>> Live from Bellevue, Washington, it's the CUBE! Covering Smartsheet ENGAGE '18. Brought to you by Smartsheet. >> Welcome back to the CUBE We are live at Smartsheet ENGAGE 2018 from Bellevue, Washington. I'm Lisa Martin with Jeff Frick and we're pleased to welcome to the CUBE for the first time, Margo Visitacion, VP and Principal Analyst at Forrester. Margaret, it's great to have you here. >> Thank you. It's a pleasure to be here. >> You have a session this afternoon, so we'll get a little preview of that. You recently at Forrester were doing a lot of work with some Smartsheet customers on a white paper, regarding digital transformation, looking at how project management has typically been done and how it's evolving. Give us a little bit of an overview of that research and what people are going to hear about today. >> Absolutely, absolutely. Well, what we've seen is that digital transformation is really changing the way that companies need to work today, and that everybody in an organization is now a project manager, whether they recognize it or not. So what we've seen is three quarters of the respondents that we've surveyed, what they've seen is that they've seen their project management activities, and the scale of their projects, increase significantly in size. They've seen projects being far more distributed throughout the organization, so it isn't we have a central group that does project management, it's now everybody does projects. And what we've also seen is that the rate and pace of change creates a lot of uncertainty, and that organizations are dealing with a lot of unplanned tasks, instead of having something that was highly controlled, when you saw more traditional project management. People have to be a lot more flexible, a lot more adaptable, and they need to have a much greater visibility to be able to manage through that rate of change. >> Seems like a dichotomy though, cause on one hand, you're saying that project management is getting more complicated or complex, more pieces, more people need to do it. On the other hand, you need tools that are not for professional project managers. We need the ability to do things for people that aren't trained on those tools. And the amount of work and reach of that order is just growing, so how do you square that circle? >> It is a dichotomy. It really is a dichotomy. The nature of technology and software being central to everything a company does. All companies are software companies today, and what that means is that you have to have more collaboration, and you have a greater need for transparency and interaction between teams so that they can work together more effectively. So while elements of the project are more complex, the fact that you have more stakeholders and more people involved means that you have to create a balance that you have very highly usable technology to get everybody to work together more effectively. Especially when you think about the demographics of the workplace is changing. When I started in a technology world, I expected green screens, I expected difficult, highly complex applications. I thought that went along with the job, but in today's demographics, people want consumer grade applications. I want something that is as pleasing as it is on my device, as it is going on my desktop, and I want to be able to have the same experience no matter where I go, because work isn't nine to five where I'm sitting at a desk any longer. It is wherever I'm going, because the majority of information workers today or knowledge workers today, work on the road. They need to be able to have that experience, so you can balance complexity if you increase accessibility and usability. That allows you to reduce risk within your projects. >> Ultimately, the top line of any enterprise is the same. We got to grow revenue, we've got to do it faster, we've got to deliver better products and services that are based on feedback and data that we can glean. That's a lot of cultural challenge. I imagine in this emerging market of collaborative workforce management versus portfolio program, or project management, how have you seen companies of, and across industry, actually embrace the cultural shift that is essential to drive digital transformation. >> It's a journey and companies are really still moving through this. As we heard in the keynote today, you're seeing pockets of innovation that are growing and as companies are seeing these results, because of accessibility in schools, and because of the transparency and usability of the tools that are on the market today, you're now seeing that, "Oh, you know what, there is value." I get to see it, because it's visible to me. I'm less resistant to the change, so I'm more willing to try and, frankly, sometimes a company really has to get burnt. What we found is if a project fails, half of the respondents said, "Our company lost revenue because a project failed." Well, nobody needs to have that happen. Nobody wants to have that happen, actually. So what they really want to do is say, "What can I do to mitigate that risk?" And they're finding that, because team's today are more willing to work with technology, and more willing to have that transparency, you know everybody's life is an open book now in technology, it actually promotes teamwork. You move from the project manager as the only person, the single throat to choke, to recognize that it is a team that works together more effectively. That's what helps drive that cultural change, because when everybody's empowered to drive to a successful outcome, you're going to see that cultural resistance move away. >> I imagine that sort of, I don't know if shared accountability's the right thing. >> Absolutely. >> Also is a facilitator of that cultural shift? >> Absolutely, absolutely. When you can see the intelligence behind why a decision was being made, and people can contribute to that decision being made, you get better decision making. It's not a decision made in a vacuum, and you don't have people waiting around for someone to make a decision, or you create cost of delay and waste in a process where no company wants that today. Nobody has time for that today. >> It's pretty interesting cause all we see, that diversity of opinions and background, makes better decisions. We've seen that time and time again. And then also, there's this little thing where if people are part of the decision that was made, they generally have a little bit more buy-in. So that's all-- >> Correct. >> All goodness. So you call it collaborative workflow management as a-- >> Collaborative work management. >> Work management. >> Yes. >> Excuse me, work. Not work flow. I'm just curious, in terms of this kind of struggle for the desktop, right, there's so many SAAS tools out there now, whether you're in Slack or you're in Salesforce, or in G Suite or Office 365. As you look at that competition for what is the top level that is driving what I do, how are people sorting through that? Are we just in this multi-app world? Is there a place for something to be on top? Or is it horses for courses depending on where you are in that process? Cause, man oh man, I find myself tapping from app to app to app to app to app. I've got so many browsers open on my desk, just to get through my day. >> Well, we see the average knowledge worker opening between 8 and 13 apps a day to get their job done, and they spend a third to half of their time in email just looking for information. So you're right, it's a morass of applications and it's very difficult. I don't think we're ever going to get to a one stop shop, but what I do think is that organizations can build an operational system of record. When you think about this, you have CRM system where you know everything about your customer. All their contact information, all the deal data, everything that's going on. You have a financial system of record. You know exactly the revenue that your company is generating, the costs that they're incurring, but when you think about how you actually balance that, how you know and deliver to your customers, and know revenue and costs, what's in the middle is just a jumble of different types of applications. And what we're seeing at Forrester is a trend, is that organizations are trying to create an operational system of record. Now as I said, I don't think it's going to be a one-stop shop, but I do think that there will be a planning and delivery ecosystem that will allow organizations to bring together the tools that work for them. As they said in the keynote this morning, as Mark said in the keynote, if you want to tell somebody, "We're going to work together more effectively," stop what you're doing, that's never going to work. So it's really incumbent upon the tools that are able to work with other tools that make people in your organization productive, because employees have to feel productive to really be able to grow a great customer experience. So collaborative work management is an essential element. It's the core part of the execution layer. Project management tools, like I said, are never going to go away. They're going to be for that formal, critical path from building a ship, for building a road or something very plan intensive. They're always going to be there. If you're going to be managing a services organization, you still need to have your people allocated. You don't want people on the bench. You still need that, but to actually get the work done, collaborative work management is really that core that brings together contextual information around the work that's being done. So it gives collaboration purpose. So I really think that's a central core application. >> You guys at Forrester just collaborated, we'll say there in the spirit of marketing terms, with Smartsheet. You interviewed several hundred Smartsheet customers and-- >> Not just Smartsheet customers, really across the industry. >> This was across even some of their competitors. >> Yes. Project managers, professionals, collaboration workers, information workers. >> Okay. >> PMO directors. We really were trying to get into the user community. That's what we were really focusing on. >> Okay, this was agnostic. One of the things Jeff and I were chatting about before we went live is wanting to understand, okay, Smartsheet has a lot of competition, right, so if I'm going to manage a marketing project and I use JIRA, and my sales team is using Salesforce, but I communicate with a lot of people across the company in Slack, how does that integration work? They've got a lot of connectors, and a lot of integrations. What was some of the feedback that you heard from, in this sort of agnostic city, about the workers in terms of confusion, or "I just want to be able to go into one tool and have everything talk to it." >> Right. Depending on the persona there were different requirements. So what we've found is that for PMO leaders, PMO directors, they had a set of tools. They really created a tool kit for their organizations. So you had at the PMO level, they still use project management tools, they still use spreadsheets, but they increasingly used collaborative work management tools. Collaborative work management has only been around for a few years, and a quarter of the respondents that we saw were adding collaborative work management to their tool kits to reach out to that team member, to bring in more information. That became a stronger, a secondary persona, being the team member that was going to be delivering. What was interesting is the high performers, the high maturity organizations that we interviewed, they really latched on to collaborative work management, seeing this as sort of a secret sauce to say, "Okay, now I can get in better data." We don't have people rushing to fill in a time sheet on Friday, we're getting data real time. Where the integration comes in is if you have people happily and actively using tools that are sticky for them, you get better data and you're not running around at 5 o'clock on a Friday saying, "I need your time sheets." "I need your status reports." And speaking with the folks from Office Depot, they have a great saying. They said, "We move from status to progress. We weren't looking backwards, we knew where we were going." And that's a really important element. Speaking of tools like Slack and some of the other messaging tools that are out there, you might be working with somebody in legal, or you might be working with somebody in HR. That doesn't necessarily need to be in a collaborative work management tool. Almost certainly, probably never need to be in a project management tool, but you need input from them. You need to review something. "Is this contract okay? Are we allowed to say this in a marketing campaign?" Slack allows them to share that information, and then you can bring it back into the collaborative work management tool and see the information and the context around the information real time. It takes you from being able to have some transparency into the project, or the work stream that you're working on, to really actually being able to live in that work stream, and have all of that visibility around you. >> Margo, I'm curious in terms of priorities to move into this space, when you talk about all these customers. How much of it was the digital transformation prerogative? How much of it was, "We just can't move fast enough with the old way and our old tools?" How much of it was competitive threats? Either because we have to respond quickly or how much was it, "My goodness, we have so much institutional knowledge and all these greats heads that we're just not leveraging into this process." What are some of those drivers that are moving this next evolution of, well it's project management now into the work management. >> I think it's a little of everything. Digital is definitely accelerating all of those areas. Tribal knowledge, institutional knowledge, being able to move faster, being able to move more efficiently, again, another great phrase I heard in the keynote today was, "Once we move from efficiency to effectiveness, we really were able to drive better outcomes." That, to me, was a very telling statement, because that's a pain point that I hear from my clients all the time and digital is just the accelerant, because, again, customers today are more knowledgeable than ever. They don't interact in one or two ways, physically or over the phone. They now want to interact in multiple ways, and very often the very first way that they're going to interact with a company is online. It's going to be on a device, and they want that same experience throughout every channel that they're interacting with. What that does is that really puts pressure on a company to be able to design experiences for their customers that are consistent throughout their entire journey with a business. With their business. Otherwise, it takes 30 seconds to lose somebody and have them move on to the next company. >> It's so interesting to me, both the consumerization of IT, which you touched on, right. Our expectation is driven by our interaction with a lot of different applications. >> Absolutely. >> And the other thing is how quickly the gold standard becomes baseline. How quickly we just get used to something new and now we just expect that, not only in that application, but now we expect that, "Doesn't that reapplication have that capability?" >> Oh yeah. >> The competitive thread, the competitive speed in which you have to react is way faster than it ever has been before, and you're competing with my Amazon app. You're competing with the way I interact with Netflix. You're not necessarily competing with how I interact with your competitor down the street. It's a completely different paradigm. >> Absolutely. When you think about companies that have been around for a very long time in the banking industry, is such a great example of this. Millennials don't go into branches. Gen Z does not go into a branch. The need for great digital experiences that that demographic requires, needs to also appeal to a generation that was used to going into branches. You need to be able to balance that, and that puts a lot of pressure on a traditional bank, especially when you see that there are digital banking applications that have no real estate. Everything is digital and you have to be competing with that. It really does put pressure on, so that's why the digital transformation was the accelerant that makes all of the other pain points just that much more magnified. >> I like that. I like thinking about digital transformation in that accelerating version. We're out of time, but I have to ask you one more question. >> Sure. >> We're hearing that there's over 50 customers speaking at this event, which is huge. They gave us some great examples of customers in quotes, as well as presenters during the keynote. I heard a lot of strong qualitative, measurable business outcomes. From the survey that you've recently done, the research, can you give us one or two really strong qualitative, like was a company able to increase revenue by 2X or 3X, or reduce costs by 40 percent? >> Sure. What we saw where a lot of productivity increases and satisfaction increases. What we saw was that productivity increased by three to four times. That you were able to reduce the amount of time you were in email. You were enabled to speed up decision making capabilities. When you thought about how organizations were seeing higher customer satisfaction scores coming back, we saw increases there that were 3 to 4X. And from a little tidbit that we saw just from our own research, is that when we interview information workers about what collaborative tools were most valuable to them, over 70% said collaborative work management tools were the most valuable tools for them in how they leverage collaboration to deliver successful outcomes. >> Margo, thanks so much for stopping by. >> Sure, it was my pleasure. >> Sharing with us about collaborative work management in this emerging market. Excited to hear what comes next. >> Great. >> Thank you for your time. >> Thank you very much for having me. >> We want to thank you for watching the CUBE. I'm Lisa Martin with Jeff Frick. We are live from Smartsheet ENGAGE 2018. Stick around, we'll be back. (Outro Music)

