 Good morning everyone, Lisa Martin with theCUBE, live at the eighth annual Women in Data Science Conference. This is one of my absolute favorite events of the year. We engage with tons of great inspirational speakers, men and women, and what's happening with WIZ is a global movement. I've got two fabulous co-hosts with me today that you're going to be hearing and meeting. Please welcome Tracy Zhang and Hannah Freitag, who are both from the Data Journalism Program, Master's Program at Stanford. So great to have you guys. So excited to be here. So Data Journalism, so interesting. Tracy, tell us a little bit about you, what you're interested in, and then Hannah will have you do the same thing. Yeah, definitely. I definitely think Data Journalism is very interesting, and in fact, I think data, like what is Data Journalism is definitely one of the big questions that we ask during the span of one year, which is the length of our program. And yeah, I'm, like you said, I'm in the Data Journalism Master Program, and I think coming in, I just wanted to pivot from my undergrad studies, which is more like a traditional journalism, into like data, we're finding stories through data, so that's why I'm also very excited about meeting these speakers for today, because they're all, they have different backgrounds, but they all ended up in data science. So I think they'll be very inspirational, and I can't wait to talk to them. Data in Stories, I love that. Hannah, tell us a little bit about you. Yeah, so before coming to Stanford, I was a research assistant at Humboldt University in Berlin, so I was in political science research, and I love to work with data sets and data, but I figured that for me, I don't want this, like the story to end up in a research paper, which is only very limited, like in terms of the audience, and I figured, okay, Data Journalism is the perfect way to tell stories and use data, to illustrate anecdotes, but to, you know, make it comprehensive and accessible for a broader audience. So then I found this program at Stanford, and I was like, okay, that's a perfect transition from like political science to journalism and to use data to, you know, tell data-driven stories. So I'm excited to be in this program. I'm excited for the conference today and to hear from these amazing women who work in data science. You both brought up great points, and we were chatting earlier, that there's a lot of diversity in background. Definitely. Not everyone was in STEM as a young kid or studied computer science. Maybe some were engineering. Maybe some are philosophy or economic. It's so interesting. What I find year after year at WIDS is it brings in so much thought diversity, and that's what being data-driven really demands. It demands that unbiased approach, that diverse, a spectrum of diverse perspectives, and we definitely get that at WIDS. There's about 350 people in person here, but as I mentioned in the opening, hundreds of thousands will engage throughout the year. Tens of thousands, probably today, at local events going on across the globe, and it just underscores the importance of every organization, whether it's a bank or a grocer, has to be data-driven. We have that expectation as consumers in our consumer lives and even in our business lives that I'm going to engage with a business, whatever it is, and they're going to know about me, they're going to deliver me a personalized experience that's relevant to me and my history, and all that is powered by data science, which is, I think, it's fascinating. Yeah, and the great way is if you combine data with people, because after all large data sets, they oftentimes consist of stories or data that affects people, and to find these stories or advanced research in whatever fields it may be in the financial business or in health, as you mentioned, the variety of fields, it's a very powerful tool to use. It's a very powerful, go ahead, Tracy. Definitely, I just wanted to build off of that. It's important to put a face on data, so the data set without a name is just some numbers, but if there's a story, then I think it means something, too, and I think Margot was talking about how data science is about knowing, understanding the past. I think that's very interesting. That's a method for us to know who we are. Definitely, there's so many opportunities. I wanted to share some of the statistics from AnitaB.org that I was just looking at from 2022, you know, we always talk at events like WIDS and some of the other women in tech things. theCUBE is very much pro women in tech and has been for a very long, this is the beginning of theCUBE, but we've seen the numbers of women technologists historically well below 25%, and we see attrition rates are high, and so we often talk about, well, what can we do? And part of that is raising the awareness, and that's one of the great things about WIDS, especially WIDS happening on International Women's Day, March 8th, and around the event. Happy holidays. Exactly, but one of the nice things I was looking at the AnitaB.org research is that representation of tech women is on the rise, still below pre-pandemic levels, but it's actually nearly 27% of women in technical roles, and that's an increase, slow increase, but the needle is moving. We're seeing much more gender diversity across a lot of career levels, which is exciting, but some of the challenges remain. I mean, the representation of women technologists is growing except at the intern level, and I thought that was really poignant. We need to be opening up that pipeline and going younger, and you'll hear a lot of those conversations today about what are we doing to reach girls in grade school? 