>> Narrator: Live from Orlando, Florida. It's theCube. Covering Grace Hopper's Celebration of Women In Computing. Brought to you by, SiliconANGLE Media. >> Welcome back to theCube's coverage of the Grace Hopper conference here in Orlando, Florida. I'm your host, Rebecca Knight. We are joined by Raji Arasu, she is the CTO of Development at Intuit. And also, by Marianna Tessel. She is the Chief Product Officer at Intuit. So thank you both for joining us. >> I think you got the title wrong. >> Oh no! (laughter) Please correct me! >> It's SVP. >> SVP. >> SVP of our organization is called CTO Dev, and I manage the platform and infrastructure services for our... >> Great. >> So now we've got that under control. >> Wonderful. >> So tell a little bit about your background. We'll start with you Raji, how you got into this business. >> I have been about 27 years in the consumer and retail space. And a ton of background on ecommerce and payments. This actually my first job sort of focusing on platform and core services for the company. A huge responsibility, my job is not just to provide and you know, delightful services for both my internal and external customers. But to really make sure that we are really thinking about the future and the capabilities that we're building for the future. So, super excited about my role at Intuit. >> How about you, Marianna? >> First of all, thanks for having me here. >> Yes. >> And I have to confess, this is my first time at the Grace Hopper Conference. >> That's wonderful! That's, that's great! >> And I'm completely blown away from the wonderful people here and the representation and the energy. So, I'm now a fan. So, anyway, just wanted to say that. You know, my background has always been engineering, I've done multiple engineering roles. I actually, before this, I spent a lot of time in systems and infrastructure and I really get a kick right now out of using some of the products I built. And actually using them in other products. And seeing how customers are using it. So, that's an interesting kind of journey, and interesting to see kind of full picture, of kind of the industry. >> Both of you, and we are here at Grace Hopper, which is the celebration of women in computing. And both of you are passionate about creating a more inclusive engineering culture. Can you talk about why, why this is a passion project of yours. And then also, what you're doing to make, to help that happen. Raji? >> I think, I mean, Grace Hopper. This is my seventh year in the conference and I love it. >> So you're a veteran. She's a virgin, you're a veteran. >> I'm definitely a veteran, absolutely. (laughter) >> And I think it's such a joy because it not only, I have started to recognize some familiar faces. It's a fantastic opportunity for us to network, with women in technology, and talk about actually, what's cool, is not just the issue around fixing the numbers, but actually, we talk about capabilities and building, you know, what's really important for our craft. And so I'm actually excited about that. The more and more I see, you know we have about 112 people attending from Intuit. And you know, a ton of men as well, participating in that but a lot of people are going to be talking about things that are very core to us. Like, data engineering, data science, architecture, services-oriented journey, and all of that which is awesome. Because I think, that's what people want to hear, the work that we do. And they want to understand, what it would be like to work at Intuit. So, there's a ton of opportunity for companies and for individuals who work there to really show what they do everyday. And really connect in a very authentic way. And show off their work. More than actually be, you know, really talking about the Uber problem that many of us do care about that as well. But I see, down here, especially where we are sitting, everybody's connecting on where they work, what is the work that I'm going to do, or what is the stuff that actually interests me. Which I think is pretty cool. >> During the keynote, Melinda Gates had a very quotable quote and she said, "Not every idea is wrapped in a hoodie," not every good idea is wrapped in a hoodie. And this is really bemoaning the brogrammer culture. Is that message getting through, do you think, to young women? In the sense of, this is not all the sea of white dudes. >> You know, I, I think it is but there's still like work to do. Both for like, women that enter the field, as well as women that been here for, for awhile. And, you know, there's still plenty of opportunity. So, you know, the culture is definitely, at least, I'll have to tell you that, again, being a bit in the industry now, and gaining a bit of a perspective, just the fact that it's being talked about and the fact that there's more energy towards solving it is already, you know, a great win. And, you know, to your question before, if I can jump on that as well. >> Knight: Yes! Absolutely. >> You know, this whole idea of diversity in the work place, there is nothing, I don't know if there's much to say there beyond what's already said about how it's good for businesses, how the customers at many of the of, I know definitely for us, in the small businesses, a lot of our customers are diverse. And we want to have diverse people build product for our customers, right? You know, so all of these are true, it makes sense for the business. But now I can tell you from my own lens, and my own kind of perspective and experience, you know, women are just awesome. And they make like, outstanding engineers, outstanding leaders, and every time I have a group of, you know, that has all sorts of people, again all kinds of diversity it's just a stronger group. So, some of it, you know, I love to have a diverse team selfishly, because it's an awesome team and that's kind of what I think we should all be pursuing. Just, be awesome, not just diverse. >> So you're passionate about getting more women into this industry, keeping them, retaining them in the industry. But, tell me a little bit about the tech. I mean, because that is, that was obviously your first love and that's why you do what you do. So tell me about what you're working on that's really exciting to you at Intuit. >> I think, you know, as I look at my past, one of the things that always excited me is to work on complex stuff that actually makes a difference in the world. And it started fairly early on in my career where I started to, when I worked at eBay it was about actually connecting to our customers and sellers and having that sort of a social impact. Moving on to StubHub it was a lot about actually entertainment and how do you really get people to the game and that perfect evening they were looking for. And then moving on