Gabriela de Queiroz, Microsoft | WiDS 2023
(upbeat music) >> Welcome back to theCUBE's coverage of Women in Data Science 2023 live from Stanford University. This is Lisa Martin. My co-host is Tracy Yuan. We're excited to be having great conversations all day but you know, 'cause you've been watching. We've been interviewing some very inspiring women and some men as well, talking about all of the amazing applications of data science. You're not going to want to miss this next conversation. Our guest is Gabriela de Queiroz, Principal Cloud Advocate Manager of Microsoft. Welcome, Gabriela. We're excited to have you. >> Thank you very much. I'm so excited to be talking to you. >> Yeah, you're on theCUBE. >> Yeah, finally. (Lisa laughing) Like a dream come true. (laughs) >> I know and we love that. We're so thrilled to have you. So you have a ton of experience in the data space. I was doing some research on you. You've worked in software, financial advertisement, health. Talk to us a little bit about you. What's your background in? >> So I was trained in statistics. So I'm a statistician and then I worked in epidemiology. I worked with air pollution and public health. So I was a researcher before moving into the industry. So as I was talking today, the weekly paths, it's exactly who I am. I went back and forth and back and forth and stopped and tried something else until I figured out that I want to do data science and that I want to do different things because with data science we can... The beauty of data science is that you can move across domains. So I worked in healthcare, financial, and then different technology companies. >> Well the nice thing, one of the exciting things that data science, that I geek out about and Tracy knows 'cause we've been talking about this all day, it's just all the different, to your point, diverse, pun intended, applications of data science. You know, this morning we were talking about, we had the VP of data science from Meta as a keynote. She came to theCUBE talking and really kind of explaining from a content perspective, from a monetization perspective, and of course so many people in the world are users of Facebook. It makes it tangible. But we also heard today conversations about the applications of data science in police violence, in climate change. We're in California, we're expecting a massive rainstorm and we don't know what to do when it rains or snows. But climate change is real. Everyone's talking about it, and there's data science at its foundation. That's one of the things that I love. But you also have a lot of experience building diverse teams. Talk a little bit about that. You've created some very sophisticated data science solutions. Talk about your recommendation to others to build diverse teams. What's in it for them? And maybe share some data science project or two that you really found inspirational. >> Yeah, absolutely. So I do love building teams. Every time I'm given the task of building teams, I feel the luckiest person in the world because you have the option to pick like different backgrounds and all the diverse set of like people that you can find. I don't think it's easy, like people say, yeah, it's very hard. You have to be intentional. You have to go from the very first part when you are writing the job description through the interview process. So you have to be very intentional in every step. And you have to think through when you are doing that. And I love, like my last team, we had like 10 people and we were so diverse. Like just talking about languages. We had like 15 languages inside a team. So how beautiful it is. Like all different backgrounds, like myself as a statistician, but we had people from engineering background, biology, languages, and so on. So it's, yeah, like every time thinking about building a team, if you wanted your team to be diverse, you need to be intentional. >> I'm so glad you brought up that intention point because that is the fundamental requirement really is to build it with intention. >> Exactly, and I love to hear like how there's different languages. So like I'm assuming, or like different backgrounds, I'm assuming everybody just zig zags their way into the team and now you're all women in data science and I think that's so precious. >> Exactly. And not only woman, right. >> Tracy: Not only woman, you're right. >> The team was diverse not only in terms of like gender, but like background, ethnicity, and spoken languages, and language that they use to program and backgrounds. Like as I mentioned, not everybody did the statistics in school or computer science. And it was like one of my best teams was when we had this combination also like things that I'm good at the other person is not as good and we have this knowledge sharing all the time. Every day I would feel like I'm learning something. In a small talk or if I was reviewing something, there was always something new because of like the richness of the diverse set of people that were in your team. >> Well what you've done is so impressive, because not only have you been intentional with it, but you sound like the hallmark of a great leader of someone who hires and builds teams to fill gaps. They don't have to know less than I do for me to be the leader. They have to have different skills, different areas of expertise. That is really, honestly Gabriela, that's the hallmark of a great leader. And that's not easy to come by. So tell me, who were some of your mentors and sponsors along the way that maybe influenced you in that direction? Or is that just who you are? >> That's a great question. And I joke that I want to be the role model that I never had, right. So growing up, I didn't have anyone that I could see other than my mom probably or my sister. But there was no one that I could see, I want to become that person one day. And once I was tracing my path, I started to see people looking at me and like, you inspire me so much, and I'm like, oh wow, this is amazing and I want to do do this over and over and over again. So I want to be that person to inspire others. And no matter, like I'll be like a VP, CEO, whoever, you know, I want to be, I want to keep inspiring people because that's so valuable. >> Lisa: Oh, that's huge. >> And I feel like when we grow professionally and then go to the next level, we sometimes we lose that, you know, thing that's essential. And I think also like, it's part of who I am as I was building and all my experiences as I was going through, I became what I mentioned is unique person that I think we all are unique somehow. >> You're a rockstar. Isn't she a rockstar? >> You dropping quotes out. >> I'm loving this. I'm like, I've inspired Gabriela. (Gabriela laughing) >> Oh my God. But yeah, 'cause we were asking our other guests about the same question, like, who are your role models? And then we're talking about how like it's very important for women to see that there is a representation, that there is someone they look up to and they want to be. And so that like, it motivates them to stay in this field and to start in this field to begin with. So yeah, I think like you are definitely filling a void and for all these women who dream to be in data science. And I think that's just amazing. >> And you're a founder too. In 2012, you founded R Ladies. Talk a little bit about that. This is present in more than 200 cities in 55 plus countries. Talk about R Ladies and maybe the catalyst to launch it. >> Yes, so you always start, so I'm from Brazil, I always talk about this because it's such, again, I grew up over there. So I was there my whole life and then I moved to here, Silicon Valley. And when I moved to San Francisco, like the doors opened. So many things happening in the city. That was back in 2012. Data science was exploding. And I found out something about Meetup.com, it's a website that you can join and go in all these events. And I was going to this event and I joke that it was kind of like going to the Disneyland, where you don't know if I should go that direction or the other direction. >> Yeah, yeah. >> And I was like, should I go and learn about data visualization? Should I go and learn about SQL or should I go and learn about Hadoop, right? So I would go every day to those meetups. And I was a student back then, so you know, the budget was very restricted as a student. So we don't have much to spend. And then they would serve dinner and you would learn for free. And then I got to a point where I was like, hey, they are doing all of this as a volunteer. Like they are running this meetup and events for free. And I felt like it's a cycle. I need to do something, right. I'm taking all this in. I'm having this huge opportunity to be here. I want to give back. So that's what how everything started. I was like, no, I have to think about something. I need to think about something that I can give back. And I was using R back then and I'm like how about I do something with R. I love R, I'm so passionate about R, what about if I create a community around R but not a regular community, because by going to this events, I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and to, you know, be myself and to network and ask questions. I would be in the corner. So I said to myself, what about if I do something where everybody feel included, where everybody can participate, can share, can ask questions without judgment? So that's how R ladies all came together. >> That's awesome. >> Talk about intentions, like you have to, you had that go in mind, but yeah, I wanted to dive a little bit into R. So could you please talk more about where did the passion for R come from, and like how did the special connection between you and R the language, like born, how did that come from? >> It was not a love at first sight. >> No. >> Not at all. Not at all. Because that was back in Brazil. So all the documentation were in English, all the tutorials, only two. We had like very few tutorials. It was not like nowadays that we have so many tutorials and courses. There were like two tutorials, other documentation in English. So it's was hard for me like as someone that didn't know much English to go through the language and then to learn to program was not easy task. But then as I was going through the language and learning and reading books and finding the people behind the language, I don't know how I felt in love. And then when I came to to San Francisco, I saw some of like the main contributors who are speaking in person and I'm like, wow, they are like humans. I don't know, it was like, I have no idea why I had this love. But I think the the people and then the community was the thing that kept me with the R language. >> Yeah, the community factors is so important. And it's so, at WIDS it's so palpable. I mean I literally walk in the door, every WIDS I've done, I think I've been doing them for theCUBE since 2017. theCUBE has been here since the beginning in 2015 with our co-founders. But you walk in, you get this sense of belonging. And this sense of I can do anything, why not? Why not me? Look at her up there, and now look at you speaking in the technical talk today on theCUBE. So inspiring. One of the things that I always think is you can't be what you can't see. We need to be able to see more people that look like you and sound like you and like me and like you as well. And WIDS gives us that opportunity, which is fantastic, but it's also helping to move the needle, really. And I was looking at some of the Anitab.org stats just yesterday about 2022. And they're showing, you know, the percentage of females in technical roles has been hovering around 25% for a while. It's a little higher now. I think it's 27.6 according to any to Anitab. We're seeing more women hired in roles. But what are the challenges, and I would love to get your advice on this, for those that might be in this situation is attrition, women who are leaving roles. What would your advice be to a woman who might be trying to navigate family and work and career ladder to stay in that role and keep pushing forward? >> I'll go back to the community. If you don't have a community around you, it's so hard to navigate. >> That's a great point. >> You are lonely. There is no one that you can bounce ideas off, that you can share what you are feeling or like that you can learn as well. So sometimes you feel like you are the only person that is going through that problem or like, you maybe have a family or you are planning to have a family and you have to make a decision. But you've never seen anyone going through this. So when you have a community, you see people like you, right. So that's where we were saying about having different people and people like you so they can share as well. And you feel like, oh yeah, so they went through this, they succeed. I can also go through this and succeed. So I think the attrition problem is still big problem. And I'm sure will be worse now with everything that is happening in Tech with layoffs. >> Yes and the great resignation. >> Yeah. >> We are going back, you know, a few steps, like a lot of like advancements that we did. I feel like we are going back unfortunately, but I always tell this, make sure that you have a community. Make sure that you have a mentor. Make sure that you have someone or some people, not only one mentor, different mentors, that can support you through this trajectory. Because it's not easy. But there are a lot of us out there. >> There really are. And that's a great point. I love everything about the community. It's all about that network effect and feeling like you belong- >> That's all WIDS is about. >> Yeah. >> Yes. Absolutely. >> Like coming over here, it's like seeing the old friends again. It's like I'm so glad that I'm coming because I'm all my old friends that I only see like maybe once a year. >> Tracy: Reunion. >> Yeah, exactly. And I feel like that our tank get, you know- >> Lisa: Replenished. >> Exactly. For the rest of the year. >> Yes. >> Oh, that's precious. >> I love that. >> I agree with that. I think one of the things that when I say, you know, you can't see, I think, well, how many females in technology would I be able to recognize? And of course you can be female technology working in the healthcare sector or working in finance or manufacturing, but, you know, we need to be able to have more that we can see and identify. And one of the things that I recently found out, I was telling Tracy this earlier that I geeked out about was finding out that the CTO of Open AI, ChatGPT, is a female. I'm like, (gasps) why aren't we talking about this more? She was profiled on Fast Company. I've seen a few pieces on her, Mira Murati. But we're hearing so much about ChatJTP being... ChatGPT, I always get that wrong, about being like, likening it to the launch of the iPhone, which revolutionized mobile and connectivity. And here we have a female in the technical role. Let's put her on a pedestal because that is hugely inspiring. >> Exactly, like let's bring everybody to the front. >> Yes. >> Right. >> And let's have them talk to us because like, you didn't know. I didn't know probably about this, right. You didn't know. Like, we don't know about this. It's kind of like we are hidden. We need to give them the spotlight. Every woman to give the spotlight, so they can keep aspiring the new generation. >> Or Susan Wojcicki who ran, how long does she run YouTube? All the YouTube influencers that probably have no idea who are influential for whatever they're doing on YouTube in different social platforms that don't realize, do you realize there was a female behind the helm that for a long time that turned it into what it is today? That's outstanding. Why aren't we talking about this more? >> How about Megan Smith, was the first CTO on the Obama administration. >> That's right. I knew it had to do with Obama. Couldn't remember. Yes. Let's let's find more pedestals. But organizations like WIDS, your involvement as a speaker, showing more people you can be this because you can see it, >> Yeah, exactly. is the right direction that will help hopefully bring us back to some of the pre-pandemic levels, and keep moving forward because there's so much potential with data science that can impact everyone's lives. I always think, you know, we have this expectation that we have our mobile phone and we can get whatever we want wherever we are in the world and whatever time of day it is. And that's all data driven. The regular average person that's not in tech thinks about data as a, well I'm paying for it. What's all these data charges? But it's powering the world. It's powering those experiences that we all want as consumers or in our business lives or we expect to be able to do a transaction, whether it's something in a CRM system or an Uber transaction like that, and have the app respond, maybe even know me a little bit better than I know myself. And that's all data. So I think we're just at the precipice of the massive impact that data science will make in our lives. And luckily we have leaders like you who can help navigate us along this path. >> Thank you. >> What advice for, last question for you is advice for those in the audience who might be nervous or maybe lack a little bit of confidence to go I really like data science, or I really like engineering, but I don't see a lot of me out there. What would you say to them? >> Especially for people who are from like a non-linear track where like going onto that track. >> Yeah, I would say keep going. Keep going. I don't think it's easy. It's not easy. But keep going because the more you go the more, again, you advance and there are opportunities out there. Sometimes it takes a little bit, but just keep going. Keep going and following your dreams, that you get there, right. So again, data science, such a broad field that doesn't require you to come from a specific background. And I think the beauty of data science exactly is this is like the combination, the most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists, we started programming when we were nine, that's not true, right. You can be 30, 40, shifting careers, starting to program right now. It doesn't matter. Like you get there no matter how old you are. And no matter what's your background. >> There's no limit. >> There was no limits. >> I love that, Gabriela, >> Thank so much. for inspiring. I know you inspired me. I'm pretty sure you probably inspired Tracy with your story. And sometimes like what you just said, you have to be your own mentor and that's okay. Because eventually you're going to turn into a mentor for many, many others and sounds like you're already paving that path and we so appreciate it. You are now officially a CUBE alumni. >> Yes. Thank you. >> Yay. We've loved having you. Thank you so much for your time. >> Thank you. Thank you. >> For our guest and for Tracy's Yuan, this is Lisa Martin. We are live at WIDS 23, the eighth annual Women in Data Science Conference at Stanford. Stick around. Our next guest joins us in just a few minutes. (upbeat music)
SUMMARY :
but you know, 'cause you've been watching. I'm so excited to be talking to you. Like a dream come true. So you have a ton of is that you can move across domains. But you also have a lot of like people that you can find. because that is the Exactly, and I love to hear And not only woman, right. that I'm good at the other Or is that just who you are? And I joke that I want And I feel like when You're a rockstar. I'm loving this. So yeah, I think like you the catalyst to launch it. And I was going to this event And I was like, and like how did the special I saw some of like the main more people that look like you If you don't have a community around you, There is no one that you Make sure that you have a mentor. and feeling like you belong- it's like seeing the old friends again. And I feel like that For the rest of the year. And of course you can be everybody to the front. you didn't know. do you realize there was on the Obama administration. because you can see it, I always think, you know, What would you say to them? are from like a non-linear track that doesn't require you to I know you inspired me. you so much for your time. Thank you. the eighth annual Women
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PTC | Onshape 2020 full show
>>from around the globe. It's the Cube presenting innovation for good, brought to you by on shape. >>Hello, everyone, and welcome to Innovation for Good Program, hosted by the Cuban. Brought to You by on Shape, which is a PTC company. My name is Dave Valentin. I'm coming to you from our studios outside of Boston. I'll be directing the conversations today. It's a very exciting, all live program. We're gonna look at how product innovation has evolved and where it's going and how engineers, entrepreneurs and educators are applying cutting edge, cutting edge product development techniques and technology to change our world. You know, the pandemic is, of course, profoundly impacted society and altered how individuals and organizations they're gonna be thinking about an approaching the coming decade. Leading technologists, engineers, product developers and educators have responded to the new challenges that we're facing from creating lifesaving products to helping students learn from home toe how to apply the latest product development techniques and solve the world's hardest problems. And in this program, you'll hear from some of the world's leading experts and practitioners on how product development and continuous innovation has evolved, how it's being applied toe positive positively affect society and importantly where it's going in the coming decades. So let's get started with our first session fueling Tech for good. And with me is John Hirschbeck, who is the president of the Suffers, a service division of PTC, which acquired on shape just over a year ago, where John was the CEO and co founder, and Dana Grayson is here. She is the co founder and general partner at Construct Capital, a new venture capital firm. Folks, welcome to the program. Thanks so much for coming on. >>Great to be here, Dave. >>All right, John. >>You're very welcome. Dana. Look, John, let's get into it for first Belated congratulations on the acquisition of Von Shape. That was an awesome seven year journey for your company. Tell our audience a little bit about the story of on shape, but take us back to Day zero. Why did you and your co founders start on shape? Well, >>actually, start before on shaping the You know, David, I've been in this business for almost 40 years. The business of building software tools for product developers and I had been part of some previous products in the industry and companies that had been in their era. Big changes in this market and about, you know, a little Before founding on shape, we started to see the problems product development teams were having with the traditional tools of that era years ago, and we saw the opportunity presented by Cloud Web and Mobile Technology. And we said, Hey, we could use Cloud Web and Mobile to solve the problems of product developers make their Their business is run better. But we have to build an entirely new system, an entirely new company, to do it. And that's what on shapes about. >>Well, so notwithstanding the challenges of co vid and difficulties this year, how is the first year been as, Ah, division of PTC for you guys? How's business? Anything you can share with us? >>Yeah, our first year of PTC has been awesome. It's been, you know, when you get acquired, Dave, you never You know, you have great optimism, but you never know what life will really be like. It's sort of like getting married or something, you know, until you're really doing it, you don't know. And so I'm happy to say that one year into our acquisition, um, PTC on shape is thriving. It's worked out better than I could have imagined a year ago. Along always, I mean sales are up. In Q four, our new sales rate grew 80% vs Excuse me, our fiscal Q four Q three. In the calendar year, it grew 80% compared to the year before. Our educational uses skyrocketing with around 400% growth, most recently year to year of students and teachers and co vid. And we've launched a major cloud platform using the core of on shape technology called Atlas. So, um, just tons of exciting things going on a TTC. >>That's awesome. But thank you for sharing some of those metrics. And of course, you're very humble individual. You know, people should know a little bit more about you mentioned, you know, we founded Solid Works, co founded Solid where I actually found it solid works. You had a great exit in the in the late nineties. But what I really appreciate is, you know, you're an entrepreneur. You've got a passion for the babies that you you helped birth. You stayed with the salt systems for a number of years. The company that quiet, solid works well over a decade. And and, of course, you and I have talked about how you participated in the the M I T. Blackjack team. You know, back in the day, a zai say you're very understated, for somebody was so accomplished. Well, >>that's kind of you, but I tend to I tend Thio always keep my eye more on what's ahead. You know what's next, then? And you know, I look back Sure to enjoy it and learn from it about what I can put to work making new memories, making new successes. >>Love it. Okay, let's bring Dana into the conversation. Hello, Dana. You look you're a fairly early investor in in on shape when you were with any A And and I think it was like it was a serious B, but it was very right close after the A raise. And and you were and still are a big believer in industrial transformation. So take us back. What did you see about on shape back then? That excited you. >>Thanks. Thanks for that. Yeah. I was lucky to be a early investment in shape. You know, the things that actually attracted me. Don shape were largely around John and, uh, the team. They're really setting out to do something, as John says humbly, something totally new, but really building off of their background was a large part of it. Um, but, you know, I was really intrigued by the design collaboration side of the product. Um, I would say that's frankly what originally attracted me to it. What kept me in the room, you know, in terms of the industrial world was seeing just if you start with collaboration around design what that does to the overall industrial product lifecycle accelerating manufacturing just, you know, modernizing all the manufacturing, just starting with design. So I'm really thankful to the on shape guys, because it was one of the first investments I've made that turned me on to the whole sector. And while just such a great pleasure to work with with John and the whole team there. Now see what they're doing inside PTC. >>And you just launched construct capital this year, right in the middle of a pandemic and which is awesome. I love it. And you're focused on early stage investing. Maybe tell us a little bit about construct capital. What your investment thesis is and you know, one of the big waves that you're hoping to ride. >>Sure, it construct it is literally lifting out of any what I was doing there. Um uh, for on shape, I went on to invest in companies such as desktop metal and Tulip, to name a couple of them form labs, another one in and around the manufacturing space. But our thesis that construct is broader than just, you know, manufacturing and industrial. It really incorporates all of what we'd call foundational industries that have let yet to be fully tech enabled or digitized. Manufacturing is a big piece of it. Supply chain, logistics, transportation of mobility or not, or other big pieces of it. And together they really drive, you know, half of the GDP in the US and have been very under invested. And frankly, they haven't attracted really great founders like they're on in droves. And I think that's going to change. We're seeing, um, entrepreneurs coming out of the tech world orthe Agnelli into these industries and then bringing them back into the tech world, which is which is something that needs to happen. So John and team were certainly early pioneers, and I think, you know, frankly, obviously, that voting with my feet that the next set, a really strong companies are going to come out of the space over the next decade. >>I think it's a huge opportunity to digitize the sort of traditionally non digital organizations. But Dana, you focused. I think it's it's accurate to say you're focused on even Mawr early stage investing now. And I want to understand why you feel it's important to be early. I mean, it's obviously riskier and reward e er, but what do you look for in companies and and founders like John >>Mhm, Um, you know, I think they're different styles of investing all the way up to public market investing. I've always been early stage investors, so I like to work with founders and teams when they're, you know, just starting out. Um, I happened to also think that we were just really early in the whole digital transformation of this world. You know, John and team have been, you know, back from solid works, etcetera around the space for a long time. But again, the downstream impact of what they're doing really changes the whole industry. And and so we're pretty early and in digitally transforming that market. Um, so that's another reason why I wanna invest early now, because I do really firmly believe that the next set of strong companies and strong returns for my own investors will be in the spaces. Um, you know, what I look for in Founders are people that really see the world in a different way. And, you know, sometimes some people think of founders or entrepreneurs is being very risk seeking. You know, if you asked John probably and another successful entrepreneurs, they would call themselves sort of risk averse, because by the time they start the company, they really have isolated all the risk out of it and think that they have given their expertise or what they're seeing their just so compelled to go change something, eh? So I look for that type of attitude experience a Z. You can also tell from John. He's fairly humble. So humility and just focus is also really important. Um, that there's a That's a lot of it. Frankly, >>Excellent. Thank you, John. You got such a rich history in the space. Uh, and one of you could sort of connect the dots over time. I mean, when you look back, what were the major forces that you saw in the market in in the early days? Particularly days of on shape on? And how is that evolved? And what are you seeing today? Well, >>I think I touched on it earlier. Actually, could I just reflect on what Dana said about risk taking for just a quick one and say, throughout my life, from blackjack to starting solid works on shape, it's about taking calculated risks. Yes, you try to eliminate the risk Sa's much as you can, but I always say, I don't mind taking a risk that I'm aware of, and I've calculated through as best I can. I don't like taking risks that I don't know I'm taking. That's right. You >>like to bet on >>sure things as much as you sure things, or at least where you feel you. You've done the research and you see them and you know they're there and you know, you, you you keep that in mind in the room, and I think that's great. And Dana did so much for us. Dana, I want to thank you again. For all that, you did it every step of the way, from where we started to to, you know, your journey with us ended formally but continues informally. Now back to you, Dave, I think, question about the opportunity and how it's shaped up. Well, I think I touched on it earlier when I said It's about helping product developers. You know, our customers of the people build the future off manufactured goods. Anything you think of that would be manufacturing factory. You know, the chair you're sitting in machine that made your coffee. You know, the computer you're using, the trucks that drive by on the street, all the covert product research, the equipment being used to make vaccines. All that stuff is designed by someone, and our job is given the tools to do it better. And I could see the problems that those product developers had that we're slowing them down with using the computing systems of the time. When we built solid works, that was almost 30 years ago. If people don't realize that it was in the early >>nineties and you know, we did the >>best we could for the early nineties, but what we did. We didn't anticipate the world of today. And so people were having problems with just installing the systems. Dave, you wouldn't believe how hard it is to install these systems. You need toe speck up a special windows computer, you know, and make sure you've got all the memory and graphics you need and getting to get that set up. You need to make sure the device drivers air, right, install a big piece of software. Ah, license key. I'm not making this up. They're still around. You may not even know what those are. You know, Dennis laughing because, you know, zero cool people do things like this anymore. Um, and it only runs some windows. You want a second user to use it? They need a copy. They need a code. Are they on the same version? It's a nightmare. The teams change, you know? You just say, Well, get everyone on the software. Well, who's everyone? You know, you got a new vendor today? A new customer tomorrow, a new employee. People come on and off the team. The other problem is the data stored in files, thousands of files. This isn't like a spreadsheet or word processor, where there's one file to pass around these air thousands of files to make one, even a simple product. People were tearing their hair out. John, what do we do? I've got copies everywhere. I don't know where the latest version is. We tried like, you know, locking people out so that only one person can change it At the time that works against speed, it works against innovation. We saw what was happening with Cloud Web and mobile. So what's happened in the years since is every one of the forces that product developers experience the need for speed, the need for innovation, the need to be more efficient with their people in their capital. Resource is every one of those trends have been amplified since we started on shape by a lot of forces in the world. And covert is amplified all those the need for agility and remote work cove it is amplified all that the same time, The acceptance of cloud. You know, a few years ago, people were like cloud, you know, how is that gonna work now They're saying to me, You know, increasingly, how would you ever even have done this without the cloud. How do you make solid works work without the cloud? How would that even happen? You know, once people understand what on shapes about >>and we're the >>Onley full SAS solution software >>as a service, >>full SAS solution in our industry. So what's happened in those years? Same problems we saw earlier, but turn up the gain, their bigger problems. And with cloud, we've seen skepticism of years ago turn into acceptance. And now even embracement in the cova driven new normal. >>Yeah. So a lot of friction in the previous environments cloud obviously a huge factor on, I guess. I guess Dana John could see it coming, you know, in the early days of solid works with, you know, had Salesforce, which is kind of the first major independent SAS player. Well, I guess that was late nineties. So his post solid works, but pre in shape and their work day was, you know, pre on shape in the mid two thousands. And and but But, you know, the bet was on the SAS model was right for Crick had and and product development, you know, which maybe the time wasn't a no brainer. Or maybe it was, I don't know, but Dana is there. Is there anything that you would invest in today? That's not Cloud based? >>Um, that's a great question. I mean, I think we still see things all the time in the manufacturing world that are not cloud based. I think you know, the closer you get to the shop floor in the production environment. Um e think John and the PTC folks would agree with this, too, but that it's, you know, there's reliability requirements, performance requirements. There's still this attitude of, you know, don't touch the printing press. So the cloud is still a little bit scary sometimes. And I think hybrid cloud is a real thing for those or on premise. Solutions, in some cases is still a real thing. What what we're more focused on. And, um, despite whether it's on premise or hybrid or or SAS and Cloud is a frictionless go to market model, um, in the companies we invest in so sass and cloud, or really make that easy to adopt for new users, you know, you sign up, started using a product, um, but whether it's hosted in the cloud, whether it's as you can still distribute buying power. And, um, I would I'm just encouraging customers in the customer world and the more industrial environment to entrust some of their lower level engineers with more budget discretionary spending so they can try more products and unlock innovation. >>Right? The unit economics are so compelling. So let's bring it, you know, toe today's you know, situation. John, you decided to exit about a year ago. You know? What did you see in PTC? Other than the obvious money? What was the strategic fit? >>Yeah, Well, David, I wanna be clear. I didn't exit anything. Really? You >>know, I love you and I don't like that term exit. I >>mean, Dana had exit is a shareholder on and so it's not It's not exit for me. It's just a step in the journey. What we saw in PTC was a partner. First of all, that shared our vision from the top down at PTC. Jim Hempleman, the CEO. He had a great vision for for the impact that SAS can make based on cloud technology and really is Dana of highlighted so much. It's not just the technology is how you go to market and the whole business being run and how you support and make the customers successful. So Jim shared a vision for the potential. And really, really, um said Hey, come join us and we can do this bigger, Better, faster. We expanded the vision really to include this Atlas platform for hosting other SAS applications. That P D. C. I mean, David Day arrived at PTC. I met the head of the academic program. He came over to me and I said, You know, and and how many people on your team? I thought he'd say 5 40 people on the PTC academic team. It was amazing to me because, you know, we were we were just near about 100 people were required are total company. We didn't even have a dedicated academic team and we had ah, lot of students signing up, you know, thousands and thousands. Well, now we have hundreds of thousands of students were approaching a million users and that shows you the power of this team that PTC had combined with our product and technology whom you get a big success for us and for the teachers and students to the world. We're giving them great tools. So so many good things were also putting some PTC technology from other parts of PTC back into on shape. One area, a little spoiler, little sneak peek. Working on taking generative design. Dana knows all about generative design. We couldn't acquire that technology were start up, you know, just to too much to do. But PTC owns one of the best in the business. This frustrated technology we're working on putting that into on shaping our customers. Um, will be happy to see it, hopefully in the coming year sometime. >>It's great to see that two way exchange. Now, you both know very well when you start a company, of course, a very exciting time. You know, a lot of baggage, you know, our customers pulling you in a lot of different directions and asking you for specials. You have this kind of clean slate, so to speak in it. I would think in many ways, John, despite you know, your install base, you have a bit of that dynamic occurring today especially, you know, driven by the forced march to digital transformation that cove it caused. So when you sit down with the team PTC and talk strategy. You now have more global resource is you got cohorts selling opportunities. What's the conversation like in terms of where you want to take the division? >>Well, Dave, you actually you sounds like we should have you coming in and talking about strategy because you've got the strategy down. I mean, we're doing everything said global expansion were able to reach across selling. We got some excellent PTC customers that we can reach reach now and they're finding uses for on shape. I think the plan is to, you know, just go, go, go and grow, grow, grow where we're looking for this year, priorities are expand the product. I mentioned the breath of the product with new things PTC did recently. Another technology that they acquired for on shape. We did an acquisition. It was it was small, wasn't widely announced. It, um, in an area related to interfacing with electrical cad systems. So So we're doing We're expanding the breath of on shape. We're going Maura, depth in the areas were already in. We have enormous opportunity to add more features and functions that's in the product. Go to market. You mentioned it global global presence. That's something we were a little light on a year ago. Now we have a team. Dana may not even know what we have. A non shape, dedicated team in Barcelona, based in Barcelona but throughout Europe were doing multiple languages. Um, the academic program just introduced a new product into that space that z even fueling more success and growth there. Um, and of course, continuing to to invest in customer success and this Atlas platform story I keep mentioning, we're going to soon have We're gonna soon have four other major PTC brands shipping products on our Atlas Saas platform. And so we're really excited about that. That's good for the other PTC products. It's also good for on shape because now there's there's. There's other interesting products that are on shape customers can use take advantage of very easily using, say, a common log in conventions about user experience there, used to invest of all they're SAS based, so they that makes it easier to begin with. So that's some of the exciting things going on. I think you'll see PTC, um, expanding our lead in SAS based applications for this sector for our our target, uh, sectors not just in, um, in cat and data management, but another area. PTC's Big and his augmented reality with of euphoria, product line leader and industrial uses of a R. That's a whole other story we should do. A whole nother show augmented reality. But these products are amazing. You can you can help factory workers people on, uh, people who are left out of the digital transformation. Sometimes we're standing from machine >>all day. >>They can't be sitting like we are doing Zoom. They can wear a R headset in our tools, let them create great content. This is an area Dana is invested in other companies. But what I wanted to note is the new releases of our authoring software. For this, our content getting released this month, used through the Atlas platform, the SAS components of on shape for things like revision management and collaboration on duh workflow activity. All that those are tools that we're able to share leverage. We get a lot of synergy. It's just really good. It's really fun to have a good time. That's >>awesome. And then we're gonna be talking to John MacLean later about that. Let's do a little deeper Dive on that. And, Dana, what is your involvement today with with on shape? But you're looking for you know, which of their customers air actually adopting. And they're gonna disrupt their industries. And you get good pipeline from that. How do you collaborate today? >>That sounds like a great idea. Um, Aziz, John will tell you I'm constantly just asking him for advice and impressions of other entrepreneurs and picking his brain on ideas. No formal relationship clearly, but continue to count John and and John and other people in on shaping in the circle of experts that I rely on for their opinions. >>All right, so we have some questions from the crowd here. Uh, one of the questions is for the dream team. You know, John and Dana. What's your next next collective venture? I don't think we're there yet, are we? No. >>I just say, as Dana said, we love talking to her about. You know, Dana, you just returned the compliment. We would try and give you advice and the deals you're looking at, and I'm sort of casually mentoring at least one of your portfolio entrepreneurs, and that's been a lot of fun for May on, hopefully a value to them. But also Dana. We uran important pipeline to us in the world of some new things that are happening that we wouldn't see if you know you've shown us some things that you've said. What do you think of this business? And for us, it's like, Wow, it's cool to see that's going on And that's what's supposed to work in an ecosystem like this. So we we deeply value the ongoing relationship. And no, we're not starting something new. I got a lot of work left to do with what I'm doing and really happy. But we can We can collaborate in this way on other ventures. >>I like this question to somebody asking With the cloud options like on shape, Wilmore students have stem opportunities s Oh, that's a great question. Are you because of sass and cloud? Are you able to reach? You know, more students? Much more cost effectively. >>Yeah, Dave, I'm so glad that that that I was asked about this because Yes, and it's extremely gratified us. Yes, we are because of cloud, because on shape is the only full cloud full SAS system or industry were able to reach. Stem education brings able to be part of bringing step education to students who couldn't get it otherwise. And one of most gratifying gratifying things to me is the emails were getting from teachers, um, that that really, um, on the phone calls that were they really pour their heart out and say We're able to get to students in areas that have very limited compute resource is that don't have an I T staff where they don't know what computer that the students can have at home, and they probably don't even have a computer. We're talking about being able to teach them on a phone to have an android phone a low end android phone. You can do three D modeling on there with on shape. Now you can't do it any other system, but with on shape, you could do it. And so the teacher can say to the students, They have to have Internet access, and I know there's a huge community that doesn't even have Internet access, and we're not able, unfortunately to help that. But if you have Internet and you have even an android phone, we can enable the educator to teach them. And so we have case after case of saving a stem program or expanding it into the students that need it most is the ones we're helping here. So really excited about that. And we're also able to let in addition to the run on run on whatever computing devices they have, we also offer them the tools they need for remote teaching with a much richer experience. Could you teach solid works remotely? Well, maybe if the student ran it had a windows workstation. You know, big, big, high end workstation. Maybe it could, but it would be like the difference between collaborating with on shape and collaborate with solid works. Like the difference between a zoom video call and talking on the landline phone. You know, it's a much richer experience, and that's what you need. And stem teaching stem is hard, So yeah, we're super super. Um, I'm excited about bringing stem to more students because of cloud yond >>we're talking about innovation for good, and then the discussion, John, you just had it. Really? There could be a whole another vector here. We could discuss on diversity, and I wanna end with just pointing out. So, Dana, your new firm, it's a woman led firm, too. Two women leaders, you know, going forward. So that's awesome to see, so really? Yeah, thumbs up on that. Congratulations on getting that off the ground. >>Thank you. Thank you. >>Okay, so thank you guys. Really appreciate It was a great discussion. I learned a lot and I'm sure the audience did a swell in a moment. We're gonna talk with on shaped customers to see how they're applying tech for good and some of the products that they're building. So keep it right there. I'm Dave Volonte. You're watching innovation for good on the Cube, the global leader in digital tech event coverage. Stay right there. >>Oh, yeah, it's >>yeah, yeah, around >>the globe. It's the Cube presenting innovation for good. Brought to you by on shape. >>Okay, we're back. This is Dave Volonte and you're watching innovation for good. A program on Cuba 3 65 made possible by on shape of PTC company. We're live today really live tv, which is the heritage of the Cube. And now we're gonna go to the sources and talkto on shape customers to find out how they're applying technology to create real world innovations that are changing the world. So let me introduce our panel members. Rafael Gomez Furberg is with the Chan Zuckerberg bio hub. A very big idea. And collaborative nonprofit was initiative that was funded by Mark Zuckerberg and his wife, Priscilla Chan, and really around diagnosing and curing and better managing infectious diseases. So really timely topic. Philip Tabor is also joining us. He's with silver side detectors, which develops neutron detective detection systems. Yet you want to know if early, if neutrons and radiation or in places where you don't want them, So this should be really interesting. And last but not least, Matthew Shields is with the Charlottesville schools and is gonna educate us on how he and his team are educating students in the use of modern engineering tools and techniques. Gentlemen, welcome to the Cuban to the program. This should be really interesting. Thanks for coming on. >>Hi. Or pleasure >>for having us. >>You're very welcome. Okay, let me ask each of you because you're all doing such interesting and compelling work. Let's start with Rafael. Tell us more about the bio hub and your role there, please. >>Okay. Yeah. So you said that I hope is a nonprofit research institution, um, funded by Mark Zuckerberg and his wife, Priscilla Chan. Um, and our main mission is to develop new technologies to help advance medicine and help, hopefully cure and manage diseases. Um, we also have very close collaborations with Universe California, San Francisco, Stanford University and the University California Berkeley on. We tried to bring those universities together, so they collaborate more of biomedical topics. And I manage a team of engineers. They by joining platform. Um, and we're tasked with creating instruments for the laboratory to help the scientist boats inside the organization and also in the partner universities Do their experiments in better ways in ways that they couldn't do before >>in this edition was launched Well, five years ago, >>it was announced at the end of 2016, and we actually started operation with at the beginning of 2017, which is when I joined, um, So this is our third year. >>And how's how's it going? How does it work? I mean, these things take time. >>It's been a fantastic experience. Uh, the organization works beautifully. Um, it was amazing to see it grow From the beginning, I was employee number 12, I think eso When I came in, it was just a nem P office building and empty labs. And very quickly we had something running about. It's amazing eso I'm very proud of the work that we have done to make that possible. Um And then, of course, that's you mentioned now with co vid, um, we've been able to do a lot of very cool work attire being of the pandemic in March, when there was a deficit of testing, uh, capacity in California, we spun up a testing laboratory in record time in about a week. It was crazy. It was a crazy project, Um, but but incredibly satisfying. And we ended up running all the way until the beginning of November, when the lab was finally shut down. We could process about 3000 samples a day. I think at the end of it all, we were able to test about 100 on the order of 100 and 50,000 samples from all over the state. We were providing free testing toe all of the Department of Public Health Department of Public Health in California, which at the media pandemic, had no way to do testing affordably and fast. So I think that was a great service to the state. Now the state has created that testing system that would serve those departments. So then we decided that it was unnecessary to keep going with testing in the other biopsy that would shut down. >>All right. Thank you for that. Now, Now, Philip, you What you do is mind melting. You basically helped keep the world safe. Maybe describe a little bit more about silver sod detectors and what your role is there and how it all works. >>Tour. So we make a nuclear bomb detectors and we also make water detectors. So we try and do our part thio keep the world from blowing up and make it a better place at the same time. Both of these applications use neutron radiation detectors. That's what we make. Put them out by import border crossing places like that. They can help make sure that people aren't smuggling. Shall we say very bad things. Um, there's also a burgeoning field of research and application where you can use neutrons with some pretty cool physics to find water so you could do things. Like what? A detector up in the mountains and measure snowpack. Put it out in the middle of the field and measure soil moisture content. And as you might imagine, there's some really cool applications in, uh, research and agronomy and public policy for this. >>All right, so it's OK, so it's a It's much more than, you know, whatever fighting terrorism, it's there's a riel edge or I kind of i o t application for what you guys >>do. We do both its's to plowshares. You might >>say a mat. I I look at your role is kind of scaling the brain power for for the future. Maybe tell us more about Charlottesville schools and in the mission that you're pursuing and what you do. >>Thank you. Um, I've been in Charlottesville City schools for about 11 or 12 years. I started their teaching, um, a handful of classes, math and science and things like that. But Thescore board and my administration had the crazy idea of starting an engineering program about seven years ago. My background is an engineering is an engineering. My masters is in mechanical and aerospace engineering and um, I basically spent a summer kind of coming up with what might be a fun engineering curriculum for our students. And it started with just me and 30 students about seven years ago, Um, kind of a home spun from scratch curriculum. One of my goals from the outset was to be a completely project based curriculum, and it's now grown. We probably have about six or 700 students, five or six full time teachers. We now have pre engineering going on at the 5th and 6th grade level. I now have students graduating. Uh, you know, graduating after senior year with, like, seven years of engineering under their belt and heading off to doing some pretty cool stuff. So it's It's been a lot of fun building a program and, um, and learning a lot in the process. >>That's awesome. I mean, you know, Cuba's. We've been passionate about things like women in tech, uh, diversity stem. You know, not only do we need more, more students and stem, we need mawr underrepresented women, minorities, etcetera. We were just talking to John Herstek and integrate gration about this is Do you do you feel is though you're I mean, first of all, the work that you do is awesome, but but I'll go one step further. Do you feel as though it's reaching, um, or diverse base? And how is that going? >>That's a great question. I think research shows that a lot of people get funneled into one kind of track or career path or set of interests really early on in their educational career, and sometimes that that funnel is kind of artificial. And so that's one of the reasons we keep pushing back. Um, so our school systems introducing kindergartners to programming on DSO We're trying to push back how we expose students to engineering and to stem fields as early as possible. And we've definitely seen the first of that in my program. In fact, my engineering program, uh, sprung out of an after school in Extracurricular Science Club that actually three girls started at our school. So I think that actually has helped that three girls started the club that eventually is what led to our engineering programs that sort of baked into the DNA and also our eyes a big public school. And we have about 50% of the students are under the poverty line and we e in Charlottesville, which is a big refugee town. And so I've been adamant from Day one that there are no barriers to entry into the program. There's no test you have to take. You don't have to have be taking a certain level of math or anything like that. That's been a lot of fun. To have a really diverse set of kids enter the program and be successful, >>that's final. That's great to hear. So, Philip, I wanna come back to you. You know, I think about maybe some day we'll be able to go back to a sporting events, and I know when I when I'm in there, there's somebody up on the roof looking out for me, you know, watching the crowd, and they have my back. And I think in many ways, the products that you build, you know, our similar. I may not know they're there, but they're keeping us safe or they're measuring things that that that I don't necessarily see. But I wonder if you could talk about a little bit more detail about the products you build and how they're impacting society. >>Sure, so There are certainly a lot of people who are who are watching, trying to make sure things were going well in keeping you safe that you may or may not be aware of. And we try and support ah lot of them. So we have detectors that are that are deployed in a variety of variety of uses, with a number of agencies and governments that dio like I was saying, ports and border crossing some other interesting applications that are looking for looking for signals that should not be there and working closely to fit into the operations these folks do. Onda. We also have a lot of outreach to researchers and scientists trying to help them support the work they're doing. Um, using neutron detection for soil moisture monitoring is a some really cool opportunities for doing it at large scale and with much less, um, expense or complication than would have been done. Previous technologies. Um, you know, they were talking about collaboration in the previous segment. We've been able to join a number of conferences for that, virtually including one that was supposed to be held in Boston, but another one that was held out of the University of Heidelberg