(upbeat music) >> Hey, girls and guys. Welcome back to theCUBE. We are live at Stanford University, covering the 8th Annual Women in Data Science Conference. One of my favorite events. Lisa Martin here. Got a couple of guests from Total Energies. We're going to be talking all things data science, and I think you're going to find this pretty interesting and inspirational. Please welcome Alexandre Lapene, Tech Advisor Data Science at Total Energy. It's great to have you. >> Thank you. >> And Myriam Fayad is here as well, product and value manager at Total Energies. Great to have you guys on theCUBE today. Thank you for your time. >> Thank you for - >> Thank you for receiving us. >> Give the audience, Alexandre, we'll start with you, a little bit about Total Energies, so they understand the industry, and what it is that you guys are doing. >> Yeah, sure, sure. So Total Energies, is a former Total, so we changed name two years ago. So we are a multi-energy company now, working over 130 countries in the world, and more than 100,000 employees. >> Lisa: Oh, wow, big ... >> So we're a quite big company, and if you look at our new logo, you will see there are like seven colors. That's the seven energy that we basically that our business. So you will see the red for the oil, the blue for the gas, because we still have, I mean, a lot of oil and gas, but you will see other color, like blue for hydrogen. >> Lisa: Okay. >> Green for gas, for biogas. >> Lisa: Yeah. >> And a lot of other solar and wind. So we're definitely multi-energy company now. >> Excellent, and you're both from Paris? I'm jealous, I was supposed to go. I'm not going to be there next month. Myriam, talk a little bit about yourself. I'd love to know a little bit about your role. You're also a WiDS ambassador this year. >> Myriam: Yes. >> Lisa: Which is outstanding, but give us a little bit of your background. >> Yes, so today I'm a product manager at the Total Energies' Digital Factory. And at the Digital Factory, our role is to develop digital solutions for all of the businesses of Total Energies. And as a background, I did engineering school. So, and before that I, I would say, I wasn't really aware of, I had never asked myself if being a woman could stop me from being, from doing what I want to do in the professional career. But when I started my engineering school, I started seeing that women are becoming, I would say, increasingly rare in the environment >> Lisa: Yes. >> that, where I was evolving. >> Lisa: Yes. >> So that's why I was, I started to think about, about such initiatives. And then when I started working in the tech field, that conferred me that women are really rare in the tech field and data science field. So, and at Total Energies, I met ambassadors of, of the WiDS initiatives. And that's how I, I decided to be a WiDS Ambassador, too. So our role is to organize events locally in the countries where we work to raise awareness about the importance of having women in the tech and data fields. And also to talk about the WiDS initiative more globally. >> One of my favorite things about WiDS is it's this global movement, it started back in 2015. theCUBE has been covering it since then. I think I've been covering it for theCUBE since 2017. It's always a great day full of really positive messages. One of the things that we talk a lot about when we're focusing on the Q1 Women in Tech, or women in technical roles is you can't be what you can't see. We need to be able to see these role models, but also it, we're not just talking about women, we're talking about underrepresented minorities, we're talking about men like you, Alexander. Talk to us a little bit about what your thoughts are about being at a Women and Data Science Conference and your sponsorship, I'm sure, of many women in Total, and other industries that appreciate having you as a guide. >> Yeah, yeah, sure. First I'm very happy because I'm back to Stanford. So I did my PhD, postdoc, sorry, with Margot, I mean, back in 20, in 2010, so like last decade. >> Lisa: Yeah, yep. >> I'm a film mechanics person, so I didn't start as data scientist, but yeah, WiDS is always, I mean, this great event as you describe it, I mean, to see, I mean it's growing every year. I mean, it's fantastic. And it's very, I mean, I mean, it's always also good as a man, I mean, to, to be in the, in the situation of most of the women in data science conferences. And when Margo, she asked at the beginning of the conference, "Okay, how many men do we have? Okay, can you stand up?" >> Lisa: Yes. I saw that >> It was very interesting because - >> Lisa: I could count on one hand. >> What, like 10 or ... >> Lisa: Yeah. >> Maximum. >> Lisa: Yeah. >> And, and I mean, you feel that, I mean, I mean you could feel what what it is to to be a woman in the field and - >> Lisa: Absolutely. >> Alexandre: That's ... >> And you, sounds like you experienced it. I experienced the same thing. But one of the things that fascinates me about data science is all of the different real world problems it's helping to solve. Like, I keep saying this, we're, we're in California, I'm a native Californian, and we've been in an extreme drought for years. Well, we're getting a ton of rain and snow this year. Climate change. >> Guests: Yeah. We're not used to driving in the rain. We are not very good at it either. But the, just thinking about data science as a facilitator of its understanding climate change better; to be able to make better decisions, predictions, drive better outcomes, or things like, police violence or healthcare inequities. I think the power of data science to help unlock a lot of the unknown is so great. And, and we need that thought diversity. Miriam, you're talking about being in engineering. Talk to me a little bit about what projects interest you with respect to data science, and how you are involved in really creating more diversity and thought. >> Hmm. In fact, at Total Energies in addition to being an energy company we're also a data company in the sense that we produce a lot of data in our activities. For example with the sensors on the fuel on the platforms. >> Lisa: Yes. >> Or on the wind turbines, solar panels and even data related to our clients. So what, what is really exciting about being, working in the data science field at Total Energies is that we really feel the impact of of the project that we're working on. And we really work with the business to understand their problems. >> Lisa: Yeah. >> Or their issues and try to translate it to a technical problem and to solve it with the data that we have. So that's really exciting, to feel the impact of the projects we're working on. So, to take an example, maybe, we know that one of the challenges of the energy transition is the storage of of energy coming from renewable power. >> Yes. >> So I'm working currently on a project to improve the process of creating larger batteries that will help store this energy, by collecting the data, and helping the business to improve the process of creating these batteries. To make it more reliable, and with a better quality. So this is a really interesting project we're working on. >> Amazing, amazing project. And, you know, it's, it's fun I think to think of all of the different people, communities, countries, that are impacted by what you're doing. Everyone, everyone knows about data. Sometimes we think about it as we're paying we're always paying for a lot of data on our phone or "data rates may apply" but we may not be thinking about all of the real world impact that data science is making in our lives. We have this expectation in our personal lives that we're connected 24/7. >> Myriam: Yeah. >> I can get whatever I want from my phone wherever I am in the world. And that's all data driven. And we expect that if I'm dealing with Total Energies, or a retailer, or a car dealer that they're going to have the data, the data to have a personal conversation, conversation with me. We have this expectation. I don't think a lot of people that aren't in data science or technology really realize the impact of data all around their lives. Alexander, talk about some of the interesting data science projects that you're working on. >> There's one that I'm working right now, so I stake advisor. I mean, I'm not the one directly working on it. >> Lisa: Okay. >> But we have, you know, we, we are from the digital factory where we, we make digital products. >> Lisa: Okay. >> And we have different squads. I mean, it's a group of different people with different skills. And one of, one of the, this squad, they're, they're working on the on, on the project that is about safety. We have a lot of site, work site on over the world where we deploy solar panels on on parkings, on, on buildings everywhere. >> Lisa: Okay. Yeah. >> And there's, I mean, a huge, I mean, but I mean, we, we have a lot of, of worker and in term of safety we want to make sure that the, they work safely and, and we want to prevent accidents. So what we, what we do is we, we develop some computer vision approach to help them at improving, you know, the, the, the way they work. I mean the, the basic things is, is detecting, detecting some equipment like the, the the mean the, the vest and so on. But we, we, we, we are working, we're working to really extend that to more concrete recommendation. And that's one a very exciting project. >> Lisa: Yeah. >> Because it's very concrete. >> Yeah. >> And also, I, I'm coming from the R&D of the company and that's one, that's one of this project that started in R&D and is now into the Digital Factory. And it will become a real product deployed over the world on, on our assets. So that's very great. >> The influence and the impact that data can have on every business always is something that, we could talk about that for a very long time. >> Yeah. >> But one of the things I want to address is there, I'm not sure if you're familiar with AnitaB.org the Grace Hopper Institute? It's here in the States and they do this great event every year. It's very pro-women in technology and technical roles. They do a lot of, of survey of, of studies. So they have data demonstrating where are we with respect to women in technical roles. And we've been talking about it for years. It's been, for a while hovering around 25% of technical roles are held by women. I noticed in the AnitaB.org research findings from 2022, It's up to 27.6% I believe. So we're seeing those numbers slowly go up. But one of the things that's a challenge is attrition; of women getting in the roles and then leaving. Miryam, as a woman in, in technology. What inspires you to continue doing what you're doing and to elevate your career in data science? >> What motivates me, is that data science, we really have to look at it as a mean to solve a problem and not a, a fine, a goal in itself. So the fact that we can apply data science to so many fields and so many different projects. So here, for example we took examples of more industrial, maybe, applications. But for example, recently I worked on, on a study, on a data science study to understand what to, to analyze Google reviews of our clients on the service stations and to see what are the the topics that, that are really important to them. So we really have a, a large range of topics, and a diversity of topics that are really interesting, so. >> And that's so important, the diversity of topics alone. There's, I think we're just scratching the surface. We're just at the very beginning of what data science can empower for our daily lives. For businesses, small businesses, large businesses. I'd love to get your perspective as our only male on the show today, Alexandre, you have that elite title. The theme of International Women's Day this year which is today, March 8th, is "Embrace equity." >> Alexandre: Yes. >> Lisa: What is that, when you hear that theme as as a male in technology, as a male in the, in a role where you can actually elevate women and really bring in that thought diversity, what is embracing equity, what does it look like to you? >> To me, it, it's really, I mean, because we, we always talk about how we can, you know, I mean improve, but actually we are fixing a problem, an issue. I mean, it's such a reality. I mean, and the, the reality and and I mean, and force in, in the company. And that's, I think in Total Energy, we, we still have, I mean things, I mean, we, we haven't reached our objective but we're working hard and especially at the Digital Factory to, to, to improve on that. And for example, we have 40% of our women in tech. >> Lisa: 40? >> 40% of our tech people that are women. >> Lisa: Wow, that's fantastic! >> Yeah. That's, that's ... >> You're way ahead of, of the global average. >> Alexandre: Yeah. Yeah. >> That outstanding. >> We're quite proud of that. >> You should be. >> But we, we still, we still know that we, we have at least 10% >> Lisa: Yes. because it's not 50. The target is, the target is to 50 or more. And, and, but I want to insist on the fact that we have, we are correcting an issue. We are fixing an issue. We're not trying to improve something. I mean, that, that's important to have that in mind. >> Lisa: It is. Absolutely. >> Yeah. >> Miryam, I'd love to get your advice to your younger self, before you studied engineering. Obviously you had an interest when you were younger. What advice would you give to young Miriam now, looking back at what you've accomplished and being one of our female, visible females, in a technical role? What do you, what would you say to your younger self? >> Maybe I would say to continue as I started. So as I was saying at the beginning of the interview, when I was at high school, I have never felt like being a woman could stop me from doing anything. >> Lisa: Yeah. Yeah. >> So maybe to continue thinking this way, and yeah. And to, to stay here for, to, to continue this way. Yeah. >> Lisa: That's excellent. Sounds like you have the confidence. >> Mm. Yeah. >> And that's something that, that a lot of people ... I struggled with it when I was younger, have the confidence, "Can I do this?" >> Alexandre: Yeah. >> "Should I do this?" >> Myriam: Yeah. >> And you kind of went, "Why not?" >> Myriam: Yes. >> Which is, that is such a great message to get out to our audience and to everybody else's. Just, "I'm interested in this. I find it fascinating. Why not me?" >> Myriam: Yeah. >> Right? >> Alexandre: Yeah, true. >> And by bringing out, I think, role models as we do here at the conference, it's a, it's a way to to help young girls to be inspired and yeah. >> Alexandre: Yeah. >> We need to have women in leadership positions that we can see, because there's a saying here that we say a lot in the States, which is: "You can't be what you can't see." >> Alexandre: Yeah, that's true. >> And so we need more women and, and men supporting women and underrepresented minorities. And the great thing about WiDS is it does just that. So we thank you so much for your involvement in WiDS, Ambassador, our only male on the program today, Alexander, we thank you. >> I'm very proud of it. >> Awesome to hear that Total Energies has about 40% of females in technical roles and you're on that path to 50% or more. We, we look forward to watching that journey and we thank you so much for joining us on the show today. >> Alexandre: Thank you. >> Myriam: Thank you. >> Lisa: All right. For my guests, I'm Lisa Martin. You're watching theCUBE Live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. We'll be back after a short break, so stick around. (upbeat music)

>>Mhm. Mhm. Yeah. Hello, everybody. Welcome to the Cubes. Coverage of Mongo db World 2022. This is the first Mongo live mongo DB World. Since 2019, the Cube has covered a number of of mongo shows actually going back to when the company was called Engine. Some of you may recall Margo since then has done an i p o p o in 2017, it's It's been a rocket ship company. It's up. It'll probably do 1.2 billion in revenue this year. It's got a billion dollars in cash on the balance sheet. Uh, despite the tech clash, it's still got a 19 or $20 million valuation growing above 50% a year. Uh, company just had a really strong quarter, and and there seems to be hitting on all cylinders. My name is Dave Volonte. And here to kick it off with me as Sanjeev Mohan, who was the principal at Sanremo. So great to see you. You become a wonderful cube contributor, Former Gartner analyst. Really sharp? No, the database space in the data space generally really well, so thanks for coming back on >>you. You know, it's just amazing how exciting. The entire data space is like they used to say. Companies are All companies are software companies. All companies are data >>companies, >>so data has become the the foundation. >>They say software is eating the world. Data is eating software and a little little quips here. But this is a good size show. Four or 5000 people? I don't really know exactly. You know the numbers, but it's exciting. And of course, a lot of financial services were here at the Javits Centre. Um, let's let's lay down the basics for people of Mongo, DB is a is a document database, but they've been advancing. That's a document database as an alternative to R D. B M s. Explain that, but explain also how Mongo has broadened its capabilities and serving a lot more use cases. >>So that's my forte is like databases technology. But before even I talk about that, I have to say I am blown away by this mongo db world because mongo db uh, in beckons to all of us during the pandemic has really come of age, and it's a billion dollar company. Now we are in this brand new Javits Centre That's been built during the pandemic. And and now the company is holding this event the high 1000 people last year. So I think this company has really grown. And why has it drawn is because its offerings have grown to more developers than just a document database document databases. Revolution revolutionised the whole DBM s space where no sequel came up. Because for a change, you don't need a structured schema. You could start bringing data in this document model scheme, uh, like varying schema. But since then, they've added, uh, things like such. So they have you seen such? They added a geospatial. They had a time series last year, and this year they keep adding more and more so like, for example, they are going to add some column store indexes. So from being a purely transactional, they are now starting to address analytical. And they're starting to address more use cases, like, you know, uh, like what? What was announced this morning at keynote was faceted search. So they're expanding the going deeper and deeper into these other data >>structures. Taking Lucy made a search of first class citizens, but I want to ask you some basic questions about document database. So it's no fixed schemes. You put anything in there? Actually, so more data friendly. They're trying to simplify the use of data. Okay, that's that's pretty clear. >>What are the >>trade offs of a document database? >>So it's not like, you know, one technology has solved every problem. Every technology comes with its own tradeoffs. So in a document, you basically get rid of joining tables with primary foreign keys because you can have a flexible schemer and so and wouldn't sing single document. So it's very easy to write and and search. But when you have a lot of repeated elements and you start getting more and more complex, your document size can start expanding quite a bit because you're trying to club everything into a single space. So So that is where the complexity goes >>up. So what does that mean for for practitioner, it means they have to think about what? How they how they are ultimately gonna structure, how they're going to query so they can get the best performances that right. So they're gonna put some time in up front in order to make it pay back at the tail end, but clearly it's it's working. But is that the correct way of thinking about >>100% in, uh, the sequel world? You didn't care about the sequel. Analytical queries You just cared about how your data model was structured and then sequel would would basically such any model. But in the new sequel world, you have to know your patterns before you. You invest into the database so it's changed that equation where you come in knowing what you are signing up. >>So a couple of questions, if I can kind of Colombo questions so to Margo talks about how it's really supporting mission critical applications and at the same time, my understanding is the architecture of mongo specifically, or a document database in general. But specifically, you've got a a primary, uh, database, and you and that is the sort of the master, if you will, right and then you can create secondaries. But so help me square the circle between mission critical and really maybe a more of a focus on, say, consistency versus availability. Do customers have to sort of think about and design in that availability? How