to Intuit, it's about making that financial freedom possible for many of our customers. And I think when I look at that, for Intuit, there's a huge opportunity. Which we are actively working on is, to start looking at our data and be able to create some delightful customer experiences for our people. And to, to really give them more time and more money at the end of the day. And I think, and that sort of confidence in our own products, about the decisions we make for them and the expertise that we provide, and so as part of that, a lot of that can only come alive with technology. So, when we start to look at that, you know, there's a huge focus within the company on building great tools for developers so they can move faster. There's a huge focus on trying to do AI and machine learning on our data and looking at what we can do to personalize our experiences for our customers and reduce friction in the flow. There's a ton of work that's being done there. And I also think that we, we're very excited about our journey to the cloud. And having gone through the whole services-oriented architecture, re-architecture that we are being embarked on for many years. So, I think really, really there's a ton of good work that's happening inside with all towards the focus of servicing the customer. So there's a ton of conversations that we have around customer empathy. And then all of the technology towards making the lives of our customers better from a financial perspective. >> And giving them back time and money as you said, yes. Yes, absolutely. >> If I can add, to that, like our mission as a company is to power prosperity around the world and you know, and that's like a great mission. But, as Raji was saying, it's even awesome when you get to connect technology to a mission that is really inspiring like this. >> Knight: Yes. >> And is really something we put in practice. You know, I'll talk specifically in one of my products, Quick Books Online, QBO. You know, we have, a lot of the problems that, a lot of the challenges, we shouldn't call them problems. Challenges that many of the SAS companies are facing in terms of scale, in terms of velocity, how are we doing DEV ops in the most modern way? What's our CICD pipeline look like? How do we use, we have all this great data, how do we use the right data? Because, obviously we want to respect privacy. How do we use the right data to giving even more value to our, getting more value to our customers? How do we apply machine learning and AI? And, you know et cetera, to make it even more interesting because we have some touch with financial data. There's a lot of view on security and what we do there. So, lots of problems to solve that are deep technical problems. Lots of modern technology. Some that other, that you know, we have to look at but you know, really interesting set of challenges. From all the way to, in close to the infrastructure, all the way to the UI and some really cool things that we're doing there. >> I think that's a really great point, and the fact that, you know, as you're women technologists so you face issues of biases and sexism in the industry. But as technologists, as human technologists, you face questions about, am I looking at the right data, is this data secure, am I doing enough around privacy? Do you think that this conference does enough to acknowledge both sides of this coin in the sense that you are technical leaders in your field and you are here, at a tech conference, but then you're also here to rally around this issue of getting more women and retaining more women in the industry? What do you think? >> I think, I think that I am in this, in these booths here, I sense it. I sense that we're talking about the real problems around technology. The conversations around the specialties that are required in data science or maybe architecture, maybe engineering. I mean any parts of that, we do have those conversations. I think at the keynotes and maybe at the higher level, it's a lot more about developing women and addressing the problem and probably building leadership. So, there's probably two flavors that you find in this conference. Which I think cater to different sets of women and some about staying in the field and not sort of, you know, dealing with the problems that we have. So I think it does. But I think it'd be awesome to have a panel where we have very different points of view on a technology, and having a really good debate about that. Which would be really cool I think, if we had something like that. I don't know if it's in our curriculum. I'm definitely not aware of everything in our curriculum but it would be cool to have a panel like that. >> I want to wrap up here but I want to ask, what is your best advice for aspiring women in this field? And it could be someone who is just starting her computer science journey in college, or it could be someone who maybe is feeling as though, do I stay in this field, I don't know if this is for me. What would you say to that young woman? >> You know, again, maybe something that she heard before, but I would say, you know, go for it, stick with it, be ready to fall down. And come back up and be ready, be open-minded, know that you can learn anything. And, you know, but stick with it. >> Just stay, stick with it. (laughter) >> Yes, through hard and through easy. >> I love that. I mean, I want to definitely second Marianna saying don't be afraid of failures. Take it on, and use that as an opportunity to convert that into success in the next opportunity that you have. I think the part that I would also say, is protect being a leader in tech and staying true to it. You got to have a learning mindset. Every single day you come in, you got to learn new skills, you have to open to change, and constant change. And if you learn, and every one of us has different ways to learn. You know, some of us learn through conversation, some of us learn through reading papers, whatever that might be. But if you do that, you will stay as a credible and relevant leader for the longer run. >> Knight: The growth mindset. >> Absolutely. >> Well Raji, Marianna, thank you so much for joining us. It's been a lot of fun. >> Thank you. >> Thanks for having us. >> I'm Rebecca Knight, here at the Grace Hopper Conference, we will have more, just after this. (electronic music)