in Germany. And, uh, this is sort of things that in some ways, the pandemic is pushing people towards greater collaboration than they would have been able to do. Had it all but in person. >>Yeah, we did. Uh, the cube did live works a couple years ago in Boston. It was awesome show. And I think, you know, with this whole trend toward digit, I call it the Force march to digital. Thanks to cove it I think that's just gonna continue. Thio grow. Rafael. What if you could describe the process that you use to better understand diseases? And what's your organization's involvement? Been in more detail, addressing the cove in pandemic. >>Um, so so we have the bio be structured in, Um um in a way that foster so the combination of technology and science. So we have to scientific tracks, one about infectious diseases and the other one about understanding just basic human biology, how the human body functions, and especially how the cells in the human body function on how they're organized to create tissues in the body. On Ben, it has this set of platforms. Um, mind is one of them by engineering that are all technology rated. So we have data science platform, all about data analysis, machine learning, things like that. Um, we have a mass spectrometry platform is all about mass spectrometry technologies to, um, exploit those ones in service for the scientist on. We have a genomics platform that it's all about sequencing DNA and are gonna, um and then an advanced microscopy. It's all about developing technologies, uh, to look at things with advanced microscopes and developed technologies to marry computation on microscopy. So, um, the scientists set the agenda and the platforms, we just serve their needs, support their needs, and hopefully develop technologies that help them do their experiments better, faster, or allow them to the experiment that they couldn't do in any other way before. Um And so with cove, it because we have that very strong group of scientists that work on have been working on infectious disease before, and especially in viruses, we've been able to very quickly pivot to working on that s O. For example, my team was able to build pretty quickly a machine to automatically purified proteins on is being used to purify all these different important proteins in the cove. It virus the SARS cov to virus Onda. We're sending some of those purified proteins all over the world. Two scientists that are researching the virus and trying to figure out how to develop vaccines, understand how the virus affects the body and all that. Um, so some of the machines we built are having a very direct impact on this. Um, Also for the copy testing lab, we were able to very quickly develop some very simple machines that allowed the lab to function sort of faster and more efficiently. Sort of had a little bit of automation in places where we couldn't find commercial machines that would do it. >>Um, eso Matt. I mean, you gotta be listening to this and thinking about Okay, So someday your students are gonna be working at organizations like like, like Bio Hub and Silver Side. And you know, a lot of young people they're just don't know about you guys, but like my kids, they're really passionate about changing the world. You know, there's way more important than you know, the financial angles and it z e. I gotta believe you're seeing that you're right in the front lines there. >>Really? Um, in fact, when I started the curriculum six or seven years ago, one of the first bits of feedback I got from my students is they said Okay, this is a lot of fun. So I had my students designing projects and programming microcontrollers raspberry, PiS and order we nose and things like that. The first bit of feedback I got from students was they said Okay, when do we get to impact the world? I've heard engineering >>is about >>making the world a better place, and robots are fun and all, but, you know, where is the real impact? And so um, dude, yeah, thanks to the guidance of my students, I'm baking that Maurin. Now I'm like day one of engineering one. We talk about how the things that the tools they're learning and the skills they're gaining, uh, eventually, you know, very soon could be could be used to make the world a better place. >>You know, we all probably heard that famous line by Jeff Hammer Barker. The greatest minds of my generation are trying to figure out how to get people to click on ads. I think we're really generally generationally, finally, at the point where young students and engineering a really, you know, a passionate about affecting society. I wanna get into the product, you know, side and understand how each of you are using on shape and and the value that that it brings. Maybe Raphael, you could start how long you've been using it. You know, what's your experience with it? Let's let's start there. >>I begin for about two years, and I switched to it with some trepidation. You know, I was used to always using the traditional product that you have to install on your computer, that everybody uses that. So I was kind of locked into that. But I started being very frustrated with the way it worked, um, and decided to give on ship chance. Which reputation? Because any change always, you know, causes anxiety. Um, but very quickly my engineers started loving it, Uh, just because it's it's first of all, the learning curve wasn't very difficult at all. You can transfer from one from the traditional product to entree very quickly and easily. You can learn all the concepts very, very fast. It has all the functionality that we needed and and what's best is that it allows to do things that we couldn't do before or we couldn't do easily. Now we can access the our cat documents from anywhere in the world. Um, so when we're in the lab fabricating something or testing a machine, any computer we have next to us or a tablet or on iPhone, we can pull it up and look at the cad and check things or make changes. That's something that couldn't do before because before you had to pay for every installation off the software for the computer, and I couldn't afford to have 20 installations to have some computers with the cat ready to use them like once every six months would have been very inefficient. So we love that part. And the collaboration features are fantastic, especially now with Kobe, that we have to have all the remote meetings eyes fantastic, that you can have another person drive the cad while the whole team is watching that person change the model and do things and point to things that is absolutely revolutionary. We love it. The fact that you have very, very sophisticated version control before it was always a challenge asking people, please, if you create anniversary and apart, how do we name it so that people find it? And then you end up with all these collection of files with names that nobody ever remembers, what they are, the person left. And now nobody knows which version is the right one. A mess with on shape on the version ING system it has, and the fact that you can go back in history off the document and go back to previous version so easily and then go back to the press and version and explore the history of the part that is truly, um, just world changing for us, that we can do that so easily on for me as a manager to manage this collection of information that is critical for our operations. It makes it so much easier because everything is in one place. I don't have to worry about file servers that go down that I have to administer that have to have I t taken care off that have to figure how to keep access to people to those servers when they're at home, and they need a virtual private network and all of that mess disappears. I just simply give give a person in accounting on shape and then magically, they have access to everything in the way I want. And we can manage the lower documents and everything in a way that is absolutely fantastic. >>Feel what was your what? What were some of the concerns you had mentioned? You had some trepidation. Was it a performance? Was it security? You know some of the traditional cloud stuff, and I'm curious as to how, How, whether any of those act manifested really that you had to manage. What were your concerns? >>Look, the main concern is how long is it going to take for everybody in the team to learn to use the system like it and buy into it? Because I don't want to have my engineers using tools against their will write. I want everybody to be happy because that's how they're productive. They're happy, and they enjoyed the tools they have. That was my main concern. I was a little bit worried about the whole concept of not having the files in a place where I couldn't quote unquote seat in some server and on site, but that That's kind of an outdated concept, right? So that took a little bit of a mind shift, but very quickly. Then I started thinking, Look, I have a lot of documents on Google Drive. Like, I don't worry about that. Why would I worry about my cat on on shape, right? Is the same thing. So I just needed to sort of put things in perspective that way. Um, the other, um, you know, the concern was the learning curve, right? Is like, how is he Will be for everybody to and for me to learn it on whether it had all of the features that we needed. And there were a few features that I actually discussed with, um uh, Cody at on shape on, they were actually awesome about using their scripting language in on shape to sort of mimic some of the features of the old cat, uh, in on, shaped in a way that actually works even better than the old system. So it was It was amazing. Yeah, >>Great. Thank you for that, Philip. What's your experience been? Maybe you could take us through your journey within shape. >>Sure. So we've been we've been using on shaped silver side for coming up on about four years now, and we love it. We're very happy with it. We have a very modular product line, so we make anything from detectors that would go into backpacks. Two vehicles, two very large things that a shipping container would go through and saw. Excuse me. Shape helps us to track and collaborate faster on the design. Have multiple people working a same time on a project. And it also helps us to figure out if somebody else comes to us and say, Hey, I want something new how we congrats modules from things that we already have put them together and then keep track of the design development and the different branches and ideas that we have, how they all fit together. A za design comes together, and it's just been fantastic from a mechanical engineering background. I will also say that having used a number of different systems and solid works was the greatest thing since sliced bread. Before I got using on shape, I went, Wow, this is amazing and I really don't want to design in any other platform. After after getting on Lee, a little bit familiar with it. >>You know, it's funny, right? I'll have the speed of technology progression. I was explaining to some young guns the other day how I used to have a daytime er and that was my life. And if I lost that daytime, er I was dead. And I don't know how we weigh existed without, you know, Google maps eso we get anywhere, I don't know, but, uh but so So, Matt, you know, it's interesting to think about, you know, some of the concerns that Raphael brought up, you hear? For instance, you know, all the time. Wow. You know, I get my Amazon bill at the end of the month that zip through the roof in, But the reality is that Yeah, well, maybe you are doing more, but you're doing things that you couldn't have done before. And I think about your experience in teaching and educating. I mean, you so much more limited in terms of the resource is that you would have had to be able to educate people. So what's your experience been with With on shape and what is it enabled? >>Um, yeah, it was actually talking before we went with on shape. We had a previous CAD program, and I was talking to my vendor about it, and he let me know that we were actually one of the biggest CAD shops in the state. Because if you think about it a really big program, you know, really big company might employ. 5, 10, 15, 20 cad guys, right? I mean, when I worked for a large defense contractor, I think there were probably 20 of us as the cad guys. I now have about 300 students doing cat. So there's probably more students with more hours of cat under their belt in my building than there were when I worked for the big defense contractor. Um, but like you mentioned, uh, probably our biggest hurdle is just re sources. And so we want We want one of things I've always prided myself and trying to do in this. Programs provide students with access two tools and skills that they're going to see either in college or in the real world. So it's one of the reason we went with a big professional cad program. There are, you know, sort of K 12 oriented software and programs and things. But, you know, I want my kids coding and python and using slack and using professional type of tools on DSO when it comes to cat. That's just that That was a really hurt. I mean, you know, you could spend $30,000 on one seat of, you know, professional level cad program, and then you need a $30,000 computer to run it on if you're doing a heavy assemblies, Um and so one of my dreams And it was always just a crazy dream. And I was the way I would always pitcher in my school system and say, someday I'm gonna have a kid on a school issued chromebook in subsidized housing, on public WiFi doing professional level bad and that that was a crazy statement until a couple of years ago. So we're really excited that I literally and you know, March and you said the forced march, the forced march into, you know, modernity, March 13th kids sitting in my engineering lab that we spent a lot of money on doing cad March 14th. Those kids were at home on their school issued chromebooks on public WiFi, uh, keeping their designs going and collaborating. And then, yeah, I could go on and on about some of the things you know, the features that we've learned since then they're even better. So it's not like this is some inferior, diminished version of Academy. There's so much about it. Well, I >>wanna I wanna ask you that I may be over my skis on this, but we're seeing we're starting to see the early days of the democratization of CAD and product design. It is the the citizen engineer, I mean, maybe insulting to the engineers in the room, But but is that we're beginning to see that >>I have to believe that everything moves into the cloud. Part of that is democratization that I don't need. I can whether you know, I think artists, you know, I could have a music studio in my basement with a nice enough software package. And Aiken, I could be a professional for now. My wife's a photographer. I'm not allowed to say that I could be a professional photographer with, you know, some cloud based software, and so, yeah, I do think that's part of what we're seeing is more and more technology is moving to the cloud. >>Philip. Rafael Anything you Dad, >>I think I mean, yeah, that that that combination of cloud based cat and then three d printing that is becoming more and more affordable on ubiquitous It's truly transformative, and I think for education is fantastic. I wish when I was a kid I had the opportunity to play with those kinds of things because I was always the late things. But, you know, the in a very primitive way. So, um, I think this is a dream for kids. Teoh be able to do this. And, um, yeah, there's so many other technologies coming on, like Arduino on all of these electronic things that live kids play at home very cheaply with things that back in my day would have been unthinkable. >>So we know there's a go ahead. Philip, please. >>We had a pandemic and silver site moved to a new manufacturing facility this year. I was just on the shop floor, talking with contractors, standing 6 ft apart, pointing at things. But through it all, our CAD system was completely unruffled. Nothing stopped in our development work. Nothing stopped in our support for existing systems in the field. We didn't have to think about it. We had other server issues, but none with our, you know, engineering cad, platform and product development in support world right ahead, which was cool, but also a in that's point. I think it's just really cool what you're doing with the kids. The most interesting secondary and college level engineering work that I did was project based, taken important problem to the world. Go solve it and that is what we do here. That is what my entire career has been. And I'm super excited to see. See what your students are going to be doing, uh, in there home classrooms on their chromebooks now and what they do building on that. >>Yeah, I'm super excited to see your kids coming out of college with engineering degrees because, yeah, I think that Project based experience is so much better than just sitting in a classroom, taking notes and doing math problems on day. I think it will give the kids a much better flavor. What engineering is really about Think a lot of kids get turned off by engineering because they think it's kind of dry because it's just about the math for some very abstract abstract concept on they are there. But I think the most important thing is just that hands on a building and the creativity off, making things that you can touch that you can see that you can see functioning. >>Great. So, you know, we all know the relentless pace of technology progression. So when you think about when you're sitting down with the folks that on shape and there the customer advisor for one of the things that that you want on shape to do that it doesn't do today >>I could start by saying, I just love some of the things that does do because it's such a modern platform. And I think some of these, uh, some some platforms that have a lot of legacy and a lot of history behind them. I think we're dragging some of that behind them. So it's cool to see a platform that seemed to be developed in the modern era, and so that Z it is the Google docks. And so the fact that collaboration and version ing and link sharing is and like platform agnostic abilities, the fact that that seems to be just built into the nature of the thing so far, That's super exciting. As far as things that, uh, to go from there, Um, I don't know, >>Other than price. >>You can't say >>I >>can't say lower price. >>Yeah, so far on P. D. C. S that work with us. Really? Well, so I'm not complaining. There you there, >>right? Yeah. Yeah. No gaps, guys. Whitespace, Come on. >>We've been really enjoying the three week update. Cadence. You know, there's a new version every three weeks and we don't have to install it. We just get all the latest and greatest goodies. One of the trends that we've been following and enjoying is the the help with a revision management and release work flows. Um, and I know that there's more than on shape is working on that we're very excited for, because that's a big important part about making real hardware and supporting it in the field. Something that was cool. They just integrated Cem markup capability. In the last release that took, we were doing that anyway, but we were doing it outside of on shapes. And now we get to streamline our workflow and put it in the CAD system where We're making those changes anyway when we're reviewing drawings and doing this kind of collaboration. And so I think from our perspective, we continue to look forward. Toa further progress on that. There's a lot of capability in the cloud that I think they're just kind of scratching the surface on you, >>right? I would. I mean, you're you're asking to knit. Pick. I would say one of the things that I would like to see is is faster regeneration speed. There are a few times with convicts, necessities that regenerating the document takes a little longer than I would like. It's not a serious issue, but anyway, I I'm being spoiled, >>you know? That's good. I've been doing this a long time, and I like toe ask that question of practitioners and to me, it It's a signal like when you're nit picking and that's what you're struggling to knit. Pick that to me is a sign of a successful product, and and I wonder, I don't know, uh, have the deep dive into the architecture. But are things like alternative processors. You're seeing them hit the market in a big way. Uh, you know, maybe helping address the challenge, But I'm gonna ask you the big, chewy question now. Then we maybe go to some audience questions when you think about the world's biggest problems. I mean, we're global pandemics, obviously top of mind. You think about nutrition, you know, feeding the global community. We've actually done a pretty good job of that. But it's not necessarily with the greatest nutrition, climate change, alternative energy, the economic divides. You've got geopolitical threats and social unrest. Health care is a continuing problem. What's your vision for changing the world and how product innovation for good and be applied to some of the the problems that that you all are passionate about? Big question. Who wants toe start? >>Not biased. But for years I've been saying that if you want to solve the economy, the environment, uh, global unrest, pandemics, education is the case. If you wanna. If you want to, um, make progress in those in those realms, I think funding funding education is probably gonna pay off pretty well. >>Absolutely. And I think Stam is key to that. I mean, all of the ah lot of the well being that we have today and then industrialized countries. Thanks to science and technology, right improvements in health care, improvements in communication, transportation, air conditioning. Um, every aspect of life is touched by science and technology. So I think having more kids studying and understanding that is absolutely key. Yeah, I agree, >>Philip, you got anything to add? >>I think there's some big technical problems in the world today, Raphael and ourselves there certainly working on a couple of them. Think they're also collaboration problems and getting everybody to be able to pull together instead of pulling separately and to be able to spur the ideas on words. So that's where I think the education side is really exciting. What Matt is doing and it just kind of collaboration in general when we could do provide tools to help people do good work. Uh, that is, I think, valuable. >>Yeah, I think that's a very good point. And along those lines, we have some projects that are about creating very low cost instruments for low research settings, places in Africa, Southeast Asia, South America, so that they can do, um, um, biomedical research that it's difficult to do in those place because they don't have the money to buy the fancy lab machines that cost $30,000 an hour. Um, so we're trying to sort of democratize some of those instruments. And I think thanks to tools like Kahn shape then is easier, for example, to have a conversation with somebody in Africa and show them the design that we have and discuss the details of it with them on. But it's amazing, right to have somebody, you know, 10 time zones away, Um, looking really life in real time with you about your design and discussing the details or teaching them how to build a machine, right? Because, um, you know, they have a three D printer. You can you can just give them the design and say like, you build it yourself, uh, even cheaper than and, you know, also billing and shipping it there. Um, so all that that that aspect of it is also super important. I think for any of these efforts to improve some of the hardest part was in the world for climate change. Do you say, as you say, poverty, nutrition issues? Um, you know, availability of water. You have that project at about finding water. Um, if we can also help deploy technologies that teach people remotely how to create their own technologies or how to build their own systems that will help them solve those forms locally. I think that's very powerful. >>Yeah, the point about education is right on. I think some people in the audience may be familiar with the work of Erik Brynjolfsson and Andrew McAfee, the second machine age where they sort of put forth the premise that, uh, is it laid it out. Look, for the first time in history, machines air replacing humans from a cognitive perspective. Machines have always replaced humans, but that's gonna have an impact on jobs. But the answer is not toe protect the past from the future. The answer is education and public policy that really supports that. So I couldn't agree more. I think it's a really great point. Um, we have We do have some questions from the audience. If if we could If I can ask you guys, um, you know, this one kind of stands out. How do you see artificial intelligence? I was just talking about machine intelligence. Um, how do you see that? Impacting the design space guys trying to infuse a I into your product development. Can you tell me? >>Um, absolutely, like, we're using AI for some things, including some of these very low cost instruments that will hopefully help us diagnose certain diseases, especially this is that are very prevalent in the Third World. Um, and some of those diagnostics are these days done by thes armies of technicians that are trained to look under the microscope. But, um, that's a very slow process. Is very error prone and having machine learning systems that can to the same diagnosis faster, cheaper and also little machines that can be taken to very remote places to these villages that have no access to a fancy microscope. To look at a sample from a patient that's very powerful. And I we don't do this, but I have read quite a bit about how certain places air using a Tribune attorneys to actually help them optimize designs for parts. So you get these very interesting looking parts that you would have never thought off a person would have never thought off, but that are incredibly light ink. Earlier, strong and I have all sort of properties that are interesting thanks to artificial intelligence machine learning in particular >>yet another. The advantage you get when when your work is in the cloud I've seen. I mean, there's just so many applications that so if the radiology scan is in the cloud and the radiologist is goes to bed at night, Radiologist could come in in the morning and and say, Oh, the machine while you were sleeping was using artificial intelligence to scan these 40,000 images. And here's the five that we picked out that we think you should take a closer look at. Or like Raphael said, I can design my part. My, my, my, my, my you know, mount or bracket or whatever and go to sleep. And then I wake up in the morning. The machine has improved. It for me has made it strider strider stronger and lighter. Um And so just when your when your work is in the cloud, that's just that's a really cool advantage that you get that you can have machines doing some of your design work for you. >>Yeah, we've been watching, uh, you know, this week is this month, I guess is AWS re invent and it's just amazing to see how much effort is coming around machine learning machine intelligence. You know Amazon has sage maker Google's got, you know, embedded you no ML and big query. Uh, certainly Microsoft with Azure is doing tons of stuff and machine learning. I think the point there is that that these things will be infused in tow R and D and in tow software product by the vendor community. And you all will apply that to your business and and build value through the unique data that your collecting, you know, in your ecosystems. And and that's how you add value. You don't have to be necessarily, you know, developers of artificial intelligence, but you have to be practitioners to apply that. Does that make sense to you, Philip? >>Yeah, absolutely. And I think your point about value is really well chosen. We see AI involved from the physics simulations all the way up to interpreting radiation data, and that's where the value question, I think, is really important because it's is the output of the AI giving helpful information that the people that need to be looking at it. So if it's curating a serious of radiation alert, saying, Hey, like these air the anomalies. You need to look at eyes it, doing that in a way that's going to help a good response on. In some cases, the II is only as good as the people. That sort of gave it a direction and turn it loose. And you want to make sure that you don't have biases or things like that underlying your AI that they're going to result in less than helpful outcomes coming from it. So we spend quite a lot of time thinking about how do we provide the right outcomes to people who are who are relying on our systems? >>That's a great point, right? Humans air biased and humans build models, so models are inherently biased. But then the software is hitting the market. That's gonna help us identify those biases and help us, you know? Of course. Correct. So we're entering Cem some very exciting times, guys. Great conversation. I can't thank you enough for spending the time with us and sharing with our audience the innovations that you're bringing to help the world. So thanks again. >>Thank you so much. >>Thank you. >>Okay. Welcome. Okay. When we come back, John McElheny is gonna join me. He's on shape. Co founder. And he's currently the VP of strategy at PTC. He's gonna join the program. We're gonna take a look at what's next and product innovation. I'm Dave Volonte and you're watching innovation for good on the Cube, the global leader. Digital technology event coverage. We'll be right back. >>Okay? Okay. Yeah. Okay. >>From around >>the globe, it's the Cube. Presenting innovation for good. Brought to you by on shape. >>Okay, welcome back to innovation. For good. With me is John McElheny, who is one of the co founders of On Shape and is now the VP of strategy at PTC. John, it's good to see you. Thanks for making the time to come on the program. Thanks, Dave. So we heard earlier some of the accomplishments that you've made since the acquisition. How has the acquisition affected your strategy? Maybe you could talk about what resource is PTC brought to the table that allowed you toe sort of rethink or evolve your strategy? What can you share with us? >>Sure. You know, a year ago, when when John and myself met with Jim Pepperman early on is we're we're pondering. Started joining PTC one of things became very clear is that we had a very clear shared vision about how we could take the on shape platform and really extended for, for all of the PTC products, particular sort of their augmented reality as well as their their thing works or the i o. T business and their product. And so from the very beginning there was a clear strategy about taking on shape, extending the platform and really investing, um, pretty significantly in the product development as well as go to market side of things, uh, toe to bring on shape out to not only the PTC based but sort of the broader community at large. So So So PTC has been a terrific, terrific, um, sort of partner as we've we've gonna go on after this market together. Eso We've added a lot of resource and product development side of things. Ah, lot of resource and they go to market and customer success and support. So, really, on many fronts, that's been both. Resource is as well a sort of support at the corporate level from from a strategic standpoint and then in the field, we've had wonderful interactions with many large enterprise customers as well as the PTC channels. So it's been really a great a great year. >>Well, and you think about the challenges of in your business going to SAS, which you guys, you know, took on that journey. You know, 78 years ago. Uh, it's not trivial for a lot of companies to make that transition, especially a company that's been around as long as PTC. So So I'm wondering how much you know, I was just asking you How about what PCP TC brought to the table? E gotta believe you're bringing a lot to the table to in terms of the mindset, uh, even things is, is mundane is not the right word, but things like how you compensate salespeople, how you interact with customers, the notion of a service versus a product. I wonder if you could address >>that. Yeah, it's a it's a really great point. In fact, after we had met Jim last year, John and I one of the things we walked out in the seaport area in Boston, one of things we sort of said is, you know, Jim really gets what we're trying to do here and and part of let me bring you into the thinking early on. Part of what Jim talked about is there's lots of, you know, installed base sort of software that's inside of PTC base. That's helped literally thousands of customers around the world. But the idea of moving to sass and all that it entails both from a technology standpoint but also a cultural standpoint. Like How do you not not just compensate the sales people as an example? But how do you think about customer success? In the past, it might have been that you had professional services that you bring out to a customer, help them deploy your solutions. Well, when you're thinking about a SAS based offering, it's really critical that you get customers successful with it. Otherwise, you may have turned, and you know it will be very expensive in terms of your business long term. So you've got to get customers success with software in the very beginning. So you know, Jim really looked at on shape and he said that John and I, from a cultural standpoint, you know, a lot of times companies get acquired and they've acquired technology in the past that they integrate directly into into PTC and then sort of roll it out through their products, are there just reached channel, he said. In some respects, John John, think about it as we're gonna take PTC and we want to integrate it into on shape because we want you to share with us both on the sales side and customer success on marketing on operations. You know all the things because long term, we believe the world is a SAS world, that the whole industry is gonna move too. So really, it was sort of an inverse in terms of the thought process related to normal transactions >>on That makes a lot of sense to me. You mentioned Sharon turns the silent killer of a SAS company, and you know, there's a lot of discussion, you know, in the entrepreneurial community because you live this, you know what's the best path? I mean today, You see, you know, if you watch Silicon Valley double, double, triple triple, but but there's a lot of people who believe, and I wonder, if you come in there is the best path to, you know, in the X Y axis. If if it's if it's uh, growth on one and retention on the other axis. What's the best way to get to the upper right on? Really? The the best path is probably make sure you've nailed obviously the product market fit, But make sure that you can retain customers and then throw gas on the fire. You see a lot of companies they burn out trying to grow too fast, but they haven't figured out, you know that. But there's too much churn. They haven't figured out those metrics. I mean, obviously on shape. You know, you were sort of a pioneer in here. I gotta believe you've figured out that customer retention before you really, You know, put the pedal to the >>metal. Yeah, and you know, growth growth can mask a lot of things, but getting getting customers, especially the engineering space. Nobody goes and sits there and says, Tomorrow we're gonna go and and, you know, put 100 users on this and and immediately swap out all of our existing tools. These tools are very rich and deep in terms of capability, and they become part of the operational process of how a company designs and builds products. So any time anybody is actually going through the purchasing process. Typically, they will run a try along or they'll run a project where they look at. Kind of What? What is this new solution gonna help them dio. How are we gonna orient ourselves for success? Longer term. So for us, you know, getting new customers and customer acquisition is really critical. But getting those customers to actually deploy the solution to be successful with it. You know, we like to sort of, say, the marketing or the lead generation and even some of the initial sales. That's sort of like the Kindle ing. But the fire really starts when customers deploy it and get successful. The solution because they bring other customers into the fold. And then, of course, if they're successful with it, you know, then in fact, you have negative turn which, ironically, means growth in terms of your inside of your install. Bates. >>Right? And you've seen that with some of the emerging, you know, SAS companies, where you're you're actually you know, when you calculate whatever its net retention or renew ALS, it's actually from a dollar standpoint. It's up in the high nineties or even over 100%. >>So >>and that's a trend we're gonna continue. See, I >>wonder >>if we could sort of go back. Uh, and when you guys were starting on shape, some of the things that you saw that you were trying to strategically leverage and what's changed, you know, today we were talking. I was talking to John earlier about in a way, you kinda you kinda got a blank slate is like doing another startup. >>You're >>not. Obviously you've got installed base and customers to service, but But it's a new beginning for you guys. So one of the things that you saw then you know, cloud and and sas and okay, but that's we've been there, done that. What are you seeing? You know today? >>Well, you know, So So this is a journey, of course, that that on shape on its own has gone through it had I'll sort of say, you know, several iterations, both in terms of of of, you know, how do you How do you get customers? How do you How do you get them successful? How do you grow those customers? And now that we've been part of PTC, the question becomes okay. One, There is certainly a higher level of credibility that helps us in terms of our our megaphone is much bigger than it was when we're standalone company. But on top of that now, figuring out how to work with their channel with their direct sales force, you know, they have, um, for example, you know, very large enterprises. Well, many of those customers are not gonna go in forklift out their existing solution to replace it with with on shape. However, many of them do have challenges in their supply chain and communications with contractors and vendors across the globe. And so, you know, finding our fit inside of those large enterprises as they extend out with their their customers is a very interesting area that we've really been sort of incremental to to PTC. And then, you know, they they have access to lots of other technology, like the i o. T business. And now, of course, the augmented reality business that that we can bring things to bear. For example, in the augmented reality world, they've they've got something called expert capture. And this is essentially imagine, you know, in a are ah, headset that allows you to be ableto to speak to it, but also capture images still images in video. And you could take somebody who's doing their task and capture literally the steps that they're taking its geo location and from their builds steps for new employees to be, we'll learn and understand how todo use that technology to help them do their job better. Well, when they do that, if there is replacement products or variation of of some of the tools that that they built the original design instruction set for they now have another version. Well, they have to manage multiple versions. Well, that's what on shape is really great at doing and so taking our technology and helping their solutions as well. So it's not only expanding our customer footprint, it's expanding the application footprint in terms of how we can help them and help customers. >>So that leads me to the tam discussion and again, as part of your strategist role. How do you think about that? Was just talking to some of your customers earlier about the democratization of cat and engineering? You know, I kind of joked, sort of like citizen engineering, but but so that you know, the demographics are changing the number of users potentially that can access the products because the it's so much more of a facile experience. How are you thinking about the total available market? >>It really is a great question, You know, it used to be when you when you sold boxes of software, it was how many engineers were out there. And that's the size of the market. The fact that matter is now when, When you think about access to that information, that data is simply a pane of glass. Whether it's a computer, whether it's a laptop, UH, a a cell phone or whether it's a tablet, the ability to to use different vehicles, access information and data expands the capabilities and power of a system to allow feedback and iteration. I mean, one of the one of the very interesting things is in technology is when you can take something and really unleash it to a larger audience and builds, you know, purpose built applications. You can start to iterate, get better feedback. You know there's a classic case in the clothing industry where Zara, you know, is a fast sort of turnaround. Agile manufacturer. And there was a great New York Times article written a couple years ago. My wife's a fan of Zara, and I think she justifies any purchases by saying, You know, Zara, you gotta purchase it now. Otherwise it may not be there the next time. Yet you go back to the store. They had some people in a store in New York that had this woman's throw kind of covering Shaw. And they said, Well, it would be great if we could have this little clip here so we can hook it through or something. And they sent a note back toe to the factory in Spain, and literally two weeks later they had, you know, 4000 of these things in store, and they sold out because they had a closed loop and iterative process. And so if we could take information and allow people access in multiple ways through different devices and different screens, that could be very specific information that, you know, we remove a lot of the engineering data book, bring the end user products conceptually to somebody that would have had to wait months to get the actual physical prototype, and we could get feedback well, Weaken have a better chance of making sure whatever product we're building is the right product when it ultimately gets delivered to a customer. So it's really it's a much larger market that has to be thought of rather than just the kind of selling A boxes software to an engineer. >>That's a great story. And again, it's gonna be exciting for you guys to see that with. The added resource is that you have a PTC, Um, so let's talk. I promise people we wanna talk about Atlas. Let's talk about the platform. A little bit of Atlas was announced last year. Atlas. For those who don't know it's a SAS space platform, it purports to go beyond product lifecycle management and you You're talking cloud like agility and scale to CAD and product design. But John, you could do a better job than I. What do >>we need to know about Atlas? Well, I think Atlas is a great description because it really is metaphorically sort of holding up all of the PTC applications themselves. But from the very beginning, when John and I met with Jim, part of what we were intrigued about was that he shared a vision that on shape was more than just going to be a cad authoring tool that, in fact, you know, in the past these engineering tools were very powerful, but they were very narrow in their purpose and focus. And we had specialty applications to manage the versions, etcetera. What we did in on shape is we kind of inverted that thinking. We built this collaboration and sharing engine at the core and then kind of wrap the CAD system around it. But that collaboration sharing and version ING engine is really powerful. And it was that vision that Jim had that he shared that we had from the beginning, which was, how do we take this thing to make a platform that could be used for many other applications inside of inside of any company? And so not only do we have a partner application area that is is much like the APP store or Google play store. Uh, that was sort of our first Stan Shih ation of this. This this platform. But now we're extending out to broader applications and much meatier applications. And internally, that's the thing works in the in the augmented reality. But there'll be other applications that ultimately find its way on top of this platform. And so they'll get all the benefits of of the collaboration, sharing the version ing the multi platform, multi device. And that's an extremely extremely, um, strategic leverage point for the company. >>You know, it's interesting, John, you mentioned the seaport before. So PTC, for those who don't know, built a beautiful facility down at the Seaport in Boston. And, of course, when PTC started, you know, back in the mid 19 eighties, there was nothing at the seaport s. >>So it's >>kind of kind of ironic, you know, we were way seeing the transformation of the seaport. We're seeing the transformation of industry and of course, PTC. And I'm sure someday you'll get back into that beautiful office, you know? Wait. Yeah, I'll bet. And, uh and but I wanna bring this up because I want I want you to talk about the future. How you how you see that our industry and you've observed this has moved from very product centric, uh, plat platform centric with sass and cloud. And now we're seeing ecosystems form around those products and platforms and data flowing through the ecosystem powering, you know, new innovation. I wonder if you could paint a picture for us of what the future looks like to you from your vantage point. >>Yeah, I think one of the key words you said there is data because up until now, data for companies really was sort of trapped in different applications. And it wasn't because people were nefarious and they want to keep it limited. It was just the way in which things were built. And, you know, when people use an application like on shape, what ends up happening is there their day to day interaction and everything that they do is actually captured by the platform. And, you know, we don't have access to that data. Of course it's it's the customer's data. But as as an artifact of them using the system than doing their day to day job, what's happening is they're creating huge amounts of information that can then be accessed and analyzed to help them both improve their design process, improve their efficiencies, improve their actual schedules in terms of making sure they can hit delivery times and be able to understand where there might be roadblocks in the future. So the way I see it is companies now are deploying SAS based tools like on shape and an artifact of them. Using that platform is that they have now analytics and tools to better understand and an instrument and manage their business. And then from there, I think you're going to see, because these systems are all you know extremely well. Architected allow through, you know, very structured AP. I calls to connect other SAS based applications. You're gonna start seeing closed loop sort of system. So, for example, people design using on shape, they end up going and deploying their system or installing it, or people use the end using products. People then may call back into the customers support line and report issues, problems, challenges. They'll be able to do traceability back to the underlying design. They'll be able to do trend analysis and defect analysis from the support lines and tie it back and closed loop the product design, manufacture, deployment in the field sort of cycles. In addition, you can imagine there's many things that air sort of as designed. But then when people go on site and they have to install it. There's some alterations modifications. Think about think about like a large air conditioning units for buildings. You go and you go to train and you get a large air conditioning unit that put up on top of building with a crane. They have to build all kinds of adaptors to make sure that that will fit inside of the particulars of that building. You know, with on shape and tools like this, you'll be able to not only take the design of what the air conditioning system might be, but also the all the adapter plates, but also how they installed it. So it sort of as designed as manufactured as stalled. And all these things can be traced, just like if you think about the transformation of customer service or customer contacts. In the early days, you used to have tools that were PC based tools called contact management solution, you know, kind of act or gold mine. And these were basically glorified Elektronik role in Texas. It had a customer names and they had phone numbers and whatever else. And Salesforce and Siebel, you know, these types of systems really broadened out the perspective of what a customer relationship? Waas. So it wasn't just the contact information it was, you know, How did they come to find out about you as a company? So all of the pre sort of marketing and then kind of what happens after they become a customer and it really was a 3 60 view. I think that 3 60 view gets extended to not just to the customers, but also tools and the products they use. And then, of course, the performance information that could come back to the manufacturer. So, you know, as an engineer, one of the things you learn about with systems is the following. And if you remember, when the CD first came out CDs that used to talk about four times over sampling or eight times over sampling and it was really kind of, you know, the fidelity the system. And we know from systems theory that the best way to improve the performance of a system is to actually have more feedback. The more feedback you have, the better system could be. And so that's why you get 16 60 for example, etcetera. Same thing here. The more feedback we have of different parts of a company that a better performance, The company will be better customer relationships. Better, uh, overall financial performance as well. So that's that's the view I have of how these systems all tied together. >>It's a great vision in your point about the data is I think right on. It used to be so fragmented in silos, and in order to take a system view, you've gotta have a system view of the data. Now, for years, we've optimized maybe on one little component of the system and that sometimes we lose sight of the overall outcome. And so what you just described, I think is, I think sets up. You know very well as we exit. Hopefully soon we exit this this covert era on John. I hope that you and I can sit down face to face at a PTC on shape event in the near term >>in the seaport in the >>seaport would tell you that great facility toe have have an event for sure. It >>z wonderful >>there. So So John McElhinney. Thanks so much for for participating in the program. It was really great to have you on, >>right? Thanks, Dave. >>Okay. And I want to thank everyone for participating. Today we have some great guest speakers. And remember, this is a live program. So give us a little bit of time. We're gonna flip this site over toe on demand mode so you can share it with your colleagues and you, or you can come back and and watch the sessions that you heard today. Uh, this is Dave Volonte for the Cube and on shape PTC. Thank you so much for watching innovation for good. Be well, Have a great holiday. And we'll see you next time. Yeah.
SUMMARY :
for good, brought to you by on shape. I'm coming to you from our studios outside of Boston. Why did you and your co founders start on shape? Big changes in this market and about, you know, a little Before It's been, you know, when you get acquired, You've got a passion for the babies that you you helped birth. And you know, I look back Sure to enjoy And and you were and still are a What kept me in the room, you know, in terms of the industrial world was seeing And you just launched construct capital this year, right in the middle of a pandemic and you know, half of the GDP in the US and have been very under invested. And I want to understand why you feel it's important to be early. so I like to work with founders and teams when they're, you know, Uh, and one of you could sort of connect the dots over time. you try to eliminate the risk Sa's much as you can, but I always say, I don't mind taking a risk And I could see the problems You know, a few years ago, people were like cloud, you know, And now even embracement in the cova driven new normal. And and but But, you know, the bet was on the SAS model was right for Crick had and I think you know, the closer you get to the shop floor in the production environment. So let's bring it, you know, toe today's you know, I didn't exit anything. know, I love you and I don't like that term exit. It's not just the technology is how you go to market and the whole business being run and how you support You know, a lot of baggage, you know, our customers pulling you in a lot of different directions I mentioned the breath of the product with new things PTC the SAS components of on shape for things like revision management And you get good pipeline from that. Um, Aziz, John will tell you I'm constantly one of the questions is for the dream team. pipeline to us in the world of some new things that are happening that we wouldn't see if you know you've shown Are you able to reach? And so the teacher can say to the students, They have to have Internet access, you know, going forward. Thank you. Okay, so thank you guys. Brought to you by on shape. where you don't want them, So this should be really interesting. Okay, let me ask each of you because you're all doing such interesting and compelling San Francisco, Stanford University and the University California Berkeley on. it was announced at the end of 2016, and we actually started operation with at the beginning of 2017, I mean, these things take time. of course, that's you mentioned now with co vid, um, we've been able to do a lot of very cool Now, Now, Philip, you What you do is mind melting. And as you might imagine, there's some really cool applications do. We do both its's to plowshares. kind of scaling the brain power for for the future. Uh, you know, graduating after senior year with, like, seven years of engineering under their belt I mean, you know, Cuba's. And so that's one of the reasons we keep pushing back. And I think in many ways, the products that you build, you know, our similar. Um, you know, they were talking about collaboration in the previous segment. And I think, you know, with this whole trend toward digit, I call it the Force march to digital. and especially how the cells in the human body function on how they're organized to create tissues You know, there's way more important than you know, the financial angles one of the first bits of feedback I got from my students is they said Okay, this is a lot of fun. making the world a better place, and robots are fun and all, but, you know, where is the real impact? I wanna get into the product, you know, side and understand how each of that person change the model and do things and point to things that is absolutely revolutionary. What were some of the concerns you had mentioned? Um, the other, um, you know, the concern was the learning curve, right? Maybe you could take us through your journey within I want something new how we congrats modules from things that we already have put them together And I don't know how we weigh existed without, you know, Google maps eso we I mean, you know, you could spend $30,000 on one seat wanna I wanna ask you that I may be over my skis on this, but we're seeing we're starting to see the early days I can whether you know, I think artists, you know, But, you know, So we know there's a go ahead. it. We had other server issues, but none with our, you know, engineering cad, the creativity off, making things that you can touch that you can see that you can see one of the things that that you want on shape to do that it doesn't do today abilities, the fact that that seems to be just built into the nature of the thing so There you there, right? There's a lot of capability in the cloud that I mean, you're you're asking to knit. of the the problems that that you all are passionate about? But for years I've been saying that if you want to solve the I mean, all of the ah lot to be able to pull together instead of pulling separately and to be able to spur the Um, you know, availability of water. you guys, um, you know, this one kind of stands out. looking parts that you would have never thought off a person would have never thought off, And here's the five that we picked out that we think you should take a closer look at. You don't have to be necessarily, you know, developers of artificial intelligence, And you want to make sure that you don't have biases or things like that I can't thank you enough for spending the time with us and sharing And he's currently the VP of strategy at PTC. Okay. Brought to you by on shape. Thanks for making the time to come on the program. And so from the very beginning not the right word, but things like how you compensate salespeople, how you interact with customers, In the past, it might have been that you had professional services that you bring out to a customer, I mean today, You see, you know, if you watch Silicon Valley double, And then, of course, if they're successful with it, you know, then in fact, you have negative turn which, know, when you calculate whatever its net retention or renew ALS, it's actually from a dollar standpoint. and that's a trend we're gonna continue. some of the things that you saw that you were trying to strategically leverage and what's changed, So one of the things that you saw then you know, cloud and and sas and okay, And this is essentially imagine, you know, in a are ah, headset that allows you to but but so that you know, the demographics are changing the number that could be very specific information that, you know, we remove a lot of the engineering data book, And again, it's gonna be exciting for you guys to see that with. tool that, in fact, you know, in the past these engineering tools were very started, you know, back in the mid 19 eighties, there was nothing at the seaport s. I wonder if you could paint a picture for us of what the future looks like to you from your vantage point. In the early days, you used to have tools that were PC I hope that you and I can sit down face to face at seaport would tell you that great facility toe have have an event for sure. It was really great to have you on, right? And we'll see you next time.
