(upbeat music) >> Welcome back to The Cubes coverage of WIDS 2023 the eighth Annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, thank you for having me today. I'm super excited to be here and share my journey with you guys. >> Let's talk about that journey. You recently got your PhD in information sciences, congratulations. >> Thank you. Yes, I just graduated, I completed my PhD in information sciences from University of Illinois Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. >> And you're in better climate. Well, we do get snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. >> Well, I consider myself maybe I was a lucky case. I did grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this roots, like this foundation but then you still need to kind of like figure out yourself what is it? Is it really the career that you want to pursue? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have you. >> Yeah. So you' re with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health, or is that something you explored throughout your studies? >> Oh, that's an excellent question. So I did have background in computer science but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP and machine learning and their applications in biomedical domains. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the advanced analytics department, and our focus is to bring artificial intelligence and machine learning into supporting clinical decision making. And really the ultimate goal is how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that which is very close to my research at school. >> That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? >> Yep. So as you may know already with the digitalization of medical records, this is nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient about their treatments, how we can mine these data how we can understand and tailor the treatment to individuals. And give everyone better care. >> I love that you brought up precision medicine. You know, I always think, if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives. We can get anything we want. Not only can we get anything we want but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have, based on data, your genetics, and being able to use NLP, machine learning and AI to drive that is really exciting. >> Yeah. You recap it very well, but then you also bring up a good point about the challenges to bring AI into this field right? Definitely this is an emerging field, but also very challenging because we talk about human health. We are doing the work that have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems for shopping or anything like that. We're talking about our actual health. So yes, it's challenging that way. >> Yeah. With that, you already answered one of the next questions I had because like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, who are the data in the end. >> The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data. Or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security, in your everyday work practices within data science? Is it... I imagine it's a fundamental piece. >> Yes, for sure. We at Gilead, for sure, in data science organization we have like intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies to follow to put that in place in our organization. >> Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? >> Yes. I did. I attended. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at different level of their professional life, sharing their journeys. It's really inspiring. >> And Hannah, and I've been talking about, a lot of those journeys look like this. >> I know >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my pa... Sorry, my bachelor degree in Vietnam, in STEM and in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room. And I kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. So you just like navigate yourself, keep pushing yourself over those journey. And like being where I am right now. >> And look what you've accomplished. >> Thank you. >> Yeah. That's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, "Okay, is this the right thing?" "Is this the right thing for me?" What motivated you to keep going? >> Well, I think personally for me, as a data scientist or for woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% women in your computer science undergrad in Vietnam. Yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles. We've seen it hover around 25% the last few years, probably five to 10. I was reading some data from anitab.org over the weekend, and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose, 27.6%. So we're seeing the needle move slowly. But one of the challenges that still remains is attrition. Women who are leaving the role. You've got your PhD. You have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career in climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? >> I think it's really down to that you need to follow your passion. Like in any kind of job, not only like in data science right? If you want to be a baker, or you want to be a chef, or you want to be a software engineer. It's really like you need to ask yourself is it something that you're really passionate about? Because if you really passionate about something, regardless how difficult it is, like regardless like you have so many kids to take care of, you have the whole family to take care of. You have this and that. You still can find your time to spend on it. So it's really like let yourself drive your own passion. Drive the way where you leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> What role have mentors played in your career path, to where you are now? Have you had mentors on the way or people who inspired you? >> Well, I did. I certainly met quite a lot of women who inspired me during my journey. But right now, at this moment, one person, particular person that I just popped into my mind is my current manager. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too, and now led a group, all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspire me a lot, like someone I look up to in this career. >> I love that. You went from being one of five females in a class of 100, to now having a PhD in information sciences, and being on an all female data science team. That's pretty cool. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme, and your career path, and what you're doing now, and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? >> So I feel like I'm very lucky to get my first job at Gilead. Not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless your background, your gender. So at Gilead, we have women at Gilead which is a global network of female employees, that help us to strengthen our inclusion culture, and also to influence our voices into the company cultural company policy and practice. So yeah, I'm very lucky to work in the environment nowadays. >> It's impressive to not only hear that you're on an all female data science team, but what Gilead is doing and the actions they're taking. It's one thing, we've talked about this Hannah, for companies, and regardless of industry, to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper. To actually be creating a strategy that they're executing on. That's awesome. And it must feel good to be a part of a company who's really adapting its culture to be more inclusive, because there's so much value that comes from inclusivity, thought diversity, that ultimately will help Gilead produce better products and services. >> Yeah. Yes. Yeah. Actually this here is the first year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS in the future. >> Excellent. Kelly we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. >> Thank you so much for having me. >> Oh, it was our pleasure. For our guests, and Hannah Freytag this is Lisa Martin from theCUBE. Coming to you from WIDS 2023, the eighth annual conference. Stick around. Our final guest joins us in just a minute.

(gentle upbeat music) >> Hey, friends of theCUBE. It's Lisa Martin live at Stanford University covering the Eighth Annual Women In Data Science. But you've been a Cube fan for a long time. So you know that we've been here since the beginning of WiDS, which is 2015. We always loved to come and cover this event. We learned great things about data science, about women leaders, underrepresented minorities. And this year we have a special component. We've got two grad students from Stanford's Master's program and Data Journalism joining. One of my them is here with me, Hannah Freitag, my co-host. Great to have you. And we are pleased to welcome from Intuit for the first time, Shir Meir Lador Group Manager at Data Science. Shir, it's great to have you. Thank you for joining us. >> Thank you for having me. >> And I was just secrets girl talking with my boss of theCUBE who informed me that you're in great company. Intuit's Chief Technology Officer, Marianna Tessel is an alumni of theCUBE. She was on at our Supercloud event in January. So welcome back into it. >> Thank you very much. We're happy to be with you. >> Tell us a little bit about what you're doing. You're a data science group manager as I mentioned, but also you've had you've done some cool things I want to share with the audience. You're the co-founder of the PyData Tel Aviv Meetups the co-host of the unsupervised podcast about data science in Israel. You give talks, about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? >> So I was always interested in mathematics when I was small, I went to this special program for youth going to university. So I did my test in mathematics earlier and studied in university some courses. And that's when I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. >> Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WiDS and Hannah and I and our co-host Tracy, have been talking about this all day is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity telecom, and of course we have these expectation that we're going to be connected 24/7 from wherever we are in the world and we can do whatever we want. I can do an Uber transaction, I can watch Netflix, I can do a bank transaction. It all is powered by data. And data science is, some of the great applications of it is what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on at Intuit. I'm an intuit user myself, but talk to me about some of those things. Give the audience really a feel for what you're doing. >> So if you are a Intuit product user, you probably use TurboTax. >> I do >> In the past. So for those who are not familiar, TurboTax help customers submit their taxes. Basically my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So- >> Thank you. >> Yeah, and this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the you know, financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bill, receipts, different bank statements. Yeah, so this is really exciting for me, the opportunity to work to apply data science and machine learning to solution that actually help people. Yeah >> Yeah, in the past years there have been more and more digital products emerging that needs some sort of data security. And how did your team, or has your team developed in the past years with more and more products or companies offering digital services? >> Yeah, so can you clarify the question again? Sorry. >> Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? >> Well, definitely. I think, you know, since I joined Intuit, I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined, there were like a dozens of data scientists and machine learning engineers on Intuit. And now there are a few hundreds. So we've definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing, so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. >> I like both of those. More money in my pocket and less work. That's awesome. >> Exactly. >> So keep going Intuit. But one of the things that is so cool is just the the abstraction of the complexity that Intuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background, but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now. And so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. >> Definitely. And I think that's what's also really amazing about Intuit it, is how it combines human in the loop as well as AI. Because in some of the tax situation it's very complicated maybe to do it yourself. And then there's an option to work with an expert online that goes on a video with you and helps you do your taxes. And the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. >> And that's what it's all about is you know, using data to be more efficient, to be faster, to be smarter, but also to make complicated processes in our daily lives, in our business lives just a little bit easier. One of the things I've been geeking out about recently is ChatGPT. I was using it yesterday. I was telling everyone I was asking it what's hot in data science and I didn't know would it know what hot is and it did, it gave me trends. But one of the things that I was so, and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the the CTO of OpenAI is a female. I didn't know that. And I thought why are we not putting her on a pedestal? Because people are likening ChatGPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not, is another role model. Because really ultimately what WiDS is great at doing is showcasing women in technical roles. Because I always say you can't be what you can't see. We need to be able to see more role models, female role role models, underrepresented minorities of course men, because a lot of my sponsors and mentors are men, but we need more women that we can look up to and see ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished what inspires you to continue and be one of those females that we say oh my God, I could be like Shir. >> I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generative AI, for example. There's so much that can be done to further help you know, people make more money and do less work because there's still so much work that we do that we don't need to. You know, this is with Intuit, but also there are so many other use cases like I heard today you know, with the talk about the police. So that was really exciting how you can apply machine learning and data to actually help people, to help people that been through wrongful things. So I was really moved by that. And I'm also really excited about all the medical applications that we can have with data. >> Yeah, yeah. It's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have like equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? >> Yeah, so I have so much to say on this. >> Good. >> But in my work in Tel Aviv, I had the opportunity to start with Intuit women in data science branch in Tel Aviv. So that's why I'm super excited to be here today for that because basically this is the original conference, but as you know, there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018. And we've been through already this year it's going to be it's next week, it's going to be the sixth conference. And every year our number of submission to make talk in the conference doubled itself. >> Nice. >> We started with 20 submission, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. >> Ah, that's fantastic. >> And beyond the fact that there's so much traction, I also feel the great impact it has on the community in Israel because one of the reason we started WiDS was that when I was going to conferences I was seeing so little women on stage in all the technical conferences. You know, kind of the reason why I guess you know, Margaret and team started the WiDS conference. So I saw the same thing in Israel and I was always frustrated. I was organizing PyData Meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough, you know. We need more. So once we started WiDS and people saw you know, so many examples on the stage and also you know females got opportunity to talk in a place for that. Then it also started spreading and you can see more and more female speakers across other conferences, which are not women in data science. So I think just the fact that Intuits started this conference back in Israel and also in Bangalore and also the support Intuit does for WiDS in Stanford here, it shows how much WiDS values are aligned with our values. Yeah, and I think that to chauffeur that I think we have over 35% females in the data science and machine learning engineering roles, which is pretty amazing I think compared to the industry. >> Way above average. Yeah, absolutely. I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that 'cause usually if we look at the industry to you point, over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org showed that that number is now 27.6%. So it's very slowly- >> It's very slowly increasing. >> Going in the right direction. >> Too slow. >> And that representation of women technologists increase at every level, except intern, which I thought was really interesting. And I wonder is there a covid relation there? >> I don't know. >> What do we need to do to start opening up the the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that the hiring we've seen improvements, but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked, Hannah about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have you know, 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it, and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural. >> Yeah. >> At an organization and culture can be hard to change, but it sounds like you guys kind of have it dialed in. >> I think we definitely do. That's why I really like working and Intuit. And I think that a lot of it is with the role modeling, diversity and inclusion, and by having women leaders. When you see a woman in leadership position, as a woman it makes you want to come work at this place. And as an evidence, when I build the team I started in Israel at Intuit, I have over 50% women in my team. >> Nice. >> Yeah, because when you have a woman in the interviewers panel, it's much easier, it's more inclusive. That's why we always try to have at least you know, one woman and also other minorities represented in our interviews panel. Yeah, and I think that in general it's very important as a leader to kind of know your own biases and trying to have defined standard and rubrics in how you evaluate people to avoid for those biases. So all of that inclusiveness and leadership really helps to get more diversity in your teams. >> It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time, I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that so the biases start to be dialed down and I think a conference like WiDS and it sounds like someone like you and what you've already done so far in the work that you're doing having so many females raise their hands to want to do talks at events is a good situation. It's a good scenario and hopefully it will continue to move the needle on the percentage of females in technical roles. So we thank you Shir for your time sharing with us your story, what you're doing, how Intuit and WiDS are working together. It sounds like there's great alignment there and I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. >> Thank you very much. >> All right. >> I enjoyed very, very much >> Good. We hope, we aim to please. Thank you for our guests and for Hannah Freitag. This is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.

>> Hello everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE been profiling the leaders in the technology world, women in technology from developers to the boardroom, everything in between. We have two great guests promoting in from Malaysia. Nancy Wang is the general manager, also CUBE alumni from AWS Data Protection, and founder and board chair of Advancing Women in Tech, awit.org. And of course Kate Watts who's the executive director of Advancing Women in Tech.org. So it's awit.org. Nancy, Kate, thanks for coming all the way across remotely from Malaysia. >> Of course, we're coming to you as fast as our internet bandwidth will allow us. And you know, I'm just thrilled today that you get to see a whole nother aspect of my life, right? Because typically we talk about AWS, and here we're talking about a topic near and dear to my heart. >> Well, Nancy, I love the fact that you're spending a lot of time taking the empowerment to go out and help the industries and helping with the advancement of women in tech. Kate, the executive director it's a 501C3, it's nonprofit, dedicating to accelerating the careers of women in groups in tech. Can you talk about the organization? >> Yes, I can. So Advancing Women in Tech was founded in 2017 in order to fix some of the pathway problems that we're seeing on the rise to leadership in the industry. And so we specifically focus on supporting mid-level women in technical roles, get into higher positions. We do that in a few different ways through mentorship programs through building technical skills and by connecting people to a supportive community. So you have your peer network and then a vertical sort of relationships to help you navigate the next steps in your career. So to date we've served about 40,000 individuals globally and we're just looking to expand our reach and impact and be able to better support women in the industry. >> Nancy, talk about the creation, the origination story. How'd this all come together? Obviously the momentum, everyone in the industry's been focused on this for a long time. Where did AWIT come from? Advancing Women in Technology, that's the acronym. Advancing Women in Technology.org, where'd it come from? What's the origination story? >> Yeah, so AWIT really originated from this desire that I had, to Kate's point around, well if you look around right and you know, don't take my word for it, right? Look at stats, look at news reports, or just frankly go on your LinkedIn and see how many women in underrepresented groups are in senior technical leadership roles right out in the companies whose names we all know. And so that was my case back in 2016. And so when I first got the idea and back then I was actually at Google, just another large tech company in the valley, right? It was about how do we get more role models, how we get more, for example, women into leadership roles so they can bring up the next generation, right? And so this is actually part of a longer speech that I'm about to give on Wednesday and part of the US State Department speaker program. In fact, that's why Kate and I are here in Malaysia right now is working with over 200 women entrepreneurs from all over in Southeast Asia, including Malaysia Philippines, Vietnam, Borneo, you know, so many countries where having more women entrepreneurs can help raise the GDP right, and that fits within our overall mission of getting more women into top leadership roles in tech. >> You know, I was talking about Teresa Carlson she came on the program as well for this year this next season we're going to do. And she mentioned the decision between the US progress and international. And she's saying as much as it's still bad numbers, it's worse than outside the United States and needs to get better. Can you comment on the global aspect? You brought that up. I think it's super important to highlight that it's just not one area, it's a global evolution. >> Absolutely, so let me start, and I'd love to actually have Kate talk about our current programs and all of the international groups that we're working with. So as Teresa aptly mentioned there is so much work to be done not just outside the US and North Americas where typically tech nonprofits will focus, but rather if you think about the one to end model, right? For example when I was doing the product market fit workshop for the US State Department I had women dialing in from rice fields, right? So let me just pause there for a moment. They were holding their cell phones up near towers near trees just so that they can get a few minutes of time with me to do a workshop and how to accelerate their business. So if you don't call that the desire to propel oneself or accelerate oneself, not sure what is, right. And so it's really that passion that drove me to spend the next week and a half here working with local entrepreneurs working with policy makers so we can take advantage and really leverage that passion that people have, right? To accelerate more business globally. And so that's why, you know Kate will be leading our contingent with the United Nations Women Group, right? That is focused on women's economic empowerment because that's super important, right? One aspect can be sure, getting more directors, you know vice presidents into companies like Google and Amazon. But another is also how do you encourage more women around the world to start businesses, right? To reach economic and freedom independence, right? To overcome some of the maybe social barriers to becoming a leader in their own country. >> Yes, and if I think about our own programs and our model of being very intentional about supporting the learning development and skills of women and members of underrepresented groups we focused very much on providing global access to a number of our programs. For instance, our product management certification on Coursera or engineering management our upcoming women founders accelerator. We provide both access that you can get from anywhere. And then also very intentional programming that connects people into the networks to be able to further their networks and what they've learned through the skills online, so. >> Yeah, and something Kate just told me recently is these courses that Kate's mentioning, right? She was instrumental in working with the American Council on Education and so that our learners can actually get up to six college credits for taking these courses on product management engineering management, on cloud product management. And most recently we had our first organic one of our very first organic testimonials was from a woman's tech bootcamp in Nigeria, right? So if you think about the worldwide impact of these upskilling courses where frankly in the US we might take for granted right around the world as I mentioned, there are women dialing in from rice patties from other, you know, for example, outside the, you know corporate buildings in order to access this content. >> Can you think about the idea of, oh sorry, go ahead. >> Go ahead, no, go ahead Kate. >> I was going to say, if you can't see it, you can't become it. And so we are very intentional about ensuring that we have we're spotlighting the expertise of women and we are broadcasting that everywhere so that anybody coming up can gain the skills and the networks to be able to succeed in this industry. >> We'll make sure we get those links so we can promote them. Obviously we feel the same way getting the word out. I think a couple things I'd like to ask you guys cause I think you hit a great point. One is the economic advantage the numbers prove that diverse teams perform better number one, that's clear. So good point there. But I want to get your thoughts on the entrepreneurial equation. You mentioned founders and startups and there's also different makeups in different countries. It's not like the big corporations sometimes it's smaller business in certain areas the different cultures have different business sizes and business types. How do you guys see that factoring in outside the United States, say the big tech companies? Okay, yeah. The easy lower the access to get in education than stay with them, in other countries is it the same or is it more diverse in terms of business? >> So what really actually got us started with the US State Department was around our work with women founders. And I love for Kate to actually share her experience working with AWS startups in that capacity. But frankly, you know, we looked at the content and the mentor programs that were providing women who wanted to be executives, you know, quickly realize a lot of those same skills such as finding customers, right? Scaling your product and building channels can also apply to women founders, not just executives. And so early supporters of our efforts from firms such as Moderna up in Seattle, Emergence Ventures, Decibel Ventures in, you know, the Bay Area and a few others that we're working with right now. Right, they believed in the mission and really helped us scale out what is now our existing platform and offerings for women founders. >> Those are great firms by the way. And they also are very founder friendly and also understand the global workforce. I mean, that's a whole nother dimension. Okay, what's your reaction to all that? >> Yes, we have been very intentional about taking the product expertise and the learnings of women and in our network, we first worked with AWS startups to support the development of the curriculum for the recent accelerator for women founders that was held last spring. And so we're able to support 25 founders and also brought in the expertise of about 20 or 30 women from Advancing Women in Tech to be able to be the lead instructors and mentors for that. And so we have really realized that with this network and this individual sort of focus on product expertise building strong teams, we can take that information and bring it to folks everywhere. And so there is very much the intentionality of allowing founders allowing individuals to take the lessons and bring it to their individual circumstances and the cultures in which they are operating. But the product sense is a skill that we can support the development of and we're proud to do so. >> That's awesome. Nancy, I want to ask you some never really talk about data storage and AWS cloud greatness and goodness, here's different and you also work full-time at AWS and you're the founder or the chairman of this great organization. How do you balance both and do you get, they're getting behind you on this, Amazon is getting behind you on this. >> Well, as I say it's always easier to negotiate on the way in. But jokes aside, I have to say the leadership has been tremendously supportive. If you think about, for example, my leaders Wayne Duso who's also been on the show multiple times, Bill Vaas who's also been on the show multiple times, you know they're both founders and also operators entrepreneurs at heart. So they understand that it is important, right? For all of us, it's really incumbent on all of us who are in positions to do so, to create a pathway for more people to be in leadership roles for more people to be successful entrepreneurs. So, no, I mean if you just looked at LinkedIn they're always uploading my vote so they reach to more audiences. And frankly they're rooting for us back home in the US while we're in Malaysia this week. >> That's awesome. And I think that's a good culture to have that empowerment and I think that's very healthy. What's next for you guys? What's on the agenda? Take us through the activities. I know that you got a ton of things happening. You got your event out there, which is why you're out there. There's a bunch of other activities. I think you guys call it the Advancing Women in Tech week. >> Yes, this week we are having a week of programming that you can check out at Advancing Women in Tech.org. That is spotlighting the expertise of a number of women in our space. So it is three days of programming Tuesday, Wednesday and Thursday if you are in the US so the seventh through the ninth, but available globally. We are also going to be in New York next week for the event at the UN and are looking to continue to support our mentorship programs and also our work supporting women founders throughout the year. >> All right. I have to ask you guys if you don't mind get a little market data so you can share with us here at theCUBE. What are you hearing this year that's different in the conversation space around the topics, the interests? Obviously I've seen massive amounts of global acceleration around conversations, more video, things like this more stories are scaling, a lot more LinkedIn activity. It just seems like it's a lot different this year. Can you guys share any kind of current trends you're seeing relative to the conversations and topics being discussed across the the community? >> Well, I think from a needle moving perspective, right? I think due to the efforts of wonderful organizations including the Q for spotlighting all of these awesome women, right? Trailblazing women and the nonprofits the government entities that we work with there's definitely more emphasis on creating access and creating pathways. So that's probably one thing that you're seeing is more women, more investors posting about their activities. Number two, from a global trend perspective, right? The rise of women in security. I noticed that on your agenda today, you had Lena Smart who's a good friend of mine chief information security officer at MongoDB, right? She and I are actually quite involved in helping founders especially early stage founders in the security space. And so globally from a pure technical perspective, right? There's right more increasing regulations around data privacy, data sovereignty, right? For example, India's in a few weeks about to get their first data protection regulation there locally. So all of that is giving rise to yet another wave of opportunity and we want women founders uniquely positioned to take advantage of that opportunity. >> I love it. Kate, reaction to that? I mean founders, more pathways it sounds like a neural network, it sounds like AI enabled. >> Yes, and speaking of AI, with the rise of that we are also hearing from many community members the importance of continuing to build their skills upskill learn to be able to keep up with the latest trends. There's a lot of people wondering what does this mean for my own career? And so they're turning to organizations like Advancing Women in Tech to find communities to both learn the latest information, but also build their networks so that they are able to move forward regardless of what the industry does. >> I love the work you guys are doing. It's so impressive. I think the economic angle is new it's more amplified this year. It's always kind of been there and continues to be. What do you guys hope for by next year this time what do you hope to see different from a needle moving perspective, to use your word Nancy, for next year? What's the visual output in your mind? >> I want to see real effort made towards 50-50 representation in all tech leadership roles. And I'd like to see that happen by 2050. >> Kate, anything on your end? >> I love that. I'm going to go a little bit more touchy-feely. I want everybody in our space to understand that the skills that they build and that the networks they have carry with them regardless of wherever they go. And so to be able to really lean in and learn and continue to develop the career that you want to have. So whether that be at a large organization or within your own business, that you've got the potential to move forward on that within you. >> Nancy, Kate, thank you so much for your contribution. I'll give you the final word. Put a plug in for the organization. What are you guys looking for? Any kind of PSA you want to share with the folks watching? >> Absolutely, so if you're in a position to be a mentor, join as a mentor, right? Help elevate and accelerate the next generation of women leaders. If you're an investor help us invest in more women started companies, right? Women founded startups and lastly, if you are women looking to accelerate your career, come join our community. We have resources, we have mentors and who we have investors who are willing to come in on the ground floor and help you accelerate your business. >> Great work. Thank you so much for participating in our International Women's Day 23 program and we'd look to keep this going quarterly. We'll see you next year, next time. Thanks for coming on. Appreciate it. >> Thanks so much John. >> Thank you. >> Okay, women leaders here. >> Nancy: Thanks for having us >> All over the world, coming together for a great celebration but really highlighting the accomplishments, the pathways the investment, the mentoring, everything in between. It's theCUBE. Bring as much as we can. I'm John Furrier, your host. Thanks for watching.