>> Narrator: From Austin, Texas, it's theCUBE. Covering DockerCon 2017. Brought to you by Docker and support from it's ecosystem partners. >> Hi, I'm Stu Miniman joining with my co-host Jim Kobielus. We're here with theCUBE at DockerCon 2017. When I talked to John Furrier, he said Stu, at DockerCon, we're going to get Solomon Hykes, the founder. We're going to get Ben Golub, the CEO. And we're also, of course, going to get Marianna Tessle, who is the EVP of Strategic Development. Marianna, thank you for having us back again, we've been having a great event. How is everything with you? >> Thank you first of all, it's great. This is the second day of DockerCon. I think we had a great set of announcement yesterday, and an amazing set of announcement today as well. It's really going great. You know I have been roaming the exhibit hall, and actually a couple of people said this is one of the best shows they have been part of, and this very engaged audience is great to hear. >> From the keynote yesterday, the word that stuck out to me is really scaling. We talk about scaling employment, scaling the ecosystem, and the show itself. I was at that first DockerCon when we were wedged into that hotel room, as Ben joked. We had 100 more people than we told the Fire Marshall. Because it was tight. TheCUBE is usually a little bit smaller footprint than we have at some other shows. But, Austin, first of all, you pick great locations. I mean, San Francisco, Seattle, here. I'm looking forward to... Have we announced yet where next year's is? >> I don't think we've announced it yet. Usually it happens in the afternoon. >> Here in Austin. Talk to us a little about some of those announcements and stuff that you're excited about with growing the ecosystem. >> You know, I'm going to continue the theme you started with scale, and obviously like you said, a lot of things are changing, and scaling. One of the things we have noticed more and more are companies and enterprises have really started to use us more in scale and more in production, more apps, more of that going on. One of the trends we've noticed that actually Ben covered on stage today is that there's not just the leading edge of development and all new apps, web apps, but actually, we are starting to see more of traditional apps coming on board as well. More traditions Ops saying, I want those benefits as well. I do not want to go all the way to the extreme of re-writing my code, and going to microservices. But I can reap a lot of the benefits from Docker rising and putting our tools on top. So we're actually seeing more and more of that. And more and more companies. >> The discussion with Solomon, we talked this morning. He said, Oh, I don't know what Lego set we are. And I said, You know that green, flat piece that you can build everything on top of, so you can have your spaceset, your castle, and all the pieces there. You want to be a platform that can build. One of the announcements you guys had today, it's the modernized traditional applications. Maybe you can walk us through a little bit what that means, you know that mix of microservices verses traditional apps. How you guys see yourself participating in a customer's journey. >> Right. So, when we call this program, by the way it has a nickname, MTA. It's like you said, what we've seen is customers and users that want to have benefit across the board was if they write new code as they have more traditional apps with traditional stacks. What we came up with is a way for you to move from a more traditional to the new and Dockerizing really quickly. One of the things we also announced today, is a go-to market and a program helping customers to do that. We have great partners we announced today and I'm sure we're going to have even more, whether it's Microsoft, Avalon, HPE, and Cisco. What we're going to basically provide is a way for you to very quickly start seeing the benefits. Taking the traditional app, and within days, like five days, you should be able to get it in a modern state and start seeing the benefits from that. It's something that we're going to encourage customers to do very quickly and see the benefits. In fact, we had a customer today, Noran Trust, who's already been doing that, talking about the benefits they've been seeing from this program. >> Marianna, in terms of developer enablement, that's everything to getting Dockerizing, a universal phenomenon for wrapping legacy systems, for refactoring existing code, for building greenfield applications. What will Docker do to continue to improve the experience of Project Moby as an enabler of your ISV ecosystem? Going forward, how do you see the experience of front-end in front of Moby evolving to enable very simplicity and speed of development? >> First of all, I have to say that one of the magic, or secret sauces of Docker is our user experience, and the way we made technologies sometimes that were already available super accessible and super useful for developers and ops and users. So I would say that's definitely something that we have the DNA to do. And a project in Moby, we see ISV's and companies, and it doesn't have to be a company, it could be like users, a company that can come in and collaborate and really create a new component, or a new project from what we're going to put there, and hopefully others as well is a whole set of these Lego building blocks they can assemble. >> Are there any plans of Dockers to provide task-oriented skins or experiences on Moby for different roles, different developer roles associated with particular projects, you know, task, or wrapping a legacy system is a different task, obviously, from developing a greenfield containerized application. So to an extend, will you evolve the tool to enable more task oriented role specific interfaces? >> I would say as far as Moby, and across the company, we do have this realization that it could be that developers started to use Docker first, but actually Ops, and even like we talked about, traditional IT, it's pretty prevalent. So our thought is really to cater to all of these audiences, kind of understand, have a conversation with them and understand what exactly they need and what would make them more productive. An example of what I mentioned with the MTA program, the Modernized Traditional Apps, that one is targeted more towards an Ops audience. Different things we do, we try to understand our audience and engage with them, and see what's going to make them most productive. Both in terms of tool sets and in terms of how we bring it to them. >> Right, right. >> Marianna, we had the opportunity to have some of the partner keynote speakers on theCUBE, John Gossman on from Microsoft yesterday, we had Mark Cavage on from Oracle, here. There's a lot going on. Maybe give our audience a little flavor as to some of the other partner activity going on that we might have missed if we weren't watching close. >> I think we had the same conversation last year, just explaining how important it is for us that we work well with our ecosystem. It's a big part of our plan and strategy, and again confirmation that customers want to use choice, different things, that we're not alone in the