>>The pandemic saw a majority of employees working remotely, as we all know, and the world turning to digital services, which caused an uptick in cyber attacks because almost all business was conducted virtually well, the unprecedented events of 2020 led to an enormous and extended upswing in innovation for threat actors. And it's not going away anytime soon. This is according to our colleagues at NetScout and an excerpt from its first half 2021 threat intelligence report. And this event, we're going to unpack Netscout's semi-annual security report for the second half of 2021, which outlines how and why these attacks are carried out and what individuals and businesses can do to prevent attacks. Now, one of the things that NetScout discovered in the second half threat intelligence report is that these cyber attacks, they're not motivated by a single factor on notable example of a recent attack just last month, where government and private websites in Ukraine were knocked offline in a massive distributed denial of service DDoSs attack. >>As Russian troops moved into contested areas in the east of the country. My name is Lisa Martin. And today on this special Q presentation, Richard Hummel joins me manager of threat intelligence at NetScout. He and I are going to explore three of the key findings in the second half of 2021 threat intelligence reports. In the first segment, Richard's going to talk with me about the dark side of DDoS for hire. And one of the things that you're going to learn is that launching DDoS attacks with illicit DDoS for hire services no longer requires a nominal fee in segment two. Richard's going to talk to me about the rise of server class bot net armies. And as Richard will discuss recently, adversaries not only increased the size of IOT botnets, but also conscripted high powered servers into larger button nuts. Then we'll come back for a third and final segment to discuss the vertical industries where attackers really zeroed in for DDoSs attacks in the second half. And here Richard's going to explore some of the verticals that haven't traditionally been in the crosshairs, such as a software publishers and computer manufacturing. All right, guys, let's do this. Here comes our first segment.

(upbeat music) >> Narrator: Live from the Wynn Hotel in Las Vegas, it's theCUBE, covering Magento Imagine, 2018. Brought to you by Magento. >> Welcome back to theCUBE, I'm Lisa Martin. We've had a really informative day talking all things commerce, open commerce, and digital commerce innovation at Magento Imagine 2018. I'm joined by Amanda Batista, who is the head of content marketing for Magento. Amanda, thanks so much for all your help in coordinating this. We've had a really educational day with your folks, with your customers and partners. >> Yeah, we've had a really great community. It's been wonderful to have theCUBE here, and I'm so thrilled to be able to be here with you closing out the show. >> So this is the eighth Imagine event, There's over 3000 people here. You guys had some great speakers on stage today. I'm always very excited to see female leaders on stage. >> Absolutely. >> We talked about Baked by Melissa, she was our first guest today sharing her story. You've been growing this event year over year. What is it about #LeadingTheCharge, your hashtag and message for this event, that really differentiates this eighth event from the last several? >> Well #LeadingTheCharge is a really exciting message for us because ultimately we're focused on empowering merchants and developers and really allowing them to not worry about the technology component of things. Whatever you can dream, you can do on Magento. So, #LeadingTheCharge for us here today is really about bringing people together, making connections, and really thinking about, How do you use this community? How do you tap into all these resources? How do you see people that you haven't seen in a while? It's kind of our coming out party, our big coming together. You know, #LeadingTheCharge I think means different things for different folks, but I think for us we're really aiming to empower individuals to do the work that they do really well but also come together. So I actually heard a gentleman say that part of Leading the Charge for him is a matter of making connection. It's almost stepping out as a leader and allowing other people to come together. I think #LeadingTheCharge has been a really nice message for us today and I think our speakers have really brought that to life. >> I agree and with the sentiment that we've heard. Magento started reputation-wise, helping retailers to target the online shoppers and the experiences there. We talked with Peter Sheldon today about what you guys are doing in really formalizing how you're helping businesses, B2B organizations. There's so much opportunity that's really being driven by all of us as consumers and we have this expectation that we can get anything, anywhere, anytime. >> That's right. >> And have it delivered day or night. Amazon sets the bar really high. You guys had Amazon on main stage this morning talking about the fact that there's now 100 million Prime subscribers and how half of Amazon's revenue doesn't come from products they sell, this third-party marketplace just kicks open the doors of opportunity- >> Amanda: Right. >> for businesses from small to large alike. >> Yeah, I think it's really exciting, too, because, you know we can't all compete on price. We can't all be Amazon, but I think as we're really encouraging merchants to think about, What are you offering that's special? What are you doing from a content standpoint? Obviously, content is near and dear to me, that's my bread and butter and what I've been doing for a long time, but we really think about, what are we offering people that's value-add? Is it an added catalog, is it a manual? Is it something that helps you do your job better? Is it something that helps you go back to your organization and feel celebrated and feel excited?" I think when it comes to how we're empowering people, we're really focused on, from a content perspective, enabling you to, again, not really worry about the tech component, but think about how you can innovate your business. That's really important to us. >> Well, that's one of the things that Melissa Ben-Ishay, she's product officer at Baked by Melissa Cupcakes and how- >> Amanda: Sweet it is. >> I still want, it is, and I still want a cupcake. >> Amanda: Yes! >> It was very evident when we were talking with her that she gets, because of technology, that makes things simple for folks like herself, it allows her not to just grow the business, to open more stores, to reach hundreds of thousands of people, but to do so in a way that she doesn't have to worry about the technology. >> Amanda: Right, right. >> And that really- >> That's a great example, really, for us. I think when we look at who we're looking to enable, you know, Melissa started a business ten years ago, was let go from her job and said, Let me take a passion and bring it to life with business. They had e-commerce even before they had stores. They had e-commerce before they were up and running. I think using that as a linchpin, as a springboard to really bring her business to life, delivering a hundred cupcakes on foot on the New York City subway. I'm from New York, I ride the subway, I wouldn't want to do it with a hundred cupcakes, frankly, but these are the sort of bootstrap methods that she was enabled to do not worrying about that sort of tech component, right? She's bootstraps, she only had about five founders, five people around her with her business. Really great to hear from her and I don't see any cupcakes anywhere but I'm dying for one. >> Me too! >> Or five. >> We need to get some. One of the things that you mentioned, content, earlier, in being a content marketer, look at media as an example, with Netflix and Spotify and Amazon, and what's happened to traditional media. It's now that the way a service is delivered is as important as the content >> Absolutely. >> and what we've heard a lot from your customers that have been on the program today is they have the opportunity to deliver services in a responsive way, and in a way that's really personalized, which is really key, right? As consumers, we all want to have an experience that's tailored to us, and we've heard that as sort of an enabling capability that Magento is helping. We had a gentleman from Coca-Cola on, talking about the Share a Coke experience and how that started as a program in Australia. >> Amanda: Right. >> With one bottler, then went to Europe, then became something that was focused in store, and then the consumers are going, Hey, Coca-Cola, I can't find a bottle with my name on it. And it became this really big program for them, that they had to figure out, How do we do this in the U.S. with 70 bottlers? They needed technology that would allow them to identify and have this visibility of inventory, which you guys allow them to do, but to enable their customers to have an experience with a personalized bottle of Coca-Cola. >> Right. >> Amazing how the technology opens up doors like that, and allows these businesses, whether it's something as an establishment like Coca-Cola, or a Baked by Melissa, to be able to deliver this relevant, personal experience, at the touch of a button. It's Amazing. >> Well, listen, and it's non-negotiable, right? Think about your own experiences as a consumer. Who are you shopping with? I'm shopping with brands that understand me, that know what I need, that are offering value-add. You know, you might also revolutionize the way that we view our experiences, and we really don't have patience. Like you said, we have digital, everything is very quick, and I think the experience is the differentiator. We're really focused, again, on taking the technology out of your planning equation so that you can focus on what are you offering? What are you delivering? How are you delighting? That's a big, big area of opportunity and I think what you do to delight and engage and if you're using data intelligently, and not just the nitty gritty of data, but also simple things, the way that you welcome people via email, the way you engage on Instagram. There's a number of ways to do things that don't really require a lot of planning, a lot of cost, and so in our content efforts, we're really encouraging merchants to think about that. How do you do things in a sort of home-grown way without spending a lot of time or money? We have to be agile, we have to be quick as marketers, I certainly know that, that's the world I live in, and again, it's non-negotiable. I think as a consumer, if I don't feel that you understand me, if I don't feel that you're paying attention to the things that I'm buying or not buying, I'm going somewhere else. I'm going to go to a place that makes me feel as though I'm going to be fulfilled and delighted. I think delight is such an understated thing, but we're here at the Wynn which does a wonderful job with experience and everywhere you go it's so delightful and wonderful. >> Lisa: It is delightful! >> I came back to the room last night and my computer cord was just rattled up ever so gently, and I thought, That's delightful! You know, I Instagrammed that. That's a perfect example of providing experience that is superior. >> Speaking of experience, we just had the gentleman from the Accent Group on, Mark Teperson. It was so interesting how they've taken this company down in Australia and New Zealand, with multiple, many, many, many brands of footwear. And, you know, the online and the physical world have been merging in retail for a while now, but what they're wanting to do, to click and collect, and to create this in-store experience. It was such an interesting way of thinking about and hearing from a Chief Digital Officer say, We want to be able to enable people, especially mobile first, we're sitting on the couch with these things often, but to enable them to be able to come into my store and have an experience. That word is, we heard that referenced in many different times today, the Accent Group was a great example of that, as well as when we had your V.P. of Strategy on saying, A lot of cases depending on the, whether it's B2B or B2C, it's not mobile too, it's mobile only. It's not just leveraging technology and data and analytics to understand what I want as a consumer, but it's how I want to consume it. So it's what I was saying earlier about we're seeing this level playing field of how services are delivered, equally as important as the content that you're going to deliver to me. >> Yeah, absolutely. Again, non-negotiable, right? This idea of an omni-channel experience bridging the gaps between online and in-store, like you said, we're on the couch. I almost never shop on a computer any more, right? I'm mobile, we're enabled, we have PayPal, we our credit cards saved. I think to keep that momentum going, you want it to be a seamless experience. How many times have you gone online and found that an item is supposed to be available in the store. When you go, it's not there, right? I've even done due diligence as a savvy shopper who works in retail and says, Let me call the store and make sure it's there. There's really no margin for error there, because when we talk about experience, if you do go in store, and if you do take the initiative to make that purchase and take time out of your day, right, we're all busy people. I think mobile and digital has made it easy, especially Amazon Prime revolutionized that. (mimics beeping noise) Two days, it's on your doorstep. I think as we look to see who's sort of mimicking that experience, I think an easy way to do it, is simply put, have your systems connected, ensure that things are integrated, ensure that your inventory visibility is on point. It's a non-negotiable experience, really. >> Well, Amanda, we've had a blast at Magento Imagine 2018. Our first one, looking forward to being back next year. Thank you for putting together a great array of guests. I know we've learned a ton about this. I won't look at online shopping again the same. We want to thank you for helping us have a really enlightened and delightful conversation. >> And likewise, we've loved having theCUBE. You guys have been wonderful. I've learned a great deal and it's been really nice spending this time with you. So thanks for having me, Lisa. >> Absolutely. We hope you've had a delightful experience today with us on theCUBE. We've been live at Magento Imagine 2018. Check out theCUBE.net where you can find all the replays of the segments that we filmed today. You can also find the editorial components on SiliconANGLE.com. I'm Lisa Martin for theCUBE. We'll see you next time. (upbeat music)

