide diversity of where data science and where women who are practicing data science come from. I think it's really empowering because you can see yourself in the representation does matter quite a bit. >>Absolutely. And where do you see data science going forward? >>Oh, that is a, uh, tough and interesting question, actually. And I think that in the current environment today, we could talk about where it could go wrong or where it could actually open the doors. And for me, I'm an eternal optimist on one of the things that I think is really, really exciting for the future is we're getting to a stage where we're building models, not just for the general population. We have enough data and we have enough compute where we can build a model. Taylor just for you, for all of your life's on for me. I think that that is really, really powerful because we can build exactly the right solution to help our customers and our users succeed. Specifically, me working in the personal friend, Small business finance lease. That means I can hope that cupcake shop owner actually manage her cash flow and help her succeed to me that I think that's really powerful. And that's where data science is headed. >>None. Thank you so much for being on the Cube and thank you for your insight. Thank you so much. I'm so sorry. Thanks for watching the Cube. Stay tuned for more. Yeah, Yeah, yeah, yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah. Hi. And welcome to the Cube. I'm your host, Sonia Atari. And we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is Lillian Kearse. Keo, who's the Insights manager at Spotify. Slowly and welcome to the Cube. Thank you so much for having me. So tell us a little bit about your role at a Spotify. >>Yeah, So I'm actually one of the few insights managers in the personalization team. Um, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So, from your limits to discover weekly to your year and wrap stories to your experience on home and the search results, that's >>awesome. Can you tell us a little bit more about the personalization? Um, team? >>Yes. We actually have a variety of different product areas that come together to form the personalization mission, which is the mission is like the term that we use for a big department at Spotify, and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app? >>Great. Um, and so you're going to be on the career panel today? How do you feel about that? I'm >>really excited. Yeah, Yeah, the would seem is in a great job of bringing together Diverse is very, uh, it's overused term. Sometimes they're a very diverse group of people with lots of different types of experiences, which I think is core. So how I think about data science, it's a wide definition. And so I think it's great to show younger and mid career women all of the different career paths that we can all take. >>And what advice would you would you give to? Women were coming out of college right now about data science. >>Yeah, so my my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientists or a team that says Data scientist, You can follow your interest into your data science. Use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of different different types of expertise coming together to work on problems. >>And speaking of mixed methods, insights is a team that's a mixed methods research groups. So tell us more about that. Yes, I >>personally manage a data scientist, Um, user researcher and the three of us collaborate highly together across their disciplines. We also collaborate across research science, the research science team right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative, and the idea is to understand the problem. Space deeply together, be able to understand. What is it that we're trying to even just form in our head is like the need that a user work and human and user human has, um, in bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, you know, a product owner you can really think through and understand the current space and like the product opportunities >>and to understand that user insight do use a B testing. >>We use a lot of >>a B testing, so that's core to how we think about our users at Spotify. So we use a lot of a B testing. We do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think a B testing, you know, there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features. You also have to think about unintended or second order effects of algorithmic a B test. So it's been just like a huge area of learning in a huge area of just very interesting outcomes. And like every test that we run, we learn a lot about not just the individual thing. We're testing with just the process overall. >>And, um, what are some features of Spotify that customers really love anything? Anything >>that's like we know use a daily mix people absolutely love every time that I make a new friend and I saw them what they work on there like I was just listening to my daily makes this morning discover weekly for people who really want >>to stay, >>you know, open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have just the nostalgia that people have, even just for the last year. But in 2019 we were actually able to do 10 years, and that amount of nostalgia just went through the roof like people were just like, Oh my goodness, you captured the time that I broke up with that, you >>know, the 1st 5 years ago, or just like when I discovered that I love Taylor Swift, even though I didn't think I like their or something like that, you know? >>Are there any surprises or interesting stories that you have about, um, interesting user experiences? Yeah. >>I mean, I could give I >>can give you an example from my experience. So recently, A few a few months ago, I was scrolling through my home feed, and I noticed that one of the highly rated things for me was women in >>country, and I was like, Oh, that's kind of weird. I don't consider >>myself a country fan, right? And I was like having this moment where I went through this path of Wait, That's weird. Why would Why would this recommend? Why would the home screen recommend women in country, country music to me? And then when I click through it, um, it would show you a little bit of information about it because it had, you know, Dolly Parton. It had Margo Price and it had the high women and those were all artistes. And I've been listening to a lot, but I just had not formed an identity as a country music. And then I click through It was like, Oh, this is a great play list and I listen to it and it got me to the point where I was realizing I really actually do like country music when the stories were centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the song, writing these other country acts that >>so quickly discovered that so you have a degree in industrial mathematics, went to a liberal arts college on purpose because you want to try out different classes. So how is that diversity of education really helped >>you in your Yes, in my undergrad is from Smith College, which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he, you know, he considers studying math, not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what? The problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I haven't undeclared major in Latin American studies, and I studied like neuroscience and quantum physics for non experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school, and I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. >>And would you recommend having this kind of like diverse education to young women now who are looking >>and I absolutely love it? I mean, I think, you know, there's some people believe that instead of thinking about steam, we should be talking instead of thinking about stem. Rather, we should be talking about steam, which adds the arts education in there, and liberal arts is one of them. And I think that now, in these conversations that we have about biases in data and ML and AI and understanding, fairness and accountability, accountability bitterly, it's a hardware. Apparently, I think that a strong, uh, cross disciplinary collaborative and even on an individual level, cross disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects for having based on the decisions of parameters for a model. In a local sense, we're optimizing and doing a great job. But what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. >>And speaking about bias. Earlier, we heard that diversity is great because it brings out new perspectives, and it also helps to reduce that unfair bias. So how it Spotify have you managed? Or has Spotify managed to create a more diverse team? >>Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there, and there's a great team that thinks about that exclusively. And they're really pushing all of us as managers. As I seizes leaders to really think about the decisions in the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environments, it's not just about diversity. It's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out what their place is in technology, whether it be because they come from a different culture, >>there are, >>you know, they might be gender, non binary. They might be women who feel like there is in a place for them. It's really about, You know, the things that I think about is because you're different. Your voice is needed even more. You know, like your voice matters and we need to figure out. And I always ask, How can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence. You know, more than some other folks. How can I help other people acquire that as well? >>Lilian, thank you so much for your insight. Thank you for being on the Cube. Thank you. I'm your host, Sonia today. Ari. Thank you for watching and stay tuned for more. Yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data Science 2020. Brought to you by Silicon Angle Media. >>Hi, and welcome to the Cube. I'm your host, Sonia today, Ari. And we're live at Stanford University covering wigs, Women in Data Science Conference 2020 And this is the fifth annual one. Joining us today is John Hoegger, who is the principal data scientist manager at Microsoft. John. Welcome to the Cube. Thanks. So tell us a little bit about your role at Microsoft. >>I manage a central data science team for myself. 