do they do that? How a Mongol customers handling that. >>So I have to say, uh, my experience of mongo db was was that the whole company, the whole ethos was developed a friendly. So, to be honest, I don't think Mongo DB was as much focused on high availability, disaster, recovery, even security. To some extent, they were more focused on developer productivity. >>And you've experienced >>simplicity. Make it simple, make the developers productive as fast as you can. What has really, uh, was an inflexion point for Mongo DB was the launch of Atlas because the atlas they were able to introduce all of these management features and hide it abstracted from the end users. So now they've got, you know, like 2014 is when Atlas came out and it was in four regions. But today they're in 100 regions, so they keep expanding, then every hyper scale cloud provider, and they've abstracted that whole managed. >>So Atlas, of course, is the managed database as a service in the cloud. And so it's those clouds, cloud infrastructure and cloud tooling that has allowed them to go after those high available application. My other question is when you talk about adding search, geospatial time series There are a lot of specialised databases that take time series persons. You have time series specialists that go deep into time series can accompany like Mongo with an all in one strategy. Uh, how close can they get to that functionality? Do they have to be? You know, it's kind of a classic Microsoft, you know, maybe not perfect, but good enough. I mean, can they compete with those other areas? Uh, with those other specialists? And what happens to those specialists if the answer is yes. What's your take on that? If that question >>makes sense So David, this is not a mongo db only issue This is this is an issue with, you know, anytime serious database, any graph database Should I put a graph database or should I put a multifunctional database multidimensional database? And and I really think there is no right or wrong answer. It just really comes down to your use case. If you have an extremely let's, uh, complex graph, you know, then maybe you should go with best of breed purpose built database. But more and more, we're starting to see that organisations are looking to simplify their environment by going in for maybe a unified database that has multiple data structures. Yeah, well, >>it's certainly it's interesting when you hear Mongo speak. They don't They don't call out Oracle specifically, but when they talk about legacy r d m r d B m s that don't scale and are complex and are expensive, they're talking about Oracle first. And of course, there are others. Um, And then when they talk about, uh, bespoke databases the horses for courses, databases that they show a picture of that that's like the poster child for Amazon. Of course, they don't call out Amazon. They're a great partner of Amazon's. But those are really the sort of two areas that mangoes going after, Um, now Oracle. Of course, we'll talk about their converged strategy, and they're taking a similar approach. But so help us understand the difference. There is just because they're sort of or close traditional r d B M s, and they have all the drawbacks associated with that. But by the way, there are some benefits as well. So how do you see that all playing >>out? So you know it. Really, uh, it's coming down to the the origins of these databases. Uh, I think they're converging to a point where they are offering similar services. And if you look at some of the benchmark numbers or you talk to users, I from a business point of view, I I don't think there's too much of a difference. Uh, technology writes. The difference is that Mongo DB started in the document space. They were more interested in availability rather than consistency. Oracle started in the relation database with focus on financial services, so asset compliance is what they're based on. And since then they've been adding other pieces, so so they differ from where they started. Oracle has been in the industry for some since 19 seventies, so they have that maturity. But then they have that legacy, >>you know, I love. Recently, Oracle announced the mongo db uh, kpi. So basically saying why? Why leave Oracle when you can just, you know, do the market? So that, to me, is a sign that Mongo DB is doing well because the Oracle calls you out, whether your workday or snowflake or mongo. You know, whoever that's a sign to me that you've got momentum and you're stealing share in that marketplace, and clearly Mongo is they're growing at 50 plus percent per year. So thinking about the early I mentioned 10 gen Early on, I remember that one of the first conferences I went to mongo conferences. It was just It was all developers. A lot of developers here as well. But they have really, since 2014, expanded the capabilities you talk about, Atlas, you talked about all these other you know, types of databases that they've added. If it seems like Mongo is becoming a platform company, uh, what are your thoughts on that in terms of them sort of up levelling the message there now, a billion dollar plus company. What's the next? You know, wave for Mongo. >>So, uh, Oracle announced mongo db a p i s a W s has document d. B has cost most db so they all have a p. I compatible a p. I s not the source code because, you know, mongo DB has its own SPL licence, so they have written their own layer on top. But at the end of the day, you know, if you if you these companies have to keep innovating to catch up with Mongo DB because we can announce a brand new capability, then all these other players have to catch up. So other cloud providers have 80% or so of capabilities, but they'll never have 100% of what Mongo DB has. So people who are diehard Mongo DB fans they prefer to stay on mongo db. They are now able to write more applications like you know, mongo DB bought realm, which is their front end. Uh, like, you know, like, if you're on social media kind of thing, you can build your applications and sink it with Atlas. So So mongo DB is now at a point where they are adding more capabilities that more like developers like, You know, five G is coming. Autonomous cars are coming, so now they can address Iot kind of use cases. So that's why it's becoming such a juggle, not because it's becoming a platform rather than a single document database. >>So atlases, the near the midterm future. Today it's about 60% of revenues, but they have what we call self serve, which is really the traditional on premise stuff. They're connecting those worlds. You're bringing up the point that. Of course, they go across clouds. You also bring up the point that they've got edge plays. We're gonna talk to Verizon later on today. And they're they've got, uh, edge edge activity going on with developers. I I call it Super Cloud. Right, This layer that floats above. Now, of course, a lot of the super Cloud concert says we're gonna hide the underlying complexity. But for developers, they wanna they might want to tap those primitives, so presumably will let them do that. But But that hybrid that what we call Super Cloud that is a new wave of innovation, is it not? And do you? Do you agree with that? And do you see that as a real opportunity from Mongo in terms of penetrating a new tan? >>Yes. So I see this is a new opportunity. In fact, one of the reasons mongo DB has grown so quickly is because they are addressing more markets than they had three pandemic. Um, Also, there are all gradations of users. Some users want full control. They want an eye as kind of, uh, someone passed. And some businesses are like, you know, we don't care. We don't want to deal with the database. So today we heard, uh, mongo db. Several went gear. So now they have surveillance capability, their past. But if you if you're more into communities, they have communities. Operator. So they're addressing the full stack of different types of developers different workloads, different geographical regions. So that that's why the market is expected. >>We're seeing abstraction layers, you know, throughout the started a physical virtual containers surveillance and eventually SuperClubs Sanjeev. Great analysis. Thanks so much for taking your time to come with the cube. Alright, Keep it right there. But right back, right after this short break. This is Dave Volonte from the Javits Centre. Mongo db World 2022. Thank you. >>Mm.

>>Welcome back to the cubes coverage of women in data science, 2022. We've been live all day at Stanford at the Arriaga alumni center. Lisa Martin, John furrier joins me next, trying to, to cure your FOMO that you have. >>I love this events. My favorite events is 2015. We've been coming, growing community over 60 countries, 500 ambassadors and growing so many members. Widths has become a global phenomenon. And it's so exciting to be part of just being part of the ride. Judy and Karen, the team have been amazing partners and it's been fun to watch the progression and international women's day is tomorrow. And just the overall environment's changed a lot since then. It's gotten better. I'm still a lot more work to do, but we're getting the word out, but this year seems different. It seems more like a tipping point is happening and real-time cultural change. A lot of problems. COVID pulled forward. A lot of things, there's a war going on in Europe. It's just really weird time. And it's just seems like it's a tipping point. >>I think that's what we felt today was that it was a tipping point. There was a lot of our guests on the program that are first time with attendees. So in seven, just seven short years, this is the seventh annual width it's gone from this one day technical conference to this global movement, as you talked about. And I think that we definitely felt that women of all ages and men that are here as well understand we're at that tipping point and what needs to be done next to push it over the edge. >>Well, I'm super excited that you are able to do all the amazing interviews. I watched some of them online. I had to come by and, and join the team because I have FOMO. I love doing the interviews, but they're including me. I'm happy to be included, but I got to ask you, I mean, what was different this year? Because it was interesting. It's a hybrid event. It's in part, they didn't have it in person last year, right? So it's hybrid. I showed the streams where everywhere good interviews, what was some of the highlights? >>Just a very inspiring stories of women who really this morning's conversation that I got to hear before I came to set was about mentors and sponsors and how important it is for women of any age and anybody really to build their own personal board of directors with mentors and sponsors. And they were very clear in the difference between a mentor and a sponsor and John something. I didn't understand the difference between the two until a few years ago. I think it was at a VMware event and it really surprised me that I have mentors do ask sponsors. And so that was a discussion that everybody on this onset talked about. >>It was interesting. We're doing also the international women's day tomorrow, big 24 interviews, including the winds of content, as well as global women leaders around the world and to new J Randori, who runs all of AWS, Amy are your maps. And she told me the same thing. She's like, there's too many mentors, not enough sponsors. And she said that out loud. I felt, wow. That was a defining moment because he or she is so impressive. Worked at McKinsey, okay. Was an operator in, in running businesses. Now she heads up AWS saying out loud, we have too many mentors, this get down to business and get sponsors. And I asked her the same thing and she said, sponsors, create opportunities. Mentors, give feedback. And mentors go both ways. And she said, my S my teenage son is a mentor to me for some of the cool new stuff, but ventures can go both ways. Sponsors is specifically about opportunities, and I'm like, I felt like that comment hit home. >>It's so important, but it's also important to teach girls. And especially the there's younger girls here this year, there's high school and middle, I think even middle school girls here, how to have the confidence to, to find those mentors, those sponsors and cultivate those relationships. That's a whole, those are skills that are incredibly important, as important as it is to understand AI data science, machine learning. It's to be able to, to have the confidence in a capability to create that and find those sponsors to help you unlock those opportunities. >>You know, I feel lucky to do the interviews, certainly being included as a male, but you've been doing a lot of the interviews as females and females. I got to ask you what was the biggest, because every story is different. Some people will it's about taking charge of their career. Sometimes it's maybe doing something different. What some of the story themes did you see in your interviews out there? What were some of the, the coverings personal? Yeah. >>Yeah. A lot of, a lot of the guests had stem backgrounds and were interested in the stem studies from when they were quite young and had strong family backgrounds that helps to nurture that. I >>Also heard that role models. Yes, >>Exactly, exactly. A strong family backgrounds. I did talk to a few women who come from different backgrounds, like international business and, but loved data and wanted to be able to apply that and really learn data analytics and understand data science and understand the opportunities that, that it brings. And also some of the challenges there. Everybody had an inspiring story. >>Yeah. It's interesting. One of the senior women I interviewed, she was from Singapore and she fled India during a bombing war and then ended up getting her PhD. Now she's in space and weld and all that stuff. And she said, we're now living in nerd, native environment, me and the younger generation they're nerds. And I, you know, were at Stanford dirt nation. Of course we're Stanford, it's nerd nerd nation here. But her point is, is that everything's digital now. So the younger generation, they're not necessarily looking for programmers, certainly coding. Great. But if you're not into coding, you can still solve society problems. There's plenty of jobs that are open for the first time that weren't around years ago, which means there's problems that are new to that need new minds and new, fresh perspectives. So I thought that aperture of surface area of opportunities to contribute in women in tech is not just coding. No, and that was a huge, >>That was, and we also, this morning, I got to hear, and we've talked about, we talked with several of the women before the event about data science in healthcare, data science, in transportation equity. That was a new thing for me, John, that I didn't know, I didn't, I never thought about transient equity and transportation or lack thereof. And so w what this conference showed, I think this year is that the it's not just coding, but it's every industry. As we know, every company is a data company. Every company is a tech company. If they're not, they're not going to be here for a long. So the opportunities for women is the door is just blown. >>And I said, from my interviews, it's a data problem. That's our line. We always say in the cube, people who know our program programming, we say that, but it actually, when we get the data on the pipeline and the pipeline, it has data points where the ages of drop-off of girls and young women is 12 to 14 and 16 to 18, where the drop-off is significant. So attack the pipelining problem is one that I heard a lot of. And the other one that comes out a lot, it's kind of common sense, and it's talked about it, but it's nuanced, but it became very elevated this year in the breaking, the bias theme, which was role models are huge. So seeing powerful women in leadership positions is really a focus and that's inspires people and they can see themselves. And so I think when people see role models of women and, and folks on in positions, not just coded, but even at the executive suite huge focus. So I think that's going to be a next step function in my mind. That's that's, if I had to predict the trend, it would be you see a lot more role modeling, flexing that big time. >>Good that's definitely needed. You know, we, we often used to say she can't be what she can't see, but one of the interviews that I had said, she can be what she can see. And I loved the pivot on that because it put a positive light, but to your point, there needs to be more female role models that, that girls can look up to. So they can see, I can do this. Like she's doing leading, you know, YouTube, for example, or Sheryl Sandberg of Facebook. We need more of these role models to show the tremendous amount of opportunities that are there, and to help those, not just the younger girls, those even that are maybe more mature find that confidence to build. >>And I think that was another king that came out role models from family members, dad, or a relative, or someone that could see was a big one. The other common thread was, yeah. I tend to break stuff and like to put it together. So at a young age, they kind of realized that they were kind of nerdy and they like to do stuff very engineering, but mind is where math or science. And that was interesting. Sally eaves from in the UK brought this up, she's a professor and does cyber policy. She said, it's a stems gray, but put the arts in there, make it steam. So steam and stem are in two acronyms. Stem is, is obviously the technical, but adding arts because of the creativity needs, we need creativity and problem solving with technical. Yes. So it's not just stem it's theme. We've heard that before, but not as much this year, it's amplified big >>Time. Sally's great. I had the chance to interview her in the last couple of months. And you, you bring up creativity, which is an incredibly important point. You know, there are the, obviously the hard skills, the technical skills that are needed, but there's also creativity. Curiosity being curious to ask a question, there's probably many questions that we haven't even thought to ask yet. So encouraging that curiosity, that natural curiosity is as important as maybe someone say as the actual technical knowledge, >>What was the biggest thing you saw this year? If you zoom out and you look at the forest from the trees, what was the big observation for you this year? >>I think it's the growth of woods. We've decided seven years. It's now in 60 countries, 200 events, 500 ambassadors, probably 500 plus. And the number of people that I had on the program, John, that this is their first woods. So just the fact that it's growing, we, we we've seen it for years, but I think we really saw a lot of the fresh faces and heard from them today had stories of how they got involved and how they met Margo, how she found them. I had a younger Alon who'd just graduated from Harvard back in the spring. So maybe not even a year ago, working at Skydio, doing drone work and had a great perspective on why it's important to have women in the drone industry, the opportunities Jones for good. And it was just nice to hear that fresh perspective. And also to S to hear the women who are new to woods, get it immediately. You walk into the Arriaga alumni center in the morning and you feel the energy and the support and that it was just perpetuated year after year. >>Yeah, it's awesome. I think one of the things I think it was reflecting on this morning was how many women we've interviewed in our cube alumni database now. And