>> Announcer: Live from the Computer History Museum, in the heart of Silicon Valley, it's theCUBE, covering Food IT, Fork to Farm. Brought to you by Western Digital. >> Welcome back to theCUBE, I'm Lisa Martin, with Jeff Frick, and we have just spent a really interesting educational day at the Fork to Farm event, Food IT. Jeff we've spoken with investors, ag-tech experts, folks in academia who are training the next generation of farmers, to Campbell Soup, who's been around since the late 1800s, are really focused on helping the agriculture and food industry combat the challenges of environmental sustainability, of climate change, of labor shortages, it's been a really, really intriguing day, where tech meets food and agriculture. >> Yeah and just a huge opportunity. One of the themes that kept coming up over and over again, is the average age of the farmers today. Heard 70 something, 60 something, whatever, they're getting old, so there's going to be a huge turnover in this industry, so both a challenge as well as an opportunity for the next generation of ag-people to make some of these changes, and change the way the industry works. The other thing that's really interesting that I found Lisa, is that there's really big social issues that are at play here. We talked about water, we talked about labor, that play into this whole thing, sustainability. And again, tying it back to their theme of its fork to farm, how much of that's now driven by the consumer and the industry, it's kind of a reaction to the consumer, which we see over and over and over in all the other shows that we go. The consumerization IT, driven by younger people's interactions with their phones, is setting an expectation of the way they want everything to work. And so, it sounds like the food industry is really at the cutting edge of this, still really early on, but as we saw in some of those market maps, and the innovation is rich, feels like we're really at the start of this thing. So even though this show has been around for a few years, they have the big show in Salinas next week, the Forbes show, that's still really early days of leveraging tech, innovation, to change the food industry. >> It is, and you brought up that the labor shortages, and that was echoed quite a bit today, for a number of reasons. One, the aging population of farmers as you mentioned. Two, also in California, the minimum wage going up, and that's not only going to be a problem Jeff for farmers, but it's actually now pervading into the retail space, where they're going to have to start depending on robotics to be able to create, or to reduce their cost, to provide even fast food. That was something that was quite interesting to me, I hadn't really quite thought about, from that channel perspective. >> Right, right. >> And then as you mentioned, on the tech enabled consumer side, I was talking with Jeff earlier, I kept thinking farm to fork, 'cause farm to table is so trendy now, right? There's a lot of apps. And you gave me this a-ha grasshopper look, and it was really because as consumers we've really demanded so much. We want transparency, we want to know exactly what's in things, and we want organic, and hormone-free, and we also want things delivered whenever, and wherever we want them. We think of the distribution model, has really become very decentralized, and a lot of that being driven by the consumer. On the farm side too, regarding the attrition, there's also a lot of antiquated, especially in the post-harvest supply chain, things that are still written down on paper, traceability is a huge challenge for them. And I think from some of the things we heard today, a lot of the farming, especially in California, they can't really quite see all the data that they have, but they are sitting on a lot of information, that not only could make their farms more efficient, but could also facilitate you think, even knowledge transfer to the next generation of farmers. Right, right. Yeah a lot of talk about kind of there wasn't a lot of data, now it's a data flood. So how do you use those data sources to be more intelligent in what you do? And I specifically asked some of the guests, you know, are kind of the classic big data players participating in this space, and she said, "Not really." They're all kind of holding off on the side waiting to get in. But these are big numbers, this is a big impact. The professor from St. Louis Episcopal talked about a billion dollars worth of strawberries that you got to get off the field, and if you don't have the labor to get it off, and the data to get the labor and to time it right, it's a billion dollars worth of strawberries, and these are big numbers. And the other thing that just fascinated me, is again, this power of the consumer. The Google guy who took basically what was a service just to feed employees and keep them around so they write more code, but using that as a platform to drive much more thoughtfulness and intelligence. And supply chain changes around food, and even called it food shot in reference to the moon shot. >> The moon shot, yes. >> Enabled better diets, shift diets, food transparency, reduced loss and waste, accelerate transformation to a circular food economy. So, and they said, I think he's been at it for 15 years or thereabout. So really an interesting kind of a twist, on what you would not expect from the food service people, you think of them just supplying food. >> Exactly. >> Not trying to drive cultural change. >> Exactly, and trying to scale, but they're using data from their own googlers, to help determine and evaluate what people are doing, what they want, preferences, making it more personal, and using data in that way to also then facilitate some of the upstream, you know from the supply perspective, making things, meeting those challenges that the consumers are demanding, but you said he's been at Google for five years, and when he first got the call being in hospitality for so long, he just thought, "Google, what do they want to talk to me for?" And how revolutionary they've been, and you can think of how much education can happen from Google Food alone. I was quite blown away by that. >> Yeah, the other kind of theme is unused resources. So, one of the food trucks that they had seaweed. Why seaweed? Because it takes no fresh water, it takes no fertilizer, and it's carbon negative. So not really about how does it taste, but some specific reasons to try to make seaweed a better food, a more satisfying food. Talked about kale, and really again what a great example of a, can't say it, Fork to Farm tradition, 'cause before kale was a throwaway, nobody grew kale, now suddenly everybody wants kale smoothies, and so there's nothing, plant became something of importance, driven by the consumer, not necessarily by the producers. So, very dynamic times. I think again, the trend we see over and over and over, finding the hollowing out of the middle. You know, you don't want to be just a generic provider in the middle, you better have massive scale, or you better be a real specialty provider. And then finally the ramifications of the Amazon purchase of Whole Foods, really validating, yes you want digital, yes you want data, yes you want to provide better customer service. But at the same time, you still need a physical presence, kind of validating the physical presence of the store like Whole Foods. So really a very dynamic activity going on in this space. >> And it'll be interesting to see what happens over the next five to 10 years, as farming generationally changes hands. And there is technology that's available today, right? We talked about big data, there's many, many sources of public data, whether it's satellite imagery, water data that can be utilized and then paired with private data that a farm has. Or using GPS devices on tractors and combines, robotics. You talked to the inventor of the Sally Salad machine, there's a lot of technology that might be, I don't know if I'd say ahead of its time, but I think from a farming perspective, there's a little bit of a gap there right now. So it'll be very interesting to see how farms evolve from a technology perspective. I love how the Forbes AgTech Summit, I think it's tomorrow and Thursday in Salinas Valley, what a great juxtaposition of Silicon Valley and a world hub of technology innovation, to Salinas, which is the salad bowl of the world. I think that is quite interesting, and some of the dynamics that they've seen, I think this was their fourth event tomorrow. >> Jeff: Fourth event, right, right. >> Really starting to get more farmers interested in understanding the potential that ag-tech can have on profitability, efficiencies, reducing waste, even things like discovering and preventing foodborne pathogens. >> Right, and robots, we need robots, we don't have enough labor. Michael Rose said there's going to be a shortage of hundreds of thousands of line cooks. Just regular, ordinary line cooks at restaurants, and that's really kind of one of the applications of the salad machine, because as you hit the button below that cook, you can hit the button to load that salad, while you run off and pull the rest of the entree meals together. So, again, it's really fun to see the consistent themes that we see over and over, that's computing cloud and data-driven decision making, applied to what's arguably one of the most important things going on, which is feeding us a lot of conversation about the world's population getting to 10 billion in the not too distant future, that have to be fed. And again, with the aging of the population, the traditional farmers, a real opportunity to do kind of a refresh with a bunch of people that have grown up with these things. So, really cool show, a great day, hope you had fun, I had fun. >> Oh, I had a great time, it was really educational. I think that you hit the nail on the head, there's a tremendous amount of opportunity. I think what the Mixing Bowl is doing, along with Better Foods, is really bringing the people that are creating food, and producing it together, and connecting them with the people that are creating technology. So, I think this is the tip of the iceberg head of lettuce, maybe? So, I am excited to see what happens over time, but not only was it a great event, but I'm now very hungry. >> Now you're very hungry, there's more food trucks outside. Alright Lisa, well thank you again for hosting. >> Thank you. >> Again, another great show. I think last time we were together was at the NAB. >> NAB. >> Talking about media entertainment, so the digitization, transformation continues, driven by all these huge macro-factors of cloud, big data, so the beat rolls on. >> It does. >> Alright, she's Lisa Martin, and I'm Jeff Frick, you're watching theCube. Thanks for watching, we've got a busy spring coming to an end. Had a little bit of a lull in the summer then we'll hit it hard again in the fall, so thanks for watching siliconangle.tv, youtube.com/siliconangle, and siliconangle.com for complete coverage of a lot of stories beyond just theCUBE. I'm Jeff Frick, signing off with Lisa Martin from Food IT, from Fork to Food, thanks for watching.

(Exciting Techno Music) >> Live from the Computer History Museum in the heart of Silicon Valley, it's theCUBE covering food IT: Fork to Farm. Brought to you by Western Digital. >> Hi, welcome to theCUBE. We are live at the fourth annual food IT Fork to Farm Event. I am Lisa Martin with my Co-Host Jeff Frick. Jeff, this is a really interesting event. The first time we've been here with The Cube, and one of the first things I said to you this morning was "Fork to Farm - we always kind of think of it as "farm to fork, right, farm to table." But it's really interesting mix of investors here, people that are very educated in food and agriculture and one of the things they're focused on is connecting people who are feeding the world, billions and billions of people, with the people who are changing the world through technologies. And nowadays we're all this tech enabled food consumer which has really flipped farm to fork from fork to farm which I found really interesting. >> It's pretty interesting, our first kickoff call with Michael Rose from The Mixing Bowl and we were talking about the Conference. I'm like, "Michael didn't you get that mixed up? "Isn't it supposed to be Farm to Fork?" But as you said it's really now the tech enabled consumer and what they want to eat, like everything else, is being so consumer driven that we see in our other shows is driving now what the food producers have to create. And what's interesting is they don't necessarily think of all the ramifications of those decisions upstream and downstream. And so that's a big topic of the theme here. The other thing that struck me is some of the sponsors: Yamaha is here >> Yes. >> And one of the main sponsors. Google is here as one of the main sponsors. And we just had one of the opening keynotes from one of the guys from Google talking about how they've taken really just the task of feeding the employees to a much greater responsibility in both what people eat, how it gets produced, and really more sustainable longterm food kind of as a circle he called it. So it's pretty interesting, I'm excited. We've got Deans from a lot of big schools, we've got, of course, like I said, Yamaha. I'm really curious to find out what they're doing in this space. And it's fun to get, you know, out of the tech infrastructure space to see what's really happening on the front lines. I really want to get into edge computing, I really want to get into cloud, data, you know, all of the themes that we follow over and over and over again, but now a real specific application. And doing some of the research, you know, we have to feed 10 billion people in just a couple years and we're not growing any more land. So how are those challenges being addressed with technology? How are cloud, mobile, data helping solve those problems? And then how are the consumer driven prioritization impacting all of this? So it should be a great day. >> Absolutely, like you said, a great spectrum of guests on the show today. And we think of food and agriculture as one of the largest industries globally and as you said, there's a daunting responsibility feeding billions of people in a very short period of time. Having to deal with environmental sustainability, we're going to be talking about that on the program today, climate change, and also the consumer. But there's tremendous potential for big data and IOT and analytics to improve farming efficiencies from planting to weeding to fertilizing to the post-harvest supply chain logistics, traceability. There's, you know, opportunities for GPS sensors on tractors and columbines, as well as robotics and automation. We're going to be talking to a guy, the CEO Chell Botics, who invented Sally, a robot that makes salads. So there's a tremendous amount of opportunity and I'm really curious to see how these, from the University Folks, the Deans, to the investors, how Venture Capital is really seeing big data as revolutionary, the potential to be revolutionary, for the entire food supply, the food chain. >> Right, right. And another topic that's come up is really transparency and enabling consumers to see kind of where their food comes from, how it was raised, but as come up again in one of the earlier Keynotes, there's no perfect solution, right? There's always trade offs. So how are people creating values, making trade offs based on those values, and how are the food producers now being able to deliver to those values? So it should be, like I said, a fantastic day. We're going to go wall to wall. We'll be here till 5 o'clock today, full slate of guests, a lot of two guests, so we're going to pack them in. And it should be fantastic. >> Absolutely, I'm excited. >> Alright. >> A lot of great topics. >> So she's Lisa Martin, I'm Jeff Frick, you're watching theCUBE from Food IT: from Fork to Farm. We'll be right back with our first guest after this short break. Thanks for watching.

