>> So to come together and see thousands of people talking and walking into a room of diverse age and diverse experience. It feels good and it makes me hopeful about the future because people is a great challenge to date a science. City of Data scientists to have multiple models that are completely divorce on DH. We have to be very responsible when we start to create. Creators are by default, have to be responsible for the way to get the information out there in a visual way because people will hear the word data and they like I start, Yeah, right zero they connected with a cartoon or drawing it humanizes it for a little bit, and if I could do that? So collaboration is extremely pivotal. It's extremely important for us to put ourselves in other shoes and see look at the problem on looking problem from different places. But their collaboration of key to Be ableto successfully defends Dominus. Try to do different things. Only then you'll find out very a passion, lies. And just don't be scared off throwing yourself in that situation, which you have never done.

>> Live from Stanford University, it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE I'm Lisa Martin. We've been live all day at the fourth annual Women in Data Science Conference. I'm with John Furrier, John, this is not just WiDS fourth annual, it's theCUBE's fourth time covering this event. There were, as Margot Gerritsen, Co-Founder stopped by this afternoon and was chatting with me saying, there's over 20,000 people they expect today just to watch the WiDS livestream from Stanford. Another 100,000 engaging in over 150 regional WiDS events, and 50 countries, CUBE's been there since the beginning tell us a little bit about that. >> Well what's exciting about this event is that we've been there from the beginning, present at creation with these folks. Great community, Judy Logan, Karen Matthys, Margot. They're all been great, but the vision from day one has been put together smart people, okay, on a stage, in a room, and bring it, syndicate it out to anyone who's available, meet ups and groups around the world. And if you bet on good content and quality people the community with self-form. And with the Stanford brand behind it, it really was a formula for success from day one. And this is the new model, this is the new reality, where, if you have high quality people in context, the global opportunity around the content and community work well together, and I think they cracked the code. Something that we feel similar at theCUBE is high quality conversations, builds community so content drives community and keep that fly wheel going this is what Women in Data Science have figured out. And I'm sure they have the data behind it, they have the women who can analyze the data. But more importantly is a great community and it's just it's steamrolling forward ahead, it's just great to see. 50 countries, 125 cities, 150 events. And it's just getting started so, we're proud to be part of it, and be part of the creation but continue to broadcast and you know you're doing a great job, and I wish I was interviewing, some of the ladies myself but, >> I know you do >> I get jealous. >> you're always in the background, yes I know you do. You know you talk about fly wheel and Margot Gerritsen we had her on the WiDS broadcast last year, and she said, you know, it's such a short period of time its been three and a half years. That they have generated this incredible momentum and groundswell that every time, when you walk in the door, of the Stanford Arrillaga Alumni Center it's one of my favorite events as you know, you feel this support and this positivity and this movement as soon as you step foot in the door. But Margot said this actually really was an idea that she and her Co-Founders had a few years ago. As almost sort of an anti, a revenge conference. Because they go to so many events, as do we John, where there are so many male, non-female, keynote speakers. And you and theCUBE have long been supporters of women in technology, and the time is now, the momentum is self-generating, this fly wheel is going as you mentioned. >> Well I think one of the things that they did really well was they, not only the revenge on the concept of having women at the event, not being some sort of, you know part of an event, look we have brought women in tech on stage, you know this is all power women right? It's not built for the trend of having women conference there's actual horsepower here, and the payload of the content agenda is second to none. If you look at what they're talking about, it's hardcore computer science, its data analytics, it's all the top concepts that the pros are talking about and it just happens to be all women. Now, you combine that with what they did around openness they created a real open environment around opening up the content and not making it restrictive. So in a way that's, you know, counter intuitive to most events and finally, they created a video model where they livestream it, theCUBE is here, they open up the video format to everybody and they have great people. And I think the counter intuitive ones become the standard because not everyone is doing it. So that's how success is, it's usually the ones you don't see coming that are doing it and they think they did it. >> I agree, you know this is a technical conference and you talked about there's a lot of hardcore data science and technology being discussed today. Some of the interesting things, John, that I really heard thematically across all the guests that I was able to interview today is, is the importance, maybe equal weight, maybe more so some of the other skills, that, besides the hardcore data analysis, statistical analysis, computational engineering and mathematics. But it's skills such as communication, collaboration collaboration was key throughout the day, every person in academia and the industry that we talked to. Empathy, the need to have empathy as you're analyzing data with these diverse perspectives. And one of the things that kind of struck me as interesting, is that some of the training in those other skills, negotiation et cetera, is not really infused yet in a lot of the PhD Programs. When communication is one of the key things that makes WiDS so effective is the communication medium, but also the consistency. >> I think one of the things I'm seeing out of this trend is the humanization of data and if you look at I don't know maybe its because its a women's conference and they have more empathy than men as my wife always says to me. But in seriousness, the big trend right now in machine learning is, is it math or is it cognition? And so if you look at the debate that machine learning concepts, you have two schools of thought. You have the Berkeley School of thought where it's all math all math, and then you have, you know kind of another school of thought where learning machines and unsupervised machine learning kicks in. So, machines have to learn, so, in order to have a humanization side is important and people who use data the best will apply human skills to it. So it's not just machines that are driving it, it's the role of the humans and the machines. This is something we have been talking a lot in theCUBE about and, it's a whole new cutting edge area of science and social science and look at it, fake news and all these things in the mainstream press as you see it playing out everyday, without that contextual analysis and humanization the behavioral data gets lost sometimes. So, again this is all data, data science concepts but without a human application, it kind of falls down. >> And we talked about that today and one of the interesting elements of conversation was, you know with respect to data ethics, there's 2.5 trillion data sets generated everyday, everything that we do as people is traceable there's a lot of potential there. But one of the things that we talked about today was this idea of, almost like a Hippocratic Oath that MDs take, for data scientists to have that accountability, because the human component there is almost one that can't really be controlled yet. And it's gaining traction this idea of this oath for data science. >> Yeah and what's interesting about this conference is that they're doing two things at the same time. If you look at the data oath, if you will, sharing is a big part, if you