>>How y'all doing? My name's Savannah Peterson, coming to you from Detroit, Michigan, where the cube is excited to be at Cube Con. Our guest this afternoon is a wonderfully brilliant woman who's been leading in the space for over eight years. Please welcome Gar Kapur. Gar, thanks for being with us. >>Well, thank you for having me to, It's a pleasure. Good >>To see you. So, update what's going on here? Co saw you at VMware Explorer. Yes. Welcome back to the Cube. Yes. What's, what's going on for you guys here? What's the message? What's the story >>Soupcon like I always say, it's our event, it's our audience. So, you know, Minayo, I dunno if you've been keeping track, Mani ha did reach like a billion docker downloads recently. So >>Congratulations. >>This is your tribe right here. Yes, >>It is. It is. Our >>Tribe's native infrastructure. Come on. Yes. >>You know, this audience understands us. We understand them. You know, you were asking when did we start the company? So we started in 2014, and if you see, Kubernetes was born in 2015 in all sorts of ways. So we kind of literally grew up together along with the Kubernetes journey. So all the decisions that we took were just, you know, making sure that we addressed the Kubernetes and the cloud native audiences, the first class citizens when it comes to storage. So I think that has been very instrumental in leading us up to the point where we have reached a billion docker downloads and we are the most loved object storage out >>There. So, So do you like your younger brother Kubernetes? Or not? Is this is It's a family that gets along. >>It does get along. I think in, in Kubernetes space, what we are seeing from customer standpoint as well, right? They're warming up to Kubernetes and you know, they are using Kubernetes as a framework to deploy anything at scale. And especially when you're, you know, offering storage as a service to your, whether it is for your internal audience or to the external audience, Kubernetes becomes extremely instrumental because it makes Multitenancy extremely easy. It makes, you know, access control points extremely easy for different user sets and so on. Yeah. So Kubernetes is definitely the way to go. I think enterprises need to just have little bit more skill set when it comes to Kubernetes overall, because I think there are still little bit areas in which they need to invest in, but I think this is the right direction, This is the right way. If you, if you want multi-tenant, you need Kubernetes for compute, you need Kubernetes for storage. So >>You guys hit an interesting spot here with Kubernetes. You have a product that targets builders. Yes. But also it's a service that's consumed. >>Yes. Yes. >>How do you see those two lanes shaping out as the world starts to grow, the ecosystems growing, You've got products for builders and products for people who are developers consuming services. How do you see that shaking out? Is just, is there intersections there? There is. You seem to be hitting that. >>There is. There is definitely an intersection. And I think it's getting merged because a lot of these users are the ones who dictate what kind of stack they want as part of their application ecosystem overall, right? So that is where, when an application, for example, in the big data workloads, right? They tell their IT or their storage department, this is the S3 compatible storage that they want their applications to run on or sit on. So the bridges definitely like becoming very narrow in that way from builders versus the service consumers overall. And I think, you know, at the end of the day, people need to get their job done from application users perspective. They want to just get in and get out. They don't want to deal with the underlying complexity when it comes to storage or any of the framework, right? So I think what we enable is for the builders to make sure they have extremely easy, simple, high performance software service that they can offer it to their customers, which is as three compatible. So now they can take their applications wherever they need to go, whether it is edge, whether it is on-prem, whether it is any of the public cloud, wherever you need to be, go be with it. With >>Mei, I mean, I wanna get your thoughts on a really big trend that's happening now. That's right. In your area of expertise. That is people are realizing that, hey, I don't necessarily need AWS S3 for storage. I gotta do my own storage or build my own. So there's a cost slash value for commodity storage. Yes. When does a company just dive to what to do there? Do they do their own? You see, CloudFlare, you seeing Wasabi, other companies? Yes. Merging. You guys are here. Yeah, yeah. Common services then there's a differentiator in the cloud. What's the, what's this all about? >>Yeah, so there are a couple of things going on in this space, right? So firstly, I think cloud model is the way to go. And what, what we mean by cloud is not public cloud, it's the cloud operating model overall, right? You need to build the applications the correct way so that they can consume cloud native infrastructure correctly. So I think that is what is going on. And secondly, I think cloud is great for your burst workloads. It's all about productivity. It's all about getting your applications to the market as fast as you can. And that is where of course, MIN IO comes into play when you know you can develop your applications natively on something like mania. And when, when you take it to production, it's very easy no matter where you go. And thirdly, I think when it comes to the cost perspective, you know, what we offer to the customers is predictability of the cost and no surprise in the builds when it comes, which is extremely important to like a CFO of a company because everyone knows that cloud is not the cheapest place to run your sustainable workloads. And there is unpredictability element involved because, you know, people leave their buckets on, people leave their compute nodes on it, it happens all the time. So I think if you take that uncertainty out of it and have more predictability around it, I think that is, that is where the true value lies. >>You're really hitting on a theme that we've been hearing a lot on the cube today, which is standardization, predictability. Yes. We, everyone always wants to move fast, but I think we're actually stepping away from that Mark Zuckerberg parity, move fast and break things and let's move fast, but know how much it's gonna cost and also decrease the complexity. Drugs >>Don't things. >>Yeah, yeah, yeah, exactly. And try, you know, minimize the collateral damage when Yeah. I, I love that you're enabling folks like that. How is, I'm curious because I see that your background, you have a PhD in philosophy, so we don't always see philosophy and DevOps and Kubernetes in the same conversation. Yeah. So how does this translate into your leadership within your team and the, And Min i's culture, >>So it's PhD in financial management and financial economics. So that is where my specialization lies. And I think after that I came to Bay Area. So once you're in Bay Area, you cannot escape technology. It is >>To you, >>It is just the way things are. You cannot escape startups, you cannot escape technology overall. So that's how I got introduced to it. And yeah, that it has been a great journey so far. And from the culture standpoint of view, you know, I always tell like if I can learn technology, anyone can learn technology. So what we look for is the right attitude, the right kind of, you know, passion to learn is what is most important in this world if you want to succeed. And that's what I tell everyone who joins the, who joins win I, two months, three months, you'll be up and going. I, I'm not too worried about it. >>But pet pedigree doesn't always play into it because no, the changing technology you could level up. So for sure you get into those and be contributing. >>I think one of the reasons why we have been successful the way we have been successful with storage is because we've not hired storage experts. Because they come with their own legacy and mindset of how to build things. And we are like, and we always came from a point of view, we are not a storage company. We are a data company and we want to be close to the data. So when you come to that mindset, you build a product directly attacking data, not just like, you know, in traditional appliance world and so on, so forth. So I think those things have been very instrumental in terms of getting the right people on board, making sure that they're very aligned with how we do things and you know, the dnf, the company's, >>That's for passion and that's actually counterintuitive, but it's makes sense. Yes. In new markets it doesn't always seem to take the boiler plate. Yes. Skill set or person. No, we're doing journalism, but we don't hire journalists. No, >>I mean you gotta be, It's adventurers. It is. It's curious. >>Exactly. Exactly. Yeah, I, yeah, I think also, you know, for you to disrupt any space, you cannot approach it from how they approach the problem. You need to completely turn the tables upside down as they say, right? You need to disrupt it and have the surprise element. And I think that is what always makes a technology very special. You cannot follow the path that others have followed. You need to come from a different space, different mindset altogether. So that is where it's important that you, like you said, adventurous are the people >>That that is for sure. Talk to us about the company. Are you growing scaling? How do people find out more? >>Oh yeah, for sure. So people can find out more by visiting our website. Min dot i, we are growing. We just closed last year, end of last year we closed our CDC round unicorn valuation and so on, so forth. So >>She says unicorn valuation, so casually, I just wanna point that out, that, that, that, that's funny. Like a true strong female leader. I love that. I >>Love that. Thank you. Yes. So in terms of, you know, in terms of growth and scalability, we are growing the team. We are, you know, onboarding more commercial customers to the platform. So yeah, it's growth all across growth from the community standpoint, growth from commercial number standpoint. So congratulations. Yeah, thank you. >>Yeah, that's very exciting. Grma, thank you so much for being, >>Being with us. Thank you for >>Having me. Always. Thanks for hanging out and to all of you, thank you so much for tuning into the Cube, especially for this exciting edition for all of us here in Detroit, Michigan, where we're coming to you from Cuban. See you back here in a little bit.

(upbeat music) >> Hello everyone, welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE in theCUBE Studios. We've got a special video here. We love when we have startups that are launching. It's an exclusive video of a hot startup that's launching. Got great reviews so far. You know, word on the street is, they got something different and unique. We're going to' dig into it. Amit Govrin who's the CEO and co-founder of Kubiya, which stands for Cube in Hebrew, and they're headquartered in Bay Area and in Tel Aviv. Amit, congratulations on the startup launch and thanks for coming in and talk to us in theCUBE >> Thank you, John, very nice to be here. >> So, first of all, a little, 'cause we love the Cube, 'cause theCUBE's kind of an open brand. We've never seen the Cube in Hebrew, so is that true? Kubiya is? >> Kubiya literally means cube. You know, clearly there's some additional meanings that we can discuss. Obviously we're also launching a KubCon, so there's a dual meaning to this event. >> KubCon, not to be confused with CubeCon. Which is an event we might have someday and compete. No, I'm only kidding, good stuff. I want to get into the startup because I'm intrigued by your story. One, you know, conversational AI's been around, been a category. We've seen chat bots be all the rage and you know, I kind of don't mind chat bots on some sites. I can interact with some, you know, form based knowledge graph, whatever, knowledge database and get basic stuff self served. So I can see that, but it never really scaled or took off. And now with Cloud Native kind of going to the next level, we're starting to see a lot more open source and a lot more automation, in what I call AI as code or you know, AI as a service, machine learning, developer focused action. I think you guys might have an answer there. So if you don't mind, could you take a minute to explain what you guys are doing, what's different about Kubiya, what's happening? >> Certainly. So thank you for that. Kubiya is what we would consider the first, or one of the first, advanced virtual assitants with a domain specific expertise in DevOps. So, we respect all of the DevOps concepts, GitOps, workflow automation, of those categories you've mentioned, but also the added value of the conversational AI. That's really one of the few elements that we can really bring to the table to extract what we call intent based operations. And we can get into what that means in a little bit. I'll save that maybe for the next question. >> So the market you're going after is kind of, it's, I love to hear starters when they, they don't have a Gartner Magic quadrant, they can fit nicely, it means they're onto something. What is the market you're going after? Because you're seeing a lot of developers driving a lot of the key successes in DevOps. DevOps has evolved to the point where, and DevSecOps, where developers are driving the change. And so having something that's developer focused is key. Are you guys targeting the developers, IT buyers, cloud architects? Who are you looking to serve with this new opportunity? >> So essentially self-service in the world of DevOps, the end user typically would be a developer, but not only, and obviously the operators, those are the folks that we're actually looking to help augment a lot of their efforts, a lot of the toil that they're experiencing in a day to day. So there's subcategories within that. We can talk about the different internal developer tools, or platforms, shared services platforms, service catalogs are tangential categories that this kind of comes on. But on top of that, we're adding the element of conversational AI. Which, as I mentioned, that's really the "got you". >> I think you're starting to see a lot of autonomous stuff going on, autonomous pen testing. There's a company out there doing I've seen autonomous AI. Automation is a big theme of it. And I got to ask, are you guys on the business side purely in the cloud? Are you born in the cloud, is it a cloud service? What's the product choice there? It's a service, right? >> Software is a service. We have the classic, Multi-Tenancy SAAS, but we also have a hybrid SAAS solution, which allows our customers to run workflows using remote runners, essentially hosted at their own location. >> So primary cloud, but you're agnostic on where they could consume, how they want to' consume the product. >> Technology agnostic. >> Okay, so that's cool. So let's get into the problem you're solving. So take me through, this will drive a lot of value here, when you guys did the company, what problems did you hone in on and what are you guys seeing as the core problem that you solve? >> So we, this is a unique, I don't know how unique, but this is a interesting proposition because I come from the business side, so call it the top down. I've been in enterprise sales, I've been in a CRO, VP sales hat. My co-founder comes from the bottom up, right? He ran DevOps teams and SRE teams in his previous company. That's actually what he did. So, we met each other halfway, essentially with me seeing a lot of these problems of self-service not being so self-service after all, platforms hitting walls with adoption. And he actually created his own self-service platform, within his last company, to address his own personal pains. So we essentially kind of met with both perspectives. >> So you're absolutely hardcore on self-service. >> We're enabling self-service. >> And that basically is what everybody wants. I mean, the developers want self-service. I mean, that's kind of like, you know, that's the nirvana. So take us through what you guys are offering, give us an example of use cases and who's buying your product, why, and take us through that whole piece. >> Do you mind if I take a step back and say why we believe self-service has somewhat failed or not gotten off. >> Yeah, absolutely. >> So look, this is essentially how we're looking at it. All the analysts and the industry insiders are talking about self-service platforms as being what's going to' remove the dependency of the operator in the loop the entire time, right? Because the operator, that scarce resource, it's hard to hire, hard to train, hard to retain those folks, Developers are obviously dependent on them for productivity. So the operators in this case could be a DevOps, could be a SecOps, it could be a platform engineer. It comes in different flavors. But the common denominator, somebody needs an access request, provisioning a new environment, you name it, right? They go to somebody, that person is operator. The operator typically has a few things on their plate. It's not just attending and babysitting platforms, but it's also innovating, spinning up, and scaling services. So they see this typically as kind of, we don't really want to be here, we're going to' go and do this because we're on call. We have to take it on a chin, if you may, for this. >> It's their child, they got to' do it. >> Right, but it's KTLOs, right, keep the lights on, this is maintenance of a platform. It's not what they're born and bred to do, which is innovate. That's essentially what we're seeing, we're seeing that a lot of these platforms, once they finally hit the point of maturity, they're rolled out to the team. People come to serve themselves in platform, and low and behold, it's not as self-service as it may seem. >> We've seen that certainly with Kubernetes adoption being, I won't say slow, it's been fast, but it's been good. But I think this is kind of the promise of what SRE was supposed to be. You know, do it once and then babysit in the sense of it's working and automated. Nothing's broken yet. Don't call me unless you need something, I see that. So the question, you're trying to make it easier then, you're trying to free up the talent. >> Talent to operate and have essentially a human, like in the loop, essentially augment that person and give the end users all of the answers they require, as if they're talking to a person. >> I mean it's basically, you're taking the virtual assistant concept, or chat bot, to a level of expertise where there's intelligence, jargon, experience into the workflows that's known. Not just talking to chat bot, get a support number to rebook a hotel room. >> We're converting operational workflows into conversations. >> Give me an example, take me through an example. >> Sure, let's take a simple example. I mean, not everyone provisions EC2's with two days (indistinct). But let's say you want to go and provision new EC2 instances, okay? If you wanted to do it, you could go and talk to the assistant and say, "I want to spin up a new server". If it was a human in the loop, they would ask you the following questions: what type of environment? what are we attributing this to? what type of instance? security groups, machine images, you name it. So, these are the questions that typically somebody needs to be armed with before they can go and provision themselves, serve themselves. Now the problem is users don't always have these questions. So imagine the following scenario. Somebody comes in, they're in Jira ticket queue, they finally, their turn is up and the next question they don't have the answer to. So now they have to go and tap on a friend, or they have to go essentially and get that answer. By the time they get back, they lost their turn in queue. And then that happens again. So, they lose a context, they lose essentially the momentum. And a simple access request, or a simple provision request, can easily become a couple days of ping pong back and forth. This won't happen with the virtual assistant. >> You know, I think, you know, and you mentioned chat bots, but also RPA is out there, you've seen a lot of that growth. One of the hard things, and you brought this up, I want to get your reaction to, is contextualizing the workflow. It might not be apparent, but the answer might be there, it disrupts the entire experience at that point. RPA and chat bots don't have that contextualization. Is that what you guys do differently? Is that the unique flavor here? Is that difference between current chat bots and RPA? >> The way we see it, I alluded to the intent based operations. Let me give a tangible experience. Even not from our own world, this will be easy. It's a bidirectional feedback loop 'cause that's actually what feeds the context and the intent. We all know Waze, right, in the world of navigation. They didn't bring navigation systems to the world. What they did is they took the concept of navigation systems that are typically satellite guided and said it's not just enough to drive down the 280, which typically have no traffic, right, and to come across traffic and say, oh, why didn't my satellite pick that up? So they said, have the end users, the end nodes, feed that direction back, that feedback, right. There has to be a bidirectional feedback loop that the end nodes help educate the system, make the system be better, more customized. And that's essentially what we're allowing the end users. So the maintenance of the system isn't entirely in the hands of the operators, right? 'Cause that's the part that they dread. And the maintenance of the system is democratized across all the users that they can teach the system, give input to the system, hone in the system in order to make it more of the DNA of the organization. >> You and I were talking before you came on this camera interview, you said playfully that the Siri for DevOps, which kind of implies, hey infrastructure, do something for me. You know, we all know Siri, so we get that. So that kind of illustrates kind of where the direction is. Explain why you say that, what does that mean? Is that like a NorthStar vision that you guys are approaching? You want to' have a state where everything's automated in it's conversational deployments, that kind of thing. And take us through why that Siri for DevOps is. >> I think it helps anchor people to what a virtual assistant is. Because when you hear virtual assistant, that can mean any one of various connotations. So the Siri is actually a conversational assistant, but it's not necessarily a virtual assistant. So what we're saying is we're anchoring people to that thought and saying, we're actually allowing it to be operational, turning complex operations into simple conversations. >> I mean basically they take the automate with voice Google search or a query, what's the score of the game? And, it also, and talking to the guy who invented Siri, I actually interviewed on theCUBE, it's a learning system. It actually learns as it gets more usage, it learns. How do you guys see that evolving in DevOps? There's a lot of jargon in DevOps, a lot of configurations, a lot of different use cases, a lot of new technologies. What's the secret sauce behind what you guys do? Is it the conversational AI, is it the machine learning, is it the data, is it the model? Take us through the secret sauce. >> In fact, it's all the above. And I don't think we're bringing any one element to the table that hasn't been explored before, hasn't been done. It's a recipe, right? You give two people the same ingredients, they can have complete different results in terms of what they come out with. We, because of our domain expertise in DevOps, because of our familiarity with developer workflows with operators, we know how to give a very well suited recipe. Five course meal, hopefully with Michelin stars as part of that. So a few things, maybe a few of the secret sauce element, conversational AI, the ability to essentially go and extract the intent of the user, so that if we're missing context, the system is smart enough to go and to get that feedback and to essentially feed itself into that model. >> Someone might say, hey, you know, conversational AI, that was yesterday's trend, it never happened. It was kind of weak, chat bots were lame. What's different now and with you guys, and the market, that makes a redo or a second shot at this, a second bite at the apple, as they say. What do you guys see? 'Cause you know, I would argue that it's, you know, it's still early, real early. >> Certainly. >> How do you guys view that? How would you handle that objection? >> It's a fair question. I wasn't around the first time around to tell you what didn't work. I'm not afraid to share that the feedback that we're getting is phenomenal. People understand that we're actually customizing the workflows, the intent based operations to really help hone in on the dark spots. We call it last mile, you know, bottlenecks. And that's really where we're helping. We're helping in a way tribalize internal knowledge that typically hasn't been documented because it's painful enough to where people care about it but not painful enough to where you're going to' go and sit down an entire day and document it. And that's essentially what the virtual assistant can do. It can go and get into those crevices and help document, and operationalize all of those toils. And into workflows. >> Yeah, I mean some will call it grunt work, or low level work. And I think the automation is interesting. I think we're seeing this in a lot of these high scale situations where the talented hard to hire person is hired to do, say, things that were hard to do, but now harder things are coming around the corner. So, you know, serverless is great and all this is good, but it doesn't make the complexity go away. As these inflection points continue to drive more scale, the complexity kind of grows, but at the same time so is the ability to abstract away the complexity. So you're starting to see the smart, hired guns move to higher, bigger problems. And the automation seems to take the low level kind of like capabilities or the toil, or the grunt work, or the low level tasks that, you know, you don't want a high salaried person doing. Or I mean it's not so much that they don't want to' do it, they'll take one for the team, as you said, or take it on the chin, but there's other things to work on. >> I want to add one more thing, 'cause this goes into essentially what you just said. Think about it's not the virtual system, what it gives you is not just the intent and that's one element of it, is the ability to carry your operations with you to the place where you're not breaking your workflows, you're actually comfortable operating. So the virtual assistant lives inside of a command line interface, it lives inside of chat like Slack, and Teams, and Mattermost, and so forth. It also lives within a low-code editor. So we're not forcing anyone to use uncomfortable language or operations if they're not comfortable with. It's almost like Siri, it travels in your mobile phone, it's on your laptop, it's with you everywhere. >> It makes total sense. And the reason why I like this, and I want to' get your reaction on this because we've done a lot of interviews with DevOps, we've met at every CubeCon since it started, and Kubernetes kind of highlights the value of the containers at the orchestration level. But what's really going on is the DevOps developers, and the CICD pipeline, with infrastructure's code, they're basically have a infrastructure configuration at their disposal all the time. And all the ops challenges have been around that, the repetitive mundane tasks that most people do. There's like six or seven main use cases in DevOps. So the guardrails just need to be set. So it sounds like you guys are going down the road of saying, hey here's the use cases you can bounce around these use cases all day long. And just keep doing your jobs cause they're bolting on infrastructure to every application. >> There's one more element to this that we haven't really touched on. It's not just workflows and use cases, but it's also knowledge, right? Tribal knowledge, like you asked me for an example. You can type or talk to the assistant and ask, "How much am I spending on AWS, on US East 1, on so and so customer environment last week?", and it will know how to give you that information. >> Can I ask, should I buy a reserve instances or not? Can I ask that question? 'Cause there's always good trade offs between buying the reserve instances. I mean that's kind of the thing that. >> This is where our ecosystem actually comes in handy because we're not necessarily going to' go down every single domain and try to be the experts in here. We can tap into the partnerships, API, we have full extensibility in API and the software development kit that goes into. >> It's interesting, opinionated and declarative are buzzwords in developer language. So you started to get into this editorial thing. So I can bring up an example. Hey cube, implement the best service mesh. What answer does it give you? 