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

(light upbeat music) >> Hey, everyone. Welcome back to theCUBE's day long coverage of Women in Data Science 2023. Live from Stanford University, I'm Lisa Martin. We've had some amazing conversations today with my wonderful co-host, as you've seen. Tracy Zhang joins me next for a very interesting and inspiring conversation. I know we've been bringing them to you, we're bringing you another one here. Dr. Irene Dankwa-Mullan joins us, the Chief Medical Officer at Marti Health, and a speaker at WIDS. Welcome, Irene, it's great to have you. >> Thank you. I'm delighted to be here. Thank you so much for this opportunity. >> So you have an MD and a Master of Public Health. Covid must have been an interesting time for you, with an MPH? >> Very much so. >> Yeah, talk a little bit about you, your background, and Marti Health? This is interesting. This is a brand new startup. This is a digital health equity startup. >> Yes, yes. So, I'll start with my story a little bit about myself. So I was actually born in Ghana. I finished high school there and came here for college. What would I say? After I finished my undergraduate, I went to medical school at Dartmouth and I always knew I wanted to go into public health as well as medicine. So my medical education was actually five years. I did the MPH and my medical degree, at the same time, I got my MPH from Yale School of Public Health. And after I finished, I trained in internal medicine, Johns Hopkins, and after that I went into public health. I am currently living in Maryland, so I'm in Bethesda, Maryland, and that's where I've been. And really enjoyed public health, community health, combining that aspect of sort of prevention and wellness and also working in making sure that we have community health clinics and safety net clinics. So a great experience there. I also had the privilege, after eight years in public health, I went to the National Institute of Health. >> Oh, wow. >> Where I basically worked in clinical research, basically on minority health and health disparities. So, I was in various leadership roles and helped to advance the science of health equity, working in collaboration with a lot of scientists and researchers at the NIH, really to advance the science. >> Where did your interest in health equity come from? Was there a defining moment when you were younger and you thought "There's a lot of inequities here, we have to do something about this." Where did that interest start? >> That's a great question. I think this influence was basically maybe from my upbringing as well as my family and also what I saw around me in Ghana, a lot of preventable diseases. I always say that my grandfather on my father's side was a great influence, inspired me and influenced my career because he was the only sibling, really, that went to school. And as a result, he was able to earn enough money and built, you know, a hospital. >> Oh wow. >> In their hometown. >> Oh my gosh! >> It started as a 20 bed hospital and now it's a 350 bed hospital. >> Oh, wow, that's amazing! >> In our hometown. And he knew that education was important and vital as well for wellbeing. And so he really inspired, you know, his work inspired me. And I remember in residency I went with a group of residents to this hospital in Ghana just to help over a summer break. So during a summer where we went and helped take care of the sick patients and actually learned, right? What it is like to care for so many patients and- >> Yeah. >> It was really a humbling experience. But that really inspired me. I think also being in this country. And when I came to the U.S. and really saw firsthand how patients are treated differently, based on their background or socioeconomic status. I did see firsthand, you know, that kind of unconscious bias. And, you know, drew me to the field of health disparities research and wanted to learn more and do more and contribute. >> Yeah. >> Yeah. So, I was curious. Just when did the data science aspect tap in? Like when did you decide that, okay, data science is going to be a problem solving tool to like all the problems you just said? >> Yeah, that's a good question. So while I was at the NIH, I spent eight years there, and precision medicine was launched at that time and there was a lot of heightened interest in big data and how big data could help really revolutionize medicine and healthcare. And I got the opportunity to go, you know, there was an opportunity where they were looking for physicians or deputy chief health officer at IBM. And so I went to IBM, Watson Health was being formed as a new business unit, and I was one of the first deputy chief health officers really to lead the data and the science evidence. And that's where I realized, you know, we could really, you know, the technology in healthcare, there's been a lot of data that I think we are not really using or optimizing to make sure that we're taking care of our patients. >> Yeah. >> And so that's how I got into data science and making sure that we are building technologies using the right data to advance health equity. >> Right, so talk a little bit about health equity? We mentioned you're with Marti Health. You've been there for a short time, but Marti Health is also quite new, just a few months old. Digital health equity, talk about what Marti's vision is, what its mission is to really help start dialing down a lot of the disparities that you talked about that you see every day? >> Yeah, so, I've been so privileged. I recently joined Marti Health as their Chief Medical Officer, Chief Health Officer. It's a startup that is actually trying to promote a value-based care, also promote patient-centered care for patients that are experiencing a social disadvantage as a result of their race, ethnicity. And were starting to look at and focused on patients that have sickle cell disease. >> Okay. >> Because we realize that that's a population, you know, we know sickle cell disease is a genetic disorder. It impacts a lot of patients that are from areas that are endemic malaria. >> Yeah. >> Yeah. >> And most of our patients here are African American, and when, you know, they suffer so much stigma and discrimination in the healthcare system and complications from their sickle cell disease. And so what we want to do that we feel like sickle cell is a litmus test for disparities. And we want to make sure that they get in patient-centered care. We want to make sure that we are leveraging data and the research that we've done in sickle cell disease, especially on the continent of Africa. >> Okay. >> And provide, promote better quality care for the patients. >> That's so inspiring. You know, we've heard so many great stories today. Were you able to watch the keynote this morning? >> Yes. >> I loved how it always inspires me. This conference is always, we were talking about this all day, how you walk in the Arrillaga Alumni Center here where this event is held every year, the vibe is powerful, it's positive, it's encouraging. >> Inspiring, yeah. >> Absolutely. >> Inspiring. >> Yeah, yeah. >> It's a movement, WIDS is a movement. They've created this community where you feel, I don't know, kind of superhuman. "Why can't I do this? Why not me?" We heard some great stories this morning about data science in terms of applications. You have a great application in terms of health equity. We heard about it in police violence. >> Yes. >> Which is an epidemic in this country for sure, as we know. This happens too often. How can we use data and data science as a facilitator of learning more about that, so that that can stop? I think that's so important for more people to understand all of the broad applications of data science, whether it's police violence or climate change or drug discovery or health inequities. >> Irene: Yeah. >> The potential, I think we're scratching the surface. But the potential is massive. >> Tracy: It is. >> And this is an event that really helps women and underrepresented minorities think, "Why not me? Why can't I get involved in that?" >> Yeah, and I always say we use data to make an make a lot of decisions. And especially in healthcare, we want to be careful about how we are using data because this is impacting the health and outcomes of our patients. And so science evidence is really critical, you know? We want to make sure that data is inclusive and we have quality data. >> Yes. >> And it's transparent. Our clinical trials, I always say are not always diverse and inclusive. And if that's going to form the evidence base or data points then we're doing more harm than good for our patients. And so data science, it's huge. I mean, we need a robust, responsible, trustworthy data science agenda. >> "Trust" you just brought up "trust." >> Yeah. >> I did. >> When we talk about data, we can't not talk about security and privacy and ethics but trust is table stakes. We have to be able to evaluate the data and trust in it. >> Exactly. >> And what it says and the story that can be told from it. So that trust factor is, I think, foundational to data science. >> We all see what happened with Covid, right? I mean, when the pandemic came out- >> Absolutely. >> Everyone wanted information. We wanted data, we wanted data we could trust. There was a lot of hesitancy even with the vaccine. >> Yeah. >> Right? And so public health, I mean, like you said, we had to do a lot of work making sure that the right information from the right data was being translated or conveyed to the communities. And so you are totally right. I mean, data and good information, relevant data is always key. >> Well- >> Is there any- Oh, sorry. >> Go ahead. >> Is there anything Marti Health is doing in like ensuring that you guys get the right data that you can put trust in it? >> Yes, absolutely. And so this is where we are, you know, part of it would be getting data, real world evidence data for patients who are being seen in the healthcare system with sickle cell disease, so that we can personalize the data to those patients and provide them with the right treatment, the right intervention that they need. And so part of it would be doing predictive modeling on some of the data, risk, stratifying risk, who in the sickle cell patient population is at risk of progressing. Or getting, you know, they all often get crisis, vaso-occlusive crisis because the cells, you know, the blood cell sickles and you want to avoid those chest crisis. And so part of what we'll be doing is, you know, using predictive modeling to target those at risk of the disease progressing, so that we can put in preventive measures. It's all about prevention. It's all about making sure that they're not being, you know, going to the hospital or the emergency room where sometimes they end up, you know, in pain and wanting pain medicine. And so. >> Do you see AI as being a critical piece in the transformation of healthcare, especially where inequities are concerned? >> Absolutely, and and when you say AI, I think it's responsible AI. >> Yes. >> And making sure that it's- >> Tracy: That's such a good point. >> Yeah. >> Very. >> With the right data, with relevant data, it's definitely key. I think there is so much data points that healthcare has, you know, in the healthcare space there's fiscal data, biological data, there's environmental data and we are not using it to the full capacity and full potential. >> Tracy: Yeah. >> And I think AI can do that if we do it carefully, and like I said, responsibly. >> That's a key word. You talked about trust, responsibility. Where data science, AI is concerned- >> Yeah. >> It has to be not an afterthought, it has to be intentional. >> Tracy: Exactly. >> And there needs to be a lot of education around it. Most people think, "Oh, AI is just for the technology," you know? >> Yeah, right. >> Goop. >> Yes. >> But I think we're all part, I mean everyone needs to make sure that we are collecting the right amount of data. I mean, I think we all play a part, right? >> We do. >> We do. >> In making sure that we have responsible AI, we have, you know, good data, quality data. And the data sciences is a multi-disciplinary field, I think. >> It is, which is one of the things that's exciting about it is it is multi-disciplinary. >> Tracy: Exactly. >> And so many of the people that we've talked to in data science have these very non-linear paths to get there, and so I think they bring such diversity of thought and backgrounds and experiences and thoughts and voices. That helps train the AI models with data that's more inclusive. >> Irene: Yes. >> Dropping down the volume on the bias that we know is there. To be successful, it has to. >> Definitely, I totally agree. >> What are some of the things, as we wrap up here, that you're looking forward to accomplishing as part of Marti Health? Like, maybe what's on the roadmap that you can share with us for Marti as it approaches the the second half of its first year? >> Yes, it's all about promoting health equity. It's all about, I mean, there's so much, well, I would start with, you know, part of the healthcare transformation is making sure that we are promoting care that's based on value and not volume, care that's based on good health outcomes, quality health outcomes, and not just on, you know, the quantity. And so Marti Health is trying to promote that value-based care. We are envisioning a world in which everyone can live their full life potential. Have the best health outcomes, and provide that patient-centered precision care. >> And we all want that. We all want that. We expect that precision and that personalized experience in our consumer lives, why not in healthcare? Well, thank you, Irene, for joining us on the program today. >> Thank you. >> Talking about what you're doing to really help drive the volume up on health equity, and raise awareness for the fact that there's a lot of inequities in there we have to fix. We have a long way to go. >> We have, yes. >> Lisa: But people like you are making an impact and we appreciate you joining theCUBE today and sharing what you're doing, thank you. >> Thank you. >> Thank you- >> Thank you for having me here. >> Oh, our pleasure. For our guest and Tracy Zhang, this is Lisa Martin from WIDS 2023, the eighth Annual Women in Data Science Conference brought to you by theCUBE. Stick around, our show wrap will be in just a minute. Thanks for watching. (light upbeat music)