10-year-olds, 12-year-olds, those in high school, how do we help foster them through their undergrad studies? And excite them about science and all these fields, for sure. What do you think, Hannah, on that note, and I'll ask you the same question, what do you think can be done that the theme of this year's International Women's Day is embrace equity? What do you think can be done on that intern problem to help really dial up the volume on getting those younger kids interested, one, earlier, and two, helping them stay interested? Yeah, yeah, that's a great question. I think it's important to start early, as you said, in school, back in the day when I went to high school, we had this one day per year where we could explore, as girls, explore a STEM job and go into the job for one day and see how it's like to work in IT or in data science, so that's a great first step. But as you mentioned, it's important to keep girls and women excited about this field and make them actually pursue this path. So I think conferences or networking is very powerful. Also, these days with social media and technology, we have more ability and greater ways to connect and I think we should even empower ourselves even more to pursue this path if we're interested in data science and not be like, okay, maybe it's not for me or maybe as a woman, I have less chances. So I think it's very important to connect with other women and this is what Wiz is great about. Wiz is so fantastic for that network effect as you talked about. It's always such as I was telling you both before we went live, I've covered five or six Wiz for theCUBE and it's always such a day of positivity. It's a day of inclusivity, which is exactly what Embrace Equity is really kind of about. Tracy, talk a little bit about some of the things that you see that will help with that hashtag Embrace Equity kind of pulling in, not just to tech because we're talking and we saw Metta was a keynote who's going to come to talk with Hannah and me in a little bit. We see Total Energy's on the program today. We see Microsoft into it, Boeing, Air Company. What are some of the things you think that can be done to help inspire, say, little Tracy back in the day to become interested in STEM or in technology or in data? What do you think companies can and should be doing to dial up the volume for those youngsters? Yeah, because I think somebody was talking about one of the keynote speakers was talking about how there is a notion that girls just can't be data scientists, girls just can't do science. And I think representation definitely matters. If three-year-old me see on TV that all the scientists are women, I think I would definitely have the notion that, oh, this might be a career choice for me and I can definitely also be a scientist if I want. So yeah, I think representation definitely matters and that's why conferences like this will just show us how these women are great in their fields. They're great data scientists that are bringing great insights of the company and even to the social good as well. So yeah, I think that's very important to make women feel seen in this data science field and to listen to the great woman who's doing amazing work. Absolutely. There's a saying you can't be what you can't see. Exactly. And I like to say, I like to flip it on its head because we can talk about some of the negatives but there's a lot of positives and I want to share some of those in a minute. Is that we need to be, the visibility that you talked about, the awareness that you talked about, it needs to be there but it needs to be sustained and maintained and an organization like WIDS and some of the other women in tech events that happen around the Valley here and globally are all aimed at raising the profile of these women so that the younger, really all generations can see what they can be. We all, the funny thing is, we all have this expectation whether we're transacting on Uber ride or we are on Netflix or we're buying something on Amazon, we can get it like that. They're going to know who I am, they're going to know what I want, they're going to want to know what I just bought or what I just watched. Don't serve me up something that I've already done that. So that expectation that everyone has is all about data though we don't necessarily think about it like that. Exactly. But it's all about the data, the past data, the data science as well as the real-time data because we want to have these experiences that are fresh in the moment and super relevant. So whether women recognize it or not, they're data-driven too. Whether or not you're in data science. We're all driven by data and we have these expectations that every business is going to meet it. Exactly. And circling back to young women, I think it's crucial and important to have role models. As you said, if you see someone and you're younger and you're like, oh, I want to be like her. I want to follow this path and have inspiration in a role model someone you look up to and be like, okay, this is possible. If I study the math part or do the physics and you kind of have a goal and like a vision in mind, I think that's really important to drive you. It definitely, having those mentors and sponsors, you know, something that's interesting is I always, everyone knows what a mentor is. Somebody that you look up to, that can guide you, that you admire. I didn't learn what a sponsor was until a women in tech event a few years ago that we did on theCUBE. And I was kind of, my eyes were open. I didn't understand the difference between a mentor and a sponsor. And they sort of think of me thinking, who are my sponsors? And I started going through LinkedIn, he's a sponsor, she's a sponsor. People that help really propel you forward, your recommenders, your champions. And it's so important at every level to build that network. And we have, to your point Hannah, there's so much potential here for data-drivenness across the globe and there's so much potential for women. One of the things I also learned recently and I wanted to share this with you because I'm not sure if you know this, chat GPT, exploding. I was on it yesterday looking at- Everyone talking about- What's hot in data science? And it was kind of like, and I actually asked it, what was hot in data science in 2023? And it told me that it didn't know anything prior to 2021. Yeah. So I said, I'm so sorry. But everyone's talking about chat GPT, it is the most advanced AI chat bot ever released to the masses. It's on fire. They're likening it to the launch of the iPhone, 100 million plus users, but did you know that the CTO of chat GPT is a woman? I did not know, but I learned that. I learned that a couple of days ago. Mira Murati. And of course- I love it. She's been, I saw this great profile piece on her on Fast Company. But of course, everything that we're hearing about with respect to chat GPT, a lot on the CEO, but I thought we need to help dial up the profile of the CTO because she's only 35, yet she is at the helm of one of the most groundbreaking things in our lifetime we'll probably ever see. Isn't