look at cyber security, we are going to be at the RSA conference this week. You know, people who share data get the best insights because data, contextual data, is relevant. So, if you have data and I'm looking at data but your data could help me figure out my data, data blending together works well. So that's an important concept of data sharing and there's an oath involved, trust, obviously, privacy and monitoring and being a steward of the data. The second thing that's going on at this event is because it's a global event broadcast out of Stanford, they're activating over 50 countries, over 125 cities, they're creating a localization dynamic inside other cities so, they're sharing their data from this event which is the experts on stage, localizing it in these markets, which feeds into the community. So, the concept of sharing is really important to this conference and I think that's one of the highlights I see coming out of this is just that, well, the people are amazing but this concept of data sharing it's one of those big things. >> And something to that they're continuing to do is not just leverage the power of the WiDS brand that they're creating in this one time of year in the March of the year where they are generating so much interest. But Margot talked about this last year, and the idea of developing content to have this sustained inspiration and education and support. They just launched a podcast a few months ago, which is available on iTunes and GooglePlay. And also they had their second annual datathon this year which was looking at palm oil production, plantations rather, because of the huge biodiversity and social impact that these predictive analytics can have, it's such an interesting, diverse, set of complex challenges that they tackle and that they bring more awareness to everyday. >> And Padmasree Warrior talked about her keynote around, former Cisco CTO, and she just ran, car, she's working on a new start up. She was talking about the future of how the trends are, the old internet days, as the population of internet users grew it changed the architecture. Now mobile phones, that's changing the architecture. Now you have a global AI market, that's going to change the architecture of the solutions, and she mentioned at the end, an interesting tidbit, she mentioned Blockchain. And so I think that's something that's going to be kind of interesting in this world is, because there's, you know about data and data science, you have Blockchain it's the data store potentially out there. So, interesting to see as you start getting to these supply chains, managing these supply chains of decentralization, how that's going to impact the WiDS community, I'm curious to see how the team figures that out. >> Well I look forward to being here at the fifth annual next year, and watching and following the momentum that WiDS continues to generate throughout the rest of 2019. For John Furrier, I'm Lisa Martin, thanks so much for watching theCUBE's coverage, of the fourth annual Women in Data Science Conference Bye for now. (upbeat electronic music)

>> Live from Stanford University it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's live coverage of Women in Data Science fourth annual global conference. I'm Lisa Martin here at the Arrillaga Alumni Center at Stanford joined by, a WiDS speaker and Standford alum Madeleine Udell. You are now an assistant professor at Cornell University. Madeleine welcome to theCUBE. >> Thank you it's great to be here. >> So this is your first WiDS. >> This is my first WiDS. >> But you were at Stanford a few years ago when the WiDS movement began. So tell us a little bit about what you do at Cornell. The research that you do, the classes that you teach, and the people men and women that you work with. >> Sure so at Cornell I'm studying optimization and machine learning. I'm really interested in understanding low dimensional structure in large messy data sets. So we can figure out ways of looking at the data set that make them seem cleaner, and smaller, and easier to work with. I teach a bunch of classes related to these topics. PhD classes on optimization and on optimization for machine learning. But one that I'm really excited about is an undergrad class that I teach called, Learning With Big Messy Data. That introduces undergraduates to what messy data sets look like which they often don't see in their undergraduate curriculum. And ways to wrangle them into the kinds of forms that they could use with other tools that they have learned about as undergraduates. >> You say messy, big messy data. >> Yes. >> With a big smile on your face. >> Yes. >> So this is something that might be introduced to these students as they enter their PhD program. Define messy data and some applications of it. >> Often times people only learn about big messy data when they go to industry and that's actually how I understood what these kinds of data sets looked like. I took a break from my PhD while my advisor was on sabbatical and I scampered off to the Obama 2012 campaign, and on the campaign they had these horrible data sets. They had you know hundreds of millions or rows. One for every voter in the United States, and maybe tens of thousands of columns about things that we knew about those voters. And they were weird kinds of things, right? They were things like gender, which in this data set was boolean, State, which took one of fifty values, Approximate education level, Approximate income weather or not they had voted in each of the last elections and I looked at this and I was like I don't know what to do, right? these are not numbers, right? They are boolean, they're categorical they're ordinals and a bunch of the data was missing so there were many people for which we didn't know their level of education or we didn't know their approximation of income or we didn't know weather or not they had voted in the last elections. So with this kind of horrible data set how do you do like basic things, how do you cluster, how do you even visualize this kind of data set so I came back to my PhD thinking, I want to figure out how this works I want to figure out the right way of approaching this data set Cause a lot of people would just sort of hack it and I wanted to understand what's really going on here what's the right model to think about this stuff. >> So that really was quite influential in the rest of your PhD and what your doing now, cause you found this interesting but also tangible in a way, right? especially working with a political campaign >> That's right so, I mean I'm both interested in the application and I'm interested in the math so I like to be able to come back to Stanford at the time we're now at Cornell and really think about what the mathematical structure is of these data sets what are good models for what the underlying latent spaces look like, but then I also like to take it back to people in industry, take it back to political campaigns but you know here at WiDS I'm excited to tell people about the kinds of mathematics that can help you deal with this kind of data set more easily. >> Did you have a talk this afternoon called filling in missing-- >> Yup >> Data with low rank models >> that's right >> One of the things before we get into that, that id love to kind of unpack with you is looking at, taking the campaign Obama 2012 campaign messy data as an example of something that is interesting there's a lot of science and mathematics behind it but there's also other skill I'd like to get your perspective on and that's creativity that's empathy it's being able to clearly understand and communicate to your audience, Where do those other skills factor into what you do as a professor and also the curriculum you're teaching >> Sure, I think they are incredibly important if you want your technical work to have an impact you need to be able to communicate it to other people you need to make, number one make sure you are working on the right problems which means talking to people to figure out what the right problems are and this is one aspect that I consider really fundamental to my career is going around talking to people in industry about what problems they are facing that they don't know how to solve, right? Then you go back to your universities you squirrel away and try and figure it out, often sometimes I can't figure it out on