 from Stanford University in Palo Alto, California. It's theCUBE, covering Women in Data Science Conference 2018. Brought to you by Stanford. Welcome back to theCUBE. We are live at Stanford University for the third annual Women in Data Science Conference, WIDDS. I'm Lisa Martin, very honored to be joined by one of the co-founders of this incredible WIDDS movement and phenomenon, Dr. Margo Garrison. Welcome to theCUBE. Yeah, it's great to be here. Thanks so much for being at our conference. Oh, likewise. You are the senior associate dean and director of the Institute for Computational Mathematics and Engineering at Stanford. That's right, yep. Wow, that's a mouthful and I'm glad I could actually pronounce that. So you have been, I would love to give our audience a sense of the history of WIDDS, which is very short. You've been on this incredible growth and scale trajectory. But you've been in this field of computational science for what, 30 over 30 years? Yeah, probably since I was 16. So that is, you know, 35 years ago. Yeah, and you were used to being one of few or if not the only woman in a meeting in a room and you were okay with that, but you realized, you know what, there's probably women that are not comfortable with this and it's probably going to be a barrier. Tell us about the conception of WIDDS that you and your co-founders had. So May 2015, Esteban from Walmart Labs now at Facebook and Karen Mathis, who is still very active, you know, one of the organizers of the conference and I were having coffee at a cafe at Stanford. And we were lamenting the fact that yet another data science conference that we had been to had only had male speakers. And so we connected with the organizers and asked them, you know, why? Did you notice? Because very often people not even aware, you know, it's just such a, such the norm to only have male speakers that people don't even notice. And so we asked, why is that? And they said, well, you know, we really tried to find speakers, but we couldn't find any. And that really was for me, the last straw. I've been in so many of these situations and I thought, you know, we're going to show them. So we joke sometimes a little bit to say it was sort of a revenge conference. Said, let's show them. We can get some really outstanding women and in fact, only women. And that's, that's how it started. Now we were sitting at this coffee shop and I said, let's do a conference. And they said, well, that would be great. Next year I said, no, this year, let's just do it. Let's do it in November. We had six months to put it together. We, it was just a local conference here. We got outstanding speakers, was really great, mostly from the area. And then we started live streaming because we thought that would be fun to do. And to our big surprise, we had 6,000 people on the live stream just without really advertising. That made us realize in November, 2015, my goodness, we're onto something. And we had such amazing responses. We wanted to then scale up the conference and then you can hire, you know, a fantastic conference center in San Francisco and get 10,000 people in, like they do, for example, at Grace Hopper. But we thought, why not use online technology and scale it up virtually and make this a global event using the live stream that we will then provide to people and asking for regional events, local events to be set up all around the world. And we created this ambassador program that is now in its second year. The first year, the responses were actually overwhelming to us already then. We got 75 ambassadors who set up 75 events around the world in about 40 countries. This last year, 30 year ago, 2017. Yeah, almost exactly 30 months ago. And then this year, now we have over 200 ambassadors. We have 177 events in 155 cities in 53 countries. That's incredible. So we're on every continent apart from Antarctica, but we're working on that one. I was going to say that's probably next year. The scale though that you've achieved in such a short time period, I think not only speaks to the power, like you said, of using technology and using live streaming, but also there is a massive demand. There's a great need. For not only supporting like from a perspective of the conference, you want to support and inspire and educate data scientists worldwide and support females in the field, but it really, I think underscores. There is still in 2018, a massive need to start raising more profiles and not just inspiring undergrad females, but also reinvigorating those of us that have been in the STEM field and technology for a while. That's right. So what are some of the things? So this year, not only are you reaching hopefully about 100,000 people, you mentioned some of the countries involved today, but you also have a new first this year with the WIDDs Datathon. That's right. Tell us about the WIDDs Datathon. What was the idea behind it? You announced some winners today. Yeah. Yeah, so with WIDDs last year, we really felt that we hit a nerf. You know, there is an incredible need for women to see other women perform so well in this field. And you know, that's why we do WIDDs to inspire. But it's a one-time event, right? It's once a year. And we start to think about what are some of the ways that we could make this movement because it's really become a movement into something more than just an annual once a year conference. And so Datathon is a fantastic way to do that. You can engage people for several months before the conference, and you can announce the winner at the conference. It is something that can be done really easily worldwide if it is supported, again, by the ambassadors, so the local WIDDs organizations. And so we thought we'd just try. But again, it's one of those things. We say, oh, let's do it. I think thought about this about six months ago. Finding a good dataset is always a challenge, but we