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Rafael Gómez-Sjöberg, Philip Taber and Dr. Matt Shields | Onshape Innovation For Good
>>from around the globe. It's the Cube presenting innovation for good. Brought to you by on shape. >>Okay, we're back. This is Dave Volonte and you're watching innovation for good. A program on Cuba 3 65 made possible by on shape of BTC company. We're live today really live TV, which is the heritage of the Cuban. Now we're gonna go to the sources and talkto on shape customers to find out how they're applying technology to create real world innovations that are changing the world. So let me introduce our panel members. Rafael Gomez Fribourg is with the Chan Zuckerberg bio hub. A very big idea. And collaborative nonprofit was initiative that was funded by Mark Zuckerberg and his wife, Priscilla Chan, and really around diagnosing and curing and better managing infectious diseases. So really timely topic. Philip Tabor is also joining us. He's with silver side detectors which develops neutron detective detection systems. Yet you want to know if early if neutrons and radiation or in places where you don't want them, so this should be really interesting. And last but not least, Matthew Shields is with the Charlottesville schools and is gonna educate us on how he and his team are educating students in the use of modern engineering tools and techniques. Gentlemen, welcome to the Cuban to the program. This should be really interesting. Thanks for coming on. >>Hi. Or pleasure >>for having us. >>You're very welcome. Okay, let me ask each of you because you're all doing such interesting and compelling work. Let's start with Rafael. Tell us more about the bio hub and your role there, please. >>Okay. Yes. As you said, the Bio Hope is a nonprofit research institution, um, funded by Mark Zuckerberg and his wife, Priscilla Chan. Um and our main mission is to develop new technologies to help advance medicine and help, hopefully cure and manage diseases. Um, we also have very close collaborations with Universe California, San Francisco, Stanford University and the University California Berkeley on. We tried to bring those universities together, so they collaborate more of biomedical topics. And I manage a team of engineers in by joining platform. Um, and we're tasked with creating instruments for the laboratory to help the scientist boats inside the organization and also in the partner universities do their experiments in better ways in ways that they couldn't do before >>in this edition was launched five years ago. It >>was announced at the end of 2016, and we actually started operations in the beginning of 2017, which is when I joined um, so this is our third year. >>And how's how's it going? How does it work? I mean, these things >>take time. It's been a fantastic experience. Uh, the organization works beautifully. Um, it was amazing to see it grow from the beginning. I was employee number 12, I think eso When I came in, it was just a nem p off his building and MP labs. And very quickly we had something running about from anything. Eso I'm very proud of the work that we have done to make that possible. Um And then, of course, that's you mentioned now, with co vid, um, we've been able to do a lot of very cool work, um, very being of the pandemic In March, when there was a deficit of testing, uh, capacity in California, we spun up a testing laboratory in record time in about a week. It was crazy. It was a crazy project. Um, but but incredibly satisfying. And we ended up running all the way until the beginning of November, when the lab was finally shut down, we could process about 3000 samples a day. I think at the end of it all, we were able to test about 100 on the road, 150,000 samples from all over the state. We were providing free testing toe all of the Department of Public Health Department of Public Health in California, which, at the media pandemic, had no way to do testing affordably and fast. So I think that was a great service to the state. Now the state has created a testing system that will serve those departments. So then we decided that it was unnecessary to keep going with testing in the other biopsy that would shut down, >>right? Thank you for that. Now, Now, Philip, you What you do is mind melting. You basically helped keep the world safe. Maybe you describe a little bit more about silver side detectors and what your role is there and how it all works. >>Tour. So we make a nuclear bomb detectors and we also make water detectors. So we try and do our part. Thio Keep the world from blowing up and make it a better place at the same time. Both of these applications use neutron radiation detectors. That's what we make. Put them out by a port border crossing Places like that they can help make sure that people aren't smuggling, shall we say, very bad things. Um, there's also a burgeoning field of research and application where you can use neutrons with some pretty cool physics to find water so you can do things like but a detector up in the mountains and measure snowpack. Put it out in the middle of the field and measure soil moisture content. And as you might imagine, there's some really cool applications in, uh, research and agronomy and public policy for this. >>All right, so it's OK, so it's It's much more than you know, whatever fighting terrorism, it's there's a riel edge, or I kind of i o t application for what you guys do. >>You do both Zito shares. You might >>say a mat. I I look at your role is kind of scaling the brain power for for the future. Maybe tell us more about Charlottesville schools and in the mission that you're pursuing and what you do. >>Thank you. Um, I've been in Charlottesville city schools for about 11 or 12 years. I started their teaching, Um, a handful of classes, math and science and things like that. But Thescore board and my administration had the crazy idea of starting an engineering program about seven years ago. My background is an engineering is an engineering. My masters is in mechanical and aerospace engineering. And, um, I basically spent a summer kind of coming up with what might be a fun engineering curriculum for our students. And it started with just me and 30 students about seven years ago, Um, kind of a home spun from scratch curriculum. One of my goals from the outside was to be a completely project based curriculum, and it's now grown. We probably have about six or 700 students, five or six full time teachers. We now have pre engineering going on at the 5th and 6th grade level. I now have students graduating. Uh, you know, graduating after senior year with, like, seven years of engineering under their belt and heading off to doing some pretty cool stuff. So it's It's been a lot of fun building up a program and, um, and learning a lot in the process. >>That's awesome. I mean, you know, Cuba's. We've been passionate about things like women in tech, uh, diversity stem. You know, not only do we need more more students in stem, we need mawr underrepresented women, minorities, etcetera. We were just talking to John her stock and integrate Grayson about this is do you do you feel is though you're I mean, first of all, the work that you do is awesome, but but I'll go one step further. Do you feel as though it's reaching, um, or, you know, diverse base and And how is that going? >>That's a great question. I think research shows that a lot of people get funneled into one kind of track or career path or set of interests really early on in their educational career. And sometimes that that funnels kind of artificial. And so that's one of the reasons we keep pushing back. Um, so our school systems introducing kindergartners to programming on DSO. We're trying to push back how we expose students to engineering and to stem fields as early as possible, and we've definitely seen the fruits of that in my program. In fact, my engineering program, uh, sprung out of an after school in Extracurricular Science Club that actually three girls started at our school. So I think that actually has helped that three girls started the club That eventually is what led our engineering programs that sort of baked into the DNA and also are a big public school. And we have about 50% of the students are under the poverty line, and we should I mean, Charlottesville, which is a big refugee town. And so I've been adamant from Day one that there are no barriers to entry into the program. There's no test you have to take. You don't have to have be taking a certain level of math or anything like that. That's been a lot of fun. To have a really diverse set of kids and or the program and be successful, >>that's phenomenal. That's great to hear. So, Philip, I wanna come back to you. You know, I think about maybe some day we'll be able to go back to a sporting events, and I know when I when I'm in there, there's somebody up on the roof looking out for me, you know, watching the crowd. And they have my back. And I think in many ways, the products that you build, you know, our similar I may not know they're there, but they're keeping us safe or they're measuring things that that that I don't necessarily see. But I wonder if you could talk about a little bit more detail about the products you build and how they're impacting society. >>Sure, So there are certainly a lot of people who are who are watching, trying to make sure things were going well in keeping you safe that you may or may not be aware of. And we try and support ah lot of them. So we have detectors that are that are deployed in a variety of variety of uses with a number of agencies and governments that dio like I was saying, ports and border crossing some other interesting applications that are looking for looking for signals that should not be there and working closely to fit into the operations these folks do Onda. We also have ah lot of outreach to researchers and scientists trying to help them support the work they're doing, um, using neutron detection for soil moisture monitoring is a some really cool opportunities for doing it at large scale and with much less, um, expense or complication then would have been done previous technologies. Mhm. You know, they were talking about collaboration in the previous segment. We've been able to join a number of conferences for that, virtually including one that was supposed to be held in Boston. But another one that was held, uh, of the University of Heidelberg in Germany. And, uh, this is sort of things that in some ways, the pandemic is pushing people towards greater collaboration than there would have been able to do. Had it all but in person. >>Yeah, we did. Uh, the cube did live works a couple years ago in Boston. It was awesome show. And I think, you know, with this whole trend toward digit, I call it the forced march to digital. Thanks to cove it I think that's just gonna continue. Thio grow Raphael one. If you could describe the process that you used to better understand diseases and what's your organization's involvement? Been in more detail, addressing the cove in pandemic. >>Um, so so we have the bio be structured in, Um um, in a way that foster So the combination of technology and science. So we have to scientific tracks, one about infectious diseases and the other one about understanding just basic human biology how the human body functions and especially how the cells in the human body function on how they're organized to create teachers in the body. Um, and then it has the set of platforms. Um, mind is one of them by engineering that are all technology. Read it. So we have data science platform, all about data analysis, machine learning, things like that. Um, we have a mass spectrometry platform is all about mass spectrometry technologies to, um, exploit those ones in service for the scientists on. We have a genomics platform. That is all about sequencing DNA in our DNA. Um, and then an advanced microscopy. It's all about developing technologies, uh, to look at things with advanced microscopes and the little technologies to marry computation on microscope. So, um, the scientists said the agenda and the platforms we just serve their needs, support their needs, and hopefully develop technologies that help them do their experiments better, faster, or allow them to the experiment that they couldn't do in any other way before. Um And so with cove, it because we have that very strong group of scientists that work on. I have been working on infectious disease before, and especially in viruses, we've been able to very quickly pivot to working on that s O, for example, my team was able to build pretty quickly a machine to automatically purified proteins, and it's being used to purify all these different important proteins in the cove. It virus the SARS cov to virus on Dwyer, sending some of those purified proteins all over the world. Two scientists that are researching the virus and trying to figure out how to develop vaccines, understand how the virus affects the body and all that. So some of the machines we built are having a very direct impact on this. Um, Also for the copy testing lab, we were able to very quickly develop some very simple machines that allowed the lab to function sort of faster and more efficiently. Sort of had a little bit of automation in places where we couldn't find commercial machines that would do it. >>Um, God s o mat. I mean, you gotta be listening to this in thinking about, Okay? Some. Someday your students are gonna be working at organizations like Like like Bio Hub and Silver Side. And you know, a lot of young people that just have I don't know about you guys, but like my kids, they're really passionate about changing the world. You know, there's way more important than, you know, the financial angles and that z e I gotta believe you're seeing that you're right in the front lines there. >>Really? Um, in fact, when I started the curriculum six or seven years ago, one of the first bits of feedback I got from my students is they said Okay, this is a lot of fun. So I had my students designing projects and programming microcontrollers raspberry, PiS and order We nose and things like that. The first bit of feedback I got from students was they said Okay, when do we get to impact the world? I've heard engineering is about making the world a better place, and robots are fun and all, but, you know, where is the real impact? And so, um do Yeah, thanks to the guidance of my students, I'm baking that Maurin. Now I'm like Day one of engineering one. We talk about how the things that the tools they're learning and the skills they're gaining eventually you know, very soon could be could be used to make the world a better place. >>You know, we all probably heard that famous line By Jeff Hammond Barker. The greatest minds of my generation are trying to figure out how to get people to click on ads. E. I think we're really generally generationally finally, at the point where you know young students and engineering and really you know it passionate about affecting society. I wanna get into the product, you know, side and understand how each of you are using on shape and and the value that that it brings. Maybe Raphael, you could start how long you've been using it. You know, what's your experience with it? Let's let's start there. >>I begin for about two years, and I switched to it with some trepidation. You know, I was used to always using the traditional product that you have to install on your computer, that everybody uses that. So I was kind of locked into that, but I started being very frustrated with the way it worked, um, and decided to give on ship chance. Which reputation? Because any change always, you know, causes anxiety. But very quickly my engineers started loving it. Uh, just because it's it's first of all, the learning curve wasn't very difficult at all. You can transfer from one from the traditional product to entree very quickly and easily. You can learn all the concepts very, very fast. It has all the functionality that we needed, and and what's best is that it allows to do things that we couldn't do before or we couldn't do easily. Um, now we can access the our cat documents from anywhere in the world. Um, so when we're in the lab fabricating something or testing a machine, any computer we have next to us or a tablet or on iPhone, we can pull it up and look at the cad and check things or make changes that something that couldn't do before because before you had to pay for every installation off the software for the computer, and I couldn't afford to have 20 installations to have some computers with the cat ready to use them like once every six months would have been very inefficient. So we love that part. And the collaboration features are fantastic. Especially now with Kobe, that we have to have all the remote meetings, eyes fantastic, that you can have another person drive the cad while the whole team is watching that person change the model and do things and point to things that is absolutely revolutionary. We love it. The fact that you have very, very sophisticated version control before it was always a challenge asking people, please, if you create anniversary and apart, how do we name it so that people find it? And then you end up with all these collection of files with names that nobody remembers, what they are, the person left and now nobody knows which version is the right one m s with on shape on the version ING system it has, and the fact that you can go back in history off the document and go back to previous version so easily and then go back to the press and version and explore the history of the part that is truly, um, just world changing for us, that we can do that so easily on for me as a manager to manage this collection of information that is critical for our operations. It makes it so much easier because everything is in one place. I don't have to worry about file servers that go down that I have to administer that have to have I t taken care off that have to figure how to keep access to people to those servers when they're at home. And they need a virtual private network and all of that mess disappears. I just simply give give a personal account on shape. And then, magically, they have access to everything in the way I want. And we can manage the lower documents and everything in a way, that is absolutely fantastic. >>Rafael, what was your what? What were some of the concerns you had mentioned? You had some trepidation. Was it a performance? Was it security? You know, some of the traditional cloud stuff and I'm curious as to how How whether any of those act manifested were they really that you had to manage? What were your concerns? >>Look, the main concern is how long is it going to take for everybody in the team? to learn to use the system like it and buy into it because I don't want to have my engineers using tools against their will write. I want everybody to be happy because that's how they're productive. They're happy and they enjoyed the tools they have. That was my main concern. I was a little bit worried about the whole concept of not having the files in a place where I couldn't quote unquote seat in some serving on site, but that that's kind of an outdated concept, right? So that took a little bit of a mind shift. But very quickly. Then I started thinking, Look, I have a lot of documents on Google Drive like I don't worry about that. Why would I worry about my cat on on shape? Right is the same thing. So I just needed to sort of put things in perspective that way. Um, the other, um, you know, their concern was the learning curve right is like how is he will be for everybody to and for me to learn it on whether it had all of the features that we needed and there were a few features that I actually discussed with, um uh, Cody at on shape on. They were actually awesome about using their scripting language in on shape to sort of mimic some of the features of the old cat, uh, in on shaped in a way that actually works even better than the old system. So it was It was amazing. Yeah. >>Great. Thank you for that, Phillip. What's your experience been? Maybe you could take us through your journey with on shape? >>Sure. So we've been we've been using on shaped Silver Side for coming up on about four years now, and we love it. We're very happy with it. We have a very modular product line, so and we make anything from detectors that would go into backpacks? Two vehicles, two very large things that a shipping container would go through and saw. Excuse me. Shape helps us to track and collaborate faster on the design, have multiple people working a same time on a project. And it also helps us to figure out if somebody else comes to us and say, Hey, I want something new. How we congrats modules from things that we already have. Put them together and then keep track of the design development and the different branches and ideas that we have, how they all fit together. A za design comes together and it's just been fantastic from a mechanical engineering background. I will also say that having used a number of different systems and solid works was the greatest thing since sliced bread. Before I got using on shape, I went, Wow, this is amazing. And I really don't want to design in any other platform after after getting on Lee a little bit familiar with it. >>You know, it's funny, right? I will have the speed of technology progression. I was explaining to some young guns the other day how e used to have a daytime er and that was my life. And if I lost that day, timer, I was dead. And I don't know how we weigh existed without, you know, Google Maps. Eso did we get anywhere? I don't know, but, uh, but so So, Matt, you know, it's interesting to think about, um, you know, some of the concerns that Raphael brought up, you hear? For instance, you know, all the time. Wow. You know, I get my Amazon bill at the end of the month It's through the roof in. But the reality is that Yeah, well, maybe you are doing more, but you're doing things that you couldn't have done before. And I think about your experience in teaching and educating. I mean, you so much more limited in terms of the resource is that you would have had to be able to educate people. So what's your experience been with With on shape and what is it enabled? >>Um, yeah, it was actually talking before we went with on shape. We had a previous CAD program and I was talking to my vendor about it, and he let me know that we were actually one of the biggest CAD shops in the state. Because if you think about it a really big program, you know, really big company might employ 5, 10, 15, 20 cad guys, right? I mean, when I worked for a large defense contractor, I think there were probably 20 of us as the cad guys. I now have about 300 students doing cat. So there's probably more students with more hours of cat under their belt in my building than there were when I worked for the big defense contractor. Um, but like you mentioned, uh, probably our biggest hurdle is just re sources. And so we want We want one of things I've always prided myself and trying to do in this programs provide students with access two tools and skills that they're going to see either in college or in the real world. So it's one of the reason we went with a big professional cad program. There are, you know, sort of k 12 oriented software and programs and things. But, you know, I want my kids coding and python and using slack and using professional type of tools on DSO when it comes to cat. That's just that that was a really hurt. I mean, you know, you could spend $30,000 on one seat of, you know, professional level cad program, and then you need a $30,000 computer to run it on if you're doing a heavy assemblies, Um, and so one of my dreams and it was always just a crazy dream. And I was the way I would always pitcher in my school system and say someday I'm gonna have a kid on a school issued chromebook in subsidized housing on public WiFi doing professional level bad and that that was a crazy statement until a couple of years ago. So we're really excited that I literally and, you know, march in, um, you said the forced march the forced march into, you know, modernity, March 13th kids sitting in my engineering lab that we spent a lot of money on doing. Cad March 14th. Those kids were at home on their school shoot chromebooks on public WiFi, uh, keeping their designs going and collaborating. And then, yeah, I could go on and on about some of the things you know, the features that we've learned since then they're even better. So it's not like this is some inferior, diminished version of the cat. And there's so much about it, E >>wanna I wanna ask you that I may be over my skis on this, but we're seeing we're starting to see the early days of the democratization of CAD and product design. It is the the citizen engineer. I mean, maybe insulting to the engineers in the room, but but is that we're beginning to see that >>I have to believe that everything moves into the cloud. Part of that is democratization that I don't need. I can whether you know, I think artists, you know, I could have a music studio in my basement with a nice enough software package. And Aiken, I could be a professional for now. My wife's a photographer. I'm not allowed to say that I could be a professional photographer with, you know, some cloud based software. And so, yeah, I do think that's part of what we're seeing is more and more technology is moving to the cloud >>Philip or Rafael anything. Your dad, >>I think I mean yeah, that that that combination of cloud based cat and then three D printing that is becoming more and more affordable on ubiquitous It's truly transformative, and I think for education is fantastic. I wish when I was a kid I had the opportunity to play with those kinds of things because I was always the late things. But, you know, the in a very primitive way. So, um, I think there's a dream for kids Thio to be able to do this. And, um, yeah, there's so many other technologies coming on, like Arduino and all of these electronic things that live. Kids play at home very cheaply with things that back in my day would have been unthinkable. >>So we know there's a go ahead. Philip Way >>had a pandemic and silver site moved to a new manufacturing facility this year. I was just on the shop floor, talking with contractors, standing 6 ft apart, pointing at things. But through it all, our CAD system was completely unruffled. Nothing stopped in our development work. Nothing stopped in our support for existing systems in the field. We didn't have to think about it. We had other server issues, but none with our, you know, engineering cad, platform and product development and support world right ahead, which was cool, but also a That's point. I think it's just really cool what you're doing with the kids. The most interesting secondary and college level engineering work that I did was project based. It's an important problem to the world. Go solve it and that is what we do here. That is what my entire career has been. And I'm super excited to see See what your students are gonna be doing, uh, in there home classrooms on their chromebooks now and what they do. Building on that. >>Yeah, I'm super excited to see your kids coming out of college with engineering degrees because yeah, I think that project based experience is so much better than just sitting in a classroom, taking notes and doing math problems on. And I think he will give the kids a much better flavor What engineering is really about. Think a lot of kids get turned off by engineering because they think it's kind of dry because it's just about the math for some very abstract abstract concept, and they are there. But I think the most important thing is just that. Hands on a building and the creativity off, making things that you can touch that you can see that you can see functioning. >>Great. So you know, we all know the relentless pace of technology progression. So when you think about when you're sitting down with the folks that on shape and there the customer advisor for one of the things that you want on shape to do that it doesn't do today >>I could start by saying, I just love some of the things that does do because it's such a modern platform and I think some of these, uh, some some platforms that have a lot of legacy and a lot of history behind them. I think we're dragging some of that behind them. So it's cool to see a platform that seemed to be developed in a modern era. And so that's, you know, it is the Google docks. And so the fact that collaboration and version ing and link sharing is, and, like, platform agnostic abilities the fact that that seems to be just built into the nature of the thing so far, that's super exciting as far as things that it to go from there, Um, I don't know. >>Other than price, >>you can't say I >>can't say lower price. >>Yeah, so far on a PTC s that worked with us. Really well, so I'm not complaining. There. You there? >>Yeah. Yeah. No Gaps, guys. Whitespace, Come on. >>We've been really enjoying the three week update Cadence. You know, there's a new version every three weeks and we don't have to install it. We just get all the latest and greatest goodies. One of the trends that we've been following and enjoying is the the help with a revision management and release work flows. Um, and I know that there's more than on shape is working on that we're very excited for, because that's a big important part about making real hardware and supporting it in the field. Um, something that was cool. They just integrated Cem markup capability In the last release that took, we were doing that anyway, but we were doing it outside of on shapes, and now we get to streamline our workflow and put it in the CAD system where we're making those changes anyway, when we're reviewing drawings and doing this kind of collaboration. And so I think from our perspective, we continue to look forward toa further progress on that. There's a lot of capability in the cloud that I think they're just kind of scratching the surface on you. >>I would. I mean, you're you're asking to knit. Pick. I would say one of the things that I would like to see is is faster regeneration speed. There are a few times with comics necessities that regenerating the document takes a little longer than I would like to. It's not a serious issue, but anyway, I'm being spoiled, >>you know. That's good. I've been doing this a long time and I like toe Ask that question of practitioners and to me, it it's a signal like when you're nit picking and that you're struggling to knit. Pick that to me is a sign of a successful product. And And I wonder, I don't know, uh, have the deep dive into the architecture, But are things like alternative processors? You're seeing them hit the market in a big way. Uh, you know, maybe a helping address the challenge, But I'm gonna ask you the big, chewy question now, then would maybe go to some audience questions when you think about the world's biggest problems. I mean, we're global pandemics. Obviously top of mind. You think about nutrition, you know, feeding the global community. We've actually done a pretty good job of that. But it's not necessarily with the greatest nutrition climate change, alternative energy, the economic divides. You've got geopolitical threats and social unrest. Health care is a continuing problem. What's your vision for changing the world and how product innovation for good can be applied to some of the the problems that that you all are passionate about? Big question. But who wants toe start >>not biased. But for years I've been saying that if you want to solve the economy, the environment, uh, global unrest, pandemics education is the case If you wanna if you want to, um, make progress in those in those realms, I think funding funding education is probably gonna pay off pretty well. >>Absolutely. And I think stem is key to that. I mean, all of the, ah lot of the well being that we have today and then industrialized countries, thanks to science and technology, right, improvements in health care, improvements in communication, transportation, air conditioning. Um, every aspect of life is touched by science and technology. So I think having more kids studying and understanding that is absolutely key. Yeah, I agree, >>Philip, you got anything they had? >>I think there's some big technical problems in the world today, Raphael and ourselves there certainly working on a couple of them. Think they're also collaboration problems and getting everybody doing ableto pull together instead of pulling, pulling separately and to be able to spur the idea is onwards. So that's where I think the education side is really exciting. What Matt is doing and and it just kind of collaboration in general when we could do provide tools to help people do good work? Uh, that is, I think, valuable. >>Yeah, I think that's a very good point. And along those lines, we have some projects that are about creating very low cost instruments for low research settings places in Africa, Southeast Asia, South America so that they can do, um, um, biomedical research that it's difficult to do in those place because they don't have the money to buy the fancy lab machines that cost $30,000 an hour. Um, so we're trying to sort of democratize some of those instruments. And I think thanks to tools like Kahn shaped and is easier, for example, to have a conversation with somebody in Africa and show them the design that we have and discuss the details of it with them. Andi, that's amazing. Right? To have somebody you know, 10 time zones away, Um, looking really life in real time with you about your design and discussing the details or teaching them how to build a machine. Right? Because, um, you know, they have a three d printer. You can you just give them the design and say, like, you build it yourself, uh, even cheaper than and, you know, also billing and shipping it there. Um, so all that that that aspect of it is also so super important, I think, for any of these efforts to improve, um, some of the hardest part was in the world from climate change. Do you say, as you say, poverty, nutrition issues? Um, you know, availability of water. You have that project at about finding water. Um, if we can also help deploy technologies that teach people remotely how to create their own technologies or how to build their own systems that will help them solve those forms locally. I think that's very powerful. >>Yeah, that point about education is right on. I think some people in the audience may be familiar with the work of Erik Brynjolfsson and Andrew McAfee, the second machine age where they sort of put forth the premise that, uh, is it laid it out. Look, for the first time in history, machines air replacing humans from a cognitive perspective. Machines have always replaced humans, but that's gonna have an impact on jobs. But the answer is not toe protect the past from the future. Uh, the answer is education and public policy. That really supports that. So I couldn't agree more. I think it's a really great point. Um, we have We do have some questions from the audience. If if we can. If I can ask you guys, um, you know, this one kind of stands out. How do you see artificial intelligence? I was just talking about machine intelligence. Um, how do you see that? Impacting the design space guys trying to infuse a I into your product development. What can you tell me? >>Um, absolutely. Like, we're using AI for some things, including some of these very low cost instruments that will hopefully help us diagnose certain diseases, especially this is that are very prevalent in the Third World. Um, and some of those diagnostics are these days done by thes armies of technicians that are trained to look under the microscope. But, um, that's a very slow process. Is very error prone and having machine learning systems that can, to the same diagnosis faster, cheaper and also little machines that can be taken to very remote places to these villages that have no access to a fancy microscope to look at a sample from a patient that's very powerful, and I we don't do this. But I have read quite a bit about how certain places air, using a Tribune attorneys to actually help them optimize designs for parts. So you get these very interesting looking parts that you would have never thought off. A person would have never thought off, but that are incredibly light ink earlier strong and I have all sort of properties that are interesting thanks to artificial intelligence machine learning in particular, >>yet another, uh, advantage you get when when your work is in the cloud I've seen. I mean, there's just so many applications that so if the radiology scan is in the cloud and the radiologist is goes to bed at night, radiologist could come in in the morning and and say, Oh, the machine while you were sleeping was using artificial intelligence to scan these 40,000 images. And here's the five that we picked out that we think you should take a closer look at or like Raphael said. I can design my part. My, my, my, my, my you know, mount or bracket or whatever and go to sleep. And then I wake up in the morning. The machine has improved. It for me has made it strider strider stronger and lighter. Um And so just when your when your work is in the cloud, that's just that's a really cool advantage that you get that you can have machines doing some of your design work for you. >>Yeah, we've been watching, uh, you know, this week is this month, I guess is aws re invent and it's just amazing to see how much effort is coming around machine learning machine intelligence. You know, Amazon has sage maker Google's got, you know, embedded you no ML and big query. Certainly Microsoft with Azure is doing tons of stuff and machine learning. I think the point there is that that these things will be infused in tow R and D and in tow software products by the vendor community. And you all will apply that to your business and and build value through the unique data that your collecting you know, in your ecosystems. And and that's how you add value. You don't have to be necessarily, you know, developers of artificial intelligence, but you have to be practitioners to apply that. Does that make sense to you, Philip? >>Yeah, absolutely. And I think your point about value is really well chosen. We see AI involved from the physics simulations all the way up to interpreting radiation data, and that's where the value question, I think, is really important because it's is the output of the AI giving helpful information that the people that need to be looking at it. So if it's curating a serious of radiation alert, saying, Hey, like these are the anomalies you need to look at eyes it, doing that in a way that's going to help a good response on. In some cases, the II is only as good as the people. That sort of gave it a direction and turn it loose. And you want to make sure that you don't have biases or things like that underlying your AI that air going to result in, uh in less than helpful outcomes coming from it. So we spend quite a lot of time thinking about how do we provide the right outcomes to people who are who are relying on our systems? >>That's a great point, right? Humans, air biased and humans build models, so models are inherently biased. But then software is hitting the market. That's gonna help us identify those biases and help us, you know? Of course. Correct. So we're entering Cem some very exciting times, guys. Great conversation. I can't thank you enough for spending the time with us and sharing with our audience the innovations that you're bringing to help the world. So thanks again. >>Thank you so much. >>Thank you. >>Okay. You're welcome. Okay. When we come back, John McElheny is gonna join me. He's on shape. Co founder. And he's currently the VP of strategy at PTC. He's gonna join the program. We're gonna take a look at what's next and product innovation. I'm Dave Volonte and you're watching innovation for good on the Cube, the global leader. Digital technology event coverage. We'll be right back
SUMMARY :
Brought to you by on shape. and his team are educating students in the use of modern engineering tools and techniques. Okay, let me ask each of you because you're all doing such interesting and compelling San Francisco, Stanford University and the University California Berkeley on. in this edition was launched five years ago. was announced at the end of 2016, and we actually started operations in the beginning of 2017, I think at the end of it all, we were able to test about 100 on the road, 150,000 Now, Now, Philip, you What you do is mind melting. can use neutrons with some pretty cool physics to find water so you can do things like but All right, so it's OK, so it's It's much more than you know, whatever fighting terrorism, You do both Zito shares. kind of scaling the brain power for for the future. One of my goals from the outside was to be a completely I mean, you know, Cuba's. And so that's one of the reasons we keep pushing back. And I think in many ways, the products that you build, you know, our similar I may not know they're there, trying to make sure things were going well in keeping you safe that you may or may not be aware of. And I think, you know, with this whole trend toward digit, I call it the forced march to digital. machines that allowed the lab to function sort of faster and more efficiently. You know, there's way more important than, you know, the financial angles and robots are fun and all, but, you know, where is the real impact? I wanna get into the product, you know, side and understand that person change the model and do things and point to things that is absolutely revolutionary. You know, some of the traditional cloud stuff and I'm curious as to how How Um, the other, um, you know, their concern was the learning curve right is like how is he will be Maybe you could take us through your journey with And I really don't want to design in any other platform after And I don't know how we weigh existed without, you know, I mean, you know, you could spend $30,000 on one seat of, I mean, maybe insulting to the engineers in the room, but but is that we're I can whether you know, I think artists, you know, Philip or Rafael anything. But, you know, So we know there's a go ahead. you know, engineering cad, platform and product development and support world right ahead, Hands on a building and the creativity off, making things that you can touch that you can see that one of the things that you want on shape to do that it doesn't do today And so that's, you know, it is the Google docks. Yeah, so far on a PTC s that worked with us. Whitespace, Come on. There's a lot of capability in the cloud that I mean, you're you're asking to knit. maybe a helping address the challenge, But I'm gonna ask you the big, chewy question now, pandemics education is the case If you wanna if you want to, of the well being that we have today and then industrialized countries, thanks to science and technology, and it just kind of collaboration in general when we could do provide And I think thanks to tools like Kahn shaped and is easier, I think some people in the audience may be familiar with the work of Erik Brynjolfsson and I have all sort of properties that are interesting thanks to artificial intelligence machine learning And here's the five that we picked out that we think you should take a closer look at or like Raphael You don't have to be necessarily, you know, developers of artificial intelligence, And you want to make sure that you don't have biases or things like that I can't thank you enough for spending the time with us and sharing And he's currently the VP of strategy at PTC.
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Harnessing the Power of Sound for Nature – Soundscape Ecological Research | Exascale Day 2020
>> From around the globe, it's theCUBE, with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We are celebrating Exascale Day. 10, 18, I think it's the second year of celebrating Exascale Day, and we're really excited to have our next guest and talk about kind of what this type of compute scale enables, and really look a little bit further down the road at some big issues, big problems and big opportunities that this is going to open up. And I'm really excited to get in this conversation with our next guest. He is Bryan Pijanowski the Professor of Landscape and Soundscape Ecology at Purdue University. Bryan, great to meet you. >> Great to be here. >> So, in getting ready for this conversation, I just watched your TED Talk, and I just loved one of the quotes. I actually got one of quote from it that's basically saying you are exploring the world through sound. I just would love to get a little deeper perspective on that, because that's such a unique way to think about things and you really dig into it and explain why this is such an important way to enjoy the world, to absorb the world and think about the world. >> Yeah, that's right Jeff. So the way I see it, sound is kind of like a universal variable. It exists all around us. And you can't even find a place on earth where there's no sound, where it's completely silent. Sound is a signal of something that's happening. And we can use that information in ways to allow us to understand the earth. Just thinking about all the different kinds of sounds that exist around us on a daily basis. I hear the birds, I hear the insects, but there's just a lot more than that. It's mammals and some cases, a lot of reptiles. And then when you begin thinking outside the biological system, you begin to hear rain, wind, thunder. And then there's the sounds that we make, sounds of traffic, the sounds of church bells. All of this is information, some of it's symbolic, some of it's telling me something about change. As an ecologist that's what I'm interested in, how is the earth changing? >> That's great and then you guys set up at Purdue, the Purdue Center for Global Soundscapes. Tell us a little bit about the mission and some of the work that you guys do. >> Well, our mission is really to use sound as a lens to study the earth, but to capture it in ways that are meaningful and to bring that back to the public to tell them a story about how the earth kind of exists. There's an incredible awe of nature that we all experience when we go out and listen into to the wild spaces of the earth. I've gone to the Eastern Steppes of Mongolian, I've climbed towers in the Paleotropics of Borneo and listened at night. And ask the question, how are these sounds different? And what is a grassland really supposed to sound like, without humans around? So we use that information and bring it back and analyze it as a means to understand how the earth is changing and really what the biological community is all about, and how things like climate change are altering our spaces, our wild spaces. I'm also interested in the role that people play and producing sound and also using sound. So getting back to Mongolia, we have a new NSF funded project where we're going to be studying herders and the ways in which they use sonic practices. They use a lot of sounds as information sources about how the environment is changing, but also how they relate back to place and to heritage a special sounds that resonate, the sounds of a river, for example, are the resonance patterns that they tune their throat to that pay homage to their parents that were born at the side of that river. There's these special connections that people have with place through sound. And so that's another thing that we're trying to do. In really simple terms, I want to go out and, what I call it sounds rather simple, record the earth-- >> Right. >> What does that mean? I want to go to every major biome and conduct a research study there. I want to know what does a grassland sound like? What is a coral reef sound like? A kelp forest and the oceans, a desert, and then capture that as baseline and use that information-- >> Yeah. >> For scientific purposes >> Now, there's so much to unpack there Bryan. First off is just kind of the foundational role that sound plays in our lives that you've outlined in great detail and you talked about it's the first sense that's really activated as we get consciousness, even before we're born right? We hear the sounds of our mother's heartbeat and her voice. And even the last sense that goes at the end a lot of times, in this really intimate relationship, as you just said, that the sounds represent in terms of our history. We don't have to look any further than a favorite song that can instantly transport you, almost like a time machine to a particular place in time. Very, very cool. Now, it's really interesting that what you're doing now is taking advantage of new technology and just kind of a new angle to capture sound in a way that we haven't done before. I think you said you have sound listening devices oftentimes in a single location for a year. You're not only capturing sound, the right sound is changes in air pressure, so that you're getting changes in air pressure, you're getting vibration, which is kind of a whole different level of data. And then to be able to collect that for a whole year and then start to try to figure out a baseline which is pretty simple to understand, but you're talking about this chorus. I love your phrase, a chorus, because that sound is made up of a bunch of individual inputs. And now trying to kind of go under the covers to figure out what is that baseline actually composed of. And you talk about a bunch of really interesting particular animals and species that combine to create this chorus that now you know is a baseline. How did you use to do that before? I think it's funny one of your research papers, you reach out to the great bird followers and bird listeners, 'cause as you said, that's the easiest way or the most prolific way for people to identify birds. So please help us in a crowdsource way try to identify all the pieces that make this beautiful chorus, that is the soundscape for a particular area. >> Right, yeah, that's right. It really does take a team of scientists and engineers and even folks in the social sciences and the humanities to really begin to put all of these pieces together. Experts in many fields are extremely valuable. They've got great ears because that's the tools that they use to go out and identify birds or insects or amphibians. What we don't have are generalists that go out and can tell you what everything sounds like. And I'll tell you that will probably never ever happen. That's just way too much, we have millions of species that exist on this planet. And we just don't have a specific catalog of what everything sounds like, it's just not possible or doable. So I need to go out and discover and bring those discoveries back that help us to understand nature and understand how the earth is changing. I can't wait for us to eventually develop that catalog. So we're trying to develop techniques and tools and approaches that allow us to develop this electronic catalog. Like you're saying this chorus, and it doesn't necessarily have to be a species specific chorus, it can be a chorus of all these different kind of sounds that we think relate back to this kind of animal or that kind of animal based upon the animals instrument-- >> Right, great. >> And this is the sound. >> Now again, you know, keep it to the exascale theme, right? You're collecting a lot of data and you mentioned in one of the pieces I've dug up, that your longest study in a single location is 17 years. You've got over 4 million recordings. And I think you said over 230 years if you wanted to listen to them all back to back. I mean, this is a huge, a big data problem in terms of the massive amount of data that you have and need to run through an analysis. >> Yeah, that's right. We're collecting 48,000 data points per second. So that's 48 kilohertz. And then so you multiply everything and then you have a sense of how many data points you actually have to put them all together. When you're listening to a sound file over 10 minutes, you have hundreds of sounds that exist in them. Oftentimes you just don't know what they are, but you can more or less put some kind of measure on all of them and then begin to summarize them over space and time and try to understand it from a perspective of really science. >> Right, right. And then I just love to get your take as you progress down this kind of identification road, we're all very familiar with copyright infringement hits on YouTube or social media or whatever, when it picks up on some sound and the technology is actually really sophisticated to pick up some of those sound signatures. But to your point, it's a lot easier to compare against the known and to search for that known. Then when you've got this kind of undefined chorus that said we do know that there can be great analysis done that we've seen AI and ML applied, especially in the surveillance side on the video-- >> Right. >> With video that it can actually do a lot of computation and a lot of extracting signal from the noise, if you will. As you look down the road on the compute side for the algorithms that you guys are trying to build with the human input of people that know what you're listening to, what kind of opportunities do you see and where are we on that journey where you can get more leverage out of some of these technology tools? >> Well, I think what we're doing right now is developing the methodological needs, kind of describe what it is we need to move into that new space, which is going to require these computational, that computational infrastructure. So, for example, we have a study right now where we're trying to identify certain kinds of mosquitoes (chuckling) a vector-borne mosquitoes, and our estimates is that we need about maybe 900 to 1200 specific recordings per species to be able to put it into something like a convolutional neural network to be able to extract out the information, and look at the patterns and data, to be able to say indeed this is the species that we're interested in. So what we're going to need and in the future here is really a lot of information that allow us to kind of train these neural networks and help us identify what's in the sound files. As you can imagine the computational infrastructure needed to do that for data storage and CPU, GPU is going to be truly amazing. >> Right, right. So I want to get your take on another topic. And again the basis of your research is really all bound around the biodiversity crisis right? That's from the kind of-- >> Yeah. >> The thing that's started it and now you're using sound as a way to measure baseline and talk about loss of species, reduced abundancies and rampant expansion of invasive species as part of your report. But I'd love to get your take on cities. And how do you think cities fit the future? Clearly, it's an efficient way to get a lot of people together. There's a huge migration of people-- >> Right. >> To cities, but one of your themes in your Ted Talk is reconnecting with nature-- >> Yeah. >> Because we're in cities, but there's this paradox right? Because you don't want people living in nature can be a little bit disruptive. So is it better to kind of get them all in a tip of a peninsula in San Francisco or-- >> Yeah. >> But then do they lose that connection that's so important. >> Yeah. >> I just love to get your take on cities and the impacts that they're have on your core research. >> Yeah, I mean, it truly is a paradox as you just described it. We're living in a concrete jungle surrounded by not a lot of nature, really, honestly, occasional bird species that tend to be fairly limited, selected for limited environments. So many people just don't get out into the wild. But visiting national parks certainly is one of those kinds of experience that people oftentimes have. But I'll just say that it's getting out there and truly listening and feeling this emotional feeling, psychological feeling that wraps around you, it's a solitude. It's just you and nature and there's just no one around. >> Right. >> And that's when it really truly sinks in, that you're a part of this place, this marvelous place called earth. And so there are very few people that have had that experience. And so as I've gone to some of these places, I say to myself I need to bring this back. I need to tell the story, tell the story of the awe of nature, because it truly is an amazing place. Even if you just close your eyes and listen. >> Right, right. >> And it, the dawn chorus in the morning in every place tells me so much about that place. It tells me about all the animals that exist there. The nighttime tells me so much too. As a scientist that's spent most of his career kind of going out and working during the day, there's so much happening at night. Matter of fact-- >> Right. >> There's more sounds at night than there were during the day. So there is a need for us to experience nature and we don't do that. And we're not aware of these crises that are happening all over the planet. I do go to places and I listen, and I can tell you I'm listening for things that I think should be there. You can listen and you can hear the gaps, the gaps and that in that chorus, and you think what should be there-- >> Right. >> And then why isn't it there? And that's where I really want to be able to dig deep into my sound files and start to explore that more fully. >> It's great, it's great, I mean, I just love the whole concept of, and you identified it in the moment you're in the tent, the thunderstorm came by, it's really just kind of changing your lens. It's really twisting your lens, changing your focus, because that sound is there, right? It's been there all along, it's just, do you tune it in or do you tune it out? Do you pay attention? Do not pay attention is an active process or a passive process and like-- >> Right. >> I love that perspective. And I want to shift gears a little bit, 'cause another big environmental thing, and you mentioned it quite frequently is feeding the world's growing population and feeding it-- >> Yeah. >> In an efficient way. And anytime you see kind of factory farming applied to a lot of things you wonder is it sustainable, and then all the issues that come from kind of single output production whether that's pigs or coffee or whatever and the susceptibility to disease and this and that. So I wonder if you could share your thoughts on, based on your research, what needs to change to successfully and without too much destruction feed this ever increasing population? >> Yeah, I mean, that's one of the grand challenges. I mean, society is facing so many at the moment. In the next 20 years or so, 30 years, we're going to add another 2 billion people to the planet, and how do we feed all of them? How do we feed them well and equitably across the globe? I don't know how to do that. But I'll tell you that our crops and the ecosystem that supports the food production needs the animals and the trees and the microbes for the ecosystem to function. We have many of our crops that are pollinated by birds and insects and other animals, seeds need to be dispersed. And so we need the rest of life to exist and thrive for us to thrive too. It's not an either, it's not them or us, it has to be all of us together on this planet working together. We have to find solutions. And again, it's me going out to some of these places and bringing it back and saying, you have to listen, you have to listen to these places-- >> Right. >> They're truly a marvelous. >> So I know most of your listening devices are in remote areas and not necessarily in urban areas, but I'm curious, do you have any in urban areas? And if so, how has that signature changed since COVID? I just got to ask, (Bryan chuckling) because we went to this-- >> Yeah. >> Light switch moment in the middle of March, human activity slowed down-- >> Yeah. >> In a way that no one could have forecast ever on a single event, globally which is just fascinating. And you think of the amount of airplanes that were not flying and trains that we're not moving and people not moving. Did you have any any data or have you been able to collect data or see data as the impact of that? Not only directly in wherever the sensors are, but a kind of a second order impact because of the lack of pollution and the other kind of human activity that just went down. I mean, certainly a lot of memes (Bryan chuckling) on social media of all the animals-- >> Yeah. >> Come back into the city. But I'm just curious if you have any data in the observation? >> Yeah, we're part of actually a global study, there's couple of hundred of us that are contributing our data to what we call the Silent Cities project. It's being coordinated out of Europe right now. So we placed our sensors out in different areas, actually around West Lafayette area here in Indiana, near road crossings and that sort of thing to be able to kind of capture that information. We have had in this area here now, the 17 year study. So we do have studies that get into areas that tend to be fairly urban. So we do have a lot of information. I tell you, I don't need my sensors to tell me something that I already know and you suspect is true. Our cities were quiet, much quieter during the COVID situation. And it's continued to kind of get a little bit louder, as we've kind of released some of the policies that put us into our homes. And so yes, there is a major change. Now there have been a couple of studies that just come out that are pretty interesting. One, which was in San Francisco looking at the white-crowned sparrow. And they looked at historical data that went back something like 20 years. And they found that the birds in the cities were singing a much softer, 30% softer. >> Really? >> And they, yeah, and they would lower their frequencies. So the way sound works is that if you lower your frequencies that sound can travel farther. And so the males can now hear themselves twice as far just due to the fact that our cities are quieter. So it does have an impact on animals, truly it does. There was some studies back in 2001, during the September, the 9/11 crisis as well, where people are going out and kind of looking at data, acoustic data, and discovering that things were much quieter. I'd be very interested to look at some of the data we have in our oceans, to what extent are oceans quieter. Our oceans sadly are the loudest part of this planet. It's really noisy, sound travels, five times farther. Generally the noise is lower frequencies, and we have lots of ships that are all over the planet and in our oceans. So I'd really be interested in those kinds of studies as well, to what extent is it impacting and helping our friends in the oceans. >> Right, right, well, I was just going to ask you that question because I think a lot of people clearly understand sound in the air that surrounds us, but you talk a lot about sound in ocean, and sound as an indicator of ocean health, and again, this concept of a chorus. And I think everybody's probably familiar with the sounds of the humpback whale right? He got very popular and we've all seen and heard that. But you're doing a lot of research, as you said, in oceans and in water. And I wonder if you can, again, kind of provide a little bit more color around that, because I don't think you people, maybe we're just not that tuned into it, think of the ocean or water as a rich sound environment especially to the degree as you're talking about where you can actually start to really understand what's going on. >> Yeah, I mean, some of us think that sound in the oceans is probably more important to animals than on land, on the terrestrial side. Sound helps animals to navigate through complex waterways and find food resources. You can only use site so far underwater especially when it gets to be kind of dark, once you get down to certain levels. So there many of us think that sound is probably going to be an important component to measuring the status of health in our oceans. >> It's great. Well, Bryan, I really enjoyed this conversation. I've really enjoyed your Ted Talk, and now I've got a bunch of research papers I want to dig into a little bit more as well. >> Okay.(chuckling) >> It's a fascinating topic, but I think the most important thing that you talked about extensively in your Ted Talk is really just taking a minute to take a step back from the individual perspective, appreciate what's around us, hear, that information and I think there's a real direct correlation to the power of exascale, to the power of hearing this data, processing this data, and putting intelligence on that data, understanding that data in a good way, in a positive way, in a delightful way, spiritual way, even that we couldn't do before, or we just weren't paying attention like with what you know is on your phone please-- >> Yeah, really. >> It's all around you. It's been there a whole time. >> Yeah. (both chuckling) >> Yeah, Jeff, I really encourage your viewers to count it, just go out and listen. As we say, go out and listen and join the mission. >> I love it, and you can get started by going to the Center for Global Soundscapes and you have a beautiful landscape. I had it going earlier this morning while I was digging through some of the research of Bryan. (Bryan chuckling) Thank you very much (Bryan murmurs) and really enjoyed the conversation best to you-- >> Okay. >> And your team and your continued success. >> Alright, thank you. >> Alright, thank you. All right, he's Bryan-- >> Goodbye. >> I'm Jeff, you're watching theCUBE. (Bryan chuckling) for continuing coverage of Exascale Day. Thanks for watching. We'll see you next time. (calm ambient music)
SUMMARY :
From around the globe, it's theCUBE, And I'm really excited to and I just loved one of the quotes. I hear the birds, I hear the insects, and some of the work that you guys do. and analyze it as a means to understand A kelp forest and the oceans, a desert, And then to be able to and even folks in the social amount of data that you have and then you have a sense against the known and to for the algorithms that you and our estimates is that we need about And again the basis of your research But I'd love to get your take on cities. So is it better to kind of get them all that connection that's I just love to get your take on cities tend to be fairly limited, And so as I've gone to the dawn chorus in the and you think what should be there-- to explore that more fully. and you identified it in the and you mentioned it quite frequently a lot of things you for the ecosystem to function. of all the animals-- Come back into the city. that tend to be fairly urban. that are all over the planet going to ask you that question to be kind of dark, and now I've got a It's been there a whole time. Yeah. listen and join the mission. the conversation best to you-- and your continued success. Alright, thank you. We'll see you next time.