my own so I need to put together a team, I need to pull in other people from other disciplines who have the skills I don't have in order to figure out the full solution to the problem, right? Not just to solve the part of the problem that I know how but to solve the full problem I can see and so that also requires a lot of empathy and communication to make the team actually produce something more than what the individual members could. Then the third step is to communicate that result back to the people who could actually use it and put it into practice, and for that you know that's part of the reason I'm here at WiDS is to try show people the useful things I think that I've come up with but I'm also really excited to talk to people here and understand what gnarly problems do they not know how to solve yet. >> There's a lot of gnarly problems out there, love that you brought that word up >> (laughter) >> But I'm just curious before we go further is understanding did you understand when you was studying mathematics, computational engineering data science did you understand at that point the other important skills. A collaboration of communication or did you discover that along the way and is that something that is taught today to those students these are the other things we want to develop in you >> Yeah I think we barely teach those skills, >> Really? I think at the earliest level there's a lot of focus on the technical skills and it's hard to see the other skills that are going to enable you to get from 90 to 100% but that 90 to 100% is the most important part. Right? If you can't communicate your results back then it doesn't do so much good to have produced the results in the first place, >> Right but really a lot of the education right now at most universities is focused on the technical core and you can see that in the way we evaluate student, right? We evaluate them on their homework which are supposed to be individual on their test performance, right? maybe their projects and the projects I think are much better at helping them develop these skills of communication and teamwork, but that's you know not included in most courses because frankly it's hard to do it's hard to teach students how to work on projects It's hard to get them topics, it's hard to evaluate their results on their projects it's hard to give them time to present it to a group, but I think these are critical skills, right? The project work is much more what works becomes after they finish their studies. >> As you've been in the STEM fields for quite a while and gone so far in your academic career, tell me about the changes that you've seen in the curriculum and do you think you're going to have a chance to influence some of those other skills communication when I was in grad school studying biology, communication a long time ago was actually part of it for a semester but I'm just wondering do you think that this is something that a movement like WiDS could help inspire. >> I think it's important to help people see what, the skills they are going to need to use down the line I think that sometimes, the thing is I think that the technical foundation is really important and I think that doubling down on that particularly when your young and can concentrate on the, on the nitty gritty details I actually think that's something that becomes harder as you get older And so focusing on that for people on their undergrad and early PDH I think that actually makes sense but you want them to see what the final result is, right? You want them to see like what is their career and how is that different from what they are doing right now So I think events like WiDS are really great for showcasing that but I would also like to sort of pull that forward, to pull that project work forward, to the extent possible with the skills that the students have at any point in their curriculum in the class that I teach in big messy date the cap stone of the course is, class project where the students tackle a big messy data set that they find on their own, they define the problems and the form of what they are supposed to produce is supposed to be a report to their manager, right? To say the project proposal says, "manager this is why I should be allowed to work on this "project for the next month because it's so important "it's really going to drive growth in our business it's going to "open up new markets" But they're supposed to describe it industry terms not just academic terms, right? Then they try and figure out actually how to solve the problem and at the end they're supposed to once again write a report that's describing how what they found will help and impact the business >> That element of persuasion is key-- >> That's right that's right >> So the last thing here as we wrap up this is the fourth annual women in data science conference that I mentioned in the opening. The impact and the expansion that they have been able to drive in such a short period of time is something that I always loved seeing every year there's is a hundred and fifty plus regional events going on they're expected to reach a hundred thousand people what excites you about the opportunity that you have to present here at Stanford later today? >> I think that it's amazing that there is so many people that are excited about WiDS, I mean I can't travel to a hundred and fifty locations certainly not this year, not in many many years so the ability to, to be in touch with so many people in so many different places is really exciting to me I hope that they will be in touch with me too that direction is a little be harder with current technology but I want to learn from them as well as teaching them. >> Well Madeleine thank you so much for sharing some of your time with me this morning on theCUBE we appreciate that, and wish you good luck on your WiDS presentation this afternoon >> It was really fun to talk with you, thank you for having me here >> Ah my pleasure >> We want to thank you, you're watching theCUBE live from the forth annual women in data science conference WiDS here at Stanford, I'm Lisa Martin stick around I'll be right back after a break with my next guest. 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[Announcer] Live from Stanford University, it's The Cube! Covering global women in Data Science Conference. Brought to you by SiliconeANGLE Media. >> Welcome back to The Cube. I'm Lisa Martin, live at Stanford University for the fourth annual Women in Date Science Conference, hashtag WiDS2019. We are here with Kavita Sangwan, the Director of Technical Programs, Artificial Intelligence and Machine Learning at Intuit. Kavita, it's wonderful to have you on the program. >> Thank you, pleasure is all mine. >> So Intuit is a global and visionary sponsor of WiDs, and has been for a couple of years. Talk to us a little bit about Intuit's sponsorship of this WiDs movement. >> Sure, well, Tech Women at Intuit has been important part of our culture. It was founded sometime a couple of years back from our previous CTO Taylor Stansbury. He was the founder and sponsor for it, and it has been getting the continuous support and sponsorship from our current CTO, Marianna Tessel. We highly believe that diversity in inclusion, and diversity in talks, and diversity in employees, is an important aspect for our company because that kind of helps us to deliver awesome product experiences and seamless experiences to our customers. This is our second year at WiDs, and we are proud to be part of this event today. >> It's growing tremendously, you know I mentioned it as a movement, and in three and a half years, this is the fourth annual, as I mentioned, and Margot Gerritsen, one of the co founders, chatted with me a couple hours ago and said they're expecting 20,000 people to be engaging today alone. The live stream at the event here at Stanford, but also the impact that they're making. There's a 150 plus regional events going on around this event in 50 plus countries. >> So it's the... You and I were chatting before we went live that you feel this, this palpable energy when you walk in. Tell me a little bit about your role at Intuit, and how you're able to really kind of grow your career in this organization that really seems to support diversity. >> Sure, I head the Technical Program Management for Intuit Data Science Organization, so it's all about