world, and we really want to engage with a vast ecosystem. So you saw from Cloud providers to a more on-prem infrastructure to ISV's to networking providers, storage providers. Like a whole understanding and way to be a full platform, we really need to understand how to integrate and how to engage with that ecosystem, and how to help customers have benefits of the entire thing combined. So we've been really looking at who are the different leaders; Sometimes customers take us there, they're like, hey please partner with this company or that company. Understanding mapping of what is needed, and starting from Cloud, infrastructure, network, storage, management, monitoring, security, all the way to ISV's. I would, since you brought up that fact that Mark was here, Mark from Oracle. I do want to talk about that because I think that is maybe even a bit new and unique. Another thing that we announced today, the fact that we have Oracle, Dockerizing their apps and putting them in Docker store and that is big, and again, to us that is obviously big, but again, big for user. It's a very easy way to get software you really need. And not only that, we announced several weeks ago, a certification program. The nice thing about that, if something is certified in store, you can really use that with a lot of trust. You know it's been tested, it's secure. That we made sure that it followed best practices. We made sure that our support engagement with the publisher. Again, geared toward enterprises that really want to have that confidence of downloading something from the store and just using it. Again, Oracle is kind of groundbreaking in putting their software there, and we're very excited about that and we think there is going to be more to come. We really are looking forward to this being an amazing service for our users who want to really start from components that exist and the components that they can trust and be productive very quickly. >> I'm curious, how do you think of the Docker store in relation to things like the Amazon Marketplace, or you know, many of your other partners have their own piece. There really is no kind of enterprise app store today so what do you guys want to own? How do you integrate with partners as you look at that develop over time? >> For us, Docker Store started as an enabler as we saw more and more need from users to to basically, Hey, I want.. Let's say since I talked about Oracle I want to use a database. I don't want to go and Dockerize it again. If somebody already did it and they're already prepared, they already went through it, why wouldn't I just re-use it? So the fact that you can put things in this building block and then move them around, it actually enables the idea that you can re-use the same component between different users. So basically you have here something you can do once, and many people can benefit. So that's the benefit we see. It started with official images long ago. We saw unbelievable traction for it. Users really love it, it makes them productive very quickly. We wanted to expand it to a wider set of ISV's, a wider set of components, a wider set of apps, and make them available. We, right now, see it as more of an enabler and again it's one of those things, listening to our users, listening to our customers, we saw that that's one of the things that will make them productive really quickly. >> One of the things we saw in abundance at DockerCon this year is customers of Visa, MetLife, and so forth, up on stage, talking about how they are using Docker in their business for actual live applications. In terms of partners, are you focusing on particular vertical industries in terms of partnership with ISV's and VAR's, particular geographies? Give us a sense for where you're going in terms of diversification of geographies and industries, and in terms of your focus on partnerships. >> Yeah, and again different parts of the stack require different kinds of partnerships. Like on the South end of the stack on the infrastructure, we're looking for partners that either provide on-prem or Cloud infrastructure, or they can provide a set of plug-ins that integrate with us and a set of tools that can be used with Docker to complete and enhance the overall experience of users using Docker. So that's kind of one set of partnerships that started from hardware vendors, to different plug-ins. On the North side of it as we look at it, we just talked about the fact that we have... >> Jim: Top of the application, the application services end of the staff is the North, right? >> Exactly, and all the way to the content. What you actually put inside and what you run. >> Data, so forth and so on. >> Exactly. We'll form a set of partnerships there and making sure that those components are available in store, those components are Dockerized, that companies can really use that, and obviously Microsoft is a huge partner for us in the OS and as your others as well. >> The storage vendors, like Veritask and so forth, there is a fair amount of data inside the ecosystem that really you're going to continue to develop a partnership. >> Absolutely, Adera, Quadera, you've seen a lot, and we continue partner and seeing what's needed there. Understanding we are trying to predict where customers are today, where they're going to maybe, what they will need a year or two from now, and be ready for that. >> Marianna, that leads me to my final question. We know where you're going to be in Europe, you won't tell us yet the location of the North American show for next year, but as you look at the ecosystem, how do you see that developing? When we sit down with you a year from now, what do you hope to have as the progress? >> As I look at the exhibit hall, I am hoping that we're going to see a bigger exhibit hall with every single DockerCon. And, not just for fun, but really, it kind of indicates the collaboration we have with the ecosystem. I would like us to be known as a trusted and productive partner for our ecosystem. And a trusted and productive partner for our customer. That kind of knows to work together with all these contingencies to have amazing results. Like you said, we seen customers on stage, we seen the press releases of people say it took me months to get VM going, it takes me seconds to get this now going. So you see the kind of productivity and we would like to enhance it even more and get there faster. >> Absolutely, Marianna, always a pleasure to catch up with you. We've got a few more interviews left, two days of live coverage, for Jim Kobielus, and I'm Stu Miniman. Thanks for watching theCUBE. 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(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.

>> 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.