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Pat Gelsinger, VMware | VMworld 2020
>> Announcer: From around the globe, it's theCUBE with digital coverage of VMworld 2020 brought to you by VMware and its ecosystem partners. >> Hello, welcome back to theCUBE's coverage of VMworld 2020. This is theCUBE virtual with VMworld 2020 virtual. I'm John Furrier, your host of theCUBE with Dave Vellante. It's our 11th year covering VMware. We're not in-person, we're virtual but all the content is flowing. Of course, we're here with Pat Gelsinger, the CEO of VMware who's been on theCUBE, all 11 years. This year virtual of theCUBE as we've been covering VMware from his early days in 2010 when theCUBE started, 11 years later, Pat, it's still changing and still exciting. Great to see you, thanks for taking the time. >> Hey, you guys are great. I love the interactions that we have, the energy, the fun, the intellectual sparring and of course the audiences have loved it now for 11 years, and I look forward to the next 11 that we'll be doing together. >> It's always exciting 'cause we have great conversations, Dave, and I like to drill in and really kind of probe and unpack the content that you're delivering at the keynotes, but also throughout the entire program. It is virtual this year which highlights a lot of the cloud native changes. Just want to get your thoughts on the virtual aspect, VMworld's not in-person, which is one of the best events of the year, everyone loves it, the great community. It's virtual this year but there's a slew of content, what should people take away from this virtual VMworld? >> Well, one aspect of it is that I'm actually excited about is that we're going to be well over 100,000 people which allows us to be bigger, right? You don't have the physical constraints, you also are able to reach places like I've gone to customers and maybe they had 20 people attend in prior years. This year they're having 100. They're able to have much larger teams also like some of the more regulated industries where they can't necessarily send people to events like this, The International Audience. So just being able to spread the audience much more. A digital foundation for an unpredictable world, and man, what an unpredictable world it has been this past year. And then key messages, lots of key products announcements, technology announcements, partnership announcements, and of course in all of the VMworld is that hands-on labs, the interactions that will be delivering a virtual. You come to VMware because the content is so robust and it's being delivered by the world's smartest people. >> Yeah, we've had great conversations over the years and we've talked about hybrid cloud, I think, 2012. A lot of the stuff I look back at a lot of the videos was early on we're picking out all these waves, but there was that moment four years ago or so, maybe even four three, I can't even remember it seems like yesterday. You gave the seminal keynote and you said, this is the way the world's going to happen. And since that keynote, I'll never forget, was in Moscone and since then, you guys have been performing extremely well both on the business front as well as making technology bets and it's paying off. So what's next, you got the cloud, cloud scale, is it Space, is it Cyber? All these things are going on what is next wave that you're watching and what's coming out and what can people extract out of VMworld this year about this next wave? >> Yeah, one of the things I really am excited about and I went to my buddy Jensen, I said, boy, we're doing this work in smart mix we really like to work with you and maybe some things to better generalize the GPU. And Jensen challenged me. Now usually, I'm the one challenging other people with bigger visions. This time Jensen said, "hey Pat, I think you're thinking too small. Let's do the entire AI landscape together, and let's make AI a enterprise class works load from the data center to the cloud and to the Edge. And so I'm going to bring all of my AI resources and make VMware and Tanzu the preferred infrastructure to deliver AI at scale. I need you guys to make the GPUs work like first-class citizens in the vSphere environment because I need them to be truly democratized for the enterprise, so that it's not some specialized AI Development Team, it's everybody being able to do that. And then we're going to connect the whole network together in a new and profound way with our Monterey program as well being able to use the Smart NIC, the DPU, as Jensen likes to call it. So now with CPU, GPU and DPU, all being managed through a distributed architecture of VMware. This is exciting, so this is one in particular that I think we are now re-architecting the data center, the cloud and the Edge. And this partnership is really a central point of that. >> Yeah, the NVIDIA thing's huge and I know Dave probably has some questions on that but I asked you a question because a lot of people ask me, is that just a hardware deal? Talking about SmartNICs, you talk about data processing units. It sounds like a motherboard in the cloud, if you will, but it's not just hardware. Can you talk about the aspect of the software piece? Because again, NVIDIA is known for GPUs, we all know that but we're talking about AI here so it's not just hardware. Can you just expand and share what the software aspect of all this is? >> Yeah well, NVIDIA has been investing in their AI stack and it's one of those where I say, this is Edison at work, right? The harder I work, the luckier I get. And NVIDIA was lucky that their architecture worked much better for the AI workload. But it was built on two decades of hard work in building a parallel data center architecture. And they have built a complete software stack for all the major AI workloads running on their platform. All of that is now coming to vSphere and Tanzu, that is a rich software layer across many vertical industries. And we'll talk about a variety of use cases, one of those that we highlight at VMworld is the University, California, San Francisco partnership, UCSF, one of the world's leading research hospitals. Some of the current vaccine use cases as well, the financial use cases for threat detection and trading benefits. It really is about how we bring that rich software stack. This is a decade and a half of work to the VMware platform, so that now every developer and every enterprise can take advantage of this at scale. That's a lot of software. So in many respects, yeah, there's a piece of hardware in here but the software stack is even more important. >> It's so well we're on the sort of NVIDIA, the arm piece. There's really interesting these alternative processing models, and I wonder if you could comment on the implications for AI inferencing at the Edge. It's not just as well processor implications, it's storage, it's networking, it's really a whole new fundamental paradigm, but how are you thinking about that, Pat? >> Yeah, and we've thought about there's three aspects, what we said, three problems that we're solving. One is the developer problem where we said now you develop once, right? And the developer can now say, "hey I want to have this new AI-centric app and I can develop and it can run in the data center on the cloud or at the Edge." Secondly, my Operations Team can be able to operate this just like I do all of my infrastructure, and now it's VMs containers and AI applications. And third, and this is where your question really comes to bear most significantly, is data gravity. Right, these data sets are big. Some of them need to be very low latency as well, they also have regulatory issues. And if I have to move these large regulated data sets to the cloud, boy, maybe I can't do that generally for my Apps or if I have low latency heavy apps at the Edge, huh, I can't pull it back to the cloud or to my data center. And that's where the uniform architecture and aspects of the Monterey Program where I'm able to take advantage of the network and the SmartNICs that are being built, but also being able to fully represent the data gravity issues of AI applications at scale. 'Cause in many cases, I'll need to do the processing, both the learning and the inference at the Edge as well. So that's a key part of our strategy here with NVIDIA and I do think is going to unlock a new class of apps because when you think about AI and containers, what am I using it for? Well, it's the next generation of applications. A lot of those are going to be Edge, 5G-based, so very critical. >> We've got to talk about security now too. I'm going to pivot a little bit here, John, if it's okay. Years ago, you said security is a do-over, you said that on theCUBE, it stuck with us. But there's been a lot of complacency. It's kind of if it ain't broke, don't fix it, but but COVID kind of broke it. And so you see three mega trends, you've got cloud security, you'll see in Z-scaler rocket, you've got Identity Access Management and Octo which I hope there's I think a customer of yours and then you got Endpoint, you're seeing Crowdstrike explode you guys paid 2.7 billion, I think, for Carbon Black, yet Crowdstrike has this huge valuation. That's a mega opportunity for you guys. What are you seeing there? How are you bringing that all together? You've got NSX components, EUC components, you've got sort of security throughout your entire stack. How should we be thinking about that? >> Well, one of the announcements that I am most excited about at VMworld is the release of Carbon Black workload. 'Cause we said we're going to take those carbon black assets and we're going to combine it with workspace one, we're going to build it in NSX, we're going to make it part of Tanzu, and we're going to make it part of vSphere. And Carbon Black workload is literally the vSphere embodiment of Carbon Black in an agent-less way. So now you don't need to insert new agents or anything, it becomes part of the hypervisor itself. Meaning that there's no attack surface available for the bad guys to pursue. But not only is this an exciting new product capability, but we're going to make it free, right? And what I'm announcing at VMworld and everybody who uses vSphere gets Carbon Black workload for free for an unlimited number of VMs for the next six months. And as I said in the keynote, today is a bad day for cyber criminals. This is what intrinsic security is about, making it part of the platform. Don't add anything on, just click the button and start using what's built into vSphere. And we're doing that same thing with what we're doing at the networking layer, this is the last line acquisition. We're going to bring that same workload kind of characteristic into the container, that's why we did the Octarine acquisition, and we're releasing the integration of workspace one with Carbon Black client and that's going to be the differentiator, and by the way, Crowdstrike is doing well, but guess what? So are we, and right both of us are eliminating the rotting dead carcasses of the traditional AV approach. So there's a huge market for both of us to go pursue here. So a lot of great things in security, and as you said, we're just starting to see that shift of the industry occur that I promised last year in theCUBE. >> So it'd be safe to say that you're a cloud native and a security company these days? >> Yeah well, absolutely. And the bigger picture of us is that we're this critical infrastructure layer for the Edge, for the cloud, for the Telco environment and for the data center from every endpoint, every application, every cloud. >> So, Pat, I want to ask you a virtual question we got from the community. I'm going to throw it out to you because a lot of people look at Amazon and the cloud and they say, okay we didn't see it coming, we saw it coming, we saw it scale all the benefits that are coming out of cloud well documented. The question for you is, what's next after cloud? As people start to rethink especially with COVID highlighting and all the scabs out there as people look at their exposed infrastructure and their software, they want to be modern, they want the modern apps. What's next after cloud, what's your vision? >> Well, with respect to cloud, we are taking customers on the multicloud vision, right, where you truly get to say, oh, this workload I want to be able to run it with Azure, with amazon, I need to bring this one on-premise, I want to run that one hosted. I'm not sure where I'm going to run that application, so develop it and then run it at the best place. And that's what we mean by our hybrid multicloud strategy, is being able for customers to really have cloud flexibility and choice. And even as our preferred relationship with Amazon is going super well, we're seeing a real uptick, we're also happy that the Microsoft Azure VMware service is now GA. So there in Marketplace, are Google, Oracle, IBM and Alibaba partnerships, and the much broader set of VMware Cloud partner programs. So the future is multicloud. Furthermore, it's then how do we do that in the Telco network for the 5G build out? The Telco cloud, and how do we do that for the Edge? And I think that might be sort of the granddaddy of all of these because increasingly in a 5G world, we'll be enabling Edge use cases, we'll be pushing AI to the Edge like we talked about earlier in this conversation, we'll be enabling these high bandwidth low latency use cases at the Edge, and we'll see more and more of the smart embodiment smart city, smart street, smart factory, the autonomous driving, all of those need these type of capabilities. >> Okay. >> So there's hybrid and there's multi, you just talked about multi. So hybrid are data, are data partner ETR they do quarterly surveys. We're seeing big uptick in VMware Cloud on AWS, you guys mentioned that in your call. We're also seeing the VMware Cloud, VMware Cloud Foundation and the other elements, clearly a big uptick. So how should we think about hybrid? It looks like that's an extension of on-prem maybe not incremental, maybe a share shift, whereas multi looks like it's incremental but today multi is really running on multiple clouds, but a vision toward incremental value. How are you thinking about that? >> Yeah, so clearly, the idea of multi is truly multiple clouds. Am I taking advantage of multiple clouds being my private clouds, my hosted clouds and of course my public cloud partners? We believe everybody will be running a great private cloud, picking a primary public cloud and then a secondary public cloud. Hybrid then is saying, which of those infrastructures are identical, so that I can run them without modifying any aspect of my infrastructure operations or applications? And in today's world where people are wanting to accelerate their move to the cloud, a hybrid cloud is spot-on with their needs. Because if I have to refactor my applications, it's a couple million dollars per app and I'll see you in a couple of years. If I can simply migrate my existing application to the hybrid cloud, what we're consistently seeing is the time is 1/4 and the cost is 1/8 or less. Those are powerful numbers. And if I need to exit a data center, I want to be able to move to a cloud environment to be able to access more of those native cloud services, wow, that's powerful. And that's why for seven years now, we've been preaching that hybrid is the future, it is not a way station to the future. And I believe that more fervently today than when I declared it seven years ago. So we are firmly on that path that we're enabling a multi and hybrid cloud future for all of our customers. >> Yeah, you addressed that like Cube 2013, I remember that interview vividly was not a weigh station I got hammered answered. Thank you, Pat, for clarifying that going back seven years. I love the vision, you always got the right wave, it's always great to talk to you but I got to ask you about these initiatives that you're seeing clearly. Last year, a year and a half ago, Project Pacific came out, almost like a guiding directional vision. It then put some meat on the bone Tanzu and now you guys have that whole cloud native initiative, it's starting to flower up, thousands of flowers are blooming. This year, Project Monterey has announced. Same kind of situation, you're showing out the vision. What are the plans to take that to the next level? And take a minute to explain how Project Monterey, what it means and how you see that filling out. I'm assuming it's going to take the same trajectory as Pacific. >> Yeah, Monterey is a big deal. This is re-architecting the core of vSphere and it really is ripping apart the IO stack from the intrinsic operation of vSphere and the SX itself because in many ways, the IO, we've been always leveraging the NIC and essentially virtual NICs, but we never leverage the resources of the network adapters themselves in any fundamental way. And as you think about SmartNICs, these are powerful resources now where they may have four, eight, 16 even 32 cores running in the SmartNIC itself. So how do I utilize that resource, but it also sits in the right place? In the sense that it is the network traffic cop, it is the place to do security acceleration, it is the place that enables IO bandwidth optimization across increasingly rich applications where the workloads, the data, the latency get more important both in the data center and across data centers, to the cloud and to the Edge. So this re-architecting is a big deal, we announced the three partners, Intel, NVIDIA Mellanox and Pensando that we're working with, and we'll begin the deliveries of this as part of the core vSphere offerings beginning next year. So it's a big re-architecting, these are our key partners, we're excited about the work that we're doing with them and then of course our system partners like Dell and Lenovo who've already come forward and says, "Yeah we're going to to be bringing these to market together with VMware." >> Pat, personal question for you. I want to get your personal take, your career going back to Intel, you've seen it all but the shift is consumer to enterprise and you look at just recently Snowflake IPO, the biggest ever in the history of Wall Street. It's an enterprise data company, and the enterprise is now relevant. The consumer enterprise feels consumery, we talked about consumerization of IT years and years ago. But now more than ever the hottest financial IPO enterprise, you guys are enterprise. You did enterprise at Intel (laughing), you know the enterprise, you're doing it here at VMware. The enterprise is the consumer now with cloud and all this new landscape. What is your view on this because you've seen the waves, have you seen the historical perspective? It was consumer, was the big thing now it's enterprise, what's your take on all this? How do you make sense of it because it's now mainstream, what's your view on this? >> Well, first I do want to say congratulations to my friend, Frank and the extraordinary Snowflake IPO. And by the way they use VMware, so I not only do I feel a sense of ownership 'cause Frank used to work for me for a period of time, but they're also a customer of ours so go Frank, go Snowflake. We're excited about that. But there is this episodic to the industry where for a period of time, it is consumer-driven and CES used to be the hottest ticket in the industry for technology trends. But as you say, it has now shifted to be more business-centric, and I've said this very firmly, for instance, in the case of 5G where I do not see consumer. A faster video or a better Facebook isn't going to be why I buy 5G. It's going to be driven by more business use cases where the latency, the security and the bandwidth will have radically differentiated views of the new applications that will be the case. So we do think that we're in a period of time and I expect that it's probably at least the next five years where business will be the technology drivers in the industry. And then probably, hey there'll be a wave of consumer innovation, and I'll have to get my black turtlenecks out again and start trying to be cool but I've always been more of an enterprise guy so I like the next five to 10 years better. I'm not cool enough to be a consumer guy and maybe my age is now starting to conspire against me as well. >> Hey, Pat I know you got to go but a quick question. So you guys, you gave guidance, pretty good guidance actually. I wonder, have you and Zane come up with a new algorithm to deal with all this uncertainty or is it kind of back to old school gut feel? >> (laughing) Well, I think as we thought about the year, as we came into the year, and obviously, COVID smacked everybody, we laid out a model, we looked at various industry analysts, what we call the Swoosh Model, right? Q2, Q3 and Q4 recovery, Q1 more so, Q2 more so. And basically, we built our own theories behind that, we tested against many analyst perspectives and we had Vs and we had Ws and we had Ls and so on. We picked what we thought was really sort of grounded in the best data that we could, put our own analysis which we have substantial data of our own customers' usage, et cetera and picked the model. And like any model, you put a touch of conservatism against it, and we've been pretty accurate. And I think there's a lot of things we've been able to sort of with good data, good thoughtfulness, take a view and then just consistently manage against it and everything that we said when we did that back in March has sort of proven out incrementally to be more accurate. And some are saying, "Hey things are coming back more quickly" and then, "Oh, we're starting to see the fall numbers climb up a little bit." Hey, we don't think this goes away quickly, there's still a lot of secondary things to get flushed through, the various economies as stimulus starts tailoring off, small businesses are more impacted, and we still don't have a widely deployed vaccine and I don't expect we will have one until second half of next year. Now there's the silver lining to that, as we said, which means that these changes, these faster to the future shifts in how we learn, how we work, how we educate, how we care for, how we worship, how we live, they will get more and more sedimented into the new normal, relying more and more on the digital foundation. And we think ultimately, that has extremely good upsides for us long-term, even as it's very difficult to navigate in the near term. And that's why we are just raving optimists for the long-term benefits of a more and more digital foundation for the future of every industry, every human, every workforce, every hospital, every educator, they are going to become more digital and that's why I think, going back to the last question this is a business-driven cycle, we're well positioned and we're thrilled for all of those who are participating with Vmworld 2020. This is a seminal moment for us and our industry. >> Pat, thank you so much for taking the time. It's an enabling model, it's what platforms are all about, you get that. My final parting question for you is whether you're a VC investing in startups or a large enterprise who's trying to get through COVID with a growth plan for that future. What does a modern app look like, and what does a modern company look like in your view? >> Well, a modern company would be that instead of having a lot of people looking down at infrastructure, the bulk of my IT resources are looking up at building apps, those apps are using modern CICD data pipeline approaches built for a multicloud embodiment, right, and of course VMware is the best partner that you possibly could have. So if you want to be modern cool on the front end, come and talk to us. >> All right, Pat Gelsinger, the CEO of VMware here on theCUBE for VMworld 2020 virtual, here with theCUBE virtual great to see you virtually, Pat, thanks for coming on, thanks for your time. >> Hey, thank you so much, love to see you in person soon enough but this is pretty good. >> Yeah. >> Thank you Dave. Thank you so much. >> Okay, you're watching theCUBE virtual here for VMworld 2020, I'm John Furrier, Dave Vellante with Pat Gelsinger, thanks for watching. (gentle music)
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>> Narrator: From around the globe. It's theCUBE with digital coverage of VMworld 2020, brought to you by VMware and its ecosystem partners. >> Hello, welcome back to theCUBE's coverage of VMworld 2020. This is theCUBE virtual with VMworld 2020 virtual. I'm John Furrier your host of theCUBE with Dave Vellante. It's our 11th year covering VMware. We're not in person, we're virtual, but all the content is flowing. Of course, we're here with Pat Galsinger, the CEO of VMware. Who's been on theCUBE all 11 years. This year virtual of theCUBE as we've been covering VMware from his early days in 2010, when theCUBE started 11 years later, Pat is still changing and still exciting. Great to see you. Thanks for taking the time. >> Hey, you guys are great. I love the interactions that we have, the energy, the fun, the intellectual sparring. And of course that audiences have loved it now for 11 years. And I look forward to the next 11 that we'll be doing together. >> It's always exciting cause we'd love great conversations. Dave and I like to drill in and really kind of probe and unpack the content that you're delivering at the keynotes, but also throughout the entire program. It is virtual this year, which highlights a lot of the cloud native changes. Just want to get your thoughts on the virtual aspect of VMworld, not in person, which is one of the best events of the year. Everyone loves it. The great community. It's virtual this year, but there's a slew of content. What should people take away from this virtual VMworld? >> Well, one aspect of it is that I'm actually excited about is that we're going to be well over a hundred thousand people, which allows us to be bigger, right? You don't have the physical constraints. You also are able to reach places like I've gone to customers and maybe they had 20 people attend in prior years. This year they're having a hundred, they're able to have much larger teams. Also like some of the more regulated industries where they can't necessarily send people to events like this, the international audience. So just being able to spread the audience much more broadly well, also our key messages a digital foundation for unpredictable world. And man, what an unpredictable world it has been this past year? And then key messages, lots of key products announcements technology, announcements partnership, announcements and of course in all of the VMworld, is that hands on (murmurs) interactions that we'll be delivering our virtual, you come to the VMware because the content is so robust and it's being delivered by the world's smartest people. >> Yeah. We've had great conversations over the years. And we've talked about hybrid clothing 2012, a lot of this stuff I looked back in lot of the videos was early on, we're picking out all these waves, but it was that moment four years ago or so, maybe even four, three, I can't even remember, seems like yesterday. You gave the Seminole keynote and you said, "This is the way the world's going to happen." And since that keynote I'll never forget was in Moscone. And since then you guys have been performing extremely well both on the business as well as making technology bets and is paying off. So what's next? I mean, you've got the cloud scale. Is it space? Is it cyber? I mean, all these things are going on. What is next wave that you're watching and what's coming out and what can people extract out of VMworld this year about this next wave? >> Yeah, one of the things I really am excited about I went to my buddy Jensen. I said, "Boy, we're doing this work and smart. Next We really liked to work with you and maybe some things to better generalize the GPU." And Jensen challenged me. Now, usually, I'm the one challenging other people with bigger visions, this time Jensen said, "Hey Pat, I think you're thinking too small. Let's do the entire AI landscape together. And let's make AI a enterprise classwork stowed from the data center to the cloud and to the Edge. And so I'm going to bring all of my AI resources and make VMware, And Tansu the preferred infrastructure to deliver AI at scale. I need you guys to make the GPS work like first class citizens in the vSphere environment, because I need them to be truly democratized for the enterprise. so that it's not some specialized AI development team, it's everybody being able to do that. And then we're going to connect the whole network together in a new and profound way with our Monterey Program as well being able to use the SmartNIC, the DPU as Jensen likes to call it. So now it's CPU, GPU and DPU, all being managed through a distributed architecture of VMware." This is exciting. So this is one in particular that I think we are now rearchitecting the data center, the cloud in the Edge. And this partnership is really a central point of that. >> Yeah, the Nvid thing's huge. And I know Dave, Perharbs has some questions on that. But I ask you a question because a lot of people ask me, is it just a hardware deal? I mean, talking about SmartNIC, you talking about data processing units. It sounds like a motherboard in the cloud, if you will, but it's not just hardware. Can you talk about the aspect of the software piece? Because again, Nvidia is known for GP use, we all know that, but we're talking about AI here. So it's not just hardware. Can you just expand and share what the software aspect of all this is? >> Yeah. Well, Nvidia has been investing in their AI stack and it's one of those where I say, this is Edison at work, right? The harder I work, the luckier I get. And Nvidia was lucky that their architecture worked much better for the AI workload, but it was built on two decades of hard work in building a parallel data center architecture. And they have built a complete software stack for all of the major AI workloads running on their platform. All of that is now coming to vSphere and Tansu, that is a rich software layer across many vertical industries. And we'll talk about a variety of use cases. One of those that we highlight at Vmworld is the university of California, San Francisco partnership UCSF one of the world's leading research hospitals, some of the current vaccine use cases as well, the financial use cases for threat detection and trading benefits. It really is about how we bring that rich software stack. this is a decade and a half of work to the VMware platform so that now every developer and every enterprise could take advantage of this at scale, that's a lot of software. So in many respects, yeah, there's a piece of hardware in here, but the software stack is even more important. >> So well on the sort of Nvidia the arm piece, there's really interesting, these alternative processing models. And I wonder if you could comment on the implications for AI inferencing at the Edge. It's not just as well processor implications, it's storage, it's networking. It's really a whole new fundamental paradigm. How are you thinking about that Pat? >> Yeah, we've thought about, there's three aspects, but what we said three problems that we're solving. One is the developer problem, what we said, now you develop once, right? And the developer can now say, "Hey, I want to have this new AI centric app and I can develop, and it can run in the data center on the cloud or at the Edge." You'll secondly, my operations team can be able to operate this just like I do all my infrastructure. And now it's VMs containers and AI applications and third, and this is where your question really comes to bear. Most significantly is data gravity, right? These data sets are big. Some of them need to be very low latency as well. They also have regulatory issues. And if I have to move these large regulated data sets to the cloud, boy, maybe I can't do that generally for my apps or if I have low latency heavy apps at the Edge, ah, I can't pull it back to the cloud or to my data center. And that's where the uniform architecture and aspects of the Monterey program, where I'm able to take advantage of the network and the SmartNIC that are being built, but also being able to fully represent the data gravity issues of AI applications at scale 'cause in many cases I'll need to do the processing, both the learning and the inference at the Edge as well. So that's a key part of our strategy here with Nvidia. And I do think is going to be a lock, a new class of apps because when you think about AI and containers, what am I using it for? Well, it's the next generation of applications. A lot of those are going to be Edge 5G based. So very critical. >> We got to talk about security now, too. I mean, I'm going to pivot a little bit here John if it's okay. Years ago you said security is a do over. You said that on theCUBE, It stuck with us. There's there's been a lot of complacency it's kind of, if it didn't broke, don't fix it, but COVID kind of broke it. That's why you see three mega trends. You've got cloud security, you see in Z scaler rocket, you got identity access management and I'll check, I think a customer of yours. And then you've got endpoint you're seeing CrowdStrike explode. You guys pay 2.7 billion I think for carbon black yet CrowdStrike has this huge valuation. That's a mega opportunity for you guys. What are you seeing there? How are you bringing that all together? You've got NSX components, EUC components. You've got sort of security throughout your entire stack. How should we be thinking about that? >> Well, one of the announcements that I am most excited about at Vmworld is the release of carbon black workload, this research we're going to take those carbon black assets and we're going to combine it with workspace one. We're going to build it in NSX. We're going to make it part of Tansu and we're going to make it part of vSphere. And carbon black workload is literally the vSphere embodiment of carbon black in an agentless way. Ans so now you don't need to insert new agents or anything. It becomes part of the hypervisor itself, meaning that there's no attack surface available for the bad guys to pursue, but not only is this an exciting new product capability, but we're going to make it free, right? And what I'm announcing at VMworld and everybody who uses vSphere gets carbon black workload for free for an unlimited number of VMs for the next six months. And as I said in the keynote today is a bad day for cybercriminals. This is what intrinsic security is about, making it part of the platform. Don't add anything on, just click the button and start using what's built into vSphere. And we're doing that same thing with what we're doing at the networking layer. This is the act, the last line acquisition. We're going to bring that same workload kind of characteristic into the container. That's why we did the Octarine acquisition. And we're releasing the integration of workspace one with a carbon black client, and that's going to be the differentiator. And by the way, CrowdStrike is doing well, but guess what? So are we, and like both of us are eliminating the rotting dead carcasses of the traditional AV approach. So there is a huge market for both of us to go pursue here. So a lot of great things in security. And as you said, we're just starting to see that shift of the industry occur that I promised last year in theCUBE. >> So it'd be safe to say that you're a cloud native in a security company these days? >> You all, absolutely. And the bigger picture of us, is that we're critical infrastructure layer for the Edge for the cloud, for the telco environment and for the data center from every end point, every application, every cloud. >> So Padagonia asked you a virtual question, we got from the community, I'm going to throw it out to you because a lot of people look at Amazon, The cloud and they say, "Okay, we didn't see it coming. We saw it coming. We saw it scale all the benefits that are coming out of cloud, Well-documented." The question for you is what's next after cloud, as people start to rethink, especially with COVID highlighting all the scabs out there. As people look at their exposed infrastructure and their software, they want to be modern. They want the modern apps. What's next after cloud. What's your vision? >> Well, with respect to cloud, we are taking customers on the multicloud vision, right? Where you truly get to say, "Oh, this workload, I want to be able to run it with Azure, with Amazon. I need to bring this one on premise. I want to run that one hosted. I'm not sure where I'm going to run that application." So develop it and then run it at the best place. And that's what we mean by our hybrid multicloud strategy is being able for customers to really have cloud flexibility and choice. And even as our preferred relationship with Amazon is going super well. We're seeing a real uptick. We're also happy that the Microsoft Azure VMware services now GA so they're in marketplace, our Google, Oracle, IBM and Alibaba partnerships in the much broader set of VMware cloud Partner Program. So the future is multicloud. Furthermore, it's then how do we do that in the Telco Network for the 5G build out, The Telco cloud? And how do we do that for the Edge? And I think that might be sort of the granddaddy of all of these because increasingly in a 5G world will be a nibbling Edge use cases. We'll be pushing AI to the Edge like we talked about earlier in this conversation, will be enabling these high bandwidth, with low latency use cases at the Edge, and we'll see more and more of the smart embodiment, smart cities, smart street, smart factory, or the autonomous driving. All of those need these type of capabilities. >> So there's hybrid and there's multi, you just talked about multi. So hybrid are data partner ETR, they do quarterly surveys. We're seeing big uptick in VMware cloud and AWS, you guys mentioned that in your call. we're also seeing the VMware cloud, VMware cloud Coundation and the other elements, clearly a big uptake. So how should we think about hybrid? It looks like that's an extension of on-prem maybe not incremental, maybe a share shift whereas multi looks like it's incremental, but today multi has really running on multiple clouds, but vision toward incremental value. How are you thinking about that? >> Yeah, so clearly the idea of multi is to link multiple. Am I taking advantage of multiple clouds being my private clouds, my hosted clouds. And of course my public cloud partners, we believe everybody will be running a great private cloud, picking a primary, a public cloud, and then a secondary public cloud. Hybrid then is saying, which of those infrastructures are identical so that I can run them without modifying any aspect of my infrastructure operations or applications. And in today's world where people are wanting to accelerate their move to the cloud, a hybrid cloud is spot on with their needs because if I have to refactor my applications it's a couple million dollars per app, And I'll see you in a couple of years. If I can simply migrate my existing application to the hybrid cloud, what we're consistently seeing is the time is one quarter and the cost is one eight, four less. Those are powerful numbers. And if I need to exit a data center, I want to be able to move to a cloud environment, to be able to access more of those native cloud services. Wow. That's powerful. And that's why for seven years now we've been preaching that hybrid is the future. It is not a waystation to the future. And I believe that more fervently today than when I declared it seven years ago. So we are firmly on that path that we're enabling a multi and a hybrid cloud future for all of our customers. >> Yeah. You addressed that like CUBE 2013. I remember that interview vividly was not a waystation. I got (murmurs) the answer. Thank you Pat, for clarifying than going back seven years. I love the vision. You're always got the right wave. It's always great to talk to you, but I got to ask you about these initiatives you seeing clearly last year or a year and a half ago, project Pacific name out almost like a guiding directional vision, and then put some meat on the bone Tansu and now you guys have that whole Cloud Native Initiative is starting to flower up thousand flowers are blooming. This year Project Monterrey has announced same kind of situation. You're showing out the vision. What are the plans to take that to the next level and take a minute to explain how project Monterey, what it means and how you see that filling out. I'm assuming it's going to take the same trajectory as Pacific. >> Yeah. Monetary is a big deal. This is rearchitecting The core of vSphere. It really is ripping apart the IO stack from the intrinsic operation of a vSphere and ESX itself, because in many ways, the IO we've been always leveraging the NIC and essentially virtual NICs, but we never leverage the resources of the network adapters themselves in any fundamental way. And as you think about SmartNICs, these are powerful resources now where they may have four, eight, 16, even 32 cores running in the smartNIC itself. So how do I utilize that resource? But it also sits in the right place in the sense that it is the network traffic cop. It is the place to do security acceleration. It is the place that enables IO bandwidth optimization across increasingly rich applications where the workloads, the data, the latency get more important both in the data center and across data centers to the cloud and to the Edge. So this rearchitecting is a big deal. We announced the three partners, Intel, Nvidia, Mellanox, and Penn Sandow that we're working with. And we'll begin the deliveries of this as part of the core vSphere offerings of beginning next year. So it's a big rearchitecting. These are our key partners. We're excited about the work that we're doing with them. And then of course our system partners like Dell and Lenovo, who've already come forward and says, "Yeah, we're going to be bringing these to market together with VMware." >> Pat, personal question for you. I want to get your personal take, your career, going back to Intel. You've seen it all, but the shift is consumer to enterprise. And you look at just recently snowflake IPO, the biggest ever in the history of wall street, an enterprise data's company. And the enterprise is now relevant. Enterprise feels consumer. We talked about consumerization of IT years and years ago, but now more than ever the hottest financial IPO enterprise, you guys are enterprise. You did enterprise at Intel. (laughs) You know the enterprise, you doing it here at VMware. The enterprise is the consumer now with cloud and all this new landscape. What is your view on this? Because you've seen the waves, and you've seen the historical perspective. It was consumer, was the big thing. Now it's enterprise, what's your take on all this? How do you make sense of it? Because it's now mainstream. what's your view on this? >> Well, first I do want to say congratulations to my friend Frank, and the extraordinary snowflake IPO, and by the way, they use VMware. So not only do I feel a sense of ownership 'cause Frank used to work for me for a period of time, but they're also a customer of ours. So go Frank, go snowflake. We're we're excited about that. But there is this episodic, this to the industry where for a period of time it is consumer-driven and CES used to be the hottest ticket in the industry for technology trends. But as you say, it is now shifted to be more business centric. And I've said this very firmly, for instance, in the case of 5G where I do not see consumer a faster video or a better Facebook, isn't going to be why I buy 5G. It's going to be driven by more business use cases where the latency, the security and the bandwidth will have radically differentiated views of the new applications that will be the case. So we do think that we're in a period of time and I expect that it's probably at least the next five years where business will be the technology drivers in the industry. And then probably, hey, there'll be a wave of consumer innovation and I'll have to get my black turtlenecks out again and start trying to be cool, but I've always been more of an enterprise guy. So I like the next five to 10 years better. I'm not cool enough to be a consumer guy. And maybe my age is now starting to conspire against me as well. >> Hey, Pat, I know you've got to go, but quick question. So you guys, you gave guidance, pretty good guidance, actually. I wondered have you and Zane come up with a new algorithm to deal with all this uncertainty or is it kind of back to old school gut feel? (laughs) >> Well, I think as we thought about the year as we came into the year and obviously, COVID smacked everybody, we laid out a model, we looked at various industry analysts, what we call the swoosh model, right? Q2, Q3 and Q4 recovery, Q1 more so, Q2 more so, and basically, we build our own theories behind that. We test it against many analysts, the perspectives, and we had vs and we had Ws and we had Ls and so on. We picked what we thought was really sort of grounded of the best data that we could put our own analysis, which we have substantial data of our own customer's usage, et cetera, and pick the model. And like any model, you put a touch of conservatism against it, and we've been pretty accurate. And I think there's a lot of things, we've been able to sort of, with good data good thoughtfulness, take a view and then just consistently manage against it and everything that we said when we did that back in March, sort of proven out incrementally to be more accurate. And some are saying, "Hey, things are coming back more quickly." And then, oh we're starting to see the fall numbers climb up a little bit. Hey, we don't think this goes away quickly. There's still a lot of secondary things to get flushed through the various economies, as stimulus starts tailoring off small businesses are more impacted and we still don't have a widely deployed vaccine. And I don't expect we will have one until second half of next year. Now there's the silver lining to that, as we said, which means that these changes, these faster to the future shifts in how we learn, how we work, how we educate, how we care for, how we worship, how we live, they will get more and more sedimented into the new normal relying more and more on the digital foundation. And we think ultimately that has extremely good upsides for us longterm, even as it's very difficult to navigate in the near term. And that's why we are just raving optimists for the longterm benefits of a more and more digital foundation for the future of every industry, every human, every workforce, every hospital, every educator, they are going to become more digital. And that's why I think going back to the last question, this is a business driven cycle, we're well positioned, and we're thrilled for all of those who are participating with VMworld 2020. This is a seminal moment for us and our industry. >> Pat, thank you so much for taking the time. It's an enabling model. It's what platforms are all about. You get that. My final parting question for you is whether you're a VC investing in startups or a large enterprise who's trying to get through COVID with a growth plan for that future. What is a modern app look like? And what does a modern company look like in your view? >> Well, a modern company would be that instead of having a lot of people looking down at infrastructure, the bulk of my IT resources are looking up at building apps. Those apps are using modern CICD data pipeline approaches built for a multicloud embodiment, right? And of course, VMware is the best partner that you possibly could have. So if you want to be modern, cool on the front end, come and talk to us. >> All right. Pat Galsinger the CEO of VMware here on theCUBE for VML 2020 virtual here with theCUBE virtual. Great to see you virtually Pat. Thanks for coming on. Thanks for your time. >> Hey, thank you so much. Love to see you in person soon enough, but this is pretty good. Thank you, Dave. Thank you so much. >> Okay. You're watching theCUBE virtual here for VMworld 2020. I'm John Furrier with Dave Vallente with Pat Gelsinger. Thanks for watching. (upbeat music)
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Bill Schmarzo, Hitachi Vantara | CUBE Conversation, August 2020
>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)
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leaders all around the world. And now he teaches at the of the very first Strata Conferences into the details, you know, and how do I get it on the balance sheet? of the data, has kind of put at the value you paid but on the ability to And how do I make sure the analytics and the work of making sure the data has the time to go through that the data in and of itself and the queue of you is driven from the use case And one of the great kind And of course the first and the guy who made a really But now with the autonomy, and the data he's captured, and get past the idea of of the data around the use cases. and the two may not really and the ability that you don't need into the organizations that you work with? the birth of this new role And the idea how you embrace ambiguity people that have the ability of the organization to is the next book coming out? Then it goes back to the I just love how the Dean Thanks again and have a great week. we'll see you next time.
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Sreeram Visvanathan, IBM | IBM Think 2020
>>From the cube studios in Palo Alto in Boston covering IBM thing brought to you by IBM. >>Hi everybody. We're back and this is Dave Vellante and you're watching the cubes continuous coverage of the IBM think 2020 digital events experience Sriram these monotonous here he is the global managing director for government healthcare and life sciences three. Ron, thanks so much for coming on the cube. >>Great to be with you Dave. I wish we were Darren but it's, it's great to be here digitally indeed >>be good to be face to face and in San Francisco but this certainly will help our audience understand what's happening in these critical sectors. I mean you were at the heart of it. I mean these are three sectors and then there are sub sectors in there. Let's try to understand how you're communicating with your clients, what you've been doing in the near term and then I want to really try to understand, you know, what you see coming out of this, but please tell us what's been going on in your, in your world. >>You're right. I mean these sectors are keeping, keeping the engine running right now in terms of keeping society running, right? So if you look at the federal government, the state government, the local government, you look at providers of healthcare, you look at payers, we're making sure that their members are getting the, getting the advice and the service they need. You look at a life sciences companies or rapidly trying to find a cure for this, uh, for this virus. And then you look at education where, um, you know, the educational establishments are trying to work remotely and make sure that our children get the education they need. So kind of existential industries right front and center of this ninety-five, interestingly, they have 95% of IBM has, have continued to work from home and yet we are able to support the core operations of our clients. So if you look at some of the things that we've been doing over the last eight or nine weeks that we've been under this kind of lockdown, um, IBM, IBM is involved in the engine room. >>I would like to call it the engine room of many of these operations, right? Whether it just to keep a city running or a hospital running. Um, our systems, our software, our services teams are engaged in making sure that the core systems that allow those entities to function are actually operational, um, during these times. So we've had no blips. We've been able to support that. And that's a, that's a key part of it. Now, of course, there are extraordinary things we've done on top. For instance, you know, in the first two weeks after the crisis started, we used, um, a supercomputer with the department of energy that you must've heard about, uh, to narrow down over 8,000 compounds that could potentially be cures for the COBIT 19 virus and narrowed down to 80. That could be applicable, right? Um, so sharpening the time and allowing researchers not to focus on 80 compounds and stuff, 8,000 so that we can get a vaccine to market faster. >>And that's tremendous, right? I mean we've, we formed a, um, uh, you know, collaboration, uh, with, with 27 other, uh, partners, uh, that, who are all co innovating, uh, using modeling techniques, uh, to try and find a cure faster. The other end, um, you look at things like what we're doing with the state of New York, where we work for the government, uh, the duet to get 350,000 tablets with the right security software, with the right educational software so that students can continue to learn while, uh, you know, what they are, uh, when they're remote, but the right connectivity. So, um, extremes. And then of course as a backbone, you know, be using, we are starting to see real use of our AI tools, chat bots to stop it, that we have. Uh, we have allowed, uh, uh, customers to use for free. So they began answer that we can, we can consume the latest CDC advice, the latest advice from the governors and the state, and then, um, allow the technology to answer a lot of queries that are coming through, uh, with, with, uh, with citizens being worried about what, where they stand every single day. >>Yeah. So let's kind of break down some of the sectors that you follow. Um, let's start with, with government. I mean, certainly in the United States it's been all about the fiscal policy, the monetary policy, injecting cash into the system, liquidity, you know, supporting the credit markets. Certainly central banks around the world are facing, you know, similar, but somewhat different depending on their financial situations. Um, and so that's been the near term tactical focus and it actually seems to be working pretty well. Uh, you know, the stock market's any indicator, but going forward, I'm interested in your thoughts. You wrote a blog and you basically, it was a call to action to the government to really kind of reinvent its workforce, bringing in, uh, millennials. Um, and, and so my, my, my question is, how do you think the millennial workforce, you know, when we exit this thing, will embrace the government. What does the government have to do to attract millennials who want the latest and greatest technology? I mean, give us your thoughts on that. >>Well, it's an, it's a really interesting question. A couple of years ago I was talking about, uh, this is the time where governments have to have to really transform. They have to change. If you, if you go back in time compared governments to other industries, uh, governments have embraced technology, but it's been still kind of slow, incremental, right? Lots of systems of record, big massive systems that take 10 years, five years to implement. So we've implemented systems record. We've, we've started using data and analytics to kind of inform policymaking, but they tend to be sequential. And I think, uh, you know, coming back to the, the, the changing workforce, uh, what is it? By 2025, 75% of the workforce are going to be millennials, right? Um, and as they come into the workforce, I think they're going to demand that, uh, that we work in new ways in new, um, more integrated, more digitally savvy pace and uh, strange enough, I think this crisis is going to be a, is a proof point, right? >>Um, many governments are working remotely and yet they're functioning okay. Um, the, the, the world of, um, you know, providing policy seems to be working even if you are, if you are remote. So a lot of the naysayers who said we could not operate digit, operate digitally, um, now are starting to starting to get past that, uh, that bias if you like. And so I think as, as digital natives come into the for what we are going to see is this is a Stressless innovation of why do we do things the same way as we've done them for the last 20, 30 years. Um, granted we need to still have the, um, the, the division of policies, make sure that we are enforcing the policies of government. But at the same time, if you look at workflow, uh, this is the time where you can use automation, intelligent workflows, right? >>This is the time where we can use insights about what our citizens need so that services are tuned, a hyper-local are relevant to what the citizen is going through at that particular time. Uh, contextual and, um, are relevant to what, what that individual needs at that particular time. Uh, rather than us having to go to a portal and, uh, submit an application and submit relevant documents and then be told a few hours or a few minutes later then that you've got, you've got approval for something, right? So I think there's this period of restless innovation coming through that is from a citizen engagement perspective, but behind the scenes in terms of how budgeting works, how approvals work, how uh, uh, you know, the divisions between federal, state, local, how the handoffs between agencies work. All of that is going to be restlessly innovative. And, uh, this is the moment I think this is going to be a trigger point. We believe it's going to be a trigger point for that kind of a transformation? >>No, sure. I'm, I've talked to a number of, of CEOs in, in sort of hard hit industries, um, hospitality, you know, certainly, you know, the restaurant business, airlines and, and you know, they just basically have a dial down spending, um, and really just shift to only mission critical activities. Uh, and in your segments it's, it's mixed, right? I mean, obviously government, you use the engine room, uh, analogy before some of use the war room metaphor, but you think about healthcare, the frontline workers. So it's, it's, it's mixed what our CIO is telling you in, in the industries in which you're focused. >>Well, the CIO is right now. I mean, you're going to go through different phases, right? Phase one is just reactive. It's just coping with the, uh, with the situation today where you suddenly have 95%, a hundred percent of your workforce working remote, providing the ability to, it's providing the leadership, the ability to, to work remotely where possible. Um, and it take IBM for instance, you know, we've got 300,000 people around the world, but 95% of whom are working remotely. Um, but we've been, we've been preparing for moments like this where, uh, you know, we've got the tools, we've got the network bandwidth, we've got the security parameters. Uh, we have been modernizing our applications. Um, so you've been going to a hybrid cloud kind of architecture, but you're able to scale up and scale down, stand up additional capacity when you need it. So I think a lot of the CEOs that we talk to are, uh, you know, phase one was all about how do I keep everything running? >>Phase two is how do I prepare for the new norm where I think more collaborative tools are going to come into, into the work environment. Um, CEO's are going to be much more involved in how do I get design in the center of everything that we do no matter what kind of industry. Alright. So, um, it's, it's going gonna be an interesting change as to the role of the CIO going forward. Dave and I think, uh, again, it's a catalyst to saying why do we have to do things the same way we've been doing? Why do we need so many people in an office building doing things in traditional ways? And why can't we use these digital techniques as the new norm? >>Yeah, there are a lot of learnings going on and I think huge opportunities to, to, to, to save money going forward because we've had to do that in the near term. But, but more importantly, it's like how are we going to invest in the future? And that's, that's something that I think a lot of people are beginning now to think about. They haven't had much time to do anything other than think tactically. But now we're at the point where, okay, we're maybe starting to come out of this a little bit, trying to envision how we come back. And organizations I think are beginning to think about, okay, what is our mid to longer term strategy? It's, we're not just going to go back to 2019. So what do we do going forward? So we're starting to spend more cycles and more energy, you know, on that topic. What do you see? >>Yeah, I mean, take every segment of my, uh, my sector, right? Take the education industry, will you, uh, will you spend 60, $70,000 a year to send a child to university, um, when a lot of the learning is available digitally and when, when we've seen that they can learn as much and probably more, uh, you know, more agile manner and follow their interests. So I think the whole education industry is going to leverage digital in a big way. And I think you're going to see partnerships form, you can see more, uh, you're going to see more choice, uh, for the student and for the parents, uh, in the education industry. And so that industry, which has been kind of falling the same type of pattern, uh, you know, for a hundred years, it's suddenly going to reinvent itself. Take the healthcare industry. Um, you know, it's interesting, a lot of providers are following, uh, following staff because elective, uh, elective treatment as really, you know, uh, fallen tremendously. >>Right? On the one hand you have huge demand for covert 19 related, uh, treatment on the other hand, electives have come down. So cost is a big issue. So I, I believe we're going to see M and a activity, uh, in that sector. And as you see that what's going to happen is people are gonna, uh, restlessly reinvent. So w you know, I think telemedicine is going to, is not going to become a reality. I think, um, if you look at the payer space and if you look at the insurance providers, they're all going to be in the market saying, Harbor, how do I capture more members and retain them and how do I give them more choice? Um, and how do I keep them safe? It's interesting, I was speaking to a colleague in Japan, uh, yesterday and he was saying to me in the automotive industry that, um, I was arguing that, you know, you will see a huge downfall. >>Uh, but his argument back was people are actually so afraid of taking public transport that, uh, they're expecting to see a spike in personal transportation. Right? So I think from a government perspective, the kind of policy implications, um, you know, whether there would be economic stimulus related in the short term, governments are going to introduce inefficiencies to get the economy back to where it needs to be. But over a long term I think we're back to these efficiencies. We are going to look at supply chain, there's going to be a postmortem on how do we get where we got to now. And um, so I think in terms of citizen engagement, in terms of supply chain, in terms of back office operations, in terms of how agencies coordinate, um, do stockpiling command and control, all of that is going to change, right? And it's an exciting time in a way to be at the forefront of these industries shaping, shaping the future. >>I want to ask your thoughts on, on education and excuse me, drill into that a little bit. I've actually got pretty personal visibility in sort of let's, let's break it down. Um, you know, secondary universities, uh, nine through 12 and K through six and then you're seeing some definite differences. Uh, I think actually the universities are pretty well set up. They've been doing online courses for quite some time. They've, they've started, you know, revenue streams in that regard and, and so their technology is pretty good and their processes are pretty good at the other end of the spectrum, sort of the K through six, you know, there's a lot of homeschooling going on and, and parents are at home, they're adjusting pretty well. Whether it's young kids with manipulatives or basic math and vocabulary skills, they're able to support that and you know, adjust their work lives accordingly. >>I find in the, in the high school it's, it's really different. I mean it's new to these folks. I had an interesting conversation with my son last night and he was explaining to me, he spends literally hours a day just trying to figure out what he's got to do because every process is different from every teacher. And so that's that sort of fat middle, if you will, which is a critical time, especially for juniors in high school and so forth where that is so new. And I wonder what you're seeing and maybe those three sectors, is that sort of consistent with what you see and, and what do you see coming out of this? >>I think it's, it's broadly consistent and I have personally experienced, I have one university grade, uh, university senior and I have a high school senior and I see pretty much the same pattern no matter which part of the world they're in. Right? I, I do believe that, um, you know, this notion of choice for students and how they learn and making curriculum customized to get the best out of students is the new reality. How fast we will get there. How do you get there? It's not a linear line. I think what is going to happen is you're going to, you're going to see partnerships between, uh, content providers. You're going to see partnerships between platform providers and you're going to see these educational institutions, uh, less restless. The reinvent to say, okay, this particular student learns in this way and this is, this is how I shape a personalized curriculum, but still achieving a minimum outcome. Right? I think that's going to come, but it's going to take a few years to get there. >>I think it was a really interesting observations. I mean, many children that I observed today are sort of autodidactic and if you give them the tooling to actually set their own learning curriculum, they'll, they'll absorb that and obviously the technology has gotta be there to support it. So it's sort of hitting the escape key. Let's sort of end on that. I mean, in terms of just IBM, how you're positioning in the industries that you're focused on to help people take this new technology journey. As they said, we're not going back to the last decade. It's a whole new world that we're going to going to come out of this post. Coven, how do you see IBM has positioned their Sri round? >>Dave, I think I'd be positioned brilliantly. Um, as you know, we've, Arvind Christianized is our new CEO and, uh, he, he recently talked about this on CNBC. So if you look at the core platforms that we've been building, right? Um, so CA occupies an industry, whether it's, whether it be government, healthcare, life sciences or education are going to look for speed. They're going to look for agility, they're going to look to change processes quickly so they can, they can react to situations like this in the future in a much more agile way, right? In order to do that, their it systems, their applications, their infrastructure needs to scale up and down needs to be, uh, you need to be able to configure things in a way where you can change parameters. You can change policies without having to read a long time, right? And so if you think about things like HyperCloud our investment in, uh, in, in red hat, uh, our, uh, our, uh, position on data and open technologies and, um, you know, our policies around making sure that, that our client's data and insights are their insights and we don't, we don't want to taste that. >>On of those things. Our investments in blockchain are deep, deep, uh, incumbency in services. But there'd our technology services, our consulting services, our deep industry knowledge, allowing all of these technologies to be used at to solve these problems. Um, I think we are really well positioned and, uh, you know, a great example is the New York example, right? So, uh, getting 350,000 students to work in a completely new way in a matter of two weeks. It's not something that every single company can do. It's not just a matter of providing the tech, the tool itself, it's the content, it's the consumption, it's the design must experience. And that's where a company like IBM can bring everything together. And then you have the massive issues of government, like social reform, like mental health, like making sure the stimulus money is going to the people who need it the most, um, in, in the most useful way. And that's where I work between industries, between government and banks and other industries really comes to, comes to fruition. So I think we have the technology but the services depth. And I think we've got the relevance of the industry to make a difference. And I'm excited about the future. >>Well, it's interesting that you mentioned, you know, the basically one of my takeaways is that you've got to be agile. You've gotta be flexible. You, you've been in the consulting business for most of your career and in the early part of your career. And even up until, you know, maybe recently we were automating processes that we knew well, but today the processes are, we so much is unknown. And so you've got to move fast. You've got to be agile, you've got to experiment, uh, and apply that sort of, you know, test, experiment, methodology and iterate and have that continuous improvement. That's a different world than what we've known. Obviously. You know, as I say, you've seen this over the decades. Uh, your final thoughts on, uh, on the future. >>Well, my final thoughts are, um, yeah, you're exactly right. I mean, if I take a simple example, right, that, that, uh, controls how quickly the commerce works. Think about simple things like bill of lading. Uh, the government has to issue a federal government has to prove that a state government has to prove it and local government has to prove it. Why? That's the way we've been doing it for a long time. Right? There are control points, but to your point, imagine if you can shorten that from a seven day cycle to a seven second cycle. The impact on commerce, the impact on GDP, and this is one simple process. This is the time for us to re to, to, to break it all apart and say why not do something differently? And the technology is right. The CA, the AI is getting more and more and more mature and you've got interesting things like quantum to look forward to. So I think the timing is right for, for reinventing, uh, the core of this industry. >>Yeah, I think they really are. I mean, it's difficult as this crisis has been a lot of opportunities going to present coming out of a tree room. Thanks so much for coming on the cube and making this happen. Really appreciate your time. It's great to be here. Thank you for having me. Dave, you're very welcome and thank you everybody for watching. This is Dave Volante for the Cuban or continuous coverage of the IBM think 2020 digital event experience. Keep it right there and we right back right after this short break.