data, data science, AI Machine Learning. We apply and imbed AI Machine Learning across all of our product suites. And also try to apply AI Machine Learning in different other aspects as well. Some of the focus areas where we applying AI Machine Learning is making our products smart, security risk and fraud space, where we are all several steps ahead of the fraudsters. Also, in customer success space, and also within the organization, the products and services our work employees use to make their experiences amazing. I have been with Intuit for almost three years now, and it has been an amazing journey. Intuit is such a... It embraces diversity, and it's because of its diverse, durable, innovative culture, I think Intuit has been in Silicone Valley as a strong force for over 35 years. >> So when we think about Data Science, often we think about the technical skills that a data scientist would need to have, right? It's the computational mathematics and engineering, being able to analyze data, but there's this whole other side that seems to be, based on some of the conversations that we've had, as important but maybe lagging behind, and that is skills on being a team player, being collaborative, communication skills, empathy skills. Tell me about, from your perspective, how do you use those skills in your daily job, and how does Intuit maybe foster some of those communication negotiation skills as equal importance as the actual data itself? >> It's very important for us, as we hire our top talent in our organization to empower and grow that top talent as well. We do that by providing them opportunities to learn from different sessions we host around executive presence, negotiation skills, public speaking skills. In addition to advancing them in their technological space. As you rightly said, it's very important for us to operate in a team setting. You know, a data scientist has to interact with a product manager, and a data engineer, a business person, a legal person, because there is questions about security and privacy. So there are so much interactions happening across functional space, it is very important for us to be a team player, and having the ability to have those conversations in the right way. So, Intuit invests heavily, not just in the technology space to advance women, but also in all the other ancillary spaces, which are equally important to be successful as you advance in your career. >> So, as our viewers understand Intuit, I'm a user of it as well for my business, who understand it to a degree. What do you think would surprise our viewers about how Intuit is applying Data Science? >> So, it's important to know that we operate with a customer's mindset. Everything we do starts with our customers, and it's very important for us to build a culture which reflects the values, and the talent, and the skills of our customers. And that is why I said it's very important for us to have diversity in our teams. Our most opportunistic areas for investment in the AI machine learning is the smart products space where we are heavily investing to make our products intelligent, customize it according to the needs of our customers, and giving them great insights for our customers to save them money, make them do less work, and build more confidence in our product suites. >> Confidence, that word kind of reminds me of another word that we hear used a lot around data, and I'm making it very general, but it's trust. That's something that is critical for any business to establish with the customer, but if we look at how much data we're all generating just as people, and how every company has a trail of us with what we eat, what we buy, what we watch, what we download. Where does trust come into play, if you're really designing these things for the customer in mind, how are you delivering on that promise of trust? >> It's very rightly said, just to add to that sentiment, it has been shared in some articles that we have accumulated so much data in the last two years which is more than what we have accumulated in the last five thousand years of humanity. It is really important to have trust with your customers because we are using their data for their own benefits. Intuit operates with the principle and the mindset that this our customer's data, and we are their stewards. We make sure that we are one of the best stewards for their data, and that's what we reflect in our products, how we serve them, build intelligent products for them, and that's how we start to gain trust from our customers. >> And I imagine being quite transparent in the process. >> That's true, yes. >> So in terms of your career, I was doing some research on you, and I know that you love to give back to the community by being a champion for women in technology, encouraging young girls in STEM towards building that community. Tell me a little bit about your career as we are here at WiDS at Stanford there's a lot of involvement in the student community. Tell me a little about your background and what some of your favorite things are about giving back to the next generation. >> Sure, I actually, when I graduated from engineering, I was one of the four women students out of the, maybe, a class of around 50 students. So I think it struck me right there that there is a disparity in the industry, in the education system, and then in the industry. I felt the same thing in my different companies where I worked, and that always led me to a point that I actually, rather than just being observing this from afar, why can't I be the one who moved the needle on this? That led me to a point where I started collaborating within the companies, started forming teams, and started working with the teams who were already there to move the needle in technical women's space. I think, if I reflect back in my journey, a couple of things that stand out for me is passion for what you do, and I am really passionate about what my goal is and I try to line up my work according to that and that's why this women in tech, something which is close to my heart and I'm passionate about, always comes forward whenever I do something. The second important aspect is, I've always thrown myself into situations which I've never done before. For example we were offline talking about hackathon, which is DevelopHer. I had never done any hackathons before because I was so passionate about doing it, I just threw myself in and I ran that hackathon. And then the third thing is being persistent about what you do. I mean, you can't just do one thing and then drop it and then come back after a few weeks and then do it again. You have to have that consistency of doing it, only then do you start moving the needle. I think when I reflect and look back, these three things stand out for me and that has applied in my own personal career, as well as everything I do in my life. >> How do you give, and the last question, it seems like you sort of have that natural passion, I love this, this is what I want to do, you were persistent with it, how do you advise younger girls who might not have that natural passion to really develop that within themselves? >> I think experiment and explore. When you try to do different things, only then you find out where your passion lies. Just don't be scared of throwing yourself into a situation which you have never dealt before. Always try to find new things and throw yourself in an uncomfortable situation, and try to get out of it. It helps you become super bold, and gives you confidence, and that's the way to find what you're naturally passionate about. >> I like that, I like to say get comfortably uncomfortable. Last question in the last few seconds, I just want you to have the opportunity to tell our viewers where they can go to learn more about Intuit and their Data Science jobs. >> Yes, you can always go to intuit.com, and intuitcareers.com, and learn about the great opportunities we have for Intuit and Data Science. >> Excellent, well Kavita, it's been a pleasure to have you on The Cube this afternoon. Thank you for stopping by, and also for sharing what Intuit is doing to support WiDS. >> Thank you, it was my pleasure, thank you so much. >> We want to thank you for watching The Cube, I'm Lisa Martin live from the WiDS fourth annual WiDS global conference at Stanford. Stick around, I'll be right back with our next guest.