(gentle upbeat music) >> Welcome back everyone, to "theCUBE"'s live stage performance here in Palo Alto, California at "theCUBE" Studios. I'm John Furrier with Dave Vellante, kicking off our first inaugural Supercloud event. It's an editorial event, we wanted to bring together the best in the business, the smartest, the biggest, the up-and-coming startups, venture capitalists, everybody, to weigh in on this new Supercloud trend, this structural change in the cloud computing business. We're about to run the Ecosystem Speaks, which is a bunch of pre-recorded companies that wanted to get their voices on the record, so stay tuned for the rest of the day. We'll be replaying all that content and they're going to be having some really good commentary and hear what they have to say. I had a chance to interview and so did Dave. Dave, this is our closing segment where we kind of unpack everything or kind of digest and report. So much to kind of digest from the conversations today, a wide range of commentary from Supercloud operating system to developers who are in charge to maybe it's an ops problem or maybe Oracle's a Supercloud. I mean, that was debated. So so much discussion, lot to unpack. What was your favorite moments? >> Well, before I get to that, I think, I go back to something that happened at re:Invent last year. Nick Sturiale came up, Steve Mullaney from Aviatrix; we're going to hear from him shortly in the Ecosystem Speaks. Nick Sturiale's VC said "it's happening"! And what he was talking about is this ecosystem is exploding. They're building infrastructure or capabilities on top of the CapEx infrastructure. So, I think it is happening. I think we confirmed today that Supercloud is a thing. It's a very immature thing. And I think the other thing, John is that, it seems to me that the further you go up the stack, the weaker the business case gets for doing Supercloud. We heard from Marianna Tessel, it's like, "Eh, you know, we can- it was easier to just do it all on one cloud." This is a point that, Adrian Cockcroft just made on the panel and so I think that when you break out the pieces of the stack, I think very clearly the infrastructure layer, what we heard from Confluent and HashiCorp, and certainly VMware, there's a real problem there. There's a real need at the infrastructure layer and then even at the data layer, I think Benoit Dageville did a great job of- You know, I was peppering him with all my questions, which I basically was going through, the Supercloud definition and they ticked the box on pretty much every one of 'em as did, by the way Ali Ghodsi you know, the big difference there is the philosophy of Republicans and Democrats- got open versus closed, not to apply that to either one side, but you know what I mean! >> And the similarities are probably greater than differences. >> Berkely, I would probably put them on the- >> Yeah, we'll put them on the Democrat side we'll make Snowflake the Republicans. But so- but as we say there's a lot of similarities as well in terms of what their objectives are. So, I mean, I thought it was a great program and a really good start to, you know, an industry- You brought up the point about the industry consortium, asked Kit Colbert- >> Yep. >> If he thought that was something that was viable and what'd they say? That hyperscale should lead it? >> Yeah, they said hyperscale should lead it and there also should be an industry consortium to get the voices out there. And I think VMware is very humble in how they're putting out their white paper because I think they know that they can't do it all and that they do not have a great track record relative to cloud. And I think, but they have a great track record of loyal installed base ops people using VMware vSphere all the time. >> Yeah. >> So I think they need a catapult moment where they can catapult to the cloud native which they've been working on for years under Raghu and the team. So the question on VMware is in the light of Broadcom, okay, acquisition of VMware, this is an opportunity or it might not be an opportunity or it might be a spin-out or something, I just think VMware's got way too much engineering culture to be ignored, Dave. And I think- well, I'm going to watch this very closely because they can pull off some sort of rallying moment. I think they could. And then you hear the upstarts like Platform9, Rafay Systems and others they're all like, "Yes, we need to unify behind something. There needs to be some sort of standard". You know, we heard the argument of you know, more standards bodies type thing. So, it's interesting, maybe "theCUBE" could be that but we're going to certainly keep the conversation going. >> I thought one of the most memorable statements was Vittorio who said we- for VMware, we want our cake, we want to eat it too and we want to lose weight. So they have a lot of that aspirations there! (John laughs) >> And then I thought, Adrian Cockcroft said you know, the devs, they want to get married. They were marrying everybody, and then the ops team, they have to deal with the divorce. >> Yeah. >> And I thought that was poignant. It's like, they want consistency, they want standards, they got to be able to scale And Lori MacVittie, I'm not sure you agree with this, I'd have to think about it, but she was basically saying, all we've talked about is devs devs devs for the last 10 years, going forward we're going to be talking about ops. >> Yeah, and I think one of the things I learned from this day and looking back, and some kind of- I've been sauteing through all the interviews. If you zoom out, for me it was the epiphany of developers are still in charge. And I've said, you know, the developers are doing great, it's an ops security thing. Not sure I see that the way I was seeing before. I think what I learned was the refactoring pattern that's emerging, In Sik Rhee brought this up from Vertex Ventures with Marianna Tessel, it's a nuanced point but I think he's right on which is the pattern that's emerging is developers want ease-of-use tooling, they're driving the change and I think the developers in the devs ops ethos- it's never going to be separate. It's going to be DevOps. That means developers are driving operations and then security. So what I learned was it's not ops teams leveling up, it's devs redefining what ops is. >> Mm. And I think that to me is where Supercloud's going to be interesting- >> Forcing that. >> Yeah. >> Forcing the change because the structural change is open sources thriving, devs are still in charge and they still want more developers, Vittorio "we need more developers", right? So the developers are in charge and that's clear. Now, if that happens- if you believe that to be true the domino effect of that is going to be amazing because then everyone who gets on the wrong side of history, on the ops and security side, is going to be fighting a trend that may not be fight-able, you know, it might be inevitable. And so the winners are the ones that are refactoring their business like Snowflake. Snowflake is a data warehouse that had nothing to do with Amazon at first. It was the developers who said "I'm going to refactor data warehouse on AWS". That is a developer-driven refactorization and a business model. So I think that's the pattern I'm seeing is that this concept refactoring, patterns and the developer trajectory is critical. >> I thought there was another great comment. Maribel Lopez, her Lord of the Rings comment: "there will be no one ring to rule them all". Now at the same time, Kit Colbert, you know what we asked him straight out, "are you the- do you want to be the, the Supercloud OS?" and he basically said, "yeah, we do". Now, of course they're confined to their world, which is a pretty substantial world. I think, John, the reason why Maribel is so correct is security. I think security's a really hard problem to solve. You've got cloud as the first layer of defense and now you've got multiple clouds, multiple layers of defense, multiple shared responsibility models. You've got different tools for XDR, for identity, for governance, for privacy all within those different clouds. I mean, that really is a confusing picture. And I think the hardest- one of the hardest parts of Supercloud to solve. >> Yeah, and I thought the security founder Gee Rittenhouse, Piyush Sharrma from Accurics, which sold to Tenable, and Tony Kueh, former head of product at VMware. >> Right. >> Who's now an investor kind of looking for his next gig or what he is going to do next. He's obviously been extremely successful. They brought up the, the OS factor. Another point that they made I thought was interesting is that a lot of the things to do to solve the complexity is not doable. >> Yeah. >> It's too much work. So managed services might field the bit. So, and Chris Hoff mentioned on the Clouderati segment that the higher level services being a managed service and differentiating around the service could be the key competitive advantage for whoever does it. >> I think the other thing is Chris Hoff said "yeah, well, Web 3, metaverse, you know, DAO, Superclouds" you know, "Stupercloud" he called it and this bring up- It resonates because one of the criticisms that Charles Fitzgerald laid on us was, well, it doesn't help to throw out another term. I actually think it does help. And I think the reason it does help is because it's getting people to think. When you ask people about Supercloud, they automatically- it resonates with them. They play back what they think is the future of cloud. So Supercloud really talks to the future of cloud. There's a lot of aspects to it that need to be further defined, further thought out and we're getting to the point now where we- we can start- begin to say, okay that is Supercloud or that isn't Supercloud. >> I think that's really right on. I think Supercloud at the end of the day, for me from the simplest way to describe it is making sure that the developer experience is so good that the operations just happen. And Marianna Tessel said, she's investing in making their developer experience high velocity, very easy. So if you do that, you have to run on premise and on the cloud. So hybrid really is where Supercloud is going right now. It's not multi-cloud. Multi-cloud was- that was debunked on this session today. I thought that was clear. >> Yeah. Yeah, I mean I think- >> It's not about multi-cloud. It's about operationally seamless operations across environments, public cloud to on-premise, basically. >> I think we got consensus across the board that multi-cloud, you know, is a symptom Chuck Whitten's thing of multi-cloud by default versus multi- multi-cloud has not been a strategy, Kit Colbert said, up until the last couple of years. Yeah, because people said, "oh we got all these multiple clouds, what do we do with it?" and we got this mess that we have to solve. Whereas, I think Supercloud is something that is a strategy and then the other nuance that I keep bringing up is it's industries that are- as part of their digital transformation, are building clouds. Now, whether or not they become superclouds, I'm not convinced. I mean, what Goldman Sachs is doing, you know, with AWS, what Walmart's doing with Azure connecting their on-prem tools to those public clouds, you know, is that a supercloud? I mean, we're going to have to go back and really look at that definition. Or is it just kind of a SAS that spans on-prem and cloud. So, as I said, the further you go up the stack, the business case seems to wane a little bit but there's no question in my mind that from an infrastructure standpoint, to your point about operations, there's a real requirement for super- what we call Supercloud. >> Well, we're going to keep the conversation going, Dave. I want to put a shout out to our founding supporters of this initiative. Again, we put this together really fast kind of like a pilot series, an inaugural event. We want to have a face-to-face event as an industry event. Want to thank the founding supporters. These are the people who donated their time, their resource to contribute content, ideas and some cash, not everyone has committed some financial contribution but we want to recognize the names here. VMware, Intuit, Red Hat, Snowflake, Aisera, Alteryx, Confluent, Couchbase, Nutanix, Rafay Systems, Skyhigh Security, Aviatrix, Zscaler, Platform9, HashiCorp, F5 and all the media partners. Without their support, this wouldn't have happened. And there are more people that wanted to weigh in. There was more demand than we could pull off. We'll certainly continue the Supercloud conversation series here on "theCUBE" and we'll add more people in. And now, after this session, the Ecosystem Speaks session, we're going to run all the videos of the big name companies. We have the Nutanix CEOs weighing in, Aviatrix to name a few. >> Yeah. Let me, let me chime in, I mean you got Couchbase talking about Edge, Platform 9's going to be on, you know, everybody, you know Insig was poopoo-ing Oracle, but you know, Oracle and Azure, what they did, two technical guys, developers are coming on, we dig into what they did. Howie Xu from Zscaler, Paula