'Cause there's different choices. >> Well this is actually where the operator, there's clearly guard rails. Like you can go and say, I want to' spin up a machine, and it will give you all of the machines on AWS. Doesn't mean you have to get the X one, that's good for a SAP environment. You could go and have guardrails in place where only the ones that are relevant to your team, ones that have resources and budgetary, you know, guidelines can be. So, the operator still has all the control. >> It was kind of tongue in cheek around the editorialized, but actually the answer seems to be as you're saying, whatever the customer decided their service mesh is. So I think this is where it gets into as an assistant to architecting and operating, that seems to be the real value. >> Now code snippets is a different story because that goes on to the web, that goes onto stock overflow, and that's actually one of the things. So inside the CLI, you could actually go and ask for code snippets and we could actually go and populate that, it's a smart CLI. So that's actually one of the things that are an added value of that. >> I was saying to a friend and we were talking about open source and how when I grew up, there was no open source. If you're a developer now, I mean there's so much code, it's not so much coding anymore as it is connecting and integrating. >> Certainly. >> And writing glue layers, if you will. I mean there's still code, but it's not, you don't have to build it from scratch. There's so much code out there. This low-code notion of a smart system is interesting 'cause it's very matrix like. It can build its own code. >> Yes, but I'm also a little wary with low-code and no code. I think part of the problem is we're so constantly focused on categories and categorizing ourselves, and different categories take on a life of their own. So low-code no code is not necessarily, even though we have the low-code editor, we're not necessarily considering ourselves low-code. >> Serverless, no code, low-code. I was so thrown on a term the other day, architecture-less. As a joke, no we don't need architecture. >> There's a use case around that by the way, yeah, we do. Show me my AWS architecture and it will build the architect diagram for you. >> Again, serverless architect, this is all part of infrastructure's code. At the end of the day, the developer has infrastructure with code. Again, how they deploy it is the neuron. That's what we've been striving for. >> But infrastructure is code. You can destroy, you know, terraform, you can go and create one. It's not necessarily going to' operate it for you. That's kind of where this comes in on top of that. So it's really complimentary to infrastructure. >> So final question, before we get into the origination story, data and security are two hot areas we're seeing fill the IT gap, that has moved into the developer role. IT is essentially provisioned by developers now, but the OP side shifted to large scale SRE like environments, security and data are critical. What's your opinion on those two things? >> I agree. Do you want me to give you the normal data as gravity? >> So you agree that IT is now, is kind of moved into the developer realm, but the new IT is data ops and security ops basically. >> A hundred percent, and the lines are so blurred. Like who's what in today's world. I mean, I can tell you, I have customers who call themselves five different roles in the same day. So it's, you know, at the end of the day I call 'em operators 'cause I don't want to offend anybody because that's just the way it is. >> Architectural-less, we're going to' come back to that. Well, I know we're going to' see you at CubeCon. >> Yes. >> We should catch up there and talk more. I'm looking forward to seeing how you guys get the feedback from the marketplace. It should be interesting to hear, the curious question I have for you is, what was the origination story? Why did you guys come together, was it a shared problem? Was it a big market opportunity? Was it an itch you guys were scratching? Did you feel like you needed to come together and start this company? What was the real vision behind the origination? Take a take a minute to explain the story. >> No, absolutely. So I've been living in Palo Alto for the last couple years. Previous, also a founder. So, you know, from my perspective, I always saw myself getting back in the game. Spent a few years in AWS essentially managing partnerships for tier one DevOps partners, you know, all of the known players. Some in public, some of them not. And really the itch was there, right. I saw what everyone's doing. I started seeing consistency in the pains that I was hearing back, in terms of what hasn't been solved. So I already had an opinion where I wanted to go. And when I was visiting actually Israel with the family, I was introduced by a mutual friend to Shaked, Shaked Askayo, my co-founder and CTO. Amazing guy, unbelievable technologists, probably one the most, you know, impressive folks I've had a chance to work with. And he actually solved a very similar problem, you know, in his own way in a previous company, BlueVine, a FinTech company where he was head of SRE, having to, essentially, oversee 200 developers in a very small team. The ratio was incongruent to what the SRE guideline would tell. >> That's more than 10 x rate developer. >> Oh, absolutely. Sure enough. And just imagine it's four different time zones. He finishes day shift and you already had the US team coming, asking for a question. He said, this is kind of a, >> Got to' clone himself, basically. >> Well, yes. He essentially said to me, I had no day, I had no life, but I had Corona, I had COVID, which meant I could work from home. And I essentially programed myself in the form of a bot. Essentially, when people came to him, he said, "Don't talk to me, talk to the bot". Now that was a different generation. >> Just a trivial example, but the idea was to automate the same queries all the time. There's an answer for that, go here. And that's the benefit of it. >> Yes, so he was able to see how easy it was to solve, I mean, how effective it was solving 70% of the toil in his organization. Scaling his team, froze the headcount and the developer team kept on going. So that meant that he was doing some right. >> When you have a problem, and you need to solve it, the creativity comes out of the woodwork, you know, invention is the mother of necessity. So final question for you, what's next? Got the launch, what are you guys hope to do over the next six months to a year, hiring? Put a plug in for the company. What are you guys looking to do? Take a minute to share the future vision and get a plug in. >> A hundred percent. So, Kubiya, as you can imagine, announcing ourselves at CubeCon, so in a couple weeks. Opening the gates towards the public beta and NGA in the next couple months. Essentially working with dozens of customers, Aston Martin, and business earn in. We have quite a few, our website's full of quotes. You can go ahead. But effectively we're looking to go and to bring the next operator, generation of operators, who value their time, who value the, essentially, the value of tribal knowledge that travels between organizations that could be essentially shared. >> How many customers do you guys have in your pre-launch? >> It's above a dozen. Without saying, because we're actually looking to onboard 10 more next week. So that's just an understatement. It changes from day to day. >> What's the number one thing people are saying about you? >> You got that right. I know it's, I'm trying to be a little bit more, you know. >> It's okay, you can be cocky, startups are good. But I mean they're obviously, they're using the product and you're getting good feedback. Saving time, are they saying this is a dream product? Got it right, what are some of the things? >> I think anybody who doesn't feel the pain won't know, but the folks who are in the trenches, or feeling the pain, or experiencing this toil, who know what this means, they said, "You're doing this different, you're doing this right. You architected it right. You know exactly what the developer workflows," you know, where all the areas, you know, where all the skeletons are hidden within that. And you're attending to that. So we're happy about that. >> Everybody wants to clone themselves, again, the tribal knowledge. I think this is a great example of where we see the world going. Make things autonomous, operationally automated for the use cases you know are lock solid. Why wouldn't you just deploy? >> Exactly, and we have a very generous free tier. People can, you know, there's a plugin, you can sign up for free until the end of the year. We have a generous free tier. Yeah, free forever tier, as well. So we're looking for people to try us out and to give us feedback. >> I think the self-service, I think the point is, we've talked about it on the Cube at our events, everyone says the same thing. Every developer wants self-service, period. Full stop, done. >> What they don't say is they need somebody to help them babysit to make sure they're doing it right. >> The old dashboard, green, yellow, red. >> I know it's an analogy that's not related, but have you been to Whole Foods? Have you gone through their self-service line? That's the beauty of it, right? Having someone in a loop helping you out throughout the time. You don't get confused, if something's not working, someone's helping you out, that's what people want. They want a human in the loop, or a human like in the loop. We're giving that next best thing. >> It's really the ratio, it's scale. It's a scaling. It's force multiplier, for sure. Amit, thanks for coming on, congratulations. >> Thank you so much. >> See you at KubeCon. Thanks for coming in, sharing the story. >> KubiyaCon. >> CubeCon. Cube in Hebrew, Kubiya. Founder, co-founder and CEO here, sharing the story in the launch. Conversational AI for DevOps, the theory of DevOps, really kind of changing the game, bringing efficiency, solving a lot of the pain points of large scale infrastructure. This is theCUBE, CUBE conversation, I'm John Furrier, thanks for watching. (upbeat electronic music)

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

(calm electronic music) >> Okay, welcome back everyone to theCUBE's coverage here live in New York City for AWS Summit 2022. I'm John Furrier, host of theCUBE, but a great conversation here as the day winds down. First of all, 10,000 plus people, this is a big event, just New York City. So sign of the times that some headwinds are happening? I don't think so, not in the cloud enterprise innovation game. Lot going on, this innovation conversation we're going to have now is about the confluence of cloud scale integration data and the future of how FinTech and other markets are going to change with technology. We got Chris Thomas, the CTO of Slalom, and Rob Krugman, chief digital officer at Broadridge. Gentlemen, thanks for coming on theCUBE. >> Thanks for having us. >> So we had a talk before we came on camera about your firm, what you guys do, take a quick minute to just give the scope and size of your firm and what you guys work on. >> Yeah, so Broadridge is a global financial FinTech company. We work on, part of our business is capital markets and wealth, and that's about a third of our business, about $7 trillion a day clearing through our platforms. And then the other side of our business is communications where we help all different types of organizations communicate with their shareholders, communicate with their customers across a variety of different digital channels and capabilities. >> Yeah, and Slalom, give a quick one minute on Slalom. I know you guys, but for the folks that don't know you. >> Yeah, no problem. So Slalom is a modern consulting firm focused on strategy, technology, and business transformation. And me personally, I'm part of the element lab, which is focused on forward thinking technology and disruptive technology in the next five to 10 years. >> Awesome, and that's the scope of this conversation. The next five to 10 years, you guys are working on a project together, you're kind of customer partners. You're building something. What are you guys working on? I can't wait to jump into it, explain. >> Sure, so similar to Chris, at Broadridge, we've created innovation capability, innovation incubation capability, and one of the first areas we're experimenting in is digital assets. So what we're looking to do is we're looking at a variety of different areas where we think consolidation network effects that we could bring can add a significant amount of value. And so the area we're working on is this concept of a wallet of wallets. How do we actually consolidate assets that are held across a variety of different wallets, maybe traditional locations- >> Digital wallets. >> Digital wallets, but maybe even traditional accounts, bring that together and then give control back to the consumer of who they want to share that information with, how they want their transactions to be able to control. So the idea of, people talk about Web 3 being the internet of value. I often think about it as the internet of control. How do you return control back to the individual so that they can make decisions about how and who has access to their information and assets? >> It's interesting, I totally like the value angle, but your point is what's the chicken and the egg here, the cart before the horse, you can look at it both ways and say, okay, control is going to drive the value. This is an interesting nuance, right? >> Yes, absolutely. >> So in this architectural world, they thought about the data plane and the control plane. Everyone's trying to go old school, middleware thinking. Let's own the data plane, we'll win everything. Not going to happen if it goes decentralized, right, Chris? >> Yeah, yeah. I mean, we're building a decentralized application, but it really is built on top of AWS. We have a serverless architecture that scales as our business scales built on top of things like S3, Lambda, DynamoDB, and of course using those security principles like Cognito and AWS Gateway, API Gateway. So we're really building an architecture of Web 3 on top of the Web 2 basics in the cloud. >> I mean, all evolutions are abstractions on top of each other, IG, DNS, Key, it goes the whole nine yards. In digital, at least, that's the way. Question about serverless real quick. I saw that Redshift just launched general availability of serverless in Redshift? >> Yes. >> You're starting to see the serverless now part of almost all the services in AWS. Is that enabling that abstraction, because most people don't see it that way. They go, oh, well, Amazon's not Web 3. They got databases, you could use that stuff. So how do you connect the dots and cross the bridge to the future with the idea that I might not think Web 2 or cloud is Web 3? >> I'll jump in quick. I mean, I think it's the decentralize. If you think about decentralization. serverless and decentralization, you could argue are the same way of, they're saying the same thing in different ways. One is thinking about it from a technology perspective. One is thinking about it from an ecosystem perspective and how things come together. You need serverless components that can talk to each other and communicate with each other to actually really reach the promise of what Web 3 is supposed to be. >> So digital bits or digital assets, I call it digital bits, 'cause I think zero ones. If you digitize everything and everything has value or now control drives the value. I could be a soccer team. I have apparel, I have value in my logos, I have photos, I have CUBE videos. I mean some say that this should be an NFT. Yeah, right, maybe, but digital assets have to be protected, but owned. So ownership drives it too, right? >> Absolutely. >> So how does that fit in, how do you explain that? 'Cause I'm trying to tie the dots here, connect the dots and tie it together. What do I get if I go down this road that you guys are building? >> So I think one of the challenges of digital assets right now is that it's a closed community. And I think the people that play in it, they're really into it. And so you look at things like NFTs and you look at some of the other activities that are happening and there are certain naysayers that look at it and say, this stuff is not based upon value. It's a bunch of artwork, it can't be worth this. Well, how about we do a time out there and we actually look at the underlying technology that's supporting this, the blockchain, and the potential ramifications of that across the entire financial ecosystem, and frankly, all different types of ecosystems of having this immutable record, where information gets stored and gets sent and the ability to go back to it at all times, that's where the real power is. So I think we're starting to see. We've hit a bit of a hiccup, if you will, in the cryptocurrencies. They're going to continue to be there. They won't all be there. A lot of them will probably disappear, but they'll be a finite number. >> What percentage of stuff do you think is vapor BS? If you had to pick an order of magnitude number. >> (laughs) I would say at least 75% of it. (John laughs) >> I mean, there's quite a few projects that are failing right now, but it's interesting in that in the crypto markets, they're failing gracefully. Because it's on the blockchain and it's all very transparent. Things are checked, you know immediately which companies are insolvent and which opportunities are still working. So it's very, very interesting in my opinion. >> Well, and I think the ones that don't have valid premises are the ones that are failing. Like Terra and some of these other ones, if you actually really looked at it, the entire industry knew these things were no good. But then you look at stable coins. And you look at what's going on with CBDCs. These are backed by real underlying assets that people can be comfortable with. And there's not a question of, is this going to happen? The question is, how quickly is it going to happen and how quickly are we going to be using digital currencies? >> It's interesting, we always talk about software, software as money now, money is software and gold and oil's moving over to that crypto. How do you guys see software? 'Cause we were just arguing in the queue, Dave Vellante and I, before you guys came on that the software industry pretty much does not exist anymore, it's open source. So everything's open source as an industry, but the value is integration, innovation. So it's not just software, it's the free. So you got to, it's integration. So how do you guys see this software driving crypto? Because it is software defined money at the end of the day. It's a token. >> No, I think that's absolutely one of the strengths of the crypto markets and the Web 3 market is it's governed by software. And because of that, you can build a trust framework. Everybody knows it's on the public blockchain. Everybody's aware of the software that's driving the rules and the rules of engagement in this blockchain. And it creates that trust network that says, hey, I can transact with you even though I don't know anything about you and I don't need a middleman to tell me I can trust you. Because this software drives that trust framework. >> Lot of disruption, lot of companies go out of business as a middleman in these markets. >> Listen, the intermediaries either have to disrupt themselves or they will be disrupted. I think that's what we're going to learn here. And it's going to start in financial services, but it's going to go to a lot of different places. I think the interesting thing that's happening now is for the first time, you're starting to see the regulators start to get involved. Which is actually a really good thing for the market. Because to Chris's point, transparency is here, how do you actually present that transparency and that trust back to consumers so they feel comfortable once that problem is solved. And I think everyone in the industry welcomes it. All of a sudden you have this ecosystem that people can play in, they can build and they can start to actually create real value. >> Every structural change that I've been involved in my 30 plus year career has been around inflection points. There was always some sort of underbelly. So I'm not going to judge crypto. It's been in the market for a while, but it's a good sign there's innovation happening. So as now, clarity comes into what's real. I think you guys are talking a conversation I think is refreshing because you're saying, okay, cloud is real, Lambda, serverless, all these tools. So Web 3 is certainly real because it's a future architecture, but it's attracting the young, it's a cultural shift. And it's also cooler than boring Web 2 and cloud. So I think the cultural shift, the fact that it's got data involved, there's some disruption around middleman and intermediaries, makes it very attractive to tech geeks. You look at, I read a stat, I heard a stat from a friend in the Bay Area that 30% of Cal computer science students are dropping out and jumping into crypto. So it's attracting the technical nerds, alpha geeks. It's a cultural revolution and there's some cool stuff going on from a business model standpoint. >> There's one thing missing. The thing that's missing, it's what we're trying to work on, I think is experience. I think if you're being honest about the entire marketplace, what you would agree is that this stuff is not easy to use today, and that's got to be satisfied. You need to do something that if it's the 85 year old grandma that wants to actually participate in these markets that not only can they feel comfortable, but they actually know how to do it. You can't use these crazy tools where you use these terms. And I think the industry, as it grows up, will satisfy a lot of those issues. >> And I think this is why I want to tie back and get your reaction to this. I think that's why you guys talking about building on top of AWS is refreshing, 'cause it's not dogmatic. Well, we can't use Amazon, it's not really Web 3. Well, a database could be used when you need it. You don't need to write everything through the blockchain. Databases are a very valuable capability, you get serverless. So all these things now can work together. So what do you guys see for companies that want to be Web 3 for all the good reasons and how do they leverage cloud specifically to get there? What are some things that you guys have learned that you can point to and share, you want to start? >> Well, I think not everything has to be open and public to everybody. You're going to want to have some things that are secret. You're going to want to encrypt some things. You're going to want to put some things within your own walls. And that's where AWS really excels. I think you can have the best of both worlds. So that's my perspective on it. >> The only thing I would add to it, so my view is it's 2022. I actually was joking earlier. I think I was at the first re:Invent. And I remember walking in and this was a new industry. >> It was tiny. >> This is foundational. Like cloud is not a, I don't view like, we shouldn't be having that conversation anymore. Of course you should build this stuff on top of the cloud. Of course you should build it on top of AWS. It just makes sense. And we should, instead of worrying about those challenges, what we should be worrying about are how do we make these applications easier to use? How do we actually- >> Energy efficient. >> How do we enable the promise of what these things are going to bring, and actually make it real, because if it happens, think about traditional assets. There's projects going on globally that are looking at how do you take equity securities and actually move them to the blockchain. When that stuff happens, boom. >> And I like what you guys are doing, I saw the news out through this crypto winter, some major wallet exchanges that have been advertising are hurting. Take me through what you guys are thinking, what the vision is around the wallet of wallets. Is it to provide an experience for the user or the market industry itself? What's the target, is it both? Share the design goals for the wallet of wallets. >> My favorite thing about innovation and innovation labs is that we can experiment. So I'll go in saying we don't know what the final answer is going to be, but this is the premise that we have. In this disparate decentralized ecosystem, you need some mechanism to be able to control what's actually happening at the consumer level. So I think the key target is how do you create an experience where the consumer feels like they're in control of that value? How do they actually control the underlying assets? And then how does it actually get delivered to them? Is it something that comes from their bank, from their broker? Is it coming from an independent organization? How do they manage all of that information? And I think the last part of it are the assets. It's easy to think about cryptos and NFTs, but thinking about traditional assets, thinking about identity information and healthcare records, all of that stuff is going to become part of this ecosystem. And imagine being able to go someplace and saying, oh, you need my information. Well, I'm going to give it to you off my phone and I'm going to give it to you for the next 24 hours so you can use it, but after that you have no access to it. Or you're my financial advisor, here's a view of what I actually have, my underlying assets. What do you recommend I do? So I think we're going to see an evolution in the market. >> Like a data clean room. >> Yeah, but that you control. >> Yes! (laughs) >> Yes! >> I think about it very similarly as well. As my journey into the crypto market has gone through different pathways, different avenues. And I've come to a place where I'm really managing eight different wallets and it's difficult to figure exactly where all my assets are and having a tool like this will allow me to visualize and aggregate those assets and maybe even recombine them in unique ways, I think is hugely valuable. >> My biggest fear is losing my key. >> Well, and that's an experience problem that has to be solved, but let me give you, my favorite use case in this space is, 'cause NFTs, right? People are like, what does NFTs really mean? Title insurance, right? Anyone buy a house or refinance your mortgage? You go through this crazy process that costs seven or eight thousand dollars every single time you close on something to get title insurance so they could validate it. What if that title was actually sitting on the chain, you got an NFT that you put in your wallet and when it goes time to sell your house or to refinance, everything's there. Okay, I'm the owner of the house. I don't know, JP Morgan Chase has the actual mortgage. There's another lien, there's some taxes. >> It's like a link tree in the wallet. (laughs) >> Yeah, think about it, you got a smart contract. Boom, closing happens immediately. >> I think that's one of the most important things. I think people look at NFTs and they think, oh, this is art. And that's sort of how it started in the art and collectable space, but it's actually quickly moving towards utilities and tokenization and passes. And that's where I think the value is. >> And ownership and the token. >> Identity and ownership, especially. >> And the digital rights ownership and the economics behind it really have a lot of scale 'cause I appreciate the FinTech angle you are coming from because I can now see what's going on here with you. It's like, okay, we got to start somewhere. Let's start with the experience. The wallet's a tough nut to crack, 'cause that requires defacto participation in the industry as a defacto standard. So how are you guys doing there? Can you give an update and then how can people get, what's the project called and how do people get involved? >> Yeah, so we're still in the innovation, incubation stages. So we're not launching it yet. But what I will tell you is what a lot of our focus is, how do we make these transactional things that you do? How do we make it easy to pull all your assets together? How do we make it easy to move things from one location to the other location in ways that you're not using a weird cryptographic numeric value for your wallet, but you actually can use real nomenclature that you can renumber and it's easy to understand. Our expectation is that sometime in the fall, we'll actually be in a position to launch this. What we're going to do over the summer is we're going to start allowing people to play with it, get their feedback, and we're going to iterate. >> So sandbox in when, November? >> I think launch in the fall, sometime in the fall. >> Oh, this fall. >> But over the summer, what we're expecting is some type of friends and family type release where we can start to realize what people are doing and then fix the challenges, see if we're on the right track and make the appropriate corrections. >> So right now you guys are just together on this? >> Yep. >> The opening up friends and family or community is going to be controlled. >> It is, yeah. >> Yeah, as a group, I think one thing that's really important to highlight is that we're an innovation lab. We're working with Broadridge's innovation lab, that partnership across innovation labs has allowed us to move very, very quickly to build this. Actually, if you think about it, we were talking about this not too long ago and we're almost close to having an internal launch. So I think it's very rapid development. We follow a lot of the- >> There's buy-in across the board. >> Exactly, exactly, and we saw lot of very- >> So who's going to run this? A Dow, or your companies, is it going to be a separate company? >> So to be honest, we're not entirely sure yet. It's a new product that we're going to be creating. What we actually do with it. Our thought is within an innovation environment, there's three things you could do with something. You can make it a product within the existing infrastructure, you can create a new business unit or you can spin it off as something new. I do think this becomes a product within the organization based upon it's so aligned to what we do today, but we'll see. >> But you guys are financing it? >> Yes. >> As collective companies? >> Yeah, right. >> Got it, okay, cool. Well, let us know how we can help. If you guys want to do a remote in to theCUBE. I would love the mission you guys are on. I think this is the kind of work that every company should be doing in the new R and D. You got to jump in the deep end and swim as fast as possible. But I think you can do it. I think that is refreshing and that's smart. >> And you have to do it quick because this market, I think the one thing we would probably agree on is that it's moving faster than we could, every week there's something else that happens. >> Okay, so now you guys were at Consensus down in Austin when the winter hit and you've been in the business for a long time, you got to know the industries. You see where it's going. What was the big thing you guys learned, any scar tissue from the early data coming in from the collaboration? Was there some aha moments, was there some oh shoot moments? Oh, wow, I didn't think that was going to happen. Share some anecdotal stories from the experience. Good, bad, and if you want to be bold say ugly, too. >> Well, I think the first thing I want to say about the timing, it is the crypto winter, but I actually think now's a really great time to build something because everybody's continuing to build. Folks are focused on the future and that's what we are as well. In terms of some of the challenges, well, the Web 3 space is so new. And there's not a way to just go online and copy somebody else's work and rinse and repeat. We had to figure a lot of things on our own. We had to try different technologies, see which worked better and make sure that it was functioning the way we wanted it to function. Really, so it was not easy. >> They oversold that product out, that's good, like this team. >> But think about it, so the joke is that when winter is when real work happens. If you look at the companies that have not been affected by this it's the infrastructure companies and what it reminds me of, it's a little bit different, but 2001, we had the dot com bust. The entire industry blew up, but what came out of that? >> Everything that exists. >> Amazon, lots of companies grew up out of that environment. >> Everything that was promoted actually happened. >> Yes, but you know what didn't happen- >> Food delivery. >> But you know what's interesting that didn't happen- >> (laughs) Pet food, the soccer never happened. >> The whole Super Bowl, yes. (John laughs) In financial services we built on top of legacy. I think what Web 3 is doing, it's getting rid of that legacy infrastructure. And the banks are going to be involved. There's going to be new players and stuff. But what I'm seeing now is a doubling down of the infrastructure investment of saying okay, how do we actually make this stuff real so we can actually show the promise? >> One of the things I just shared, Rob, you'd appreciate this, is that the digital advertising market's changing because now banner ads and the old techniques are based on Web 2 infrastructure, basically DNS as we know it. And token problems are everywhere. Sites and silos are built because LinkedIn doesn't share information. And the sites want first party data. It's a hoarding exercise, so those practices are going to get decimated. So in comes token economics, that's going to get decimated. So you're already seeing the decline of media. And advertising, cookies are going away. >> I think it's going to change, it's going to be a flip, because I think right now you're not in control. Other people are in control. And I think with tokenomics and some of the other things that are going to happen, it gives back control to the individual. Think about it, right now you get advertising. Now you didn't say I wanted this advertising. Imagine the value of advertising when you say, you know what, I am interested in getting information about this particular type of product. The lead generation, the value of that advertising is significantly higher. >> Organic notifications. >> Yeah. >> Well, gentlemen, I'd love to follow up with you. I'm definitely going to ping in. Now I'm going to put CUBE coin back on the table. For our audience CUBE coin's coming. Really appreciate it, thanks for sharing your insights. Great conversation. >> Excellent, thank you for having us. >> Excellent, thank you so much. >> theCUBE's coverage here from New York City. I'm John Furrier, we'll be back with more live coverage to close out the day. Stay with us, we'll be right back. >> Excellent. (calm electronic music)