>> 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 the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the Cubes. Continuing coverage of the fourth annual Women and Data Science Conference with Hashtag with twenty nineteen to join the conversation. Lisa Martin joined by one of the speakers on the career panel today at Stanford. Natalie Evans Harris, the cofounder and head of strategic initiatives at right hive. Natalie. It's a pleasure to have you on the program so excited to be here. Thank you. So you have, which I can't believe twenty years experience advancing the public sectors. Strategic use of data. Nearly twenty. I got more. Is your career at the National Security Agency in eighteen months with the Obama administration? You clearly were a child prodigy, of course. Of course, I was born in nineteen ninety two s. So tell me a little bit about how you got involved with was. This is such an interesting movement because that's exactly what it is in such a short time period. They of a mask. You know, they're expecting about twenty thousand people watching the live stream today here from Stanford. But there's also fifty plus countries participating with one hundred fifty plus a regional events. You're here on the career panel. Tell me a little bit about what attracted you to wits and some of the advice and learnings that you're going to deliver this afternoon. Sure, >> absolutely So Wits and the Women and Data Science Program and Conference on what it's evolved to are the exact type of community collective impact initiatives we want to say. When we think about where we want data science to grow, we need to have diversity in the space. There's already been studies that have come out to talk about the majority of innovations and products that come out are built by white men and built by white men. And from that lens you often lose out on the African American experience or divers racial or demographic experiences. So you want communities like women and data science to come together and show we are a part of this community. We do have a voice and a seat at the table, and we can be a part of the conversation and innovation, and that's what we want, right? So to come together and see thousands of people talking and walking into a room of diverse age and diverse experience, it feels good, and it makes me hopeful about the future because people is what the greatest challenge to data science is going to be in the future. >> Let's talk about that because a lot of the topics around data science relate to data privacy and ethics. Cyber security. But if we look at the amount of data that's generated every day, two point five quintillion pieces of data, tremendous amount of impact for the good. You think of cancer research and machine learning in cancer research. But we also think, Wow, we're at this data revolution. I read this block that you co authored it about a year ago called It's time to Talk About Data Ethics, and I found it so interesting because how how do we get control around this when we all know that? Yes, there is so many great applications for data that were that we benefit from every day. But there's also been a lack of transparency on a growing scale. In your perspective, how do what's the human capital element and how does that become influenced to really manage data in a responsible way? I think that >> we're recognizing that data can solve all of these really hard problems and where we're collecting these quintillion bytes of data on a daily basis. So there's acknowledgment that there's things that humans just can't d'oh so a I and machine learning our great ways to increase access to that data so we can use it to start to solve problems. But we also need to recognize is that no matter how good A I gets, there's still humans that need to be a part of that context because the the algorithms air on Lee as strong as the people that have developed them. So we need data scientist. We need women with diverse experiences. We need people with diverse thoughts because they're the ones we're going to create, those algorithms that make the machine learning and the and the algorithms in the technology more powerful, more diverse and more equal. So we need to see more growth and experiences and people and learning the things that I talk about. When I when others asked me and what I'll mention on the career panel is when you think about data science. It's not just about teaching the technical skills. There's this empathy that needs to be a part of it. There's this skill of being able to ask questions in really interesting ways of the data. When I worked at National Security Agency and helped build the data science program there, every data scientist that came into the building, we, of course taught them about working in our vitamins. But we also made every single one of them take a class on asking questions. The same class that we had our intelligence analyst take so the same ways of the history and the foreign language experts needed to learn how to ask questions of data we needed, Our data scientist told. Learn that as well. That's how you start to look beyond just the ones and zeros and start to really think about not just data but the people that are impacted by the use of the data. >> Well, it's really one of the things I find interesting about data. Science is how diverse on I use that word, specifically because we talked about thought diversity. But it's not just the technical skills as you mentioned. It's empathy. It's communication. It's collaboration on DH those air. So it's such a like I said, Diverse opportunity. One of the things I think I read about in your blawg. If we look at okay, we need to not just train the people on how to analyze the data but howto be confident enough to raise their hand and ask questions. How do you also train the people? >> Two. >> Handle data responsibly. You kind of mentioned there's this notion of sort of like a Hippocratic oath that medical doctors take for data scientist. And I thought that was really intriguing. Tell me a little bit more about that. And how do you think that data scientists in training and those that are working now can be trained? Yeah, influenced to actually take something like that in terms of really individualizing that responsibility for ethical treatment of data. So, towards the >> end of my time at the White House, we it was myself deejay Patil and a number of experts and thought leaders in the space of of news and ethics and data science came together and had this conversation about the future of data ethics. And what does it look like? Especially with the rise of fake news and misinformation and all of these things? And born out of that conversation was just this. This realization that if you believe that, inherently people want to do the good thing, want to do the right thing? How do they do that? What does that look like? So I worked with Data for Democracy and Bloomberg to Teo issue a study and just say, Look, data scientist, what keeps you up at night? What are the things that as you as you build these algorithms and you're doing this? Data sharing keeps you up at night. And the things that came out of those conversations and the working groups and the community of practice. Now we're just what you're talking about. How do we communicate responsibly around this? How do we What does it look like to know that we've done enough to protect the data, to secure the data, to, to use the data in the most appropriate ways? And when we >> see a problem, what do >> we do to communicate that problem and address it >> out of >> that community of practice? And those principles really came the starts of what an ethics. Oh, the Hippocratic oath could look like it's a set of principles. It's not the answer, but it's a framework to help guide you down. Your own definition of what ethical behaviour looks like when you use data. Also, it became a starting point for many companies to create their own manifestos and their own goals to say as a company, these are the values that we're going to hold true to as we use data. And then they can create the environments that allow for data scientists to be able to communicate how they feel about what is happening around