found a wonderful dataset and we had a great response with 1100, almost 1200 people in the world participating. Several hundred teams. Yeah, and what we said at the time as well, let's have the teams be 50% female at least. So that was the requirement. We have a lot of mixed teams. And ultimately, of course, that's what we want, right? We want 50-50 men, women, have them both at the table to participate in data science activities, to do data science research and answer a lot of these data questions that are now driving so many decisions. We want everybody around the table. So with this data fund, it was just a very small event in a sense, and I'm sure next year it will be bigger, but it was a great success. Well, congratulations on that. One of the things I saw you on a YouTube video talking about over the weekend when I was doing some prep was that you wanted this data fund to be fun, creative. And I think those are two incredibly important ways to describe careers, not just in STEM, but data science, that yes, this can be fun. Should be if you're spending so much time every day, right? Doing something for a living. But I love the creativity descriptor. Tell us a little bit about the room for interpretation and creativity to start removing some of the bias that is clearly there in data interpretation. Oh. You're hitting the biggest sore point in data science. And you could even turn it around. You say, because of creativity, we have a problem too because you can be very creative in how you interpret the data. And unfortunately for most of us, you know, whenever we look at news, whenever we look at data or other information given to us, we never see this through an objective lens. We always see this through our own filters. And that, of course, when you're doing data analysis is risky and it's tricky. Because you're often not even aware that you're doing it. So that's one thing, you have this bias coming in just as a data scientist and engineer, even though we always say, well, we do objective work and we're building neutral software programs, we're not. We're not. Everything that we do in machine learning, data mining, we're looking for patterns that we think may be in the data because we have to program this data. And then even looking at some of the results, the way we visualize them, present them, can really introduce bias as well. And then we don't control the perception of people of this data, right? So we can present it the way we think is fair, but other people can interpret or use little bits of that data in other ways. So it's an incredibly difficult problem. And the more we use data to address and answer critical challenges, the more data is influencing decisions made by politicians, made in industry, made by government. The more important is that we're at least aware. One of the really interesting things this conference is that many of the speakers are talking to that. We just had Latanya Sweeney give an outstanding keynote really about this, raising this awareness. And we had Daniela Witten saying this and various other speakers. And in the first year that we had this conference, you would not have heard this. So this- Really, only two years ago? So even two years ago means some people were bringing it up, but now it is right at the forefront of almost everybody's thinking. Data ethics, the issue of reproducibility, confirmation bias. Now, at least people now are aware. And I'm always a great optimist, thinking if people are aware and they see the need to really work on this, something will happen. But it is incredibly important for the new data scientists that come into the field to really have this awareness and to have the skill sets to actually work with that. So as a data scientist, one of the reasons why I think it's so fun, you're not just a mathematician or statistician or computer scientist. You are somebody who needs to look at things with taking into account ethics and fairness. You need to understand human behavior. You need to understand the social sciences. And we're seeing that awareness now grow. The new generation of data scientists is picking that up now much more. Programs, educational programs like ours, too, have embedded these sort of aspects into the education. And I think there is a lot of hope for the future. But we're just starting. Right, yeah. But you hit the nail on the head. You've got to start with that awareness. And it sounds like another thing that you just described is we often hear, oh, the top skills that a data scientist needs to have, statistical analysis, data mining. But there's also now some of these other skills you just mentioned, maybe more on the softer side that seem to be from what we hear on theCUBE as important as really that technical training to be more well-rounded and to also, as you mentioned earlier, influence, to have the chance to influence every single sector, every single industry in our world today. And it's a pity that they're called softer skills. It is. Because they're very, very hard skills to really a master. A lot of them are probably, you're born with that, right? It's innate certain things that you can't necessarily teach. Well, I don't believe that you cannot do this without innate ability. Of course, if you have this innate ability, it helps a little, but there's a growth mindset, of course, in this and everybody can be taught. And that's what we try to do. Now, it may take a little bit of time, but you have to confront this and you have to give the people the skills and really integrate this in your education, integrate this at companies. Company culture plays a big role. This is one of the reasons why we want way more diversity in these companies, right? It's not just to have people in decision-making teams that are more diverse, but the whole culture of the company needs to change so that these sort of skills, communication, empathy, big