3 65 >>And tell us more about what you do on a daily basis. >>Yeah, so we look at it across all the different myself. 365 products Office Windows security products has really try and drive growth, whether it's trying to provide recommendations to customers to end uses to drive more engagement with the products that they use every day. >>And you're also on the Weeds Conference Planning Committee. So tell us about how you joined and how that experience has been like, >>Yeah, actually, I was at Stanford about a week after the very first conference on. I got talking to Karen, one of this co organizers of that that conference and I found out there was only one sponsor very first year, which was WalMart Labs >>on. >>The more that she talked about it, the more that I wanted to be involved on. I thought that makes it really should be a sponsor, this initiative. And so I got details. I went back and my assessment sponsor. Ever since I've been on the committee trying it help with. I didn't find speakers on and review and the different speakers that we have each year. And it's it's amazing just to see how this event has grown over the four years. >>Yeah, that's awesome. So when you first started, how many people attended in the beginning? >>So it started off as we're in this conference with 400 people and just a few other regional events, and so was live streamed but just ready to a few universities. And ever since then it's gone with the words ambassadors and people around the world. >>Yes, and outwits has is over 60 countries on every continent except Antarctica has told them in the Kino a swell as has 400 plus attendees here and his life stream. So how do you think would has evolved over the years? >>Uh, it's it's term from just a conference to a movement. Now it's Ah, there's all these new Our regional events have been set up every year and just people coming together, I'm working together. So, Mike, self hosting different events. We had events in Redmond. I had office and also in New York and Boston and other places as well. >>So as a as a data scientist manager for many years at Microsoft, I'm I'm sure you've seen it increase in women taking technical roles. Tell us a little bit about that. >>Yeah, And for any sort of company you have to try and provide that environment. And part of that is even from recruiting and ensuring that you've got a diverse into s. So we make sure that we have women on every set of interviews to be able to really answer the question. What's it like to be a woman on this team and your old men contents of that question on? So you know that helps as faras we try, encourage more were parented some of these things demos on. I've now got a team of 30 data scientists, and half of them are women, which is great. >>That's also, um So, uh, um, what advice would you give to young professional women who are just coming out of college or who just starting college or interested in a stem field? But maybe think, Oh, I don't know if they'll be anyone like me in the room. >>Uh, you ask the questions when you interview I go for those interviews and asked, like Like, say, What's it like to be a woman on the team? All right. You're really ensuring that the teams that you're joining the companies you joined in a inclusive on and really value diversity in the workforce >>and talking about that as we heard in the opening address that diversity brings more perspectives, and it also helps take away bias from data science. How have you noticed that that bias becoming more fair, especially at your time at Microsoft? >>Yeah, and that's what the rest is about. Is just having those diverse set of perspectives on opinions in heaven. More people just looking like a data and thinking through your holiday to come. Views on and ensure has been used in the right way. >>Right. Um and so, um, what do you going forward? Do you plan to still be on the woods committee? What do you see with is going how DC woods in five years? >>Ah, yeah. I live in for this conference I've been on the committee on. I just expected to continue to grow. I think it's just going right beyond a conference. Dossevi in the podcasts on all the other initiatives that occurring from that. >>Great. >>John, Thank you so much for being on the Cube. It was great having >>you here. Thank you. >>Thanks for watching the Cube. I'm your host, Sonia, to worry and stay tuned for more. Yeah.

live from Stanford University it's the hue covering Stanford women in data science 2020 brought to you by Silicon angle media hi and welcome to the cube I'm your host Sonia - Garrett and we're live at Stanford University covering wigs women in data science conference the fifth annual one and joining us today is Daphne Koller who is the co-founder who sari is the CEO and founder of in seat row that Daphne welcome to the cube nice to be here Sonia thank you for having me so tell us a little bit about in seat row how you how it you got it founded and more about your role so I've been working in the intersection of machine learning and biology and health for quite a while and it was always a bit of a an interesting journey in that the data sets were quite small and limited we're now in a different world where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems and one of those problems that I think is really important is drug discovery development where despite many important advancements the costs just keep going up and up and up and the question is can we use machine learning to solve that problem better and you talk about this more in your keynote so give us a few highlights of what you talked about so in the last you can think of drug discovery and development in the last 50 to 70 years as being a bit of a glass half-full glass half-empty the glass half-full is the fact that there's diseases that used to be a death sentence or of the sentence still a life long of pain and suffering that are now addressed by some of the modern-day medicines and I think that's absolutely amazing the other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as Arun was law being the inverse of Moore's Law which is the one we're all familiar with because the number of drugs approved per billion u.s. dollars just keeps going down exponentially so the question is can we change that curve and you talked in your keynote about the interdisciplinary cold to tell us more about that I think in order to address some of the critical problems that were facing one needs to really build a culture of people who work together at from different disciplines each bringing their own insights and their own ideas into the mix so and in seat row we actually have a company that's half-life scientists many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those but it's not a handoff where one group produces the data and the other one consumes and interpreted but really they start from the very beginning to understand what are the problems that one could solve together how do you design the experiment how do you build the model and how do you derive insights from that that can help us make better medicines for people and I also wanted to ask you you co-founded Coursera so tell us a little bit more about that platform so I founded Coursera as a result of work that I'd been doing at Stanford working on how technology can make education better and more accessible this was a project that I did here a number of my colleagues as well and at some point in the fall of 2011 there was an experiment let's take some of the content that we've been we've been developing within it's within Stanford and put it out there for people to just benefit from and we didn't know what would happen would it be a few thousand people but within a matter of weeks with minimal advertising other than one New York Times article that went viral we had a hundred thousand people in each of those