them and effect change. It's a form of empowerment. Amazing. I love >> that in the last thirty seconds, I just want to get your perspective on. Here we are spring of twenty nineteen. Where are we as a society? Mon data equaling trust? >> Oh, I love that we're having the conversation. And so we're at that point of just recognizing that data's more than ones and zeroes. And it's become such an integral part of who people are. And so we need some rules to this game. We need to recognize that privacy is more than just virus protection, that there is a trust that needs to be built between the individuals, the communities and the companies that are using this data. What the answers are is what we're still figuring out. I argue that a large part of it is just human capital. It's just making sure that you have a diverse set of voices, almost a brain trust as a part of the conversation. So you're not just going to the same three people and saying, What should we d'Oh But you're growing and each one teach one and building this community around collectively solving these problems. Well, >> Natalie's been such a pleasure talking with you today. Thank you so much for spending some time and joining us on the Cuban. Have a great time in the career panel this afternoon. Atwood's. >> Thank you so much. This is a lot of fun. >> Good. My pleasure. We want to thank you. You're watching the Cube from the fourth annual Women and Data Science Conference alive from Stanford University. I'm Lisa Martin. I'll be back with my next guest 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 Stanford University it's the cube covering global women and data science conference brought to you by Silicon angle media good morning and welcome to the cube I'm Lisa Martin and we are live at the global fourth annual women in data science conference at the Arriaga Alumni Center at Stanford I'm very pleased to be joined by one of the Wits ambassadors this year Regina cut of our math data science senior manager Accenture at Google and as I mentioned you are an ambassador for wits in Bangla Road the event is Saturday so Janelle welcome to the cube thank you pleasure it is - this is the fourth annual women in data science conference this year over 150 regional events of which you are hosting Bengaluru on Saturday March 9th 50-plus countries they're expecting a hundred thousand people to engage tell us a little bit about how you got to be involved in wins yeah so I care about data science but also what accurate representation of women in gender minority in the space and I think it's global initiative is doing amazing job in creating a significant impact globally and that kind of excited me to get involved with its initiative so you have which I can't believe you're an SME with ten plus years experience and data analytics focusing on marketing and customer analytics you've had senior analytics leadership positions at Accenture Hewlett Packard now Google tell me a little bit about before we get into some of the things that you're doing specifically the data--the on your experience as a female in technology the last ten plus years it's been exciting I started my career as an engineer I wanted to be a doctor fortunately unfortunately it couldn't happen and I ended up being an engineer and it has been an exciting ride since then I felt that had a passion for doing personal management and I posted management and specialization of operational research and project management and I started my career as a data scientist worked my way up through different leadership positions and currently leading a portfolio for Accenture at Google yeah in the read of science domain yeah it's exciting absolutely so one of the things that is happening this year wins 2019 the second annual data thon that's right really looking at predictive analytics challenge for social impact tell us a little bit about why Woods is doing this data thon and what you're doing in not respectively in Bengaluru okay so well you see data science in itself is a highly interdisciplinary domain and it requires people from different disciplines to come together look at the problem from different perspectives to be able to come up with the most amicable and optimal solution at any given point of time and Gareth on is one such avenue that fosters this collaboration and data thon is also an interesting Avenue because it helps young data science enthusiasts whom the require design skill sets and also helps the data science practitioners enhance and sustain their skill sets and that's the reason which Bangalore was keen on supporting what's global data thon initiative so this skill set so I'd like to kind of dig into that a bit because we're very familiar with those required data analytics skill sets from a subject matter expertise perspective but there's other skill sets that we talk about more and more with respect to data science and analytics and that's empathy it's communication negotiation can you talk to us a little bit about how some of those other skills help these data thon participants not just in the actual event but to further their careers absolutely so really into the real world so there are a lot of these challenges wherein you would require a domain expert you require someone who has a coding experience someone who has experience to handle multiple data sites programmatically and also you need someone who has a background of statistics and mathematics so you would need different people to come together I look at the problem and then be able to solve the challenges right so collaboration is extremely pivotal it's extremely important for us to put ourselves in other shoes and see a look at the problem and look at the problem from different perspective and collaboration or the key to be able to be successful in data science domain as such okay so let's get into the specifics about this year's data sets and the teams that were involved in the data thon all right so this year's marathon was focused on using satellite imagery to analyze the scenario of deforestation cost of oil palm plantations so what we did at which Bangalore is we conducted a community workshop because our research indicated that men dominated the Kegel leaderboard not just in Bangla but for India in general despite that region having amazing female leader scientists who are innovators in their space with multiple patents publications and innovations to the credit so we asked few questions to certain female data scientists to understand what could be the potential reason for their lower participation and the Kegel as a platform and their responses led us to these three reasons firstly they may not have the awareness about Kegel as a platform may be a little bit more about that platform so reviewers can understand that right so Kegel is a platform where in a lot of these data sets have been posted if anybody is interested to hold the required a design skill says they can definitely try explore build some codes and submit those schools and the teams that are submitting the codes which are very effective having greater accuracy he would get scored and the jiggle-ator build and you know that which is the most effective solution that can be implemented in the real world so we connected this data Sun workshop and one of the challenges that most of the female leader scientists face is having an environment to network collaborate and come up with a team to be able to attempt a specific data on challenge that is in hand so we connected data from workshop to help participants overcome this challenge and to encourage them to participate into its global hit a fun challenge so what we did as a part of this workshop was we give them on how to navigate Kegel as a platform and we connected an event specifically focused on networking so that participants could network form teams we also conducted a deep in-depth technical session focusing on deep neural nets and specifically on convolutional neural nets the understanding of which was pivotal to be able to solve this year's marathon challenge and the most interesting part of this telethon workshop was a mentorship guidance we were able to line up some amazing mentors and assign these minders to the concern or the