we yet are massing quite the database of really amazing people and there's more coming in. So that was kind of on a personal kind of reflection on the cube and what we've been working on together. All of us, the other thing that jumped out at me was the international aspect this year. It just seems like there's a community of tribal vibe where it's not just the tech industry, you know, saying rod, rod, it's a complete call to arms around more stories, tell your story. Yes. More enthusiasm outside of the corporate kind of swim lanes into like more of, Hey, let's get the stories out there. And the catalyst from an interview turned into follow up on LinkedIn, just a lot more like viral network effect so much more this year than ever before. So, you know, we just got to get the stories. >>Absolutely. And I think people given what we've been through the last two years are just really hungry for that. In-person collaboration, the opportunity to see more leadership to get inspired and any level of their career. I think the women here this today have had that opportunity and it's been overwhelmingly positive as you can imagine as it is every year. But I agree. I think it's been more international and definitely much more focused on teaching some of the other skills, the confidence, the creativity, the curiosity. >>Well, Lisa, as of right now, it's March 8th in Japan. So today, officially is kicking off right now. It's kicking off international women's day, March 8th, and the cube has a four region portal that we're going to make open, thanks to the sponsors with widths and Stanford and AWS supporting our mission. We're going to have Latin America, AMIA Asia Pacific and north America content pumping on the cube all day today, tomorrow. >>Exactly. And we've had such great conversations. I really enjoyed talking to the women. I always, I love hearing the stories as you talked about, we need more stories to make it personal, to humanize it, to learn from these people who either had some of them had linear paths, but a lot of emergency zig-zaggy, as you would say. And I always find that so interesting to understand how they got to where they are. Was it zig-zaggy, was it zig-zaggy intentionally? Yes. Some of the women that I talked to had very intentional pivots in their career to get them where they are, but I still thought that story was a very, >>And I like how you're here at Stanford university with winds the day before international Wednesday, technically now in Asia, it's starting, this is going to be a yearly trend. This is season one episode, one of the cube covering international women's day, and then every day for the rest of the year, right? >>What were some of your takeaways from some of the international women's day conversations that you had? >>Number one thing was community. The number one vibe was besides the message of more roles or available role models are important. You don't have to be a coder, but community was inherently the fabric of every conversation. The people were high energy, highly knowledgeable about on being on point around the core issue. It wasn't really politicized was much more of about this is really goodness and real examples of force multipliers of diversity, inclusion and equity, when, what works together as a competitive advantage. And, you know, as a student of business, that is a real change. I think, you know, the people who do it are going to have a competitive advantage. So community competitive advantage and just, and just overall break that bias through the mentoring and the sponsorships. >>And we've had a lot of great conversations about, I loved the theme of international women's day, this year breaking the bias. I asked everybody that I spoke with for international women's day and for width. What does that mean to you? And where are we on that journey? And everyone had a really insightful stories to share about where we are with that in their opinions, in their fields industries. Why, and ultimately, I think the general theme was we have the awareness now that we need, we have the awareness from an equity perspective, that's absolutely needed. We have to start there, shine the light on it so that the bias can be broken and opportunities for everybody can just proliferate >>Global community is going to rise and it's going to tend to rise. The tide is rising. It's going to get better and better. It was a fun year this year. And I think it was relief that COVID kind of going out, people getting back into physical events has been, been really, really great. >>Yep, absolutely. So, John, I, I appreciate all the opportunities that you've given me as a female anchor on the show. International women's day coverage was fantastic. Widths 2022 coming to an end was fantastic. Look forward to next year. >>Well, Margo, Judy and Karen who put this together, had a vision and that vision was right and it was this working and when it gets going, it has escape, velocity unstoppable. >>It's a rocket ship. That's a rocket. I love that. I love to be part of John. Thanks for joining me on the wrap. We want to thank you for watching the cubes coverage of international women's day. The women's showcase as well as women in data science, 2022. We'll see you next time.

>>Hey everyone. Welcome back to the cubes. Live coverage of women in data science, worldwide conference widths 2022. I'm Lisa Martin coming to you from Stanford university at the Arriaga alumni center. And I'm pleased to welcome my next guest. Hannah Sperling joins me business process intelligence or BPI, academic and research alliances at SAP HANA. Welcome to the program. >>Hi, thank you so much for having me. >>So you just flew in from Germany. >>I did last week. Yeah. Long way away. I'm very excited to be here. Uh, but before we get started, I would like to say that I feel very fortunate to be able to be here and that my heart and vicious still goes out to people that might be in more difficult situations right now. I agree >>Such a it's one of my favorite things about Wiz is the community that it's grown into. There's going to be about a 100,000 people that will be involved annually in woods, but you walk into the Arriaga alumni center and you feel this energy from all the women here, from what Margo and teams started seven years ago to what it has become. I was happened to be able to meet listening to one of the panels this morning, and they were talking about something that's just so important for everyone to hear, not just women, the importance of mentors and sponsors, and being able to kind of build your own personal board of directors. Talk to me about some of the mentors that you've had in the past and some of the ones that you have at SAP now. >>Yeah. Thank you. Um, that's actually a great starting point. So maybe talk a bit about how I got involved in tech. Yeah. So SAP is a global software company, but I actually studied business and I was hired directly from university, uh, around four years ago. And that was to join SAP's analytics department. And I've always had a weird thing for databases, even when I was in my undergrad. Um, I did enjoy working with data and so working in analytics with those teams and some people mentoring me, I got into database modeling and eventually ventured even further into development was working in analytics development for a couple of years. And yeah, still am with a global software provider now, which brought me to women and data science, because now I'm also involved in research again, because yeah, some reason couldn't couldn't get enough of that. Um, maybe learn about the stuff that I didn't do in my undergrad. >>And post-grad now, um, researching at university and, um, yeah, one big part in at least European data science efforts, um, is the topic of sensitive data and data privacy considerations. And this is, um, also topic very close to my heart because you can only manage what you measure, right. But if everybody is afraid to touch certain pieces of sensitive data, I think we might not get to where we want to be as fast as we possibly could be. And so I've been really getting into a data and anonymization procedures because I think if we could random a workforce data usable, especially when it comes to increasing diversity in stem or in technology jobs, we should really be, um, letting the data speak >>And letting the data speak. I like that. One of the things they were talking about this morning was the bias in data, the challenges that presents. And I've had some interesting conversations on the cube today, about data in health care data in transportation equity. Where do you, what do you think if we think of international women's day, which is tomorrow the breaking the bias is the theme. Where do you think we are from your perspective on breaking the bias that's across all these different data sets, >>Right. So I guess as somebody working with data on a daily basis, I'm sometimes amazed at how many people still seem to think that data can be unbiased. And this has actually touched upon also in the first keynote that I very much enjoyed, uh, talking about human centered data science people that believe that you can take the human factor out of any effort related to analysis, um, are definitely on the wrong path. So I feel like the sooner that we realize that we need to take into account certain bias sees that will definitely be there because data is humanly generated. Um, the closer we're going to get to something that represents reality better and might help us to change reality for the better as well, because we don't want to stick with the status quo. And any time you look at data, it's definitely gonna be a backward looking effort. So I think the first step is to be aware of that and not to strive for complete objectivity, but understanding and coming to terms with the fact just as it was mentioned in the equity panel, that that is logically impossible, right? >>That's an important, you bring up a really important point. It's important to understand that that is not possible, but what can we work with? What is possible? What can we get to, where do you think we are on the journey of being able to get there? >>I think that initiatives like widths of playing an important role in making that better and increasing that awareness there a big trend around explainability interpretability, um, an AI that you see, not just in Europe, but worldwide, because I think the awareness around those topics is increasing. And that will then, um, also show you the blind spots that you may still have, no matter how much you think about, um, uh, the context. Um, one thing that we still need to get a lot better at though, is including everybody in these types of projects, because otherwise you're always going to have a certain selection in terms of prospectus that you're getting it >>Right. That thought diversity there's so much value in thought diversity. That's something that I think I first started talking about thought diversity at a Wood's conference a few years ago, and really understanding the impact there that that can make to every industry. >>Totally. And I love this example of, I think it was a soap dispenser. I'm one of these really early examples of how technology, if you don't watch out for these, um, human centered considerations, how technology can, can go wrong and just, um, perpetuate bias. So a soap dispenser that would only recognize the hand, whether it was a certain, uh, light skin type that w you know, be placed underneath it. So it's simple examples like that, um, that I think beautifully illustrate what we need to watch out for when we design automatic decision aids, for example, because anywhere where you don't have a human checking, what's ultimately decided upon you end up, you might end up with much more grave examples, >>Right? No, it's, it's I agree. I, Cecilia Aragon gave the talk this morning on the human centered guy. I was able to interview her a couple of weeks ago for four winds and a very inspiring woman and another herself, but she brought up a great point about it's the humans and the AI working together. You can't ditch the humans completely to your point. There are things that will go wrong. I think that's a sends a good message that it's not going to be AI taking jobs, but we have to have those two components working better. >>Yeah. And maybe to also refer to the panel discussion we heard, um, on, on equity, um, I very much liked professor Bowles point. Um, I, and how she emphasized that we're never gonna get to this perfectly objective state. And then also during that panel, um, uh, data scientists said that 80% of her work is still cleaning the data most likely because I feel sometimes there is this, um, uh, almost mysticism around the role of a data scientist that sounds really catchy and cool, but, um, there's so many different aspects of work in data science that I feel it's hard to put that all in a nutshell narrowed down to one role. Um, I think in the end, if you enjoy working with data, and maybe you can even combine that with a certain domain that you're particularly interested in, be it sustainability, or, you know, urban planning, whatever that is the perfect match >>It is. And having that passion that goes along with that also can be very impactful. So you love data. You talked about that, you said you had a strange love for databases. Where do you, where do you want to go from where you are now? How much more deeply are you going to dive into the world of data? >>That's a good question because I would, at this point, definitely not consider myself a data scientist, but I feel like, you know, taking baby steps, I'm maybe on a path to becoming one in the future. Um, and so being at university, uh, again gives me, gives me the opportunity to dive back into certain courses and I've done, you know, smaller data science projects. Um, and I was actually amazed at, and this was touched on in a panel as well earlier. Um, how outdated, so many, um, really frequently used data sets are shown the realm of research, you know, AI machine learning, research, all these models that you feed with these super outdated data sets. And that's happened to me like something I can relate to. Um, and then when you go down that path, you come back to the sort of data engineering path that I really enjoy. So I could see myself, you know, keeping on working on that, the whole data, privacy and analytics, both topics that are very close to my heart, and I think can be combined. They're not opposites. That is something I would definitely stay true to >>Data. Privacy is a really interesting topic. We're seeing so many, you know, GDPR was how many years did a few years old that is now, and we've got other countries and states within the United States, for example, there's California has CCPA, which will become CPRA next year. And it's expanding the definition of what private sensitive data is. So we're companies have to be sensitive to that, but it's a huge challenge to do so because there's so much potential that can come from the data yet, we've got that personal aspect, that sensitive aspect that has to be aware of otherwise there's huge fines. Totally. Where do you think we are with that in terms of kind of compliance? >>So, um, I think in the past years we've seen quite a few, uh, rather shocking examples, um, in the United States, for instance, where, um, yeah, personal data was used or all proxies, um, that led to, uh, detrimental outcomes, um, in Europe, thanks to the strong data regulations. I think, um, we haven't had as many problems, but here the question remains, well, where do you draw the line? And, you know, how do you design this trade-off in between increasing efficiency, um, making business applications better, for example, in the case of SAP, um, while protecting the individual, uh, privacy rights of, of people. So, um, I guess in one way, SAP has a, as an easier position because we deal with business data. So anybody who doesn't want to care about the human element maybe would like to, you know, try building models and machine generated data first. >>I mean, at least I would feel much more comfortable because as soon as you look at personally identifiable data, you really need to watch out, um, there is however ways to make that happen. And I was touching upon these anonymization techniques that I think are going to be, um, more and more important in the, in the coming years, there is a proposed on the way by the European commission. And I was actually impressed by the sophisticated newness of legislation in, in that area. And the plan is for the future to tie the rules around the use of data science, to the specific objectives of the project. And I think that's the only way to go because of the data's out there it's going to be used. Right. We've sort of learned that and true anonymization might not even be possible because of the amount of data that's out there. So I think this approach of, um, trying to limit the, the projects in terms of, you know, um, looking at what do they want to achieve, not just for an individual company, but also for us as a society, think that needs to play a much bigger role in any data-related projects where >>You said getting true anonymization isn't really feasible. Where are we though on the anonymization pathway, >>If you will. I mean, it always, it's always the cost benefit trade off, right? Because if the question is not interesting enough, so if you're not going to allocate enough resources in trying to reverse engineer out an old, the tie to an individual, for example, sticking true to this, um, anonymization example, um, nobody's going to do it right. We live in a world where there's data everywhere. So I feel like that that's not going to be our problem. Um, and that is why this approach of trying to look at the objectives of a project come in, because, you know, um, sometimes maybe we're just lucky that it's not valuable enough to figure out certain details about our personal lives so that nobody will try, because I am sure that if people, data scientists tried hard enough, um, I wonder if there's challenges they wouldn't be able to solve. >>And there has been companies that have, you know, put out data sets that were supposedly anonymized. And then, um, it wasn't actually that hard to make interferences and in the, in the panel and equity one lab, one last thought about that. Um, we heard Jessica speak about, uh, construction and you know, how she would, um, she was trying to use, um, synthetic data because it's so hard to get the real data. Um, and the challenge of getting the synthetic data to, um, sort of, uh, um, mimic the true data. And the question came up of sensors in, in the household and so on. That is obviously a huge opportunity, but for me, it's somebody who's, um, very sensitive when it comes to privacy considerations straight away. I'm like, but what, you know, if we generate all this data, then somebody uses it for the wrong reasons, which might not be better urban planning for all different communities, but simple profit maximization. Right? So this is something that's also very dear to my heart, and I'm definitely going to go down that path further. >>Well, Hannah, it's been great having you on the program. Congratulations on being a Wood's ambassador. I'm sure there's going to be a lot of great lessons and experiences that you'll take back to Germany from here. Thank you so much. We appreciate your time for Hannah Sperling. I'm Lisa Martin. You're watching the QS live coverage of women in data science conference, 2020 to stick around. I'll be right back with my next guest.