courses and that was a moment in time where you know we looked at this and said can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world and so I ended up taking a what was supposed to be a teary leave of absence from Stanford to go and co-found Coursera and I thought I'd go back after two years but the but at the end of that two-year period the there was just so much more to be done and so much more impact that we could bring to people all over the world people of both genders people of the different social economic status every single country around the world we I just felt like this was something that I couldn't not do and how did you why did you decide to go from an educational platform to then going into machine learning and biomedicine so I've been doing Coursera for about five years in 2016 and the company was on a great trajectory but it's primarily a Content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to ecommerce and the natural language and to self-driving cars but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen partly because I felt like coming back to our earlier comment that in order to really have that impact you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning there's still a small group and there very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building and company in the space so it sounds like your background is pretty varied what advice would you give to women who are just starting college now who may be interested in a similar field would you tell them they have to major in math or or do you think that maybe like there are some other majors that may be influential as well I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that found the is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than a third computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection and how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies that are very strongly Rd focused like like in C tro and others having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating oh if we could only do this and yet that would be great but you can't and people end up oftentimes making ridiculous promises about what technology will or will not do because they just don't understand where the land mines sit and and where you're gonna hit real obstacles and in the path so I think it's really important to have a strong technical foundation in these companies and that being said where do you see an teacher in the future and and how do you see it solving say Nash that you talked about in your keynote so we hope that in seat row we'll be a fully integrated drug discovery and development company that is based on a slightly different foundation than a traditional pharma company where they grew up in the old approach of that is very much bespoke scientific analysis of the biology of different diseases and then going after targets or our ways of dealing with the disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data-driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that the cord with people's preconceptions of what matters and what doesn't and so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost that's great and you also mentioned in your keynote that you think that 2020s is like a digital biology era so tell us more about that so I think if you look if you take a historical perspective on science and think back you realize that there's periods in history where one discipline has made a tremendous amount of progress in a relatively short amount of time because of a new technology or a new way of looking at things in the 1870s that discipline was chemistry was the understanding of the periodic table and that you actually couldn't turn lead into gold in the 1900s that was physics with understanding the connection between matter and energy and between space and time in the 1950s that was computing where silicon chips were suddenly able to perform calculations that up until that point only people have been able to do and then in 1990s there was an interesting bifurcation one was the era of data which is related to computing but also involves elements statistics and optimization of neuroscience and the other one was quantitative biology in which biology moved from a descriptive science of techsan amaizing phenomena to really probing and measuring biology in a very detailed and a high-throughput way using techniques like microarrays that measure the activity of 20,000 genes at once Oh the human genome sequencing of the human genome and many others but these two feels kind of evolved in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail of fidelity of scale we can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials in energy in the environment in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large that sounds awesome Daphne thank you for your insight and thank you for being on cute thank you I'm so neat agario thanks for watching stay tuned for more great

>> Narrator: Live from San Francisco, it's theCube covering RSA Conference 2020 San Francisco brought to you by Silicon Angle Media. >> Hey welcome back here ready Jeff Frick here with theCube. We're at the RSA Conference downtown San Francisco, about 40,000 people In the year we're going to know everything with the benefit of fine sight. It's not really working out that way. So we're still going out to the events, getting the smartest people we can find, bringing them to you. We're excited to have our very next guest. He's Steve Chin, the senior director of developer relations for JFrog. Steve, great to meet you. >> Thanks very much for having me here at the conference. >> Absolutely so for people that don't know JFrog, give him kind of the one on one. >> So I think the simplest way to describe our company is where the database of DevOps >> The database of DevOps. (laughs) I don't know that that would be the simplest way, >> But basically when companies want to deliver software faster, when they're looking at how to speed up their feature development, how to respond quicker to security, we provide a end-to-end DevOps platform, the JFrog platform, which accomplishes this for companies. >> Okay so a lot of people know about DevOps. A lot of people have experienced with rapid iteration on their apps. I don't know why they have to keep uploading updates all the time. There's a ton of great benefits to that and this really revolutionize the software industry. That said, the other kind of theme here at RSA and a lot of the security conferences is you can no longer bolt security on. It can no longer be a moat around the castle. It can no longer be a firewall on the edge of the network that it has to be baked in all the way through the product. And that goes right back to kind of what you guys do. And on the DevOps, how do devs who didn't necessarily get trained on security don't necessarily want to know about security and probably would prefer not to have to deal they probably liked the better when they could just push it off, but kind of like they used to push it off to prod. That's not the way anymore they have to bake it in. So how do you help them do that? What do you kind of see in terms of trends in the space? >> Yeah, so I think what we're seeing in the industry is that companies want to deliver, they need to deliver software more quickly and more rapidly. Just based on user requirements. So if you think about your phone, your car, like pretty much everything is updating constantly and it's not even a choice anymore. Updates get pushed to you because you need new features. You also need security fixes for things. And this is happening weekly, daily, hourly. As new threats are exposed and for companies, the standard processes which might have been used in the past to type security or reviews to run a complicated scanners to have like different checkpoints that doesn't work in an environment where you're continuously deploying. And really if you think about it, the only way you can accomplish rapid iteration, high security is to be doing security scanning as a part of your workflow. As a part of your DevOps workflow and shifting left. So going towards the developers and giving them more tools, which give them information about potential security risks. So as an example, developers code and an IDE or some sort of visual environment. And if you can present the information up front right there and tell them, "Hey, this open source library "you're using it has a security vulnerability, "there's a new version you should upgrade." Or "Hey this component that has an incompatible license. "Like this doesn't meet our security requirements." Those sort of things if they're caught while you're developing new features, it saves time and money there. But it delays potential slippage, risks, pushback from the security team at the other end. The next step is when they check in code or when they're executing a build. You want to be scanning up front scan the bills, scan the binary's really far up the chain. And that way you're catching security vulnerabilities during the iterative