interested participating teams and these matters work with respective teams for the next three weeks and for them terms with the required guidance coaching and mentorship held them for the VidCon showed me that's fantastic so over a three-week period how many participants did you have there 110 plus people for the key right yeah for the event and there are multiple teams that have formed and we assigned those mentors we identified seven different mentors and assigned these mentors to the interested participating teams we got a great response in terms of amazing turnout for the event new teams got formed new relationships got initiated new relationships new collaborations all right tell us about those achievements so they were there was one team from engineering branch or engineering division who were really near to the killer's platform they have their engineering exams coming up but despite that they learned a lot of these new concepts they form the team they work together as a team and we were able to submit the code on the Kegel leader board they were not the top scoring team but this entire experience of being able to collaborate look at the problem from different perspective and be able to submit the code despite one of these challenges and also navigate the platforming itself was a decent achievement from my perspective a huge achievement yeah so who you are at Stanford today you're gonna be flying back to go host the event there tell us about from your perspective if we look at the future line of sight for data science let's just take a peek at the momentum this that this Woods movement is generating this is our fourth year covering this fourth annual event fourth year on the cube and we see tremendous tremendous momentum mm-hmm with not just females participating and the woods leaders providing this sustained education throughout the year the podcast for example that they released a few months ago on Google Play on iTunes but also the number of participants worldwide as you look where we are today what in your perspective is the future for data science all right so data science is a domain is evolving at a lightning speed and may possibly hold the solution to almost all the challenges faced by humanity in the near future but to be able to come up with the most amicable and sustainable solution that's more relevant to the domain achieving diversity in this field is most and initiatives like wits help achieve that diversity and foster a real impact absolutely what's original thank you so much for joining me on the cube this morning live from wins 2019 we appreciate that wish you the best of luck kids a local event in Bengaluru over the weekend thank you it was a pleasure likewise thank you we want to thank you you're watching the cube live from Stanford University at the fourth annual woods conference I'm Lisa Martin stick around my next guest will join me in just a moment

>> Narrator: Live from Stanford University in Palo Alto, California, it's the CUBE! Covering, Women in Data Science Conference 2018, brought to you by Stanford. >> Welcome back to the CUBE, we are live at Stanford University, we've been here all day at the third annual Women in Data Science Conference, WiDS 2018. This event is remarkable in its growth in scale, in its third year, and that is, in part by the partners and the sponsors that they have been able to glean quite early on. I'm excited to be joined by Vijay Raghavendra, the senior vice president of Merchant Technology and stores as well, from Walmart Labs. Vijay, welcome to the CUBE! >> Thank you, thank you for having me. >> Walmart Labs has been paramount to the success of WiDS, we had Margot Gerritsen on earlier, and I said, "How did you get the likes of a Walmart Labs as a partner?" And, she was telling me that, the coffee-- the coffee shop conversation >> Yeah, the Coupa Cafe! >> That she had with Walmart Labs a few years ago, and said, "Really, partners and sponsors like Walmart have been instrumental in the growth and the scale, of this event." And, we've got the buzz around, so we can hear the people here, but this is the big event at Stanford. There's 177 regional events, 177! In 53 countries. It's incredible. Incredible, the reach. So, tell me a little bit about the... From Walmart Labs perspective, the partnership with WiDS, what is it that really kind of was an "Aha! We've got to do this"? >> Yeah, it's just incredible, seeing all of these women and women data scientists here. It all started with Esteban Arcaute, who used to lead data science at Walmart Labs, and Search, before he moved on to Facebook with Margot. And, Karen in the cafe in Palo Alto, in 2015, I think. And Esteban and I had been talking about how we really expand the leverage of data and data science within Walmart, but more specifically, how we get more women into data science. And, that was really the genesis of that, and, it was really-- credit goes to Esteban, Margot, and Karen for, really, thinking through it, bringing it together, and, here we are. >> Right, I mean bringing it together from that concept, that conversation here at Stanford Cafe to the first event was six months. >> Yeah, from June to November, and, it's just incredible the way they put it together. And, from a Walmart Labs perspective, we were thrilled to be a huge part of it. And, all the way up the leadership chain there was complete support, including my boss Jeremy King, who was all in, and, that really helped. >> Margot was, when we were chatting earlier, she was saying, "It's still sort of surprising," and she said she's been, I think in, in the industry for, 30-plus years, and she said that, she always thought, back in the day, that by the time she was older, this problem would be solved, this gender gap. And she says, "Actually, it's not like it's still stagnant," we're almost behind, in a sense. When I look at the ... women that are here, in Stanford, and those that are participating via those regional events, the livestream that WiDS is doing, as well as their Facebook livestream. You know, the lofty goal and opportunity to reach 100,000 people shows you that there's clearly a demand, there's a need for this. I'd love to get your perspective on data science at Walmart Labs. Tell me a little bit about the team that you're leading, you lead a team of engineers, data scientists, product managers, you guys are driving some of the core capabilities that drive global e-commerce for Walmart. Tell me about, what you see as important for that female perspective, to help influence, not only what Walmart Labs is doing, but technology and industry in general. >> Yeah. So, the team I lead is called Merchant Technology, and my teams are responsible for, almost every aspect of what drives merchandising within Walmart, both on e-commerce and stores. So, within the purview of my teams are everything from the products our customers want, the products we should be carrying either in stores or online, to, the product catalog, to search, to the way the products are actually displayed within a store, to the way we do pricing. All of these are aspects of what my teams are driving. And, data and data science really put me at every single aspect of this. And the reason why we are so excited about women in data science and why getting that perspective is so important, is, we are in the retail business, and our customers are really span the entire spectrum, from, obviously a lot of women shop at Walmart, lot of moms, lot of millennials, and, across the entire spectrum. And, our workforce needs to reflect our customers. That's when you build great products. That's when you build products that you can relate to as a customer, and, to us that is a big part of what is driving, not just the interest in data science, but, really ensuring that we have as diverse and as inclusive a community within Walmart, so we can build products that customers can really relate to. >> Speaking of being relatable, I think that is a key thing here that, a theme that we're hearing from the guests that we're talking to, as well as some of the other conversations is, wanting to inspire the next generation, and helping them understand how data science relates to, every industry. It's