(upbeat music) >> Welcome back to The Cubes coverage of WIDS 2023 the eighth Annual Women in Data Science Conference which is held at Stanford University. I'm your host, Lisa Martin. I'm really excited to be having some great co-hosts today. I've got Hannah Freytag with me, who is a data journalism master student at Stanford. We have yet another inspiring woman in technology to bring to you today. Kelly Hoang joins us, data scientist at Gilead. It's so great to have you, Kelly. >> Hi, thank you for having me today. I'm super excited to be here and share my journey with you guys. >> Let's talk about that journey. You recently got your PhD in information sciences, congratulations. >> Thank you. Yes, I just graduated, I completed my PhD in information sciences from University of Illinois Urbana-Champaign. And right now I moved to Bay Area and started my career as a data scientist at Gilead. >> And you're in better climate. Well, we do get snow here. >> Kelly: That's true. >> We proved that the last... And data science can show us all the climate change that's going on here. >> That's true. That's the topic of the data fund this year, right? To understand the changes in the climate. >> Yeah. Talk a little bit about your background. You were mentioning before we went live that you come from a whole family of STEM students. So you had that kind of in your DNA. >> Well, I consider myself maybe I was a lucky case. I did grew up in a family in the STEM environment. My dad actually was a professor in computer science. So I remember when I was at a very young age, I already see like datas, all of these computer science concepts. So grew up to be a data scientist is always something like in my mind. >> You aspired to be. >> Yes. >> I love that. >> So I consider myself in a lucky place in that way. But also, like during this journey to become a data scientist you need to navigate yourself too, right? Like you have this roots, like this foundation but then you still need to kind of like figure out yourself what is it? Is it really the career that you want to pursue? But I'm happy that I'm end up here today and where I am right now. >> Oh, we're happy to have you. >> Yeah. So you' re with Gilead now after you're completing your PhD. And were you always interested in the intersection of data science and health, or is that something you explored throughout your studies? >> Oh, that's an excellent question. So I did have background in computer science but I only really get into biomedical domain when I did my PhD at school. So my research during my PhD was natural language processing, NLP and machine learning and their applications in biomedical domains. And then when I graduated, I got my first job in Gilead Science. Is super, super close and super relevant to what my research at school. And at Gilead, I am working in the advanced analytics department, and our focus is to bring artificial intelligence and machine learning into supporting clinical decision making. And really the ultimate goal is how to use AI to accelerate the precision medicine. So yes, it's something very like... I'm very lucky to get the first job that which is very close to my research at school. >> That's outstanding. You know, when we talk about AI, we can't not talk about ethics, bias. >> Kelly: Right. >> We know there's (crosstalk) Yes. >> Kelly: In healthcare. >> Exactly. Exactly. Equities in healthcare, equities in so many things. Talk a little bit about what excites you about AI, what you're doing at Gilead to really influence... I mean this, we're talking about something that's influencing life and death situations. >> Kelly: Right. >> How are you using AI in a way that is really maximizing the opportunities that AI can bring and maximizing the value in the data, but helping to dial down some of the challenges that come with AI? >> Yep. So as you may know already with the digitalization of medical records, this is nowaday, we have a tremendous opportunities to fulfill the dream of precision medicine. And what I mean by precision medicines, means now the treatments for people can be really tailored to individual patients depending on their own like characteristic or demographic or whatever. And nature language processing and machine learning, and AI in general really play a key role in that innovation, right? Because like there's a vast amount of information of patients and patient journeys or patient treatment is conducted and recorded in text. So that's why our group was established. Actually our department, advanced analytic department in Gilead is pretty new. We established our department last year. >> Oh wow. >> But really our mission is to bring AI into this field because we see the opportunity now. We have a vast amount of data about patient about their treatments, how we can mine these data how we can understand and tailor the treatment to individuals. And give everyone better care. >> I love that you brought up precision medicine. You know, I always think, if I kind of abstract everything, technology, data, connectivity, we have this expectation in our consumer lives. We can get anything we want. Not only can we get anything we want but we expect whoever we're engaging with, whether it's Amazon or Uber or Netflix to know enough about me to get me that precise next step. I don't think about precision medicine but you bring up such a great point. We expect these tailored experiences in our personal lives. Why not expect that in medicine as well? And have a tailored treatment plan based on whatever you have, based on data, your genetics, and being able to use NLP, machine learning and AI to drive that is really exciting. >> Yeah. You recap it very well, but then you also bring up a good point about the challenges to bring AI into this field right? Definitely this is an emerging field, but also very challenging because we talk about human health. We are doing the work that have direct impact to human health. So everything need to be... Whatever model, machine learning model that you are building, developing you need to be precise. It need to be evaluated properly before like using as a product, apply into the real practice. So it's not like recommendation systems for shopping or anything like that. We're talking about our actual health. So yes, it's challenging that way. >> Yeah. With that, you already answered one of the next questions I had because like medical data and health data is very sensitive. And how you at Gilead, you know, try to protect this data to protect like the human beings, you know, who are the data in the end. >> The security aspect is critical. You bring up a great point about sensitive data. We think of healthcare as sensitive data. Or PII if you're doing a bank transaction. We have to be so careful with that. Where is security, data security, in your everyday work practices within data science? Is it... I imagine it's a fundamental piece. >> Yes, for sure. We at Gilead, for sure, in data science organization we have like intensive trainings for employees about data privacy and security, how you use the data. But then also at the same time, when we work directly with dataset, it's not that we have like direct information about patient at like very granular level. Everything is need to be kind of like anonymized at some points to protect patient privacy. So we do have rules, policies to follow to put that in place in our organization. >> Very much needed. So some of the conversations we heard, were you able to hear the keynote this morning? >> Yes. I did. I attended. Like I listened to all of them. >> Isn't it fantastic? >> Yes, yes. Especially hearing these women from different backgrounds, at different level of their professional life, sharing their journeys. It's really inspiring. >> And Hannah, and I've been talking about, a lot of those journeys look like this. >> I know >> You just kind of go... It's very... Yours is linear, but you're kind of the exception. >> Yeah, this is why I consider my case as I was lucky to grow up in STEM environment. But then again, back to my point at the beginning, sometimes you need to navigate yourself too. Like I did mention about, I did my pa... Sorry, my bachelor degree in Vietnam, in STEM and in computer science. And that time, there's only five girls in a class of 100 students. So I was not the smartest person in the room. And I kept my minority in that areas, right? So at some point I asked myself like, "Huh, I don't know. Is this really my careers." It seems that others, like male people or students, they did better than me. But then you kind of like, I always have this passion of datas. So you just like navigate yourself, keep pushing yourself over those journey. And like being where I am right now. >> And look what you've accomplished. >> Thank you. >> Yeah. That's very inspiring. And yeah, you mentioned how you were in the classroom and you were only one of the few women in the room. And what inspired or motivated you to keep going, even though sometimes you were at these points where you're like, "Okay, is this the right thing?" "Is this the right thing for me?" What motivated you to keep going? >> Well, I think personally for me, as a data scientist or for woman working in data science in general, I always try to find a good story from data. Like it's not, when you have a data set, well it's important for you to come up with methodologies, what are you going to do with the dataset? But I think it's even more important to kind of like getting the context of the dataset. Like think about it like what is the story behind this dataset? What is the thing that you can get out of it and what is the meaning behind? How can we use it to help use it in a useful way. To have in some certain use case. So I always have that like curiosity and encouragement in myself. Like every time someone handed me a data set, I always think about that. So it's helped me to like build up this kind of like passion for me. And then yeah. And then become a data scientist. >> So you had that internal drive. I think it's in your DNA as well. When you were one of five. You were 5% women in your computer science undergrad in Vietnam. Yet as Hannah was asking you, you found a lot of motivation from within. You embrace that, which is so key. When we look at some of the statistics, speaking of data, of women in technical roles. We've seen it hover around 25% the last few years, probably five to 10. I was reading some data from anitab.org over the weekend, and it shows that it's now, in 2022, the number of women in technical roles rose slightly, but it rose, 27.6%. So we're seeing the needle move slowly. But one of the challenges that still remains is attrition. Women who are leaving the role. You've got your PhD. You have a 10 month old, you've got more than one child. What would you advise to women who might be at that crossroads of not knowing should I continue my career in climbing the ladder, or do I just go be with my family or do something else? What's your advice to them in terms of staying the path? >> I think it's really down to that you need to follow your passion. Like in any kind of job, not only like in data science right? If you want to be a baker, or you want to be a chef, or you want to be a software engineer. It's really like you need to ask yourself is it something that you're really passionate about? Because if you really passionate about something, regardless how difficult it is, like regardless like you have so many kids to take care of, you have the whole family to take care of. You have this and that. You still can find your time to spend on it. So it's really like let yourself drive your own passion. Drive the way where you leading to. I guess that's my advice. >> Kind of like following your own North Star, right? Is what you're suggesting. >> Yeah. >> What role have mentors played in your career path, to where you are now? Have you had mentors on the way or people who inspired you? >> Well, I did. I certainly met quite a lot of women who inspired me during my journey. But right now, at this moment, one person, particular person that I just popped into my mind is my current manager. She's also data scientist. She's originally from Caribbean and then came to the US, did her PhDs too, and now led a group, all women. So believe it or not, I am in a group of all women working in data science. So she's really like someone inspire me a lot, like someone I look up to in this career. >> I love that. You went from being one of five females in a class of 100, to now having a PhD in information sciences, and being on an all female data science team. That's pretty cool. >> It's great. Yeah, it's great. And then you see how fascinating that, how things shift right? And now today we are here in a conference that all are women in data science. >> Yeah. >> It's extraordinary. >> So this year we're fortunate to have WIDS coincide this year with the actual International Women's Day, March 8th which is so exciting. Which is always around this time of year, but it's great to have it on the day. The theme of this International Women's Day this year is embrace equity. When you think of that theme, and your career path, and what you're doing now, and who inspires you, how can companies like Gilead benefit from embracing equity? What are your thoughts on that as a theme? >> So I feel like I'm very lucky to get my first job at Gilead. Not only because the work that we are doing here very close to my research at school, but also because of the working environment at Gilead. Inclusion actually is one of the five core values of Gilead. >> Nice. >> So by that, we means we try to create and creating a working environment that all of the differences are valued. Like regardless your background, your gender. So at Gilead, we have women at Gilead which is a global network of female employees, that help us to strengthen our inclusion culture, and also to influence our voices into the company cultural company policy and practice. So yeah, I'm very lucky to work in the environment nowadays. >> It's impressive to not only hear that you're on an all female data science team, but what Gilead is doing and the actions they're taking. It's one thing, we've talked about this Hannah, for companies, and regardless of industry, to say we're going to have 50% women in our workforce by 2030, 2035, 2040. It's a whole other ballgame for companies like Gilead to actually be putting pen to paper. To actually be creating a strategy that they're executing on. That's awesome. And it must feel good to be a part of a company who's really adapting its culture to be more inclusive, because there's so much value that comes from inclusivity, thought diversity, that ultimately will help Gilead produce better products and services. >> Yeah. Yes. Yeah. Actually this here is the first year Gilead is a sponsor of the WIDS Conference. And we are so excited to establish this relationship, and looking forward to like having more collaboration with WIDS in the future. >> Excellent. Kelly we've had such a pleasure having you on the program. Thank you for sharing your linear path. You are definitely a unicorn. We appreciate your insights and your advice to those who might be navigating similar situations. Thank you for being on theCUBE today. >> Thank you so much for having me. >> Oh, it was our pleasure. For our guests, and Hannah Freytag this is Lisa Martin from theCUBE. Coming to you from WIDS 2023, the eighth annual conference. Stick around. Our final guest joins us in just a minute.