development process. By the time you get to like QA to stage to production, security vulnerabilities shouldn't be a surprise. They should be something which the teams up front know about. They're addressing and you're using tools which are designed in that workflow to really give early, often feedback to the teams up the chain and see it's the only way like all the large companies doing continuous deployments. This is how you have to approach it. You use multiple techniques, you use binary scatters, you use source code scanners even runtime scanners and you make sure you shift as much left as possible, which is exactly what the JFrog platform enables development teams to do. >> So what percentage roughly is just making sure you've got the first thing that you described that you've got the right libraries that you're using the right tools that have already gone through some security protocol check versus just writing in a bad sequence of steps or that API call or opening up some hole via just bad code choices. Yeah so I think increasingly as companies depends more on third party libraries, open source libraries. if you think about your average application, you're bundling in hundreds of different components and libraries which you have relatively little control over. And a simple way to look at this as if you created a Docker container today, you loaded up with a bunch of DB and packages, maybe a few application bundles within a few days, at the end of a month, that will be full of security vulnerabilities. So that container you build one month ago, it will be full which is outdated. You'll have hundreds of security vulnerabilities >> Just because validated patches or because people see it in attacking? >> Well the thing is you constantly have folks releasing new software, identifying vulnerability risks, patching those risks. And if you don't stay current, if you're not constantly updating your software to stay up with the latest security patches, you're putting your customers and your own business at risk. So I think today that is the number one issue with software is we all depend on open source libraries and components which are used by a lot of companies are constantly being improved and then patched. And the most important thing is knowing when their security vulnerability is identifying the risk of how those impact your customers and then patching as quickly as possible. >> And then the other piece of it is just API is to lots of other people, software that I don't necessarily have access to rights to. So the fact that so much of this stuff is all tied together. Now an attendant just opens up kind of a whole another layer of a potential attack surface. So have you seen things change in kind of IOT as kind of OT and IT come together with IOT and a lot of those OT devices, we're not necessarily set up for patching, they weren't necessarily set up with easy to get into operating systems or maybe too easy to get into operating systems. How are you seeing kind of all the growth that's happening there impact this conversation? >> Yeah, so I think especially with edge devices, I think what we've realized is that edge devices which aren't being updated or insecurity devices. So if you don't have a plan for how you update a new patch and you address security vulnerabilities in your edge devices, they're subject to the same risks. If they're running a variant of Linux, then they're running open source software. They're running a bunch of libraries. If they're on the network, they're open to network attacks. And we have even more complicated edge devices rolling around the roads now. There were some critical security patches and several of the self driving cars with braking systems, with obstacle avoidance systems. So if you don't have an aggressive plan on how you're patching your edge devices you reached the same sort of challenge. And what that involves again is identifying what libraries and components you depend upon, assessing the security risks, which those pose and then having a distribution plan. How do you go from your systems through builds, through deployments and then do the edge distribution to all the devices to get critical security updates to your end users as quickly as possible. >> I'm just curious who do you see on the teams that ultimately has responsibility that this is ready to go or not go. 'Cause we've seen too many instances of stuff that gets shipped that's not ready to go. I can certainly see the pressure to get stuff shipped and somebody says, well, that's okay, we'll just get that patch out. We'll get that patch out next week or we'll get that patch out sometime down the road. And we've seen a ton of things go out that are super easily hacked children's toys and some of these things that have all kinds of really bad implications to it. Is there somebody usually on the team that's, that needs to give the stamp of approval? Is it more of kind of a broad? >> Yes I think the traditional approach is having somebody within the company responsible for security, but increasingly to effectively address security, it needs to be the ownership of the whole team from end to end to make it successful. So the more the security team can be an ally of the QA team of the development team, of the DevOps team rather than being the gatekeeper, they want to be the ally of those teams. Then the more successful it is. So arming the other teams in your company with knowledge about security risks, arming with tools which provide visibility into different security vulnerabilities. That's the way which you have a end-to-end secure product because when you get to the release, if the security team holds up the release, you're either making a bad decision or a bad decision. Catching it up front. When you're building features, then you actually can address it and build the right security into your product, which is much better for your customers and your company. >> Well, Steve, interesting conversation, interesting times. The DevOps and the rapid deploy is certainly the way it is that we're here. So being able to effectively bake that security is only a good thing, but really a necessary thing. >> Well, this was great chatting with you and the conference here is great to see all of these folks focused on improving security and taking us to the next generation with more secure edge devices. >> I don't think there'll be any shortage of need for security professionals anytime soon. All right well thanks again Steve. >> All right, thank you. All right Steve, I'm Jeff Frick. You're watching theCube. We're at the RSA Conference in downtown San Francisco. Thanks for watching. We'll see you next time. (upbeat music)

>>Bye from San Francisco. It's the cube covering RSA conference, 2020 San Francisco brought to you by Silicon angle media. >>Hey, welcome back. Everyone's keeps coverage here in San Francisco at the Moscone center for RSA conference 2020 I'm John, your host, as cybersecurity goes to the next generation as the new cloud scale, cyber threats are out there, the real impact a company's business and society will be determined by the industry. This technology and the people that a cube alumni here, caramel Jaffer, SVP, senior vice president of strategy and corporate development for iron net. Welcome back. Thanks to Shawn. Good to be here. Thanks for having so iron net FC general Keith Alexander and you got to know new CEO of there. Phil Welsh scaler and duo knows how to scale up a company. He's right. Iron is doing really well. The iron dome, the vision of collaboration and signaling. Congratulations on your success. What's a quick update? >> Well look, I mean, you know, we have now built the capability to share information across multiple companies, multiple industries with the government in real time at machine speed. >>Really bringing people together, not just creating collected security or clip to defense, but also collaborating real time to defend one another. So you're able to divide and conquer Goliath, the enemy the same way they come after you and beat them at their own game. >> So this is the classic case of offense defense. Most corporations are playing defense, whack-a-mole, redundant, not a lot of efficiencies, a lot of burnout. Exactly. Not a lot of collaboration, but everyone's talking about the who the attackers are and collaborating like a team. Right? And you guys talk about this mission. Exactly. This is really the new way to do it. It has, the only way it works, >> it is. And you know, you see kids doing it out there when they're playing Fortnite, right? They're collaborating in real time across networks, uh, to, you know, to play a game, right? You can imagine that same construct when it comes to cyber defense, right? >>There's no reason why one big company, a second big company in a small company can't work together to identify all the threats, see that common threat landscape, and then take action on it. Trusting one another to take down the pieces they have folk to focus on and ultimately winning the battle. There's no other way a single company