very horizontal, but it also, like a tech company, or any company these days is a tech company, really, can transform to a digital business, to compete, to become more profitable. It opens up new business models, right, new opportunities for that. So does data science open up so many, almost infinite opportunities and possibilities on the career front. So that's one of the things that we're hearing, is being able to relate that to the next generation to understand, they don't have to fit in the box. As a data scientist, it sounds like from your team, is quite interdisciplinary, and collaborative. >> And, to us that is really the essence of, or the magic of, how you build great products. For us data science is not a function that is sitting on the side. For us, it is the way we operate as we have engineers, product managers, folks from the business teams, with our data scientists, really working together and collaborating every single day, to build great products. And that's, really how we see this evolving, it's not as a separate function, but, as a function that is really integrated into every single aspect of what we do. >> Right. One of the things that we talked about is, that's thematic for WiDS, is being able to inspire and educate data scientists worldwide, and obviously with the focus of helping females. But it's not just the younger generation. Some of the things that we're also hearing today at WiDS 2018 is, there's also an opportunity within this community to reinvigorate the women that have been in, in STEM and academia and industry for quite a while. Tell me a little bit more about your team and, maybe some of the more veterans and, how do you kind of get that spirit of collaboration so that those that, maybe, have been in, in the industry for a while get inspired and, maybe get that fire relit underneath them. >> That's a great question, because we, on our teams, when you look across all the different teams across different locations, we have a great mix of folks that bring very different, diverse experiences to the table. And, what we've found, especially with the way we are leveraging data, and, how that is invigorating the way we are... How people come to the table, is really almost seeing the art of what is possible. We are able to have, with data, with data science, we are able to do things that, are, really step functions in terms of the speed at which we can do things. Or, the- for example, take something as simple as search, product search, which is one of the, capabilities we own, or my team is responsible for, but, you could build the machine learning ranking, and, relevance and ranking algorithms, but, when you combine it with, for example, a merchant that really fundamentally understands their category, and you combine data science with that, you can accelerate the learning in ways that is not possible. And when folks see that, and see that in operation that really opens up a whole, slew of other ideas and possibilities that they think about. >> And, I couldn't agree more. Looking at sort of the skillset, we talk a lot about, the obvious technical skillset, that a data scientist needs to have, but there's also, the skills of, empathy, of communication, of collaboration. Tell me about your thoughts on, what is an ideal mix, of skills that that data scientist, in this interdisciplinary function, should have. >> Yeah, in fact, I was talking with a few folks over lunch about just this question! To me, some of the technical skills, the grounding in math and analytics, are table stakes. Beyond that, what we look for in data scientists really starts with curiosity. Are they really curious about the problems they're trying to solve? Do they have tenacity? Do they settle for the more obvious answers, or do they really dig into, the root cause, or the root, core of the problems? Do they have the empathy for our customers and for our business partners, because unless you're able to put yourself in those shoes, you're going to be approaching at, maybe, in somewhat of an antiseptic way? And it doesn't really work. And the last, but one of the most important parts is, we look for folks who have a good sense for product and business. Are they able to really get into it, and learn the domain? So for example, if someone's working on pricing, do they really understand pricing, or can they really understand pricing? We don't expect them to know pricing when they come in, but, the aptitude and the attitude is really, really critical, almost as much as the core technical skills, because, in some ways, you can teach the technical skills, but not some of these other skills. >> Right, and that's an interesting point that you bring up, is, what's teachable, and, I won't say what's not, but what might be, maybe not so natural for somebody. One of the things, too, that is happening at WiDS 2018 is the first annual Datathon. And, Margot was sharing this huge number of participants that they had and they set a few ground rules like wanting the teams to be 50% female, but, tell us about the Datathon from your global visionary sponsorship level; what excites you about that in terms of, the participation in the community and the potential of, "Wow, what's next"? >> Yeah... So, it's hugely exciting for us, just seeing the energy that we've seen. And, the way people are approaching different problems, using data to solve very different kinds of problems ... across the spectrum. And for us, that is a big part of what we look for. For us it is really about, not just coming up with a solution, that's in search of a problem, but really looking at real-world problems and looking at it from the perspective of, "Can I bring data, can I bring data science to bear on this problem?", to solve it in ways that, either are not possible, or can accelerate the way we would solve the problems otherwise. And that is a big part of what is exciting. >> Yeah, and the fact that the impact that data science can make to, every element of our lives is, like I said before, it's infinite, the possibilities are infinite. But that impact is something that, I think, how exciting to be able to be in an industry or a field, that is so pervasive and so horizontal, that you can make a really big social impact. One of they other things, too, that Margot said. She mentioned that the Datathon should be fun, and I loved that, and also have an element of creativity. What's that balance of, creativity in data science? Like, what's the mixture, because we can be maybe over-creative, and maybe interpret something that's in a biased way. What is your recommendation on how much creativity can creep into, and influence, positively, data science? >> Yeah, that's a great question, and there's no perfect answer for it. Ultimately, at least my biases towards using data and data science to, solve real problems. And... As opposed to, pure research, so our focus very much is on applied learning, and applied science. And, to me, within that, I do want the data science to be creative, data scientists to be creative, because, by putting too many guardrails, you limit the way in which they would explore the data, that they may come up with insights that, well, we might not see otherwise. And, which is why, I go back to the point I made, when you have data scientists who fundamentally understand a business, and the business problems we are trying to solve, or the business domains, I think they can then come up with very interesting, innovative ways of looking at the data, and the problem, that you might not otherwise. So, I would by no means want to limit their creativity, but I do have a bias towards ensuring that it is focused on problems we are trying to solve. >> Excellent. Well, Vijay, thank you so much for stopping by the CUBE, congratulations on the continued success of the partnership with WiDS and, we're looking forward to seeing what happens the rest of the year, and we'll probably see you next year at WiDS 2019! >> Absolutely, thank you! >> Excellent, we want to thank you, you're watching the CUBE, live from Stanford University, the third annual Women in Data Science Conference. I am Lisa Martin, I'll be right back after a short break with my next guest. (cool techno music)