(energetic music) >> Everyone, welcome back to theCUBE's coverage of WiDS 2023. This is the eighth annual Women in Data Science Conference. As you know, WiDS is not just a conference or an event, it's a movement. This is going to include over 100,000 people in the next year WiDS 2023 in 200-plus countries. It is such a powerful movement. If you've had a chance to be part of the Livestream or even be here in person with us at Stanford University, you know what I'm talking about. This is Lisa Martin. I have had the pleasure all day of working with two fantastic graduate students in Stanford's Data Journalism Master's Program. Hannah Freitag has been here. Tracy Zhang, ladies, it's been such a pleasure working with you today. >> Same wise. >> I want to ask you both what are, as we wrap the day, I'm so inspired, I feel like I could go build an airplane. >> Exactly. >> Probably can't. But WiDS is just the inspiration that comes from this event. When you walk in the front door, you can feel it. >> Mm-hmm. >> Tracy, talk a little bit about what some of the things are that you heard today that really inspired you. >> I think one of the keyword that's like in my mind right now is like finding a mentor. >> Yeah. >> And I think, like if I leave this conference if I leave the talks, the conversations with one thing is that I'm very positive that if I want to switch, say someday, from Journalism to being a Data Analyst, to being like in Data Science, I'm sure that there are great role models for me to look up to, and I'm sure there are like mentors who can guide me through the way. So, like that, I feel reassured for some reason. >> It's a good feeling, isn't it? What do you, Hannah, what about you? What's your takeaway so far of the day? >> Yeah, one of my key takeaways is that anything's possible. >> Mm-hmm. >> So, if you have your vision, you have the role model, someone you look up to, and even if you have like a different background, not in Data Science, Data Engineering, or Computer Science but you're like, "Wow, this is really inspiring. I would love to do that." As long as you love it, you're passionate about it, and you are willing to, you know, take this path even though it won't be easy. >> Yeah. >> Then you can achieve it, and as you said, Tracy, it's important to have mentors on the way there. >> Exactly. >> But as long as you speak up, you know, you raise your voice, you ask questions, and you're curious, you can make it. >> Yeah. >> And I think that's one of my key takeaways, and I was just so inspiring to hear like all these women speaking on stage, and also here in our conversations and learning about their, you know, career path and what they learned on their way. >> Yeah, you bring up curiosity, and I think that is such an important skill. >> Mm-hmm. >> You know, you could think of Data Science and think about all the hard skills that you need. >> Mm, like coding. >> But as some of our guests said today, you don't have to be a statistician or an engineer, or a developer to get into this. Data Science applies to every facet of every part of the world. >> Mm-hmm. >> Finances, marketing, retail, manufacturing, healthcare, you name it, Data Science has the power and the potential to unlock massive achievements. >> Exactly. >> It's like we're scratching the surface. >> Yeah. >> But that curiosity, I think, is a great skill to bring to anything that you do. >> Mm-hmm. >> And I think we... For the female leaders that we're on stage, and that we had a chance to talk to on theCUBE today, I think they all probably had that I think as a common denominator. >> Exactly. >> That curious mindset, and also something that I think as hard is the courage to raise your hand. I like this, I'm interested in this. I don't see anybody that looks like me. >> But that doesn't mean I shouldn't do it. >> Exactly. >> Exactly, in addition to the curiosity that all the women, you know, bring to the table is that, in addition to that, being optimistic, and even though we don't see gender equality or like general equality in companies yet, we make progress and we're optimistic about it, and we're not like negative and complaining the whole time. But you know, this positive attitude towards a trend that is going in the right direction, and even though there's still a lot to be done- >> Exactly. >> We're moving it that way. >> Right. >> Being optimistic about this. >> Yeah, exactly, like even if it means that it's hard. Even if it means you need to be your own role model it's still like worth a try. And I think they, like all of the great women speakers, all the female leaders, they all have that in them, like they have the courage to like raise their hand and be like, "I want to do this, and I'm going to make it." And they're role models right now, so- >> Absolutely, they have drive. >> They do. >> Right. They have that ambition to take something that's challenging and complicated, and help abstract end users from that. Like we were talking to Intuit. I use Intuit in my small business for financial management, and she was talking about how they can from a machine learning standpoint, pull all this data off of documents that you upload and make that, abstract that, all that complexity from the end user, make something that's painful taxes. >> Mm-hmm. >> Maybe slightly less painful. It's still painful when you have to go, "Do I have to write you a check again?" >> Yeah. (laughs) >> Okay. >> But talking about just all the different applications of Data Science in the world, I found that to be very inspiring and really eye-opening. >> Definitely. >> I hadn't thought about, you know, we talk about climate change all the time, especially here in California, but I never thought about Data Science as a facilitator of the experts being able to make sense of what's going on historically and in real-time, or the application of Data Science in police violence. We see far too many cases of police violence on the news. It's an epidemic that's a horrible problem. Data Science can be applied to that to help us learn from that, and hopefully, start moving the needle in the right direction. >> Absolutely. >> Exactly. >> And especially like one sentence from Guitry from the very beginnings I still have in my mind is then when she said that arguments, no, that data beats arguments. >> Yes. >> In a conversation that if you be like, okay, I have this data set and it can actually show you this or that, it's much more powerful than just like being, okay, this is my position or opinion on this. And I think in a world where increasing like misinformation, and sometimes, censorship as we heard in one of the talks, it's so important to have like data, reliable data, but also acknowledge, and we talked about it with one of our interviewees that there's spices in data and we also need to be aware of this, and how to, you know, move this forward and use Data Science for social good. >> Mm-hmm. >> Yeah, for social good. >> Yeah, definitely, I think they like data, and the question about, or like the problem-solving part about like the social issues, or like some just questions, they definitely go hand-in-hand. Like either of them standing alone won't be anything that's going to be having an impact, but combining them together, you have a data set that illustrate a point or like solves the problem. I think, yeah, that's definitely like where Data Set Science is headed to, and I'm glad to see all these great women like making their impact and combining those two aspects together. >> It was interesting in the keynote this morning. We were all there when Margot Gerritsen who's one of the founders of WiDS, and Margot's been on the program before and she's a huge supporter of what we do and vice versa. She asked the non-women in the room, "Those who don't identify as women, stand up," and there was a handful of men, and she said, "That's what it's like to be a female in technology." >> Oh, my God. >> And I thought that vision give me goosebumps. >> Powerful. (laughs) >> Very powerful. But she's right, and one of the things I think that thematically another common denominator that I think we heard, I want to get your opinions as well from our conversations today, is the importance of community. >> Mm-hmm. >> You know, I was mentioning this stuff from AnitaB.org that showed that in 2022, the percentage of females and technical roles is 27.6%. It's a little bit of an increase. It's been hovering around 25% for a while. But one of the things that's still a problem is attrition. It doubled last year. >> Right. >> And I was asking some of the guests, and we've all done that today, "How would you advise companies to start moving the needle down on attrition?" >> Mm-hmm. >> And I think the common theme was network, community. >> Exactly. >> It takes a village like this. >> Mm-hmm. >> So you can see what you can be to help start moving that needle and that's, I think, what underscores the value of what WiDS delivers, and what we're able to showcase on theCUBE. >> Yeah, absolutely. >> I think it's very important to like if you're like a woman in tech to be able to know that there's someone for you, that there's a whole community you can rely on, and that like you are, you have the same mindset, you're working towards the same goal. And it's just reassuring and like it feels very nice and warm to have all these women for you. >> Lisa: It's definitely a warm fuzzy, isn't it? >> Yeah, and both the community within the workplace but also outside, like a network of family and friends who support you to- >> Yes. >> To pursue your career goals. I think that was also a common theme we heard that it's, yeah, necessary to both have, you know your community within your company or organization you're working but also outside. >> Definitely, I think that's also like how, why, the reason why we feel like this in like at WiDS, like I think we all feel very positive right now. So, yeah, I think that's like the power of the connection and the community, yeah. >> And the nice thing is this is like I said, WiDS is a movement. >> Yes. >> This is global. >> Mm-hmm. >> We've had some WiDS ambassadors on the program who started WiDS and Tel Aviv, for example, in their small communities. Or in Singapore and Mumbai that are bringing it here and becoming more of a visible part of the community. >> Tracy: Right. >> I loved seeing all the young faces when we walked in the keynote this morning. You know, we come here from a journalistic perspective. You guys are Journalism students. But seeing all the potential in the faces in that room just seeing, and hearing stories, and starting to make tangible connections between Facebook and data, and the end user and the perspectives, and the privacy and the responsibility of AI is all... They're all positive messages that need to be reinforced, and we need to have more platforms like this to be able to not just raise awareness, but sustain it. >> Exactly. >> Right. It's about the long-term, it's about how do we dial down that attrition, what can we do? What can we do? How can we help? >> Mm-hmm. >> Both awareness, but also giving women like a place where they can connect, you know, also outside of conferences. Okay, how do we make this like a long-term thing? So, I think WiDS is a great way to, you know, encourage this connectivity and these women teaming up. >> Yeah, (chuckles) girls help girls. >> Yeah. (laughs) >> It's true. There's a lot of organizations out there, girls who Code, Girls Inc., et cetera, that are all aimed at helping women kind of find their, I think, find their voice. >> Exactly. >> And find that curiosity. >> Yeah. Unlock that somewhere back there. Get some courage- >> Mm-hmm. >> To raise your hand and say, "I think I want to do this," or "I have a question. You explained something and I didn't understand it." Like, that's the advice I would always give to my younger self is never be afraid to raise your hand in a meeting. >> Mm-hmm. >> I guarantee you half the people weren't listening or, and the other half may not have understood what was being talked about. >> Exactly. >> So, raise your hand, there goes Margot Gerritsen, the founder of WiDS, hey, Margot. >> Hi. >> Keep alumni as you know, raise your hand, ask the question, there's no question that's stupid. >> Mm-hmm. >> And I promise you, if you just take that chance once it will open up so many doors, you won't even know which door to go in because there's so many that are opening. >> And if you have a question, there's at least one more person in the room who has the exact same question. >> Exact same question. >> Yeah, we'll definitely keep that in mind as students- >> Well, I'm curious how Data Journalism, what you heard today, Tracy, we'll start with you, and then, Hannah, to you. >> Mm-hmm. How has it influenced how you approach data-driven, and storytelling? Has it inspired you? I imagine it has, or has it given you any new ideas for, as you round out your Master's Program in the next few months? >> I think like one keyword that I found really helpful from like all the conversations today, was problem-solving. >> Yeah. >> Because I think, like we talked a lot about in our program about how to put a face on data sets. How to put a face, put a name on a story that's like coming from like big data, a lot of numbers but you need to like narrow it down to like one person or one anecdote that represents a bigger problem. And I think essentially that's problem-solving. That's like there is a community, there is like say maybe even just one person who has, well, some problem about something, and then we're using data. We're, by giving them a voice, by portraying them in news and like representing them in the media, we're solving this problem somehow. We're at least trying to solve this problem, trying to make some impact. And I think that's like what Data Science is about, is problem-solving, and, yeah, I think I heard a lot from today's conversation, also today's speakers. So, yeah, I think that's like something we should also think about as Journalists when we do pitches or like what kind of problem are we solving? >> I love that. >> Or like kind of what community are we trying to make an impact in? >> Yes. >> Absolutely. Yeah, I think one of the main learnings for me that I want to apply like to my career in Data Journalism is that I don't shy away from complexity because like Data Science is oftentimes very complex. >> Complex. >> And also data, you're using for your stories is complex. >> Mm-hmm. >> So, how can we, on the one hand, reduce complexity in a way that we make it accessible for broader audience? 'Cause, we don't want to be this like tech bubble talking in data jargon, we want to, you know, make it accessible for a broader audience. >> Yeah. >> I think that's like my purpose as a Data Journalist. But at the same time, don't reduce complexity when it's needed, you know, and be open to dive into new topics, and data sets and circling back to this of like raising your hand and asking questions if you don't understand like a certain part. >> Yeah. >> So, that's definitely a main learning from this conference. >> Definitely. >> That like, people are willing to talk to you and explain complex topics, and this will definitely facilitate your work as a Data Journalist. >> Mm-hmm. >> So, that inspired me. >> Well, I can't wait to see where you guys go from here. I've loved co-hosting with you today, thank you. >> Thank you. >> For joining me at our conference. >> Wasn't it fun? >> Thank you. >> It's a great event. It's, we, I think we've all been very inspired and I'm going to leave here probably floating above the ground a few inches, high on the inspiration of what this community can deliver, isn't that great? >> It feels great, I don't know, I just feel great. >> Me too. (laughs) >> So much good energy, positive energy, we love it. >> Yeah, so we want to thank all the organizers of WiDS, Judy Logan, Margot Gerritsen in particular. We also want to thank John Furrier who is here. And if you know Johnny, know he gets FOMO when he is not hosting. But John and Dave Vellante are such great supporters of women in technology, women in technical roles. We wouldn't be here without them. So, shout out to my bosses. Thank you for giving me the keys to theCube at this event. I know it's painful sometimes, but we hope that we brought you great stories all day. We hope we inspired you with the females and the one male that we had on the program today in terms of raise your hand, ask a question, be curious, don't be afraid to pursue what you're interested in. That's my soapbox moment for now. So, for my co-host, I'm Lisa Martin, we want to thank you so much for watching our program today. You can watch all of this on-demand on thecube.net. You'll find write-ups on siliconeangle.com, and, of course, YouTube. Thanks, everyone, stay safe and we'll see you next time. (energetic music)