that cool? That is, yeah, I had completely had no idea. I didn't either. I saw it on LinkedIn over the weekend. I thought I have to talk about that because it's so important when we talk about some of the trends, the other trends from I need to be.org, I talked about some of those positive trends. Overall hiring is rebounded in 22 compared to pre-pandemic levels. And we see also 51% more women being hired in 22 than 21. So the data, it's all about data is showing us things are progressing quite silly. But one of the biggest challenges that's still persistent is attrition. So we were talking about Hannah, what would your advice be? How would you help a woman stay in tech? We saw that attrition last year in 22, according to I need to be.org, more than doubled. So we're seeing women getting into the field and dropping up for various reasons. And so that's still an extant concern that we have. What do you think would motivate you to stick around if you were in a technical role? State question for you in a minute. Right, you were talking about how there we see an increase, especially in the internal level for women. And I think like if, I don't know, like if I, like this is a great for a start point for like pushing the momentum to like start growth, like pushing the needle to like right words. But I think if like we can see more increase in the upper level, like the woman representation in the upper level too, like maybe that's definitely a big goal and something we should work towards too. But if there's more representation up in the CTO position, like in the managing level, I think that would definitely be a great factor to keep women in data science. I was looking at some trends, sorry Hannah, forgetting what the source was, so forgive me, that was showing that there was a trend in the last few years, I think it was fast company, of more women in executive positions. And COO, specifically chief operating officer positions. What that hasn't translated to, what they thought it might translate to, is more women going from COO to CEO, and we're not seeing that. You know, we think of, if you ask, name a female executive that you'd recognize, everyone would probably say Cheryl Sandberg. But I was shocked to learn the other day at a women in tech event I was doing that there was a survey done by this organization that showed that 78% of people couldn't identify. So to your point, we need more of them in that visible role in the executive suite. And there's data that show that companies that have women, companies across industries that have women in leadership positions, executive positions, I should say, are actually more profitable. So it's kind of like duh, the data is there, it's telling you this, right? And I think also a very important point is work culture and the work environment. And as a woman, maybe if you enter and you work like two or three years, and then you have to oftentimes choose, okay, do I want family or do I want my job? And I think that's one of the major tasks that companies face to make it possible for women to combine being a mother and being a great data scientist or an executive or CEO. And I think there's still a lot to be done in this regard to make it possible for women to not have to choose for one thing on the other. And I think that's also a reason why we might see more women at the entry level, but not long term because they are punished if they take a couple years off if they want to have kids. I think that's a question we need to ask to men too, like how do families work in life? Because we never ask that. We just ask like, how do you ask a woman? They just get it done probably because there's a woman on the other end who's making it happen. So yeah, another thing to think about, another thing to work towards too. Yeah, it's a good point you're raising that we have this conversation together and not exclusively only women, but we all have to come together and talk about how we can design companies in a way that it works for everyone. Yeah, and no slant to men at all. A lot of my mentors and sponsors are men. They're just people that I greatly admire who saw raw potential in me 15, 18 years ago and just added a little water to this little weed and it started to grow. In fact, the cube, the guy is Dave Vellante, John Farrier, two of those people that are sponsors of mine. But we need, it needs to be diverse. It needs to be diverse and gender. It needs to include non-binary people. Anybody shouldn't matter. We should be able to collectively work together to solve big problems like the propaganda problem that was being discussed in the keynote this morning with respect to China or climate change. Climate change is a huge challenge. We're here, we are in California. We're getting an atmospheric river tomorrow and Californians and rain, we're not so friendly. But we know that there's massive changes going on in the climate. Data science can help really unlock a lot of the challenges and solve some of the problems and help us understand better. So there's so much real world implication potential that being data driven can really lead to. And I love the fact that you guys are studying data journalism. You'll have to help me understand that even more. But we're going to have great conversations today. I'm so excited to be co-hosting with both of you. Just, you're going to be inspired to get to learn. They're going to learn from us as well. So let's just kind of think of this as a community of men, women, everything in between to really help inspire the current generations, the future generations. And to your point, let's help women feel confident to be able to stay and raise their hand for fast tracking their careers. What are you guys, last minute, what are you looking forward to most for today? Just meeting these great women. I can't wait. Yeah, learning from each other, having this conversation about how, yeah, we can make data science even more equitable. And yeah, hear from the great ideas that all these women have. Excellent, girls, we're going to have a great day. We're so glad that you're here with us on theCUBE, live at Stanford University, Women in Data Science, the eighth annual conference. I'm Lisa Martin. My two co-hosts for the day, Tracy Zhang, Hannah Freitag. You're going to be seeing a lot of us. We appreciate, stick around. Our first guest joins Hannah and me in just a minute.