is gonna be able defend itself against a huge decency that has virtually unlimited resources and virtually unlimited human capital. And you've got to come together, defend across multiple industries, uh, collectively and collaboratively. >> Do you mean, we talked about this last time and I want to revisit this and I think it's super important. I think it's the most important story that's not really being talked about in the industry. And that is that we were talking last time about the government protects businesses. If someone dropped troops on the ground in your neighborhood, the government would protect you digitally. >>That's not happening. So there's really no protection for businesses. Do they build their own militia? Do they build their own army? Who was going to, who's going to be their heat shield? So this is a big conversation and a big, it brings a question. The role of the government. We're going to need a digital air force. We're going to need a digital army, Navy, Navy seals. We need to have that force, and this has to be a policy issue, but in the short term, businesses and individuals are sitting out there being attacked by sophisticated mission-based teams of hackers and nation States, right? Either camouflaging or hiding, but attacking still. This is a huge issue. What's going on? Are people talking about this in D C well, >> John, look not enough. People are talking about it, right? And forget DC. We need to be talking about here, out here in the Silicon Valley with all these companies here at the RSA floor and bring up the things you're bringing up because this is a real problem we're facing as a nation. >>The Russians aren't coming after one company, one state. They're coming after our entire election infrastructure. They're coming after us as a nation. The Chinese maybe come after one company at a time, but their goal is to take our electoral properties, a nation, repurpose it back home. And when the economic game, right, the Iranians, the North Koreans, they're not focused on individual actors, but they are coming after individual actors. We can't defend against those things. One man, one woman, one company on an Island, one, one agency, one state. We've got to come together collectively, right? Work state with other States, right? If we can defend against the Russians, California might be really good at it. Rhode Island, small States can be real hard, defends against the Russians, but if California, Rhode Island come together, here's the threats. I see. Here's what it's. You see share information, that's great. Then we collaborate on the defense and work together. >>You take these threats, I'll take those threats and now we're working as a team, like you said earlier, like those kids do when they're playing fortnight and now we're changing the game. Now we're really fighting the real fight. >> You know, when I hear general Keith Alexander talking about his vision with iron net and what you guys are doing, I'm inspired because it's simply put, we have a mission to protect our nation, our people, and a good businesses, and he puts it into kind of military, military terms, but in reality, it's a simple concept. Yeah, we're being attacked, defend and attack back. Just basic stuff. But to make it work as the sharing. So I got to ask you, I'm first of all, I love the, I love what he has, his vision. I love what you guys are doing. How real are we? What's the progression? >>Where are we on the progress bar of that vision? Well, you know, a lot's changed to the last year and a half alone, right? The threats gotten a lot, a lot more real to everybody, right? Used to be the industry would say to us, yeah, we want to share with the government, but we want something back for, right. We want them to show us some signal to today. Industry is like, look, the Chinese are crushing us out there, right? We can beat them at a, at some level, but we really need the governor to go do its job too. So we'll give you the information we have on, on an anonymized basis. You do your thing. We're going to keep defending ourselves and if you can give us something back, that's great. So we've now stood up in real time of DHS. We're sharing with them huge amounts of data about what we're seeing across six of the top 10 energy companies, some of the biggest banks, some of the biggest healthcare companies in the country. >>Right? In real time with DHS and more to come on that more to come with other government agencies and more to come with some our partners across the globe, right? Partners like those in Japan, Singapore, Eastern Europe, right? Our allies in the middle East, they're all the four lenses threat. We can bring their better capability. They can help us see what's coming at us in the future because as those enemies out there testing the weapons in those local areas. I want to get your thoughts on the capital markets because obviously financing is critical and you're seeing successful venture capital formulas like forge point really specialized funds on cyber but not classic industry formation sectors. Like it's not just security industry are taking a much more broader view because there's a policy implication is that organizational behavior, this technology up and down the stack. So it's a much broad investment thesis. >>What's your view of that? Because as you do, you see that as a formula and if so, what is this new aperture or this new lens of investing to be successful in funding? Companies will look, it's really important what companies like forge point are doing. Venture capital funds, right? Don Dixon, Alberta Pez will land. They're really innovating here. They've created a largest cybersecurity focused fund. They just closed the recently in the world, right? And so they really focus on this industry. Partners like, Kleiner Perkins, Ted Schlein, Andrea are doing really great work in this area. Also really important capital formation, right? And let's not forget other funds. Ron Gula, right? The founder of tenable started his own fund out there in DC, in the DMV area. There's a lot of innovation happening this country and the funding on it's critical. Now look, the reality is the easy money's not going to be here forever, right? >>It's the question is what comes when that inevitable step back. We don't. Nobody likes to talk about it. I said the guy who who bets on the other side of the craps game in Vegas, right? You don't wanna be that guy, but let's be real. I mean that day will eventually come. And the question is how do you bring some of these things together, right? Bring these various pieces together to really create long term strategies, right? And that's I think what's really innovative about what Don and Alberto are doing is they're building portfolio companies across a range of areas to create sort of an end to end capability, right? Andrea is doing things like that. Ted's doing stuff like that. It's a, that's really innovation. The VC market, right? And we're seeing increased collaboration VC to PE. It's looking a lot more similar, right? And now we're seeing innovative vehicles like stacks that are taking some of these public sort of the reverse manner, right? >>There's a lot of interests. I've had to be there with Hank Thomas, the guys chief cyber wrenches. So a lot of really cool stuff going on in the financing world. Opportunities for young, smart entrepreneurs to really move out in this field and to do it now. And money's still silver. All that hasn't come as innovation on the capital market side, which is awesome. Let's talk about the ecosystem in every single market sector that I've been over, my 30 year career has been about a successful entrepreneurship check, capital two formation of partnerships. Okay. You're on the iron net, front lines here. As part of that ecosystem, how do you see the ecosystem formula developing? Is it the same kind of model? Is it a little bit different? What's your vision of the ecosystem? Look, I mean partnerships channel, it's critical to every cyber security company. You can't scale on your own. >>You've got to do it through others, right? I was at a CrowdStrike event the other day. 91% of the revenue comes from the channel. That's an amazing number. You think about that, right? It's you look at who we're trying to talk about partnering with. We're talking about some of the big cloud players. Amazon, Microsoft, right? Google, right on the, on the vendor side. Pardon me? Splunk crashes, so these big players, right? We want to build with them, right? We want to work with them because there's a story to tell here, right? When we were together, the AECOS through self is defendant stronger. There's no, there's no anonymity here, right? It's all we bring a specialty, you bring specialty, you work together, you run out and go get the go get the business and make companies safer. At the end of the day, it's all about protecting the ecosystem. What about the big cloud player? >>Cause he goes two big mega trends. Obviously cloud computing and scale, right? Multi-cloud on the horizon, hybrids, kind of the bridge between