(upbeat music) >> Welcome back to theCUBE's coverage of Women in Data Science 2023 live from Stanford University. This is Lisa Martin. My co-host is Tracy Yuan. We're excited to be having great conversations all day but you know, 'cause you've been watching. We've been interviewing some very inspiring women and some men as well, talking about all of the amazing applications of data science. You're not going to want to miss this next conversation. Our guest is Gabriela de Queiroz, Principal Cloud Advocate Manager of Microsoft. Welcome, Gabriela. We're excited to have you. >> Thank you very much. I'm so excited to be talking to you. >> Yeah, you're on theCUBE. >> Yeah, finally. (Lisa laughing) Like a dream come true. (laughs) >> I know and we love that. We're so thrilled to have you. So you have a ton of experience in the data space. I was doing some research on you. You've worked in software, financial advertisement, health. Talk to us a little bit about you. What's your background in? >> So I was trained in statistics. So I'm a statistician and then I worked in epidemiology. I worked with air pollution and public health. So I was a researcher before moving into the industry. So as I was talking today, the weekly paths, it's exactly who I am. I went back and forth and back and forth and stopped and tried something else until I figured out that I want to do data science and that I want to do different things because with data science we can... The beauty of data science is that you can move across domains. So I worked in healthcare, financial, and then different technology companies. >> Well the nice thing, one of the exciting things that data science, that I geek out about and Tracy knows 'cause we've been talking about this all day, it's just all the different, to your point, diverse, pun intended, applications of data science. You know, this morning we were talking about, we had the VP of data science from Meta as a keynote. She came to theCUBE talking and really kind of explaining from a content perspective, from a monetization perspective, and of course so many people in the world are users of Facebook. It makes it tangible. But we also heard today conversations about the applications of data science in police violence, in climate change. We're in California, we're expecting a massive rainstorm and we don't know what to do when it rains or snows. But climate change is real. Everyone's talking about it, and there's data science at its foundation. That's one of the things that I love. But you also have a lot of experience building diverse teams. Talk a little bit about that. You've created some very sophisticated data science solutions. Talk about your recommendation to others to build diverse teams. What's in it for them? And maybe share some data science project or two that you really found inspirational. >> Yeah, absolutely. So I do love building teams. Every time I'm given the task of building teams, I feel the luckiest person in the world because you have the option to pick like different backgrounds and all the diverse set of like people that you can find. I don't think it's easy, like people say, yeah, it's very hard. You have to be intentional. You have to go from the very first part when you are writing the job description through the interview process. So you have to be very intentional in every step. And you have to think through when you are doing that. And I love, like my last team, we had like 10 people and we were so diverse. Like just talking about languages. We had like 15 languages inside a team. So how beautiful it is. Like all different backgrounds, like myself as a statistician, but we had people from engineering background, biology, languages, and so on. So it's, yeah, like every time thinking about building a team, if you wanted your team to be diverse, you need to be intentional. >> I'm so glad you brought up that intention point because that is the fundamental requirement really is to build it with intention. >> Exactly, and I love to hear like how there's different languages. So like I'm assuming, or like different backgrounds, I'm assuming everybody just zig zags their way into the team and now you're all women in data science and I think that's so precious. >> Exactly. And not only woman, right. >> Tracy: Not only woman, you're right. >> The team was diverse not only in terms of like gender, but like background, ethnicity, and spoken languages, and language that they use to program and backgrounds. Like as I mentioned, not everybody did the statistics in school or computer science. And it was like one of my best teams was when we had this combination also like things that I'm good at the other person is not as good and we have this knowledge sharing all the time. Every day I would feel like I'm learning something. In a small talk or if I was reviewing something, there was always something new because of like the richness of the diverse set of people that were in your team. >> Well what you've done is so impressive, because not only have you been intentional with it, but you sound like the hallmark of a great leader of someone who hires and builds teams to fill gaps. They don't have to know less than I do for me to be the leader. They have to have different skills, different areas of expertise. That is really, honestly Gabriela, that's the hallmark of a great leader. And that's not easy to come by. So tell me, who were some of your mentors and sponsors along the way that maybe influenced you in that direction? Or is that just who you are? >> That's a great question. And I joke that I want to be the role model that I never had, right. So growing up, I didn't have anyone that I could see other than my mom probably or my sister. But there was no one that I could see, I want to become that person one day. And once I was tracing my path, I started to see people looking at me and like, you inspire me so much, and I'm like, oh wow, this is amazing and I want to do do this over and over and over again. So I want to be that person to inspire others. And no matter, like I'll be like a VP, CEO, whoever, you know, I want to be, I want to keep inspiring people because that's so valuable. >> Lisa: Oh, that's huge. >> And I feel like when we grow professionally and then go to the next level, we sometimes we lose that, you know, thing that's essential. And I think also like, it's part of who I am as I was building and all my experiences as I was going through, I became what I mentioned is unique person that I think we all are unique somehow. >> You're a rockstar. Isn't she a rockstar? >> You dropping quotes out. >> I'm loving this. I'm like, I've inspired Gabriela. (Gabriela laughing) >> Oh my God. But yeah, 'cause we were asking our other guests about the same question, like, who are your role models? And then we're talking about how like it's very important for women to see that there is a representation, that there is someone they look up to and they want to be. And so that like, it motivates them to stay in this field and to start in this field to begin with. So yeah, I think like you are definitely filling a void and for all these women who dream to be in data science. And I think that's just amazing. >> And you're a founder too. In 2012, you founded R Ladies. Talk a little bit about that. This is present in more than 200 cities in 55 plus countries. Talk about R Ladies and maybe the catalyst to launch it. >> Yes, so you always start, so I'm from Brazil, I always talk about this because it's such, again, I grew up over there. So I was there my whole life and then I moved to here, Silicon Valley. And when I moved to San Francisco, like the doors opened. So many things happening in the city. That was back in 2012. Data science was exploding. And I found out something about Meetup.com, it's a website that you can join and go in all these events. And I was going to this event and I joke that it was kind of like going to the Disneyland, where you don't know if I should go that direction or the other direction. >> Yeah, yeah. >> And I was like, should I go and learn about data visualization? Should I go and learn about SQL or should I go and learn about Hadoop, right? So I would go every day to those meetups. And I was a student back then, so you know, the budget was very restricted as a student. So we don't have much to spend. And then they would serve dinner and you would learn for free. And then I got to a point where I was like, hey, they are doing all of this as a volunteer. Like they are running this meetup and events for free. And I felt like it's a cycle. I need to do something, right. I'm taking all this in. I'm having this huge opportunity to be here. I want to give back. So that's what how everything started. I was like, no, I have to think about something. I need to think about something that I can give back. And I was using R back then and I'm like how about I do something with R. I love R, I'm so passionate about R, what about if I create a community around R but not a regular community, because by going to this events, I felt that as a Latina and as a woman, I was always in the corner and I was not being able to participate and to, you know, be myself and to network and ask questions. I would be in the corner. So I said to myself, what about if I do something where everybody feel included, where everybody can participate, can share, can ask questions without judgment? So that's how R ladies all came together. >> That's awesome. >> Talk about intentions, like you have to, you had that go in mind, but yeah, I wanted to dive a little bit into R. So could you please talk more about where did the passion for R come from, and like how did the special connection between you and R the language, like born, how did that come from? >> It was not a love at first sight. >> No. >> Not at all. Not at all. Because that was back in Brazil. So all the documentation were in English, all the tutorials, only two. We had like very few tutorials. It was not like nowadays that we have so many tutorials and courses. There were like two tutorials, other documentation in English. So it's was hard for me like as someone that didn't know much English to go through the language and then to learn to program was not easy task. But then as I was going through the language and learning and reading books and finding the people behind the language, I don't know how I felt in love. And then when I came to to San Francisco, I saw some of like the main contributors who are speaking in person and I'm like, wow, they are like humans. I don't know, it was like, I have no idea why I had this love. But I think the the people and then the community was the thing that kept me with the R language. >> Yeah, the community factors is so important. And it's so, at WIDS it's so palpable. I mean I literally walk in the door, every WIDS I've done, I think I've been doing them for theCUBE since 2017. theCUBE has been here since the beginning in 2015 with our co-founders. But you walk in, you get this sense of belonging. And this sense of I can do anything, why not? Why not me? Look at her up there, and now look at you speaking in the technical talk today on theCUBE. So inspiring. One of the things that I always think is you can't be what you can't see. We need to be able to see more people that look like you and sound like you and like me and like you as well. And WIDS gives us that opportunity, which is fantastic, but it's also helping to move the needle, really. And I was looking at some of the Anitab.org stats just yesterday about 2022. And they're showing, you know, the percentage of females in technical roles has been hovering around 25% for a while. It's a little higher now. I think it's 27.6 according to any to Anitab. We're seeing more women hired in roles. But what are the challenges, and I would love to get your advice on this, for those that might be in this situation is attrition, women who are leaving roles. What would your advice be to a woman who might be trying to navigate family and work and career ladder to stay in that role and keep pushing forward? >> I'll go back to the community. If you don't have a community around you, it's so hard to navigate. >> That's a great point. >> You are lonely. There is no one that you can bounce ideas off, that you can share what you are feeling or like that you can learn as well. So sometimes you feel like you are the only person that is going through that problem or like, you maybe have a family or you are planning to have a family and you have to make a decision. But you've never seen anyone going through this. So when you have a community, you see people like you, right. So that's where we were saying about having different people and people like you so they can share as well. And you feel like, oh yeah, so they went through this, they succeed. I can also go through this and succeed. So I think the attrition problem is still big problem. And I'm sure will be worse now with everything that is happening in Tech with layoffs. >> Yes and the great resignation. >> Yeah. >> We are going back, you know, a few steps, like a lot of like advancements that we did. I feel like we are going back unfortunately, but I always tell this, make sure that you have a community. Make sure that you have a mentor. Make sure that you have someone or some people, not only one mentor, different mentors, that can support you through this trajectory. Because it's not easy. But there are a lot of us out there. >> There really are. And that's a great point. I love everything about the community. It's all about that network effect and feeling like you belong- >> That's all WIDS is about. >> Yeah. >> Yes. Absolutely. >> Like coming over here, it's like seeing the old friends again. It's like I'm so glad that I'm coming because I'm all my old friends that I only see like maybe once a year. >> Tracy: Reunion. >> Yeah, exactly. And I feel like that our tank get, you know- >> Lisa: Replenished. >> Exactly. For the rest of the year. >> Yes. >> Oh, that's precious. >> I love that. >> I agree with that. I think one of the things that when I say, you know, you can't see, I think, well, how many females in technology would I be able to recognize? And of course you can be female technology working in the healthcare sector or working in finance or manufacturing, but, you know, we need to be able to have more that we can see and identify. And one of the things that I recently found out, I was telling Tracy this earlier that I geeked out about was finding out that the CTO of Open AI, ChatGPT, is a female. I'm like, (gasps) why aren't we talking about this more? She was profiled on Fast Company. I've seen a few pieces on her, Mira Murati. But we're hearing so much about ChatJTP being... ChatGPT, I always get that wrong, about being like, likening it to the launch of the iPhone, which revolutionized mobile and connectivity. And here we have a female in the technical role. Let's put her on a pedestal because that is hugely inspiring. >> Exactly, like let's bring everybody to the front. >> Yes. >> Right. >> And let's have them talk to us because like, you didn't know. I didn't know probably about this, right. You didn't know. Like, we don't know about this. It's kind of like we are hidden. We need to give them the spotlight. Every woman to give the spotlight, so they can keep aspiring the new generation. >> Or Susan Wojcicki who ran, how long does she run YouTube? All the YouTube influencers that probably have no idea who are influential for whatever they're doing on YouTube in different social platforms that don't realize, do you realize there was a female behind the helm that for a long time that turned it into what it is today? That's outstanding. Why aren't we talking about this more? >> How about Megan Smith, was the first CTO on the Obama administration. >> That's right. I knew it had to do with Obama. Couldn't remember. Yes. Let's let's find more pedestals. But organizations like WIDS, your involvement as a speaker, showing more people you can be this because you can see it, >> Yeah, exactly. is the right direction that will help hopefully bring us back to some of the pre-pandemic levels, and keep moving forward because there's so much potential with data science that can impact everyone's lives. I always think, you know, we have this expectation that we have our mobile phone and we can get whatever we want wherever we are in the world and whatever time of day it is. And that's all data driven. The regular average person that's not in tech thinks about data as a, well I'm paying for it. What's all these data charges? But it's powering the world. It's powering those experiences that we all want as consumers or in our business lives or we expect to be able to do a transaction, whether it's something in a CRM system or an Uber transaction like that, and have the app respond, maybe even know me a little bit better than I know myself. And that's all data. So I think we're just at the precipice of the massive impact that data science will make in our lives. And luckily we have leaders like you who can help navigate us along this path. >> Thank you. >> What advice for, last question for you is advice for those in the audience who might be nervous or maybe lack a little bit of confidence to go I really like data science, or I really like engineering, but I don't see a lot of me out there. What would you say to them? >> Especially for people who are from like a non-linear track where like going onto that track. >> Yeah, I would say keep going. Keep going. I don't think it's easy. It's not easy. But keep going because the more you go the more, again, you advance and there are opportunities out there. Sometimes it takes a little bit, but just keep going. Keep going and following your dreams, that you get there, right. So again, data science, such a broad field that doesn't require you to come from a specific background. And I think the beauty of data science exactly is this is like the combination, the most successful data science teams are the teams that have all these different backgrounds. So if you think that we as data scientists, we started programming when we were nine, that's not true, right. You can be 30, 40, shifting careers, starting to program right now. It doesn't matter. Like you get there no matter how old you are. And no matter what's your background. >> There's no limit. >> There was no limits. >> I love that, Gabriela, >> Thank so much. for inspiring. I know you inspired me. I'm pretty sure you probably inspired Tracy with your story. And sometimes like what you just said, you have to be your own mentor and that's okay. Because eventually you're going to turn into a mentor for many, many others and sounds like you're already paving that path and we so appreciate it. You are now officially a CUBE alumni. >> Yes. Thank you. >> Yay. We've loved having you. Thank you so much for your time. >> Thank you. Thank you. >> For our guest and for Tracy's Yuan, this is Lisa Martin. We are live at WIDS 23, the eighth annual Women in Data Science Conference at Stanford. Stick around. Our next guest joins us in just a few minutes. (upbeat music)