(upbeat music) >> Hello, everyone. Welcome to the theCUBE's presentation of the AWS Startup Showcase. The theme, this showcase is MarTech, the emerging cloud scale customer experiences. Season two of episode three, the ongoing series covering the startups, the hot startups, talking about analytics, data, all things MarTech. I'm your host, John Furrier, here joined by Christian Wiklund, founder and CEO of unitQ here, talk about harnessing the power of user feedback to empower marketing. Thanks for joining us today. >> Thank you so much, John. Happy to be here. >> In these new shifts in the market, when you got cloud scale, open source software is completely changing the software business. We know that. There's no longer a software category. It's cloud, integration, data. That's the new normal. That's the new category, right? So as companies are building their products, and want to do a good job, it used to be, you send out surveys, you try to get the product market fit. And if you were smart, you got it right the third, fourth, 10th time. If you were lucky, like some companies, you get it right the first time. But the holy grail is to get it right the first time. And now, this new data acquisition opportunities that you guys in the middle of that can tap customers or prospects or end users to get data before things are shipped, or built, or to iterate on products. This is the customer feedback loop or data, voice of the customer journey. It's a gold mine. And it's you guys, it's your secret weapon. Take us through what this is about now. I mean, it's not just surveys. What's different? >> So yeah, if we go back to why are we building unitQ? Which is we want to build a quality company. Which is basically, how do we enable other companies to build higher quality experiences by tapping into all of the existing data assets? And the one we are in particularly excited about is user feedback. So me and my co-founder, Nik, and we're doing now the second company together. We spent 14 years. So we're like an old married couple. We accept each other, and we don't fight anymore, which is great. We did a consumer company called Skout, which was sold five years ago. And Skout was kind of early in the whole mobile first. I guess, we were actually mobile first company. And when we launched this one, we immediately had the entire world as our marketplace, right? Like any modern company. We launch a product, we have support for many languages. It's multiple platforms. We have Android, iOS, web, big screens, small screens, and that brings some complexities as it relates to staying on top of the quality of the experience because how do I test everything? >> John: Yeah. >> Pre-production. How do I make sure that our Polish Android users are having a good day? And we found at Skout, personally, like I could discover million dollar bugs by just drinking coffee and reading feedback. And we're like, "Well, there's got to be a better way to actually harness the end user feedback. That they are leaving in so many different places." So, you know what, what unitQ does is that we basically aggregate all different sources of user feedback, which can be app store reviews, Reddit posts, Tweets, comments on your Facebook ads. It can be better Business Bureau Reports. We don't like to get to many of those, of course. But really, anything on the public domain that mentions or refers to your product, we want to ingest that data in this machine, and then all the private sources. So you probably have a support system deployed, a Zendesk, or an Intercom. You might have a chatbot like an Ada, or and so forth. And your end user is going to leave a lot of feedback there as well. So we take all of these channels, plug it into the machine, and then we're able to take this qualitative data. Which and I actually think like, when an end user leaves a piece of feedback, it's an act of love. They took time out of the day, and they're going to tell you, "Hey, this is not working for me," or, "Hey, this is working for me," and they're giving you feedback. But how do we package these very messy, multi-channel, multiple languages, all over the place data? How can we distill it into something that's quantifiable? Because I want to be able to monitor these different signals. So I want to turn user feedback into time series. 'Cause with time series, I can now treat this the same way as Datadog treats machine logs. I want to be able to see anomalies, and I want to know when something breaks. So what we do here is that we break down your data in something called quality monitors, which is basically machine learning models that can aggregate the same type of feedback data in this very fine grained and discrete buckets. And we deploy up to a thousand of these quality monitors per product. And so we can get down to the root cause. Let's say, passive reset link is not working. And it's in that root cause, the granularity that we see that companies take action on the data. And I think historically, there has been like the workflow between marketing and support, and engineering and product has been a bit broken. They've been siloed from a data perspective. They've been siloed from a workflow perspective, where support will get a bunch of tickets around some issue in production. And they're trained to copy and paste some examples, and throw it over the wall, file a Jira ticket, and then they don't know what happens. So what we see with the platform we built is that these teams are able to rally around the single source of troop or like, yes, passive recent link seems to have broken. This is not a user error. It's not a fix later, or I can't reproduce. We're looking at the data, and yes, something broke. We need to fix it. >> I mean, the data silos a huge issue. Different channels, omnichannel. Now, there's more and more channels that people are talking in. So that's huge. I want to get to that. But also, you said that it's a labor of love to leave a comment or a feedback. But also, I remember from my early days, breaking into the business at IBM and Hewlett-Packard, where I worked. People who complain are the most loyal customers, if you service them. So it's complaints. >> Christian: Yeah. >> It's leaving feedback. And then, there's also reading between the lines with app errors or potentially what's going on under the covers that people may not be complaining about, but they're leaving maybe gesture data or some sort of digital trail. >> Yeah. >> So this is the confluence of the multitude of data sources. And then you got the siloed locations. >> Siloed locations. >> It's complicated problem. >> It's very complicated. And when you think about, so I started, I came to Bay Area in 2005. My dream was to be a quant analyst on Wall Street, and I ended up in QA at VMware. So I started at VMware in Palo Alto, and didn't have a driver's license. I had to bike around, which was super exciting. And we were shipping box software, right? This was literally a box with a DVD that's been burned, and if that DVD had bugs in it, guess what it'll be very costly to then have to ship out, and everything. So I love the VMware example because the test cycles were long and brutal. It was like a six month deal to get through all these different cases, and they couldn't be any bugs. But then as the industry moved into the cloud, CI/CD, ship at will. And if you look at the modern company, you'll have at least 20 plus integrations into your product. Analytics, add that's the case, authentication, that's the case, and so forth. And these integrations, they morph, and they break. And you have connectivity issues. Is your product working as well on Caltrain, when you're driving up and down, versus wifi? You have language specific bugs that happen. Android is also quite a fragmented market. The binary may not perform as well on that device, or is that device. So how do we make sure that we test everything before we ship? The answer is, we can't. There's no company today that can test everything before the ship. In particular, in consumer. And the epiphany we had at our last company, Skout, was that, "Hey, wait a minute. The end user, they're testing every configuration." They're sitting on the latest device, the oldest device. They're sitting on Japanese language, on Swedish language. >> John: Yeah. >> They are in different code paths because our product executed differently, depending on if you were a paid user, or a freemium user, or if you were certain demographical data. There's so many ways that you would have to test. And PagerDuty actually had a study they came out with recently, where they said 51% of all end user impacting issues are discovered first by the end user, when they serve with a bunch of customers. And again, like the cool part is, they will tell you what's not working. So now, how do we tap into that? >> Yeah. >> So what I'd like to say is, "Hey, your end user is like your ultimate test group, and unitQ is the layer that converts them into your extended test team." Now, the signals they're producing, it's making it through to the different teams in the organization. >> I think that's the script that you guys are flipping. If I could just interject. Because to me, when I hear you talking, I hear, "Okay, you're letting the customers be an input into the product development process." And there's many different pipelines of that development. And that could be whether you're iterating, or geography, releases, all kinds of different pipelines to get to the market. But in the old days, it was like just customer satisfaction. Complain in a call center. >> Christian: Yeah. >> Or I'm complaining, how do I get support? Nothing made itself into the product improvement, except for slow moving, waterfall-based processes. And then, maybe six months later, a small tweak could be improved. >> Yes. >> Here, you're taking direct input from collective intelligence. Okay. >> Is that have input and on timing is very important here, right? So how do you know if the product is working as it should in all these different flavors and configurations right now? How do you know if it's working well? And how do you know if you're improving or not improving over time? And I think the industry, what can we look at, as far as when it relates to quality? So I can look at star ratings, right? So what's the star rating in the app store? Well, star ratings, that's an average over time. So that's something that you may have a lot of issues in production today, and you're going to get dinged on star ratings over the next few months. And then, it brings down the score. NPS is another one, where we're not going to run NPS surveys every day. We're going to run it once a quarter, maybe once a month, if we're really, really aggressive. That's also a snapshot in time. And we need to have the finger on the pulse of product quality today. I need to know if this release is good or not good. I need to know if anything broke. And I think that real time aspect, what we see as stuff sort of bubbles up the stack, and not into production, we see up to a 50% reduction in time to fix these end user impacting issues. And I think, we also need to appreciate when someone takes time out of the day to write an app review, or email support, or write that Reddit post, it's pretty serious. It's not going to be like, "Oh, I don't like the shade of blue on this button." It's going to be something like, "I got double billed," or "Hey, someone took over my account," or, "I can't reset my password anymore. The CAPTCHA, I'm solving it, but I can't get through to the next phase." And we see a lot of these trajectory impacting bugs and quality issues in these work, these flows in the product that you're not testing every day. So if you work at Snapchat, your employees probably going to use Snapchat every day. Are they going to sign up every day? No. Are they going to do passive reset every day? No. And these things are very hard to instrument, lower in the stack. >> Yeah, I think this is, and again, back to these big problems. It's smoke before fire, and you're essentially seeing it early with your process. Can you give an example of how this new focus or new mindset of user feedback data can help customers increase their experience? Can you give some examples, 'cause folks watching and be like, "Okay, I love this value. Sell me on this idea, I'm sold. Okay, I want to tap into my prospects, and my customers, my end users to help me improve my product." 'Cause again, we can measure everything now with data. >> Yeah. We can measure everything. we can even measure quality these days. So when we started this company, I went out to talk to a bunch of friends, who are entrepreneurs, and VCs, and board members, and I asked them this very simple question. So in your board meetings, or on all hands, how do you talk about quality of the product? Do you have a metric? And everyone said, no. Okay. So are you data driven company? Yes, we're very data driven. >> John: Yeah. Go data driven. >> But you're not really sure if quality, how do you compare against competition? Are you doing as good as them, worse, better? Are you improving over time, and how do you measure it? And they're like, "Well, it's kind of like a blind spot of the company." And then you ask, "Well, do you think quality of experience is important?" And they say, "Yeah." "Well, why?" "Well, top of fund and growth. Higher quality products going to spread faster organically, we're going to make better store ratings. We're going to have the storefronts going to look better." And of course, more importantly, they said the different conversion cycles in the product box itself. That if you have bugs and friction, or an interface that's hard to use, then the inputs, the signups, it's not going to convert as well. So you're going to get dinged on retention, engagement, conversion to paid, and so forth. And that's what we've seen with the companies we work with. It is that poor quality acts as a filter function for the entire business, if you're a product led company. So if you think about product led company, where the product is really the centerpiece. And if it performs really, really well, then it allows you to hire more engineers, you can spend more on marketing. Everything is fed by this product at them in the middle, and then quality can make that thing perform worse or better. And we developed a metric actually called the unitQ Score. So if you go to our website, unitq.com, we have indexed the 5,000 largest apps in the world. And we're able to then, on a daily basis, update the score. Because the score is not something you do once a month or once a quarter. It's something that changes continuously. So now, you can get a score between zero and 100. If you get the score 100, that means that our AI doesn't find any quality issues reported in that data set. And if your score is 90, that means that 10% will be a quality issue. So now you can do a lot of fun stuff. You can start benchmarking against competition. So you can see, "Well, I'm Spotify. How do I rank against Deezer, or SoundCloud, or others in my space?" And what we've seen is that as the score goes up, we see this real big impact on KPI, such as conversion, organic growth, retention, ultimately, revenue, right? And so that was very satisfying for us, when we launched it. quality actually still really, really matters. >> Yeah. >> And I think we all agree at test, but how do we make a science out of it? And that's so what we've done. And when we were very lucky early on to get some incredible brands that we work with. So Pinterest is a big customer of ours. We have Spotify. We just signed new bank, Chime. So like we even signed BetterHelp recently, and the world's largest Bible app. So when you look at the types of businesses that we work with, it's truly a universal, very broad field, where if you have a digital exhaust or feedback, I can guarantee you, there are insights in there that are being neglected. >> John: So Chris, I got to. >> So these manual workflows. Yeah, please go ahead. >> I got to ask you, because this is a really great example of this new shift, right? The new shift of leveraging data, flipping the script. Everything's flipping the script here, right? >> Yeah. >> So you're talking about, what the value proposition is? "Hey, board example's a good one. How do you measure quality? There's no KPI for that." So it's almost category creating in its own way. In that, this net new things, it's okay to be new, it's just new. So the question is, if I'm a customer, I buy it. I can see my product teams engaging with this. I can see how it can changes my marketing, and customer experience teams. How do I operationalize this? Okay. So what do I do? So do I reorganize my marketing team? So take me through the impact to the customer that you're seeing. What are they resonating towards? Obviously, getting that data is key, and that's holy gray, we all know that. But what do I got to do to change my environment? What's my operationalization piece of it? >> Yeah, and that's one of the coolest parts I think, and that is, let's start with your user base. We're not going to ask your users to ask your users to do something differently. They're already producing this data every day. They are tweeting about it. They're putting in app produce. They're emailing support. They're engaging with your support chatbot. They're already doing it. And every day that you're not leveraging that data, the data that was produced today is less valuable tomorrow. And in 30 days, I would argue, it's probably useless. >> John: Unless it's same guy commenting. >> Yeah. (Christian and John laughing) The first, we need to make everyone understand. Well, yeah, the data is there, and we don't need to do anything differently with the end user. And then, what we do is we ask the customer to tell us, "Where should we listen in the public domain? So do you want the Reddit post, the Trustpilot? What channels should we listen to?" And then, our machine basically starts ingesting that data. So we have integration with all these different sites. And then, to get access to private data, it'll be, if you're on Zendesk, you have to issue a Zendesk token, right? So you don't need any engineering hours, except your IT person will have to grant us access to the data source. And then, when we go live. We basically build up this taxonomy with the customers. So we don't we don't want to try and impose our view of the world, of how do you describe the product with these buckets, these quality monitors? So we work with the company to then build out this taxonomy. So it's almost like a bespoke solution that we can bootstrap with previous work we've done, where you don't have these very, very fine buckets of where stuff could go wrong. And then what we do is there are different ways to hook this into the workflow. So one is just to use our products. It's a SaaS product as anything else. So you log in, and you can then get this overview of how is quality trending in different markets, on different platforms, different languages, and what is impacting them? What is driving this unitQ Score that's not good enough? And all of these different signals, we can then hook into Jira for instance. We have a Jira integration. We have a PagerDuty integration. We can wake up engineers if certain things break. We also tag tickets in your support system, which is actually quite cool. Where, let's say, you have 200 people, who wrote into support, saying, "I got double billed on Android." It turns out, there are some bugs that double billed them. Well, now we can tag all of these users in Zendesk, and then the support team can then reach out to that segment of users and say, "Hey, we heard that you had this bug with double billing. We're so sorry. We're working on it." And then when we push fix, we can then email the same group again, and maybe give them a little gift card or something, for the thank you. So you can have, even big companies can have that small company experience. So, so it's groups that use us, like at Pinterest, we have 800 accounts. So it's really through marketing has vested interest because they want to know what is impacting the end user. Because brand and product, the lines are basically gone, right? >> John: Yeah. >> So if the product is not working, then my spend into this machine is going to be less efficient. The reputation of our company is going to be worse. And the challenge for marketers before unitQ was, how do I engage with engineering and product? I'm dealing with anecdotal data, and my own experience of like, "Hey, I've never seen these type of complaints before. I think something is going on." >> John: Yeah. >> And then engineering will be like, "Ah, you know, well, I have 5,000 bugs in Jira. Why does this one matter? When did it start? Is this a growing issue?" >> John: You have to replicate the problem, right? >> Replicate it then. >> And then it goes on and on and on. >> And a lot of times, reproducing bugs, it's really hard because it works on my device. Because you don't sit on that device that it happened on. >> Yup. >> So now, when marketing can come with indisputable data, and say, "Hey, something broke here." And we see the same with support. Product engineering, of course, for them, we talk about, "Hey, listen, you you've invested a lot in observability of your stack, haven't you?" "Yeah, yeah, yeah." "So you have a Datadog in the bottom?" "Absolutely." "And you have an APP D on the client?" "Absolutely." "Well, what about the last mile? How the product manifests itself? Shouldn't you monitor that as well using machines?" They're like, "Yeah, that'd be really cool." (John laughs) And we see this. There's no way to instrument everything, lowering the stack to capture these bugs that leak out. So it resonates really well there. And even for the engineers who's going to fix it. >> Yeah. >> I call it like empathy data. >> Yup. >> Where I get assigned a bug to fix. Well, now, I can read all the feedback. I can actually see, and I can see the feedback coming in. >> Yeah. >> Oh, there's users out there, suffering from this bug. And then when I fix it and I deploy the fix, and I see the trend go down to zero, and then I can celebrate it. So that whole feedback loop is (indistinct). >> And that's real time. It's usually missed too. This is the power of user feedback. You guys got a great product, unitQ. Great to have you on. Founder and CEO, Christian Wiklund. Thanks for coming on and sharing, and showcase. >> Thank you, John. For the last 30 seconds, the minute we have left, put a plug in for the company. What are you guys looking for? Give a quick pitch for the company, real quick, for the folks out there. Looking for more people, funding status, number of employees. Give a quick plug. >> Yes. So we raised our A Round from Google, and then we raised our B from Excel that we closed late last year. So we're not raising money. We are hiring across go-to-markets, engineering. And we love to work with people, who are passionate about quality and data. We're always, of course, looking for customers, who are interested in upping their game. And hey, listen, competing with features is really hard because you can copy features very quickly. Competing with content. Content is commodity. You're going to get the same movies more or less on all these different providers. And competing on price, we're not willing to do. You're going to pay 10 bucks a month for music. So how do you compete today? And if your competitor has a better fine tuned piano than your competitor will have better efficiencies, and they're going to retain customers and users better. And you don't want to lose on quality because it is actually a deterministic and fixable problem. So yeah, come talk to us if you want to up the game there. >> Great stuff. The iteration lean startup model, some say took craft out of building the product. But this is now bringing the craftsmanship into the product cycle, when you can get that data from customers and users. >> Yeah. >> Who are going to be happy that you fixed it, that you're listening. >> Yeah. >> And that the product got better. So it's a flywheel of loyalty, quality, brand, all off you can figure it out. It's the holy grail. >> I think it is. It's a gold mine. And every day you're not leveraging this assets, your use of feedback that's there, is a missed opportunity. >> Christian, thanks so much for coming on. Congratulations to you and your startup. You guys back together. The band is back together, up into the right, doing well. >> Yeah. We we'll check in with you later. Thanks for coming on this showcase. Appreciate it. >> Thank you, John. Appreciate it very much. >> Okay. AWS Startup Showcase. This is season two, episode three, the ongoing series. This one's about MarTech, cloud experiences are scaling. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> The cloud is evolving. You know, it's no longer a set of remote services somewhere off in the cloud, in the distance. It's expanding. It's moving to on-prem. On-prem workloads are connecting to the cloud. They're spanning clouds in a way that hides the plumbing and simplifies deployment, management, security, and governance. So hybrid multicloud is the next big thing in infrastructure, and at the recent Nutanix .NEXT conference, we got a major dose of that theme, and with me to talk about what we heard at that event, what we learned, why it matters, and what it means to customers are Monica Kumar, who's the senior vice president of marketing and cloud go-to-market at Nutanix, and Tarkan Maner, who's the chief commercial officer at Nutanix. Guys, great to see you again. Welcome to the theCUBE. >> Great to be back here. >> Great to see you, Dave. >> Okay, so you just completed another .NEXT. As an analyst, I like to evaluate the messaging at an event like this, drill into the technical details to try to understand if you're actually investing in the things that you're promoting in your keynotes, and then talk to customers to see how real it is. So with that as a warning, you guys are all in on hybrid multicloud, and I have my takeaways that I'd be happy to share, but, Tarkan, what were your impressions, coming out of the event? >> Look, you had a great entry. Our goal, as Monica is going to outline, too, cloud is not a destination. It's an operating model. Our customers are basically using cloud as a business model, as an operating model. It's not just a bunch of techno mumbo-jumbo, as, kind of, you outlined. We want to make sure we make cloud invisible to the customer so they can focus on what they need to focus on as a business. So as part of that, we want to make sure the workloads, the apps, they can run anywhere the way the customer wants. So in that context, you know, our entire story was bringing customer workloads, use-cases, partner ecosystem with ISVs and cloud providers and service providers and ISPs we're working with like Citrix on end user computing, like Red Hat on cloud native, and also bringing the right products, both in terms of infrastructure capability and management capability for both operators and application developers. So bringing all these pieces together and make it simple for the customer to use the cloud as an operating model. That was the biggest goal here. >> Great, thank you. Monica, anything you'd add in terms of your takeaways? >> Well, I think Tarkan said it right. We are here to make cloud complexity invisible. This was our big event to get thousands of our customers, partners, our supporters together and unveil our product portfolio, which is much more simplified, now. It's a cloud platform. And really have a chance to show them how we are building an ecosystem around it, and really bringing to life the whole notion of hybrid multicloud computing. >> So, Monica, could you just, for our audience, just summarize the big news that came out of .NEXT? >> Yeah, we actually made four different announcements, and most of them were focused around, obviously, our product portfolio. So the first one was around enhancements to our cloud platform to help customers build modern, software-defined data centers to speed their hybrid multicloud deployments while supporting their business-critical applications, and that was really about the next version of our flagship, AOS six, availability. We announced the general availability of that, and key features really included things like built-in virtual networking, disaster recovery enhancements, security enhancements that otherwise would need a lot of specialized hardware, software, and skills are now built into our platform. And, most importantly, all of this functionality being managed through a single interface, right? Which significantly decreases the operational overhead. So that was one announcement. The second announcement was focused around data services and really making it easy for customers to simplify data management, also optimize big data and database workloads. We announced capability that now improves performances of database workloads by 2x, big data workloads by 3x, so lots of great stuff there. We also announced a new service called Nutanix Data Lens, which is a new unstructured data governance service. So, again, I don't want to go into a lot of details here. Maybe we can do it later. That was our second big announcement. The third announcement, which is really around partnerships, and we'll talk more about that, is with Microsoft. We announced the preview of Nutanix Clusters and Azure, and that's really taking our entire flagship Nutanix platform and running it on Azure. And so, now, we are in preview on that one, and we're super excited about that. And then, last but not least, and I know Tarkan is going to go into a lot more detail, is we announced a strategic partnership with Citrix around the whole future of hybrid work. So lots of big news coming out of it. I just gave you a quick summary. There's a lot more around this, as well. >> Okay. Now, I'd like to give you my honest take, if you guys don't mind, and, Tarkan, I'll steal one of your lines. Don't hate me, okay? So the first thing I'm going to say is I think, Nutanix, you have the absolute right vision. There's no question in my mind. But what you're doing is not trivial, and I think it's going to play out. It's going to take a number of years. To actually build an abstraction layer, which is where you're going, as I take it, as a platform that can exploit all the respective cloud native primitives and run