single public cloud and multi-cloud and then AI you've got the biggies are generally will be multiple generations of innovation and value creation. What's your vision on the impact of the big waves that are coming? Well, look, I mean cloud computing is a rate change the world right? Today you can deploy capability and have a supercomputer in your fingertips in in minutes, right? You can also secure that in minutes because you can update it in real time. As the machine is functioning, you have a problem, take it down, throw up a new virtual machine. These are amazing innovations that are creating more and more capability out there in industry. It's game changing. We're happy, we're glad to be part of that and we ought to be helping defend that new amazing ecosystem. >>Partnering with companies like Microsoft. They didn't AWS did, you know, you know, I'm really impressed with your technical acumen. You've got a good grasp of the industry, but also, uh, you have really strong on the societal impact policy formulation side of government and business. So I want to get your thoughts for the young kids out there that are going to school, trying to make sense of the chaos that's going on in the world, whether it's DC political theater or the tech theater, big tech and in general, all of the things with coronavirus, all this stuff going on. It's a, it's a pretty crazy time, but a lot of work has to start getting done that are new problems. Yeah. What is your advice as someone who's been through the multiple waves to the young kids who have to figure out what half fatigue, what problems are out there, what things can people get their arms around to work on, to specialize in? >>What's your, what's your thoughts and expertise on that? Well, John, thanks for the question. What I really like about that question is is we're talking about what the future looks like and here's what I think the future looks like. It's all about taking risks. Tell a lot of these young kids out there today, they're worried about how the world looks right? Will America still be strong? Can we, can we get through this hard time we're going through in DC with the world challenges and what I can say is this country has never been stronger. We may have our own troubles internally, but we are risk takers and we always win. No matter how hard it gets them out of how bad it gets, right? Risk taking a study that's building the American blood. It's our founders came here taking a risk, leaving Eagle to come here and we've succeeded the last 200 years. >>There is no question in my mind that trend will continue. So the young people out there, I don't know what the future has to hold. I don't know if the new tape I was going to be, but you're going to invent it. And if you don't take the risks, we're not succeed as a nation. And that's what I think is key. You know, most people worry that if they take too many risks, they might not succeed. Right? But the reality is most people you see around at this convention, they all took risks to be here. And even when they had trouble, they got up, they dust themselves off and they won. And I believe that everybody in this country, that's what's amazing about the station is we have this opportunity to, to try, if we fail to get up again and succeed. So fail fast, fail often, and crush it. >>You know, some of the best innovations have come from times where you had the cold war, you had, um, you had times where, you know, the hippie revolution spawn the computer. So you, so you have the culture of America, which is not about regulation and stunting growth. You had risk-taking, you had entrepreneurship, but yet enough freedom for business to operate, to solve new challenges, accurate. And to me the biggest imperative in my mind is this next generation has to solve a lot of those new questions. What side of the street is the self driving cars go on? I see bike lanes in San Francisco, more congestion, more more cry. All this stuff's going on. AI could be a great enabler for that. Cyber security, a direct threat to our country and global geopolitical landscape. These are big problems. State and local governments, they're not really tech savvy. They don't really have a lot ID. >>So what do they do? How do they serve their, their constituents? You know, look John, these are really important and hard questions, but we know what has made technology so successful in America? What's made it large, successful is the governor state out of the way, right? Industry and innovators have had a chance to work together and do stuff and change the world, right? You look at California, you know, one of the reasons California is so successful and Silicon Valley is so dynamic. You can move between jobs and we don't enforce non-compete agreements, right? Because you can switch jobs and you can go to that next higher value target, right? That shows the value of, you know, innovation, creating innovation. Now there's a real tendency to say, when we're faced with challenges, well, the government has to step in and solve that problem, right? The Silicon Valley and what California's done, what technology's done is a story about the government stayed out and let innovators innovate, and that's a real opportunity for this nation. >>We've got to keep on down that path, even when it seemed like the easier answer is, come on in DC, come on in Sacramento, fix this problem for us. We have demonstrated as a country that Americans and individual are good at solve these problems. We should allow them to do that and innovate. Yeah. One of my passions is to kind of use technology and media to end communities to get to the truth faster. A lot of, um, access to smart minds out there, but young minds, young minds, uh, old minds, young minds though. It's all there. You gotta get the data out and that's going to be a big thing. That's the, one of the things that's changing is the dark arts of smear campaigns. The story of Bloomberg today, Oracle reveals funding for dark money, group biting, big tech internet accountability projects. Um, and so the classic astroturfing get the Jedi contract, Google WASU with Java. >>So articles in the middle of all this, but using them as an illustrative point. The lawyers seem to be running the kingdom right now. I know you're an attorney, so I'm recovering, recovering. I don't want to be offensive, but entrepreneurship cannot be stifled by regulation. Sarbanes Oxley slowed down a lot of the IPO shifts to the latest stage capital. So regulation, nest and every good thing. But also there's some of these little tactics out in the shadows are going to be revealed. What's the new way to get this straightened out in your mind? We'll look, in my view, the best solution for problematic speech or pragmatic people is more speech, right? Let's shine a light on it, right? If there are people doing shady stuff, let's talk about it's an outfit. Let's have it out in the open. Let's fight it out. At the end of the day, what America's really about is smart ideas. >>Winning. It's a, let's get the ideas out there. You know, we spent a lot of time, right now we're under attack by the Russians when it comes to our elections, right? We spent a lot of time harping at one another, one party versus another party. The president versus that person. This person who tells committee for zap person who tells committee. It's crazy when the real threat is from the outside. We need to get past all that noise, right? And really get to the next thing which is we're fighting a foreign entity on this front. We need to face that enemy down and stop killing each other with this nonsense and turn the lights on. I'm a big believer of if something can be exposed, you can talk about it. Why is it happening exactly right. This consequences with that reputation, et cetera. You got it. >>Thanks for coming on the queue. Really appreciate your insight. Um, I want to just ask you one final question cause you look at, look at the industry right now. What is the most important story that people are talking about and what is the most important story that people should be talking about? Yeah. Well look, I think the one story that's out there a lot, right, is what's going on in our politics, what's going on in our elections. Um, you know, Chris Krebs at DHS has been out here this week talking a lot about the threat that our elections face and the importance about States working with one another and States working with the federal government to defend the nation when it comes to these elections in November. Right? We need to get ahead of that. Right? The reality is it's been four years since 2016 we need to do more. That's a key issue going forward. What are the Iranians North Koreans think about next? They haven't hit us recently. We know what's coming. We got to get ahead of that. I'm going to come again at a nation, depending on staff threat to your meal. Great to have you on the QSO is great insight. Thanks for coming on sharing your perspective. I'm John furrier here at RSA in San Francisco for the cube coverage. Thanks for watching.