SUMMARY :
IBM thing brought to you by IBM. Ron, thanks so much for coming on the cube. Great to be with you Dave. you know, what you see coming out of this, but please tell us what's been going on in your, And then you look at education where, um, you know, the educational establishments are trying to work remotely Um, so sharpening the time and allowing researchers not to focus on 80 compounds and continue to learn while, uh, you know, what they are, uh, when they're remote, but the right connectivity. injecting cash into the system, liquidity, you know, supporting the credit markets. And I think, uh, you know, coming back to the, the, the changing workforce, uh, But at the same time, if you look at workflow, uh, this is the time where you can use automation, works, how approvals work, how uh, uh, you know, the divisions between um, hospitality, you know, certainly, you know, the restaurant business, Um, and it take IBM for instance, you know, we've got 300,000 people around the Um, CEO's are going to be much more involved in So we're starting to spend more cycles and more energy, you know, on that topic. of pattern, uh, you know, for a hundred years, it's suddenly going to reinvent itself. I think, um, if you look at the payer space and if you look at the insurance providers, um, you know, whether there would be economic stimulus related in the short term, they're able to support that and you know, adjust their work lives accordingly. and maybe those three sectors, is that sort of consistent with what you see and, and what do you see coming um, you know, this notion of choice for students and and if you give them the tooling to actually set their own learning curriculum, to be, uh, you need to be able to configure things in a way where you can change parameters. and, uh, you know, a great example is the New York example, And even up until, you know, maybe recently we were Uh, the government has to issue a federal government has to prove that a state government has to prove it and local I mean, it's difficult as this crisis has been a lot of opportunities going to present
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Rich Gaston, Micro Focus | Virtual Vertica BDC 2020
(upbeat music) >> Announcer: It's theCUBE covering the virtual Vertica Big Data Conference 2020 brought to you by Vertica. >> Welcome back to the Vertica Virtual Big Data Conference, BDC 2020. You know, it was supposed to be a physical event in Boston at the Encore. Vertica pivoted to a digital event, and we're pleased that The Cube could participate because we've participated in every BDC since the inception. Rich Gaston this year is the global solutions architect for security risk and governance at Micro Focus. Rich, thanks for coming on, good to see you. >> Hey, thank you very much for having me. >> So you got a chewy title, man. You got a lot of stuff, a lot of hairy things in there. But maybe you can talk about your role as an architect in those spaces. >> Sure, absolutely. We handle a lot of different requests from the global 2000 type of organization that will try to move various business processes, various application systems, databases, into new realms. Whether they're looking at opening up new business opportunities, whether they're looking at sharing data with partners securely, they might be migrating it to cloud applications, and doing migration into a Hybrid IT architecture. So we will take those large organizations and their existing installed base of technical platforms and data, users, and try to chart a course to the future, using Micro Focus technologies, but also partnering with other third parties out there in the ecosystem. So we have large, solid relationships with the big cloud vendors, with also a lot of the big database spenders. Vertica's our in-house solution for big data and analytics, and we are one of the first integrated data security solutions with Vertica. We've had great success out in the customer base with Vertica as organizations have tried to add another layer of security around their data. So what we will try to emphasize is an enterprise wide data security approach, where you're taking a look at data as it flows throughout the enterprise from its inception, where it's created, where it's ingested, all the way through the utilization of that data. And then to the other uses where we might be doing shared analytics with third parties. How do we do that in a secure way that maintains regulatory compliance, and that also keeps our company safe against data breach. >> A lot has changed since the early days of big data, certainly since the inception of Vertica. You know, it used to be big data, everyone was rushing to figure it out. You had a lot of skunkworks going on, and it was just like, figure out data. And then as organizations began to figure it out, they realized, wow, who's governing this stuff? A lot of shadow IT was going on, and then the CIO was called to sort of reign that back in. As well, you know, with all kinds of whatever, fake news, the hacking of elections, and so forth, the sense of heightened security has gone up dramatically. So I wonder if you can talk about the changes that have occurred in the last several years, and how you guys are responding. >> You know, it's a great question, and it's been an amazing journey because I was walking down the street here in my hometown of San Francisco at Christmastime years ago and I got a call from my bank, and they said, we want to inform you your card has been breached by Target, a hack at Target Corporation and they got your card, and they also got your pin. And so you're going to need to get a new card, we're going to cancel this. Do you need some cash? I said, yeah, it's Christmastime so I need to do some shopping. And so they worked with me to make sure that I could get that cash, and then get the new card and the new pin. And being a professional in the inside of the industry, I really questioned, how did they get the pin? Tell me more about this. And they said, well, we don't know the details, but you know, I'm sure you'll find out. And in fact, we did find out a lot about that breach and what it did to Target. The impact that $250 million immediate impact, CIO gone, CEO gone. This was a big one in the industry, and it really woke a lot of people up to the different types of threats on the data that we're facing with our largest organizations. Not just financial data; medical data, personal data of all kinds. Flash forward to the Cambridge Analytica scandal that occurred where Facebook is handing off data, they're making a partnership agreement --think they can trust, and then that is misused. And who's going to end up paying the cost of that? Well, it's going to be Facebook at a tune of about five billion on that, plus some other finds that'll come along, and other costs that they're facing. So what we've seen over the course of the past several years has been an evolution from data breach making the headlines, and how do my customers come to us and say, help us neutralize the threat of this breach. Help us mitigate this risk, and manage this risk. What do we need to be doing, what are the best practices in the industry? Clearly what we're doing on the perimeter security, the application security and the platform security is not enough. We continue to have breaches, and we are the experts at that answer. The follow on fascinating piece has been the regulators jumping in now. First in Europe, but now we see California enacting a law just this year. They came into a place that is very stringent, and has a lot of deep protections that are really far-reaching around personal data of consumers. Look at jurisdictions like Australia, where fiduciary responsibility now goes to the Board of Directors. That's getting attention. For a regulated entity in Australia, if you're on the Board of Directors, you better have a plan for data security. And if there is a breach, you need to follow protocols, or you personally will be liable. And that is a sea change that we're seeing out in the industry. So we're getting a lot of attention on both, how do we neutralize the risk of breach, but also how can we use software tools to maintain and support our regulatory compliance efforts as we work with, say, the largest money center bank out of New York. I've watched their audit year after year, and it's gotten more and more stringent, more and more specific, tell me more about this aspect of data security, tell me more about encryption, tell me more about money management. The auditors are getting better. And we're supporting our customers in that journey to provide better security for the data, to provide a better operational environment for them to be able to roll new services out with confidence that they're not going to get breached. With that confidence, they're not going to have a regulatory compliance fine or a nightmare in the press. And these are the major drivers that help us with Vertica sell together into large organizations to say, let's add some defense in depth to your data. And that's really a key concept in the security field, this concept of defense in depth. We apply that to the data itself by changing the actual data element of Rich Gaston, I will change that name into Ciphertext, and that then yields a whole bunch of benefits throughout the organization as we deal with the lifecycle of that data. >> Okay, so a couple things I want to mention there. So first of all, totally board level topic, every board of directors should really have cyber and security as part of its agenda, and it does for the reasons that you mentioned. The other is, GDPR got it all started. I guess it was May 2018 that the penalties went into effect, and that just created a whole Domino effect. You mentioned California enacting its own laws, which, you know, in some cases are even more stringent. And you're seeing this all over the world. So I think one of the questions I have is, how do you approach all this variability? It seems to me, you can't just take a narrow approach. You have to have an end to end perspective on governance and risk and security, and the like. So are you able to do that? And if so, how so? >> Absolutely, I think one of the key areas in big data in particular, has been the concern that we have a schema, we have database tables, we have CALMS, and we have data, but we're not exactly sure what's in there. We have application developers that have been given sandbox space in our clusters, and what are they putting in there? So can we discover that data? We have those tools within Micro Focus to discover sensitive data within in your data stores, but we can also protect that data, and then we'll track it. And what we really find is that when you protect, let's say, five billion rows of a customer database, we can now know what is being done with that data on a very fine grain and granular basis, to say that this business process has a justified need to see the data in the clear, we're going to give them that authorization, they can decrypt the data. Secure data, my product, knows about that and tracks that, and can report on that and say at this date and time, Rich Gaston did the following thing to be able to pull data in the clear. And that could be then used to support the regulatory compliance responses and then audit to say, who really has access to this, and what really is that data? Then in GDPR, we're getting down into much more fine grained decisions around who can get access to the data, and who cannot. And organizations are scrambling. One of the funny conversations that I had a couple years ago as GDPR came into place was, it seemed a couple of customers were taking these sort of brute force approach of, we're going to move our analytics and all of our data to Europe, to European data centers because we believe that if we do this in the U.S., we're going to violate their law. But if we do it all in Europe, we'll be okay. And that simply was a short-term way of thinking about it. You really can't be moving your data around the globe to try to satisfy a particular jurisdiction. You have to apply the controls and the policies and put the software layers in place to make sure that anywhere that someone wants to get that data, that we have the ability to look at that transaction and say it is or is not authorized, and that we have a rock solid way of approaching that for audit and for compliance and risk management. And once you do that, then you really open up the organization to go back and use those tools the way they were meant to be used. We can use Vertica for AI, we can use Vertica for machine learning, and for all kinds of really cool use cases that are being done with IOT, with other kinds of cases that we're seeing that require data being managed at scale, but with security. And that's the challenge, I think, in the current era, is how do we do this in an elegant way? How do we do it in a way that's future proof when CCPA comes in? How can I lay this on as another layer of audit responsibility and control around my data so that I can satisfy those regulators as well as the folks over in Europe and Singapore and China and Turkey and Australia. It goes on and on. Each jurisdiction out there is now requiring audit. And like I mentioned, the audits are getting tougher. And if you read the news, the GDPR example I think is classic. They told us in 2016, it's coming. They told us in 2018, it's here. They're telling us in 2020, we're serious about this, and here's the finds, and you better be aware that we're coming to audit you. And when we audit you, we're going to be asking some tough questions. If you can't answer those in a timely manner, then you're going to be facing some serious consequences, and I think that's what's getting attention. >> Yeah, so the whole big data thing started with Hadoop, and Hadoop is open, it's distributed, and it just created a real governance challenge. I want to talk about your solutions in this space. Can you tell us more about Micro Focus voltage? I want to understand what it is, and then get into sort of how it works, and then I really want to understand how it's applied to Vertica. >> Yeah, absolutely, that's a great question. First of all, we were the originators of format preserving encryption, we developed some of the core basic research out of Stanford University that then became the company of Voltage; that build-a-brand name that we apply even though we're part of Micro Focus. So the lineage still goes back to Dr. Benet down at Stanford, one of my buddies there, and he's still at it doing amazing work in cryptography and keeping moving the industry forward, and the science forward of cryptography. It's a very deep science, and we all want to have it peer-reviewed, we all want to be attacked, we all want it to be proved secure, that we're not selling something to a major money center bank that is potentially risky because it's obscure and we're private. So we have an open standard. For six years, we worked with the Department of Commerce to get our standard approved by NIST; The National Institute of Science and Technology. They initially said, well, AES256 is going to be fine. And we said, well, it's fine for certain use cases, but for your database, you don't want to change your schema, you don't want to have this increase in storage costs. What we want is format preserving encryption. And what that does is turns my name, Rich, into a four-letter ciphertext. It can be reversed. The mathematics of that are fascinating, and really deep and amazing. But we really make that very simple for the end customer because we produce APIs. So these application programming interfaces can be accessed by applications in C or Java, C sharp, other languages. But they can also be accessed in Microservice Manor via rest and web service APIs. And that's the core of our technical platform. We have an appliance-based approach, so we take a secure data appliance, we'll put it on Prim, we'll make 50 of them if you're a big company like Verizon and you need to have these co-located around the globe, no problem; we can scale to the largest enterprise needs. But our typical customer will install several appliances and get going with a couple of environments like QA and Prod to be able to start getting encryption going inside their organization. Once the appliances are set up and installed, it takes just a couple of days of work for a typical technical staff to get done. Then you're up and running to be able to plug in the clients. Now what are the clients? Vertica's a huge one. Vertica's one of our most powerful client endpoints because you're able to now take that API, put it inside Vertica, it's all open on the internet. We can go and look at Vertica.com/secure data. You get all of our documentation on it. You understand how to use it very quickly. The APIs are super simple; they require three parameter inputs. It's a really basic approach to being able to protect and access data. And then it gets very deep from there because you have data like credit card numbers. Very different from a street address and we want to take a different approach to that. We have data like birthdate, and we want to be able to do analytics on dates. We have deep approaches on managing analytics on protected data like Date without having to put it in the clear. So we've maintained a lead in the industry in terms of being an innovator of the FF1 standard, what we call FF1 is format preserving encryption. We license that to others in the industry, per our NIST agreement. So we're the owner, we're the operator of it, and others use our technology. And we're the original founders of that, and so we continue to sort of lead the industry by adding additional capabilities on top of FF1 that really differentiate us from our competitors. Then you look at our API presence. We can definitely run as a dup, but we also run in open systems. We run on main frame, we run on mobile. So anywhere in the enterprise or one in the cloud, anywhere you want to be able to put secure data, and be able to access the protect data, we're going to be there and be able to support you there. >> Okay so, let's say I've talked to a lot of customers this week, and let's say I'm running in Eon mode. And I got some workload running in AWS, I've got some on Prim. I'm going to take an appliance or multiple appliances, I'm going to put it on Prim, but that will also secure my cloud workloads as part of a sort of shared responsibility model, for example? Or how does that work? >> No, that's absolutely correct. We're really flexible that we can run on Prim or in the cloud as far as our crypto engine, the key management is really hard stuff. Cryptography is really hard stuff, and we take care of all that, so we've all baked that in, and we can run that for you as a service either in the cloud or on Prim on your small Vms. So really the lightweight footprint for me running my infrastructure. When I look at the organization like you just described, it's a classic example of where we fit because we will be able to protect that data. Let's say you're ingesting it from a third party, or from an operational system, you have a website that collects customer data. Someone has now registered as a new customer, and they're going to do E-commerce with you. We'll take that data, and we'll protect it right at the point of capture. And we can now flow that through the organization and decrypt it at will on any platform that you have that you need us to be able to operate on. So let's say you wanted to pick that customer data from the operational transaction system, let's throw it into Eon, let's throw it into the cloud, let's do analytics there on that data, and we may need some decryption. We can place secure data wherever you want to be able to service that use case. In most cases, what you're doing is a simple, tiny little atomic efetch across a protected tunnel, your typical TLS pipe tunnel. And once that key is then cashed within our client, we maintain all that technology for you. You don't have to know about key management or dashing. We're good at that; that's our job. And then you'll be able to make those API calls to access or protect the data, and apply the authorization authentication controls that you need to be able to service your security requirements. So you might have third parties having access to your Vertica clusters. That is a special need, and we can have that ability to say employees can get X, and the third party can get Y, and that's a really interesting use case we're seeing for shared analytics in the internet now. >> Yeah for sure, so you can set the policy how we want. You know, I have to ask you, in a perfect world, I would encrypt everything. But part of the reason why people don't is because of performance concerns. Can you talk about, and you touched upon it I think recently with your sort of atomic access, but can you talk about, and I know it's Vertica, it's Ferrari, etc, but anything that slows it down, I'm going to be a concern. Are customers concerned about that? What are the performance implications of running encryption on Vertica? >> Great question there as well, and what we see is that we want to be able to apply scale where it's needed. And so if you look at ingest platforms that we find, Vertica is commonly connected up to something like Kafka. Maybe streamsets, maybe NiFi, there are a variety of different technologies that can route that data, pipe that data into Vertica at scale. Secured data is architected to go along with that architecture at the node or at the executor or at the lowest level operator level. And what I mean by that is that we don't have a bottleneck that everything has to go through one process or one box or one channel to be able to operate. We don't put an interceptor in between your data and coming and going. That's not our approach because those approaches are fragile and they're slow. So we typically want to focus on integrating our APIs natively within those pipeline processes that come into Vertica within the Vertica ingestion process itself, you can simply apply our protection when you do the copy command in Vertica. So really basic simple use case that everybody is typically familiar with in Vertica land; be able to copy the data and put it into Vertica, and you simply say protect as part of the data. So my first name is coming in as part of this ingestion. I'll simply put the protect keyword in the Syntax right in SQL; it's nothing other than just an extension SQL. Very very simple, the developer, easy to read, easy to write. And then you're going to provide the parameters that you need to say, oh the name is protected with this kind of a format. To differentiate it between a credit card number and an alphanumeric stream, for example. So once you do that, you then have the ability to decrypt. Now, on decrypt, let's look at a couple different use cases. First within Vertica, we might be doing select statements within Vertica, we might be doing all kinds of jobs within Vertica that just operate at the SQL layer. Again, just insert the word "access" into the Vertica select string and provide us with the data that you want to access, that's our word for decryption, that's our lingo. And we will then, at the Vertica level, harness the power of its CPU, its RAM, its horsepower at the node to be able to operate on that operator, the decryption request, if you will. So that gives us the speed and the ability to scale out. So if you start with two nodes of Vertica, we're going to operate at X number of hundreds of thousands of transactions a second, depending on what you're doing. Long strings are a little bit more intensive in terms of performance, but short strings like social security number are our sweet spot. So we operate very very high speed on that, and you won't notice the overhead with Vertica, perse, at the node level. When you scale Vertica up and you have 50 nodes, and you have large clusters of Vertica resources, then we scale with you. And we're not a bottleneck and at any particular point. Everybody's operating independently, but they're all copies of each other, all doing the same operation. Fetch a key, do the work, go to sleep. >> Yeah, you know, I think this is, a lot of the customers have said to us this week that one of the reasons why they like Vertica is it's very mature, it's been around, it's got a lot of functionality, and of course, you know, look, security, I understand is it's kind of table sticks, but it's also can be a differentiator. You know, big enterprises that you sell to, they're asking for security assessments, SOC 2 reports, penetration testing, and I think I'm hearing, with the partnership here, you're sort of passing those with flying colors. Are you able to make security a differentiator, or is it just sort of everybody's kind of got to have good security? What are your thoughts on that? >> Well, there's good security, and then there's great security. And what I found with one of my money center bank customers here in San Francisco was based here, was the concern around the insider access, when they had a large data store. And the concern that a DBA, a database administrator who has privilege to everything, could potentially exfil data out of the organization, and in one fell swoop, create havoc for them because of the amount of data that was present in that data store, and the sensitivity of that data in the data store. So when you put voltage encryption on top of Vertica, what you're doing now is that you're putting a layer in place that would prevent that kind of a breach. So you're looking at insider threats, you're looking at external threats, you're looking at also being able to pass your audit with flying colors. The audits are getting tougher. And when they say, tell me about your encryption, tell me about your authentication scheme, show me the access control list that says that this person can or cannot get access to something. They're asking tougher questions. That's where secure data can come in and give you that quick answer of it's encrypted at rest. It's encrypted and protected while it's in use, and we can show you exactly who's had access to that data because it's tracked via a different layer, a different appliance. And I would even draw the analogy, many of our customers use a device called a hardware security module, an HSM. Now, these are fairly expensive devices that are invented for military applications and adopted by banks. And now they're really spreading out, and people say, do I need an HSM? Well, with secure data, we certainly protect your crypto very very well. We have very very solid engineering. I'll stand on that any day of the week, but your auditor is going to want to ask a checkbox question. Do you have HSM? Yes or no. Because the auditor understands, it's another layer of protection. And it provides me another tamper evident layer of protection around your key management and your crypto. And we, as professionals in the industry, nod and say, that is worth it. That's an expensive option that you're going to add on, but your auditor's going to want it. If you're in financial services, you're dealing with PCI data, you're going to enjoy the checkbox that says, yes, I have HSMs and not get into some arcane conversation around, well no, but it's good enough. That's kind of the argument then conversation we get into when folks want to say, Vertica has great security, Vertica's fantastic on security. Why would I want secure data as well? It's another layer of protection, and it's defense in depth for you data. When you believe in that, when you take security really seriously, and you're really paranoid, like a person like myself, then you're going to invest in those kinds of solutions that get you best in-class results. >> So I'm hearing a data-centric approach to security. Security experts will tell you, you got to layer it. I often say, we live in a new world. The green used to just build a moat around the queen, but the queen, she's leaving her castle in this world of distributed data. Rich, incredibly knowlegable guest, and really appreciate you being on the front lines and sharing with us your knowledge about this important topic. So thanks for coming on theCUBE. >> Hey, thank you very much. >> You're welcome, and thanks for watching everybody. This is Dave Vellante for theCUBE, we're covering wall-to-wall coverage of the Virtual Vertica BDC, Big Data Conference. Remotely, digitally, thanks for watching. Keep it right there. We'll be right back right after this short break. 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Edward Thomson, GitHub | Microsoft Ignite 2019
>>Lai from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Good afternoon, cube viewers. We are here at Microsoft ignite at the orange County convention center. I'm your host, Rebecca Knight. Along with my cohost Stu Miniman. We're joined by Edward Thompson. He is the product manager at get hub. Thank you so much for coming on the queue. So the get hub acquisition closed this time last year, uh, for our viewers who are maybe unfamiliar with get hub, explain what get hub is and then also tell us a little bit how it's going since the, >>yeah, I'd be happy to. So yeah, get hub is like the home for software development. If you're a, if you're a software developer, uh, you know, get hub rehost, you know, most of the open source repositories in the world. Um, you know, just to give you some stats. So at this time, last year, about the time the acquisition happened, um, we announced ad get hub universe, which is our annual developer conference, uh, that we had 30 million developers on GitHub and a hundred million repositories. So that's, that's a huge number of developers. I haven't seen the latest numbers. We'll announce the newest, uh, at get hub university this year, which is coming up next week. Uh, but the last number I saw was 40 million developers. So that's a growth of, you know, 10 million developers in just a year. Unbelievable. And that, that also means the 25% of our developers on get hub have joined within the last year. So that's just absolutely incredible. Um, and so yeah, I get hope. Is, is, is that, is that place for development? >>Yeah, it's really interesting when I look at some acquisitions that Microsoft has made back in 2016, they spent $26 billion for LinkedIn, which is most people's resume. And if you look last year it was seven and a half billion dollars for my friends in the software world. Get hub is their resume. That's right. Oh, when you talk about how you do things online, so you've got an interesting perspective on this because you've worked for Microsoft and get hub a couple of times. So give us a little bit about, you know, the relationship when you joined Microsoft 10 years ago, you know, open source developers, developers, developers weren't exactly on everyone's lips. So it gives a little bit of viewpoint through the various incarnations. >>So as you said, I joined Microsoft about 10 years ago. I came in through a little acquisition. Uh, we were just a very small software company, but we were building enterprise cross platform developer tools and we were about five engineers. And when you're building for, you know, Mac OS, Linux, Sonos, all these different platforms you use with so many people with so few rather, so few developers, you really need to take as much off the shelf as possible. You can't build all that yourself, you know. So if, if you needed a logging library, we would just go and use some open source products. We're not going to spend our time working on that when we could be building customer value in step. So when Microsoft acquired that company, they looked at, you know, they did their due diligence, they looked at the source code and they saw all this open source and they, I mean it was almost a deal breaker. >>They really lost their mind. Um, they were not geared up to deal with open source, to use open source, certainly not to contribute to open source. Uh, and so that's the Microsoft that I first saw. And, and to get from there to here is, is incredible. You know, over time. Um, we worked closely with some open source tools. We worked closely with get hub at Microsoft and that was really one of the early sort of unions between Microsoft and get hub was starting to work together on, on some open source software. And so we kind of started to know each other. We started to understand each other's companies and each other's cultures. And we started to, I don't know, I dare say like each other. Like I still count some of those early get hub employees that I met, uh, as some of my closest friends. Uh, and so at some point, uh, they became such close friends that I went to go work with them and get hub and then of course the Microsoft acquisition and so on. But I really think that that, you know, the, the transformation in Microsoft between, uh, the 10 years ago, Microsoft that really didn't get open source and today is, is just incredible. >>Well, let me just sit in that, in that culture and maybe culture clash a little bit the first time around because Microsoft developers have their own culture and their own uniform and their own way of interacting with each other. The, the, the hours that they work, which is very different from Microsoft, which is a pretty middle-aged Volvo driving kind of organization. So how, how does that work and, and what is, what has it been like the second time around the Microsoft as a middle aged Volvo driver? I think you can, you can >>wear a hoodie in and drive a Volvo. Um, no, I think it's been, I think it's been really great. The interesting thing about Microsoft is that it's not, you know, with so many people, it's not just like a homogenous big company. Um, we do have, you know, the developer tools division is a little bit different than offices, a little bit different than windows. And so they all have their own sort of unique cultures and, and now get hub slots in as its own unique culture. And we can, you know, we can talk to each other and we can understand each other, but we don't necessarily have to be all the same, you know, we can get hub team does kind of work some, some of us do work kind of weird hours. And, and I think that somehow that that works, especially with, you know, new tools coming to, um, coming to the marketplace, uh, you know, chat applications, we can be a lot less synchronous. We can be a lot more online and leave a message for each other. You know, we get out, we use get hub issues and pull requests to collaborate on almost everything, whether it's legal, uh, or, you know, our, our PR department. And it's not just developers. So we're trying to take these, these tools and, and sort of apply them to allow us to have the culture that we want at get hub. And I think Microsoft's doing the same thing as well. >>So speaking of new tools and you're, you're speaking here at ignite, you're about to announce the new repository with lots of new capabilities, enabling users to deploy at to any cloud. So tell us a little bit about, about the, this new tool. >>Yeah, so, uh, we announced, we call it get hub actions. We announced it last year at, at get hub universe. Our, uh, again, our, our annual developer conference. And our goal with GitHub actions was to allow people to take, you know, we've got 100 million repositories on get hub. We wanted our users to, to take those repositories and automate common tasks. Let me, let me give you a concrete example. Um, a lot of times somebody will open an issue on a, on a good hub repository, you know, uh, Hey, this doesn't work. I've got a bug report, you know, and they'll fill out an issue. And often either they didn't understand things or, um, the issue resolved itself, you know, who knows. We call that, uh, an issue that goes stale. And so you can build a workflow around that repository that will look for these stale issues and it will, uh, you know, just close them automatically. >>That gets rid of the mental tacks for somebody who, for a, for a developer who owns this repository to allow this, you know, this workload to just do it automatically. And so that's an example of a, a get hub actions workflow. Um, some people, uh, don't like swearing in their repository and you know, so if somebody were to open a bug report, you know, they might be angry. And so you could actually have a get hub workflow that looks for certain words and then replies and says, Hey, that's, here's our code of conduct. That's not the way we roll here. And actually a lot of people find that that feedback coming from a robot, uh, is a lot easier to take than a feedback coming from a human cause. They might want to meet with a person, can't argue with a robot. Well, not successfully. >>I think I have argued with the chat bot in my day. But anyway, >>yeah. So that's what we did a year ago and we opened it up into the beta program and we really quickly got feedback that, that people liked it and people were doing some really innovative things. But the one thing that people really wanted to automate was their bills. They wanted it to be able to build their code and deploy it. And we were just not set up for that. We, we, we didn't build, get hub actions as that platform in 2018 so we kind of had to pause our beta program. You know, I, they, they, they say that no, uh, no good plan survives first contact with the customer. So we had to, we had to hit pause on that. Uh, and we retooled. Um, we, we just sort of, I don't know, iterated on it, I guess. Uh, and we basically built a new platform that supported all of that repository automation capability that we had planned for in the first place. But also allowed for continuous integration build and deployments. So, um, we brought Macko S we brought Linux and windows runners, uh, that we host, uh, in our cloud, um, that people can use to build their software and then deploy it. And again, yeah, we want to be absolutely a tool agnostic. So any, any operating system, any, uh, language and cloud agnostic, we want to let anybody deploy anywhere, whether it's to a public cloud or on premises. >>Yeah. Uh, so, and with this, the second year we've done our program at this show and we really feel it's gone through a transformation. You know, this is a multi decade in a windows office. Uh, the business applications, uh, you know, cloud seeped in, developers are all over the place here. The day two keynote was all about app dev. Um, I'd love to get a little compare and contrast as to, you know, what you see here at Microsoft ignite versus, and I guess what I would call a pure dev show next week. Get hub universe happening in San Francisco. >>It's true. Get up universe is pretty much a pure dev show. Um, we, we have fewer booths, we have smaller booths. Uh, but, uh, and, and honestly, we have fewer sort of, um, I don't know, enterprise sorta. It, it pro crowd is what we used to call them. Um, but we do of course have a lot of dev ops. So, you know, we get up university has a lot of developers, but, uh, we're seeing a lot of dev ops, so there's a lot of meeting in the middle because, you know, I started out my career as assistant man actually. So I remember just, you know, doing everything manually. Um, but that's not the way we do things anymore. We automate all of our, uh, automate, uh, deployments. We automate all of our builds. You know, I don't want to sit there and type something into a console cause I'm going to get it wrong. Um, you know, I've accidentally deleted config files on production servers and that's, that's no good. So I think that they're, uh, get up universe is very different. A to ignite, it's much smaller, it's more intimate, but at the same time, there's a lot of, uh, overlap, especially around dev ops. >>Yeah. Uh, Satya Nadella yesterday in the keynote talked about the citizen developer as a big push for Microsoft. He said 61% of job openings for developers are outside the tech sector. Um, w what do you see in that space? Uh, the different developer roles these days? >>Uh, I think it's, it's absolutely fascinating. When I, uh, when I started my career, you know, you were, you were a developer and you, and you wrote code probably at a development company. Um, but now like everybody is automating tools, everybody's adopting machine learning. Um, when I look around at some of my friends in finance, uh, it's not about, it's not about anything but tech anymore. That's th they're, they're putting technology into absolutely everything that they do to succeed. Uh, and I think that, I think that it's amazing. Um, uh, like I said earlier, uh, 25% of developers on get hub have joined within the last year. So it's clear that it's just exploding. Um, everybody is doing, uh, software now. Yeah. >>There's something for the citizen developer on get hub though. Or is it too high level? I think >>I don't think it's too high level. I think that, uh, I think that that's a great challenge that we need to really step up to. Yeah. So Edward, >>the other big themes we heard here is talking about trust. So, you know, we talked about how Microsoft is different today than it wasn't in past, but I'm curious what good hub seen because you know, in social media when the acquisition first happened, it was, wait, I love GoodHub hub, I love all those people, but Hey, get lab. Hey, some of these other things I'm, you know, I'm fleeing for the woods. And every time I've seen an open source company get bought by a public company, there's always that online backlash. What are you seeing? How has the community reacted over the last year? >>I understand that skepticism. Uh, you know, I would be skeptical of any, uh, sort of change really. I, you know, the, the whole notion of who moved my cheese. But I think that the only way that we can, we can counter that is just to prove ourselves. And I think that we have, I think that Microsoft has allowed get hub to operate independently. And I think that, you know, I think a lot of people expect it to all of a sudden everything to change. And I don't think everything did change. I think that, uh, get hub now has more resources than it used to to be able to tackle bigger and more challenging problems. I think that get hub, uh, now can hire more and, and, and deploy to more places. And so I really just think that we're just going to keep doing exactly what we've been doing just better. So I think it's great. >>So universe happening next week teed up a little bit for us. What are some of the most exciting things that you're looking forward to? What kinds of conversations that will you be having? Presentations? >>So the big one for me is, is actions. I've, I, I've been just completely heads down working on, on get hub actions. So I'm really excited to be able to put that out there and, and you know, finally give it to everybody. Cause you know, we've been in beta now. Uh, like I said, we've been in beta for a year, which sounds like a ridiculous amount of time. Uh, but you know, it, it did involve a lot of retooling and rethinking and, and iteration with our, our beta testers. Um, and so the biggest thing for me is, is talking to people about actions and showing what they can do with actions. I'm super excited about that, but we've got a lot of other interesting stuff. You know, we've done a lot in the last year since our last universe. We've done a lot in the security space. Um, we've done, uh, we've both built tools and we've acquired some. Um, and so we'll be talking about those, uh, get hood package registry, which goes along really well with get hub actions. Uh, I'm super excited about that too. But yeah, I mean my, my calendar is, is, is just booked. Um, it's great. So many people like want to want to sit down and talk that I'm, I'm super excited about it. >>Excellent. Well great note to end on Edgar Thompson. Thank you so much for coming on the queue. We appreciate it. Thank you. I'm Rebecca Knight. First two minutes, stay tuned for more of the cubes live coverage from Microsoft ignite.
SUMMARY :
Microsoft ignite brought to you by Cohesity. Thank you so much for coming on the queue. So that's a growth of, you know, 10 million developers in just a year. So give us a little bit about, you know, the relationship when you joined Microsoft they looked at, you know, they did their due diligence, they looked at the source code and they saw all this open source But I really think that that, you know, I think you can, you can And we can, you know, we can talk to each other and we can understand each other, but we don't necessarily have to be So tell us a little bit about, about the, this new tool. actions was to allow people to take, you know, we've got 100 million repositories on get hub. swearing in their repository and you know, so if somebody were to open a bug report, I think I have argued with the chat bot in my day. So we had to, we had to hit pause on that. Uh, the business applications, uh, you know, cloud seeped in, developers are all over the place So I remember just, you know, doing everything manually. Um, w what do you see in that space? you know, you were, you were a developer and you, and you wrote code probably at a development company. I think I think that, uh, I think that that's a great challenge that we need to really is different today than it wasn't in past, but I'm curious what good hub seen because you know, And I think that, you know, I think a lot of people expect it to all of a sudden everything What kinds of conversations that will you be having? and you know, finally give it to everybody. Thank you so much for coming on the queue.
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Dee Mooney, Micron Gives | Micron Insights 2019
>>live from San Francisco. It's the Q covering Micron Insight 2019 >>Not to You, by Micron. >>Welcome back to San Francisco, everybody. This is a Micron Insight 2019 and you're watching the Cube, the leader in live coverage on Day Volonte with my co host, David Floyd. Di Mooney is here. She's the executive director of Micron gives. That's right. Give us the story. What's happening with Micron gives Tech for good. We love the tech for good stories. Tech companies are really taking this seriously. This is not just lip service. Give us the update. >>That's right. That's right. We're so proud of our company that they established a foundation 20 years ago to give back to our global communities. And since then we have given $115 million away and over 10,000 grands. So we have seen a lot of different opportunities in our global communities, and it's just been fabulous that our company supports >>you talk today about water dot or what's going on there. Why is that important in what your role there. >>So what we did is we started taking a look at an organization that we have. We have started recently binning beam or engaged with basic human needs and the grants that those support And when we were taking a look at, Really, what is the primary basic human need? Way discovered? It really is the need for water, and there are millions of people that cannot access this precious resource, and it's just was really surprising to us to think way, take it for granted so much. But yet it is very difficult to get. So as we took a look at this, there was a lot of information that this organization collects. And so we thought, Well, this will be a great opportunity for us to utilize information to enrich and bring in some of our advanced computing expertise along with our philanthropy, help them reach their mission even greater. >>This is huge. I was an event earlier this week, and the keynote speaker was an ultra marathoner, and he literally at one point he ran 4500 miles across the continent of Africa. He and two other ultra runners and people were asking what was The biggest challenge was that the heat was the painting. You know, the biggest challenge was see the challenges of of the community's getting part of the water. That was the number one thing that you know. He left the impression So I mean, this is a huge global problem. >>It really is. And our manufacturing operations were global, and we are located in water scarce areas of the world. And so what really became you know, it's a Micron issue to one of our biggest environmental issues that we talked about, and water dot org's has just been a >>leader in this space, and it has been just fabulous to work with on >>really, they have so much passion and dedication towards this. They've been ableto help. 22 million people already. >>All right, so they're lining up for the main stage. Just give us real quick some of the grants that you guys have. >>Last year at this event, we announced our advancing curiosity, and we announced three recipients last year, and since then we have four more. That's U C L. A. All right T, University of Texas at Austin and University of Washington. >>Awesome. That's great. Listen, congratulations. D on all your great work. We really appreciate your ticket sometime in the queue. All right, and thank you for watching her body. We're back with our next guest from Micron inside. 2019 on the Cube, right back.
SUMMARY :
It's the Q covering the leader in live coverage on Day Volonte with my co host, David Floyd. And since then we have given $115 million away and over 10,000 Why is that important in what your role and the grants that those support And when we were taking a look at, and he literally at one point he ran 4500 miles across the continent of Africa. And so what really became you know, it's a Micron issue to one of our biggest environmental really, they have so much passion and dedication towards this. Just give us real quick some of the grants that you guys have. and we announced three recipients last year, and since then we have four more. 2019 on the Cube,
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Alan Cohen, DCVC | CUBEConversation, September 2019
>>from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation. >>Hey, welcome back already, Jeffrey. Here with the cue, we're in our pal Amato Studios for acute conversation or excited, have ah, many Time Cube alone. I has been at all types of companies. He's moving around. We like to keep him close because he's got a great feel for what's going on. And now he's starting a new adventure. Eso really happy to welcome Alan Cohen back to the studio. Only great to see you. >>Hey, Draft, how are you >>in your new adventure? Let's get it right. It's the D C v c your partner. So this is ah, on the venture side. I'm gonna dark. You've gone to the dark side of the money side That is not a new firm, dark side. You know what's special about this town of money adventure right now, but you guys kind of have a special thesis. So tell us about yeah, and I think you've spoken >>to Matt and Zack. You know my partners in the past, So D. C. V. C is been in the venture business for about a decade and, um, you know, the 1st 5 years, the fund was very much focused on building, ah, lot of the infrastructure that we kind of take for granted. No things have gone into V m wear and into Citrix, and it's AWS, and hence the data collect of the D. C out of D. C. V. C. Really, the focus of the firm in the last five years and going forward is an area we call deep tech, which think about more about the intersection of science and engineering so less about. How do you improve the IittIe infrastructure? But how do you take all this computational power and put it to work in in specific industries, whether it's addressing supply chains, new forms of manufacturing, new forms of agriculture. So we're starting to see all that all the stuff that we've built our last 20 years and really apply it against kind of industrial transformation. So and we're excited. We just raise the $725 million fund. So we I got a little bit of ammunition to work with, >>Congratulate says, It's fun. Five. That's your eighth fund. Yeah, and really, it's consistent with where we're seeing all the time about applied a I and applied machine. Exactly. Right in New York, a company that's gonna build a I itt s'more the where you applying a i within an application, Where you applying machine, learning within what you do. And then you can just see the applications grow exactly right. Or are you targeting specific companies that are attacking a particular industrial focus and just using a eyes, their secret sauce or using deep taxes or secret uh, all of the above? Right. So, like I >>did when I think about D c v c like it's like so don't think about, um, I ops or throughput Orban with think about, um uh, rockets, robots, microbes, building blocks of effectively of human life and and of materials and then playing computational power and a I against those areas. So a little bit, you know, different focus. So, you know, it's the intersection of compute really smart computer science, but I'll give you a great example of something. It would be a little bit different. So we are investors and very active in a company called Pivot Bio, which is not exactly a household name. Pivot bio is a company that is replacing chemical fertilizer with microbes. And what I mean by that is they create microbes they used. So they've used all this big data and a I and computational power to construct microbes that when you plant corn, you insert the microbe into the planting cycle and it continuously produces nitrogen, which means you don't have to apply fertilizer. Right? Which fertilizer? Today in the U. S. A. $212 billion industry and two things happen. One you don't have. All of the runoff doesn't leech into the ground. The nitrous does. Nitrogen doesn't go into the air, and the crop yield has been a being been between about 12 and 15% higher. Right? >>Is it getting put? You know, the food industry is such a great place, and there's so many opportunities, both in food production. This is like beyond a chemical fertilizer instead of me. But it's great, but it's funny because you think of GMO, right? So all food is genetically modified. It's just It took a long time in the past because you had to get trees together, and yet you replant the pretty apples and throw the old apple trees away. Because if you look at an apple today versus an apple 50 years, 100 years, right, very, very different. And yet when we apply a man made kind of acceleration of that process than people, you know, kind of pushed back Well, this is this is not this is not nature, So I'm just curious in, in, in in, Well, this is like a microbe, you know? You know, they actually it is nature, right? So nature. But there'll be some crazy persons that wait, This is not, you know, you're introducing some foreign element into Well, you could take >>potash and pour it on corn. Or you could create a use, a microbe that creates nitrogen. So which one is the chemical on which one is nature, >>right, That that's why they get out. It's a funny part of that conversation, but but it's a different area. So >>you guys look, you guys spent a lot of time on the road. You talked a lot of startups. You talked a lot of companies. You actually talked to venture capitalists and most of the time where you know, we're working on the $4 trillion I t sector, not an insignificant sector, right? So that's globally. It's that's about the size of the economy. You know, manufacturing, agriculture and health care is more like 20 to $40 billion of the economy. So what we've also done is open the aperture to areas that have not gone through the technical disruption that we've seen an I t. Right now in these industries. And that's what's that mean? That's why I joined the firm. That's why I'm really excited, because on one hand you're right. There is a lot of cab you mentioned we were talking before. There is a lot of capital in venture, but there's not a CZ much targeted at the's area. So you have a larger part of global economy and then a much more of specific focus on it. >>Yeah, I think it's It's such a you know, it's kind of the future's here kind of the concept because no one knows, you know, the rate of which tech is advancing across all industries currently. And so that's where you wake up one day and you're like, Oh, my goodness, you know, look at the impacts on transportation. Look at the impacts on construction of the impacts on health care. Look at the impacts on on agriculture. So the opportunity is fantastic and still following the basic ideas of democratizing data. Not using a sample of old data but using, you know, real time analytics on hold data sets. You know, all these kind of concepts that come over really, really well to a more commercial application in a nightie application. Yeah. So, Jeff, I'm kind of like >>looking over your shoulder. And I'm looking at Tom Friedman's book The world is flat. And you know, if we think about all of us have been kind of working on the Internet for the last 20 years, we've done some amazing things like we've democratized information, right? Google's fairly powerful part of our lives. We've been able to allow people to buy things from all over the world and ship it. So we've done a lot of amazing things in the economy, but it hasn't been free. So if I need a 2032 c r. 20 to 32 battery for my key fob for my phone, and I buy it from Amazon and it comes in a big box. Well, there's a little bit of a carbon footprint issue that goes with that. So one of our key focus is in D. C V. C, which I think is very unique, is we think two things can happen is that weaken deal with some of the excess is over the economy that we built and as well as you know, unlock really large profit pulls. At the end of the day, you know, it has the word Venture Patrol says the word capital, right? And so we have limited partners. They expect returns. We're doing this obviously, to build large franchises. So this is not like this kind of political social thing is that we have large parts of the economy. They were not sustainable. And I'll give you some examples. Actually, you know, Jeff Bezos put out a pledge last week to try to figure out how to turn Amazon carbon neutral. >>Pretty amazing thing >>right with you from the was the richest person Now that half this richest person in the world, right? But somebody who has completely transformed the consumer economy as well as computing a comedy >>and soon transportation, right? So people like us are saying, Hey, >>how can we help Jeff meet his pledge? Right? And like, you know, there are things that we work on, like, you know, next generation of nuclear plants. Like, you know, we need renewables. We need solar, but there's no way to replace electricity. The men electricity, we're gonna need to run our economy and move off of coal and natural gas, Right? So, you know, being able to deal with the climate impacts, the social impacts are going to be actually some of the largest economic opportunities. But you can look at it and say, Hey, this is a terrible problem. It's ripping people across. I got caught in a traffic jam in San Francisco yesterday upon the top of the hill because there was climate protest, right? And you know, so I'm not kind of judging the politics of that. We could have a long conversation about that. The question is, how do you deal with these real issues, right and obviously and heady deal with them profitably and ethically, and I think that something is very unique about you know, D. C. V. C's focus and the ability to raise probably the largest deep tech fund ever to go after. It means that you know, a lot of people who back us also see the economic opportunity. And at the end of day there, you know, a lot of our our limited partners, our pension funds, you know, in universities, like, you know, there was a professor who has a pension fund who's gotta retire, right? So a little bit of that money goes into D C V C. So we have a responsibility to provide a return to them as well as go after these very interesting opportunities. >>So is there any very specific kind of investment thesis or industry focus Or, you know, kind of a subset within, you know, heavy lifting technology and science and math. That's a real loaded question in front of that little. So we like problems >>that can be solved through massive computational capability. And so and that reflects our heritage and where we all came from, right, you and I, and folks in the industry. So, you know, we're not working at the intersection of lab science at at a university, but we would take something like that and invest in it. So we like you know we have a lot of lessons in agriculture and health care were, surprisingly, one of the largest investors in space. We have investments and rocket labs, which is the preferred launch vehicle for any small satellite under two and 1/2 kilograms. We are large investors and planet labs, which is a constellation of 200 small satellites over investors and compel a space. So, uh, well, you know, we like space, and, you know, it's not space for the sake of space. It's like it's about geospatial intelligence, right? So Planet Labs is effectively the search engine for the planet Earth, right? They've been effectively Google for the planet, right? Right. And all that information could be fed to deal with housing with transportation with climate change. Um, it could be used with economic activity with shipping. So, you know, we like those kinds of areas where that technology can really impact and in the street so and so we're not limited. But, you know, we also have a bio fund, so we have, you know, we're like, you know, we like agriculture and said It's a synthetic biology types of investments and, you know, we've still invest in things like cyber we invest in physical security were investors and evolve, which is the lead system for dealing with active shooters and venues. Israel's Fordham, which is a drone security company. So, um, but they're all built on a Iot and massive >>mess. Educational power. I'm just curious. Have you private investment it if I'm tree of a point of view because you got a point of view. Most everything on the way. Just hear all this little buzz about Quantum. Um, you know, a censure opened up their new innovation hub in the Salesforce tower of San Francisco, and they've got this little dedicated kind of quantum computer quanta computer space. And regardless of how close it is, you know there's some really interesting computational opportunities last challenges that we think will come with some period of time so we don't want them in encryption and leather. We have lost their quantum >>investments were in literally investors and Righetti computing. Okay, on control, cue down in Australia, so no, we like quantum. Now, Quantum is a emerging area like it's we're not quite at the X 86 level of quantum. We have a little bit of work to get there, but it offers some amazing, you know, capabilities. >>One thing >>that also I think differentiates us. And I was listening to What you're saying is we're not afraid. The gold long, I mean a lot of our investments. They're gonna be between seven and 15 years, and I think that's also it's very different if you follow the basic economics adventure. Most funds are expected to be about 10 years old, right? And in the 1st 3 or four years, you do the bulk of the preliminary investing, and then you have reserves traditional, you know, you know, the big winners emerged that you can continue to support the companies, some of ours, they're going to go longer because of what we do. And I think that's something very special. I'm not. Look, we'd like to return in life of the fun. Of course, I mean, that's our do share a responsibility. But I think things like Quantum some of these things in the environment. They're going to take a while, and our limited partners want to be in that long ride. Now we have a thesis that they will actually be bigger economic opportunities. They'll take longer. So by having a dedicated team dedicated focus in those areas, um, that gives us, I think, a unique advantage, one of one of things when we were launching the fund that we realized is way have more people that have published scientific papers and started companies than NBA's, um, in the firm. So we are a little bit, you know, we're a little G here. That >>that's good. I said a party one time when I was talking to this guy. You were not the best people at parties we don't, but it is funny. The guy was He was a VC in medical medical tech, and I didn't ask him like So. Are you like a doctor? Did you work in a hospital where you worked at A at a university that doesn't even know I was investment banker on Wall Street and Michael, that's that's how to make money move. But do you have? Do you have the real world experience of being in the trenches? Were Some of these applications are being used, but I'm also curious. Where do you guys like to come in? ABC? What's your well, sweets? Traditionally >>we are have been a seed in Siri's. A investor would like to be early. >>Okay, Leader, follow on. Uh, everybody likes the lead, right? Right, right, right. You know what? Your term feet, you >>know? Yeah, right. And you have to learn howto something lead. Sometimes you follow. So we you know, we do both. Okay, Uh, there are increasing as because of the size of the fund. We will have the opportunity to be a little bit more multi stage than we traditionally are known for doings. Like, for example, we were seed investors in little companies, like conflict an elastic that worked out. Okay, But we were not. Later stage right. Investors and company likes companies like that with the new fund will more likely to also be in the later stages as well for some of the big banks. But we love seed we love. Precede. We'd like three guys in in a dog, right? If they have a brilliant >>tough the 7 50 to work when you're investing in the three guys in a dog and listen well and that runs and runs and you know you >>we do things we call experiments. Just you know, uh, we >>also have >>a very unique asset. We don't talk about publicly. We have a lot of really brilliant people around the firm that we call equity partners. So there's about 60 leaning scientists and executives around the world who were also attached to the firm. They actually are, have a financial stake in the firm who work with us. That gives us the ability to be early Now. Clearly, if you put in a $250,000 seed investment you don't put is the same amount of time necessarily as if you just wrote a $12 million check. What? That's the traditional wisdom I found. We actually work. Address this hard on. >>Do you have any? Do you have any formal relationships within the academic institutions? How's that >>work? Well, well, I mean, we work like everybody else with Stanford in M I t. I mean, we have many universities who are limited partners in the fund. You know, I'll give you an example of So we helped put together a company in Canada called Element A I, which actually just raised $150 million they, the founder of that company is Ah, cofounder is a fellow named Joshua Benji. Oh, he was Jeff Hinton's phD student. Him in the Vatican. These guys invented neural networks ing an a I and this company was built at a Yasha his position at the University of Montreal. There, 125 PhDs and a I that work at this firm. And so we're obviously deeply involved. Now, the Montreal A icing, my child is one of the best day I scenes in the world and cool food didn't and oh, yeah, And well, because of you, Joshua, because everybody came out of his leg, right? So I think, Yes, I think so. You know, we've worked with Carnegie Mellon, so we do work with a lot of universities. I would, I would say his university's worked with multiple venture firm Ah, >>such an important pipeline for really smart, heavy duty, totally math and tech tech guys. All right, May, that's for sure. Yeah, you always one that you never want to be the smartest guy in the room, right, or you're in the wrong room is what they say you said is probably >>an equivalent adventure. They always say you should buy the smallest house in the best neighborhood. Exactly. I was able to squeeze its PCB sees. I'm like, the least smart technical guy in the smartest technical. There >>you go. That's the way to go. All right, Alan. Well, thanks for stopping by and we look forward. Thio, you bring in some of these exciting new investment companies inside the key, right? Thanks for the time. Alright. He's Alan. I'm Jeff. You're watching the Cube. We're Interpol about the studios. Thanks for watching. We'll see you next time.