one. Now, all communication skills, presentation skills, visualization skills, negotiation skills that they really are developed everywhere in the companies at the universities. Absolutely, we speak with some companies and some today, even on theCUBE, where they really talk about how they're shifting, and SAP is one of them, their corporate culture to say, we've got a goal by 2020 to have 30% of our workforce be female. You've got some great partners in which you mentioned Walmart Labs. How challenging was it to go to some of these companies here in Silicon Valley and beyond and say, hey, we have this idea for this conference. We want to do this in six months, so step on your seatbelts. What were those conversations like to get some of those partners on board? We wouldn't have been able to do it in six months if the response had not been fantastic right from the get-go. I think we started the conference just at the right time. There was a lot of talk about diversity. Several of the companies were starting a really big diversity initiative. Intel is one of them, SAP is another one of them. We were connected with these companies, Walmart Labs, for example, one of the founders of the company, it's from Walmart Labs. And so when we said, look, we want to put this together, they said, great, this is a fantastic venue for us also. You see this with some of these companies, they don't just come and give us money for this conference. They build their own WITZ events around the world, like SAP build 30 WITZ events around the world. So they're very active everywhere. They see the need, of course, too. They do this because they really believe that the change culture is for the best of everybody. But they also believe it because they need the women. There is a great shortage of really excellent data scientists right now. So why not look at 50% of your population? There's fantastic talent in that pool and they want to attract that also. So I think within the companies, there is more awareness. There is an economic need to do so, a real need. If they want to grow, they need those people. There is an awareness that for their future, the long-term benefits of that company, they need this diversity and opinions. They need the diversity and the questions that are being asked in the way that the companies look at the data. And so I think we're at the golden age for that now. Now, am I a little bit frustrated that it's 2018 and we're doing this? Yes. When I was a student, 30, some years ago, I was one of the very few women and I thought by the time I'm old and now I'm old, you know, as far as my 18-year-old self, right? I mean, you're 50-year-old. I thought everything would be better and we would certainly be at critical mass which is 30% or so or higher and it's actually gone down since the 80s in computer science and in data science and statistics. So it is really very frustrating in that sense that we're really starting again from quite a low level. But I see much more enthusiasm and now the difference is the economical need. So this is going to be driven by business sense as well as any other sense. Well, I think you definitely, with WIDS, you are beyond onto something with what you've achieved in such a short time period. So I can only imagine WIDS 2018 reaching up to 100,000 people over these events. What do you do next year? Where do you go from here? Well, it's becoming a little bit of a challenge actually to organize and help and support all of these international events. So we're going to be thinking about how to organize ourselves maybe on every continent. Or in... Getting to Antarctica in 2019? Yeah, but to have a little bit more of a local or regional organization, so that's one thing. The main thing that we'd like to do is have even more events during the year. There are some specific needs that we cannot address right now. One need, for example, is for high school students. We have two high school students here today, which is wonderful. And quite a few of them are looking at the livestream of the conference. But if you want to really reach out to high school students and tell them about this and the sort of skill sets that they should be thinking about developing when they're at university, you have to really do a special event. The same with undergraduate students, graduate students. So there are some markets there, some subgroups of people that we would really like to tailor to. The other thing is a lot of people are very, very eager to self-educate. And so what we're going to be putting together, at least that's the plan now, we'll see if we can make this, is educational tools and really have a repository of educational tools that people can use to educate themselves and to learn more. We're going to start a podcast series of women which will be very, very interesting. We'll start this next month. And so every week or every two weeks we'll have a new podcast out there and then we'll keep the momentum going. But really the idea is to not provide just this one day of inspiration, but to provide throughout the year. Sustained inspiration. Sustained inspiration and resources. Wow. Well congratulations, Margo, to you and your co-founders. This is a movement and we are very excited to be able to have the opportunity to have you on theCUBE as well as some of the speakers and attendees from the event today. And we look forward to seeing all the great things that I think are going to come for sure the rest of this year and beyond. So thank you for giving us some of your time. Thank you so much. We're a big fan of theCUBE. Oh, we're lucky, thank you, thank you. We want to thank you for watching theCUBE. I'm Lisa Martin. We are live at the third annual Women in Data Science Conference coming to you from Stanford University. Hashtag WIDS 2018, join the conversation. I'll be back with my next guest after a short break.