>>Fly from San Francisco. It's the cube covering RSA conference, 2020 San Francisco brought to you by Silicon angle media. >>Hello and welcome back to the cube coverage here in San Francisco, the Moscone center for RSA Congress 2020 for all the coverage period for three days. I'm John, host of the cube. You know, as cybersecurity goes to the next level, as cloud computing goes, continues to go more enterprise, large scale AI and machine learning have become critical managing the data. We've got a great guest here from Intel, Kaz Borzynski, senior director of the AI price with Intel. Thanks for joining us. Oh thanks. So data is a huge, huge data problem when it comes down to cybersecurity, uh, and and generally across the enterprise. Now it's well known, well documented, but you're here giving a talk about machine learning privacy because everyone wants to know who the bad guys are. So do the bad guys deserve privacy? Okay, we'll get to that later. But first tell about your talk and give a talk here at RSA. >>We'll get into other stuff later. I gave a talk, so thanks for having me. I gave a talk on a whole suite of exciting new techniques known as privacy preserving machine learning. So this is a set of machine learning techniques that help people realize the promise of AI and machine learning. But we know that machine learning systems rely on underlying data to train. So how can you also respect the privacy and the security of the underlying data while still being able to train and use AI systems and just take it, where are you within the Intel sphere? Because Intel osseous surgery obviously chips and power to all the enterprises in large. Skip. How are you on the software side AI group? Explain where you are. And so I'm in the AI group at Intel, but I have the most fun job at Intel. I think so cause I work in the CTO office of the AI group, which means I get to think about more futuristic, you know, where is AI going? >>What are some of the major inflection points? One of these that we've been looking at for the last couple of years is this kind of collision course between the need for data to train machine learning systems to unlock all the power of AI, but still the need to keep data private. Yeah, and I think that's generally consistent with our editorial in our research, which is the confluence of cloud native, large scale cloud computing, multi-cloud and AI or machine learning, all kinds of coming together. Those are multigenerational technologies that are coming. So that's, this wave is big. That's right. And I think one thing that's kind of maybe underappreciated about machine learning, especially in production is it's almost always a multi-party interaction. So you'll have, let's say one party that owns data and other party may own a model. They're running a system on somebody else's hardware. So because of the nature of digital data, if you want to share things, you have to worry about what other parties may be doing with those data. >>Because you bring up a great point I want to get your reaction and thoughts on is that, is that it's multidisciplinary. Now as people aren't breaking into the field. I mean people are really excited about AI. I mean you talk to someone who's 12 years old, they see a Tesla, they see software, they see all these things, they see all this cool stuff. So machine learning, which powers AI is very enticing to anyone that's got kind of technical or nerdy background and social attracting a lot of young people. So it's not just getting a computer science degree. There's so much more to AI because you talk about why, what someone needs to be successful too. And to engage in the AI wave. You don't need to just be a code or you could be outside the scope because it's an integrated model or is it's very much, so my group at Intel is better, very heterogeneous. >>So what have got a, you know, kind of mathematicians, but I also have coders. I have, uh, an attorney who's a public policy expert. I have cryptographers. Uh, I think there's a number of ways to get involved in, in meaning my, my background is actually a neuroscience. So, um, it makes sense. Good. Stitch it all together. Yeah. Well, societal changes has to be the, the algorithm needs training they need to learn. So having the most diverse input seems to me to be a, a posture the industry is taking and what's, is that right? Is that the right way to think about it? How should we be thinking about how to make AI highly effective versus super scary? Right. Well, one of the efforts that we're making, part of my message here is that to make these systems better, generally more data helps, right? If you can expand the availability of data, that's always going to help machine learning systems. >>And so we're trying to unlock data silos that may exist across countries, across the organizations. So for example, you know, in healthcare you could have multiple hospitals that have patient data. If somehow they could pool all their data together, you would get much more effective models, much better patient outcomes, but for very good privacy reasons, they're not allowed to do that. So there's these interesting ideas like federated learning where you could somehow decentralize the machine learning process so that you can still respect privacy but get the statistical power. That's a double down on that for a second cause I want to explore that. I think this is the most important story that's not being talked about. It's nuance a little bit. Yeah. You know, healthcare, you had HIPAA, which was built for all the right reasons back then, but now when you start to get into much more of a cross pollination of data, you need to manage the benefit of why it existed with privacy. >>So encryption, homomorphic encryption for instance, data and use. Yes. Okay. When it's being used, not just in flight or being arrested becomes, now you have the three triads of data. Yes. This is now causing a new formula for encryption privacy. What are some of the state of the art mindset thinkings around how to make data open a usable but yet either secure, encrypted or protected. That's right. So it's kind of this paradox of how do I use the data but not actually get the data. You mentioned homomorphic encryption. So this is one of the most kind of leading edge techniques in this area where somehow you're able to, there are ways of doing math on the data while it stays encrypted and the answer that comes out, it's still encrypted and it's only the actual owner of the data who can reveal the answer. So it seems like magic, but with this capability you enable all kinds of new use cases that wouldn't be possible before where third parties can act on, you know, your sensitive data without ever being exposed to it in any way. >>So discovery and leverage of the days that what you're getting at in terms of the benefits, I mean use cases. So stay on that. They used cases of the, of this new idea. Yeah. Is discovery and usage. How would that work? Well, so when we talked about federated learning and pooling across hospitals, that's one set of techniques. Homomorphic encryption would be, for example, suppose that some AI system has already been trained, but I'd like to use it on sensitive data. How do I do that in such a way that the third party service isn't, you know, this what makes, I think machine learning different from different types of data. You know, security problems is that machine learning, you have to operate on the data. You're not just storing it, you're not just moving it around. So how do you, yeah, and this is a key thing. >>So I've got to ask you the question because one of the things that's a real interesting trade off these days is AI and machine learning is really can create great benefits, but also people just go the knee jerk reaction of, you know, Oh my God, it's scary. My privacy. So it's a frontline with Amazon, just facial recognition. Oh my God, it's evil. Yeah. So there's a lot of scared people that might not be informed. Yeah. How should companies invest in machine learning and AI from your opinion? On how should they think about the next 10 year trajectory starting today, thinking about how to invest, what's the right way to think about it, build a team. Yeah. What's your thoughts on that? Because, and this is the number one challenge right now. Yeah. Well I think the, uh, some of this scary issues that you mentioned, you know, there are legitimately scary. >>They're going to have to be resolved, not by companies, but probably, you know, by society and kind of our delegates. So lawmakers, regulators, part of what we're trying to do at the technical level is give society and regulators a, a more flexible set of tools around which you can slice and dice data privacy