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of Women in Data Science, 2022. I'm Lisa Martin, excited to be coming to you live from Stanford University at the Ariaga alumni center. I'm pleased to welcome fresh keynote stage Alex Hanna the director of research at the dare Institute. Alex, it's great to have you on the program. >> Yeah, lovely to be here. >> Talk to me a little bit about yourself. I know your background is in sociology. We were talking before we went live about your hobbies and roller derby, which I love. >> Yes. >> But talk to me a little bit about your background and what the DAIR Institute this is, distributed AI research Institute, what it actually is doing. >> Sure, absolutely. So happy to be here talking to the women in data science community. So my background's in sociology, but also in computer science and machine learning. So my dissertation work was actually focusing on developing some machine learning and natural language processing tools for analyzing protest event data and generating that and applying it to pertinent questions within social movement scholarship. After that, I was a faculty at University of Toronto and then research scientist at Google on the ethical AI team where I met Dr. Timnit Gebru who is the founder of DAIR. And so, DAIR is a nonprofit research Institute oriented on around independent community based AI work, focused really on, the kind of, lots of discussions around AI are done by big companies or companies focus on solutions that are very much oriented around collecting as much data as they can. Not really knowing if it's going to be for community benefit. At DAIR, we want to flip that, we want to really want to prioritize what that would mean if communities had input into data driven technologies what it would mean for those communities and how we can help there. >> Double click and just some of your research, where do your passions lie? >> So I'm a sociologist and a lot of that being, I think one of the big insights of sociology is to really highlight at how society can be more just, how we can interrogate inequality and understanding how to make those distances between people who are underserved and over served who already have quite a lot, how we can reduce the disparities. So finding out where that lies, especially in technology that's really what I'm passionate about. So it's not just technology, which I think can be helpful but it's really understanding what it means to reduce those gaps and make the world more just. >> And that's so important. I mean, as more and more data is generated, exponentially growing, so are some of the biases and the challenges that that causes. You just gave your tech vision talk which I had a chance to see most of it. And you were talking about something that's very interesting. That is the biases in facial recognition software. Maybe on a little bit about what you talked about and why that is such a challenge. And also what are some of the steps being made in the right direction where that's concerned? >> Yeah. So there's the work I was talking about in the talk was highlighting, not work I've done, but the work by doctors (indistinct) and (indistinct) focusing on the distance that exists and the biases that exist in facial recognition as a technical system. The fact remains also that facial recognition is used and is disproportionately deployed on marginalized population. So in the U.S, that means black and brown communities. That's where facial recognition is used disproportionately. And we also see this in refugee context where refugees will be leaving the country. And those facial recognition software will be used in those contexts and surveilling them. So these are people already in a really precarious place. And so, some of the movements there have been to debias some of the facial recognition tools. I actually don't think that's far enough. I'm fundamentally against facial recognition. I think that it shouldn't be used as a technology because it is used so pervasively in surveillance and policing. And if we're going to approach that we really need to think, rethink our models of security models of immigration and whatnot. >> Right, it's such an important topic to discuss because I think it needs more awareness about some of the the biases, but also some to your point about some of those vulnerable communities that are really potentially being harmed by technologies like that. We have to be, there's a fine line. Or maybe it's not so fine. >> I don't think it's that fine. So like, I think it's used, in an incredibly harsh way. And for instance there's research that's being done in which, so I'm a transgender woman and there's a research being done by researchers who collected data sets that people had on YouTube documenting their transitions. And already there was a researcher collecting those data and saying, well, we could have terrorists or something take hormones and cross borders. And you talk to any trans person, you're like, well, that's not how it works, first off. Second off, it's already viewing trans people and a trans body as kind of a mode of deception. And so that's, whereas researchers in this space were collecting those data and saying that well, we should collect these data to help make these facial recognitions more fair. But that's not fair if it's going to be used on a population that's already intensely surveilled and held in suspicion. >> Right. That's, the question of fairness is huge, absolutely. Were you always interested in tech, you talked about your background in sociology. Was it something that you always, were you a stem kid from the time you were little? Talk to me about your background and how you got to where you are now? >> Yeah. I've been using computers since I was four. I've been using, I was taking a part, my parents' gateway computer. yeah, when I was 10. Going to computer shows, slapping hard drives into things, seeing how much we could upgrade computer on our own and ruining more than in one computer, to my parents chagrin but I've always been that. I went to undergrad in triple major to computer science, math and sociology, and originally just in computer science and then added the other two where I got interested in things and understanding that, was really interested in this section of tech and society. And I think the more and more I sat within the field and went and did my graduate work in sociology and other social sciences really found that there was a place to interrogate those, that intersection of the two. >> Exactly. What are some of the things that excite you now about where technology is going? What are some of the positives that you see? >> I talk so much about the negatives. It's really hard to, I mean, there's I think, some of the things that I think that are positive are really the community driven initiatives that are saying, well, what can we do to remake this in such a way that is going to more be more positive for our community? And so seeing projects like, that try to do community control over certain kinds of AI models or really try to tie together different kinds of fields. I mean, that's exciting. And I think right now we're seeing a lot of people that are super politically and justice literate and they how to work and they know what's behind all these data driven technologies and they can really try to flip the script and try to understand what would it mean to kind of turn this into something that empowers us instead of being something that is really becoming centralized in a few companies >> Right. We need to be empowered with that for sure. How did you get involved with WIS? >> So Margo, one of the co-directors, we sit on a board together, the human rights data analysis group and I've been a huge fan of HR dag for a really long time because HR dag is probably one of the first projects I've seen that's really focused on using data for accountability for justice. Their methodology has been, called on to hold perpetrators of genocide to accounts to hold state violence, perpetrators to account. And I always thought that was really admirable. And so being on their board is sort of, kind of a dream. Not that they're actually coming to me for advice. So I met Margo and she said, come on down and let's do a thing for WIS and I happily obliged >> Is this your first Wis? >> This is my very first Wis. >> Oh, excellent. >> Yeah. >> What's your interpretation so far? >> I'm having a great time. I'm learning a lot meeting a lot of great people and I think it's great to bring folks from all levels here. Not only, people who are a super senior which they're not going to get the most out of it it's going to be the high school students the undergrads, grad students, folks who, and you're never too old to be mentored, so, fighting your own mentors too. >> You know, it's so great to see the young faces here and the mature faces as well. But one of the things that I was, I caught in the panel this morning was the the talk about mentors versus sponsors. And that's actually, I didn't know the difference until a few years ago in another women in tech event. And I thought it was such great advice for those panelists to be talking to the audience, talking about the importance of mentors, but also the difference between a mentor and sponsor. Who are some of your mentors? >> Yeah, I mean, great question. It's going to sound cheesy, but my boss (indistinct) I mean, she's been a huge mentor for me and with her and another mentor (indistinct) Mitchell, I wouldn't have been a research scientist. I was the first social scientist on the research scientist ladder at Google before I left and if it wasn't for their, they did sponsor but then they all also mentored me greatly. My PhD advisor, (indistinct) huge mentor by, and I mean, lots of primarily and then peer mentors, people that are kind of at the same stage as me academically but also in professionally, but are mentors. So folks like Anna Lauren Hoffman, who's at the UDub, she's a great inspiration in collaborating, co-conspirator, so yeah. >> Co-conspirator, I like that. I'm sure you have quite a few mentees as well. Talk to me a little bit about that and what excites you about being a mentor. >> Yeah. I have a lot of mentees either informally or formally. And I sought that out purposefully. I think one of the speakers this morning on the panel was saying, if you can mentor do it. And that's what I did and sought out that, I mean, it excites me because folks, I don't have all the answers, no one person does. You only get to those places, if you have a large community. And I think being smart is often something that people think comes like, there's kind of like a smart gene or whatever but like there probably is, like I'm not a biologist or a cognitive, anything, but what really takes cultivation is being kind and really advocating for other people and building solidarity. And so that's what mentorship really means to me is building that solidarity and really trying to lift other people up. I mean, I'm only here and where I'm at in my career, because many people were mentors and sponsors to me and that's only right to pay that forward. >> I love that, paying that forward. That's so true. There's nothing like a good community, right? I mean, there's so much opportunity that that ground swell just generates, which is what I love. We are, tomorrow is international women's day. And if we look at the numbers, women are 50% of the workforce, but only less than a quarter in stem positions. What's your advice and recommendation for those young girls who might be intimidated or might be being told even to this day, no, you can't do physics. You can't do computer science. What can you tell them? >> Yeah, I mean, so individual solutions to that are putting a bandaid on a very big wound. And I mean I think, finding other people in a working to change it, I mean, I think building structures of solidarity and care are really the only way we'll get out of that. >> I agree. Well, Alex, it's been great to have you on the program. Thank you for coming and sharing what you're doing at DAIR. The intersection of sociology and technology was fascinating and your roller derby, we'll have to talk well about that. >> For sure. >> Excellent. >> Thanks for joining me. >> Yeah, thank you Lisa. >> For Alex Hanna, I'm Lisa Martin. You're watching theCUBE's coverage live, of women in data science worldwide conference, 2022. Stick around, my next guest is coming right up. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

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

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the Cube. I'm Lisa Martin Live at the Stanford Ari Aga Alone, My Center for the Fourth Annual Women and Data Science Conference with twenty nineteen and were joined by a very special guest, Liza Donnelly, cartoonist for The New Yorker. But Liza, you are a visual journalists, visual journalism. You're here live, drawing a lot of the things that are going on. It would. You were just at the Oscars at the Grammys. Your work is so unique, so descriptive. Tell us a little bit our audience about what is visual journalism? >> Well, I suppose a lot of us define it different ways. But I did find it is somebody who I am, somebody who goes to events, either political or social, cultural and draw what I see. I'm not a court reporter. I'm I'm an Impressionist. I give people a feeling that they're they're with me from what? By what I draw what I see, how I draw it, and and it's I don't usually put any editorializing in those visual drawings, but my perspective is sort of a certain kind of approach. >> So you're bringing your viewers along this journey in almost real time. When people see people might be most failure with New Yorker your illustrations there. But folks that are watching the Woods event lie that engaging with that tell us a little bit about the importance of using the illustrations to bring them on this journey as if they were here. >> Well, you know, I send the drawings out immediately, do them on my iPad and I send them out on social media almost immediately, so as I do that so that people can see them immediately. So they feel like they're there, and it's a way to draw attention to whatever it is I'm drawing. Because on the Internet, there's so many words in so many photographs, people see a drawing by other stream that like, Wait, what's that? And I'm a thumb stopper, in other words, so it's. It gives people different perspective on what's going on. And I think that my background is a cartoonist for The New Yorker for forty years. Informs these drawings in an indirect background kind of way, because I have been watching culture have been watching politics for a very long time, so it gives me a, you know, a new attitude or a way to look at what's going on, >> right? And so you you call these illustrations, not cartoons. >> I do call the cartoons cartoons. Okay, we'll do the cartoons for the for >> The New Yorker and some other magazines, and those have a caption, and they often are supposed to be funny, or at least cultural commentary. I do political cartoons for medium, and those also have it have a point of view, are a caption. But the's this visual journalism like I'm doing here is more like reportage. It's more like this is what's happening here. You might be interested in seeing what people are talking about, what they're doing and I do behind the scenes to I don't just do like the Oscars. I'll do the stars if I could get them. And on the red crime on the red carpet, it's really cool. If I catch them, I'll draw them. And then But then I also do the people taking out the trash, the guy painting, you know, painting the sideboard or the counterman, things like that. So I try to give a sense of what it's like to be there. >> So you really kind of telling a story from different perspectives. Yes, right. Yeah. And so the role of I'd love to understand you mentioned being with the New Yorker for very long time and loved. You understand from your perspective, the evolution of cartoons and the impact they can make in in our society, in politics and economics. Tell us a little bit about some of the impacts that you've seen evolve over the last few decades. >> Well, I've written about >> that. I'm also a writer. I've written about that for a very sites. Did a commentary on op ed for The New York Times about the Charlie Hebdo's murders a couple years ago because we know cartoons can be very controversial. Yes and problematic Nick. And that's been true through the course of the history of our country, and I'm sure in England and other countries as well. But it's compounded. Now because of the Internet. I think cartoons could be misunderstood that could be used as weapons. People are gonna be talking about this next week at the South by Southwest. I'm talking about political cartoons and what what their impact has been in the past and how, >> how they, how they create an impact now >> and why that is, and how we could use it to the to our to good effect. You know, not a divisive tool, which I think is a problem that we're dealing with right now in our culture is everybody's so divided and so opinionated and so hateful towards each other. Can we use cartoons? Not to perpetuate that, but to make things better in some way. >> And that's kind of the theme of Wits, Women and Data Science Conference. You know, we're talking Teo and listening Teo at the live event here at Stanford and all of those around the world. It's really strong leaders and data sign. So we think of data science on DH, the technical skills. But data is generated. We generate tons of it as people, right with whatever we're buying, what we're watching on Netflix. But we're listening to on Spotify, etcetera. There's this data trail that we're all leaving, and we know you talked about using cartoons for good. Same conversations that we have on the data side, about being able to use data for good for cancer research, for example, rather than exposing and being malicious, that's interesting. Parallel that you've seen over the years that there is a lot of potential here. Tell me a little bit about the appetite in. Maybe we'll say the millennials and the younger generations for cartoons as a tool for positive the spread of positive social news and not fake news. >> Well, there. I know that >> there's more and more cartoons on the Internet now. A lot of Web comics and cartoonists are young. Cartoonists are using the Internet effectively, too. Put out their ideas. In fact, I when the Internet hit, I was mid career right, and it just took off and helped me become Mohr more well known just by leveraging the Internet. No, because I love it. You know, I love Communicate. It's >> actually it's really an extension >> of what I did as a child learning to draw, communicate with people. I was shy. I don't want to talk. The Internet is just a matter of for me. It's like a dialogue with people on DH. That's how I look at it, and I I think this new generation is really trying to find ways to use these tools in a good way. I think there's a whole new, you know, the kids in their >> twenties. I think they're trying >> to make a better world, are working on it, and that's exciting. >> You talk about communication and how you used your artistic skills from the time you were a child to communicate. Being shy. We also talk about communication in the context of events like the women, the data science, where it isn't just enough to be ableto understand and have the technical acumen to evaluate complex, messy data sets. But the communication piece kind of go back, Teo sort of basic human scaled, being able to communicate effectively. This is what I think the data say and why, and here's what we can do with it. So I think it's interesting that you're here at this event. That has a lot of parallels with communication with using a tool or information for the betterment off a little bit about how you got involved with women in data science. >> Well, I met Margot Garretson >> about five years ago, and through a mutual friend, we met in Iceland. All places >> like it's conference >> about women's rights. It was, it was the Icelandic women are so powerful anyway. We met there, really, to be good friends, and she invited me to come live, draw her new conference at the time. I think she had one year of it, and I thought, data science, OK, >> did you even know what >> that Wass? Yeah, kind of. But I didn't think I didn't see my connection. But I thought, Well, it's about women's rights and >> I'm a big part of my interest in what I want to do with my work is promote equal rights for women around the world. And so I thought, this this sounds terrific. Plus, it's global, and I do a lot of work globally to help them and help freedom of speech as well. So it seemed to be a great fit on DH and and it seems even more to be a good fit in that. It's a way to get the information out there in a visual way because people will hear that word data, and they like they probably just >> start. Yeah, zero because >> they see it connected with a cartoon or drawing it humanizes it for them a little bit. And if I could do that, that's great. And that's what's also fun is that I thought about this today was drawing the speakers, and I'm drawing one of the speakers. I forget her name right now, but I thought and I put it out on the Internet. There were no words on there, but it was just a woman speaker talking about really very technical data science. I put on the Internet with the caption on the tweet and I thought, People, it's it's it's just a constant reminder to people that women are doing this. And it's not a silly not like writing a long essay about why women should be in data signs and why they are and why they're important. But they're doing great things. But if you see it, it resonates a little bit more quickly and more forcefully. >> Absolutely. And it aligns with what we hear and say a lot of we can't be what we can't see. >> That's right. Yeah, that's a saying right where you said that. >> Yes. I'm not sure I'd love to take credit for it. Sure >> would be if she can see it, she could be it. That's another >> thing. That a young girl, she's my drawing of a professor talking on stage. Maybe she'll think about it. >> Absolutely. So in the last few seconds here, can you just give us a little bit of an idea of how you actually What What inspires you when you're seeing someone give a talk like you mentioned about maybe an esoteric or a very technical top? What do you normally look for? That's that Ah ha moment that you want to capture in ten minutes. >> Well, I try to capture that person's essence. I'm not a caricaturist. I don't pretend to be, but I draw >> a likeness of them, and they're the full body is the best body language. You know, they're just tick yah late ing. And then oftentimes I try to capture a sentence that they're saying that has has more universal appeal that somehow brings like a not like a layman into the subject A little bit. If I can find that sentence in what they're saying, I'll put that you have the speech balloon will be saying that. But I just try to capture the person best. I can >> do anything if you compare two wins. Twenty eighteen. Here we are a year later. Even more people here, the live event, even more people engaging and think Margo's that about twenty thousand live today. One hundred thousand over. I think the one hundred thirty plus regional with events, anything that you hear, see or feel that's even more exciting this year than last year. >> Um, well, I do. I do feel the >> the increase in numbers. I can feel it. There's there soon be more people here I don't true, but the senior more young people here, what else is it is it is a buzz. I think there's a >> There's an energy >> is an energy. Not that there wasn't there last. The last I've >> done three years now. It's been there, but there's a certain excitement right now. I think more women are stepping into this field of being recognized for doing so. >> And it's great that you're able Tio, reach, help wigs, reach an even bigger audience and tell this story with your illustrations in a more visual way, way also. Thank you so much, Liza, for taking some time. Must daughter by the Cuban talked to us. It's an honor to meet you And you. I love your drawings. >> Thank you so much. You >> want to thank you for watching the Cube? I'm Lisa Martin Live at the fourth annual Women and Data Science Conference at Stanford's took around. Be right back with my next guests.