virtually any workload in any cloud. And then what you're doing, as I see it, is abstracting that underlying technology complexity and bringing that same experience on-prem, across clouds, and as I say, that's hard. I will say this: the deep dives that I got at the analyst event, it convinced me that you're committed to this vision. You're spending real dollars on focused research and development on this effort, and, very importantly, you're sticking to your true heritage of making this simple. Now, you're not alone. All the non-hyperscalers are going after the multicloud opportunity, which, again, is really challenging, but my assessment is you're ahead of the game. You're certainly focused on your markets, but, from what I've seen, I believe it's one of the best examples of a true hybrid multicloud-- you're on that journey-- that I've seen to date. So I would give you high marks there. And I like the ecosystem-building piece of it. So, Tarkan, you could course-correct anything that I've said, and I'd love for you to pick up on your comments. It takes a village, you know, you're sort of invoking Hillary Clinton, to bring the right solution to customers. So maybe you could talk about some of that, as well. >> Look, actually, you hit all the right points, and I don't hate you for that. I love you for that, as you know. Look, at the end of the day, we started this journey about 10 years ago. The last two years with Monica, with the great executive team, and overall team as a whole, big push to what you just suggested. We're not necessarily, you know, passionate about cloud. Again, it's a business model. We're passionate about customer outcomes, and some of those outcomes sometimes are going to also be on-prem. That's why we focus on this terminology, hybrid multicloud. It is not multicloud, it's not just private cloud or on-prem and non-cloud. We want to make sure customers have the right outcomes. So based on that, whether those are cloud partners or platform partners like HPE, Dell, Supermicro. We just announced a partnership with Supermicro, now, we're selling our software. HPE, we run on GreenLake. Lenovo, we run on TruScale. Big support for Lenovo. Dell's still a great partner to us. On cloud partnerships, as Monica mentioned, obviously Azure. We had a big session with AWS. Lots of new work going on with Red Hat as an ISV partner. Tying that also to IBM Cloud, as we move forward, as Red Hat and IBM Cloud go hand in hand, and also tons of workarounds, as Monica mentioned. So it takes a village. We want to make sure customer outcomes deliver value. So anywhere, for any app, on any infrastructure, any cloud, regardless standards or protocols, we want to make sure we have an open system coverage, not only for operators, but also for application developers, develop those applications securely and for operators, run and manage those applications securely anywhere. So from that perspective, tons of interest, obviously, on the Citrix or the UC side, as Monica mentioned earlier, we also just announced the Red Hat partnership for cloud services. Right before that, next we highlighted that, and we are super excited about those two partnerships. >> Yeah, so, when I talked to some of your product folks and got into the technology a little bit, it's clear to me you're not wrapping your stack in containers and shoving it into the cloud and hosting it like some do. You're actually going much deeper. And, again, that's why it's hard. You could take advantage of those things, but-- So, Monica, you were on the stage at .NEXT with Eric Lockhart of Microsoft. Maybe you can share some details around the focus on Azure and what it means for customers. >> Absolutely. First of all, I'm so grateful that Eric actually flew out to the Bay Area to be live on stage with us. So very super grateful for Eric and Azure partnership there. As I said earlier, we announced the preview of Nutanix Clusters and Azure. It's a big deal. We've been working on it for a while. What this means is that a select few organizations will have an opportunity to get early access and also help shape the roadmap of our offering. And, obviously, we're looking forward to then announcing general availability soon after that. So that's number one. We're already seeing tremendous interest. We have a large number of customers who want to get their hands on early access. We are already working with them to get them set up. The second piece that Eric and I talked about really was, you know, the reason why the work that we're doing together is so important is because we do know that hybrid cloud is the preferred IT model. You know, we've heard that in spades from all different industries' research, by talking to customers, by talking to people like yourselves. However, when customers actually start deploying it, there's lots of issues that come up. There's limited skill sets, resources, and, most importantly, there's a disparity between the on-premises networking security management and the cloud networking security management. And that's what we are focused on, together as partners, is removing that barrier, the friction between on-prem and Azure cloud. So our customers can easily migrate their workloads in Azure cloud, do cloud disaster recovery, create a burst into cloud for elasticity if they need to, or even use Azure as an on-ramp to modernize applications by using the Azure cloud services. So that's one big piece. The second piece is our partnership around Kubernetes and cloud native, and that's something we've already provided to the market. It's GA with Azure and Nutanix cloud platform working together to build Kubernetes-based applications, container-based applications, and run them and manage them. So there's a lot more information on nutanix.com/azure. And I would say, for those of our listeners who want to give it a try and who want their hands on it, we also have a test drive available. You can actually experience the product by going to nutanix.com/azure and taking the test drive. >> Excellent. Now, Tarkan, we saw recently that you announced services. You've got HPE GreenLake, Lenovo, their Azure service, which is called TruScale. We saw you with Keith White at HPE Discover. I was just with Keith White this week, by the way, face to face. Awesome guy. So that's exciting. You got some investments going on there. What can you tell us about those partnerships? >> So, look, as we talked through this a little bit, the HPE relationship is a very critical relationship. One of our fastest growing partnerships. You know, our customers now can run a Nutanix software on any HPE platform. We call it DX, is the platform. But beyond that, now, if the customers want to use HPE service as-a-service, now, Nutanix software, the entire stack, it's not only hybrid multicloud platform, the database capability, EUC capability, storage capability, can run on HPE's service, GreenLake service. Same thing, by the way, same way available on Lenovo. Again, we're doing similar work with Dell and Supermicro, again, giving our customers choice. If they want to go to a public club partner like Azure, AWS, they have that choice. And also, as you know, I know Monica, you're going to talk about this, with our GSI partnerships and new service provider program, we're giving options to customers because, in some other regions, HPE might not be their choice or Azure not be choice, and a local telco might the choice in some country like Japan or India. So we give options and capability to the customers to run Nutanix software anywhere they like. >> I think that's a really important point you're making because, as I see all these infrastructure providers, who are traditionally on-prem players, introduce as-a-service, one of the things I'm looking for is, sure, they've got to have their own services, their own products available, but what other ecosystem partners are they offering? Are they truly giving the customers choice? Because that's, really, that's the hallmark of a cloud provider. You know, if we think about Amazon, you don't always have to use the Amazon product. You can use actually a competitive product, and that's the way it is. They let the customers choose. Of course, they want to sell their own, but, if you innovate fast enough, which, of course, Nutanix is all about innovation, a lot of customers are going to choose you. So that's key to these as-a-service models. So, Monica, Tarkan mentioned the GSIs. What can you tell us about the big partners there? >> Yeah, definitely. Actually, before I talk about GSIs, I do want to make sure our listeners understand we already support AWS in a public cloud, right? So Nutanix totally is available in general, generally available on AWS to use and build a hybrid cloud offering. And the reason I say that is because our philosophy from day one, even on the infrastructure side, has been freedom of choice for our customers and supporting as large a number of platforms and substrates as we can. And that's the notion that we are continuing, here, forward with. So to talk about GSIs a bit more, obviously, when you say one platform, any app, any cloud, any cloud includes on-prem, it includes hyperscalers, it includes the regional service providers, as well. So as an example, TCS is a really great partner of ours. We have a long history of working together with TCS, in global 2000 accounts across many different industries, retail, financial services, energy, and we are really focused, for example, with them, on expanding our joint business around mission critical applications deployment in our customer accounts, and specifically our databases with Nutanix Era, for example. Another great partner for us is HCL. In fact, HCL's solution SKALE DB, we showcased at .NEXT just yesterday. And SKALE DB is a fully managed database service that HCL offers which includes a Nutanix platform, including Nutanix Era, which is our database service, along with HCL services, as well as the hardware/software that customers need to actually run their business applications on it. And then, moving on to service providers, you know, we have great partnerships like with Cyxtera, who, in fact, was the service provider partner of the year. That's the award they just got. And many other service providers, including working with, you know, all of the edge cloud, Equinix. So, I can go on. We have a long list of partnerships, but what I want to say is that these are very important partnerships to us. All the way from, as Tarkan said, OEMs, hyperscalers, ISVs, you know, like Red Hat, Citrix, and, of course, our service provider, GSI partnerships. And then, last but not least, I think, Tarkan, I'd love for you to maybe comment on our channel partnerships as well, right? That's a very important part of our ecosystem. >> No, absolutely. You're absolutely right. Monica. As you suggested, our GSI program is one of the best programs in the industry in number of GSIs we support, new SP program, enterprise solution providers, service provider program, covering telcos and regional service providers, like you suggested, OVH in France, NTT in Japan, Yotta group in India, Cyxtera in the US. We have over 50 new service providers signed up in the last few months since the announcement, but tying all these things, obviously, to our overall channel ecosystem with our distributors and resellers, which is moving very nicely. We have Christian Alvarez, who is running our channel programs globally. And one last piece, Dave, I think this was important point that Monica brought up. Again, give choice to our customers. It's not about cloud by itself. It's outcomes, but cloud is an enabler to get there, especially in a hybrid multicloud fashion. And last point I would add to this is help customers regardless of the stage they're in in their cloud migration. From rehosting to replatforming, repurchasing or refactoring, rearchitecting applications or retaining applications or retiring applications, they will have different needs. And what we're trying to do, with Monica's help, with the entire team: choice. Choice in stage, choice in maturity to migrate to cloud, and choice on platform. >> So I want to close. First of all, I want to give some of my impressions. So we've been watching Nutanix since the early days. I remember vividly standing around the conference call with my colleague at the time, Stu Miniman. The state-of-the-art was converged infrastructure, at the time, bolting together storage, networking, and compute, very hardware centric. And the founding team at Nutanix told us, "We're going to have a software-led version of that." And you popularized, you kind of created the hyperconverged infrastructure market. You created what we called at the time true private cloud, scaled up as a company, and now you're really going after that multicloud, hybrid cloud opportunity. Jerry Chen and Greylock, they just wrote a piece called Castles on the Cloud, and the whole concept was, and I say this all the time, the hyperscalers, last year, just spent a hundred billion dollars on CapEx. That's a gift to companies that can add value on top of that. And that's exactly the strategy that you're taking, so I like it. You've got to move fast, and you are. So, guys, thanks for coming on, but I want you to both-- maybe, Tarkan, you can start, and Monica, you can bring us home. Give us your wrap up, your summary, and any final thoughts. >> All right, look, I'm going to go back to where I started this. Again, I know I go back. This is like a broken record, but it's so important we hear from the customers. Again, cloud is not a destination. It's a business model. We are here to support those outcomes, regardless of platform, regardless of hypervisor, cloud type or app, making sure from legacy apps to cloud native apps, we are there for the customers regardless of their stage in their migration. >> Dave: Right, thank you. Monica? >> Yeah. And I, again, you know, just the whole conversation we've been having is around this but I'll remind everybody that why we started out. Our journey was to make infrastructure invisible. We are now very well poised to helping our customers, making the cloud complexity invisible. So our customers can focus on business outcomes and innovation. And, as you can see, coming out of .NEXT, we've been firing on all cylinders to deliver this differentiated, unified hybrid multicloud platform so our customers can really run any app, anywhere, on any cloud. And with the simplicity that we are known for because, you know, our customers love us. NPS 90 plus seven years in a row. But, again, the guiding principle is simplicity, portability, choice. And, really, our compass is our customers. So that's what we are focused on. >> Well, I love not having to get on planes every Sunday and coming back every Friday, but I do miss going to events like .NEXT, where I meet a lot of those customers. And I, again, we've been following you guys since the early days. I can attest to the customer delight. I've spent a lot of time with them, driven in taxis, hung out at parties, on buses. And so, guys, listen, good luck in the next chapter of Nutanix. We'll be there reporting and really appreciate your time. >> Thank you so much. >> Thank you so much, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante for theCUBE, and, as always, we'll see you next time. (light music)

>> Narrator: From around the globe, it's theCUBE, covering Fortinet Security Summit brought to you by Fortinet. >> Welcome to theCUBE. I'm Lisa Martin. We are live at the Fortinet Championship, the PGA Tour Kickoff to the 2021-2022 FedEx Regular Season Cup. And this is so exciting to be here with Fortinet, to be at an in-person event, and to be talking about a very important topic of cybersecurity. One of our alumni is back with me, Sandra Wheatley is here, the SVP of Marketing, Threat Intelligence, and Influencer Communications at Fortinet. Sandra, it's great to see you. >> You too, Lisa. Thank you for having me. >> This is a great event. >> Yeah, it's awesome, yeah. >> Great to be outdoors, great to see people again, and great for Fortinet for being one of the first to come back to in-person events. One of the things I would love to understand is here we are at the PGA tour, what's the relationship with Fortinet and the PGA Tour? >> Well, first of all, I think the PGA tour is an amazing brand. You just have to look around here and it's extremely exciting, but beyond the brand, there's a lot of synergies between the PGA tour and Fortinet CSR initiatives, particularly around STEM, diversity inclusion, as well as veterans rescaling. And so some of the proceeds from the Fortinet Championship will go to benefit local nonprofits and the local community. So that's something we're very excited about overall. >> Lisa: Is this a new partnership? >> It is a new partnership and we will be the Fortinet Championship sponsor for about the next five years. So we're looking forward to developing this partnership and this relationship, and benefiting a lot of nonprofits in the future. >> Excellent, that's a great cause. One of the things, when you and I last saw each other by Zoom earlier in the summer, we were talking about the cybersecurity skills gap. And it's in its fifth consecutive year, and you had said some good news on the front was that data show that instead of needing four million professionals to fill that gap, it's down to three, and now there's even better news coming from Fortinet. Talk to me about the pledge that you just announced to train one million people in the next five years. >> Absolutely, we're very excited about this. You know, Fortinet has been focused on reducing the skills gap for many years now. It continues to be one of the biggest issues for cybersecurity leaders if you think about it. You know, we still need about 3.1 million professionals to come into the industry. We have made progress, but the need is growing at about 400,000 a year. So it's something that public and private partnerships need to tackle. So last week we did announce that we are committed to training a million professionals over the next five years. We're very excited about that. We're tackling this problem in many, many ways. And this really helps our customers and our partners. If you really think about it, in addition to the lack of skills, they're really tackling cybersecurity surface that's constantly changing. In our most recent FortiGuard's threat report, we saw that ransomware alone went up 10 times over the last year. So it's something that we all have to focus on going forward. And this is our way of helping the industry overall. >> It's a huge opportunity. I had the opportunity several times to speak with Derek Manky and John Maddison over the summer, and just looking at what happened in the first half, the threat landscape, we spoke last year, looking at the second half, and ransomware as a service, the amount of money that's involved in that. The fact that we are in this, as Fortinet says, this work from anywhere environment, which is probably going to be somewhat persistent with the attack surface expanding, devices on corporate networks out of the home, there's a huge opportunity for people to get educated, trained, and have a great job in cybersecurity. >> Absolutely, I like to say there's no job security like cybersecurity, and it is. I mean, I've only been in this industry about, I'm coming up on six years, and it's definitely the most dynamic industry of all of the IT areas that I've worked in. The opportunities are endless, which is why it's a little bit frustrating to see this big gap in skills, particularly around the area of women and minorities. Women make up about 20%, and minorities are even less, maybe about 3%. And so this is a huge focus of ours. And so through our Training Advancement Agenda, our TAA initiative, we have several different pillars to attack this problem. And at the core of that is our Network Security Expert Training or NSC training and certification program. We made that freely available to everybody at the beginning of COVID. It was so successful, at one point we we're seeing someone register every five minutes. And that was so successful, we extended that indefinitely. And so to date, we've had about almost 700,000 certifications. So it's just an amazing program. The other pillars are Security Academy Program, where we partner with nonprofits and academia to train young students. And we have something like 419 academies in 88 countries. >> Lisa: Wow. >> And then the other area that's very important to us is our Veterans Program. You know, we have about 250,000 veterans every year, transfer out of the service, looking for other jobs in the private sector. And so not only do we provide our training free, but we do resume building, mentoring, all of these types of initiatives. And we've trained about 2,000 veterans and spouses, and about 350 of those have successfully got jobs. So that's something we'll continue to focus on. >> That's such a great effort. As the daughter of a Vietnam combat veteran, that really just hits me right in the heart. But it's something that you guys have been dedicated for. This isn't something new, this isn't something that is coming out of a result of the recent executive order from the Biden administration. Fortinet has been focused on training and helping to close that gap for a while. >> That's exactly true. While we made the commitment to train a million people on the heels of the Biden administration at Cybersecurity Summit about two weeks ago, we have been focused on this for many years. And actually, a lot of the global companies that were part of that summit happened to be partners on this initiative with us. For example, we work with the World Economic Forum, IBM, and Salesforce offer our NSC training on their training platforms. And this is an area that we think it's really important and we'll continue to partner with larger organizations over time. We're also working with a lot of universities, both in the Bay Area, local like Berkeley, and Stanford and others to train more people. So it's definitely a big commitment for us and has been for many years. >> It'll be exciting to see over the next few years, the results of this program, which I'm sure will be successful. Talk to me a little bit about this event here. Fortinet is 100% partner driven company, more than 300 or so partners and customers here. Tell me a little bit about what some of the interesting topics are that are going to be discussed today. >> Sure, yeah, so we're delighted to bring our partners and customers together. They will be discussing some of the latest innovations in cybersecurity, as well as some of the challenges and opportunities. We are seeing, you know, during COVID we saw a lot of change with regards to cybersecurity, especially with remote working. So we'll discuss our partnership with LYNX that we just announced. We'll also be talking about some of the emerging technologies like CTNA, 5G, SASE, cloud, and really understanding how we can best help protect our customers and our partners. So it's very exciting. In addition to our Technology Summit, we have a technology exhibition here with many of our big sponsors and partners. So it's definitely going to be a lot of dynamic conversation over the next few days. >> We've seen so much change in the last year and a half. That's just an understatement. But one of the things that you touched on this a minute ago, and we're all feeling this is is when we all had to shift to work from home. And here we are using corporate devices on home networks. We're using more devices, the edge is expanding, and that became a huge security challenge for enterprises to figure out how do we secure this. Because for some percentage, and I think John Maddison mentioned a few months ago to me, at least 25% will probably stay remote. Enterprises have to figure out how to keep their data secure as people are often the weakest link. Tell me about what you guys announced with LYNX that will help facilitate that. >> Well, we're announcing an enterprise grade security offering for people who are working remotely. And the nice thing about this offering is it's very easy to set up and implement, so consumers and others can easily set this up. It also provides a dashboard for the enterprise, IT organization to, they can see who's on the network, devices, everything else. So this should really help because we did see a big increase in attacks, really targeting remote workers. As cyber criminals try to use their home as a foothold into the enterprise. So we're very excited about this partnership, and definitely see big demand for this going forward. >> Well, can you tell me about the go-to market for that and where can enterprises and people get it? >> Well, we're still working through that. I know you'll talk with John later on, he'll have more details on that. But definitely, we'll be targeting both of our different sets of customers and the channel for this. And I definitely think this is something that will, it's something that enterprises are definitely looking for, and there'll be more to come on this over the next few months. >> It's so needed. The threat landscape just exploded last year, and it's in a- >> Sandra: Yeah, absolutely. >> Suddenly your home. Maybe your kids are home, your spouse is working, you're distracted, ransomware, phishing emails, so legitimate. >> Sandra: They do. >> Lisa: But the need for what you're doing with LYNX is absolutely essential these days. >> Sandra: Yeah, these threats are so sophisticated. They're really difficult. And the other thing we did in addition to LYNX was as we got into COVID, we saw that, or the most successful organizations were really using this as an opportunity to invest for the longterm in cybersecurity. We also saw that, and this continues to be the case that, the insider threat continues to be one of the biggest challenges, where an employee will accidentally hit on a phishing email. So we did roll out an infosec awareness training, and we made that free for all of our customers and partners. So we're trying to do everything we can to really help our customers through this demanding time. >> Lisa: Right, what are some of the feedback that you're hearing from customers? I'm sure they're very appreciative of the education, the training, the focus effort from Fortinet. >> Sandra: Absolutely, it's definitely huge. And more and more we're seeing partners who want to work with us and collaborate with us on these initiatives. We've had a really positive response from some of the companies that I mentioned earlier, some of the big global names. And we're very excited about that. So we feel like we have some key initiatives on pillars, and we'll continue to expand on those and bring more partners to work with us over time. >> Lisa: Expansion as the business is growing amazingly well. Tell me a little bit about that. >> Sandra: Yeah, I think, in our last quarter we announced our largest billings growth for many, many years. And so, Fortinet, we're been very fortunate over the last few years, has continued to grow faster than the market. We now have half a million customers, and I think our platform approach to security is really being adopted heavily. And we continue to see a lot of momentum, especially around our solutions like SD-WAN. I think we're the only vendor who provides security in SD-WAN appliance. And so that's been a key differentiator for us. The other thing that's increasingly important, especially with the rollout of 5G is performance. And, you know, Fortinet, from the very beginning, created its own customized ASX or SPU, which really provides the best performance in security compute ratings in the industry. So all of this is really helping us with our growth, and we're very excited about the opportunities ahead. >> Lisa: And last question, on that front, what are some of the things that you're excited about as we wrap up 2021 calendar year and go into 2022? >> Sandra: Well, this been very exciting year for Fortinet. And I think we're in a great position to take advantage of many of the different growth areas we're seeing in this new and changing space. And, you know, we're all on board and ready to take advantage of those opportunities, and really fire ahead. >> Lisa: Fire ahead, I like that. Sandra, thank you so much for joining me today, talking about the commitment, the long standing commitment that Fortinet has to training everybody from all ages, academia, veterans, to help close that cybersecurity skills gap. And such an interesting time that we've had. There's so much opportunity, and it's great to see how committed you are to helping provide those opportunities to people of all ages, races, you name it. >> Sandra: Thank you, Lisa, I really appreciate it. >> Lisa: Ah, likewise. For Sandra Wheatley, I'm Lisa Martin. You're watching theCube at the Fortinet Championship Security Summit. (soft bright music)