and so on, so that it's not just all or none. Right. I think that's kind of my main goal as a, as an organization. I think again, the, this idea of having a heterogeneous set of talents, you know, you're going to need policy experts and applied mathematicians and linguists and you know, neuroscientists. So diversity is a huge opportunity, very much so. Not just diversity of people, but diverse data, diverse data, diverse kind of mindsets, approaches to problems that are hard but very promising. If so. Okay. Let's flip to the other side of the spectrum, which is what should people not do? >>What does, what's a, what's a fail failure formula one dimensional thinking? What's a, what's an identification of something that's not, may not go in the right way? Well, you know, one, uh, distinguishing feature of the machine learning field, and it's kind of a cultural thing, but it's given it a lot of traction is it's fundamentally, it had been a very open culture. So there's a lot of, uh, sharing of methods. It's a very, uh, collaborative academic field. So I think within a company you want to kind of be re you want to be part of that culture too. So every company is going to have its secret sauce. It's things that it needs to keep proprietary, but it's very important for companies to engage this broader community of researchers. So you're saying, which I would want, maybe I'm what I would agree with, but I'll just say it. >>You can agree or disagree to be successful, you got to be open. If you're data-driven, you've gotta be open. That's right. There's more JD equals better data. That's why more data, more approaches to data, kind of more eyes on the problem. But you know, still you can definitely keep your proprietary, you know, it kind of forces organizations to think about what are our core strengths that we really want to keep proprietary. But then other things let's, you know, open. All right. So what's the coolest thing you've working on right now? What are some of the fun projects you guys are digging into and you've got a great job. Sounds like you're excited about it. I mean, AI I think is the most exciting thing. I mean I wish I could be 20 again in computer science or whatever field. Cause I think AI is more than a multigenerational things. >>Super exciting as a technical person. But what are you working on that you're excited about? So I'm very excited about taking some of these things like homomorphic encryption and making them much more available to developers, to data scientists because it's asking too much for a data scientist to also be a kind of a post quantum crypto expert. So we've written an open source package called H E transformer, H G for homomorphic encryption. It allows the data scientists to kind of do their normal data science and Python or whatever they're used to, but then they kind of flick a switch and suddenly their model is able to run on encrypted data. Can you just take a minute to explain why homomorphic encryption trend right now is really important? I mean, give a peek into the why because this is something that is now becoming much more real. >>Yeah. The data in use kind of philosophy. Why now? Why is it so important right now? Well, I think, uh, the, because of cloud in the, the power of cloud and the fact that you know, data are collected in one place and possibly processed in another place, you're going to have to, you know, your data are moving around and they're being operated on. So if you can know that, you know, as long as my data are moving around and people are operating on it but it's staying encrypted the whole time, you know, not just in transit, that gives a much higher level of comfort around and the applications are going to probably be onboarded. I mean you can almost imagine new applications will emerge from this application discovery cataloging and API integration points. I mean you can almost imagine the trust will go up and you can also kind of end up with these different business models where you have entities that compete in some spheres but they may decide to collaborate in other ways. >>So for example, banks could compete on, you know, lending and so on under normal activities. But in terms of fraud detection, they may decide, Hey, maybe we can make some Alliance where we cross check with each other as models on certain transactions, but I'm not actually giving you any transaction data. So that's maybe okay. Right. So that's a very powerful, it's really interesting. I mean I think the uh, the compute power has allowed, the overhead seems to be much more robust because people are working on this for in the eighties and nineties I remember. Yes. But it was just so expensive overhead while that's right. Yeah. So you bring up a great point here. So, and this is one of the areas where Intel is really pushing, my team is pushing these techniques have been around for 20 years. Initially they were maybe like 10 million times slower than real time. >>So people thought, okay, this is interesting, you know, mathematically, but not practical. There've been massive improvements just in the last two years where now things are running, you know, a hundred times slower than, than kind of un-encrypted math. But still that, that means that something that you know would take 50 milliseconds now takes five seconds. That's still not an unreasonable, you're my new friend. Now, my best friend on AI. Um, and I got a business to run and I'm going to ask you, what should I do? I really want to leverage machine learning and AI in my business. Okay, I'm investing in more tech. I got cloud and building my own software. How should I be investing? How do I build out a great machine learning AI scene and then ultimately capabilities? How should I do that? Okay, well I would start with a team that has a kind of a research mindset, not because you want them to come in and like write research papers, but the path from research into production is so incredibly short in AI. >>You know, you have things that are papers one year and they're going into production at Google search and within a year. So you kind of need that research mindset. I think another thing is that you want to, uh, you're gonna, you're going to require a very close collaboration between this data science team and your CIO and kind of, you know, systems. And a lot of the challenges around AI are not just coming up with the model, but how do you actually scale it up and you know, go to production with it and interesting about the research. I totally agree with you. I think, you know, you can almost call that product management kind of new fangled Prague product management because if it's applied research, you kind of have your eye on a market generally, but you're not making hardcore product decisions. You're researching it, you're writing it so that you got to, got to do the homework, you know, dream it before you can build it. >>Well, I'm just saying that the field is moving so fast that you're going to need on your team, uh, people who can kind of consume the latest papers. Oh, you're saying consume the research as well. Yeah, I mean if they can contribute, that's great too. I mean, I think this is this kind of open culture where, you know, people consume, they find some improvement. They can then publish it at the next year's conference. It's just been this incredibly healthy eco software. Acceleration's a big part of the cloud. Awesome. Well I really appreciate your insight. This is great topic. I could go for an hour. One of my favorite things. I love the homophobic uh, encryption. I think that's going to be a game changer. I think we're going to start to see some interesting discoveries there. Uh, give a quick plug for Intel. What are you working on now? >>What are you looking to do? What's your plans, highs hiring, doing more research, what's going on? Well, so we think that this intersection of privacy and AI is kind of at the core of, of Intel's data centric mission. So we're trying to figure out, you know, whatever it takes to enable the community, whether it's, you know, uh, optimize software libraries. It could be custom Silicon, it could be even services where, you know, we really want to listen to customers, figure out what they need. Funding. Moore's law is always going to be around the next wave is going to have more compute. It's never going away. More storage, more data. It's just gets better and better. Yeah. Thanks for coming on Catherine. Thanks for having can we have Intel inside the cube breaking down the future of AI. Really exciting stuff on the technology front security day. That's all going to happen at large scale. Of course, it's the cube bringing you all the data here at RSA. I'm John furrier. Thanks for watching.