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the key. We air live at Stanford University for the fourth annual Women in Data Science Conference. The Cube has had the pleasure of being here all four years on I'm welcoming Back to the Cube, one of our distinguished alumni Janet George, the fellow chief data officer, scientists, big data and cognitive computing at Western Digital. Janet, it's great to see you. Thank you. Thank you so much. So I mentioned yes. Fourth, Annie will women in data science. And it's been, I think I met you here a couple of years ago, and we look at the impact. It had a chance to speak with Margo Garrett's in a about an hour ago, one of the co founders of Woods saying, We're expecting twenty thousand people to be engaging today with the Livestream. There are wigs events in one hundred and fifty locations this year, fifty plus countries expecting about one hundred thousand people to engage the attention. The focus that they have on data science and the opportunities that it has is really palpable. Tell us a little bit about Western Digital's continued sponsorship and what makes this important to you? >> So Western distal has recently transformed itself as a company, and we are a data driven company, so we are very much data infrastructure company, and I think that this momentum off A is phenomenal. It's just it's a foundational shift in the way we do business, and this foundational shift is just gaining tremendous momentum. Businesses are realizing that they're going to be in two categories the have and have not. And in order to be in the half category, you have started to embrace a You've got to start to embrace data. You've got to start to embrace scale and you've got to be in the transformation process. You have to transform yourself to put yourself in a competitive position. And that's why Vest Initial is here, where the leaders in storage worldwide and we'd like to be at the heart of their data is. >> So how has Western Digital transform? Because if we look at the evolution of a I and I know you're give you're on a panel tan, you're also giving a breakout on deep learning. But some of the importance it's not just the technical expertise. There's other really important skills. Communication, collaboration, empathy. How has Western digital transformed to really, I guess, maybe transform the human capital to be able to really become broad enough to be ableto tow harness. Aye, aye, for good. >> So we're not just a company that focuses on business for a We're doing a number of initiatives One of the initiatives were doing is a I for good, and we're doing data for good. This is related to working with the U. N. We've been focusing on trying to figure out how climate change the data that impacts climate change, collecting data and providing infrastructure to store massive amounts of species data in the environment that we've never actually collected before. So climate change is a huge area for us. Education is a huge area for us. Diversity is a huge area for us. We're using all of these areas as launching pad for data for good and trying to use data to better mankind and use a eye to better mankind. >> One of the things that is going on at this year's with second annual data fun. And when you talk about data for good, I think this year's Predictive Analytics Challenge was to look at satellite imagery to train the model to evaluate which images air likely tohave oil palm plantations. And we know that there's a tremendous social impact that palm oil and oil palm plantations in that can can impact, such as I think in Borneo and eighty percent reduction in the Oregon ten population. So it's interesting that they're also taking this opportunity to look at data for good. And how can they look at predictive Analytics to understand how to reduce deforestation like you talked about climate and the impact in the potential that a I and data for good have is astronomical? >> That's right. We could not build predictive models. We didn't have the data to put predictive boats predictive models. Now we have the data to put put out massively predictive models that can help us understand what change would look like twenty five years from now and then take corrective action. So we know carbon emissions are causing very significant damage to our environment. And there's something we can do about it. Data is helping us do that. We have the infrastructure, economies of scale. We can build massive platforms that can store this data, and then we can. Alan, it's the state at scale. We have enough technology now to adapt to our ecosystem, to look at disappearing grillers, you know, to look at disappearing insects, to look at just equal system that be living, how, how the ecosystem is going to survive and be better in the next ten years. There's a >> tremendous amount of power that data for good has, when often times whether the Cube is that technology conferences or events like this. The word trust issues yes, a lot in some pretty significant ways. And we often hear that data is not just the life blood of an organization, whether it's in just industry or academia. To have that trust is essential without it. That's right. No, go. >> That's right. So the data we have to be able to be discriminated. That's where the trust comes into factor, right? Because you can create a very good eh? I'm odder, or you can create a bad air more so a lot depends on who is creating the modern. The authorship of the model the creator of the modern is pretty significant to what the model actually does. Now we're getting a lot of this new area ofthe eyes coming in, which is the adversarial neural networks. And these areas are really just springing up because it can be creators to stop and block bad that's being done in the world next. So, for example, if you have malicious attacks on your website or hear militias, data collection on that data is being used against you. These adversarial networks and had built the trust in the data and in the so that is a whole new effort that has started in the latest world, which is >> critical because you mentioned everybody. I think, regardless of what generation you're in that's on. The planet today is aware of cybersecurity issues, whether it's H vac systems with DDOS attacks or it's ah baby boomer, who was part of the fifty million Facebook users whose data was used without their knowledge. It's becoming, I won't say accepted, but very much commonplace, Yes, so training the A I to be used for good is one thing. But I'm curious in terms of the potential that individuals have. What are your thoughts on some of these practices or concepts that we're hearing about data scientists taking something like a Hippocratic oath to start owning accountability for the data that they're working with. I'm just curious. What's >> more, I have a strong opinion on this because I think that data scientists are hugely responsible for what they are creating. We need a diversity of data scientists to have multiple models that are completely divorce, and we have to be very responsible when we start to create. Creators are by default, have to be responsible for their creation. Now where we get into tricky areas off, then you are the human auto or the creator ofthe Anay I model. And now the marshal has self created because it a self learned who owns the patent, who owns the copyright to those when I becomes the creator and whether it's malicious or non malicious right. And that's also ownership for the data scientist. So the group of people that are responsible for creating the environment, creating the morals the question comes into how do we protect the authors, the uses, the producers and the new creators off the original piece of art? Because at the end of the day, when you think about algorithms and I, it's just art its creation and you can use the creation for good or bad. And as the creation recreates itself like a learning on its own with massive amounts of data after an original data scientist has created the model well, how we how to be a confident. So that's a very interesting area that we haven't even touched upon because now the laws have to change. Policies have to change, but we can't stop innovation. Innovation has to go, and at the same time we have to be responsible about what we innovate >> and where do you think we are? Is a society in terms of catching As you mentioned, we can't. We have to continue innovation. Where are we A society and society and starting to understand the different principles of practices that have to be implemented in order for proper management of data, too. Enable innovation to continue at the pace that it needs. >> June. I would say that UK and other countries that kind of better than us, US is still catching up. But we're having great conversations. This is very important, right? We're debating the issues. We're coming together as a community. We're having so many discussions with experts. I'm sitting in so many panels contributing as an Aye aye expert in what we're creating. What? We see its scale when we deploy an aye aye, modern in production. What have we seen as the longevity of that? A marker in a business setting in a non business setting. How does the I perform and were now able to see sustained performance of the model? So let's say you deploy and am are in production. You're able inform yourself watching the sustained performance of that a model and how it is behaving, how it is learning how it's growing, what is its track record. And this knowledge is to come back and be part of discussions and part of being informed so we can change the regulations and be prepared for where this is going. Otherwise will be surprised. And I think that we have started a lot of discussions. The community's air coming together. The experts are coming together. So this is very good news. >> Theologian is's there? The moment of Edward is building. These conversations are happening. >> Yes, and policy makers are actively participating. This is very good for us because we don't want innovators to innovate without the participation of policymakers. We want the policymakers hand in hand with the innovators to lead the charter. So we have the checks and balances in place, and we feel safe because safety is so important. We need psychological safety for anything we do even to have a conversation. We need psychological safety. So imagine having a >> I >> systems run our lives without having that psychological safety. That's bad news for all of us, right? And so we really need to focus on the trust. And we need to focus on our ability to trust the data or a right to help us trust the data or surface the issues that are causing the trust. >> Janet, what a pleasure to have you back on the Cube. I wish we had more time to keep talking, but it's I can't wait till we talk to you next year because what you guys are doing and also your pact, true passion for data science for trust and a I for good is palpable. So thank you so much for carving out some time to stop by the program. Thank you. It's my pleasure. We want to thank you for watching the Cuba and Lisa Martin live at Stanford for the fourth annual Women in Data Science conference. We back after a short break.