Hansen is going to talk about going to market in the multi-cloud world. You mentioned Rajiv, the CEO of Nutanix, Ramesh is going to talk about multi-cloud infrastructure. So that's going to run now for, you know, quite some time here and some of the pre-record so super excited about that and I just want to thank the crew. I hope guys, I hope you have a list of credits there's too many of you to mention, but you know, awesome jobs really appreciate the work that you did in a very short amount of time. >> Well, I'm excited. I learned a lot and my takeaway was that Supercloud's a thing, there's a kind of sense that people want to talk about it and have real conversations, not BS or FUD. They want to have real substantive conversations and we're going to enable that on "theCUBE". Dave, final thoughts for you. >> Well, I mean, as I say, we put this together very quickly. It was really a phenomenal, you know, enlightening experience. I think it confirmed a lot of the concepts and the premises that we've put forth, that David Floyer helped evolve, that a lot of these analysts have helped evolve, that even Charles Fitzgerald with his antagonism helped to really sharpen our knives. So, you know, thank you Charles. And- >> I like his blog, by the I'm a reader- >> Yeah, absolutely. And it was great to be back in Palo Alto. It was my first time back since pre-COVID, so, you know, great job. >> All right. I want to thank all the crew and everyone. Thanks for watching this first, inaugural Supercloud event. We are definitely going to be doing more of these. So stay tuned, maybe face-to-face in person. I'm John Furrier with Dave Vellante now for the Ecosystem chiming in, and they're going to speak and share their thoughts here with "theCUBE" our first live stage performance event in our studio. Thanks for watching. (gentle upbeat music)

(upbeat music) >> On August 9th at 9:00 am Pacific, we'll be broadcasting live from theCUBE Studios in Palo Alto, California. Supercloud22, an open industry event made possible by VMware. Supercloud22 will lay out the future of multi-cloud services in the 2020s. John Furrier and I will be hosting a star lineup, including Kit Colbert, VMware CTO, Benoit Dageville, co-founder of Snowflake, Marianna Tessel, CTO of Intuit, Ali Ghodsi, CEO of Databricks, Adrian Cockcroft, former CTO of Netflix, Jerry Chen of Greylock, Chris Hoff aka Beaker, Maribel Lopez, Keith Townsend, Sanjiv Mohan, and dozens of thought leaders. A full day track with 17 sessions. You won't want to miss Supercloud22. Go to thecube.net to mark your calendar and learn more about this free hybrid event. We'll see you there. (upbeat music)

[Announcer] Live from Stanford University, it's The Cube! Covering global women in Data Science Conference. Brought to you by SiliconeANGLE Media. >> Welcome back to The Cube. I'm Lisa Martin, live at Stanford University for the fourth annual Women in Date Science Conference, hashtag WiDS2019. We are here with Kavita Sangwan, the Director of Technical Programs, Artificial Intelligence and Machine Learning at Intuit. Kavita, it's wonderful to have you on the program. >> Thank you, pleasure is all mine. >> So Intuit is a global and visionary sponsor of WiDs, and has been for a couple of years. Talk to us a little bit about Intuit's sponsorship of this WiDs movement. >> Sure, well, Tech Women at Intuit has been important part of our culture. It was founded sometime a couple of years back from our previous CTO Taylor Stansbury. He was the founder and sponsor for it, and it has been getting the continuous support and sponsorship from our current CTO, Marianna Tessel. We highly believe that diversity in inclusion, and diversity in talks, and diversity in employees, is an important aspect for our company because that kind of helps us to deliver awesome product experiences and seamless experiences to our customers. This is our second year at WiDs, and we are proud to be part of this event today. >> It's growing tremendously, you know I mentioned it as a movement, and in three and a half years, this is the fourth annual, as I mentioned, and Margot Gerritsen, one of the co founders, chatted with me a couple hours ago and said they're expecting 20,000 people to be engaging today alone. The live stream at the event here at Stanford, but also the impact that they're making. There's a 150 plus regional events going on around this event in 50 plus countries. >> So it's the... You and I were chatting before we went live that you feel this, this palpable energy when you walk in. Tell me a little bit about your role at Intuit, and how you're able to really kind of grow your career in this organization that really seems to support diversity. >> Sure, I head the Technical Program Management for Intuit Data Science Organization, so it's all about data, data science, AI Machine Learning. We apply and imbed AI Machine Learning across all of our product suites. And also try to apply AI Machine Learning in different other aspects as well. Some of the focus areas where we applying AI Machine Learning is making our products smart, security risk and fraud space, where we are all several steps ahead of the fraudsters. Also, in customer success space, and also within the organization, the products and services our work employees use to make their experiences amazing. I have been with Intuit for almost three years now, and it has been an amazing journey. Intuit is such a... It embraces diversity, and it's because of its diverse, durable, innovative culture, I think Intuit has been in Silicone Valley as a strong force for over 35 years. >> So when we think about Data Science, often we think about the technical skills that a data scientist would need to have, right? It's the computational mathematics and engineering, being able to analyze data, but there's this whole other side that seems to be, based on some of the conversations that we've had, as important but maybe lagging behind, and that is skills on being a team player, being collaborative, communication skills, empathy skills. Tell me about, from your perspective, how do you use those skills in your daily job, and how does Intuit maybe foster some of those communication negotiation skills as equal importance as the actual data itself? >> It's very important for us, as we hire our top talent in our organization to empower and grow that top talent as well. We do that by providing them opportunities to learn from different sessions we host around executive presence, negotiation skills, public speaking skills. In addition to advancing them in their technological