(gentle upbeat music) >> Hey, friends of theCUBE. It's Lisa Martin live at Stanford University covering the Eighth Annual Women In Data Science. But you've been a Cube fan for a long time. So you know that we've been here since the beginning of WiDS, which is 2015. We always loved to come and cover this event. We learned great things about data science, about women leaders, underrepresented minorities. And this year we have a special component. We've got two grad students from Stanford's Master's program and Data Journalism joining. One of my them is here with me, Hannah Freitag, my co-host. Great to have you. And we are pleased to welcome from Intuit for the first time, Shir Meir Lador Group Manager at Data Science. Shir, it's great to have you. Thank you for joining us. >> Thank you for having me. >> And I was just secrets girl talking with my boss of theCUBE who informed me that you're in great company. Intuit's Chief Technology Officer, Marianna Tessel is an alumni of theCUBE. She was on at our Supercloud event in January. So welcome back into it. >> Thank you very much. We're happy to be with you. >> Tell us a little bit about what you're doing. You're a data science group manager as I mentioned, but also you've had you've done some cool things I want to share with the audience. You're the co-founder of the PyData Tel Aviv Meetups the co-host of the unsupervised podcast about data science in Israel. You give talks, about machine learning, about data science. Tell us a little bit about your background. Were you always interested in STEM studies from the time you were small? >> So I was always interested in mathematics when I was small, I went to this special program for youth going to university. So I did my test in mathematics earlier and studied in university some courses. And that's when I understood I want to do something in that field. And then when I got to go to university, I went to electrical engineering when I found out about algorithms and how interested it is to be able to find solutions to problems, to difficult problems with math. And this is how I found my way into machine learning. >> Very cool. There's so much, we love talking about machine learning and AI on theCUBE. There's so much potential. Of course, we have to have data. One of the things that I love about WiDS and Hannah and I and our co-host Tracy, have been talking about this all day is the impact of data in everyone's life. If you break it down, I was at Mobile World Congress last week, all about connectivity telecom, and of course we have these expectation that we're going to be connected 24/7 from wherever we are in the world and we can do whatever we want. I can do an Uber transaction, I can watch Netflix, I can do a bank transaction. It all is powered by data. And data science is, some of the great applications of it is what it's being applied to. Things like climate change or police violence or health inequities. Talk about some of the data science projects that you're working on at Intuit. I'm an intuit user myself, but talk to me about some of those things. Give the audience really a feel for what you're doing. >> So if you are a Intuit product user, you probably use TurboTax. >> I do >> In the past. So for those who are not familiar, TurboTax help customers submit their taxes. Basically my group is in charge of getting all the information automatically from your documents, the documents that you upload to TurboTax. We extract that information to accelerate your tax submission to make it less work for our customers. So- >> Thank you. >> Yeah, and this is why I'm so proud to be working at this team because our focus is really to help our customers to simplify all the you know, financial heavy lifting with taxes and also with small businesses. We also do a lot of work in extracting information from small business documents like bill, receipts, different bank statements. Yeah, so this is really exciting for me, the opportunity to work to apply data science and machine learning to solution that actually help people. Yeah >> Yeah, in the past years there have been more and more digital products emerging that needs some sort of data security. And how did your team, or has your team developed in the past years with more and more products or companies offering digital services? >> Yeah, so can you clarify the question again? Sorry. >> Yeah, have you seen that you have more customers? Like has your team expanded in the past years with more digital companies starting that need kind of data security? >> Well, definitely. I think, you know, since I joined Intuit, I joined like five and a half years ago back when I was in Tel Aviv. I recently moved to the Bay Area. So when I joined, there were like a dozens of data scientists and machine learning engineers on Intuit. And now there are a few hundreds. So we've definitely grown with the year and there are so many new places we can apply machine learning to help our customers. So this is amazing, so much we can do with machine learning to get more money in the pocket of our customers and make them do less work. >> I like both of those. More money in my pocket and less work. That's awesome. >> Exactly. >> So keep going Intuit. But one of the things that is so cool is just the the abstraction of the complexity that Intuit's doing. I upload documents or it scans my receipts. I was just in Barcelona last week all these receipts and conversion euros to dollars and it takes that complexity away from the end user who doesn't know all that's going on in the background, but you're making people's lives simpler. Unfortunately, we all have to pay taxes, most of us should. And of course we're in tax season right now. And so it's really cool what you're doing with ML and data science to make fundamental processes to people's lives easier and just a little bit less complicated. >> Definitely. And I think that's what's also really amazing about Intuit it, is how it combines human in the loop as well as AI. Because in some of the tax situation it's very complicated maybe to do it yourself. And then there's an option to work with an expert online that goes on a video with you and helps you do your taxes. And the expert's work is also accelerated by AI because we build tools for those experts to do the work more efficiently. >> And that's what it's all about is you know, using data to be more efficient, to be faster, to be smarter, but also to make complicated processes in our daily lives, in our business lives just a little bit easier. One of the things I've been geeking out about recently is ChatGPT. I was using it yesterday. I was telling everyone I was asking it what's hot in data science and I didn't know would it know what hot is and it did, it gave me trends. But one of the things that I was so, and Hannah knows I've been telling this all day, I was so excited to learn over the weekend that the the CTO of OpenAI is a female. I didn't know that. And I thought why are we not putting her on a pedestal? Because people are likening ChatGPT to like the launch of the iPhone. I mean revolutionary. And here we have what I think is exciting for all of us females, whether you're in tech or not, is another role model. Because really ultimately what WiDS is great at doing is showcasing women in technical roles. Because I always say you can't be what you can't see. We need to be able to see more role models, female role role models, underrepresented minorities of course men, because a lot of my sponsors and mentors are men, but we need more women that we can look up to and see ah, she's doing this, why can't I? Talk to me about how you stay the course in data science. What excites you about the potential, the opportunities based on what you've already accomplished what inspires you to continue and be one of those females that we say oh my God, I could be like Shir. >> I think that what inspires me the most is the endless opportunities that we have. I think we haven't even started tapping into everything that we can do with generative AI, for example. There's so much that can be done to further help you know, people make more money and do less work because there's still so much work that we do that we don't need to. You know, this is with Intuit, but also there are so many other use cases like I heard today you know, with the talk about the police. So that was really exciting how you can apply machine learning and data to actually help people, to help people that been through wrongful things. So I was really moved by that. And I'm also really excited about all the medical applications that we can have with data. >> Yeah, yeah. It's true that data science is so diverse in terms of what fields it can cover but it's equally important to have diverse teams and have like equity and inclusion in your teams. Where is Intuit at promoting women, non-binary minorities in your teams to progress data science? >> Yeah, so I have so much to say on this. >> Good. >> But in my work in Tel Aviv, I had the opportunity to start with Intuit women in data science branch in Tel Aviv. So that's why I'm super excited to be here today for that because basically this is the original conference, but as you know, there are branches all over the world and I got the opportunity to lead the Tel Aviv branch with Israel since 2018. And we've been through already this year it's going to be it's next week, it's going to be the sixth conference. And every year our number of submission to make talk in the conference doubled itself. >> Nice. >> We started with 20 submission, then 50, then 100. This year we have over 200 submissions of females to give talk at the conference. >> Ah, that's fantastic. >> And beyond the fact that there's so much traction, I also feel the great impact it has on the community in Israel because one of the reason we started WiDS was that when I was going to conferences I was seeing so little women on stage in all the technical conferences. You know, kind of the reason why I guess you know, Margaret and team started the WiDS conference. So I saw the same thing in Israel and I was always frustrated. I was organizing PyData Meetups as you mentioned and I was always having such a hard time to get female speakers to talk. I was trying to role model, but that's not enough, you know. We need more. So once we started WiDS and people saw you know, so many examples on the stage and also you know females got opportunity to talk in a place for that. Then it also started spreading and you can see more and more female speakers across other conferences, which are not women in data science. So I think just the fact that Intuits started this conference back in Israel and also in Bangalore and also the support Intuit does for WiDS in Stanford here, it shows how much WiDS values are aligned with our values. Yeah, and I think that to chauffeur that I think we have over 35% females in the data science and machine learning engineering roles, which is pretty amazing I think compared to the industry. >> Way above average. Yeah, absolutely. I was just, we've been talking about some of the AnitaB.org stats from 2022 showing that 'cause usually if we look at the industry to you point, over the last, I don't know, probably five, 10 years we're seeing the number of female technologists around like a quarter, 25% or so. 2022 data from AnitaB.org showed that that number is now 27.6%. So it's very slowly- >> It's very slowly increasing. >> Going in the right direction. >> Too slow. >> And that representation of women technologists increase at every level, except intern, which I thought was really interesting. And I wonder is there a covid relation there? >> I don't know. >> What do we need to do to start opening up the the top of the pipeline, the funnel to go downstream to find kids like you when you were younger and always interested in engineering and things like that. But the good news is that the hiring we've seen improvements, but it sounds like Intuit is way ahead of the curve there with 35% women in data science or technical roles. And what's always nice and refreshing that we've talked, Hannah about this too is seeing companies actually put action into initiatives. It's one thing for a company to say we're going to have you know, 50% females in our organization by 2030. It's a whole other ball game to actually create a strategy, execute on it, and share progress. So kudos to Intuit for what it's doing because that is more companies need to adopt that same sort of philosophy. And that's really cultural. >> Yeah. >> At an organization and culture can be hard to change, but it sounds like you guys kind of have it dialed in. >> I think we definitely do. That's why I really like working and Intuit. And I think that a lot of it is with the role modeling, diversity and inclusion, and by having women leaders. When you see a woman in leadership position, as a woman it makes you want to come work at this place. And as an evidence, when I build the team I started in Israel at Intuit, I have over 50% women in my team. >> Nice. >> Yeah, because when you have a woman in the interviewers panel, it's much easier, it's more inclusive. That's why we always try to have at least you know, one woman and also other minorities represented in our interviews panel. Yeah, and I think that in general it's very important as a leader to kind of know your own biases and trying to have defined standard and rubrics in how you evaluate people to avoid for those biases. So all of that inclusiveness and leadership really helps to get more diversity in your teams. >> It's critical. That thought diversity is so critical, especially if we talk about AI and we're almost out of time, I just wanted to bring up, you brought up a great point about the diversity and equity. With respect to data science and AI, we know in AI there's biases in data. We need to have more inclusivity, more representation to help start shifting that so the biases start to be dialed down and I think a conference like WiDS and it sounds like someone like you and what you've already done so far in the work that you're doing having so many females raise their hands to want to do talks at events is a good situation. It's a good scenario and hopefully it will continue to move the needle on the percentage of females in technical roles. So we thank you Shir for your time sharing with us your story, what you're doing, how Intuit and WiDS are working together. It sounds like there's great alignment there and I think we're at the tip of the iceberg with what we can do with data science and inclusion and equity. So we appreciate all of your insights and your time. >> Thank you very much. >> All right. >> I enjoyed very, very much >> Good. We hope, we aim to please. Thank you for our guests and for Hannah Freitag. This is Lisa Martin coming to you live from Stanford University. This is our coverage of the eighth Annual Women in Data Science Conference. Stick around, next guest will be here in just a minute.