SUMMARY :
from our studios in the heart of Silicon Valley, Palo Alto, We like to keep him close because he's got a great feel for what's going on. You know what's special about this town of money adventure right now, but you guys kind of have a special thesis. um, you know, the 1st 5 years, the fund was very much focused on building, build a I itt s'more the where you applying a i within an application, So a little bit, you know, different focus. acceleration of that process than people, you know, kind of pushed back Well, this is this is not this Or you could create a use, It's a funny part of that conversation, but but it's a different area. You actually talked to venture capitalists and most of the time where you know, Yeah, I think it's It's such a you know, it's kind of the future's here kind of the concept because no one And you know, And at the end of day there, you know, a lot of our our limited partners, our pension funds, Or, you know, kind of a subset within, you know, heavy lifting technology So we like you know we have a lot of lessons in agriculture and health care Um, you know, a censure opened up their new innovation hub in the Salesforce tower of San Francisco, you know, capabilities. And in the 1st 3 or four years, you do the bulk of the preliminary investing, Do you have the real world experience of being in the trenches? we are have been a seed in Siri's. Your term feet, you So we you know, Just you know, uh, put is the same amount of time necessarily as if you just wrote a $12 million check. I'll give you an example of So we helped put together a company in Canada called Yeah, you always one that you never want to be the smartest guy in the room, They always say you should buy the smallest house in the best neighborhood. you bring in some of these exciting new investment companies inside the key, right?
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Cameron Mirza, University of Bahrain | AWSPS Summit Bahrain 2019
>> from Bahrain. It's the Q covering AWS Public sector Bahrain, brought to you by Amazon Web service, is, >> But we are here. The Cube in Bahrain, Middle East for Amazon Web service is some of our second year were cloud computing and their region of couple availability zones are up and running. Big news with Amazon got our next guest. Here's Cameron Years as head of strategy at the University of By Rain. You guys big news announcing a degree bachelor's degree in cloud computing? Yeah, a certificate one year that is gonna rapidly put new talent in the market. Congratulations. Thank you. Thank you. >> Thank you so much. We're really excited by this announcement today on Dhe. What's exciting about it is Ah, first of all, it's the first cloud computing degree in the Middle East on the other. The other element to this is that the the students suits from any background. Any discipline can get a really good understanding about cloud technology for the certification because the challenges we face in the region right now are we don't have enough skilled tech talent on we don't have enough skill talent to fill the jobs are available in the region. This is not just a regional thing is you know this is a global issue on universities. Have Thio adapt, be a bit more forward thinking live in the future. And we feel really optimistic with our partnership with Amazon today that we can actually fulfill the needs off public sector employers, entrepreneurs, governments throughout the region. And that's the exciting thing >> for us. I mean, let's just take a minute to explain the two components. One's a four year degree, one when you just give a little quick DT on ongoing questions. >> So I need a four year back to the program is gonna be delivered in a very different wave in the traditional academic program is gonna be heavily integrated with the needs of employers, so employees are gonna be really involved in curriculum design. We like them to be part of a teacher faculty as well. The way that the program will be delivered will be very much in a kind of project based way. So it's about developing not just knowledge, but the skills, competency values mindset required to be successful in the 21st century. That's exciting. Think about it, and of course, you know, looking at some of the detail behind the curriculum you're looking at networking, security, machine learning, artificial intelligence, big data. So the fact that this cloud base is actually just a small component to what it opens up in terms of broader skill sets >> I mean, one of the things that we always comment here on the Cube as we cover Amazons reinvent their big annual conference. And the joke is how many more announcement's gonna make this year a tsunami of new things coming. So certainly it's tough to keep up. Many people say that, but for the young people in school, this is relevant stuff. This is like pathway to success. Yeah, job making some cash, making some money, get that's what the purpose of education is. >> Well, I think I think there's a couple of That's a great point. The first thing is, education systems now need to live in the future. Living in a current or in many cases, the past is no acceptable. So it means it means taking some sort of calculated risk. But we're very clear in terms of the direction of travel with regard to technology in the future, jobs The reality is today. But 2/3 of the world's population already needs re Skilling. Those are the challenges we face today. Young people are purpose driven. They know where the where jobs are gonna be. They want to work for themselves. You know, they understand far better than anyone else where the way the future is unraveling do they >> understand how relevant this is? I mean, that's pretty obvious. We're in the industry. Yeah, we kind of obviously known you've been part of you are getting that This is wave. This wave is not gonna end for a while. This is gonna be a great upward migration for opportunity. You know, it's still learning on the young kids part. >> I think I think I think sometimes in education we do a disservice to young people. They're so well informed they understand the market, the trends, the way the technology shaping the future on reality is that what student learns in year one of the university, 50% and acknowledge will be obsolete by the time they graduate. So the focus is no just around giving him a degree. This is also about Skillet Re Skilling and upscaling. People have graduated people in the workforce. So this is a far wider opportunity, even just young people. Well, >> I'll tell you, one thing that gets my attention is that this reminds me of theeighties glider science because I got a degree. I was a freshman. 1983 was just at the beginning of the operating systems movement. Lennox was even around yet Units was just emerging on the scene and was interesting what we learned as building blocks with operating systems and that becoming obsolete in the sense that we don't use it anymore. But coding still happen. So this is had scaled to it with Amazon. You got okay. Easy to industry. Yeah. Now you got He's mentioned machine learning at Lambda Functions server lists. Yep. I'm so much more stuff there for a variety of jobs. >> I think this is just the tip of the iceberg. And I think for us, the way that education is evolving is that we we really believe that education will be more modular, as you say, credentials based, um lifelong on the channel. So some of it will be hands on. Some would be through other channels on competency base, and I think that's the thing. I think competency for us is about the kind of mobilization of the knowledge, your skills, the values attributes. And that's the bit it's gonna add. Value Thio economies throughout >> the world. So had a strategy. You gotta look at the chessboard in the future. You mentioned I live in the future. Yeah. What are some of the feedback you've gotten as you talk to folks in the industry when you roll this out? Um, doing some press interviews? I know you've had some feedback. What's the what's the general sentiment right now? >> Really excited. I think that we talk to employees all the time. We talked to sm easy. You talk to big players like Amazon. I think that in the in the region, I think when we talk about the scale of disruption, I think well, the way we talk about it in U. S. Or Europe is very different to the way we talk about it. I think the Middle East region, like Mellie developing parts of the world still playing catch up on old there. But what you'll find is once they've caught up, the adoption rates go through the roof and then that's that's the challenge for us. Because you know what? We see the uptake. Now we see the update every year growing and growing. And now the next challenge is moving into government, moving into the private sector on upscaling and re Skilling, though. So we're just at the start of this kind of huge opportunity. John and I see it being, you know, exponentially over the next five years. You >> know, it's interesting. I live in San Francisco, Bay Area and Silicon Valley. Invalid. We'll tow you. See what Berkeley's doing. Stand up for you. If you look at Berkeley in particular, number one classes are the data science class and the CS intro. Yeah, I mean, they're kind of hybrids, basically, is all cloudy? Do anything with coding. It's gonna be cloud based, right? Um, and seal, who's the deputy Group CEO? Banky, ABC. I just interviewed earlier today. He said, Aye, aye. He thinks is the biggest thing that's gonna happen. So it's not just racking and stacking standing up infrastructure with Amazon, although great to learn that it will be nerds. Geeks do that. There's a huge machine learning a I field. Yeah, I think that's gonna be something. Is head of strategy. You gotta keep your eye on the prize. They're absolutely What's your view on that? How do you see that happening? >> I think you're right. I think only CD of recently released some doctor to say that over 20% of jobs will be automated as a result of their arrive in the next few years. I think our role is to prepare young people regardless of what they're studying. Fool. Aye, aye. On the impact of machine learning. So I'll give an example. Medicine. You can make a diagnosis now for a patient diagnosis in a fraction of a second compared to what we used to be able to buy using I. Now the reality is that although I all I can give you that information you as a patient, one a robot to give you that diagnosis, right? So our job, I think, is to look at the skills that will define what defines us as human beings away from robots. And that's empathy. That's the stuff around building, building connections around team, working around collaboration. And actually those are the things the education systems of a designed not to deliver. So our job now is by embracing these types of new program is it is. It is to start to work on those softer skills on Prepare this generation of shooting for the for the A. I will that we're moving into >> camera, and I was so excited for your opportunity. Computer science cloud >> all kind, bundle >> together and software is powering this new job. As we say, it's the keys to the kingdom. In this case, it could be the keys to the kingdom. >> Well, I think for us as the national university on for many Ah, not just Bahrain. But for many developing an emerging countries around the world, this is far greater than just technology. Or create Jarvis's about sovereignty. Because if you look at many countries, they import talent. They have to import hardware, software, computers and things imported. This is a great opportunity to help create a workforce but actually flips it on its head. Becomes the innovators, becomes the job creators. So that's the exciting thing for us. It really is >> a generational accident. This is an opportunity for the younger generation to literally take the keys to the kingdom. Absolutely absolutely thanks so much for coming. Thank you. Thank you. Telling cube coverage here by rain Middle East AWS Summit. I'm John Feehery Stables for more coverage after this short break.
SUMMARY :
from Bahrain. It's the Q covering AWS the University of By Rain. the challenges we face in the region right now are we don't have enough skilled tech talent on I mean, let's just take a minute to explain the two components. So the fact that this cloud base is actually just a small component to what it opens I mean, one of the things that we always comment here on the Cube as we cover Amazons reinvent their big annual Those are the challenges we face today. You know, it's still learning on the young kids part. I think I think I think sometimes in education we do a disservice to young people. in the sense that we don't use it anymore. And I think for us, the way that education is evolving is that we we You gotta look at the chessboard in the future. the way we talk about it. data science class and the CS intro. I. Now the reality is that although I all I can give you that information you camera, and I was so excited for your opportunity. In this case, it could be the keys to the kingdom. So that's the exciting thing take the keys to the kingdom.
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Shekar Ayyar, VMware & Sachin Katti, Uhana | VMworld 2019
>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by IBM Wear and its ecosystem partners. >> Welcome back to the Cube. It's the Emerald 2019 our 10th year water wall coverage. Three days, two sets, lots of content. Instrument of my co host is Justin Warren. And one of the big stories coming into the show is VM Wear actually went on an acquisition spree. A hold number of acquisitions. Boston based Carbon Black over $2 billion Pivotal brought back into the fold for also, you know, around that ballpark of money on Happy to Welcome to the program. One of those acquisitions, such and Conti, is sitting to my right. He's the co founder of Hana is also a professor at Stanford University. Thank you so much for joining us and joining us. Also for the segment. Shakeri Air, the executive vice president general manager of Telco Edge Cloud at VM Wear, Shaker said, Yes, there's a lot of acquisitions not to play favorites, but maybe this is his favorite. No question about it. All right. Eso such in, you know, boy, you know the Paolo Alto Stanford connection. We were thinking back, You know, the Founders Of'em where, of course, you know came from Stanford. Many acquisitions over the year, including the mega next era acquisition. You know, quite a few years ago, I came out of Stanford. Give us what was the genesis in the Why of Hana. >> It's actually interesting Stanford Connection to So I've been a faculty at Stanford for the last 10 years on dhe. I have seen the SD and moment very close on up front on one of the dirty secrets of S. T M says it makes the netbooks programmable, but someone still has to write the programs on. So that's usually a very complex task on the pieces beyond the company was, Can we use the eye to learn how to program the network rather than humans having to program the network to do management or optimization? So the division really waas can be built? A network that learned how to optimize itself learns how to manage itself on the technology we're building. Is this a pipeline that basically tries to deliver on that for mobile? >> It's great, Sachin, you know, my background is networking and it feels like forever. We've been hooking well. We need to get people from the cli over to the gooey. But we know in today's rightly complex world, whether it's a I or just automation, humans will not be able to keep up with it. And, you know, we know that that's where a lot of the errors would happen is when we entered humans into doing some of this. So what are some of the key drivers that make this solution possible today that, you know, it might not have been able to do done when when one train was first rolling out the first S t n? >> Yeah, talk about it in three dimensions. The one is, Why do we need it today? Right on. Then what is being what is happening that is enabling this today, right? So, apart from what I talked about Stu and I think the other big driver is, the way I like to think about it is that the Internet is going from a means of consumption to a means of control and interaction. So, increasingly, the application to BC driving the next big decade, our very way of controlling things remotely or the network like a self driving car, or be in interacting but very highly rich visual content like E. R. India. So the applications are becoming a lot more demanding on the Net. At the same time, the network is going through a phase off, opening up on becoming disaggregated network complexity is increasing significantly. So the motivation behind the company and why I thought that was the right time to start the company was these two friends are gonna collide with five coming along the applications that are driving five g and then at the complexity increasing our five. So that's why we started the company. What actually is enabling. This is the fact that we have seen a lot of progress with the eye over the last few years. It hasn't really. It hadn't really been applied at scale to networks and specifically mobile that book. So we definitely saw no, actually there, but increasingly, ah, lot of the infrastructure that is being deployed there was more and more telemetry available. There was more and more data becoming available and that also obviously feet this whole engine. So I think the availability of all of these Big Data Technologies Maur data coming in from the network and the need because of these applications and that complexity. I think there's a perfect confluence >> that there's lots of lots of II floating around at the moment, and there's different flavors of it as well. So this machine learning there's Aye aye, sir. When when you say that there's there's a I behind this What? What particular kind of machine learning or a Y you're using to drive these networks? >> This a few different techniques because the problems we solve our anomaly detection off. Then problems are happening in the network predicting how network conditions are going to evolve. For example, predicting what your devices throughput is gonna be the next 30 seconds. We're also learning how to control the knobs in the neck using AI ai techniques. So each of these has different classes of the eye techniques. So, for example, for control we're using reinforcement learning, which is the same technique that Google used to kind of been on alphago. How do you learn how to play a game basically, but area the game you're playing it optimizing the network. But for the others, it's a record of neural networks to do predictions on Time series data. So I think it's a combination of techniques I wouldn't get to wherever the techniques. It's ultimately. But what is the problem you're trying to solve? And then they picked the right technique to solve it, >> and so on that because the aye aye is actually kind of stupid in that it doesn't know what they wouldn't. What an optimized network looks like. We have to show it what that is. So what? How do you actually train these systems to understand? But what is an optimized network? What? How does how does that tell you? Define this is what my network optimal state should be. >> So that's a great question, because in networking like that, any other discipline that wants to use the eye. There's not a lot of label data. What is the state I want to end up at what is a problem state or what is a good state? All of this is labels that someone has to enter, and that's not available axe kid, and we're never gonna be able to get it at the scale we wanted. So one of our secret sauce is if you will, is semi supervised learning but basic ideas that we're taking a lot of domain knowledge on using that domain knowledge to figure out what should be the right features for these models so that we can actually train these models in a scalable fashion. If you just throw it a lot of data any I model, it just does not converge. Hardly constructive features on the other thing is, how do I actually define what are good kind of end state conditions? What's a good network? And that's coming from domain knowledge to That's how we're making I scale for the stomach. >> I mean, overall, I would say, as you look at that, some of the parameters in terms of what you want to achieve are actually quite obvious things like fewer dropped calls for a cellular network. You know, that's good. So figuring out what the metrics need to be and what the tuning needs before the network, that's where Hana comes in in terms of the right people. >> All right, so shake her. Give us a little bit of an understanding as to where this fits into the networking portfolio. You know, we heard no we heard from Patty or two ago. You know what would have strong push? Networking is on the NSX number. Speaks for itself is what's happening with that portfolio? >> No, absolutely. In fact, what we're doing here is actually broader than networking. It's sort off very pertinent to the network off a carrier. But that is a bulk off their business, if you will. I think if you sort of go back and look at the emirs of any any, any vision, this is the notion of having any cloud in any application land on any cloud and then any device connected to those applications on that any cloud side we are looking at particularly to cloud pools, one which we call the Telco cloud and the other is the edge cloud. And both of these fortuitously are now becoming sort of transforming the context of five G. So in one case, in the telco cloudy or looking at their core and access networks, the radio networks, all of this getting more cloud ified, which essentially leads toe greater agility in service deployment, and then the edge is a much more distributed architecture. Many points over which you can have compute storage network management and security deployed. So if you now think about the sort of thousands off nodes on dhe virtualized clouds, it is just impossible to manage this manual. So what you do need is greater. I mean, orders of magnitude, greater automation in the ability to go and manage and infrastructure like this. So, with our technology now enhanced by Johanna in that network portfolio in the Telco Edge Cloud portfolio, were able to go back to the carriers and tell them, Look, we're not just foundational infrastructure providers. We can also then help you automate help you get visibility into your networks and just help you overall manager networks better for better customer expedience and better performance. >> So what are some of the use coasters that you see is being enabled by five G? There's a lot of hype about five short the moment and not just five jail. So things like WiFi six. Yeah, it would appear to me that this kind of technique would work equally well for five g Your wife. I short a WiFi six. So what are some of the use cases? You see these thieves service providers with Toko Edge clouds using this for? Yeah, So I think overall, first of all, I'd >> say enterprise use cases are going to become a pretty prominent part off five, even though a lot off the buzz and hype ends up being about consumers and how much bandwidth and data they could get in or whether five chicken passing preys or not. But in fact, things like on premise radio on whether that is private. Lt it's 40 or five t. These are the kinds of Uschi cases that were actually quite excited about because these could be deployed literally today. I mean, sometimes they're not regulated. You can go in with, like, existing architectures. You don't need to wait for standardization to break open a radio architecture. You could actually do it, Um, and >> so this sort off going in and >> providing connectivity on an enterprise network that is an enhanced state off where it is today. We've already started that journey, for example, with yellow cloud and branch networking. Now, if we can take that toe a radio based architecture for enterprise networking, So we think, ah, use case like that would be very prominent. And then based on edge architectures distributed networks now becoming the next generation Cdn is an example. That's another application that we think would be very prominent. And then I think, for consumers just sort of getting things like gaming applications off on edge network. Those are all the kinds of applications that would consume this sort off high skill, reliability and performance. >> Can you give a little sketch of the company pre acquisition, you know, is the product all g eight? How many customers you? Can you say what you have there? Sure >> it does us roughly three years old. The company itself so relatively young. We were around 33 people total. We had a product that is already deployed with chairman Telcos. So it is in production deployment with Chairman Telco Ondas in production trials with a couple of other tier one telcos. So we built a platform to scale to the largest networks in the world on If I, if I were to summarize it, be basically can observe, makes sense or in real time about every user in the network, what their experiences like actually apply. I modeled on top of that to optimize each user's expedience because one of the vision bee had was the network today is optimized for the average. But as all off our web expedience personalized netbook experiences, not personalized can be build a network Very your experiences personalized for you for the applications, your running on it. And this was kind of a foundation for that. >> I mean, we In fact, as we've been deploying our telco Cloud and carrier networks, we've also been counting roughly how many subscribers are being served up. Today we have over 800 million subscribers, and in fact, I was talking to someone and we were talking about that does. Being over 10% off the population of the world is now running on the lack of memory infrastructure. And then along comes Johanna and they can actually fine tune the data right down to a single subscriber. Okay, so now you can see the sort of two ends of the scale problem and how we can do this using a I. It's pretty powerful. Excellent. >> So So if we have any problems with our our service fighters, b tech support and I love to hear from both of you, you know what this acquisition position means for the future of the places and obviously VM wear global footprint. A lot of customers and resource is. But you know what I mean to your team in your product. >> I mean, definitely accelerating how quickly we can now start deploying. This and the rest of the world be as a small company, have very focused on a few key customers to prove the technology we have done that on. I think now it's the face to scale it on. Repeat it across a lot of other customers, but I think it also gives us a broader canvas to play that right. So we were focused on one aspect of the problem which is around, if you will, intelligence and subscriber experience. But I think with the cloud on but the orchestration products that are coming out of the ember, we can now start to imagine a full stock that you could build a network of full carrier network code off using using remote technology. So I think it's a broad, more exciting, actually, for us to be able to integrate not just the network data but also other parts of the stock itself. And >> it strikes me that this probably isn't just limited to telcos, either. The service providers and carriers are one aspect of this bit particularly five G and things like deployments into factory automation. Yes, I can see a lot of enterprise is starting to become much in some ways a little bit like a tell go. And they would definitely benefit from this >> kind of thing. Yeah, I mean, in fact, that's the basis of our internal even bringing our telco and EJ and I ot together and a common infrastructure pool. And so we're looking at that. That's the capability for deploying this type of technology across that. So you're exactly right, >> Checker want to give you the last word, you know, Telco space, you know? And then, obviously the broader cloud has been, you know, a large growth area. What, you want people taking away from the emerald 2019 when it comes to your team? >> Yeah, I think. To me, Calico's have a tremendous opportunity to not just be the plumbing and networking providers that they can in fact, be both the clowns of tomorrow as well as the application providers of tomorrow. And I think we have the technology and both organically as well as through acquisitions like Ohana. Take them there. So I'm just super excited about the journey. Because I think while most of the people are talking about five D as this wave, that is just beginning for us, it's just a perfect coming together on many of these architectures that is going to take telcos into a new world. So we're super excited about taking them. >> Shaker. Thank you so much for joining against auction. Congratulations and good luck on the next phase of you and your team's journey along the way. Thank you. Thank you for Justin. Warren comes to Minutemen, Stay with us. Still a bit more to go for VM World 2019 and, as always, thank you for watching the Cube.
SUMMARY :
brought to you by IBM Wear and its ecosystem partners. You know, the Founders Of'em where, of course, you know came from Stanford. the dirty secrets of S. T M says it makes the netbooks programmable, but someone still has to write the programs So what are some of the key drivers that make this is that the Internet is going from a means of consumption to a means of control and So this machine learning there's Aye aye, sir. Then problems are happening in the network predicting how network conditions are going to evolve. and so on that because the aye aye is actually kind of stupid in that it doesn't know what they wouldn't. Hardly constructive features on the other thing is, how do I actually define what are the metrics need to be and what the tuning needs before the network, that's where Hana Networking is on the NSX number. I mean, orders of magnitude, greater automation in the ability to go So what are some of the use coasters that you see is being enabled by five G? Lt it's 40 or five t. These are the kinds of Uschi cases that were actually quite Those are all the kinds of applications that would consume this sort off high skill, because one of the vision bee had was the network today is optimized for the average. Being over 10% off the population of the So So if we have any problems with our our service fighters, b orchestration products that are coming out of the ember, we can now start to imagine a full stock it strikes me that this probably isn't just limited to telcos, either. Yeah, I mean, in fact, that's the basis of our internal even bringing our telco And then, obviously the broader cloud has been, you know, a large growth area. So I'm just super excited about the journey. Congratulations and good luck on the next phase of you and your
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Steve Athanas, VMUG | CUBEConversation, April 2019
>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue. Here's your host. Still Minutemen. >> Hi, I'm Stew Minutemen. And welcome to a special cute conversation here in our Boston Areas studio where in spring 2019 whole lot of shows where the cubes gonna be on going to lots of events so many different technologies were covering on one of the areas we always love to be able to dig into is what's happening with the users. Many of these shows, we go to our user conferences as well as the community. Really happy to Boca Burger. Believe first time on the program. Steve Methodists famous. Who is the newly elected president of the mug s. So I think most of Ronan should know the V mug organization to the VM where User group. We've done cube events at, you know, the most related events. Absolute talked about the mug we've had, you know, the CEO of the mug on the program. And of course, the VM were Community 2019 will be the 10th year of the Cube at VM World. Still figuring out if we should do a party and stuff like that. We know all the ins and outs of what happened at that show. But you know the V mugs itself? I've attended many. Your Boston V mug is one that I've been, too. But before we get into the mug stuff, Steve could just give us a little bit of your back, because you are. You're practicing your user yourself. >> Yeah, well, first thanks for having me. You know what? I've been watching the cube for years, and it's ah, it's great to be on this side of the of the screen, right? So, yes. So I'm Steve. I think I, you know, show up every day as the associate chief information officer of the University of Massachusetts. Little just for 95 here, and that's my day job. That's my career, right? But what? You know what? I'm excited to be here to talk about what I'm excited in general with the mug is it's a community organization. And so it's a volunteer gig, and that's true of all of our leadership, right? So the from the president of the board of directors to our local leaders around the world, they're all volunteers, and that's I think, what makes it special is We're doing this because we're excited about it. We're passionate about it. >> Yeah, you know the mugs, It's, you know, created by users for user's. You go to them, talk a little bit. It's evolved a lot, you know, It started as just a bunch of independent little events. Is now you know, my Twitter feed. I feel like constantly every day. It's like, Oh, wait, who is at the St Louis? The Wisconsin one? I'll get like ads for like, it's like a weight is the Northeast one. I'm like, Oh, is that here in New England that I don't know about? No, no, no. It's in the UK on things like that. So I get ads and friends around the world and I love seeing the community. So, boy, how do you guys keep it all straight? Man, is that allow both the organic nature as well as some of the coordination and understanding of what's going on. How do you balance that? >> Yeah, that's a great question. And you know, So I was a V mug member for many, many years before I ever got interested in becoming a leader, and you're right it when it started, it was 10 of us would get around with a six pack of beer and a box of pizza, right? And we'd be talking shop and that, you know, that was awesome. And that's what would that was, how it started. But you get to a certain scale when you start talking about having 50,000 now, over 125,000 members around the world. You gotta coordinate that somehow you're right on the money with that. And so that's why you know, we have, you know, a strong, um, coordination effort that is our offices down in Nashville, Tennessee, and their their role is to enable our leaders to give back to their community and take the burden out of running these things. You know, sourcing venues and, you know, working with hotels and stuff. That is effort that not everybody wants to do all the time. And so to do that for them lets them focus on the really cool stuff which is the tech and connecting users. >> Yeah. Can you speak a little bit too? You know what were some of the speeds and feed to the event? How many do you have How much growing, you know, Like I'm signed up. I get the newsletter for activities as well as you know, lots of weapons. I've spoken on some of the webinars too. >> Yeah, well, first thanks for that s o. We have over 30 user cons around the world on three continents. >> In fact, what's the user cough? >> Great questions. So user kind is user conference, you know, consolidated into user Connery. And those are hundreds of end users getting together around the world were on three continents. In fact, I was fortunate enough in March, I went to Australia and I spoke at Sydney and Melbourne on That was awesome, getting to meet users literally, almost a sw far away from Boston. As you can get having the same challenges in the office day today, solving the same business problems with technology. So that was exciting. And so we've got those all over. We also have local meetings which are, you know, smaller in scope and often more focused on content. We've got 235 or Maur local chapters around the world. They're talking about this, and so we're really engaged at multiple levels with this and like you talk about. We have the online events which are global in scope. And we do those, you know, we time so that people in our time zone here in the States could get to them as well as folks in, you know, e m b A and a factory. >> Yeah, and I have to imagine the attendees have to vary. I mean, is it primarily for, you know, Sylvie, um, where admin is the primary title there up to, you know, people that are CEOs or one of the CEOs? >> Yes. So that actually we've seen that change over the past couple years, which is exciting for me being in the role that I'm in is you're right historically was vey Sphere admits, right? And we're all getting together. We're talking about how do we partition our lungs appropriately, right? And now it has switched. We see a lot more architect titles. We Seymour director titles coming in because, you know, I said the other day I was in Charlotte talking and I said, You know, business is being written in code, right? And so there's a lot more emphasis on what it's happening with V m wearing his VM worth portfolio expands. We've got a lot of new type of members coming into the group, which is exciting. >> Yeah, And what about the contents out? How much of it is user generated content versus VM were content and then, you know, I understand sponsorships or part of it vendors. The vendor ecosystem, which vm where has a robust ecosystem? Yes, you know, help make sure that it's financially viable for things to happen and as well as participate in the contest. >> Yes, I feel like I almost planted that question because it's such a good one. So, you know, in 2018 we started putting a strong emphasis on community content because we were, you know, we heard from remembers that awesome VM were content, awesome partner content. But we're starting to miss some of the user to user from the trenches, battle war stories, right? And so we put an emphasis on getting that back in and 2018 we've doubled down in 2019 in a big way, so if you've been to a user kind yet in 2019 but we've limited the number of sponsors sessions that we have, right so that we have more room for community content. We're actually able to get people from around the world to these events. So again, me and a couple folks from the States went toe Australia to share our story and then user story, right? And at the end of the day, we used to have sponsored sessions to sort of close it out. Now we have a community, our right, and Sophie Mug provides food and beverages and a chance to get together a network. And so that is a great community. Our and you know, I was at one recently and I was able to watch Ah, couple folks get to them. We're talking about different problems. They're having this and let me get your card so we can touch base on this later, which at the end of the day, that's what gets me motivated. That's what >> it's about. It's Steve. I won't touch on that for a second. You know what? Get you motivated. You've been doing this for years. You're, you know, putting your time in your president. I know. When I attended your Boston V mark the end of the day, it was a good community member talking about career and got some real good, you know, somebody we both know and it really gets you pumped up in something very, a little bit different from there. So talk a little bit without kind of your goals. For a CZ president of Emma, >> Sure eso I get excited about Vima because it's a community organization, right? And because, you know, I've said this a bunch of times. But for me, what excites me is it's a community of people with similar interests growing together right and reinforcing each other. I know for a fact that I can call ah whole bunch of people around the world and say, Hey, I'm having a problem technically or hey, I'm looking for some career advice or hey, one of my buddies is looking for work. Do you know of any opening somewhere? And that's really powerful, right? Because of the end of the day, I think the mug is about names and people and not logos, right? And so that's what it motivates me is seeing the change and the transformation of people and their career growth that V mug can provide. In fact, I know ah ton of people from Boston. In fact, several of them have. You know, they were administrators at a local organization. Maybe they moved into partners. Maybe they moved into vendors. Maybe they stay where they are, and they kept accelerating their growth. But I've seen tons of career growth and that that gets me excited watching people take the next step to be ableto to build a >> career, I tell you, most conferences, I go to the kind of jobs take boards, especially if you're kind of in the hot, cool new space they're all trying to hire. But especially when you go to a local on the smaller events, it's so much about the networking and the people. When I go to a local user, event it. Hey, what kind of jobs you hiring for who you're looking for and who do I know that's looking for those kind of things and trying to help connect? You know, people in cos cause I mean, you know, we all sometime in our career, you know we'll need help alone those lines that I have, something that's personally that you know, I always love to help >> you. I have a friend who said it. I think best, and I can't take credit for this, right? But it's It can be easy to get dismissed from your day job, right? One errant click could be the career limiting click. It is nigh impossible to be fired from the community, right? And that that, to me, is a powerful differentiator for folks that are plugged into a community versus those that are trying to go it >> alone. Yeah, there are some community guidelines that if you don't follow, you might be checking for sure, but no, if if we're there in good faith and we're doing everything like out, tell me it's speaking. You know, this is such, you know, change. Is this the constant in our world? You know, I've been around in the interview long enough. That's like, you know, I remember what the, um where was this tiny little company that had, you know, once a week, they had a barbecue for everybody in the company because they were, like, 100 of them. And, you know, you know, desktop was what they started working on first. And, you know, we also hear stories about when we first heard about the emotion and the like. But, you know, today you know Veum world is so many different aspects. The community is, you know, in many ways fragmented through so many different pieces. What are some of the hot, interesting things? How does seem a deal with the Oh, hey, I want the Aye Aye or the Dev Ops or the you know where where's the vmc cloud versus all these various flavors? How do you balance all that out? All these different pieces of the community? >> Yeah, it's an interesting question. And to be fair with you, I think that's an area that were still getting better at. And we're still adapting to write. You know, if you look at V mug Five years ago, we were the V's fear, sort of first, last and always right. And now you know, especially is VM. Where's portfolio keeps increasing and they keep moving into new areas. That's new areas for us, too. And so, you know, we've got a big, uh, initiative over the next year to really reach out and and see where we can connect with, you know, the kubernetes environment, right? Cause that the hefty oh acquisition is a really big deal. and I think fundamentally changes or potential community, right? And so you know, we've launched a bunch of special interest groups over the span of the past couple years, and I think that's a big piece of it, which is, if you're really interested in networking and security, here's an area that you can connect in and folks that are like minded. If you're really interested in and user computing, here's what you can connect into. And so I think, you know, as we continue to grow and you know, we're, you know, hundreds of thousands of people now around the world so that you can be a challenge. But I think it's It's also a huge opportunity for us to be ableto keep building that connection with folks and saying, Hey, you know, as you continue to move through your career, it's not always gonna be this. You're right. Change is constant. So hey, what's on the horizon for >> you? When I look at like the field organization for being where boy, I wonder when we're gonna have the sand and NSX user groups just because there's such a strong emphasis on the pieces, the business right now? Yeah, All right, Steve, let's change that for a second. Sure said, You know, you're you got CEO is part of your title, their eyes, what you're doing. Tell me about your life these days and you know the stresses and strains And what what's changing these days and what's exciting? You >> sure? So you know, it's exciting to have moved for my career because I'm an old school admin, right? I mean, that's my background. Uh, so, you know, as I've progressed, you know, I keep getting different things in my portfolio, right? So it started out as I was, you know, I was the admin, and then I was managing the systems engineering team. And then they added desktop support that was out of necessity was like, I'm not really a dustup person, right? So something new you need to learn. But then you start seeing where these synergies are, right? Not to hate, like the words energies. But the reality is that's where we launched our VD. I project at U Mass. Lowell, and that has been transformative for how we deliver education. And it has been a lot of ways. Reduced barriers to students to get access to things they couldn't before. So we had engineering students that would have to go out and finance a 3 $4000 laptop to get the horsepower to do their work. Now, that can use a chromebook, right? They don't have to have that because we do that for them and just they have to have any device t get access via via where horizon. Right, So that happened, and then, you know, then they moved in. Our service is operation, right? So what I'm interested now is how do we deliver applications seamlessly to users to give them the best possible experience without needing to think about it? Because if you and I have been around long enough that it used to be a hassle to figure out okay, I need to get this done. That means they need to get this new applications I have to go to I t there and I have my laptop. Now it's the expectation is just like you and I really want to pull out my phone now and go to the APP store and get it right. So how do we enable that to make it very seamless and remove any friction to people getting their work >> done? Yeah, absolutely. That the enterprise app store is something we've talked about is not just the Amazon marketplace these days. >> In some ways, it is so not all applications rate. Some applications are more specific to platforms. And so that's a challenge, which is, you know, I'm a professor. I really like my iPad. Well, how do I get S P ss on that? Okay, well, let me come up with some solutions. >> Yeah, it's interesting. I'm curious if you have any thoughts just from the education standpoint, how that ties into i t. Personally myself, I think I was in my second job out of school before I realized I was in the i t industry because I studied engineering they didn't teach us about. Oh, well, here's the industry's You're working. I knew tech, and I knew various pieces of it and, you know, was learning networking and all these various pieces there. But, you know, the industry viewpoint as a technology person wasn't something. I spend a lot of time. I was just in a conference this week and they were talking about, you know, some of the machine learning pieces. There was an analyst got up on stage is like here I have a life hack for you, he said. What you need to do is get a summer intern that's been at least a junior in college that studied this stuff, and they can educate you on all these cool new things because those of us have been here a while that there's only tools and they're teaching them at the universities. And therefore that's one of those areas that even if you have years, well, if you need to get that retraining and they can help with that >> no, that's that to me is one of most exciting parts about working in education is that our faculty are constantly pushing us in new directions that we haven't even contemplated yet. So we were buying GPU raise in order to start doing a I. Before I even knew why we were doing and there was like, Hey, I need this and I was like, Are you doing like a quake server? Like they were mining Bitcoins? I don't think so, but it was, you know, that was that was that was an area for us and now we're old. Had it this stuff, right? And so that is a exciting thing to be able to partner with people that are on the bleeding edge of innovation and hear about the work that they're doing and not just in in the tech field, but how technology is enabling Other drew some groundbreaking research in, you know, the life sciences space that the technology is enabling in a way that it wasn't possible before. In fact, I had one faculty member tell me, Geez, maybe six months ago. That said, the laboratory of the past is beakers and Silla scopes, right? The laboratory of the future is how many cores can you get? >> Yeah, all right, So next week is Del Technologies world. So you know the show. The combination of what used to be A M, C World and Del World put together a big show expecting around 15,000 people in Las Vegas to be the 10th year actually of what used to be M. C world. We actually did a bunch of dead worlds together. For me personally, it's like 17 or 18 of the M C world that I've been, too, just because disclaimer former emcee employees. So V mugs there on dhe, Maybe explain. You know, the mugs roll there. What you're looking to accomplish what you get out of a show like that. >> Sure. So V mug is a part of the affiliation of del Technologies user communities. Right? And what I love about user communities is they're not mutually exclusive, right? You absolutely can. Being a converged and Avi mug and a data protection user group. It's all about what fits your needs and what you're doing back in the office. And, you know, we're excited to be there because there's a ton of the move members that are coming to Deltek World, right? And so we're there to support our community and be a resource for them. And that's exciting for us because, you know, Del Del Technologies World is a whole bunch of really cool attack that were that were seeing people run vm were on Ray. We're seeing via more partner with, and so that's exciting for us. >> Yeah, and it's a try. Hadn't realized because, like, I've been to one of the converted user group events before, didn't realize that there was kind of an affiliation between those but makes all the sense in the world. >> Yeah, right. And it's, you know, again, it's an open hand thing, right? Beaten and one being the other. You realize them both. For what? They're what They're great at connecting with people that are doing the same thing. There's a ton of people running VM wear on. Ah, myriad. Like you talked about earlier VM Where's partner? Ecosystem is massive, right? But many, many, many in fact, I would say a huge majority of converged folks are running VM we're >> on it. All right. So, Steve want to give you the final word? What's the call to action? Understand? A lot of people in the community, but always looking from or always, ways for people to get involved. So where do they go? What? What would you recommend? >> Yeah, thanks. So if if you are not plugged into user community now, when you're in the tech field, I would strongly encourage you to do so. Right? V mug, obviously, is the one that's closest to my heart, right? If you're in that space, we'd love to have you as part of our community. And it's really easy. Go to V mug. dot com and sign up and see where the next meet up is and go there, right? If you're not into the VM where space and I know you have lots of folks that air, they're doing different things. Go check out your community, right? But I tell you, the career advantages to being in a user community are immense, and I frankly was able to track my career growth from admin to manager to director to associate CEO, right alongside my community involvement. And so it's something I'm passionate about, and I would encourage everybody to check out. >> Yeah, it's Steve. Thank you so much for joining us. Yeah, I give a personal plug on this. There are a lot of communities out there, the virtual ization community, especially the VM. One specifically is, you know, a little bit special from the rest. You know, I've seen it's not the only one, but is definitely Maur of. It's definitely welcoming. They're always looking for feedback, and it's a good collaborative environment. I've done surveys in the group that you get way better feedback than I do in certain other sectors in just so many people that are looking to get involved. So it's one that you know, I'm not only interviewing, but, you know, I can personally vouch for its steeple. Thank you. Thank you so much. Always a pleasure to see you. >> Thanks for having me. >> Alright. And be sure to check out the cube dot net. Of course, we've got dealt technologies world in the immediate future. Not that long until we get to the end of summer. And vm World 2019 back in San Francisco, the Q will be there. Double set. So for both del world del Technologies world and VM World. So come find us in Las Vegas. If you're Adele or Mosconi West in the lobby is where will be for the emerald 2019 and lots and lots of other shows. So thank you so much for watching. Thank you.
SUMMARY :
It's the cue. you know, the CEO of the mug on the program. you know, show up every day as the associate chief information officer of the University of Massachusetts. Is now you know, And so that's why you know, we have, you know, a strong, as well as you know, lots of weapons. Yeah, well, first thanks for that s o. We have over 30 user cons around the world And we do those, you know, we time so that people in our time zone here in the States could there up to, you know, people that are CEOs or one of the CEOs? We Seymour director titles coming in because, you know, I said the other day I was in VM were content and then, you know, I understand sponsorships or part of it vendors. Our and you know, I was at one recently and I was able to watch it was a good community member talking about career and got some real good, you know, And because, you know, I've said this a bunch of times. something that's personally that you know, I always love to help And that that, to me, You know, this is such, you know, change. And so I think, you know, as we continue to grow and you know, we're, you know, days and you know the stresses and strains And what what's changing these days and what's exciting? Right, So that happened, and then, you know, That the enterprise app store is something we've talked about is not just the Amazon marketplace And so that's a challenge, which is, you know, I'm a professor. But, you know, the industry viewpoint as a technology I don't think so, but it was, you know, that was that was that was an area for us and now we're old. So you know the show. And that's exciting for us because, you know, Hadn't realized because, like, I've been to one of the converted user group events before, And it's, you know, again, it's an open hand thing, right? So, Steve want to give you the final word? So if if you are not plugged into user community now, when you're in the tech field, So it's one that you know, So thank you so much for watching.