space. As you rightly said, it's very important for us to operate in a team setting. You know, a data scientist has to interact with a product manager, and a data engineer, a business person, a legal person, because there is questions about security and privacy. So there are so much interactions happening across functional space, it is very important for us to be a team player, and having the ability to have those conversations in the right way. So, Intuit invests heavily, not just in the technology space to advance women, but also in all the other ancillary spaces, which are equally important to be successful as you advance in your career. >> So, as our viewers understand Intuit, I'm a user of it as well for my business, who understand it to a degree. What do you think would surprise our viewers about how Intuit is applying Data Science? >> So, it's important to know that we operate with a customer's mindset. Everything we do starts with our customers, and it's very important for us to build a culture which reflects the values, and the talent, and the skills of our customers. And that is why I said it's very important for us to have diversity in our teams. Our most opportunistic areas for investment in the AI machine learning is the smart products space where we are heavily investing to make our products intelligent, customize it according to the needs of our customers, and giving them great insights for our customers to save them money, make them do less work, and build more confidence in our product suites. >> Confidence, that word kind of reminds me of another word that we hear used a lot around data, and I'm making it very general, but it's trust. That's something that is critical for any business to establish with the customer, but if we look at how much data we're all generating just as people, and how every company has a trail of us with what we eat, what we buy, what we watch, what we download. Where does trust come into play, if you're really designing these things for the customer in mind, how are you delivering on that promise of trust? >> It's very rightly said, just to add to that sentiment, it has been shared in some articles that we have accumulated so much data in the last two years which is more than what we have accumulated in the last five thousand years of humanity. It is really important to have trust with your customers because we are using their data for their own benefits. Intuit operates with the principle and the mindset that this our customer's data, and we are their stewards. We make sure that we are one of the best stewards for their data, and that's what we reflect in our products, how we serve them, build intelligent products for them, and that's how we start to gain trust from our customers. >> And I imagine being quite transparent in the process. >> That's true, yes. >> So in terms of your career, I was doing some research on you, and I know that you love to give back to the community by being a champion for women in technology, encouraging young girls in STEM towards building that community. Tell me a little bit about your career as we are here at WiDS at Stanford there's a lot of involvement in the student community. Tell me a little about your background and what some of your favorite things are about giving back to the next generation. >> Sure, I actually, when I graduated from engineering, I was one of the four women students out of the, maybe, a class of around 50 students. So I think it struck me right there that there is a disparity in the industry, in the education system, and then in the industry. I felt the same thing in my different companies where I worked, and that always led me to a point that I actually, rather than just being observing this from afar, why can't I be the one who moved the needle on this? That led me to a point where I started collaborating within the companies, started forming teams, and started working with the teams who were already there to move the needle in technical women's space. I think, if I reflect back in my journey, a couple of things that stand out for me is passion for what you do, and I am really passionate about what my goal is and I try to line up my work according to that and that's why this women in tech, something which is close to my heart and I'm passionate about, always comes forward whenever I do something. The second important aspect is, I've always thrown myself into situations which I've never done before. For example we were offline talking about hackathon, which is DevelopHer. I had never done any hackathons before because I was so passionate about doing it, I just threw myself in and I ran that hackathon. And then the third thing is being persistent about what you do. I mean, you can't just do one thing and then drop it and then come back after a few weeks and then do it again. You have to have that consistency of doing it, only then do you start moving the needle. I think when I reflect and look back, these three things stand out for me and that has applied in my own personal career, as well as everything I do in my life. >> How do you give, and the last question, it seems like you sort of have that natural passion, I love this, this is what I want to do, you were persistent with it, how do you advise younger girls who might not have that natural passion to really develop that within themselves? >> I think experiment and explore. When you try to do different things, only then you find out where your passion lies. Just don't be scared of throwing yourself into a situation which you have never dealt before. Always try to find new things and throw yourself in an uncomfortable situation, and try to get out of it. It helps you become super bold, and gives you confidence, and that's the way to find what you're naturally passionate about. >> I like that, I like to say get comfortably uncomfortable. Last question in the last few seconds, I just want you to have the opportunity to tell our viewers where they can go to learn more about Intuit and their Data Science jobs. >> Yes, you can always go to intuit.com, and intuitcareers.com, and learn about the great opportunities we have for Intuit and Data Science. >> Excellent, well Kavita, it's been a pleasure to have you on The Cube this afternoon. Thank you for stopping by, and also for sharing what Intuit is doing to support WiDS. >> Thank you, it was my pleasure, thank you so much. >> We want to thank you for watching The Cube, I'm Lisa Martin live from the WiDS fourth annual WiDS global conference at Stanford. Stick around, I'll be right back with our next guest.