>> Live, from Stanford University, it's theCUBE! Covering Global Women in Data Science Conference, brought to you by SiliconANGLE Media. >> Welcome back to the CUBE, continuing coverage of the forth annual Women in Data Science Conference or WiDS, I am Lisa Martin, we are live at Stanford University but WiDS is going on at a 150 plus regional events in more than 50 countries. In fact there are 20 thousand people expected to be engaging with our livestream today. Joining us on the program is Kristina Draper, the chief technology officer at Wells Fargo, Wells Fargo, one of the sponsors, Kristina welcome to theCUBE. >> Thank you so much Lisa, it's a real pleasure to be here. >> So this is the forth annual WiDS, and as I was mentioning some of the numbers, it's incredible, the momentum that this event has generated, we'd like to call it a movement. Tell a little bit about your involvement in WiDS, as well as Wells Fargo's involvement as a sponsor. >> Yes, um, so we are really honored to be able to be a part of WiDS. I was introduced to WiDS from an employee of mine, Catherine Lee, she joined our team just about a year ago, and she's been part of WiDS since the Inception. So working with Margo and the team and we believe so strongly that in the consumer bank space we have a tremendous opportunity and responsibility to understand how our customers interact with Wells Fargo and that will require a discipline around data science and so we had an opportunity, and had asked this year to be an executive sponsor and we jumped at it and I think we'll continue to be here at that sponsor level in future years. >> So you've been in Wells Fargo for a long time, tell me a little bit about your background of rising to become the chief technology officer. >> Sure, thank you so much for the question. It's been an interesting journey, I haven't always been at Well. So I did a few start ups here in the Silicon Valley. Um, kind of middle of my career and I came back to Wells Fargo. Most recently, I have a responsibility for the consumer bank technology space, that's the majority of branch technology. It's all of the ATMs, the point of sale network for customers. It also is a lot of business services, so how we think about services oriented architecture to ensure that we're always thinking about our customer and their accounts, in a consistent way regardless of how customers interact with Wells Fargo. So, all channels consistently trusted, so that data set's really important. And then, I also have the customer feedback and customer complaints so the idea that from survey all the way through complaints are being able to understand how our customers are interacting with us. >> And data is an interesting topic, because it's to broad. And I think so many people now across generations understand data privacy, to some degree you can think of you know, the baby-boomers that were affected by the Facebook information and things being shared. From a financial perspective, tell as a little bit about the discipline of data science, not just from the technology background and understanding that your team needs to have, but also other skills such as empathy, communication, negotiation, how are all of those contribute to what your team is delivering? >> Yeah, I would tell you we are in the business of trust. And three years ago, after sales practice came into Wells Fargo, was a very interesting time for our company. We kind of lost our way. And the opportunity with data science is an opportunity to reestablish trust with our customers. And so, you've seen a lot of the rebranding that Wells Fargo is doing in about... We were invented in 1852, but we're reinventing ourselves now. And we have to understand our customers, we have to know our responsibilities to be that trusted advisor to really care for our customers in every interaction. And so, I would think empathy, absolutely. Trust is all about every interaction consistent every time. And so, you think about even just a personal relationship and how you establish trust. It's very hard to reestablish trust, and so for us right now, the commitment to data science is about that reestablishing trust and to really thinking about every interaction with every customer and ensuring we're getting it right. >> You've been there a long time as I've mentioned, I'd love to understand your, some of the things that you've seen along the way as technology changes in terms of more females becoming interested, as we know that there was you know, from where we were in the 80s, where it has been a downwards spiral but you were recently named one of the 50 most powerful women in technology. What are some of the things as you think of how technology in Wells Fargo is re-imagining data and trust? What are the things that you've seen in terms of the evolution of females in technology and in leadership roles? >> Sure, absolutely. Thank you so much. You know think about industry recognition, and I think about how important it is to recognize women's value in the industry. So the recognition women in technology and most powerful women for me, it's an opportunity to really demonstrate that we should be very confident in the value that we bring as leaders, and that confidence as a woman is hard to come by. I think of my own personal career and the way that doors were opened for me along the way often we are our own worst enemies we second guess ourselves, we second guess our value, and we have to really work for that seat at the table. There's certainly been, I wouldn't have come back to Wells if I didn't believed that I had the right sponsors and the right mentors that were not only willing to help me kind of see the doors to walk through but to walk through those doors. And so my coming back to Wells was really about a opportunity as a leader in technology. I just had two start ups here in Silicon Valley, and so I was invited to come back and it was really the leaders and the leadership that brought me back to Wells. I felt I could make a real impact and I think that there's, when I think about the couple of jobs I've had since my second return to Wells Fargo it's really been about impact and recognizing my voice and starting to step into that accountability. When I think about what we can do as women leaders in technology and in data science a lot of it is owning that accountability to leadership and to really kind of paving the way for leaders behind us. There comes a part in a career certainly mine, where you no longer thinking about the next job for yourself and you know, I'm really fortunate that I've been able to get to a CTO level a tech division executive level, I have, you know the recognition on most powerful woman. But I don't do that alone. I do that with a team of women and men who've helped to really create value in the space that we're in. And we're in a consumer banking space and financial services and so there's certainly a lot of places to innovate, there's a lot of places to think about how technology can help to serve a Wells Fargo customer and if you think about when you need your bank you need your bank throughout your entire life. And whether you are thinking about a home purchase, an auto purchase, college for your children, retirement, there's so many big markers in life and that's where I get excited about, not only the leadership role that I have now, but I have the opportunity to bring a team with me to contribute real value. And so that's for me what really brought me back was an opportunity to have that impact to think about data science and technology in a way that there's true visible value being added to the market place to the industry. >> So it's almost like can we have pay it forward added to, how are you using that to expand your team with the right skills and the right people regardless of gender, regardless of any of that, to continue this big movement, this re-imagination that Wells Fargo is a business in undergoing. >> Yeah, well I would tell you WiDS is one way. WiDS is certainly a tremendous network opportunity if you think about the breadth and the reach across countries, across landscapes, across geographies, this is just one example of how I think about that. There's real power in relationships. There's real power in ability to establish not only a strong industry network a strong personal brand, but also a personal network. Even in the last couple of hours, WiDS started today, so inspired by the keynote speaker, so inspired about how they're turning data science and really thinking about different problems, different ways that we can improve, not only our lives, but the lives of future generations to come. I think part of how I think about it is finding that inspiration, because we have to inspire future generations of leaders, of women, and of men to really tackle the problem and have the right skills and confidence to be able to jump into that space. >> I agree with you. I think one of my favorite things in this, theCUBE has been covering WiDS since the beginning for four years and I always love coming here because you walk in and you immediately feel inspired. But you also feel that sense of collaboration, you talked about how important that is, not just for people that are in academia but in industry as well, you know I can't do what I do, you can't be a successful CTO at anywhere, at Wells Fargo let alone, any organization, without that collaborative spirit and I think I always feel that very strongly every time I walked in the door at a WiDS event, that people, they really do live up to their mission statement which is to inspire and educate women in data science and people in data science in general. >> Yeah, and I would offer that there's a lot of magic in the empty space, so the space in between and the way I would describe that is that so you come in to WidS data conference and certainly I come from a financial services background that the primary, you know, my primary professional background has been in financial services and technology, but the problems that our future generations will face can't be solved with just one lens. You can't solve problems with just a financial services expertise or just a technical expertise. You need to really look for how do you... It's the AND, and sometimes the space in between and bringing art and science. It's an ability to bring to think across industry and to apply solutions and innovation that have been brought forward through other industries, through other companies, through other academia and thinking about how that could apply in solving the problems that we're faced with in the financial services space. And so, to me coming to WiDS conference or spending time with the women that we'll meet in the room or the men that we'll meet in the room it's really about listening to their stories, listening to their passions, thinking about the problems they're solving and stepping back and identifying well, gosh if I really turned some of the problems that we're faced with upside down and thought about it with that perspective of with that lens, and maybe invited some people to your point, the collaboration to help solve problems with us, we might come up with a better answer, it's the space that's in between that might have called the difference. >> I like that! The space in between, there's so much applicability, I mean there's 2.5 quintillion data generated everyday across every industry. Whether it's you know, personal banking information or what we eat or where we travel, we do everything through mobile these days, and companies like Wells Fargo have such potential to be able to utilize that data to you know, create solutions that helps so many people. But you're right it's what can, how can financial services and the data that you deal with and to help customers and that sense, with the opportunity to influence all these other disciplines. I think that's one of the things that excites me about data science, it's how broad and symbiotic this discipline really is. >> Totally agree with you. And I have a new leader, Jason Strle, who just came in to Wells Fargo, just over a year ago, and he talks about a vision where we are 100 percent transparent in our data with our customers, so think about that value proposition in financial services, where there's a 100 percent data symmetry. What we know, you know. What you know, we know, when you want us to know it. And that can be so powerful, and that's really how we're thinking about the transformation around technology, the investment that we're going to make in data science, an AI and machine learning, because that 100 percent data symmetry comes back to trust. If we're a 100 percent transparent with everyone of our customers about what we know think about how that establishes trust. I mean that is a rock solid foundation for trust in the future, and I think that's really something that can be very powerful if we capitalize it, but we can't to it alone, we're going to need partners. We're going to need partners like so many of the companies in the academics that are in this room today. And we'll have to reach even broader because some of the solutions won't be found if we just look internal to Wells Fargo. >> Exactly. That diversity in so many ways is so impactful. Kristina, thank you so much for stopping by theCUBE and sharing with us some of the things that you're doing, how you've ascended to the CTO at Wells Fargo and how Wells Fargo is sponsoring in contributing to this WiDS movement, we appreciate your time. >> It's a real honor, thank you so much Lisa. >> Thank you! >> Pleasure! >> We want to thank you for watching the CUBE live at Stanford University, from the forth annual Women in Data Science Conference. I'm Lisa Martin. Stick around, my next guest will be here momentarily. (upbeat music)

>> Live from Stanford University. It's the Cube covering Global Women and Data Science Conference brought to you by Silicon Angle media. >> Welcome back to the Cubes Coverage of the fourth annual Women in Data Science Conference. This global winds event is the fourth annual our fourth year here, covering it for the Cuban Lisa Martin, joined by Gianluca Pecorino, the director on the Stanford Institute for Computational and Mathematical Engineering. Gianluca, it's a pleasure to have you on the program. Thank you. So the Institute for Computational and Mathematical Engineering. I see M e. Tell us a little bit about that and its involvement in wins. >> Yes, so the status has. Bean was funded fifteen years ago at Stanford as a really hard before computation of mathematics at Stanford. The intention was to connect computations and in general, the disciplines around campus towards using computing for evolution, for starting new ideas for pursuing new endeavors. And I think it's being extremely successful over the years in creating a number of different opportunities. Now weeds started four years ago. As you mentioned, it's part of an idea that the prior director advising me, Margo Garretson, had with few others, and I think the position of I see me at the center of campus really helped bring these events sort of across different fields and this different disciplines. And I think, has Bean extremely successful in expanding and creating a new, a completely new movement, a completely new way of off off engaging with with a large, very large community. And I think I seem, has Bean very happy to play this role? And I'm continuing to be excited about the opportunities >> you mentioned expansion and movement to things that jump out. Expansion way mentioned fourth annual on Lee started This Is three and a half years ago knew that twenty fifteen and we were had the pleasure of having Margo Garrett send one of the co founders of Woods on the Cube last year at wigs. And I loved how she actually said. Very cheeky winds really started sort of as a revenge conference for her and the co founders, looking at all of the technology, events and industry events and single a lack of diversity. But in terms of expansion, this there are one hundred fifty plus regional winds events this year in fifty plus countries. They're expecting over one hundred thousand people to engage this expansion. In this movement that you mentioned, it's palpable. Tell us about your Where's the impetus for you to be involved in the woods movement. >> Well, I think my interest in in data science and which particular is because of the role that I seem years in the education at Stanford. We obviously have a very important opportunity toe renew and remodel our curriculum and provide new opportunities for for education off the new generations and clearly starting with with the opportunity off being such an audience and reaching so many different discipline. It's a very different fields. Helps us understand exactly how to put that curriculum together. And so my focus of my interest has been mostly on making sure that I see me alliance with these new directions. And when we establish the institute, computational mathematics didn't really not have data is a very, very critical component, but we are adjusting to that clearly is becoming more and more important. We want to make sure we are ready for it, and we make sure that the students through our curriculum are ready for the world out there. >> So let's talk about this. The students and the curriculum. You've been a professor at Stanford for a very long time before we get into the specifics of today's curriculum. Tell me a little bit about how you have seen that evolve over time as we know that. You know, we're sort of in terms of where the involvement and women and technology and stump field words in the eighties and how that's dropped off. Tell me a little bit about the evolution in that curriculum that you've seen and where the ice Amy is today with that adaptation. >> Yes, certainly. The evolution has bean very quick. In the last few years, we have seen, um in a number of opportunity emerging because of the technology that is out there. The fact that certainly for data science, both the software and the artwork and the technology, the methodology, the algorithms are all in the open so that there is no real barrier into sort of getting started. And I think that helps everybody sort of getting excited about the idea and the opportunity very, very quickly. So we don't really need to goto an extensive curriculum to be ableto ready, solve problems and have an impact. And I think that, perhaps is one one other reason why we are sort of in a level playing field right. Everything is is available to everybody with relatively minor investment at the beginning. And so I think that certainly a difference with respect what the disciplines, where instead, it was much more laborious process to go through before you can actually start having an impact. Suffering every o opportunity, toe change world to toe come, you know, sort of your your vision's sort of impact in the world. So I think that's That's definitely something that the data science and the recent development into the science have created. And so I think, in terms of our role, sort of continuing role in this is tow Pet Shop six. You know, expand the view ofthe data. Science is not just the algorithm, the technology, the statistical learning that you need to accomplish. A student is a new comet into the field, but also is other other elements. And I would say certainly the challenges that we are that are opposed to data. Since they are challenges that have to do with the attics with privacy on DSO, these are clear, clearly difficult to handle because they require knowledge across disciplines the typical air not related to stem in In a traditional sense. But then, on the other hand, I think is the opportunity to be really creative. Data is not analyzing on its own right. He needs the input are sort of help in creating a story. And I think that's that's another element that he makes data science a little bit different. Another stem disciplines intend to be much more ascetic, much more sort of a cold if you like. I think >> that's where the things to you that I find really interesting is if you look at all the statistical and computational skills as you mentioned, that a good data scientist needs to have as we look at some of the challenges with the amount of data being created. So you mentioned privacy, ethics, cybersecurity issues. The create creative element is key for the analysis. Other things, too. That interest me, and I'd love to get your thoughts on how you see this being developed on the curriculum. Helping is is empathy, collaboration, communication skills. Where is that in the curriculum and how important you are? Those other skills to the hard skills >> that that's That's a great question. And I think where is in the curriculum? I think we're lagging behind that. This is one of the opportunities that we have to actually connect to our other places on campus, where instead the education is built much more closely around some of these topics is that you mentioned. So I think you know, again, I the real advantage in the real opportunity we have is that the data science in general reaches out to all these different disciplines in a very, very new way if you like. I think it's it's probably one of the reasons why so attractive toe younger generation is the fact that it's not just the art skills. You do need to have a lot off understanding of the technology, the foundational statistics and mathematics and so on. But it's much more than that. Communication is very important. Teamwork is extremely important. Transparency is very important. There are there are really all these elements that do not really make that they really didn't have a place in some of the more traditional dissidents. And I think that that's definitely a great way off. Sort of refreshing are way off, even considering education and curriculum. >> When you talk to some like the next to the younger generations. Is that one of the things that they find are they pleasantly surprised, knowing that I need to actually be pretty well rounded to me? A successful data scientists? It's how I analyzed the data. How I tell a story, is that something that you still find that excites but surprises this younger generation of well, that >> certainly is a component, very important component of the excitement of the sea. Are there the fact that you can really build the story, tell a story, communicated story and oven, in fact, immediately, quickly, I think is a is something that the newer generation really see it assess a great opportunity and, you know, and it tried to me. So I mean, it has been very difficult for more traditional disciplines to have the same level of impact, partly because the communities tend to be very close, very limited with with a lot of scrutiny. I think what we have in India, the scientists, that is really a lot off you no can do attitude the lot off, Really. You know, creative force that is >> behind, you know, >> basically this movement, but in general data science, I think that >> you write. The impacts is so potent and we've seen it and we're seeing it in every industry across the globe. But it's such an exciting time with Gianluca. We thank you so much for sharing some of your time on the program this morning and look forward to hearing more great things that the ice Amy is helping with prospective women in Stem over the next year. >> Absolutely. Thank you very much. >> My pleasure. We want to thank you. You're watching the Cube live from the fourth annual Women and Data Science Conference here at Stanford University. I'm Lisa Martin. Stick around. My next guest will join me in just a moment.