(bright music) >> Welcome back to HPE Discover 2021. My name is Dave Vellante and you're watching theCUBE's virtual coverage of HPE's big customer event. Of course, the virtual edition and we're going to dig into transformations, the role of technology and the role of senior technology leadership. Look, let's face it, HPE has gone through a pretty dramatic transformation itself in the past few years so it makes a great example in case study and with me is Rashmi Kumar who is the senior vice president and CIO at HPE, Rashmi welcome come on inside theCUBE. >> Hi Dave nice to be here. >> Well it's been almost a year since COVID you know changed the world as we know it. How would you say the role of the CIO specifically in generally IT has changed? I mean you got digital, zero trust has gone from buzzword to mandate, digital, everybody was you know complacent about digital in many ways and now it's really accelerated, remote work, hybrid, how do you see it? >> Absolutely, as I said in the last Discover that COVID has been the biggest reason to accelerate digital transformation in the companies. I see CIO's role has changed tremendously in the last 15 months. It's no more just keep the operations running, that's become a table stake. Our roles have become not only to create digital customer experience, engage with our customers in different ways, but also to transform the company operations from inside out to be able to give that digital experience from beginning to end of the customer engagement going forward. We have also become responsible for switching our strategies around the companies as the COVID hit in different parts of the world at different times and how companies structured their operations to go from one region to another, a global company like HPE had to look into its supply chain differently, had to look into strategies to mitigate the risk that was created because of the supply chain disruptions, as well as you go to taking care of our employees. How do you create this digital collaboration experience where teams can still come together and make the work happen for our end customers? How do we think about future employee engagement when people are not coming into these big buildings and offices and working together, but how do you create the same level of collaboration, coordination, as well as delivery of faster, good and services which is enabled by technology going forward. So CIO and IT's role has gone from giving a different level of customer experience to different level of employee experience, as well as enabling day-to-day operations of the companies. CEOs have realized that digital is the way to go forward, it does not matter what industry you are in and now CIOs have their seat at the table to define what the future of every company now which is a technology company irrespective you are in oil and gas, or mining, or a technical product, or a car or a mobility company, end of the day you have to act and behave like a technology company. >> So I want to ask you about that because you've been a CIO at a leading technology provider now for the last three years and you've had previous roles and were, you know non-technical, technology, you know, selling to IT companies and as you point out those worlds are coming together. Everybody's a technology company today. How do you think that changes the role of the CIO because it would always seem to me that there was a difference between a CIO at a tech company you know what I mean by that and a CIO at sort of every other company is, are those two worlds converging? >> Absolutely and it's interesting you pointed out that I have worked in many different industries from healthcare and pharma, to entertainment, to utilities and now at a technology company. End of the day the issues that IT deals with are pretty similar across the organization. What is different here is now my customers are people like me in other industries and I have little bit of an advantage because just having the experience across various ecosystem even that HPE look I was fortunate at HPE because of Antonio's leadership we had top-down mandate to transform how we did business and I talked about my NextGEN IT program in last year's CUBE interview. But at the same time while we were changing our customer, partner's experience from ordering, to order processing, to supply chain, to finance, we decided this pivot of becoming as a service company. And if you think about that pivot, it's pretty common. If it was a technology company or non-technology company. At HPE we were very used to selling a product and coming back three years later at the time of refresh of infrastructure or hardware. That's no more true for us. Now we are becoming an as a service or a subscription company and IT played a major role to enable that quote-to-cash experience which is very different than the traditional experience, around how we stay connected with our customer, how we proactively understand their behavior. I always talk about this term digital exhaust which results into data, which can result into better insight and you can not only upsell, cross-sell because now you have more data about your product usage, but first and foremost give what your customer wants in a much better way because you can proactively understand their needs and wants because you are providing a digital product versus a physical product. So this is the change that most of the companies are now going through. If you look at Domino's transition, they are pizza sellers but they did better because they had better digital experience. If you look at Chipotle, these are food service companies. Ikea which is a furniture manufacturer, across the board we have helped our customers and industries to understand how to become a more digital provider. And remember when HPE says edge to cloud platform as a service, edge is the product, the customers is what we deal with and how do we get that, help them get that data, understand how the product is behaving and then get the information to cloud for further analysis and understanding from the data that comes out of the products that they sell. >> I think you've been at HPE now I think around three years and I've been watching of course for decades, you know HPE, well HP then HPE is, I feel like it's entering now that sort of third phase of its transformation, your phase one was okay we got to figure out how to deal or operate as separate companies, okay, that took some time and then it was okay, now how do we align our resources? And you know what are the waves that we're going to ride? And how do we take our human capital, our investments and what bets do we place? And you're all in on as a service and now it's like okay, you know how do we deliver on all those promises? So pretty massive transformations. You talked about edge to cloud as a service so you've got this huge pivot in your business. What's the technology strategy to support that transformation? >> Yeah, that's a great question. So as I mentioned first, your second phase which was becoming a stand-alone company was the NextGEN IT program where we brought in S4 and 60 related ecosystem application where even in the traditional business there was a realization that we were 120 billion company, we are a 30 billion company, we need different types of technologies as well as more integrated across our product line, across the globe and we, I'm very happy to report that we are the last leg of NextGEN IT transformation. Where we have brought in new customer experience through low-touch or no-touch order processing, a very strong S4 capabilities where we are now able to run all global orders across all our hardware and services business together and I'm happy to report that we have been able to successfully run through the transformation which a typical company of our size would take five or six years to do in around close to three years. But at the same time while we were building this foundation and the capabilities to be able to do order management supply chain and data and analytics platforms, we also made the pivot to go to as a service. Now for as a service and subscription selling, it needs a very different quote-to-cash experience for our customers. And that's where we had bring in platforms like BRIM to do subscription billing, convergent charging and a whole different way to address. But we were lucky to have this transformation completed on which we could bolt on this new capability and we had the data analytics platform built which now these as a service products can also use to drive better insight into our customer behavior as well as how they're using our product real time for our operations teams. >> Well they say follow the money, in theCUBE we love to say follow the data. I mean data is obviously a crucial component of competitive advantage, business value, so talk a little bit more about the role of data, I'm interested in where IT fits. You know a lot of companies they'll have a chief data officer, or a CIO, sometimes they're separate sometimes they work, you know for each other, or CDO works for CIO, how do you guys approach the whole data conversation? >> Yeah that's a great question and has been top of the mind of a lot of CEOs, CIOs, chief digital officers in many different companies. The way we have set it up here is we do have a chief data officer and we do have a head of technology and platform and data lake within IT. Look the way I see is that I call the term data torture. If they have multiple data lakes, if they have multiple data locations and the data is not coming together at one place at the first time that it comes out to the source system, we end up with data swamps and it's very difficult to drive insights, it's very difficult to have single version of truth. So HPE had two-pronged approach. First one was as part of this NextGEN IT transformation we embarked upon the journey first of all to define our customers and products in a very uniform way across the globe. It's called entity master data and product master data program. These were very, very difficult program. We are now happy to report that we can understand the customer from cold stage to servicing stage beginning to end across all our system. It's been a tough journey but it was effort well spent. At the same time while we were building this master data capability we also invested time in our analytics platform. Because we are generating so much data now globally as one footprint, how do we link our data lake to our SAP and Salesforce and all these systems where our customer data flows through and create analytics and insight from it from our customers or our operations team. At the same time we also created a chief data officer role where the responsibility is really to drive business from understanding what decision making and analytics they need around product, around customer, around their usage around their experience to be able to drive better alignment with our customers and products going forward. So this creates efficiencies in the organization. If you have a leader who is taking care of your platforms and data, building single source of truth and you have a leader who is propagating this mature notion of handling data as enterprise data and driving that focus on understanding the metrics and the insight that the businesses need to drive better customer alignment, that's when we gain those efficiencies and behind the scenes the chief data officer and the data leader within my organization work very, very closely to understand each other needs, sometimes art of the possible, where do we need the data processing? Is it at the edge? Is it in the cloud? What's the best way to drive the technology and the platform forward? And they kind of rely on each other's knowledge and intelligence to give us superior results. And I have done data analytics in many different companies, this model works. Where you have focus on insight and analytics without, because data without insight is of no value. But at the same time you need clean data, you need efficient, fast platforms to process that insight at the functional non-functional requirement that our business partners have. And that's how we have established in here and we have seen many successes recently as of now. >> I want to ask you a kind of a harder, maybe it's not a harder question it's a weird question around single version of the truth. 'Cause it's clearly a challenge for organizations and there's many applications, workloads that require that single version of the truth, the operational systems, the transaction systems, the HR, the Salesforce and clearly you have to have a single version of the truth. I feel like, however we're on the cusp of a new era where business lines see an opportunity for whatever, their own truth to work with a partner to create some kind of new data product. And it's early days in that but I wonder, maybe not the right question for HPE but I wonder if you see it with in your ecosystems where it's yes, single version of truth is sort of one class of data and analytics got to have that nailed down, data quality, everything else. But then there's this sort of artistic version of the data where business people need more freedom, they need more latitude to create. Are you seeing that? Maybe you can help me put that into context. >> That's a great question Dave and I'm glad you asked it so. I think Tom Davenport, who is known in the data space talks about the offensive and the defensive use cases of leveraging data. I think the piece that you talked about where it's clean, it's pristine, it's quality, it's all that, most of those offer the offensive use cases where you are improving companies' operations incrementally because you have very clean data, you have very good understanding of how my territories are doing, how my customers are doing, how my products are doing, how am I meeting my SLAs or how my financials are looking, there's no room for failure in that area. The other area is though which works on the same set of data. It's not a different set of data but the need is more around finding needles in the haystack to come up with new needs, new wants in customers or new business models that we go with. The way we have done it is we do take this data, take out what's not allowed for everybody to be seen and then what we call is a private space but that's this entire data available to our business leader not real time, because the need is not as real time because they are doing more, what we call this predictive analytics to be able to leverage the same data set and run their analytics. And we work very closely with business units, we educate them, we tell them how to leverage this data set and use it and gather their feedback to understand what they need in that space to continue to run with their analytics. I think as we talk about hindsight, insight and foresight, hindsight and insight happens more from this clean data lakes where you have authenticity, you have quality and then most of the foresight happens in a different space where the users have more leverage to use data in many different ways to drive analytics and insights which is not readily available. >> Great thank you for that. That's an interesting discussion. You know digital transformation it's a journey and it's going to take you know many years. I know a lot of ways, not a lot of ways, 2020 was a forced march to digital you know. If you weren't a digital business you were out of business and so you really didn't have much time to plan. So now organizations are stepping back saying, okay, let's really lean into our strategy, the journey and along the way, there's going to be blind spots, there's bumps in the road, when you look out what are the potential disruptions that you see maybe in terms of how companies are currently approaching their digital transformations? >> That's a great question Dave and I'm going to take a little bit more longer-term view on this topic, right? And what's top of my mind recently is the whole topic of ESG, environmental, social and governance. Most of the companies have governance in place right? Because they are either public companies, or they're under some kind of scrutiny from different regulatory bodies or whatnot even if you're a startup you need to do things with our customers and whatnot. It has been there for companies, it continues to be there. We the public companies are very good at making sure that we have the right compliance, right privacy, right governance in place. Now we'll talk about cybersecurity I think that creates a whole new challenge in that governance space, however we have the setup within our companies to be able to handle that challenge. Now, when we go to social, what happened last year was really important. And now as each and every company we need to think about what are we doing from our perspective to play our part in that and not only the bigger companies, leaders at our level I would say that between last March and this year I have hired more than 400 people during pandemic which was all virtual, but me and my team have made sure that we are doing the right thing to drive inclusion and diversity which is also very big objective for HPE and Antonio himself has been very active in various round tables in US at the World Economic Forum level and I think it's really important for companies to create that opportunity, remove that disparity that's there for the underserved communities. If we want to continue to be successful in this world to create innovative product and services we need to sell it to the broader cross section of populations and to be able to do that we need to bring them in our fold and enable them to create that equal consumption capabilities across different sets of people. HPE has taken many initiatives and so are many companies. I feel like the momentum that companies have now created around the topic of equality is very important. I'm also very excited to see that a lot of startups are now coming up to serve that 99% versus just the shiny ones as you know in the Bay Area to create better delivery methods of food or products right? But the third piece which is environmental is extremely important as well. As we have seen recently in many companies and where even the dollar or the economic value is flowing are around the companies which are serious about environmental. HPE recently published it's a Living Progress Report, we have been in the forefront of innovation to reduce carbon emissions, we help our customers through those processes. Again, if we don't, if our planet is on fire none of us will exist right? So we all have to do that every little part to be able to do better. And I'm happy to report I myself as a person solar panels, battery, electric cars, whatever I can do. But I think something more needs to happen right? Where as an individual I need to pitch in but maybe utilities will be so green in the future that I don't need to put panels on my roof which again creates a different kind of race going forward. So when you ask me about disruptions, I personally feel that successful company like ours have to have ESG top of their mind and think of product and services from that perspective, which creates equal opportunity for people, which creates better environment sustainability going forward and you know our customers, our investors are very interested in seeing what we are doing to be able to serve that cause for bigger cross section of companies. And I'm most of the time very happy to share with my CIO cohort around how our HPEFS capabilities creates or feeds into the circular economy, how much e-waste we have recycled or kept it off of landfills, our green lake capabilities, how it reduces the e-waste going forward, as well as our sustainability initiatives which can help other CIOs to be more carbon neutral going forward as well. >> You know that's a great answer Rashmi thank you for that 'cause I got to tell you I hear a lot of mumbo jumbo about ESG but that was a very substantive, thoughtful response that I think tech companies in particular are, have to lead and are leading in this area. So I really appreciate that sentiment. I want to end with a very important topic which is cyber it's, obviously you know escalated in the news the last several months, it's always in the news but, you know 10 or 15 years ago there was this mentality of failure equals fire. And now we realize, hey they're going to get in, it's how you handle it. Cyber has become a board-level topic. You know years ago there was a lot of discussion, oh you can't have the SecOps team working for the CIO because that's like the fox watching the hen house that's changed. It's been a real awakening, a kind of a rude awakening so the world is now more virtual, you've got a secure physical assets. I mean any knucklehead can now become a ransomware attacker, they can buy ransomware as a service in the dark web so that's something we've never seen before. You're seeing supply chains get hacked and self-forming malware I mean it's a really scary time. So you've got these intellectual assets it's a top priority for organizations. Are you seeing a convergence of the CISO role, the CIO role, the line of business roles relative to sort of prior years in terms of driving security throughout organizations? >> Yeah this is a great question and this was a big discussion at my public board meeting a couple of days ago. It's, as I talk about many topics, if you think digital, if you think data, if you think ESG, it's no more one organization's business, it's now everybody's responsibility. I saw a Wall Street Journal article a couple of days ago where somebody has compared cyber to 9/11 type scenario that if it happens for a company that's the level of impact you feel on your operations. So, you know all models are going to change where CISO reports to CIO, at HPE we are also into product security and that's why CISO is a peer of mine who I work with very closely, who also worked with product teams where we are saving our customers from lot of pain in this space going forward and HPE itself is investing enormous amount of efforts and time in coming out of products which are secure and are not vulnerable to these types of attacks. The way I see it is CISO role has become extremely critical in every company and a big part of that role is to make people understand that cybersecurity is also everybody's responsibility. That's why an IT we propagate DevSecOps, as we talk about it we are very, very careful about picking the right products and services. This is one area where companies cannot shy away from investing. You have to continuously looking at cybersecurity architecture, you have to continuously look at and understand where the gaps are and how do we switch our product or service that we use from the providers to make sure our companies stay secure. The training not only for individual employees around anti-phishing or what does cybersecurity mean, but also to the executive committee and to the board around what cyber security means, what zero trust means, but at the same time doing drive-ins. We did it for business continuity and disaster recovery before, now it is time we do it for a ransomware attack and stay prepared. As you mentioned and we all say in tech community, it's always if not when. No company can take them their chest and say, "oh we are fully secure," because something can happen going forward. But what is the readiness for something that can happen? It has to be handled at the same risk level as a pandemic, or a earthquake, or a natural disaster and assume that it's going to happen and how as a company we will behave when something like this happens. So I'm huge believer in the framework of protect, detect, govern and respond as these things happen. So we need to have exercises within the company to ensure that everybody's aware of the part that they play day to day but at the same time when some event happen and making sure we do very periodic reviews of IT and cyber practices across the company, there is no more differentiation between IT and OT. That was 10 years ago. I remember working with different industries where OT was totally out of reach of IT and guess what happened? WannaCry and Petya and XP machines were still running your supply chains and they were not protected. So, if it's a technology it needs to be protected. That's the mindset people need to go with. Invest in education, training, awareness of your employees, your management committee, your board and do frequent exercises to understand how to respond when something like this happen. See it's a big responsibility to protect our customer data, our customer's operations and we all need to be responsible and accountable to be able to provide all our product and services to our customers when something unforeseen like this happens. >> Rashmi you're very generous with your time thank you so much for coming back in theCUBE it was great to have you again. >> Thank you Dave, it was really nice chatting with you. >> And thanks for being with us for our ongoing coverage of HPE Discover '21. This is Dave Vellante you're watching the virtual CUBE, the leader in digital tech coverage we'll be right back. (bright music)

>> Announcer: From around the globe it's theCUBE. Presenting the Convergence of File and Object brought to you by Pure Storage. >> Okay now we're going to get the customer perspective on object and we'll talk about the convergence of file and object, but really focusing on the object pieces this is a content program that's being made possible by Pure Storage and it's co-created with theCUBE. Christopher CB Bohn is here. He's a lead architect for MicroFocus the enterprise data warehouse and principal data engineer at MicroFocus. CB welcome good to see you. >> Thanks Dave good to be here. >> So tell us more about your role at Microfocus it's a pan Microfocus role because we know the company is a multi-national software firm it acquired the software assets of HP of course including Vertica tell us where you fit. >> Yeah so Microfocus is you know, it's like I can says it's wide, worldwide company that it sells a lot of software products all over the place to governments and so forth. And it also grows often by acquiring other companies. So there is there the problem of integrating new companies and their data. And so what's happened over the years is that they've had a number of different discreet data systems so you've got this data spread all over the place and they've never been able to get a full complete introspection on the entire business because of that. So my role was come in, design a central data repository and an enterprise data warehouse, that all reporting could be generated against. And so that's what we're doing and we selected Vertica as the EDW system and Pure Storage FlashBlade as the communal repository. >> Okay so you obviously had experience with with Vertica in your previous role, so it's not like you were starting from scratch, but paint a picture of what life was like before you embarked on this sort of consolidated approach to your data warehouse. Was it just dispared data all over the place? A lot of M and A going on, where did the data live? >> CB: So >> Right so again the data is all over the place including under people's desks and just dedicated you know their own private SQL servers, It, a lot of data in a Microfocus is one on SQL server, which has pros and cons. Cause that's a great transactional database but it's not really good for analytics in my opinion. So but a lot of stuff was running on that, they had one Vertica instance that was doing some select reporting. Wasn't a very powerful system and it was what they call Vertica enterprise mode where it had dedicated nodes which had the compute and storage in the same locus on each server okay. So Vertica Eon mode is a whole new world because it separates compute from storage. Okay and at first was implemented in AWS so that you could spin up you know different numbers of compute nodes and they all share the same communal storage. But there has been a demand for that kind of capability, but in an on-prem situation. Okay so Pure storage was the first vendor to come along and have an S3 emulation that was actually workable. And so Vertica worked with Pure Storage to make that all happen and that's what we're using. >> Yeah I know back when back from where we used to do face-to-face, we would be at you know Pure Accelerate, Vertica was always there it stopped by the booth, see what they're doing so tight integration there. And you mentioned Eon mode and the ability to scale, storage and compute independently. And so and I think Vertica is the only one I know they were the first, I'm not sure anybody else does that both for cloud and on-prem, but so how are you using Eon mode, are you both in AWS and on-prem are you exclusively cloud? Maybe you could describe that a little bit. >> Right so there's a number of internal rules at Microfocus that you know there's, it's not AWS is not approved for their business processes. At least not all of them, they really wanted to be on-prem and all the transactional systems are on-prem. And so we wanted to have the analytics OLAP stuff close to the OLTP stuff right? So that's why they called there, co-located very close to each other. And so we could, what's nice about this situation is that these S3 objects, it's an S3 object store on the Pure Flash Blade. We could copy those over if we needed it to AWS and we could spin up a version of Vertica there, and keep going. It's like a tertiary GR strategy cause we actually have a, we're setting up a second, Flash Blade Vertica system geo located elsewhere for backup and we can get into it if you want to talk about how the latest version of the Pure software for the Flash Blade allows synchronization across network boundaries of those Flash Blade which is really nice because if, you know there's a giant sinkhole opens up under our Koll of facility and we lose that thing then we just have to switch to DNS. And we were back in business of the DR. And then the third one was to go, we could copy those objects over to AWS and be up and running there. So we're feeling pretty confident about being able to weather whatever comes along. >> Yeah I'm actually very interested in that conversation but before we go there. you mentioned you want, you're going to have the old lab close to the OLTP, was that for latency reasons, data movement reasons, security, all of the above. >> Yeah it's really all of the above because you know we are operating under the same sub-net. So to gain access to that data, you know you'd have to be within that VPN environment. We didn't want to going out over the public internet. Okay so and just for latency reasons also, you know we have a lot of data and we're continually doing ETL processes into Vertica from our production data, transactional databases. >> Right so they got to be approximate. So I'm interested in so you're using the Pure Flash Blade as an object store, most people think, oh object simple but slow. Not the case for you is that right? >> Not the case at all >> Why is that. >> This thing had hoop It's ripping, well you have to understand about Vertica and the way it stores data. It stores data in what they call storage containers. And those are immutable, okay on disc whether it's on AWS or if you had a enterprise mode Vertica, if you do an update or delete it actually has to go and retrieve that object container from disc and it destroys it and rebuilds it, okay which is why you don't, you want to avoid updates and deletes with vertica because the way it gets its speed is by sorting and ordering and encoding the data on disk. So it can read it really fast. But if you do an operation where you're deleting or updating a record in the middle of that, then you've got to rebuild that entire thing. So that actually matches up really well with S3 object storage because it's kind of the same way, it gets destroyed and rebuilt too okay. So that matches up very well with Vertica and we were able to design the system so that it's a panda only. Now we have some reports that we're running in SQL server. Okay which we're taking seven days. So we moved that to Vertica from SQL server and we rewrote the queries, which were had, which had been written in TC SQL with a bunch of loops and so forth and we were to get, this is amazing it went from seven days to two seconds, to generate this report. Which has tremendous value to the company because it would have to have this long cycle of seven days to get a new introspection in what they call the knowledge base. And now all of a sudden it's almost on demand two seconds to generate it. That's great and that's because of the way the data is stored. And the S3 you asked about, oh you know it, it's slow, well not in that context. Because what happens really with Vertica Eon mode is that it can, they have, when you set up your compute nodes, they have local storage also which is called the depot. It's kind of a cache okay. So the data will be drawn from the Flash Blade and cached locally. And that was, it was thought when they designed that, oh you know it's that'll cut down on the latency. Okay but it turns out that if you have your compute nodes close meaning minimal hops to the Flash Blade that you can actually tell Vertica, you know don't even bother caching that stuff just read it directly on the fly from the from the Flash Blade and the performance is still really good. It depends on your situation. But I know for example a major telecom company that uses the same topologies we're talking about here they did the same thing. They just dropped the cache cause the Flash Blade was able to deliver the data fast enough. >> So that's, you're talking about that's speed of light issues and just the overhead of switching infrastructure is that, it's eliminated and so as a result you can go directly to the storage array? >> That's correct yeah, it's like, it's fast enough that it's almost as if it's local to the compute node. But every situation is different depending on your needs. If you've got like a few tables that are heavily used, then yeah put them in the cache because