 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 WID's fourth annual, it's theCUBE's fourth time covering this event. There were, as Margot Garrett's and 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 WID's live stream from Stanford, another 100,000 engaging in over 150 regional WID's events in 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 Mathis, Margot, they're all been great, but the vision from day one has been to put together smart people on a stage, in a room, and bring it syndicated out to anyone who's available, meetups and groups around the world. And if you bet on good content and quality people, the community will 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 to CUBE is high quality conversations, builds communities of content, drives community, and keep that flywheel going. This is what women in data science have figured out. I'm sure they have the data behind it. They have the women who can analyze the data, but more importantly, it's a great community. And it's just, it's steam rolling 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. We're going to continue to broadcast and you're doing a great job and I wish I was interviewing some of the ladies myself, but I get jealous. I know you do. You're always in the background. Yes, I know you do. You talk about Flywheel and Margo Garrison. She, we had her on the WID's broadcast last year and she said, it's such a short period of time. It's been three and a half years that they have generated this incredible momentum and groundswell that every, when you walk in the door of the Stanford Ariyaga 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 Margo said, this actually really was an idea that she and her co-founders had a few years ago as almost sort of an anti-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 Flywheel is going, as you mentioned. Well, I think one of the things that they did really well was they, not only revenge on the concept of having women at the event, not being some sort of, you know, part of an event. Oh, look, we got women on tech on stage. Now, 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, it's 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, not making it restrictive. So in a way that's, you know, counterintuitive to most events. And finally, they created a video model where they live stream it, theCUBE is here. They open up the video format to everybody and they have great people. And I think it's kind of the counterintuitive ones become the standard because not everyone's doing it. So that's how success is. It's usually ones who 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 the importance maybe equal weight, maybe more so of 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 in every person in academia and industry that we talked to. Empathy, the need to have empathy is 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 as the communication medium, but also the consistency. Yeah. 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 it's because it's a women's conference and they have more empathy than men. My wife always says to me, but in seriousness, the big trend right now in machine learning is it math or is it cognition? And so if you look at, if you debate that machine learning concepts, you have two schools of thought. You have the Berkeley School of Thought. It's all math, all math. And then you have kind of another school of thought where learning machines and unsupervised kind of machine learning kicks in. So machines have to learn. So in order to have a humanization side of it 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. And there's something that we've been talking a lot in theCUBE about, it's a whole new cutting edge area of science and social science. And look at fake news and all these things in the mainstream press as you see it playing out every day. 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. 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 every day. 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 of, if you look at cybersecurity, we're 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 am 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 is 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 one, the people are amazing, but this concept of data sharing is one of those big things. And something too 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 Google Play 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 every day. And probably Warrior talked about her keynote around former Cisco CTO and she just ran Car Start, she's working on a new startup. She was talking about the future of how the trends are, the old internet days, as the population of internet users grew, changed the architecture. Now mobile phones, that's changed 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, 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 if you know about data, data science, you have blockchain as the data store potentially out there. So interesting to see is you start getting the supply chains, managing the supply chains of decentralization, how that's going to impact the WIDs community. Great 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.