>> Live, from Bellevue, Washington. It's theCUBE. Covering, Smartsheet Engage 18. Brought to you by, Smartsheet. >> Welcome back to theCUBE's continuing coverage of Smartsheet Engage 2018. I am Lisa Martin with Jeff Frick. We are in Bellevue, Washington or, as I like to call it, not Vegas. Excited to welcome to theCUBE, Robin Sherwood, the Senior Director of Product Management at Smartsheet. Hey Robin. >> Hi, how's it goin? >> Great. This is, been a very buzzy morning, for Jeff and I here on this side. Lot's of people, this event has doubled in size. This is your second annual, so... >> Big growth in just a year. There's a, I think, Mark Mader, your CEO, shared some sats this morning. There are 1100 companies represented here customers. >> Correct. >> From twenty countries, there are more than fifty customer speakers, which is, I think there's no more validating voice, than the voice of a customer using the technology. When I was doing some research on Smartsheet, was looking at, you guys are partners with, some of your competitors. One of things I wanted to understand is, where do you have integrations with technology, versus where do you have connectors? What's the difference between those two, and how does is work >> Yeah. >> In a Smartsheet world. >> You know, I think, the integrations really are, where you're going to, you're really interacting with that other product directly, right? So, maybe it's, I want my outbound messages and notifications to go into a Slack channel, right? That's an integration. Or, I want to be able to connect to Google Drive, or 03 Secure, One Drive document, in those native stores. So, that's where we really see an integration. It's something that the end user themselves, is really interacting with. Where you see connectors is more around where I've got big systems of record in my organization, and I need data to flow between those tools. >> Like a Sales Force. >> Like a Sales Force, or a JEAR, or something like that. Microsoft Dynamics, right? I've got data there, when something happens in that system, I need it to flow magically into Smartsheet, or when something happens in Smartsheet, I need it to flow back into those systems. Cause, those are the systems of record, that my company cares about. >> So, a connection is a much bigger step in integration? >> They're just different. >> Connectors are really about the flow of data back and forth between systems of record and integrations are more about user content and user direct interactions. So, things like Drive and Box and Dropbox, and Slack and Teams and, stuff like that. Or, the web content, which we just announced. We want to be able to embed a Youtube video in a dashboard. That's not integrations, it's not, there's no data flowing back and forth, it's just a link, right? >> Got it, thank you. >> Yeah. >> So, lot of customer's we have, I think fifty customer's presenting, which is amazing out of 2,000 people in the whole conference. I don't know what the percentage is, but it's, (laughs), >> Yeah. >> Awfully large. So, just some of the all chatter here. You've been here for a couple of day now, you guys had some early training yesterday. What is some of the things you're picking up? You obviously love to hear back from the customer's. Kind of, what's the buzz on some of the new offerings, and what are you hearing, amongst the constituent here? >> I mean, it's always, you know, this is only our second year. But the energy from them is always amazing. And, you know, people were, I was talking to someone earlier and they were just blown away. By just the big list of things that we shipped, this week. And, as I was reflecting, like, I don't remember doing all that much. But then, when you see it all on one big slide, with everything listed out, it's incredible. So, it's hard to say if anybody latched on to one thing or another. Obviously, there was lots of applause during the product... >> Yes. >> Session, and we're really excited to have shipped, the multi-assign to feature, which has been our number one customer request for a while. But, it's not a, game-changing feature. Whereas, I think some of the Automation Rules ,and Updates there, and Workflow Builder, are really. People are going to go back and it's going to to change the way that they work. And, so people are really excited about that. But, really excited about Dynamic View. And being able to really, taylor the information that is shared across their organization. >> The word collaboration, like symbionic or bi-directional collaboration, popped into my mind. When Gene Pharaoh, your SVP of Product, who we had on earlier, was talking about some of the features and it was a really interesting dynamic with the audience. In that, number of times, you mentioned, the audience broke into applause. And, it probably feels pretty good. Like, yes, we're listening to you, we're doing this. Enabling, them to have technology that allows them to collaborate with and amongst teams and functions within an organization. But, you're also taking their feedback, directly and collaborating with customer's, to further innovate your product. With the spirit of collaboration, we had, Margo Visitacion on from Forrester. And she was talking about the collaborative work management CW as an emerging market. With respect to collaboration, you guys can enable sharing. I can be a licensed user, and share it with you who's not. How is that type of collaboration a differentiator for Smartsheet? >> Well, you know, I think there's a lot of tools where they're collaborative where you can comment on them. Google Doc, and that's great. But, I think where Smaresheet really excels, is really in this free collaborator model. That's not bounded by your particular organization or your team. And it really allows you to create, to spread, and create connections across customer's and vendors and other orgs within your team. And, this is where you're starting to see this these sort of step function changes in these organizations. Where, you know, you see this Office Depot example. And, he talks about, you know, taking a workflow in their organization they, going from, you know, four to six weeks, down to twenty-four hours. And, enabling people who are putting in budget request, to take action on that request, the next day. And, those are the kinds of things, that are going to fundamentally change those businesses. And so, that's where I think the collaboration piece is really powerful. You can't get that kind of compression in time. Unless, you can really span those traditional business hours. >> So Robin, one of the great things that happens always is, with tech companies is the application versus the platform exchange, right? Everybody wants to have a platform, it's really important. You get an ecosystem, lot of stuff going on, but nobody's got a line item in their budget for 2019 to buy a new platform, right? >> It's always, >> Correct >> Application centric, right. I got a problem, I've got to fix it. At the same time, you guys, you do have a platform. Meaning, you can go across a lot of different applications. So, when you're trying to balance out your priorities with the platform. Priority, in terms of more of, kind of a general purpose underly, versus and app priority, like you said, multi, how do you call... >> Multi-assignment. Yeah. >> Multi-assignment, you assign two people to the (laughs). To the no correct product management protocol, but everybody wants it, cause it's the real world. How do you kind of prioritize that? How do yo kind of look at the world when you're deciding, what are you going to roll out next, what are you going to roll out next, ware are you going to roll out next? >> It starts and ends with having conversations with real people. We've taken lots of data and we have enhancement request and usage data on how people use the product. Multi-assigning, actually, was less than 3% of all answered request in the last couple of years. But, it's our number one request. And so, it sort of. >> Oh, Wait, wait wait. So it was less than 3%. >> Of all enhancement request. >> But it was number one? >> But it's our number one. >> So you've got a giant laundry list. >> Giant laundry list of things, right. So, we can't just look at some metric and go, these are the next features we should build because we have this really strong signal. We actually, have a very, very weak signal when we look at it from a quantitative standpoint. So what we have to do is we really have to dig into these customer use cases. We have to meet with them. All of our project teams have dedicated researchers, and dedicated user experience. People that are going out, we're actually talking to people. We're testing stuff with them and we're trying to understand what commonalities exist between multiple cases across all of these different use cases. Because, there're so many different ways people use the product. There not enough people asking for one thing. >> Right. >> They're all asking for slightly different things. So, we really have to dig in and have a real, qualitative conversation with them. To understand, and bring that back and say okay, these things are related. We can build something that solves, all of these problems in a compelling way. >> Well, it's definitely more than 3% of the people cheer. When, when that. (laughs) >> Yes. >> When the feature was announced, that's for sure. So the other, kind of (mumbles), that you've got to wrestle with is, kind of a low code, no code, we want to be for everybody, yet at the same time, you want a sophisticated application. You want integrations and connectors to all these other applications. So, again, that's kind of a delicate, balancing act as well. Cause, you want to let everyone have access to be able to manipulate the tool, work with the tool, set up the tool, but at the same time, you got to keep it, pretty sophisticated to connect to all these other things. How do you kind of balance those. >> Well we... >> Priorities. >> We just try to hide as much of that as possible. You know, Smartsheets always been this tool, where it's like, it sort of looks like a spreadsheet, and it sort of looks like project management. But it's got this underlying flexibility built into it. We don't force you to, you know, if you've got a date column, we don't force you to put a date in there. If you don't know the answer, you can type in TBD. Whereas, a lot of purpose built applications, their like, this is a date, you have to enter it in the proper date format, or it doesn't work. We've always had this, sort of, flexibility and complexity trade off. The trade off is, if you give us real data, if you give us something that looks like a date, we'll draw a Gantt Chart for you. We don't need much more, it doesn't need to be more (mumbles) than that. We just won't draw the bar if you type in TBD. And so, we've always sort of danced this line, with making the tool super flexible and assume the users know what they're doing. When they're interacting withhe tool we assume they an intention and they're trinna do something. And, we shouldn't force them down a particular path. And that, sort of, plays out in all these features. The other thing that we do, is like I mentioned earlier, we do a lot of user research and we get in front of a lot of customers. And we put stuff out there, well in advance in releasing it. In a situation like this, we announced a bunch a capabilities around workflow and multi-step approvals and multi-step workflows. And, I think that's a complex feature set. That's gone through more iterations of design and review and scrapping it and back to the drawing board, than any feature I've seen at this company. But, it's probably one of the more complex features we've ever build, as well. And so that's what we would expect, right? We're not going to get this right, by just having a bunch of designers and engineers sit in a room and go, oh, we know that perfect solution to workflow management. >> Right. >> Most of our customer's don't even necessarily, use the term workflow. >> And if you look in the app, it doesn't even say. It says words and actions. You know? And little things with words matter. We have technical writers that are very specific on what we label something. It's not an if statement. It's when this happens, do this. And there's a lot of nuance and subtlety into all of this. To try and drive the complexity out of it as much as possible. >> Right. >> You can't avoid it, but you know. >> So, in hiding it, the last thing which your going to do, going forward is machine learning and artificial intelligence. Which we hear about all the time, but really the great opportunity in the field, is for you to leverage that under the covers. To hide. >> Absolutely. >> The nasty complexity to help suggest the right answer. To help suggest the right path. So, that's got to be a huge part of your roadmap. Integrating those types of capabilities, underneath the covers. >> Yeah and, there's been a lot of, we've have had tons of discussions and obviously we bought the Converse Chatbot Company back in January. And, that's been a huge sort of arrow in our quiver, so to speak, right, in that regard. We feel that we have a lot of really good information. But, at the same time, there's a lot of talk about machine learning and AI. And, the reality is, that relies on huge data sets. And it relies on a lot of analysis. And that data is not something that we can just look at, right? We take our customer's data, security data privacy very seriously. And we don't have access to that kind of information. So we need to look at this, the machine learning and the AI capabilities from a very different lens, then say a consumer product. That's sort of, you're getting to use it for free, they sort of do whatever they want with your data. And you don't really have a lot of recourse, other than leave the product. We don't start from that, we start from, your data is yours, you own it, we can't look at it. But we want to enable you, to turn these types of features on. So, we need to look at more of like an off-end model, where a customer can say oh, if I'm a big enterprise user at Smartsheet, I can turn certain capabilities on for my users, knowing that that information is going to stay in our, is going to comply with our data governance, and our data privacy rules. That our IT team puts forward. >> So the spirit of talking about abstraction, abstracting complexity, Hiding it, (mumbles). I'm curious, when you walk into a customer. Cause here we are in Bellevue, we're not in Vegas, But, we're neighors with AWS, with Microsoft, Microsoft announced Teams, about eighteen months, or so, ago. You partner with both, you compete, but you, also, you're competing with Teams. When you walk into a customer and an enterprise, likely has a mixture of, tons of different software appications, right. But they probably have, 360, Office 365, Para Bi, Excel... Why would a customer, who has such a familiarity with, say a Microsoft, work with Smartsheet versus, well we'll just extend our Microsoft expertese and bring in something like Teams? >> Yeah. >> I'm just curious, what...You've seen in that? >> Well, you know, I think it's that Smartsheet's always been good at sort of, orchestrating the actual work that's being done. And, there's a lot of tools out there where, you're having conversations and tools out there where you're creating content, and there's not a lot of tools out there, that are sort of bringing the conversation and the content together. In an actionable and accountable way, right? And that's the sort of, Gene will, you'll sometimes here hims say, use this term, shared fabric. The Smartsheet, really provides this shared fabric, that ties a bunch of these tool together. And we really, we want to partner with all these people, because every organization is different. Every organization has a different set of tools that they've already embraced. They have a different set of goals around how many tools they're going to embrace. You talk to some customer, they're like, I love Smartsheet, it's going to allow me to get rid of ten apps. And, you talk to another customer that's equal size or equal complexity two minutes later, then they'll be like, I love Smartsheet, it allows me to work with all the tools that I've already got. Very different, and they just have to different coperate goals and objectives there. And so, I think that the reason people like Smartsheet, is it doesn't, it's back to that kind of, hey, you don't have to put a date in a date cell. It's flexible. It's going to work with you and not force you to adopt the Smartsheet way about things. It's going to say look, oh, if you want to use, if you want to us Teams for your communications vehicle, and One Drive for all of your document storage, great. You want to embed a PowerPoint document in a dashboard in Smartsheet, great. We want that to be the case. We do that internally, right, we use all those. If you look at us internally, we're just like every other mordern company. We have a dozen tools or two dozen tools that we're using. And it's different from team to team and department to department. So, it's all about just embracing the reality, that as modern business and modern application, the ecosystem of applications that we all deal with on a day-to-day basis. >> So that flexibility is key. So we said about 1100 companies represented here, at this event. 2,000 people or so, fifty plus customer speakers. Is there one customer example that comes to mind, whether they're speaking here or not, that really is a great demonstrator of, we have a plethora of applications in our environment. We want to work with Smartsheet because it enables us to integrate and use these tools so much better? I didn't mean to put you on the spot. >> Yeah, no. I'm trinna think of a good. I don't know that I have a good standout example. I think that we hear little tidbits of that from everyone. And it's not, it's a very common theme. So, I don't know that. It's sort of back to the 3% thing, right? Nobody really stands out because everyone is doing that. Everyone is, I hear things, I'm going to replace this tool because you did this. Or, I'm going to now pull, integrate with this tool because, you've added this. So, you sort of take some and give some, on the same sentence almost. >> Yeah. You can do both. >> Yeah. >> Well Robin, thanks so much for stopping by. We appreciate your time. We're excited to be here. This is our first Smartsheet event. And we have some customers coming up, so looking forward to hearing some more these cases in action. >> Great, thanks a lot. >> Thank you. >> Thanks. >> We want to thank you for watching theCUBE, I'm Lisa Martin with Jeff Frick. You're watching us from Smartsheet Engage, in Bellevue, Washington. Stick around, Jeff and I will be right back, with our next guest. (tech music) (tech music) (tech music)