that'll be probably a little bit faster. But if you're have a lot of ad hoc queries that are going on, you know you may exceed the storage of the local cache and then you're better off having it just read directly from the, from the Flash Blade. >> Got it so it's >> Okay. >> It's an append only approach. So you're not >> Right >> Overwriting on a record, so but then what you have automatically re index and that's the intelligence of the system. how does that work? >> Oh this is where we did a little bit of magic. There's not really anything like magic but I'll tell you what it is I mean. ( Dave laughing) Vertica does not have indexes. They don't exist. Instead I told you earlier that it gets a speed by sorting and encoding the data on disk and ordering it right. So when you've got an append-only situation, the natural question is well if I have a unique record, with let's say ID one, two, three, what happens if I append a new version of that, what happens? Well the way Vertica operates is that there's a thing called a projection which is actually like a materialized columnar data store. And you can have a, what they call a top-K projection, which says only put in this projection the records that meet a certain condition. So there's a field that we like to call a discriminator field which is like okay usually it's the latest update timestamp. So let's say we have record one, two, three and it had yesterday's date and that's the latest version. Now a new version comes in. When the data at load time vertical looks at that and then it looks in the projection and says does this exist already? If it doesn't then it adds it. If it does then that one now goes into that projection okay. And so what you end up having is a projection that is the latest snapshot of the data, which would be like, oh that's the reality of what the table is today okay. But inherent in that is that you now have a table that has all the change history of those records, which is awesome. >> Yeah. >> Because, you often want to go back and revisit, you know what it will happen to you. >> But that materialized view is the most current and the system knows that at least can (murmuring). >> Right so we then create views that draw off from that projection so that our users don't have to worry about any of that. They just get oh and say select from this view and they're getting the latest greatest snapshot of what the reality of the data is right now. But if they want to go back and say, well how did this data look two days ago? That's an easy query for them to do also. So they get the best of both worlds. >> So could you just plug any flash array into your system and achieve the same results or is there anything really unique about Pure? >> Yeah well they're the only ones that have got I think really dialed in the S3 object form because I don't think AWS actually publishes every last detail of that S3 spec. Okay so it had, there's a certain amount of reverse engineering they had to do I think. But they got it right. When we've, a couple maybe a year and a half ago or so there they were like at 99%, but now they worked with Vertica people to make sure that that object format was true to what it should be. So that it works just as if Vertica doesn't care, if it is on AWS or if it's on Pure Flash Blade because Pure did a really good job of dialing in that format and so Vertica doesn't care. It just knows S3, doesn't know what it doesn't care where it's going it just works. >> So the essentially vendor R and D abstracted that complexity so you didn't have to rewrite the application is that right? >> Right, so you know when Vertica ships it's software, you don't get a specific version for Pure or AWS, it's all in one package, and then when you configure it, it knows oh okay well, I'm just pointed at the, you know this port, on the Pure storage Flash Blade, and it just works. >> CB what's your data team look like? How is it evolving? You know a lot of customers I talked to they complain that they struggled to get value out of the data and they don't have the expertise, what does your team look like? How is it, is it changing or did the pandemic change things at all? I wonder if you could bring us up to date on that? >> Yeah but in some ways Microfocus has an advantage in that it's such a widely dispersed across the world company you know it's headquartered in the UK, but I deal with people I'm in the Bay Area, we have people in Mexico, Romania, India. >> Okay enough >> All over the place yeah all over the place. So when this started, it was actually a bigger project it got scaled back, it was almost to the point where it was going to be cut. Okay, but then we said, well let's try to do almost a skunkworks type of thing with reduced staff. And so we're just like a hand. You could count the number of key people on this on one hand. But we got it all together, and it's been a traumatic transformation for the company. Now there's, it's one approval and admiration from the highest echelons of this company that, hey this is really providing value. And the company is starting to get views into their business that they didn't have before. >> That's awesome, I mean, I've watched Microfocus for years. So to me they've always had a, their part of their DNA is private equity I mean they're sharp investors, they do great M and A >> CB: Yeah >> They know how to drive value and they're doing modern M and A, you know, we've seen what they what wait, what they did with SUSE, obviously driving value out of Vertica, they've got a really, some sharp financial people there. So that's they must have loved the the Skunkworks, fast ROI you know, small denominator, big numerator. (laughing) >> Well I think that in this case, smaller is better when you're doing development. You know it's a two-minute cooks type of thing and if you've got people who know what they're doing, you know I've got a lot of experience with Vertica, I've been on the advisory board for Vertica for a long time. >> Right And you know I was able to learn from people who had already, we're like the second or third company to do a Pure Flash Blade Vertica installation, but some of the best companies after they've already done it we are members of the advisory board also. So I learned from the best, and we were able to get this thing up and running quickly and we've got you know, a lot of other, you know handful of other key people who know how to write SQL and so forth to get this up and running quickly. >> Yeah so I mean, look it Pure is a fit I mean I sound like a fan boy, but Pure is all about simplicity, so is object. So that means you don't have to ra, you know worry about wrangling storage and worrying about LANs and all that other nonsense and file names but >> I have burned by hardware in the past you know, where oh okay they built into a price and so they cheap out on stuff like fans or other things in these components fail and the whole thing goes down, but this hardware is super good quality. And so I'm happy with the quality of that we're getting. >> So CB last question. What's next for you? Where do you want to take this initiative? >> Well we are in the process now of, we're when, so I designed a system to combine the best of the Kimball approach to data warehousing and the inland approach okay. And what we do is we bring over all the data we've got and we put it into a pristine staging layer. Okay like I said it's a, because it's append-only, it's essentially a log of all the transactions that are happening in this company, just as they appear okay. And then from the Kimball side of things we're designing the data marts now. So that's what the end users actually interact with. So we're taking the, we're examining the transactional systems to say, how are these business objects created? What's the logic there and we're recreating those logical models in Vertica. So we've done a handful of them so far, and it's working out really well. So going forward we've got a lot of work to do, to create just about every object that the company needs. >> CB you're an awesome guest really always a pleasure talking to you and >> Thank you. >> congratulations and good luck going forward stay safe. >> Thank you, you too Dave. >> All right thank you. And thank you for watching the Convergence of File and Object. This is Dave Vellante for theCUBE. (soft music)

>>Yeah, yeah. >>Welcome back. I'm so excited to introduce our next session empowerment through inclusion, reimagining society and technology. This is a topic that's personally very near and dear to my heart. Did you know that there's only 2% of Latinas in technology as a Latina? I know that there's so much more we could do collectively to improve these gaps and diversity. I thought spot diversity is considered a critical element across all levels of the organization. The data shows countless times. A diverse and inclusive workforce ultimately drives innovation better performance and keeps your employees happier. That's why we're passionate about contributing to this conversation and also partnering with organizations that share our mission of improving diversity across our communities. Last beyond, we hosted the session during a breakfast and we packed the whole room. This year, we're bringing the conversation to the forefront to emphasize the importance of diversity and data and share the positive ramifications that it has for your organization. Joining us for this session are thought spots Chief Data Strategy Officer Cindy Housing and Ruhollah Benjamin, associate professor of African American Studies at Princeton University. Thank you, Paola. So many >>of you have journeyed with me for years now on our efforts to improve diversity and inclusion in the data and analytic space. And >>I would say >>over time we cautiously started commiserating, eventually sharing best practices to make ourselves and our companies better. And I do consider it a milestone. Last year, as Paola mentioned that half the room was filled with our male allies. But I remember one of our Panelists, Natalie Longhurst from Vodafone, suggesting that we move it from a side hallway conversation, early morning breakfast to the main stage. And I >>think it was >>Bill Zang from a I G in Japan. Who said Yes, please. Everyone else agreed, but more than a main stage topic, I want to ask you to think about inclusion beyond your role beyond your company toe. How Data and analytics can be used to impact inclusion and equity for the society as a whole. Are we using data to reveal patterns or to perpetuate problems leading Tobias at scale? You are the experts, the change agents, the leaders that can prevent this. I am thrilled to introduce you to the leading authority on this topic, Rou Ha Benjamin, associate professor of African studies at Princeton University and author of Multiple Books. The Latest Race After Technology. Rou ha Welcome. >>Thank you. Thank you so much for having me. I'm thrilled to be in conversation with you today, and I thought I would just kick things off with some opening reflections on this really important session theme. And then we could jump into discussion. So I'd like us to as a starting point, um, wrestle with these buzzwords, empowerment and inclusion so that we can have them be more than kind of big platitudes and really have them reflected in our workplace cultures and the things that we design in the technologies that we put out into the world. And so to do that, I think we have to move beyond techno determinism, and I'll explain what that means in just a minute. Techno determinism comes in two forms. The first, on your left is the idea that technology automation, um, all of these emerging trends are going to harm us, are going to necessarily harm humanity. They're going to take all the jobs they're going to remove human agency. This is what we might call the techno dystopian version of the story and this is what Hollywood loves to sell us in the form of movies like The Matrix or Terminator. The other version on your right is the techno utopian story that technologies automation. The robots as a shorthand, are going to save humanity. They're gonna make everything more efficient, more equitable. And in this case, on the surface, he seemed like opposing narratives right there, telling us different stories. At least they have different endpoints. But when you pull back the screen and look a little bit more closely, you see that they share an underlying logic that technology is in the driver's seat and that human beings that social society can just respond to what's happening. But we don't really have a say in what technologies air designed and so to move beyond techno determinism the notion that technology is in the driver's seat. We have to put the human agents and agencies back into the story, the protagonists, and think carefully about what the human desires worldviews, values, assumptions are that animate the production of technology. And so we have to put the humans behind the screen back into view. And so that's a very first step and when we do that, we see, as was already mentioned, that it's a very homogeneous group right now in terms of who gets the power and the resource is to produce the digital and physical infrastructure that everyone else has to live with. And so, as a first step, we need to think about how to create more participation of those who are working behind the scenes to design technology now to dig a little more a deeper into this, I want to offer a kind of low tech example before we get to the more hi tech ones. So what you see in front of you here is a simple park bench public bench. It's located in Berkeley, California, which is where I went to graduate school and on this particular visit I was living in Boston, and so I was back in California. It was February. It was freezing where I was coming from, and so I wanted to take a few minutes in between meetings to just lay out in the sun and soak in some vitamin D, and I quickly realized, actually, I couldn't lay down on this bench because of the way it had been designed with these arm rests at intermittent intervals. And so here I thought. Okay, the the armrest have, ah functional reason why they're there. I mean, you could literally rest your elbows there or, um, you know, it can create a little bit of privacy of someone sitting there that you don't know. When I was nine months pregnant, it could help me get up and down or for the elderly, the same thing. So it has a lot of functional reasons, but I also thought about the fact that it prevents people who are homeless from sleeping on the bench. And this is the Bay area that we were talking about where, in fact, the tech boom has gone hand in hand with a housing crisis. Those things have grown in tandem. So innovation has grown within equity because we haven't thought carefully about how to address the social context in which technology grows and blossoms. And so I thought, Okay, this crisis is growing in this area, and so perhaps this is a deliberate attempt to make sure that people don't sleep on the benches by the way that they're designed and where the where they're implemented and So this is what we might call structural inequity. By the way something is designed. It has certain effects that exclude or harm different people. And so it may not necessarily be the intense, but that's the effect. And I did a little digging, and I found, in fact, it's a global phenomenon, this thing that architects called hostile architecture. Er, I found single occupancy benches in Helsinki, so only one booty at a time no laying down there. I found caged benches in France. And in this particular town. What's interesting here is that the mayor put these benches out in this little shopping plaza, and within 24 hours the people in the town rallied together and had them removed. So we see here that just because we have, uh, discriminatory design in our public space doesn't mean we have to live with it. We can actually work together to ensure that our public space reflects our better values. But I think my favorite example of all is the meter bench. In this case, this bench is designed with spikes in them, and to get the spikes to retreat into the bench, you have to feed the meter you have to put some coins in, and I think it buys you about 15 or 20 minutes. Then the spikes come back up. And so you'll be happy to know that in this case, this was designed by a German artists to get people to think critically about issues of design, not just the design of physical space but the design of all kinds of things, public policies. And so we can think about how our public life in general is metered, that it serves those that can pay the price and others are excluded or harm, whether we're talking about education or health care. And the meter bench also presents something interesting. For those of us who care about technology, it creates a technical fix for a social problem. In fact, it started out his art. But some municipalities in different parts of the world have actually adopted this in their public spaces in their parks in order to deter so called lawyers from using that space. And so, by a technical fix, we mean something that creates a short term effect, right. It gets people who may want to sleep on it out of sight. They're unable to use it, but it doesn't address the underlying problems that create that need to sleep outside in the first place. And so, in addition to techno determinism, we have to think critically about technical fixes that don't address the underlying issues that technology is meant to solve. And so this is part of a broader issue of discriminatory design, and we can apply the bench metaphor to all kinds of things that we work with or that we create. And the question we really have to continuously ask ourselves is, What values are we building in to the physical and digital infrastructures around us? What are the spikes that we may unwittingly put into place? Or perhaps we didn't create the spikes. Perhaps we started a new job or a new position, and someone hands us something. This is the way things have always been done. So we inherit the spike bench. What is our responsibility when we noticed that it's creating these kinds of harms or exclusions or technical fixes that are bypassing the underlying problem? What is our responsibility? All of this came to a head in the context of financial technologies. I don't know how many of you remember these high profile cases of tech insiders and CEOs who applied for Apple, the Apple card and, in one case, a husband and wife applied and the husband, the husband received a much higher limit almost 20 times the limit as his wife, even though they shared bank accounts, they lived in Common Law State. And so the question. There was not only the fact that the husband was receiving a much better interest rate and the limit, but also that there was no mechanism for the individuals involved to dispute what was happening. They didn't even know what the factors were that they were being judged that was creating this form of discrimination. So in terms of financial technologies, it's not simply the outcome that's the issue. Or that could be discriminatory, but the process that black boxes, all of the decision making that makes it so that consumers and the general public have no way to question it. No way to understand how they're being judged adversely, and so it's the process not only the product that we have to care a lot about. And so the case of the apple cart is part of a much broader phenomenon of, um, racist and sexist robots. This is how the headlines framed it a few years ago, and I was so interested in this framing because there was a first wave of stories that seemed to be shocked at the prospect that technology is not neutral. Then there was a second wave of stories that seemed less surprised. Well, of course, technology inherits its creator's biases. And now I think we've entered a phase of attempts to override and address the default settings of so called racist and sexist robots, for better or worse. And here robots is just a kind of shorthand, that the way people are talking about automation and emerging technologies more broadly. And so as I was encountering these headlines, I was thinking about how these air, not problems simply brought on by machine learning or AI. They're not all brand new, and so I wanted to contribute to the conversation, a kind of larger context and a longer history for us to think carefully about the social dimensions of technology. And so I developed a concept called the New Jim Code, which plays on the phrase Jim Crow, which is the way that the regime of white supremacy and inequality in this country was defined in a previous era, and I wanted us to think about how that legacy continues to haunt the present, how we might be coding bias into emerging technologies and the danger being that we imagine those technologies to be objective. And so this gives us a language to be able to name this phenomenon so that we can address it and change it under this larger umbrella of the new Jim Code are four distinct ways that this phenomenon takes shape from the more obvious engineered inequity. Those were the kinds of inequalities tech mediated inequalities that we can generally see coming. They're kind of obvious. But then we go down the line and we see it becomes harder to detect. It's happening in our own backyards. It's happening around us, and we don't really have a view into the black box, and so it becomes more insidious. And so in the remaining couple minutes, I'm just just going to give you a taste of the last three of these, and then a move towards conclusion that we can start chatting. So when it comes to default discrimination. This is the way that social inequalities become embedded in emerging technologies because designers of these technologies aren't thinking carefully about history and sociology. Ah, great example of this came Thio headlines last fall when it was found that widely used healthcare algorithm affecting millions of patients, um, was discriminating against black patients. And so what's especially important to note here is that this algorithm healthcare algorithm does not explicitly take note of race. That is to say, it is race neutral by using cost to predict healthcare needs. This digital triaging system unwittingly reproduces health disparities because, on average, black people have incurred fewer costs for a variety of reasons, including structural inequality. So in my review of this study by Obermeyer and colleagues, I want to draw attention to how indifference to social reality can be even more harmful than malicious intent. It doesn't have to be the intent of the designers to create this effect, and so we have to look carefully at how indifference is operating and how race neutrality can be a deadly force. When we move on to the next iteration of the new Jim code coded exposure, there's attention because on the one hand, you see this image where the darker skin individual is not being detected by the facial recognition system, right on the camera or on the computer. And so coated exposure names this tension between wanting to be seen and included and recognized, whether it's in facial recognition or in recommendation systems or in tailored advertising. But the opposite of that, the tension is with when you're over included. When you're surveiled when you're to centered. And so we should note that it's not simply in being left out, that's the problem. But it's in being included in harmful ways. And so I want us to think carefully about the rhetoric of inclusion and understand that inclusion is not simply an end point. It's a process, and it is possible to include people in harmful processes. And so we want to ensure that the process is not harmful for it to really be effective. The last iteration of the new Jim Code. That means the the most insidious, let's say, is technologies that are touted as helping US address bias, so they're not simply including people, but they're actively working to address bias. And so in this case, There are a lot of different companies that are using AI to hire, create hiring software and hiring algorithms, including this one higher view. And the idea is that there there's a lot that AI can keep track of that human beings might miss. And so so the software can make data driven talent decisions. After all, the problem of employment discrimination is widespread and well documented. So the logic goes, Wouldn't this be even more reason to outsource decisions to AI? Well, let's think about this carefully. And this is the look of the idea of techno benevolence trying to do good without fully reckoning with what? How technology can reproduce inequalities. So some colleagues of mine at Princeton, um, tested a natural learning processing algorithm and was looking to see whether it exhibited the same, um, tendencies that psychologists have documented among humans. E. And what they found was that in fact, the algorithm associating black names with negative words and white names with pleasant sounding words. And so this particular audit builds on a classic study done around 2003, before all of the emerging technologies were on the scene where two University of Chicago economists sent out thousands of resumes to employers in Boston and Chicago, and all they did was change the names on those resumes. All of the other work history education were the same, and then they waited to see who would get called back. And the applicants, the fictional applicants with white sounding names received 50% more callbacks than the black applicants. So if you're presented with that study, you might be tempted to say, Well, let's let technology handle it since humans are so biased. But my colleagues here in computer science found that this natural language processing algorithm actually reproduced those same associations with black and white names. So, too, with gender coded words and names Amazon learned a couple years ago when its own hiring algorithm was found discriminating against women. Nevertheless, it should be clear by now why technical fixes that claim to bypass human biases are so desirable. If Onley there was a way to slay centuries of racist and sexist demons with a social justice box beyond desirable, more like magical, magical for employers, perhaps looking to streamline the grueling work of recruitment but a curse from any jobseekers, as this headline puts it, your next interview could be with a racist spot, bringing us back to that problem space we started with just a few minutes ago. So it's worth noting that job seekers are already developing ways to subvert the system by trading answers to employers test and creating fake applications as informal audits of their own. In terms of a more collective response, there's a federation of European Trade unions call you and I Global that's developed a charter of digital rights for work, others that touches on automated and a I based decisions to be included in bargaining agreements. And so this is one of many efforts to change their ecosystem to change the context in which technology is being deployed to ensure more protections and more rights for everyday people in the US There's the algorithmic accountability bill that's been presented, and it's one effort to create some more protections around this ubiquity of automated decisions, and I think we should all be calling from more public accountability when it comes to the widespread use of automated decisions. Another development that keeps me somewhat hopeful is that tech workers themselves are increasingly speaking out against the most egregious forms of corporate collusion with state sanctioned racism. And to get a taste of that, I encourage you to check out the hashtag Tech won't build it. Among other statements that they have made and walking out and petitioning their companies. Who one group said, as the people who build the technologies that Microsoft profits from, we refuse to be complicit in terms of education, which is my own ground zero. Um, it's a place where we can we can grow a more historically and socially literate approach to tech design. And this is just one, um, resource that you all can download, Um, by developed by some wonderful colleagues at the Data and Society Research Institute in New York and the goal of this interventionist threefold to develop an intellectual understanding of how structural racism operates and algorithms, social media platforms and technologies, not yet developed and emotional intelligence concerning how to resolve racially stressful situations within organizations, and a commitment to take action to reduce harms to communities of color. And so as a final way to think about why these things are so important, I want to offer a couple last provocations. The first is for us to think a new about what actually is deep learning when it comes to computation. I want to suggest that computational depth when it comes to a I systems without historical or social depth, is actually superficial learning. And so we need to have a much more interdisciplinary, integrated approach to knowledge production and to observing and understanding patterns that don't simply rely on one discipline in order to map reality. The last provocation is this. If, as I suggested at the start, inequity is woven into the very fabric of our society, it's built into the design of our. Our policies are physical infrastructures and now even our digital infrastructures. That means that each twist, coil and code is a chance for us toe. We've new patterns, practices and politics. The vastness of the problems that we're up against will be their undoing. Once we accept that we're pattern makers. So what does that look like? It looks like refusing color blindness as an anecdote to tech media discrimination rather than refusing to see difference. Let's take stock of how the training data and the models that we're creating have these built in decisions from the past that have often been discriminatory. It means actually thinking about the underside of inclusion, which can be targeting. And how do we create a more participatory rather than predatory form of inclusion? And ultimately, it also means owning our own power in these systems so that we can change the patterns of the past. If we're if we inherit a spiked bench, that doesn't mean that we need to continue using it. We can work together to design more just and equitable technologies. So with that, I look forward to our conversation. >>Thank you, Ruth. Ha. That was I expected it to be amazing, as I have been devouring your book in the last few weeks. So I knew that would be impactful. I know we will never think about park benches again. How it's art. And you laid down the gauntlet. Oh, my goodness. That tech won't build it. Well, I would say if the thoughts about team has any saying that we absolutely will build it and will continue toe educate ourselves. So you made a few points that it doesn't matter if it was intentional or not. So unintentional has as big an impact. Um, how do we address that does it just start with awareness building or how do we address that? >>Yeah, so it's important. I mean, it's important. I have good intentions. And so, by saying that intentions are not the end, all be all. It doesn't mean that we're throwing intentions out. But it is saying that there's so many things that happened in the world, happened unwittingly without someone sitting down to to make it good or bad. And so this goes on both ends. The analogy that I often use is if I'm parked outside and I see someone, you