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StrongyByScience Podcast | Bill Schmarzo Part One
produced from the cube studios this is strong by science in-depth conversations about science based training sports performance and all things health and wellness here's your host max smart [Music] [Applause] [Music] all right thank you guys tune in today I have the one and only Dean of big data the man the myth the legend bill Schwarz oh also my dad is the CTO of Hitachi van Tara and IOC in analytics he has a very interesting background because he is the well he's known as the Dean of big data but also the king of the court and all things basketball related when it comes to our household and unlike most people in the data world and I want to say most as an umbrella term but a some big bill has an illustrious sports career playing at Coe College the Harvard of the Midwest my alma mater as well but I think having that background of not just being computer science but where you have multiple disciplines involved when it comes to your jazz career you had basketball career you have obviously the career Iran now all that plays a huge role in being able to interpret and take multiple domains and put it into one so thank you for being here dad yeah thanks max that's a great introduction I rep reciate that no it's it's wonderful to have you and for our listeners who are not aware bill is referring him is Bill like my dad but I call my dad the whole time is gonna drive me crazy bill has a mind that thinks not like most so he he sees things he thinks about it not just in terms of the single I guess trajectory that could be taken but the multiple domains that can go so both vertically and horizontally and when we talk about data data is something so commonly brought up in sports so commonly drop in performance and athletic development big data is probably one of the biggest guess catchphrases or hot words or sayings that people have nowadays but doesn't always have a lot of meaning to it because a lot of times we get the word big data and then we don't have action out of big data and bill specialty is not just big data but it's giving action out of big data with that going forward I think a lot of this talk to be talking about how to utilize Big Data how do you guys data in general how to organize it how to put yourself in a situation to get actionable insights and so just to start it off Becky talked a little bit on your background some of the things you've done and how you develop the insights that you have thanks max I have kind of a very nos a deep background but I've been doing data analytics a long time and I was very fortunate one of those you know Forrest Gump moments in life where in the late 1980s I was involved in a project at Procter & Gamble I ran the project where we brought in Walmart's point of sales data for the first time into a what we would now call a data warehouse and for many of this became the launching point of the data warehouse bi marketplace and we can trace the effect the origins of many of the BI players to that project at Procter & Gamble in 87 and 88 and I spent a big chunk of my life just a big believer in business intelligence and data warehousing and trying to amass data together and trying to use that data to report on what's going on and writing insights and I did that for 20 25 years of my life until as you probably remember max I was recruited out Business Objects where I was the vice president of analytic applications I was recruited out of there by Yahoo and Yahoo had a very interesting problem which is they needed to build analytics for their advertisers to help those advertisers to optimize or spend across the Yahoo ad network and what I learned there in fact what I unlearned there was that everything that I had learned about bi and data warehouse and how you constructed data warehouses how you were so schema centric how everything was evolved around tabular data at Yahoo there was an entirely different approach the of my first introduction to Hadoop and the concept of a data Lake that was my first real introduction into data science and how to do predictive analytics and prescriptive analytics and in fact it was it was such a huge change for me that I was I was asked to come back to the TD WI data world Institute right was teaching for many years and I was asked to do a keynote after being at Yahoo for a year or so to share sort of what were the observations what did I learn and I remember I stood up there in front of about 600 people and I started my presentation by saying everything I've taught you the past 20 years is wrong and it was well I didn't get invited back for 10 years so that probably tells you something but it was really about unlearning a lot about what I had learned before and probably max one of the things that was most one of the aha moments for me was bi was very focused on understanding the questions that people were trying to ask an answer davus science is about us to understand the decisions they're trying to take action on questions by their very nature our informative but decisions are actionable and so what we did at Yahoo in order to really drive the help our advertisers optimize your spend across the Yahoo ad network is we focus on identifying the decisions the media planners and buyers and the campaign managers had to make around running a campaign know what what how much money to allocate to what sides how much how many conversions do I want how many impressions do I want so all the decisions we built predictive analytics around so that we can deliver prescriptive actions to these two classes of stakeholders the media planners and buyers and the campaign managers who had no aspirations about being analysts they're trying to be the best digital marketing executives or you know or people they could possibly be they didn't want to be analysts so and that sort of leads me to where I am today and my my teaching my books my blogs everything I do is very much around how do we take data and analytics and help organizations become more effective so everything I've done since then the books I've written the teaching I do with University of San Francisco and next week at the National University of Ireland and Galway and all the clients I work with is really how do we take data and analytics and help organizations become more effective at driving the decisions that optimize their business and their operational models it's really about decisions and how do we leverage data and analytics to drive those decisions so what would how would you define the difference between a question that someone's trying to answer versus a decision but they're trying to be better informed on so here's what I'd put it I call it the Sam test I am and that is it strategic is it actionable is it material and so you can ask questions that are provocative but you might not fast questions that are strategic to the problems you're trying to solve you may not be able to ask questions that are actionable in a sense you know what to do and you don't necessarily ask questions that are material in the sense that the value of that question is greater than the cost of answering that question right and so if I think about the Sam test when I apply it to data science and decisions when I start mining the data so I know what decisions are most important I'm going through a process to identify to validate the value and prioritize those decisions right I understand what decisions are most important now when I start to dig through the data all this structured unstructured data across a number different data sources I'm looking for I'm trying to codify patterns and relationships buried in that data and I'm applying the Sam test is that against those insights is it strategic to the problem I'm trying to solve can I actually act on it and is it material in the sense that it's it's it's more valuable to act than it is to create the action around it so that's the to me that big difference is by their very nature decisions are actually trying to make a decision I'm going to take an action questions by their nature are informative interesting they could be very provocative you know questions have an important role but ultimately questions do not necessarily lead to actions so if I'm a a sport coach I'm writing a professional basketball team some of the decisions I'm trying to make are I'm deciding on what program best develops my players what metrics will help me decide who the best prospect is is that the right way of looking at it yeah so we did an exercise at at USF too to have the students go through an exercise - what question what decisions does Steve Kerr need to make over the next two games he's playing right and we go through an exercise of the identifying especially in game decisions exercise routes oh no how often are you gonna play somebody no how long are they gonna play what are the right combinations what are the kind of offensive plays that you're gonna try to run so there's a know a bunch of decisions that Steve Kerr is coach of the Warriors for example needs to make in the game to not only try to win the game but to also minimize wear and tear on his players and by the way that's a really good point to think about the decisions good decisions are always a conflict of other ideas right win the game while minimizing wear and tear on my players right there's there are there are all the important decisions in life have two three or four different variables that may not be exactly the same which is by this is where data science comes in the data science is going to look across those three or four very other metrics against what you're going to measure success and try to figure out what's the right balance of those given the situation I'm in so if going back to the decision about about playing time well think about all the data you might want to look at in order to optimize that so when's the next game how far are they in this in this in the season where do they currently sit ranking wise how many minutes per game has player X been playing looking over the past few years what's there you know what's their maximum point so there's there's a there's not a lot of decisions that people are trying to make and by the way the beauty of the decisions is the decisions really haven't changed in years right what's changed is not the decisions it's the answers and the answers have changed because we have this great bound of data available to us in game performance health data you know all DNA data all kinds of other data and then we have all these great advanced analytic techniques now neural networks and unstructured supervised machine learning on right all this great technology now that can help us to uncover those relationships and patterns that are buried in the data that we can use to help individualize those decisions one last point there the point there to me at the end when when people talk about Big Data they get fixated on the big part the volume part it's not the volume of big data that I'm going to monetize it's the granularity and what I mean by that is I now have the ability to build very detailed profiles going back to our basketball example I can build a very detailed performance profile on every one of my players so for every one of the players on the Warriors team I can build a very detailed profile it the details out you know what's their optimal playing time you know how much time should they spend before a break on the feet on the on the on the court right what are the right combinations of players in order to generate the most offense or the best defense I can build these very detailed individual profiles and then I can start mission together to find the right combination so when we talk about big it's not the volume it's interesting it's the granularity gotcha and what's interesting from my world is so when you're dealing with marketing and business a lot of that when you're developing whether it be a company that you're trying to find more out about your customers or your startup trying to learn about what product you should develop there's tons of unknowns and a lot of big data from my understanding it can help you better understand some patterns within customers how to market you know in your book you talk about oh we need to increase sales at Chipotle because we understand X Y & Z our current around us now in the sports science world we have our friend called science and science has helped us early identify certain metrics that are very important and correlated to different physiological outcomes so it almost gives us a shortcut because in the big data world especially when you're dealing with the data that you guys are dealing with and trying to understand customer decisions each customer is individual and you're trying to compile all together to find patterns no one's doing science on that right it's not like a lab work where someone is understanding muscle protein synthesis and the amount of nutrients you need to recover from it so in my position I have all these pillars that maybe exist already where I can begin my search there's still a bunch of unknowns with that kind of environment do you take a different approach or do you still go with the I guess large encompassing and collect everything you can and siphon after maybe I'm totally wrong I'll let you take it away no that's it's a it's a good question and what's interesting about that max is that the human body is governed by a series of laws we'll say in each me see ology and the things you've talked about physics they have laws humans as buyers you know shoppers travelers we have propensity x' we don't have laws right I have a propensity that I'm gonna try to fly United because I get easier upgrades but I might fly you know Southwest because of schedule or convenience right I have propensity x' I don't have laws so you have laws that work to your advantage what's interesting about laws that they start going into the world of IOT and this concept called digital twins they're governed by laws of physics I have a compressor or a chiller or an engine and it's got a bunch of components in it that have been engineered together and I can actually apply the laws I can actually run simulations against my digital twins to understand exactly when is something likely to break what's the remaining useful life in that product what's the severity of the the maintenance I need to do on that so the human body unlike the human psyche is governed by laws human behaviors are really hard right and we move the las vegas is built on the fact that human behaviors are so flawed but body mate but bat body physics like the physics that run these devices you can actually build models and one simulation to figure out exactly how you know what's the wear and tear and what's the extensibility of what you can operate in gotcha yeah so that's when from our world you start looking at subsystems and you say okay this is your muscular system this is your autonomic nervous system this is your central nervous system these are ways that we can begin to measure it and then we can wrote a blog on this that's a stress response model where you understand these systems and their inferences for the most part and then you apply a stress and you see how the body responds and even you determine okay well if I know the body I can only respond in a certain number of ways it's either compensatory it's gonna be you know returning to baseline and by the mal adaptation but there's only so many ways when you look at a cell at the individual level that that cell can actually respond and it's the aggregation of all these cellular responses that end up and manifest in a change in a subsystem and that subsystem can be measured inferential II through certain technology that we have but I also think at the same time we make a huge leap and that leap is the word inference right we're making an assumption and sometimes those assumptions are very dangerous and they lead to because that assumptions unknown and we're wrong on it then we kind of sway and missed a little bit on our whole projection so I like the idea of looking at patterns and look at the probabilistic nature of it and I'm actually kind of recently change my view a little bit from my room first I talked about this I was much more hardwired and laws but I think it's a law but maybe a law with some level of variation or standard deviation and it we have guardrails instead so that's kind of how I think about it personally is that something that you say that's on the right track for that or how would you approach it yeah actually there's a lot of similarities max so your description of the human body made up of subsystems when we talk to organizations about things like smart cities or smart malls or smart hospitals a smart city is comprised of a it's made up of a series of subsystems right I've got subsystems regarding water and wastewater traffic safety you know local development things like this look there's a bunch of subsystems that make a city work and each of those subsystems is comprised of a series of decisions or clusters of decisions with equal use cases around what you're trying to optimize so if I'm trying to improve traffic flow if one of my subsystems is practically flow there are a bunch of use cases there about where do I do maintenance where do I expand the roads you know where do I put HOV lanes right so and so you start taking apart the smart city into the subsystems and then know the subsystems are comprised of use cases that puts you into really good position now here's something we did recently with a client who is trying to think about building the theme park of the future and how do we make certain that we really have a holistic view of the use cases that I need to go after it's really easy to identify the use cases within your own four walls but digital transformation in particular happens outside the four walls of an organization and so what we what we're doing is a process where we're building journey maps for all their key stakeholders so you've got a journey map for a customer you have a journey map for operations you have a journey map for partners and such so you you build these journey maps and you start thinking about for example I'm a theme park and at some point in time my guest / customer is going to have a pity they want to go do something you want to go on vacation at that point in time that theme park is competing against not only all the other theme parks but it's competing against major league baseball who's got things it's competing against you know going to the beach in Sanibel Island just hanging around right there they're competing at that point and if they only start engaging the customer when the customers actually contacted them they must a huge part of the market they made you miss a huge chance to influence that person's agenda and so one of the things that think about I don't know how this applies to your space max but as we started thinking about smart entities we use design thinking and customer journey match there's a way to make certain that we're not fooling ourselves by only looking within the four walls of our organization that we're knocking those walls down making them very forest and we're looking at what happens before somebody engages it with us and even afterwards so again going back to the theme park example once they leave the theme park they're probably posting on social media what kind of fun they had or fun they didn't have they're probably making plans for next year they're talking to friends and other things so there's there's a bunch of stuff we're gonna call it afterglow that happens after event that you want to make certain that you're in part of influencing that so again I don't know how when you combined the data science of use cases and decisions with design thinking of journey Maps what that might mean to do that your business but for us in thinking about smart cities it's opened up all kinds of possibilities and most importantly for our customers it's opened up all kinds of new areas where they can create new sources of value so anyone listening to this need to understand that when the word client or customer is used it can be substituted for athlete and what I think is really important is that when we hear you talk about your the the amount of infrastructure you do for an idea when you approach a situation is something that sports science for in my opinion especially across multiple domains it's truly lacking what happens is we get a piece of technology and someone says go do science while you're taking the approach of let's actually think out what we're doing beforehand let's determine our key performance indicators let's understand maybe the journey that this piece of technology is going to take with the athlete or how the athletes going to interact with this piece of technology throughout their four years if you're in the private sector right that afterglow effect might be something that you refer to as a client retention and their ability to come back over and over and spread your own word for you if you're in the sector with student athletes maybe it's those athletes talking highly about your program to help with recruiting and understanding that developing athletes is going to help you know make that college more enticing to go to or that program or that organization but what really stood out was the fact that you have this infrastructure built beforehand and the example I give I spoke with a good number of organizations and teams about data utilization is that if if you're to all of a sudden be dropped in the middle of the woods and someone says go build a cabin now how was it a giant forest I could use as much wood as I want I could just keep chopping down trees until I had something that had with a shelter of some sort right even I could probably do that well if someone said you know what you have three trees to cut down to make a cabin you could become very efficient and you're going to think about each chop in each piece of wood and how it's going to be used and your interaction with that wood and conjunction with that woods interaction with yourself and so when we start looking at athlete development and we're looking at client retention or we're looking at general health and wellness it's not just oh this is a great idea right we want to make the world's greatest theme park and we want to make the world's greatest training facility but what infrastructure and steps you need to take and you said stakeholders so what individuals am i working with am I talking with the physical therapist am i talking with the athletic trainer am I talking with the skill coach how does the skill coach want the data presented to them maybe that's different than how the athletic trainer is going to have a day to present it to them maybe the sport coach doesn't want to see the data unless something a red flag comes up so now you have all these different entities just like how you're talking about developing this customer journey throughout the theme park and making sure that they have a you know an experience that's memorable and causes an afterglow and really gives that experience meaning how can we now take data and apply it in the same way so we get the most value like you said on the granular aspect of data and really turn that into something valuable max you said something really important and one of the things that let me share one of many horror stories that that that comes up in my daily life which is somebody walking up to me and saying hey I got a client here's their data you know go do some science on it like well well what the heck right so when we created this thing called the hypothesis development canvas our sales teams hate it or do the time our data science teams love it because we do all this pre work we just say we make sure we understand the problem we're going after the decision they're trying to make the KPI is it's what you're going to measure success in progress what are they the operational and financial business benefits what are the data sources we want to consider here's something by the way that's it's important that maybe I wish Boeing would have thought more about which is what are the costs of false positives and false negatives right do you really understand where your risks points are and the reason why false positive and false negatives are really important in data science because data size is making predictions and by virtue of making predictions we are never 100% certain that's right or not predictions hath me built on I'm good enough well when is good enough good enough and a lot of that determination as to when is good enough good enough is really around the cost of false positives and false negatives think about a professional athlete like the false the you know the ramifications of overtraining professional athlete like a Kevin Durant or Steph Curry and they're out for the playoffs as huge financial implications them personally and for the organization so you really need to make sure you understand exactly what's the cost of being wrong and so this hypothesis development canvas is we do a lot of this work before we ever put science to the data that yeah it's it's something that's lacking across not just sports science but many fields and what I mean by that is especially you referred to the hypothesis canvas it's a piece of paper that provides a common language right it's you can sit it out before and for listeners who aren't aware a hypothesis canvas is something bill has worked and developed with his team and it's about 13 different squares and boxes and you can manipulate it based on your own profession and what you're diving into but essentially it goes through the infrastructure that you need to have setup in order for this hypothesis or idea or decision to actually be worth a damn and what I mean by that is that so many times and I hate this but I'm gonna go in a little bit of a rant and I apologize that people think oh I get an idea and they think Thomas Edison all son just had an idea and he made a light bulb Thomas Edison's famous for saying you know I did you know make a light bulb I learned was a 9000 ways to not make a light bulb and what I mean by that is he set an environment that allowed for failure and allowed for learning but what happens often people think oh I have an idea they think the idea comes not just you know in a flash because it always doesn't it might come from some research but they also believe that it comes with legs and it comes with the infrastructure supported around it that's kind of the same way that I see a lot of the data aspect going in regards to our field is that we did an idea we immediately implement and we hope it works as opposed to set up a learning environment that allows you to go okay here's what I think might happen here's my hypothesis here's I'm going to apply it and now if I fail because I have the infrastructure pre mapped out I can look at my infrastructure and say you know what that support beam or that individual box itself was the weak link and we made a mistake here but we can go back and fix it
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Greg Benson, SnapLogic | SnapLogic Innovation Day 2018
>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time CUBE alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.
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Brought to you by SnapLogic. and look at all the buildings, So I think the last time we see you was at Fleet Forward. And then even when you do choose, and artificial intelligence to help make integration easier. to help make recommendations so that you can So you guys have really taken advantage of that Yeah, absolutely, and you know, and the augmented intelligence. "Hey, the next thing you need to do," and I guess it would flag you if there's some strange thing and the goal is how to get that concept or thought the person you had an accident learns a little bit, and what we're doing in our domain, our space, and how does it tie back to of the industry academia fence will tell you that We continuously have lots of other projects in the works. and cool startups that come out. SnapLogic in San Mateo, California.
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Greg Benson, SnapLogic | SnapLogic Innovation Day 2018
>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time Cube alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.
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DeLisa Alexander, Zui Dighe & Dana Lewis | Red Hat Summit 2018
>> Announcer: Live from San Francisco, it's theCUBE, covering Red Hat Summit 2018. Brought to you by Red Hat. >> Welcome back, here, when we here live, it's theCube, here in San Francisco live for Red Hat, Summit 2018. I'm John Furrier, the host of theCUBE. Our next three guests is the Delisa Alexander, Executive Vice President, Chief People Officer at Red Hat. Welcome to theCUBE. >> Thanks. >> Zui Dighe, who's the... Did I get that right? Zz-- >> Zui, yeah, mm-hmm. >> Zui? OK, winner of the Open Source Academic Award from Duke University, Go Blue Devils (chuckles). >> Zui: Yes. >> And we have Dana Lewis, winner of Open AP with OpenAPS, which stands for? >> The Open Source Artificial Pancreas System. >> Artificial open-source Pancreas System, great stuff. So congratulations, you guys are all award winners. Before we get into some of the questions, love your applications, talk about the program. What is this about? What's the awards program here at Red Hat Summit, and why are these guys here? >> So as Red Hat, we believe, as an open-source leader, we have a responsibility to promote women in technology and particularly women in open-source. And so, one of the things we thought we could do is to create an award that really spotlights the contributions women are making in open-source to inspire future generations to consider being open-source developers or contributors. >> Congrat, Delisa, love that you're doing that. It's fantastic. We'll start with the young student gun here. What's your degree, first of all? What are you studying? >> I'm studying biomedical engineering and computer science. >> John: Tough major, huh? >> Yep, very tough. (Delisa laughing) Not easy, but I'm-- >> This is an easy-- >> First question is, how do you in a block chain impact? It's funny, Jim always asked that question on day one. No, in all serious, tell about what your application is. This is super important. >> Yeah, yeah. So I'm basically working on researching and creating a tracking system for vaccines that enter into developing countries. So through that, you're able to understand how exactly do vaccines travel through these countries as well as where does the system break. And if you can pinpoint that, you can actually solve the problem. >> And how did you get the idea? How did this all come together? >> I was in a research course at Duke, which has collaboration with the university in Uganda, and we actually got to travel to Uganda and interview various stakeholders, pharmaceutical companies, health system, and understand how does the-- We wanted to be in vaccines, but we didn't know what exactly to do. And so after interviewing, I kind of came up with the idea of why don't we actually put a tracker on these devices that gives off the GPS location and the temperature so we can actually understand the entire system. >> It's going to get that ground truth, too, and again, the local areas. >> Yeah. >> The big walk away, what, about vaccines. This is important to track it from the origination to destination and making sure it all kind of matches up. >> Making sure, first of all, you don't have any data on exactly where they're going because this box is just carried by hand. And the pharmaceutical companies, once they ship the vaccines into Uganda, after that, they don't provide any data on what's going on. So that data is also important, and it's also, you want to know when does the system break because often in last end, when the vaccines are actually administered, they've already gone out of their cold chain cycle, and so they don't work anymore. >> That's a great story. How 'about your story? This is a good one. This is a real practical one for people with diabetes. Talk about, first of all, show the product 'cause it's always good to a little live prop there. So turn, yeah, there it is. So what is that? >> So this is an open-source hardware board. It's actually got an Intel Edison on the back side. But what this does is, it talks to my insulin pump and my continuous glucose monitor, brings the data together, runs it through an algorithm, and sends commands back to the insulin pump to tell it what to do. So this is what we call a close-loop system where we have the computer doing the math instead of the human with diabetes doing the math several times throughout the day. >> And does it do auto-injections as well? So it kind of feeds the glucose levels as well? So it's data-- >> Right. So the insulin pump is automatically dosing the insulin, and we also have a continuous feed of the blood sugar every five minutes as well. >> And that's what you mean by close-loop. >> Exactly. >> For people have these monitors, they have remotes, statistics. Does it talk to a device as well? The mobile device, how does that work? >> Yeah, so I can glance down at my watch and see how I'm doing, on my phone. My loved ones, wherever they are, can see how things are going. So if they need to intervene, they're able to do that remotely. So it really provides peace of mind as well as a lot better outcomes for those of us living with Type I diabetes. >> And what was the motivation here, to get involved deeply in this project? >> It was really selfish, I wanted to sleep, and I couldn't hear my CGM alarm, my glucose alarm. And so my project actually started of, just get the data off to make a louder alarm. And then we built an algorithm that allowed us to look into the future and do proactive alarms. And then we worked with other people to actually communicate with the insulin pump, and that's how we progressed to closing the loop. And because I've been helped so much by other people in open-source, it was a no-brainer to also make our work open-source. >> And so you open-source everything. What other progress can you share? I mean, you have predictive analytics that tell you that, "OK, I'm going to go for a hike soon, "so therefore, I'm going to do this," and all kinds of cool data gathering. Does that play into it? Is it a lifestyle and-- >> Absolutely. >> So it's like a FitBit meets close-loop. (Women laugh) >> It's more like taking standard medical devices and boosting their capacities with the help of computing technologies. It's not fancy machine learning. It's the same math a person with diabetes would do, but the benefit is, it's automated to go every five minutes, and it doesn't fall asleep, it doesn't get lazy, it doesn't round up or vary down. It's going to be giving really precise increments so that when your situation changes, you skip a meal that you though you were going to eat, you're going to go hiking, for whatever reason, if you're going up or down more than expected, it can react instantaneously and much better than a human can. >> I'm so glad you're doing that, too. How does someone get involved with this project? Obviously, it's open-source software, but you have devices. Is it in market? Is there? >> So this is an open-source project because we are not a company, so we cannot distribute medical devices. That's frowned upon by the FDA. And so this is an open-source DIY project for people who want to get involved either to help with the project or build one themselves. They can go to OpenAPS.org. We've written a plain language reference design to help anybody, whether you're a person with diabetes, a loved one, a healthcare provider, a researcher or developer understand how the system works, and then that leads you to the documentation of how to build one as well as to the code where anybody can get involved and help out. >> So that's the loophole, (Dana laughs) to say it plainly, get around that whole being a company. You build your own. >> Yes. >> So that's the way, that's here. OK, great, so congratulations. So where's this all going? This is fantastic, this story. How many other people are involved in the program that you have? Share more about how people can get involved, too. >> This is our fourth year of having the program, and we're really just thrilled with the quality of the nominations. We had over 100 nominations. Our judges then narrowed the field down to 10, and then the community selected the winners. We don't really see an end to this. We just see the community adding and growing organically. So one thing we did this time is, we introduced our winners to our CO.LAB students, and so now they're creating a network. And that network density is just increasing and improving and, I think, getting stronger. >> It's really amazing. And one thing I've always loved about open-source, and you guys see the benefit of it, obviously, with winning and succeeding, is that democratization and community are coming together at a whole nother level. And I think what's interesting about the projects that you guys have is, you got good things happening with tech. So it's tech for good. But since Obama put the Jobs Act in, means fund these projects now as entrepreneurial ventures and be mission-driven OFFLEM. You don't have to do it as a non-profit. So we're seeing a huge growth in entrepreneurial activity around tech for good on projects that would never would funded before. So you're seeing a whole nother generation of great tools and technologies saying, "Hey, let's solve a problem." >> Yeah, and I think that's one of thing I love about us both being in healthcare is, it really shows that there's amazing applications. We can take this technology and apply it in healthcare and do it in different ways, and it doesn't have to be a company right away. It doesn't have to be either a for profit or non for profit. There's a lot of ways open-source is bringing people together to solve the very problems we need to be solving. >> Do you feel good that you built something great like that, and think now you got people using the software? What's the feeling like? >> Oh, it's just incredibly rewarding. I mean, myself, I just have the peace of mind to be able to go to sleep at night. That is a priceless feeling, but then when I hear other people using it, they build the project for different reasons. Some, they want to be able to remotely monitor their loved ones. Others are doing it for their children so that they have better health outcomes. But there's just these amazing stories outpouring from the community. And to me, that's the beauty of open sources. You can really apply it however you need to apply it to your lifestyle. >> Where can someone get involved in your project? Is there like a GitHub repository? >> Yep. >> Is there a site? >> Everything's on GitHub for us, but I would go to OpenAPS.org first. It links to the documentation and the code where people can connect. >> OpenAPS.org. >> That's right. >> OK, great. How 'about your project? How do people get involved with what you're doing? >> Ours is on GitHub right now, so you can get involved through there. But I guess we're kind of right now developing in the backend stages. Soon we'll be at that stage where you can contribute more. And right now, we've just been using other open-source libraries and kind of contributed in that way. But actually, we talked earlier about how do you get involved in open-source, and especially being a student, I kind of fell into coding because of open-source in a sense >> Working on your project? where, yeah, yeah, yeah. So coming into college, I wanted to apply the engineering concepts I was learning in the classroom, and I got involved in a lot of entrepreneurship on campus, and through that, I was asked to make a front-end interface, and I didn't really know how to go about doing that. So then I found an open-source library stumbling around that was doing a similar thing. And that's how I kind of taught myself, and then from there, I branched out and learned more and more. And I think for any budding student, budding entrepreneur, open-source is a great way to take your ideas further. And my interest is in healthcare, so that's where I went, but anyone could have an idea, "Oh, I want to start this business in this way." And they might not think that open-source is a way to go about doing that, but it is a great way to learn more. >> It's a good way to change a lot of things, not just career or projects. >> Yeah. >> There's a nonlinear progression of learning happening. You can come in, you're stumbling around, quote, learning. >> Yeah, yeah. >> It's not like chapter one course, online course. Go to chapter two. >> Right, that is true. >> There's a YouTube, there's stuff on GitHub, open-source. There's people involved. This points to a whole new generational shift. >> It is. >> Of learning, connecting, you're tapping into it. >> It's so exciting because she's the role model we're talking about. We want girls to see that you can become a coder later. You don't have to necessarily start-- >> She's 14, she'd coding in unity. >> Yeah! >> I tell a soliloquy, great. (Delisa laughing) Do some smart contracts and get the bobchain action. (Delisa laughing) Bobchain's the future, you're the Bitcoin in intheoreum. Some cool stuff. >> Yeah. Congratulations, thanks for doing this. >> Thank you very much. >> Very inspirational, and thanks for sharing the story on theCUBE, and keep in touch, thanks for coming, appreciate it. >> Thank you. >> Thanks for having us. >> Great women in tech, great leaders doing some great stuff. Award winners, celebrities here on theCUBE. I'm John Furrier. Be back with more live coverage after this short break. (electronic musical flourish)
SUMMARY :
Brought to you by Red Hat. Welcome to theCUBE. Did I get that right? OK, winner of the Open Source Academic Award So congratulations, you guys are all award winners. And so, one of the things we thought we could do is What are you studying? (Delisa laughing) First question is, how do you in a block chain impact? And if you can pinpoint that, And so after interviewing, I kind of came up with the idea and again, the local areas. from the origination to destination and it's also, you want to know when does the system break 'cause it's always good to a little live prop there. and sends commands back to the insulin pump and we also have a continuous feed of the blood sugar Does it talk to a device as well? So if they need to intervene, just get the data off to make a louder alarm. And so you open-source everything. So it's like a FitBit meets close-loop. but the benefit is, it's automated to go every five minutes, but you have devices. and then that leads you to the documentation So that's the loophole, (Dana laughs) in the program that you have? and so now they're creating a network. and you guys see the benefit of it, obviously, and it doesn't have to be a company right away. And to me, that's the beauty of open sources. and the code where people can connect. How do people get involved with what you're doing? and kind of contributed in that way. and I didn't really know how to go about doing that. It's a good way to change a lot of things, You can come in, you're stumbling around, Go to chapter two. This points to a whole new generational shift. connecting, you're tapping into it. You don't have to necessarily start-- Bobchain's the future, you're the Bitcoin in intheoreum. Yeah. and thanks for sharing the story on theCUBE, Be back with more live coverage after this short break.
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Chris Wright, Red Hat | Red Hat Summit 2018
>> Narrator: Live from San Francisco. It's theCUBE! Covering RedHat Summit 2018. Brought to you by Red Hat. >> Alright welcome back, this is theCUBE's exclusive coverage of Red Hat 2018. I'm John Furrier, the co host of theCUBE with John Troyer, co-founder of TechReckoning Advisory Firm. Next guest is Chris Wright, Vice President and CTO Chief of Technology of his Red Hat. Great to see you again, thanks for joining us today. >> Yeah, great to be here. >> Day one of three days of CUBE coverage, you got, yesterday had sessions over there in Moscone South, yet in classic Red Hat fashion, good vibes, things are rocking. Red Hat's got a spring to their step, making some good calls technically. >> Chris: That's right. >> Kubernetes' one notable, Core OS Acquisition, really interesting range, this gives, I mean I think people are now connecting the dots from the tech side, but also now on the business side, saying "Okay we can see now some, a wider market opportunity for Red Hat". Not just doing it's business with Linux, software, you're talking about a changing modern software architecture, for application developers. I mean, this is a beautiful thing, I mean. >> Chris: It's not just apps but it's the operator, you know, operation side as well, so we've been at it for a long time. We've been doing something that's really similar for quite some time, which is building a platform for applications, independent from the underlying infrastructure, in the Linux days I was X86 hardware, you know, you get this HeteroGenius hardware underneath, and you get a consistent standardized application run time environment on top of Linux. Kubernetes is helping us do that at a distributive level. And it's taken some time for the industry to kind of understand what's going on, and we've been talking about hybrid cloud for years and, you really see it real and happening and it's in action and for us that distributed layer round Kubernetes which just lights up how do you manage distributed applications across complex infrastructure, makes it really real. >> Yeah it's also timing's everything too right? I mean, good timing, that helps, the evolution of the business, you always have these moments and these big waves where you can kind of see clunking going on, people banging against each other and you know, the glue layers developing, and then all of a sudden snaps into place, and then it just scales, right? So you're starting to see that, we've seen this in other ways, TCPIP, Linux itself, and you guys are certainly making that comparison, being Red Hat, but what happens next is usually an amazing growth phase. Again, small little, and move the ball down the field, and then boom, it opens up. As a CTO, you have to look at that 20 mile stair now, what's next? What's that wave coming that you're looking at in the team that you have on Red Hat's side and across your partners? What's the wave next? >> Well there's a lot of activity going on that's beyond what we're building today. And so much of it, first of all, is happening in Open Source. So that itself is awesome. Like we're totally tuned into these environments, it's core to who we are, it's our DNA to be involved in these Open Source communities, and you look across all of the different projects and things like machine learning and blockchain, which are really kind of native Open Source developments, become really relevant in ways that we can change how we build functionality and build business, and build business value in the future. So, those are the things that we look at, what's emerging out of the Open Source communities, what's going to help continue to accelerate developers' ability to quickly build applications? Operations team's ability to really give that broad scale, policy level view of what's going on inside your infrastructure to support those applications, and all the data that we're gathering and needing to sift through and build value from inside the applications, that's very much where we're going. >> Well I think we had a really good example of machine learning used in an everyday enterprise application this morning, they kicked off the keynote, talking about optimizing the schedule and what sessions were in what rooms, you know, using an AI tool right? >> Chris: That's right. >> And so, that's reality as you look at, is that going to be the new reality as you're looking into the future of building in these kind of machine learning opportunities into everyday business applications that, you know, in the yesteryear would've been just some, I don't know, visual basic, or whatever, depending on how far back you look, right? You know, is that really going to be a reality in the enterprise? It seems so. >> It is, absolutely. And so what we're trying to do is build the right platforms, and build the right tools, and then interfaces to those platforms and tools to make it easier and easier for developers to build, you know, what we've been calling "Intelligent Apps", or applications that take advantage of the data, and the insights associated with that data, right in the application. So, the scheduling optimization that you saw this morning in the keynote is a great example of that. Starting with basic rules engine, and augmenting that with machine learning intelligence is one example, and we'll see more and more of that as the sophisticated tools that are coming out of Open Source communities building machine learning platforms, start to specialize and make it easier and easier to do specific machine learning tasks within an application. So you don't have to be a data scientist and an app developer all in one, you know, that's, there's different roles and different responsibilities, and how do we build, develop, life cycle managed models is one question, and how do we take advantage of those models and applications is another question, and we're really looking at that from a Red Hat perspective. >> John F: And the enterprises are always challenged, they always (mumbles), Cloud Native speaks to both now, right? So you got hybrid cloud and now multi-cloud on the horizon, set perfectly up with Open Shift's kind of position in that, kind of the linchpin, but you got, they're still two different worlds. You got the cloud-native born in the cloud, and that's pretty much a restart-up these days, and then you've got legacy apps with container, so the question is, that people are asking is, okay, I get the cloud-native, I see the benefits, I know what the investment is, let's do it upfront, benefits are horizontally scalable, asynchronous, et cetera et cetera, but I got legacy. I want to do micro-servicing, I want to do server-less, do I re-engineer that or just containers, what's the technical view and recommendation from Red Hat when you say, when the CIO says or enterprise says, "Hey I want to go cloud native for over here and new staff, but I got all this old staff, what do I do?". Do I invest more region, or just containerize it, what's the play? >> I think you got to ask kind of always why? Why you're doing something. So, we hear a lot, "Can I containerize it?", often the answer is yes. A different question might be, "What's the value?", and so, a containerized application, whether it's an older application that's stateful or whether it's a newer cloud-native application (mumbles), horizontally scalable, and all the great things, there's value potentially in just the automation around the API's that allow you to lifecycle manage the application. So if the application itself is still continuing to change, we have some great examples with some of our customers, like Keybank, doing what we call the "Fast moving monolith". So it's still a traditional application, but it's containerized and then you build a CICD model around it, and you have automation on how you deliver and deploy production. There's value there, there's also value in your existing system, and maybe building some different services around the legacy system to give you access, API access, to data in that system. So different ways to approach that problem, I don't think there's a one size fits all. >> So Chris, some of this is also a cultural and a process shift. I was impressed this morning, we've already talked with two Red Hat customers, Macquarie and Amadeus, and you know Macquarie was talking about, "Oh yeah we moved 40 applications in a year, you know, onto Open Shift", and it turns out they were already started to be containerized and dockerized and, oh yeah yeah you know, that is standard operating procedure, for that set of companies. There's a long tail of folks who are still dealing with the rest of the stuff we've had to deal, the stack we've had to deal with for years. How is Red Hat, how are you looking at this kind of cultural shift? It's nice that it's real, right? It's not like we're talking about microservices, or some sort of future, you know, Jettison sort of thing, that's going to save us all, it's here today and they're doing it. You know, how are you helping companies get there? >> So we have a practice that we put in place that we call the "Open Innovation Lab". And it's very much an immersive practice to help our customers first get experience building one of these cloud native applications. So we start with a business problem, what are you trying to solve? We take that through a workshop, which is a multi-week workshop, really to build on top of a platform like Open Shift, real code that's really useful for that business, and those engineers that go through that process can then go back to their company and be kind of the change agent for how do we build the internal cultural shift and the appreciation for Agile development methodologies across our organization, starting with some of this practical, tangible and realist. That's one great example of how we can help, and I think part of it is just helping customers understand it isn't just technology, I'm a technologist so there's part of me that feels pain to say that but the practical reality is there's whole organizational shifts, there's mindset and cultural changes that need to happen inside the organization to take advantage of the technology that we put in place to build that optimize. >> John F: And roles are changing too, I'll see the system admin kind of administrative things getting automated way through more operating role. I heard some things last week at CubeCon in Copenhagen, Denmark, and I want to share some quotes and I want to get your reaction. >> Alright. >> This is the hallway, I won't attribute the names but, these were quotes, I need, quote, "I need to get away from VP Engine firewalls. I need user and application layer security with unfishable access, otherwise I'm never safe". Second quote, "Don't confuse lift and shift with running cloud-native global platform. Lot of actors in this system already running seamlessly. Versus say a VM Ware running environment wherein V Center running in a data center is an example of a lift and shift". So the comments are one for (mumbles) cloud, you need to have some sort of security model, and then two, you know we did digital transformation before with VM's, that was a different world, but the new world's not a lift and shift, it's re-architect of a cloud-native global platform. Your reaction to those two things, and what that means to customers as they think about what they're going to look like, as they build that bridge to the future. >> Security peace is critical, so every CIO that we're talking to, it's top of mind, nobody wants to be on the front page of The Wall Street Journal for the wrong reasons. And so understanding, as you build a micro-services software architected application, the components themselves are exposed to services, those services are API's that become potentially part of the attack surface. Thinking of it in terms of VPN's and firewalls, is the kind of traditional way that we manage security at the edge. Hardened at the edge, soft in the middle isn't an acceptable way to build a security policy around applications that are internally exposing parts of their API's to other parts of the application. So, looking at it for me, application use case perspective, which portions of the application need to be able to talk to one another, and it's part of why somebody like Histio are so exciting, because it builds right in to the platform, the notion of mutual authentication between services. So that you know you're talking to a service that you're allowed to talk to. Encryption associated with that, so that you get another level of security for data and motion, and all of that is not looking at what is the VPN or what is the VLAN tag, or what is the encapsulation ID, and thinking layer two, layer three security, it's really application layer, and thinking in terms of that policy, which pieces of the application have to talk to each other, and nobody else can talk to that service unless it's, you know, understood that that's an important part for how the application works. So I think, really agree, and you could even say DevSecOps to me is something that I've come around to. Initially I thought it was a bogus term and I see the value in considering security at every step of build, test and deliver an application. Lift and shift, totally different topic. What does it mean to lift and shift? And I think there's still, some people want to say there's no value in lift and shift, and I don't fully agree, I think there's still value in moving, and modernizing the platform without changing the application, but ultimately the real value does come in re-architecting, and so there's that balance. What can you optimize by moving? And where does that free up resources to invest in that real next generation application re-architecting? >> So Chris, you've talked about machine learning, right? Huge amounts of data, you've just talked about security, we've talked about multi-cloud, to me that says we might have an issue in the future with the data layer. How are people thinking about the data layer, where it lives, on prem, in the cloud, think about GDPR compliance, you know, all that sort of good stuff. You know, how are you and Red Hat, how are you asking people to think about that? >> So, data management is a big question. We build storage tooling, we understand how to put the bytes on disc, and persist, and maintain the storage, it's a different question what are the data services, and what is the data governance, or policy around placement, and I think it's a really interesting part of the ecosystem today. We've been working with some research partners in the Massachusetts Open Cloud and Boston University on a project called "Cloud Dataverse", and it has a whole policy question around data. 'Cause there, scientists want to share data sets, but you have to control and understand who you're sharing your data sets with. So, it's definitely a space that we are interested in, understand, that there's a lot of work to be done there, and GDPR just kind of shines a light right on it and says policy and governance around where data is placed is actually fundamental and important, and I think it's an important part, because you've seen some of the data issues recently in the news, and you know, we got to get a handle on where data goes, and ultimately, I'd love to see a place where I'm in control of how my data is shared with the rest of the world. >> John F: Yeah, certainly the trend. So a final question for you, Open Source absolutely greatness going on, more and more good things are happening in projects, and bigger than ever before, I mean machine learning's a great example, seeing not just code snippets, code bases being you know, TensorFlow jumps out at me (mumbles), what are you doing here this year that's new and different from an Open Source standpoint, but also from a Red Hat standpoint that's notable that people should pay attention to? >> Well, one of the things that we're focused on is that platform layer, how do we enable a machine learning workload to run well on our platform? So it starts actually at the very bottom of the stack, hardware enablement. You got to get GPUs functional, you got to get them accessible to virtual machine based applications, and container based applications, so that's kind of table stakes. Accelerate a machine learning workload to make it usable, and valuable, to an enterprise by reducing the training and interference times for a machine learning model. Some of the next questions are how do we embed that technology in our own products? So you saw Access Insights this morning, talking about how we take machine learning, look at all of the data that we're gathering from the systems that our customers are deploying, and then derive insights from those and then feed those back to our customers so they can optimize the infrastructure that they're building and running and maintaining, and then, you know, the next step is that intelligent application. How do we get that machine learning capability into the hands of the developer, and pair the data scientist with the developers so you build these intelligent applications, taking advantage of all the data that you're gathering as an enterprise, and turning that into value as part of your application development cycle. So those are the areas that we're focused on for machine learning, and you know, some of that is partnering, you know, talking through how do we connect some of these services from Open Shift to the cloud service providers that are building some of these great machine learning tools, so. >> Any new updates on (mumbles) the success of Red Hat just in the past two years? You see the growth, that correlates, that was your (mumbles) Open Shift, and a good calls there, positioned perfectly, analysts, financial analysts are really giving you guys a lot of props on Wall Street, about the potential revenue growth opportunities on the business side, what's it like now at Red Hat? I mean, do you look back and say, "Hey, it was only like three years ago we did this", and I mean, the vibes are good, I mean share some inside commentary on what's happening inside Red Hat. >> It's really exciting. I mean, we've been working on these things for a long time. And, the simplest example I have is the combination of tools like the JBoss Middleware Suite and Linux, well they could run well together and we have a lot of customers that combine those, but when you take it to the next step, and you build containerized services and you distribute those broadly, you got a container platform, you got middleware components, you know, even providing functionality as services, you see how it all comes together and that's just so exciting internally. And at the same time we're growing. And a big part of-- >> John F: Customers are using it. >> Customers are using it, so putting things into production is critical. It's not just exciting technology but it's in production. The other piece is we're growing, and as we grow, we have to maintain the core of who we are. There's some humility that's involved, there's some really core Open Source principles that are involved, and making sure that as we continue to grow, we don't lose sight of who we are, really important thing for our internal culture, so. >> John F: Great community driven, and great job. Chris, thanks for coming on theCUBE, appreciate it. Chris Wright, CTO of Red Hat, sharing his insights here on theCUBE. Of course, bringing you all a live action as always here in San Francisco in Moscone West, for Red Hat Summit 2018, we'll be right back. (electronic music) (intense music)
SUMMARY :
Brought to you by Red Hat. Great to see you again, thanks for joining us today. you got, yesterday had sessions over there from the tech side, but also now on the business side, and you get a consistent standardized application run time in the team that you have on Red Hat's side and all the data that we're gathering is that going to be the new reality So, the scheduling optimization that you in that, kind of the linchpin, but you got, around the legacy system to give you access, Macquarie and Amadeus, and you know and be kind of the change agent for I'll see the system admin kind of administrative and then two, you know we did digital transformation and I see the value in considering think about GDPR compliance, you know, and you know, we got to get a handle on code bases being you know, TensorFlow jumps out at me and then, you know, the next step is that I mean, do you look back and say, and you build containerized services and as we grow, we have to maintain Of course, bringing you all a live action as always
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Greg Benson, SnapLogic | Flink Forward 2018
>> Announcer: Live from San Francisco, it's theCUBE covering Flink Forward brought to you by Data Artisans. >> Hi this is George Gilbert. We are at Flink Forward on the ground in San Francisco. This is the user conference for the Apache Flink Community. It's the second one in the US and this is sponsored by Data Artisans. We have with us Greg Benson, who's Chief Scientist at Snap Logic and also professor of computer science at University of San Francisco. >> Yeah that's great, thanks for havin' me. >> Good to have you. So, Greg, tell us a little bit about how Snap Logic currently sets up its, well how it builds its current technology to connect different applications. And then talk about, a little bit, where you're headed and what you're trying to do. >> Sure, sure, so Snap Logic is a data and app integration Cloud platform. We provide a graphical interface that lets you drag and drop. You can open components that we call Snaps and you kind of put them together like Lego pieces to define relatively sophisticated tasks so that you don't have to write Java code. We use machine learning to help you build out these pipelines quickly so we can anticipate based on your data sources, what you are going to need next, and that lends itself to rapid building of these pipelines. We have a couple of different ways to execute these pipelines. You can think of it as sort of this specification of what the pipeline's supposed to do. We have a proprietary engine that we can execute on single notes, either in the Cloud or behind your firewall in your data center. We also have a mode which can translate these pipelines into Spark code and then execute those pipelines at scale. So, you can do sort of small, low latency processing to sort of larger, batch processing on very large data sets. >> Okay, and so you were telling me before that you're evaluating Flink or doing research with Flink as another option. Tell us what use cases that would address that the first two don't. >> Yeah, good question. I'd love to just back up a little bit. So, because I have this dual role of Chief Scientist and as a professor of Computer Science, I'm able to get graduate students to work on research projects for credit, and then eventually as interns at SnapLogic. A recent project that we've been working on since we started last fall so working on about six or seven months now is investigating Flink as a possible new back end for the SnapLogic platform. So this allows us to you know, to explore and prototype and just sort of figure out if there's going to be a good match between an emerging technology and our platform. So, to go back to your question. What would this address? Well, so, without going into too much of the technical differences between Flink and Spark which I imagine has come up in some of your conversations or it comes up here because they can solve similar use cases our experience with Flink is the code base is easy to work with both from taking our specification of pipelines and then converting them into Flink code that can run. But there's another benefit that we see from Flink and that is, whenever any product, whether it's our product or anybody else's product, that uses something like Spark or Flink as a back end, there's this challenge because you're converting something that your users understand into this target, right, this Spark API code or Flink API code. And the challenge there is if something goes wrong, how do you propagate that back to the users so the user doesn't have to read log files or get into the nuts and bolts of how Spark really works. >> It's almost like you've compiled the code, and now if something doesn't work right, you need to work at the source level. >> That's exactly right, and that's what we don't want our users to do, right? >> Right. >> So one promising thing about Flink is that we're able to integrate the code base in such a way that we have a better understanding of what's happening in the failure conditions that occur. And we're working on ways to propagate those back to the user so they can take actionable steps to remedy those without having to understand the Flink API code iself. >> And what is it, then, about Flink or its API that gives you that feedback about errors or you know, operational status that gives you better visibility than you would get in something else like Spark. >> Yeah, so without getting too too deep on the subject, what we have found is, one thing nice about the Flink code base is the core is written in Scala, but there's a lot of, all the IO and memory handling is written in Java and that's where we need to do our primary interfacing and the building blocks, sort of the core building blocks to get to, for example, something that you build with a dataset API to execution. We have found it easier to follow the transformation steps that Flink takes to end up with the resulting sort of optimized, optimized Flink pipeline. Now by understanding that transformation, like you were saying, the compilation step, by understanding it, then we can work backwards, and understand how, when something happens, how to trace it back to what the user was originally trying to specify. >> The GUI specification. >> Yeah. Right. >> So, help me understand though it sounds like you're the one essentially building a compiler from a graphical specification language down to Spark as the, you know, sort of, pseudo, you know, psuedo compile code, >> Yep. >> Or Flink. And, but if you're the one doing that compilation, I'm still struggling to understand why you would have better reverse engineering capabilities with one. >> It just is a matter of getting visibility into the steps that the underlying frameworks are taking and so, I'm not saying this is impossible to do in Spark, but we have found that we've had, it's been easier for us to get into the transformation steps that Flink is taking. >> Almost like, for someone who's had as much programming as a one semester in night school, like a variable and specter that's already there, >> Yeah, that's a good, there you go, yeah, yeah, yeah. >> Okay, so you don't have to go try and you can't actually add it, and you don't have to then infer it from all this log data. >> Now, I should add, there's another potential Flink. You were asking about use cases and what does Flink address. As you know, Flink is a streaming platform, in addition to being a batch platform, and Flink does streaming differently than how Spark does. Spark takes a microbatch approach. What we're also looking at in my research effort is how to take advantage of Flink's streaming approach to allow the SnapLogic GUI to be used to specify streaming Flink applications. Initially we're just focused on the batch mode but now we're also looking at the potential to convert these graphical pipelines into streaming Flink applications, which would be a great benefit to customers who want-- >> George: Real time integration. >> Want to do what Alibaba and all the other companies are doing but take advantage of it without having to get to the nuts and bolts of the programming. Do it through the GUI. >> Wow, so it's almost like, it's like, Flink, Beam, in terms of obstruction layers, >> Sure. >> And then SnapLogic. >> Greg: Sure, yes. >> Not that you would compile the beam, but the idea that you would have perv and processing and a real-time pipeline. >> Yes. >> Okay. So that's actually interesting, so that would open up a whole new set of capabilities. >> Yeah and, you know, it follows our you know, company's vision in allowing lots of users to do very sophisticated things without being, you know, Hadoop developers or Spark developers, or even Flink developers, we do a lot of the hard work of trying to give you a representation that's easier to work with, right but, also allow you to sort of evolve that and de-bug it and also eventually get the performance out of these systems One of the challenges of course of Spark and Flink is that they have to be tuned, and you have to, and so what we're trying to do is, using some of our machine learning, is eventually gather information that can help us identify how to tune different types of work flows in different environments. And that, if we're able to do that in it's entirety, then we, you know, we take out a lot of the really hard work that goes into making a lot of these streaming applications both scalable and performing. >> Performimg. So this would be, but you would have, to do that, you would probably have to collect well, what's the term? I guess data from the operations of many customers, >> Right. >> Because, as training data, just as the developer alone, you won't really have enough. >> Absolutely, and that's, so that you have to bootstrap that. For our machine learning that we currently use today, we leverage, you know, the thousands of pipelines, the trillions of documents that we now process on a monthly basis, and that allows us to provide good recommendations when you're building pipelines, because we have a lot of information. >> Oh, so you are serving the runtime, these runtime compilations. >> Yes. >> Oh, they're not all hosted on the customer premises. >> Oh, no no no, we do both. So it's interesting, we do both. So you can, you can deploy completely in the cloud, we're a complete SASS provider for you. Most of our customers though, you know, Banks Healthcare, want to run our engine behind their firewalls. Even when we do that though, we still have metadata that we can get introspection, sort of anonymized, but we can get introspection into how things are behaving. >> Okay. That's very interesting. Alright, Greg we're going to have to end it on that note, but uh you know, I guess everyone stay tuned. That sounds like a big step forward in sort of specification of real time pipelines at a graphical level. >> Yeah, well, it's, I hope to be talking to you again soon with more results. >> Looking forward to it. With that, this is George Gilbert, we are at Flink Forward, the user conference for the Apache Flink conference, sorry for the Apache Flink user community, sponsored by Data Artisans, we will be back shortly. (upbeat music)
SUMMARY :
brought to you by Data Artisans. We are at Flink Forward on the ground in San Francisco. and what you're trying to do. so that you don't have to write Java code. Okay, and so you were telling me before So this allows us to you know, to explore and prototype you need to work at the source level. so they can take actionable steps to remedy those that gives you that feedback something that you build with a dataset API to execution. you would have better and so, I'm not saying this is impossible to do in Spark, and you don't have to then infer it from all this log data. As you know, Flink is a streaming platform, Want to do what Alibaba and all the other companies the idea that you would have perv and processing so that would open up a whole new is that they have to be tuned, and you have to, So this would be, but you would have, to do that, just as the developer alone, you won't really have enough. we leverage, you know, the thousands of pipelines, Oh, so you are serving the runtime, Most of our customers though, you know, Banks Healthcare, you know, I guess everyone stay tuned. Yeah, well, it's, I hope to be talking to you again soon Looking forward to it.
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Monica Houston, Hackster.io | DevNet Create 2018
>> Announcer: Live from the Computer History Museum in Mountain View, California. It's theCUBE covering DevNet Create 2018, brought to you by Cisco. (techy music playing) >> Hello, everyone, welcome back to theCUBE's exclusive coverage here in Silicon Valley. We're in Mountain View, California, for Cisco's DevNet Create. I'm here with Lauren Cooney, our analyst here with Wikibon, of course. Our next guest is Monica Houston, director of Hackster Live, Hackster IO, Hackster.io, open source hardware, really kind of creating a great community model. Really started from a great idea. Great to have you, thanks for coming, joining us. >> Thanks for having me. >> So, we're here at Cisco Live, so no better place to talk about hardware and software coming together, but first talk about how Hackster started, how it grew, where it is now today. >> Okay, so Hackster got started about four years ago here in San Francisco. The founders, Adam and Ben, they said they wanted to make a community for people that were interested in building open source hardware. Adam had actually come from starting his own hardware startup and realized that there were very few resources for people like him that wanted to build electronics, and so started a community. I got involved, started... We actually bought a DeLorean, drove it around the country and did hardware hack-a-thons in 12 different cities in the US. >> And so where's it today in terms of numbers, community members, and you're based in Seattle, is that right? >> I'm in Seattle. >> Okay. >> Yeah. >> So, what's the community look like, what's the numbers look like? >> There are half a million people on our site and 15,000 open source projects. >> John: Wow, awesome. >> Yeah. >> That is totally awesome, what projects do you see being the most popular on your site? >> Lots of home automation, home automation's a really popular topic. >> Lauren: Mm-hmm. >> We also get a lot of some pretty cool, like, music synthesizers, amplifiers. All kinds of stuff, yeah. >> That's great, now, say I'm like a... I, you know, have coded before but I'm not necessarily truly a developer. Like I'm a moonlighter, per se. >> Monica: Sure. (laughing) >> How could I get involved in this? >> Oh, man, there's tons of resources. So, actually on our site you can sort by difficulty. >> Lauren: Mm-hmm. >> So, if you want to find some beginner projects you can sort by difficulty and find only beginner projects. Also we have tutorials, so tutorials, getting started projects. >> Lauren: Mm-hmm. >> Like, so you buy an Arduino or you buy a particle board and you want to learn how to use it you can search for a getting started project on the site. >> Lauren: That's great. >> Yeah. >> How are you, like... So, you're at DevNet Create-- >> Monica: Yeah. >> With Cisco and what are you doing here? Like, what are you talking about, what are you really interested in? >> So, I have a project called Breadboard to PCB. I'm actually... So, I was a front-end web developer and then I got into all this, so I'm fairly new to it as well. I've been doing it for about six years. I'm not an electrical engineer. (laughing) I have to tell myself, but I'm doing a project called Breadboard to PCB. I made a PCB last year, sort of taught myself how to do that and realized it's actually not that hard and I want to spread that around to people and make them realize that they can build their own PCBs, too. >> That's terrific, that's awesome. Are you and Cisco, DevNet looking to share content or anything like that that might be cool? >> Yes, definitely. (laughing) >> Lauren: Okay. >> Yeah. >> All right, and is there any more you can tell us about that or is that still in the works? >> Still in the works, yeah, we offer... So, we have all these different partners, like Microsoft, Intel, hopefully Cisco as well-- >> Lauren: Mm-hmm. >> That do, they have, their hub is hosted on our site and people share their projects there with full instructions of a template. We actually go through and make sure everyone shares their code and their schematics. So, they're very well put together projects. >> Lauren: Great. >> Talk about some of the most exciting things you worked on, because one of the things I love about the open source culture is the creativity kind of comes out of nowhere. (laughing) We were just talking, before you came out, about my son, how he's been hacking his own thing. With this culture now you have so much online information. You go to YouTube, there's always a how-to. These communities have great resources. You guys got a robust community. So, there's always that natural, organic, "Whoa, look what this person did." >> Monica: Yeah. >> Can you share some stories around some killer things that happened. Not "killer," good things or just some things that are just creatively cool that you never would've thought would happen. >> Oh, man, so Allen Pan is a maker, I think he's based in LA. He has a YouTube channel, Allen Pan, and he did this really cool... I guess The Last Airbender is a popular movie and he did this flame activated, or punch activated flamethrower on his wrist. So, you try this at home. (laughing) Might be a little dangerous. >> What's his YouTube channel? >> I believe it's Allen Pan, is his name. >> So, there's some creative stuff, so people just tinkering around but there's also some serious hardware engineers. >> Monica: Mm-hmm. >> Any businesses starting out of this? Have you seen any, like, good ventures emerge? >> There's been a few things that I forgot about. There was a really cool watch that was Kickstarted that if you're a cyclist you can wear it and it tells you, like, which way to turn based on your GPS. >> John: Mm-hmm. >> There was some really nice Bluetooth, very elegant, like, Bluetooth controlled lights, that with different colors those are nice, yeah. >> So, what are some of the things you guys are doing in the community that you think's notable that you could share, people might be interested and like, how do you guys organize? There's some things that you guys do differently. What are some of the community activities that, you know, are standard. You know, the normal thing, you have meetups and whatnot, but like, how do you guys run your community, what are some of your guiding principles. Can you share how things work? >> So, we are always open, so you can go to our site and there's no, you know, there's no pay wall or barrier to view all of our content because our content comes from our community, and like, they're the ones that are... We're encouraging them to really document their work. Also, yeah, so we do hardware hack-a-thons where we try to make sure-- >> John: Yeah. >> Everyone's very... We're very beginner friendly, I guess. That's one of our goals is to make sure that people are coming from all different... You know, it's the artists that are making cool projects and-- >> So, when new people come in-- >> Mm-hmm. >> They get welcome letter, kind of community email haze or chat, all that stuff going on, all that's in place. >> We have a news feed, we have discussion, comments on the project that we moderate a bit, so yeah. >> So, Hackster.io. >> Hackster.io, yeah. >> All right, what's the coolest thing you guys are doing right now that you think we should know about? >> So, sort of related, actually this weekend I was at a workshop to learn how to make my own fire projects. (laughing) I like fire, (laughing) yeah. >> John: A pyromaniac community. I want the flamethrower fist thing. >> Yeah, I know. (laughing) >> Lauren: Over here is, yeah. >> I'm instantly like maybe on YouTube or something, I want that. That's a great party trick. >> I think it's great. >> Until something lights up. >> Right. (laughing) >> All right, so what are you doing here at Cisco? What's the focus here, obviously great culture they're building here. Very developer, not a lot of Cisco Kool-Aid being injected here, but much more of an outreach for Cisco, what's your focus here? >> This is all new for me, actually. So, I did not realize that this has got such a huge developer community and was really involved and like, this is a great conference. >> Lauren: That's true. >> People are so nice. >> Yeah, and the internet of things is a big hardware-- >> Yeah. >> Focused market. >> Yes, yeah. >> So, they've got all the software there. >> It's only getting bigger, yeah. >> Cool. >> Mm-hmm. >> Cool, all right, so what's new in Seattle? Give us an update on what you're doing. >> All right, it's still raining there. (laughing) >> That is actually very good to know. (laughing) >> You have Microsoft up there. >> Monica: Yeah. >> You have Amazon. >> Monica: Mm-hmm. >> University of Washington, so you have kind of a nice, kind of geek culture developing up there. So, yeah, good open source hardware vibe up there? >> Yeah. >> What's the community like in Seattle? >> Yeah, I run meetups, there's lots of people that come out to different hardware meetups and there are, like, a lot of new, cool hardware startups. Like for instance, Glowforge is a laser cutter that was Kickstarted recently. >> Lauren: Yep. >> There's some other really neat... DiGo is a home automation light switch. >> Lauren: Mm-hmm. >> Yeah, there's some pretty cool startups. >> So, if someone wants to join your community, what would you say to them if they're watching this video right now, hey. What are they, what's it like to join, what are they going to be... What's the vibe like, what are some of the things that are involved? What's the value for someone watching, that might want to join this, totally into tinkering with hardware? >> One thing is a great format to share your projects, and also to document your projects. Documentation is really important and I like to say that a project doesn't exist unless it's documented. (laughing) >> John: Yeah. >> So, documenting it and then we'll boost your project, we'll share it on our social media and it'll get lots of views. >> Yeah. >> Yeah. >> It's an open, it's a nerd culture. I mean, by the way, robotics is the hottest thing going on. You can't get involved, a lot of the younger generation are absolutely enamored with robotics, just at all levels. From, you know, you've got drones, which is super cool, right. Then you've got all the kind of stuff that's, it's all about hardware, hardware. >> Well, this is great, I'm on the site and I'm looking right now at the mind control drone. >> Monica: Oh, yeah. (laughing) >> That is, you know, my question is does that really work? Can I, you know, can I actually do something. You know, take this and learn from the site and actually build that? >> There are, and there are some developer, I guess it's EEG or EKG, EMG is another one, that you can really, you know, you can think left or think right and it will go left or right, it figures it out, yeah. >> That is so cool. >> Are people meeting up on the site and doing work together. Is it like a collaborative kind of hub going on there? >> There are some people that are doing that. Yeah, there was a few people on our site that were doing work on the, what was it... Elon Musk's thing, the... Hyperloop. >> John: Yeah. >> There's, like, the Hyperloop contest, and so a few people on our site were doing some work for that. >> Lauren: That's great. >> So, yeah, people are meeting there, yeah, for sure. >> Monica, great to have you here in theCUBE. Thanks for sharing about Hackster.io. We're going to check it out and thanks for the tip on the YouTube channel. We'll get the fire flamethrower. >> Yeah, make your own flamethrowers. >> John's going to be busy this weekend. (laughing) >> I'm a pyromaniac, I keep playing with matches all the time. So, thanks for coming on, I really appreciate it. >> Yeah, thank you. >> Hackster.io, we are theCUBE here live in Mountain View, California. Cisco DevNet Create, the Computer History Museum. We'll be right back with more after this short break. (techy music playing)
SUMMARY :
2018, brought to you by Cisco. Great to have you, thanks for coming, joining us. So, we're here at Cisco Live, so no better place in 12 different cities in the US. and 15,000 open source projects. Lots of home automation, home We also get a lot of some pretty cool, I, you know, have coded before but I'm So, actually on our site you can sort by difficulty. So, if you want to find some beginner projects Like, so you buy an Arduino or you buy So, you're at DevNet Create-- So, I have a project called Breadboard to PCB. Are you and Cisco, DevNet looking to share content (laughing) Still in the works, yeah, we offer... So, they're very well put together projects. With this culture now you have so much online information. Can you share some stories around So, you try this at home. So, there's some creative stuff, so people that if you're a cyclist you can wear it and it tells you, that with different colors those are nice, yeah. So, what are some of the things you guys So, we are always open, so you can go to our site You know, it's the artists that email haze or chat, all that stuff comments on the project that we moderate a bit, so yeah. So, sort of related, actually this weekend I want the flamethrower fist thing. (laughing) I'm instantly like maybe on YouTube (laughing) All right, so what are you doing here at Cisco? So, I did not realize that this has got Cool, all right, so what's new in Seattle? (laughing) That is actually very good to know. University of Washington, so you have kind of a nice, that come out to different hardware meetups DiGo is a home automation light switch. what would you say to them if they're and also to document your projects. So, documenting it and then we'll boost You can't get involved, a lot of the younger generation and I'm looking right now at the mind control drone. Monica: Oh, yeah. That is, you know, my question is does that really work? that you can really, you know, you can think left Is it like a collaborative kind of hub going on there? There are some people that are doing that. There's, like, the Hyperloop contest, Monica, great to have you here in theCUBE. John's going to be busy this weekend. So, thanks for coming on, I really appreciate it. Cisco DevNet Create, the Computer History Museum.
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Dawn Woodard, Uber | WiDS 2018
>> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE! Covering Women In Data Science Conference 2018. Brought to you by-- >> Coverage of Women in Data Science 2018. I am Lisa Martin. We're at Stanford University. This is where the big in-person event is, but there are more than 177 regional WiDS events going on around the globe today. They are in 53 countries, and they're actually expecting to have about 100,000 people engaged with WiDS 2018. Pretty awesome. I'm joined by one of the speakers for WiDS 2018, Dawn Woodard, the senior data science manager of maps at Uber. Welcome to theCUBE! >> Thank you so much, Lisa. >> It's exciting to have you here. This is your first WiDS, and you are already a speaker. Tell us a little bit about what attracted you to WiDS. What was it that kind of spoke to you as a female leader in data science? >> Well, I tried to do a fair amount of reach-out to women in data science. I really feel like I've been blessed throughout my career with inspiring female mentors, including my mother, for example. Not every woman comes into her career with that kind of mentorship, so I really wanted to reach out and help provide that to some of the younger folks in our community. >> That's fantastic. One of the things that's remarkable about WiDS, one, is the growth and scale that they've achieved reaching such big, broad audiences in such a short time period. But it's also from a thematic perspective, aiming to inspire and to educate data scientists worldwide, and of course, to support females in that. What are some of the, tell us a little bit about your talk is Dynamic Pricing and Matching in Ride Sharing. What are some of the takeaways that the audience watching the livestream and here in person are going to hear from your talk? >> There are two technical takeaways, and then there's one non-technical takeaway. The first technical takeaway is that the matching algorithms that we use are really designed to reduce the amount of time that riders and drivers have to spend waiting in the app. For drivers, that means that we're working to increase the amount of time that they spend on-trip and getting paid. For riders, that means that we're reducing the amount of time that they have to wait to be picked up by a car. That's the first takeaway. The second takeaway is around dynamic pricing, and why it's important in ride-hailing services in particular. It turns out that it's really important in creating a seamless and reliable experience, both for riders and for drivers, so I talk through the technical reasons for that. Interestingly, these technical arguments are based not just on machine learning and statistics, but also on economic analyses and some optimization concepts. The third takeaway is really that data science is this incredibly interdisciplinary environment in which we have economics, statistics, optimization, machine learning, and more. >> It's really, data sciences has the opportunity, or really is, very horizontal. Every sector, every area of our lives is impacted by it. I mean, we think of all of us that use Uber and ride-sharing apps. I think that's one of the neat things that we're hearing from the event and from the speakers like yourself is these demarcated lines of career paths are blurring, or some of 'em are evaporating. And so, I think having the opportunity to talk to the younger generation, showing them how much impact they can make in this field has got to sort of be maybe, I would even guess, invigorating for you, as someone who's been in the tech in both industry and academia for a while. >> Absolutely. I think about data science as being the way that we learn about the world, statistics and data science. So, how do we use data to learn about the world, and how do we use data to improve, to make great products, to make great apps, for example. >> Exactly. Tell me a little bit about your career path. You have your PhD in statistics from Duke University. Tell me about how you got there, and then how you also got into industry. Were you always a STEM fan as a kid, or was it something that you had a passion for early on, or developed over time? >> I was always passionate about math and science. When I was an undergraduate, I did an internship with a defense contractor. That's how I got interested in machine learning in particular. That's where it took off. I decided to get a PhD in statistics from there. Statistics and machine learning are really closely related. And then, continued down that path throughout my academic career, and now my career in tech. >> What are some of the things that you think that prepared you for a being a female leader? Was it those mentors that you mentioned before? Was it the fact that you just had a passion for it and thought, "If I'm one of the only females in the room, I don't care. "This is something that's interesting to me." What were some of those foundational elements that really guided you? >> One is the inspiration of some women in my life, and if we have to be completely honest, I'm a person who, when, the very rare times in my career when somebody has acted like I couldn't hack it or couldn't make it, it always really got me angry. The way that I channeled that was really to turn it around and to say, "No problem. "I'm going to show you that I can go well beyond "anything that you had conceived of." >> You know, I love that you said that, 'cause Margot Gerritsen, one of the founders of WiDS actually said a couple hours ago, a few years ago, when they had this idea, from concept to first conference was six months, and she said she almost thought of it like a revenge conference. Like, "We can do this!" I think it's kind of, when they had this idea in 2015, the fact that even in 2015, there's still not only demand for, but the demand is growing. As we're seeing, the statistics that show a low percentage of women that have degrees in engineering, I want to say 20%, but only 11% of them are actually working in their field. We still have a lot of work to do to ignite the fire in this next generation of prospective leaders in technology. There's still a lot of groundwork to make up there. I think we're hearing that a lot at WiDS. Are you hearing that in your peer groups as well? >> Absolutely. I think one of the things that I've really focused on is mentoring women as leaders and managers within my organization, and I really find that that's an amazing way to reach out, is not just to reach out myself, but also to do that through female leaders in my own organization. For example, I've mentored and managed two women through the transition from individual contributor to manager. Just watching their trajectory afterwards is incredibly inspiring. But then, of course, those female managers bring in additional female contributors, and it grows from there. >> Right. And you have a pretty good, pretty diverse team at Uber. Tell us a little bit about your rise at Uber. One of the things that I saw on your LinkedIn profile, that you achieved pretty quickly in the first three years, or probably less, was that you led the marketplace data science team through a period of transformative growth. You started that team with 10 data scientists, and by the time you transitioned into your next role, there were 49 data scientists, including seven managers. How were you able to come in and make such a big impact so quickly? >> Well, the whole team chipped in in terms of hiring and reaching out. But at the time when I joined Uber, data science was still relatively small. Those 10 people were being asked to do all of the pricing and matching algorithms, all of the data science for Uber Pool, all of the data science for Uber Eats. We just had one person in each of these areas, and those people very quickly stepped up to the plate and said, "Okay, I need help." We worked together to help grow their teams. It's really a collaborative effort involving the whole team. >> The current team that you're managing, what does that look like from a male/female ratio standpoint? >> The current team is more than 50% female at this point, which is something that I'm really proud of. It's definitely not only my achievement. There was a manager who was leading the team just before I switched to leading maps, and that person also helped increase the presence of women in data science for Uber's mapping organization. The first data scientist on maps at Uber was a woman, actually. >> That's fantastic. And you were saying before we went live that there's a good-sized contingent of women data scientists at Uber today that are participating in WiDS up in San Francisco? >> That's right, yes. We're live-streaming it. There's a Women in Data Science organization at Uber, and that organization is sponsoring the internal events for the live stream, not just for my talk, but really, the whole conference. >> That's one of the things that Margot Gerritsen was also saying, that from a timing perspective, they really knew they were on to something pretty quickly, and being able to take advantage of technology, live streaming, they're also doing it on Facebook, gives them that opportunity to reach a bigger audience. It also is, for you and your peers as speakers, gives you an even bigger platform to be able to reach that audience. But one of the things I find interesting about WiDS is it's not just the younger audience. Like Maria Klawe had said in her opening remarks this morning and before, that the optimal time that she's found of reaching women to get them interested in STEM subjects is first year college, first semester of college. I actually had the same exact experience many years ago, and I didn't realize that was a timing that was actually proven to be the most successful. But it's not just young women at that stage of their university career. It's also those who've been in tech, academia, and industry for a while who, we're hearing, are feeling invigorated by events like WiDS. Do you feel the same? Is this something that just sort of turns up that bunsen burner maybe a little bit higher? >> Oh, it's incredibly empowering to be in a room full of such technically powerful women. It's a wonderful opportunity. >> It really is, and I think that reinvigoration is key. Some of the things like, as we look at what you've already achieved at Uber so far, and we're in 2018, what are some of the things that you're looking forward to your team helping to impact for Uber in 2018? >> In 2018, we're looking to magnify the impact of data science within Uber's mapping organization, which is my main focus right now. Maps at Uber does several things. Think of Uber as being a physical logistics platform. We move people and things from point A to point B. Maps, as our physical world, really impacts every aspect of the user experience, both for riders and for drivers. And then, whenever we're making a dispatch decision or a pricing decision, we need to know something about how long it would take this driver to get to this rider, for example, which is really a mapping prediction. We are looking at increasing the presence of data science within the mapping organization, really bringing that perspective to the table, both at the individual contributor level, but really also growing leadership of data science within the mapping organization so that we can help drive the direction of maps at Uber through data-driven insights. >> Data-driven insights, I'm glad that you brought that up. That's something that, as we talk about data science. Data science is helping to make decisions on policy, healthcare, so many different things, you name it. It really seems like these blurred lines of job categories, as businesses use data science, and even Uber, to extend, grow the business, open new business models, so can the next generation leverage data science to just open up this infinite box, if you will, of careers that they can go into and industries they can impact by having this foundation of data science. >> Absolutely. Well, any time we have to make a decision about what direction we go in, right, as a business, for example, as an organization, then doing that starting from data, understanding what is the world really like, what are the opportunities, what are the places in which we as a company are not doing very well, for example, and can make a simple change and get an incredible impact? Those are incredibly powerful insights. What do you think, last question-ish, 'cause we're getting low on time. We talk a lot about, there's the hard skills/soft skills. Soft is kind of a weird word these days to describe that. You know, statistical analysis, data mining. But there's also this, the softer skills, empathy, things like that. How do you find those two sides, maybe it's right brain/left brain, as being essential for people to become well-rounded data scientists? >> The couple of soft skills that I really look for heavily when I'm hiring a data scientist, one is being really focused on impact, as opposed to focused on building a new shiny thing. That's quite a different approach to the world, and if we stay focused on the product that we're creating, that means that we're willing to chip in, even if the work that's being done is not as glamorous, or is not going to get as much attention, or is not as fancy of a model. We can really stay focused on what are some simple approaches that we can use that can really drive the product forward. That kind of impact focus, and also, that great attitude about being willing to chip in on something, even if it's not that fancy or if I'm not going to get in the limelight for doing this. Those are the kinds of soft skills that really are so critical for us. >> Attitude and impact. I've heard impact a number of times today. Dawn, thank you so much for carving out some time to chat with us on theCUBE. We congratulate you on being a speaker at this year's event, and look forward to talking to you next year. >> Thank you, Lisa. >> We want to thank you for watching theCUBE. We are live at Stanford for the third annual Women in Data Science Conference, hashtag #WiDS2018. Get involved in the conversation. It is happening in over 53 countries. After this short break, I will be right back with my next guest. (fast electronic music)
SUMMARY :
Brought to you by-- and they're actually expecting to have about 100,000 people It's exciting to have you here. to women in data science. and here in person are going to hear from your talk? that they have to wait to be picked up by a car. and from the speakers like yourself the way that we learn about the world, and then how you also got into industry. I decided to get a PhD in statistics from there. What are some of the things that you think "I'm going to show you that I can go well beyond You know, I love that you said that, and I really find that that's an amazing way and by the time you transitioned into your next role, all of the data science for Uber Pool, and that person also helped increase And you were saying before we went live and that organization is sponsoring the internal events that the optimal time that she's found Oh, it's incredibly empowering to be Some of the things like, really bringing that perspective to the table, to just open up this infinite box, if you will, the softer skills, empathy, things like that. that can really drive the product forward. and look forward to talking to you next year. We are live at Stanford for the third annual
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Jennifer Prendki, Atlassian | WiDS 2018
>> Narrator: Live from Stanford University in Palo Alto California, it's theCUBE, covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Back to the cube, our continuing coverage of Women in Data Science 2018 continues. I am Lisa Martin, live from Stanford University. We have had a great array of guests this morning, from speakers, panelists, as well as attendees. This is an incredible one day technical event, and we're very excited to be joined by one of the panelists on the career panel this afternoon, Dr. Jennifer Prendki, the Head of Data Science at Atlassian. Welcome to theCUBE. >> Hi, it's my pleasure to be here. >> It's exciting to have you here. >> So you lead all search and machine learning initiatives at Atlassian, but you were telling me something interesting about your team, tell us about that. >> The interesting thing about my team is even though I'm the Head of Data Science, my team is not 100% data scientists. The belief of the company is that we really wanted to be in charge of our own destiny and be able to deploy our models ourselves and not be depending on other people to make deployment faster. >> Was that one of the interesting kind of culture elements that attracted you last year to Atlassian? >> What is really interesting about Atlassian, it's definitely a company that create products that I would say virtually every single software company in the world is using. They have a very strong software engineering culture, and so last year they decided to embrace data science. I thought it was a very interesting challenge for me to try and infuse a little bit of my passion for data and data-driven est to the company. >> You had quite a fast ramp at Atlassian. You joined last summer, and in less than six months, you grew your team of data scientists and engineers from three people to fifteen, and it gets better, in less than six months, across three locations, Mountain View, San Francisco, and Sydney. What were some of the key things for you that led you to make that impact so quickly? >> I think most data scientists on the world are interested in making an impact, and this is a company that obviously does a lot of impact, and a lot of people talk about this company, and there is obviously a lot of interesting data, and so I think one of the amazing things is that we have a very important role to play, because we are in a position where we have data related to the way people work with each other, collaborate with each other, and this is a very unique data set, so it's usually pretty easy to attract people to Atlassian. >> You mentioned collaboration, and that's certainly an undertone here at WiDS. In its third year, you were here last year as an attendee, now you're here this year as a speaker. They've grown this event dramatically in a couple of years alone. The opportunity to reach, they're expecting, a hundred thousand, to engage. It's a hundred and seventy-seven regional events, Margot Gerritsen gave us that number about an hour ago, in fifty-three countries. What is it about WiDS that attracted you, not only back, this year, but to welcome the opportunity to be on this career panel? >> I'll actually tell you something, so, we talk about diversity, and I think people usually think of diversity as meeting some kind of racial bar, to have, equality between male and female, or specific minorities. I think people tend to forget that the real diversity is diversity of thought, and so I actually found out that the very data science job I actually got, I was actually the only person who had a background in applied math, and everybody else was coming from a background in computer science. I quickly realized that I'm the only person who is really trained to push for, let's validate our models really properly, etc., and so that made realize how important that is to have a lot of diversity. I think WiDS is definitely a place where you see lots of women interested in the same thing, but coming from different perspective, different horizons, at different levels, and this is really something unique in the industry. >> Diversity of thought, I love that. I've not heard that before, I'm going to use that, but I'll give you credit for it. That is one of the things that is so, the more people we speak to, not just at WiDS, but at events like this on theCUBE, you hear, there's still such a need, obviously, the scale of which that WiDS has grown, shows clear demand for, we need more awareness that this diversity is missing, but in the fact that data science is so horizontal, across every industry, and it sort of is blurring the boundaries between rigid job roles, doctor, lawyer, attorney, teacher, whatever. This is quite pervasive and it provides the opportunity for data scientists globally to be able to make massive impact, but also, it still, as Margot Gerritsen was sharing earlier, it still requires what you said is that diversity in thought because having a particular small set of perspectives evaluating data, you think about it from an enterprise perspective, the types of companies that Atlassian deals with, and they are looking to grow and expand and launch new business models, but if the thought diversity is narrow, there's probably a lot of opportunity that is never going to be discovered. One of the things also I found interesting in your background, was that you found yourself sort of at this interesting juxtaposition of being a mentor, and going, wait a minute, this now gives you a great opportunity, but it also comes with some overhead. You've got it from a management perspective. What is that sort of crossroads that you've found yourself reaching and what have you done with that? >> I think it's true of probably every single technical role, but maybe data science more than others, you have to be technical to be part of the story. I think people need to have a leader that they can relate to and I think it's very important that you're still part of this. It's particularly interesting for data science, because data science is a field that moves so quickly. Usually you have people moving on to data science manager positions after being in IC and so if you don't make a conscious effort to remain that technical point of contact person, that people trust and people go to, then, when I think back of the technologies that were trendy when I was still in IC compared to now, it's really important for the managers to be still aware of that, to do a good job as a mentor and as a leader. >> You also said something I think before we went live, that is an important element for the women that WiDS is aiming to inspire and educate, today. Those that are new to the field or thinking about it, as well as those who've been it for a while. There is not just getting there, and going yes I'm interested, this is my passion, I want to have a career in this, it's also having to learn how to be a female leader, and you mentioned from a management perspective, you got to learn, you have to know how to be assertive. Tell us a little bit about the trials and tribulations that you have encountered in that respect. >> That's a very interesting question, because I'm actually very happy to see that nowadays, it's becoming easier and easier for women to step into individual contributor positions, because I think that people realize now that a woman can do just as good a job as men for a defined position, but when you're actually in a leadership position, you have to step into like a thought leadership role. Basically, you sometimes have to be in a meeting where you only have all the male engineers or male data scientists over there and say, you know what, I disagree with you, right? This as a woman becomes a little bit challenging because following the processes that are already in place, I believe that people have realized that it's okay for a woman to do that, but then being the assertive person that goes against the flow and says you are not thinking about it the right way, might sometimes be a problem, because women are not being perceived as creatures that are naturally assertive. It's typical for people, like a Head of Data Science, female data scientists, to be in a situation where they are perceived as being maybe a little bit aggressive or a little bit pushy, and you sometimes fall into this old saying, "he's the boss, she's bossy," kind of thing, and that is a challenge. >> I had someone once tell me a couple years ago, and I'm in tech as well, that I was pushy, and I think this was a language barrier thing, I think he meant to say persistent, but on that front, tell me a little bit more about your team of data scientists and engineers, and the females on your team, how do you help coach them to embrace, it's okay to speak your mind? What's that been like for you? >> I would say I was actually pretty soft-spoken myself. At some point I realized that public speaking actually helped me out there. Somebody at some point told me like, you should go, you're a brilliant, technical like go speak at a conference, and then I realized people are listening to me. You always have a little bit of like imposter syndrome kind of problem as a woman, so it helped me overcome this. Now I'm kind of trained to stimulate the ladies on my group to do the same thing, because that has worked really well for me I think. You have to get outside your comfort zone, and try to, things that help you have the self-confidence for you to get to the level of assertiveness you need to become successful. >> Exactly right, we've had a number of women on the show, today alone, talk about getting outside of your comfort zone, and one of my mentors always says, get comfortably uncomfortable. That's not an easy thing to achieve, but I think you walk in the door at WiDS, and you instantly feel inspired, and empowered. I think a number of the women that we've had on today, already, have talked about having, sort of being charged as a mentor with the responsibility like you just said, of helping those that are following your footsteps, to maybe understand how to have that confidence, and then have that right balance, so that there's professionalism there, there's respect, but it's not just about getting them into the field. It's about teaching them how to, once you're there, how to navigate a career path that is successful. >> That's an interesting thought, because I actually believe that getting comfortable with the uncomfortable is definitely something that data science is about, because you have new technologies, you have new models, you have lateral moves, like I actually was in the advertising industry as a data scientist, before switching to e-commerce and then eventually to the software industry, so I think that people who are trained to be data scientists are like that, and they should also be comfortable with the uncomfortable in their daily lives. >> Yeah, so you were mentioning before we went on that some of the people that you work with are like, it's my hope and dream to be at WiDS next year. What are some of the things that you've heard as we're at the halfway mark of WiDS today, that you're going to go back and share with your team, as well as maybe your friends, other females that are working in STEM fields as well? >> I would say, last year I was here just listening to all the people and whatever. This year, I'm on the panel, so I mean, I'm just like, nothing is impossible, I think. We've proven that over and over again in data science, I mean, who would have thought that ten years ago, we would be at the level of understanding of artificial intelligence and the entire field, right? It's all about waiting and seeing what the future has to bring to you, and we have all these amazing women today, to actually show us that, it's possible to get there, and it's exciting to be here. >> It is possible, and it's exciting. Well, Jennifer, thanks so much for carving out some of your time today to speak with us. We wish you continued success at Atlassian and we look forward to seeing you back at WiDS next year. >> Thank you. >> We want to thank you for watching theCUBE, we're live at Stanford University at the third annual Women in Data Science Conference, hashtag WiDS2018, join the conversation. I'll be right back with my next guest after a short break. (upbeat music)
SUMMARY :
Brought to you by Stanford. of the panelists on the career panel this afternoon, at Atlassian, but you were telling me something interesting in charge of our own destiny and be able to deploy for data and data-driven est to the company. you grew your team of data scientists and engineers and a lot of people talk about this company, What is it about WiDS that attracted you, not only back, I think people tend to forget that the real diversity a lot of opportunity that is never going to be discovered. it's really important for the managers to be still Those that are new to the field or thinking about it, that goes against the flow and says you are not thinking and try to, things that help you have the but I think you walk in the door at WiDS, because you have new technologies, you have new models, that some of the people that you work with to all the people and whatever. and we look forward to seeing you back at WiDS next year. We want to thank you for watching theCUBE,
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Margot Gerritsen, Stanford University | WiDS 2018
>> Narrator: Alumni. (upbeat music) >> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, WiDS. I'm Lisa Martin, very honored to be joined by one of the co-founders of this incredible WiDS movement and phenomenon, Dr. Margot Gerritsen. Welcome to theCUBE! >> It's great to be here, thanks so much for being at our conference. >> Oh, likewise. You were the senior associate dean and director of the Institute for Computational Mathematics and Engineering at Stanford. >> Gerritsen: That's right, yep. >> Wow, that's a mouthful and I'm glad I could actually pronounce that. So you have been, well, I would love to give our audience a sense of the history of WiDS, which is very short. You've been on this incredible growth and scale trajectory. But you've been in this field of computational science for what, 30, over 30 years? >> Yeah, probably since I was 16, so that was 35 years ago. >> Yeah, and you were used to being one of few, or if not the only woman >> That's right. >> In a meeting, in a room. You were okay with that but you realized, you know what? There are probably women who are not comfortable with this and it's probably going to be a barrier. Tell us about the conception of WiDS that you and your co-founders had. >> So, May, 2015, Esteban from Walmart Labs, now at Facebook, and Karen Matthys, who's still very active, you know, one of the organizers of the conference, and I were having coffee at a cafe in Stanford and we were lamenting the fact that at another data science conference that we had been to had only had male speakers. And so we connected with the organizers and asked them why? Did you notice? Because very often people are not even aware, it's just such the norm to only have male speakers, >> Right, right. >> That people don't even notice. And so we asked why is that? And they said, "Well, you know we really tried to find "speakers but we couldn't find any." And that really was, for me, the last straw. I've been in so many of these situations and I thought, you know, we're going to show them. So we joke sometimes, a little bit, we say it's sort of a revenge conference. (laughs) We said, let's show them we can get some really outstanding women, and in fact only women. And that's how it started. Now we were sitting at this coffee shop and I said, "Let's do a conference." And they said, "Well, that would be great, next year." And I said, "No, this year. "Let's just do it. "Let's do it in November." We had six months to put it together. It was just a local conference here. We got outstanding speakers, which were really great. Mostly from the area. And then we started live-streaming because we thought it would be fun to do. And to our big surprise, we had 6,000 people on the livestream just without really advertising. That made us realize, in November 2015, my goodness, we're onto something. And we had such amazing responses. We wanted to then scale up the conference and then you can hire a fantastic conference center in San Francisco and get 10,000 people in like they do, for example, at Grace Hopper. But we thought, why not use online technology and scale it up virtually and make this a global event using the livestream, that we will then provide to people, and asking for regional events, local events to be set up all around the world. And we created this ambassador program, that is now in its second year. the first year the responses were actually overwhelming to us already then. We got 75 ambassadors who set up 75 events around the world >> In about 40 countries. >> This was last year, 2017? >> Yeah, almost exactly 13 months ago, and then this year now we have over 200 ambassadors. We have 177 events in 155 cities in 53 countries. >> That's incredible. >> So we're on every continent apart from Antarctica but we're working on that one. >> Martin: I was going to say, that's probably next year. >> Yeah, that's right. >> The scale, though, that you've achieved in such a short time period, I think, not only speaks to the power, like you said, of using technology and using live-streaming, but also, there is a massive demand. >> Gerritsen: There is a great need, yeah. >> For not only supporting, like from the perspective of the conference, you want to support and inspire and educate data scientists worldwide and support females in the field, but it really, I think, underscores, there is still in 2018, a massive need to start raising more profiles and not just inspiring undergrad females, but also reinvigorating those of us that have been in the STEM field and technology for a while. >> Gerritsen: That's right. >> So, what are some of the things, so, this year, not only are you reaching, hopefully about 100,000 people, you mentioned some of the countries involved today, but you also have a new first this year with the WiDS Datathon. >> That's right. >> Tell us about the WiDS Datathon, what was the idea behind it? You announced some winners today? >> Yeah. Yeah, so with WiDS last year, we really felt that we hit a nerve. Now there is an incredible need for women to see other women perform so well in this field. And, you know, that's why we do it, to inspire. But it's a one-time event, it's once a year. And we started to think about, what are some of the ways that we can make this movement, because it's really become a movement, into something more than just an annual, once-a-year conference? And so, Datathon is a fantastic way to do that. You can engage people for several months before the conference, and you can announce the winner at the conference. It is something that can be done really easily worldwide if it is supported again by the ambassadors, so the local WiDS organizations. So we thought we'd just try. But again, it's one of those things we say, "Oh, let's do it." We, I think, thought about this about six months ago. Finding a good data set is always a challenge but we found a wonderful data set, and we had a great response with 1100, almost 1200 people in the world participating. >> That's incredible. >> Several hundred teams. Yeah, and what we said at the time was, well, let's have the teams be 50% female at least, so that was the requirement, we have a lot of mixed teams. And ultimately, of course, that's what we want. We want 50-50, men-women, have them both at the table, to participate in data science activities, to do data science research, and answer a lot of these data questions that are now driving so many decisions. Now we want everybody around the table. So with this Datathon, it was just a very small event in the sense, and I'm sure next year it will be bigger, but it was a great success now. >> Well, congratulations on that. One of the things I saw you on a Youtube video talking about over the weekend when I was doing some prep was that you wanted this Datathon to be fun, creative, and I think those are two incredibly important ways to describe careers, not just in STEM but in data science, that yes, this can be fun. >> Yep. >> Should be if you're spending so much time every day, right, doing something for a living. But I love the creativity descriptor. Tell us a little bit about the room for interpretation and creativity to start removing some of the bias that is clearly there in data interpretation? >> Oh. (laughs) You're hitting the biggest sore point in data science. And you could even turn it around, you say, because of creativity, we have a problem too. Because you can be very creative in how you interpret the data, and unfortunately, for most of us, whenever we look at news, whenever we look at data or other information given to us, we never see this through an objective lens. We always see this through our own filters. And that, of course, when you're doing data analysis is risky, and it's tricky. 'cause you're often not even aware that you're doing it. So that's one thing, you have this bias coming in just as a data scientist and engineer. Even though we always say we do objective work and we're building neutral software programs, we're not. We're not. Everything that we do in machine learning, data mining, we're looking for patterns that we think may be in the data because we have to program this data. And then even looking at some of the results, the way we visualize them, present them, can really introduce bias as well. And then we don't control the perception of people of this data. So we can present it the way we think is fair, but other people can interpret or use little bits of that data in other ways. So it's an incredibly difficult problem and the more we use data to address and answer critical challenges, the more data is influencing decisions made by politicians, made in industry, made by government, the more important it is that we are at least aware. One of the really interesting things this conference, is that many of the speakers are talking to that. We just had Latanya Sweeney give an outstanding keynote really about this, raising this awareness. We had Daniela Witten saying this, and various other speakers. And in the first year that we had this conference, you would not have heard this. >> Martin: Really? Only two years ago? >> Yeah. So even two years ago, some people were bringing it up, but now it is right at the forefront of almost everybody's thinking. Data ethics, the issue of reproducibility, confirmations bias, now at least people now are aware. And I'm always a great optimist, thinking if people are aware, and they see the need to really work on this, something will happen. But it is incredibly important for the new data scientists that come into the field to really have this awareness, and to have the skill sets to actually work with that. So as a data scientist, one of the reasons why I think it's so fun, you're not just a mathematician or statistician or computer scientist, you are somebody who needs to look at things taking into account ethics, and fairness. You need to understand human behavior. You need to understand the social sciences. And we're seeing that awareness now grow. The new generation of data scientists is picking that up now much more. Educational programs like ours too have embedded these sort of aspects into the education and I think there is a lot of hope for the future. But we're just starting. >> Right. But you hit the nail on the head. You've got to start with that awareness. And it sounds like, another thing that you just described is we often hear, the top skills that a data scientist needs to have is statistical analysis, data mining. But there's also now some of these other skills you just mentioned, maybe more on the softer side, that seem to be, from what we hear on theCUBE, as important, >> Gerritsen: That's right. >> As really that technical training. To be more well-rounded and to also, as you mentioned earlier, to have to the chance to influence every single sector, every single industry, in our world today. >> And it's a pity that they're called softer skills. (laughs) >> It is. >> Because they're very very hard skills to really master. >> A lot of them are probably you're born with it, right? It's innate, certain things that you can't necessarily teach? >> Well, I don't believe that you cannot do this without innate ability. Of course if you have this innate ability it helps a little, but there's a growth mindset of course, in this, and everybody can be taught. And that's what we try to do. Now, it may take a little bit of time, but you have to confront this and you have to give the people the skills and really integrate this in your education, integrate this at companies. Company culture plays a big role. >> Absolutely. >> This is one of the reasons why we want way more diversity in these companies, right. It's not just to have people in decision-making teams that are more diverse, but the whole culture of the company needs to change so that these sort of skills, communication, empathy, big one, communication skills, presentation skills, visualization skills, negotiation skills, that they really are developed everywhere, in the companies, at the universities. >> Absolutely. We speak with some companies, and some today, even, on theCUBE, where they really talk about how they're shifting, and SAP is one of them, their corporate culture to say we've got a goal by 2020 to have 30% of our workforce be female. You've got some great partners, you mentioned Walmart Labs, how challenging was it to go to some of these companies here in Silicon Valley and beyond and say, hey we have this idea for a conference, we want to do this in six months so strap on your seatbelts, what were those conversations like to get some of those partners onboard? >> We wouldn't have been able to do it in six months if the response had not been fantastic right from the get-go. I think we started the conference just at the right time. There was a lot of talk about diversity. Several of the companies were starting really big diversity initiatives. Intel is one of them, SAP is another one of them. We were connected with these companies. Walmart Labs, for example, one of the founders of the company was from Walmart Labs. And so when we said, look, we want to put this together, they said great. This is a fantastic venue for us also. You see this with some of these companies, they don't just come and give us money for this conference. They build their own WiDS events around the world. Like SAP built 30 WiDS events around the world. So they're very active everywhere. They see the need, of course, too. They do this because they really believe that a changed culture is for the best of everybody. But they also believe that because they need the women. There is a great shortage of really excellent data scientists right now, so why not look at 50% of your population? >> Martin: Exactly. >> You know, there's fantastic talent in that pool and they want to track that also. So I think that within the companies, there is more awareness, there is an economic need to do so, a real need, if they want to grow, they need those people. There is an awareness that for their future, the long term benefit of the company, they need this diversity in opinions, they need the diversity in the questions that are being asked, and the way that the companies look at the data. And so, I think we're at a golden age for that now. Now am I a little bit frustrated that it's 2018 and we're doing this? Yes. When I was a student 30 some years ago, I was one of the very few women, and I thought, by the time I'm old, and now I'm old, you know, as far as my 18-year-old self, right, I mean in your 50s, you're old. I thought everything would be better. And we certainly would be at critical mass, which is 30% or higher, and it's actually gone down since the 80s, in computer science and in data science and statistics, so it is really very frustrating in that sense that we're really starting again from quite a low level. >> Right. Right. >> But I see much more enthusiasm and now the difference is the economical need. So this is going to be driven by business sense as well as any other sense. >> Well I think you definitely, with WiDS, you are beyond onto something with what you've achieved in such a short time period. So I can only imagine, WiDS 2018 reaching up to 100,000 people over these events, what do you do next year? Where do you go from here? (laughs) >> Well, it's becoming a little bit of a challenge actually to organize and help and support all of these international events, so we're going to be thinking about how to organize ourselves, maybe on every continent. >> Getting to Antarctica in 2019? >> Yeah, but have a little bit more of a local or regional organization, so that's one thing. The main thing that we'd like to do is have even more events during the year. There are some specific needs that we cannot address right now. One need, for example, is for high school students. We have two high school students here today, which is wonderful, and quite a few of them are looking at the live-stream of the conference. But if you want to really reach out to high school students and tell them about this and the sort of skill sets that they should be thinking about developing when they are at university, you have to really do a special event. The same with undergraduate students, graduate students. So there are some markets there, some subgroups of people that we would really like to tailor to. The other thing is a lot of people are very very eager to self-educate, and so what we are going to be putting together, at least that's the plan now, we'll see, if we can make this, is educational tools, and really have a repository of educational tools that people can use to educate themselves and to learn more. We're going to start a podcast series of women, which will be very, very interesting. We'll start this next month, and so every week or every two weeks we'll have a new podcast out there. And then we'll keep the momentum going. But really the idea is to not provide just this one day of inspiration, but to provide throughout the year, >> Sustained inspiration. >> Sustained inspiration and resources. >> Wow, well, congratulations, Margot, to you and your co-founders. This is a movement, and we are very excited for the opportunity to have you on theCUBE as well as some of the speakers and the attendeees from the event today. And we look forward to seeing all the great things that I think are going to come for sure, the rest of this year and beyond. So thank you for giving us some of your time. >> Thank you so much, we're a big fan of theCUBE. >> Oh, we're lucky, thank you, thank you. We want to thank you for watching theCUBE. I'm Lisa Martin, we are live at the third annual Women in Data Science Conference coming to you from Stanford University, #WiDS2018, join the conversation. I'll be back with my next guest after a short break. (upbeat music)
SUMMARY :
(upbeat music) Brought to you by Stanford. Welcome back to theCUBE, we are live It's great to be here, thanks so much and director of the Institute for Computational a sense of the history of WiDS, which is very short. and it's probably going to be a barrier. And so we connected with the organizers and asked them why? And to our big surprise, we had 6,000 people now we have over 200 ambassadors. So we're on every continent apart from Antarctica not only speaks to the power, like you said, that have been in the STEM field and technology for a while. so, this year, not only are you reaching, before the conference, and you can announce so that was the requirement, we have a lot of mixed teams. One of the things I saw you on a Youtube video talking about and creativity to start removing some of the bias is that many of the speakers are talking to that. that come into the field to really have this awareness, that seem to be, from what we hear on theCUBE, as you mentioned earlier, to have to the chance to influence And it's a pity that they're called softer skills. and you have to give the people the skills that are more diverse, but the whole culture of the company You've got some great partners, you mentioned Walmart Labs, of the company was from Walmart Labs. by the time I'm old, and now I'm old, you know, Right. and now the difference is the economical need. what do you do next year? how to organize ourselves, maybe on every continent. But really the idea is to not provide for the opportunity to have you on theCUBE coming to you from Stanford University,
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