know breaking into my car, I don't run out there and say Now, do you feel Do you feel in your heart that you're a thief? Do you intend to be a thief? I don't go and grill their identity or their intention. Thio harm me, but I look at the effect of their actions, and so in terms of art, the teams that we work on, I think one of the things that we can do again is to have a range of perspectives around the table that can think ahead like chess, about how things might play out, but also once we've sort of created something and it's, you know, it's entered into, you know, the world. We need to have, ah, regular audits and check ins to see when it's going off track just because we intended to do good and set it out when it goes sideways, we need mechanisms, formal mechanisms that actually are built into the process that can get it back on track or even remove it entirely if we find And we see that with different products, right that get re called. And so we need that to be formalized rather than putting the burden on the people that are using these things toe have to raise the awareness or have to come to us like with the apple card, Right? To say this thing is not fair. Why don't we have that built into the process to begin with? >>Yeah, so a couple things. So my dad used to say the road to hell is paved with good intentions, so that's >>yes on. In fact, in the book, I say the road to hell is paved with technical fixes. So they're me and your dad are on the same page, >>and I I love your point about bringing different perspectives. And I often say this is why diversity is not just about business benefits. It's your best recipe for for identifying the early biases in the data sets in the way we build things. And yet it's such a thorny problem to address bringing new people in from tech. So in the absence of that, what do we do? Is it the outside review boards? Or do you think regulation is the best bet as you mentioned a >>few? Yeah, yeah, we need really need a combination of things. I mean, we need So on the one hand, we need something like a do no harm, um, ethos. So with that we see in medicine so that it becomes part of the fabric and the culture of organizations that that those values, the social values, have equal or more weight than the other kinds of economic imperatives. Right. So we have toe have a reckoning in house, but we can't leave it to people who are designing and have a vested interest in getting things to market to regulate themselves. We also need independent accountability. So we need a combination of this and going back just to your point about just thinking about like, the diversity on teams. One really cautionary example comes to mind from last fall, when Google's New Pixel four phone was about to come out and it had a kind of facial recognition component to it that you could open the phone and they had been following this research that shows that facial recognition systems don't work as well on darker skin individuals, right? And so they wanted Thio get a head start. They wanted to prevent that, right? So they had good intentions. They didn't want their phone toe block out darker skin, you know, users from from using it. And so what they did was they were trying to diversify their training data so that the system would work better and they hired contract workers, and they told these contract workers to engage black people, tell them to use the phone play with, you know, some kind of app, take a selfie so that their faces would populate that the training set, But they didn't. They did not tell the people what their faces were gonna be used for, so they withheld some information. They didn't tell them. It was being used for the spatial recognition system, and the contract workers went to the media and said Something's not right. Why are we being told? Withhold information? And in fact, they told them, going back to the park bench example. To give people who are homeless $5 gift cards to play with the phone and get their images in this. And so this all came to light and Google withdrew this research and this process because it was so in line with a long history of using marginalized, most vulnerable people and populations to make technologies better when those technologies are likely going toe, harm them in terms of surveillance and other things. And so I think I bring this up here to go back to our question of how the composition of teams might help address this. I think often about who is in that room making that decision about sending, creating this process of the contract workers and who the selfies and so on. Perhaps it was a racially homogeneous group where people didn't want really sensitive to how this could be experienced or seen, but maybe it was a diverse, racially diverse group and perhaps the history of harm when it comes to science and technology. Maybe they didn't have that disciplinary knowledge. And so it could also be a function of what people knew in the room, how they could do that chest in their head and think how this is gonna play out. It's not gonna play out very well. And the last thing is that maybe there was disciplinary diversity. Maybe there was racial ethnic diversity, but maybe the workplace culture made it to those people. Didn't feel like they could speak up right so you could have all the diversity in the world. But if you don't create a context in which people who have those insights feel like they can speak up and be respected and heard, then you're basically sitting on a reservoir of resource is and you're not tapping into it to ensure T to do right by your company. And so it's one of those cautionary tales I think that we can all learn from to try to create an environment where we can elicit those insights from our team and our and our coworkers, >>your point about the culture. This is really inclusion very different from just diversity and thought. Eso I like to end on a hopeful note. A prescriptive note. You have some of the most influential data and analytics leaders and experts attending virtually here. So if you imagine the way we use data and housing is a great example, mortgage lending has not been equitable for African Americans in particular. But if you imagine the right way to use data, what is the future hold when we've gotten better at this? More aware >>of this? Thank you for that question on DSO. You know, there's a few things that come to mind for me one. And I think mortgage environment is really the perfect sort of context in which to think through the the both. The problem where the solutions may lie. One of the most powerful ways I see data being used by different organizations and groups is to shine a light on the past and ongoing inequities. And so oftentimes, when people see the bias, let's say when it came to like the the hiring algorithm or the language out, they see the names associated with negative or positive words that tends toe have, ah, bigger impact because they think well, Wow, The technology is reflecting these biases. It really must be true. Never mind that people might have been raising the issues in other ways before. But I think one of the most powerful ways we can use data and technology is as a mirror onto existing forms of inequality That then can motivate us to try to address those things. The caution is that we cannot just address those once we come to grips with the problem, the solution is not simply going to be a technical solution. And so we have to understand both the promise of data and the limits of data. So when it comes to, let's say, a software program, let's say Ah, hiring algorithm that now is trained toe look for diversity as opposed to homogeneity and say I get hired through one of those algorithms in a new workplace. I can get through the door and be hired. But if nothing else about that workplace has changed and on a day to day basis I'm still experiencing microaggressions. I'm still experiencing all kinds of issues. Then that technology just gave me access to ah harmful environment, you see, and so this is the idea that we can't simply expect the technology to solve all of our problems. We have to do the hard work. And so I would encourage everyone listening to both except the promise of these tools, but really crucially, um, Thio, understand that the rial kinds of changes that we need to make are gonna be messy. They're not gonna be quick fixes. If you think about how long it took our society to create the kinds of inequities that that we now it lived with, we should expect to do our part, do the work and pass the baton. We're not going to magically like Fairy does create a wonderful algorithm that's gonna help us bypass these issues. It can expose them. But then it's up to us to actually do the hard work of changing our social relations are changing the culture of not just our workplaces but our schools. Our healthcare systems are neighborhoods so that they reflect our better values. >>Yeah. Ha. So beautifully said I think all of us are willing to do the hard work. And I like your point about using it is a mirror and thought spot. We like to say a fact driven world is a better world. It can give us that transparency. So on behalf of everyone, thank you so much for your passion for your hard work and for talking to us. >>Thank you, Cindy. Thank you so much for inviting me. Hey, I live back to you. >>Thank you, Cindy and rou ha. For this fascinating exploration of our society and technology, we're just about ready to move on to our final session of the day. So make sure to tune in for this customer case study session with executives from Sienna and Accenture on driving digital transformation with certain AI.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Live coverage here for eight of us. Reinvent 2020. Virtual normally were on the show floor getting all of the interviews and talking about the top newsmakers and we have one of them here on the Cube were remote. I'm John for your host of the Cube. George Ellis Eros, GM and director of product manager for AWS. Talking about Wavelength George. Welcome to the remote Cube Cube. Virtual. Thanks for coming on. >>Good to be here. Thanks for having a John >>Eso Andy's Kino. One of the highlights last year, I pointed out that the five g thing is gonna be huge with the L A Wavelength Metro thing going on this year. Same thing. Mawr Proofpoint S'more expansion. Take us through what was announced this year. What's the big update on wavelength? >>Yes, so John Wavelength essentially brings a W services at the edge of the five G network, allowing our AWS customers and developers to reach their own end users and devices. Five devices with very low latency enabling a number off emerging applications ranging from industrial automation and I O. T. All the way to weigh AR VR smart cities, connected vehicles and much more this year we announced earlier in the year the general availability of wavelength in two locations one in the Bay Area and one in the Boston area. And since then we've seen we've been growing with Verizon or five D partner in the U. S. And and increasing that coverage in multiple off the larger U. S cities, including Miami and D. C in New York. And we launched Las Vegas yesterday at Andy's keynote with Verizon. We also announced that we are going toe to have a global footprint with K d D I in Japan launching a wavelength in Tokyo with SK detail SK Telecom in in South Korea or launching indigestion and with Vodafone in London >>so significant its expansion. Um, we used to call these points of presence back in the old days. I don't know what you call them now. I guess they're just zones like you calling them zones, but this really is gonna be a critical edge network, part of the edge, whether it's stadiums, metro area things and the density and the group is awesome. And everyone loves at about five gs. More of a business at less consumer. When you think about it, what has been some of the response as you guys had deployed mawr, What's the feedback? Um, can you take us through what the response has been? What's it been like? What have been some of the observations? >>Yeah, customers air really excited with the promise of five G and really excited to get their hands on these new capabilities that we're offering. Um, And they're telling us, you know, some consistent feedback that we're getting is that they're telling us that they love that they can use the same A W s, a P I S and tools and services that they used today in the region to get their hands on this new capabilities. So that's being pretty pretty consistent. Feedback these off use and the you know, Sometimes customers tell us that within a day they are able to deploy their applications in web. So that's a that's pretty consistent there. We've seen customers across a number of areas arranging, you know, from from manufacturing to healthcare to a ar and VR and broadcasting and live streaming all the way to smart cities and and connected vehicles. So a number of customers in these areas are using wavelength. Some of my favorite you know, examples are in in actually connected vehicles where you really can see that future materialized. You get, you know, customers like LG that are building the completely secularized vehicle, tow everything platform, and customers like safari that allow multiple devices to do, you know, talkto the Waveland, the closest Waveland Zone process. All of those device data streams at the edge. And then, um, it back. You know messages to the drivers, like for emergency situations, or even construct full dynamic maps for consumption off the off the vehicle themselves. >>I mean, it's absolutely awesome. And, you know, one of things that someone Dave Brown yesterday around the C two and the trend with smaller compute. You have the compute relationship at the edge to moving back and forth so I can see those dots connecting and looking forward to see how that plays out. Sure, and it will enable more capabilities. I do want to get your your thoughts, or you could just for the audience and our perspective just define the difference between wavelength and local zones because we know what regions are. Amazon regions are well understood all around the world. But now you have this new concept called locals owns part of wavelength, not part of wavelengths. Are they different technology? Can you just explain? Take him in to exclaim wavelength versus local zones how they work together? >>Yeah, So let me take a step back at AWS. Basically, what we're trying to do is we're trying to enable our customers to reach their end users with low latency and great performance, wherever those end users are and whatever network they're they're using to get connected, whether that's the five g mobile network with the Internet or in I o t Network. So we have a number of products that help our customers do that. And we expect, like, in months off other areas of the AWS platform, that customers are gonna pick and twos and mix and match and combine some of these products toe master use case. So when you're talking about wavelength and local zones, wavelength is about five g. There is obviously a lot off excitement as you said yourself about five g about the promise off those higher throughput. They're Lowell agencies. You know, the large number of devices supported and with wavelengths were enabling our customers toe to make the most of that. You know, of the five G technology and toe work on these emerging new use cases and applications that we talked about When it comes to local zones, we're talking more about extending AWS out two more locations. So if you think about you mentioned AWS regions, we have 24 regions in another five coming. Those are worldwide and enabled most of our customers to run their workloads. You know all of their workloads with low latency and adequate performance across the world. But we are hearing from customers that they want AWS in more locations. So local zones basically bring a W S extend those regions to more locations by bringing a W s closer to population I t and industrial centers. You know, l A is a great example of that. We launched the lay last year toe to local zones in L. A and toe toe a mainly at the media and entertainment customers that are, you know, in the L. A Metro, and we've seen customers like Netflix, for example, moving their artist workstations to the local zones. If they were to move that somewhere, you know, to the cloud somewhere further out the Laden's, he might have been too much for their ass artists work clothes and having some local AWS in the L. A. Metro allows them to finally move those workstation to the cloud while preserving that user experience. You know, interacting with the workstations that's happened. The cloud. >>So just like in conceptualizing is local zone, like a base station is in the metro point of physical location. Is it outpost on steroids? Been trying to get the feel for what it is >>you can think off regions consisting off availability zones. So these are, you know, data center clusters that deliver AWS services. So a local zone is much like an availability zone. But instead of being co located with the rest of the region, is in another locations that, for example, in L. A. Rather than being, you know, in in Virginia, let's say, um, they are internally. We use the same technology that we use for outpost, I suppose, is another great example of how AWS is getting closer to customers for on premises. Deployments were using much of the same technology that you you probably know as Nitro System and a number of other kind of technology that we've been working on for years, actually, toe make all this possible. >>You know, anyone who's been to a football game or any kind of stadium knows you got a great WiFi signal, but you get terrible bandwidth that is essentially kind of the back hall component for the telecom geeks out there. This is kind of what we're talking about here, right? We're talking about more of an expansionary at that edge on throughput, not just signal. So there's, you know, there's there's a wireless signal, and it's like really conductivity riel functionality for applications. >>Yeah, and many. Many of those use case that we're talking about are about, you know, immersive experiences for for end users. So with five t, you get that increasing throughput, you can get up to 10 GPS. You know, it is much higher with what you get 40. You also get lower latents is, but in order to really get make the most out of five G. You need to have the cloud services closer to the end user. So that's what Wavelength is doing is bringing all of those cloud services closer to the end user and combined with five G delivers on these on these applications. You know, um, a couple of customers are actually doing very, very, very exciting things on immersive application, our own immersive experiences. Um, why be VR is a customer that's working on wavelength today to deliver a full 3 60 video off sports events, and it's like you're there. They basically take all of those video streams. They process them in the waving zone and then put them back down to your to your VR headset. But don't you have seen those? We are headsets there, these bulky, awkward, big things because we can do a lot of the processing now at the edge rather than on the heads of itself. We are envisioning that these headsets will Will will string down to something that's indistinguishable potential from, you know, your glasses, making that user experience much better. >>Yeah, from anything from first responders toe large gatherings of people having immersive experiences, it's only gonna get better. Jorge. Thanks for coming on. The Cuban explaining wavelength graduates on the news and expansion. A lot more cities. Um, what's your take for reinvent while I got you? What's the big take away for you this year? Obviously. Virtual, but what's the big moment for you? >>Well, I think that the big moment for me is that we're continuing to, you know, to deliver for our customers. Obviously, a very difficult year for everyone and being able to, you know, with our help off our customers and our partners deliver on the reinvent promised this year as well. It is really impressed for >>me. All right. Great to have you on. Congratulations on local news. Great to see Andy pumping up wavelength. Ah, lot more work. We'll check in with you throughout the year. A lot to talk about. A lot of societal issues and certainly a lot of a lot of controversy as well as tech for good, great stuff. Thanks for coming. I appreciate it. >>Thanks for having me. Thanks. >>Okay, That's the cube. Virtual. I'm John for your host. Thanks for watching. We'll be back with more coverage from reinvent 2023 weeks of coverage. Walter Wall here in the Cube. Thanks for watching. Yeah,

(lively music) >> Thank you, Frank, for kicking us off, setting the stage, and providing the vision for the Snowflake Data Cloud. Hi, everyone, I hope you're all doing well and staying safe. Thank you for joining me at the Snowflake Summit today to dive into the role of the Data Cloud in mobilizing data at Disney Streaming. Together, we're going to discuss data governance and how to leverage some of the unique benefits of Snowflake's data platform to unlock business value for better customer experiences. I am Anita Lynch, Vice President of Data Governance at Disney Streaming, home of Disney+. I fell in love with technology at an early age. My family is originally from Chicago and we came to the Bay Area when my dad's sales career led him to Silicon Valley. Because of the exciting advancements he saw in the devices he sold and the engineers he worked with, I am so fortunate that my father created the early opportunities for me to learn about technology, like starting to code when I was 10. Decades later, over the course of my career spanning tech startups, business school, strategy consulting, and leading data at global enterprises, I have learned it is not enough to create a technology solution. It takes a real understanding of what problems your customers are trying to solve, and what resources or capabilities they can mobilize to do it. Today, this is the focus of my career in data. At Disney Streaming, we pride ourselves on delighting our customers. We commit each day to bringing beloved characters, timeless stories, and epic sporting events to a global audience. I am one member of a global data team at Disney Streaming, continuing to work through these challenging times for our world. We are deeply appreciative to be able to continue doing our part to deliver the entertainment people love on Disney+, including my new, personal favorite series, "The Mandalorian." It is important to all of us that we maintain our viewers' highest level of trust. As our data volume grows continuously on a daily basis, we need to ensure data is compliant, secure, and well-governed. Therefore, how we execute is critical. Our work ensures our business is guiding decisions with high-quality data. Doing this empowers us to challenge convention and innovate, which brings us to the role of the organization I lead at Disney Streaming. I lead data governance, which includes instrumentation, compliance, integrations, and data architecture. Collectively, we are responsible for the value, protection, and mobilization of data for Disney+. With data volumes in the thousands of petabytes after just one year and global teams depending on us to be able to perform their analysis, data science modeling, and machine learning, it is critical to maintain compliance protocols and governance standards. However, our approach to locking down the data and limiting access without becoming a blocker to critical information needs is key. Poorly informed business decisions could ultimately lead to suboptimal customer experiences. Recognizing this, I've established eight operating principles to maintain a balance between technology, people, and process. Data lifecycle, stewardship, and data quality together define the mechanisms by which we maintain, measure, and improve the value of data as an asset. Regulatory compliance and data access establish key partnerships with our legal and information security to help us ensure data complies with internal and external legal guidelines in each region. Auditability, traceability, and risk management ensure we monitor, educate, influence, and enforce best practices. And lastly, data sharing, which serves to socialize valuable datasets and shared definitions in a secure, easy way that allows us to keep pace with the fast-moving and rapidly changing nature of our world today. Principles serve only as guardrails. In real practice, we measure the value data governance delivers based on these six, quantifiable goals for the teams we serve. Underpinning all of them is the Snowflake Data Cloud. It is our platform to store, secure, integrate, and mobilize data across the organization. It enables us to make compliant data accessible for teams to collaborate without copying, moving, or reprocessing. Going beyond the notion of a single source of truth, Snowflake's Data Cloud allows us to truly have a single copy of the data, plus the ability to scale to support a near-unlimited number of concurrent users without contention for resources, and the flexibility to prioritize or deprioritize compute workloads where concurrency matters less than our ability to manage cost. What does this mean to me? Put simply, it means the ability to support business intelligence, analytics, data science, and machine learning use cases on-demand, exceeding expectations for speed and performance where they matter without sacrificing anything on governance. And that is how we deliver value through data governance for Disney+. Data sharing is at the heart of how we make this work. We'll look at three important use cases, data clean rooms that enable restricted data sharing, data discovery that ensures data is easily found and understood, and partner data management for collaboration outside of our team. Data sharing creates the opportunity to access the power of the integrated dataset in an environment that ensures both quality and compliance. Let's start with data clean rooms and the example of restricted data sharing. Better understanding the interests and preferences of our audience through analysis is how we improve experiences for our customers, such as in-app personalization or making a recommendation on what to watch. The challenge is to mobilize the right data as it is needed while blocking distribution of any data that is not required, preventing the disclosure of sensitive information and prohibiting the merging of data that should not be combined. Simultaneously, while we seek to deliver compliance, we also want to avoid the typical process delays and enormous manual repetitive work that often comes with it. Data clean rooms enable the secure sharing of data, again, without creating copies, the combining of datasets without PII or sensitive information, and the restricting of queries by use of parameterized inputs and filtered query outputs, so only permissible data can be extracted. Outlining in advance how data will be used properly ensures consistency and execution of our compliance workflows and improves transparency on constraints, so teams don't waste their valuable time. This accelerates our ability to act on data insights. Decisions can be made for the benefit of our customers. For example, for me on Disney+, I would see right away the season two trailer for "The Mandalorian," including exciting scenes with Baby Yoda, more formerly known to some of you as the Child. Sometimes unintended data silos arise due to architectural complexities. In a traditional model for data infrastructure, complexity can evolve over time as various teams need to access, integrate, and transform data from different data sources in ways that uniquely serve their specific stakeholders. This proliferation in the analytical supply chain could result in multiple instances of copying, loading, and transforming the same data and introduce significant risks to data quality throughout the system, such as a lack of traceability. For example, changing one data pipeline may create unforeseen consequences in the calculations that occur in downstream tables and reports with no clear resolution. In the spirit of challenging convention to innovate, we knew we had to do better. With the Snowflake Data Cloud, our teams are able to discover the data sources they need through a centrally organized platform for data management and data sharing. Each user knows the data visible to them is available to them. They know they can trust it, and they know how it can properly be used to drive broader customer insights. And if a team wants to share their insights for further collaboration, they can easily publish those datasets to the Data Cloud, where they benefit from the protection of our managed platform, making sure all governance protocols are in place, including who can access for what purpose and at what level of granularity. This facilitates data sharing without the administration worry that comes with sharing files. And since there is one single copy, future updates happen at once for all consumers of the data, keeping it fresh for everyone without sacrificing business continuity. Finally, data sharing improves the performance of our partner relationships with the same degree of simplicity. In this model, our partner teams can also participate in the Data Cloud by invitation to access data specifically shared to them. Or conversely, a partner can request to share their data, and upon authorization for quality and compliance, we can safely publish that data, making it simultaneously available to all the right teams who need it. As a thought exercise, one way for us to envision making it easier to work with partners is in the way we collect and analyze data from media serving and content distribution networks. Today, customer stream Disney+ on more than 13 different types of devices. Their streaming is made possible through a collection of services that vary by geography and consumer choice. Better understanding the experience for an individual client may require integration of data collected across the unique combination of services available to that customer. To better serve our content and delight our customers, data-driven analysis to detect anomalies and service impacts might benefit from a data management platform for partner data that requires a high level of data governance similar to what we do today through our Snowflake Data Cloud. Now in closing, data is at the core of our mission at Disney Streaming to delight our customers. And when it comes to data governance, we strive to always hold ourselves to the highest standard. With the Data Cloud, we power our business with a single source of truth. As we grow, it enables data sharing with data governance at massive scale and performance. I will also leave you with this often quoted African proverb I like. "If you want to go fast, go alone. But if you want to go far, go together." We share an important cultural value. Commitment to innovation accelerated our ability to address unique use cases and the successful growth of Disney+. It was both the technology and the commitment to meet our data governance needs that has resulted in more than just another cloud data platform. We have a solution that works for us. Thank you for joining me on this journey, and thank you to Snowflake for the ongoing partnership. With the product keynote coming up next, I'm excited to see how future innovation will continue to enable us to challenge convention going forward.