(gentle music) >> Hey! Welcome back to theCUBE's coverage of WiDS 2023, the eighth Annual Women In Data Science Conference. I'm your host, Lisa Martin. We are at Stanford University, as you know we are every year, having some wonderful conversations with some very inspiring women and men in data science and technical roles. I'm very pleased to introduce Tracy Zhang, my co-host, who is in the Data Journalism program at Stanford. And Tracy and I are pleased to welcome our next guest, Rhonda Crate, Principal Data Scientist at Boeing. Great to have you on the program, Rhonda. >> Tracy: Welcome. >> Hey, thanks for having me. >> Were you always interested in data science or STEM from the time you were young? >> No, actually. I was always interested in archeology and anthropology. >> That's right, we were talking about that, anthropology. Interesting. >> We saw the anthropology background, not even a bachelor's degree, but also a master's degree in anthropology. >> So you were committed for a while. >> I was, I was. I actually started college as a fine arts major, but I always wanted to be an archeologist. So at the last minute, 11 credits in, left to switch to anthropology. And then when I did my master's, I focused a little bit more on quantitative research methods and then I got my Stat Degree. >> Interesting. Talk about some of the data science projects that you're working on. When I think of Boeing, I always think of aircraft. But you are doing a lot of really cool things in IT, data analytics. Talk about some of those intriguing data science projects that you're working on. >> Yeah. So when I first started at Boeing, I worked in information technology and data analytics. And Boeing, at the time, had cored up data science in there. And so we worked as a function across the enterprise working on anything from shared services to user experience in IT products, to airplane programs. So, it has a wide range. I worked on environment health and safety projects for a long time as well. So looking at ergonomics and how people actually put parts onto airplanes, along with things like scheduling and production line, part failures, software testing. Yeah, there's a wide spectrum of things. >> But I think that's so fantastic. We've been talking, Tracy, today about just what we often see at WiDS, which is this breadth of diversity in people's background. You talked about anthropology, archeology, you're doing data science. But also all of the different opportunities that you've had at Boeing. To see so many facets of that organization. I always think that breadth of thought diversity can be hugely impactful. >> Yeah. So I will say my anthropology degree has actually worked to my benefit. I'm a huge proponent of integrating liberal arts and sciences together. And it actually helps me. I'm in the Technical Fellowship program at Boeing, so we have different career paths. So you can go into management, you can be a regular employee, or you can go into the Fellowship program. So right now I'm an Associate Technical Fellow. And part of how I got into the Fellowship program was that diversity in my background, what made me different, what made me stand out on projects. Even applying a human aspect to things like ergonomics, as silly as that sounds, but how does a person actually interact in the space along with, here are the actual measurements coming off of whatever system it is that you're working on. So, I think there's a lot of opportunities, especially in safety as well, which is a big initiative for Boeing right now, as you can imagine. >> Tracy: Yeah, definitely. >> I can't go into too specifics. >> No, 'cause we were like, I think a theme for today that kind of we brought up in in all of our talk is how data is about people, how data is about how people understand the world and how these data can make impact on people's lives. So yeah, I think it's great that you brought this up, and I'm very happy that your anthropology background can tap into that and help in your day-to-day data work too. >> Yeah. And currently, right now, I actually switched over to Strategic Workforce Planning. So it's more how we understand our workforce, how we work towards retaining the talent, how do we get the right talent in our space, and making sure overall that we offer a culture and work environment that is great for our employees to come to. >> That culture is so important. You know, I was looking at some anitab.org stats from 2022 and you know, we always talk about the number of women in technical roles. For a long time it's been hovering around that 25% range. The data from anitab.org showed from '22, it's now 27.6%. So, a little increase. But one of the biggest challenges still, and Tracy and I and our other co-host, Hannah, have been talking about this, is attrition. Attrition more than doubled last year. What are some of the things that Boeing is doing on the retention side, because that is so important especially as, you know, there's this pipeline leakage of women leaving technical roles. Tell us about what Boeing's, how they're invested. >> Yeah, sure. We actually have a publicly available Global Diversity Report that anybody can go and look at and see our statistics for our organization. Right now, off the top of my head, I think we're hovering at about 24% in the US for women in our company. It has been a male majority company for many years. We've invested heavily in increasing the number of women in roles. One interesting thing about this year that came out is that even though with the great resignation and those types of things, the attrition level between men and women were actually pretty close to being equal, which is like the first time in our history. Usually it tends on more women leaving. >> Lisa: That's a good sign. >> Right. >> Yes, that's a good sign. >> And we've actually focused on hiring and bringing in more women and diversity in our company. >> Yeah, some of the stats too from anitab.org talked about the increase, and I have to scroll back and find my notes, the increase in 51% more women being hired in 2022 than 2021 for technical roles. So the data, pun intended, is showing us. I mean, the data is there to show the impact that having females in executive leadership positions make from a revenue perspective. >> Tracy: Definitely. >> Companies are more profitable when there's women at the head, or at least in senior leadership roles. But we're seeing some positive trends, especially in terms of representation of women technologists. One of the things though that I found interesting, and I'm curious to get your thoughts on this, Rhonda, is that the representation of women technologists is growing in all areas, except interns. >> Rhonda: Hmm. >> So I think, we've got to go downstream. You teach, I have to go back to my notes on you, did my due diligence, R programming classes through Boeings Ed Wells program, this is for WSU College of Arts and Sciences, talk about what you teach and how do you think that intern kind of glut could be solved? >> Yeah. So, they're actually two separate programs. So I teach a data analytics course at Washington State University as an Adjunct Professor. And then the Ed Wells program is a SPEEA, which is an Aerospace Union, focused on bringing up more technology and skills to the actual workforce itself. So it's kind of a couple different audiences. One is more seasoned employees, right? The other one is our undergraduates. I teach a Capstone class, so it's a great way to introduce students to what it's actually like to work on an industry project. We partner with Google and Microsoft and Boeing on those. The idea is also that maybe those companies have openings for the students when they're done. Since it's Senior Capstone, there's not a lot of opportunities for internships. But the opportunities to actually get hired increase a little bit. In regards to Boeing, we've actually invested a lot in hiring more women interns. I think the number was 40%, but you'd have to double check. >> Lisa: That's great, that's fantastic. >> Tracy: That's way above average, I think. >> That's a good point. Yeah, it is above average. >> Double check on that. That's all from my memory. >> Is this your first WiDS, or have you been before? >> I did virtually last year. >> Okay. One of the things that I love, I love covering this event every year. theCUBE's been covering it since it's inception in 2015. But it's just the inspiration, the vibe here at Stanford is so positive. WiDS is a movement. It's not an initiative, an organization. There are going to be, I think annually this year, there will be 200 different events. Obviously today we're live on International Women's Day. 60 plus countries, 100,000 plus people involved. So, this is such a positive environment for women and men, because we need everybody, underrepresented minorities, to be able to understand the implication that data has across our lives. If we think about stripping away titles in industries, everybody is a consumer, not everybody, most of mobile devices. And we have this expectation, I was in Barcelona last week at a Mobile World Congress, we have this expectation that we're going to be connected 24/7. I can get whatever I want wherever I am in the world, and that's all data driven. And the average person that isn't involved in data science wouldn't understand that. At the same time, they have expectations that depend on organizations like Boeing being data driven so that they can get that experience that they expect in their consumer lives in any aspect of their lives. And that's one of the things I find so interesting and inspiring about data science. What are some of the things that keep you motivated to continue pursuing this? >> Yeah I will say along those lines, I think it's great to invest in K-12 programs for Data Literacy. I know one of my mentors and directors of the Data Analytics program, Dr. Nairanjana Dasgupta, we're really familiar with each other. So, she runs a WSU program for K-12 Data Literacy. It's also something that we strive for at Boeing, and we have an internal Data Literacy program because, believe it or not, most people are in business. And there's a lot of disconnect between interpreting and understanding data. For me, what kind of drives me to continue data science is that connection between people and data and how we use it to improve our world, which is partly why I work at Boeing too 'cause I feel that they produce products that people need like satellites and airplanes, >> Absolutely. >> and everything. >> Well, it's tangible, it's relatable. We can understand it. Can you do me a quick favor and define data literacy for anyone that might not understand what that means? >> Yeah, so it's just being able to understand elements of data, whether that's a bar chart or even in a sentence, like how to read a statistic and interpret a statistic in a sentence, for example. >> Very cool. >> Yeah. And sounds like Boeing's doing a great job in these programs, and also trying to hire more women. So yeah, I wanted to ask, do you think there's something that Boeing needs to work on? Or where do you see yourself working on say the next five years? >> Yeah, I think as a company, we always think that there's always room for improvement. >> It never, never stops. >> Tracy: Definitely. (laughs) >> I know workforce strategy is an area that they're currently really heavily investing in, along with safety. How do we build safer products for people? How do we help inform the public about things like Covid transmission in airports? For example, we had the Confident Traveler Initiative which was a big push that we had, and we had to be able to inform people about data models around Covid, right? So yeah, I would say our future is more about an investment in our people and in our culture from my perspective >> That's so important. One of the hardest things to change especially for a legacy organization like Boeing, is culture. You know, when I talk with CEO's or CIO's or COO's about what's your company's vision, what's your strategy? Especially those companies that are on that digital journey that have no choice these days. Everybody expects to have a digital experience, whether you're transacting an an Uber ride, you're buying groceries, or you're traveling by air. That culture sounds like Boeing is really focused on that. And that's impressive because that's one of the hardest things to morph and mold, but it's so essential. You know, as we look around the room here at WiDS it's obviously mostly females, but we're talking about women, underrepresented minorities. We're talking about men as well who are mentors and sponsors to us. I'd love to get your advice to your younger self. What would you tell yourself in terms of where you are now to become a leader in the technology field? >> Yeah, I mean, it's kind of an interesting question because I always try to think, live with no regrets to an extent. >> Lisa: I like that. >> But, there's lots of failures along the way. (Tracy laughing) I don't know if I would tell myself anything different because honestly, if I did, I wouldn't be where I am. >> Lisa: Good for you. >> I started out in fine arts, and I didn't end up there. >> That's good. >> Such a good point, yeah. >> We've been talking about that and I find that a lot at events like WiDS, is women have these zigzaggy patterns. I studied biology, I have a master's in molecular biology, I'm in media and marketing. We talked about transportable skills. There's a case I made many years ago when I got into tech about, well in science you learn the art of interpreting esoteric data and creating a story from it. And that's a transportable skill. But I always say, you mentioned failure, I always say failure is not a bad F word. It allows us to kind of zig and zag and learn along the way. And I think that really fosters thought diversity. And in data science, that is one of the things we absolutely need to have is that diversity and thought. You know, we talk about AI models being biased, we need the data and we need the diverse brains to help ensure that the biases are identified, extracted, and removed. Speaking of AI, I've been geeking out with ChatGPT. So, I'm on it yesterday and I ask it, "What's hot in data science?" And I was like, is it going to get that? What's hot? And it did it, it came back with trends. I think if I ask anything, "What's hot?", I should be to Paris Hilton, but I didn't. And so I was geeking out. One of the things I learned recently that I thought was so super cool is the CTO of OpenAI is a woman, Mira Murati, which I didn't know until over the weekend. Because I always think if I had to name top females in tech, who would they be? And I always default to Sheryl Sandberg, Carly Fiorina, Susan Wojcicki running YouTube. Who are some of the people in your history, in your current, that are really inspiring to you? Men, women, indifferent. >> Sure. I think Boeing is one of the companies where you actually do see a lot of women in leadership roles. I think we're one of the top companies with a number of women executives, actually. Susan Doniz, who's our Chief Information Officer, I believe she's actually slotted to speak at a WiDS event come fall. >> Lisa: Cool. >> So that will be exciting. Susan's actually relatively newer to Boeing in some ways. A Boeing time skill is like three years is still kind of new. (laughs) But she's been around for a while and she's done a lot of inspiring things, I think, for women in the organization. She does a lot with Latino communities and things like that as well. For me personally, you know, when I started at Boeing Ahmad Yaghoobi was one of my mentors and my Technical Lead. He came from Iran during a lot of hard times in the 1980s. His brother actually wrote a memoir, (laughs) which is just a fun, interesting fact. >> Tracy: Oh my God! >> Lisa: Wow! >> And so, I kind of gravitate to people that I can learn from that's not in my sphere, that might make me uncomfortable. >> And you probably don't even think about how many people you're influencing along the way. >> No. >> We just keep going and learning from our mentors and probably lose sight of, "I wonder how many people actually admire me?" And I'm sure there are many that admire you, Rhonda, for what you've done, going from anthropology to archeology. You mentioned before we went live you were really interested in photography. Keep going and really gathering all that breadth 'cause it's only making you more inspiring to people like us. >> Exactly. >> We thank you so much for joining us on the program and sharing a little bit about you and what brought you to WiDS. Thank you so much, Rhonda. >> Yeah, thank you. >> Tracy: Thank you so much for being here. >> Lisa: Yeah. >> Alright. >> For our guests, and for Tracy Zhang, this is Lisa Martin live at Stanford University covering the eighth Annual Women In Data Science Conference. Stick around. Next guest will be here in just a second. (gentle music)

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

(upbeat music) >> Hey everyone. Welcome back to "The Cube"'s live coverage of "Women in Data Science 2023". As every year we are here live at Stanford University, profiling some amazing women and men in the fields of data science. I have my co-host for this segment is Hannah Freitag. Hannah is from Stanford's Data Journalism program, really interesting, check it out. We're very pleased to welcome our first guest of the day fresh from the keynote stage, Gayatree Ganu, the VP of Data Science at Meta. Gayatree, It's great to have you on the program. >> Likewise, Thank you for having me. >> So you have a PhD in Computer Science. You shared some really cool stuff. Everyone knows Facebook, everyone uses it. I think my mom might be one of the biggest users (Gayatree laughs) and she's probably watching right now. People don't realize there's so much data behind that and data that drives decisions that we engage with. But talk to me a little bit about you first, PhD in Computer Science, were you always, were you like a STEM kid? Little Gayatree, little STEM, >> Yeah, I was a STEM kid. I grew up in Mumbai, India. My parents are actually pharmacists, so they were not like math or stats or anything like that, but I was always a STEM kid. I don't know, I think it, I think I was in sixth grade when we got our first personal computer and I obviously used it as a Pacman playing machine. >> Oh, that's okay. (all laugh) >> But I was so good at, and I, I honestly believe I think being good at games kind of got me more familiar and comfortable with computers. Yeah. I think I always liked computers, I, yeah. >> And so now you lead, I'm looking at my notes here, the Engagement Ecosystem and Monetization Data Science teams at Facebook, Meta. Talk about those, what are the missions of those teams and how does it impact the everyday user? >> Yeah, so the engagement is basically users coming back to our platform more, there's, no better way for users to tell us that they are finding value on the things that we are doing on Facebook, Instagram, WhatsApp, all the other products than coming back to our platform more. So the Engagement Ecosystem team is looking at trends, looking at where there are needs, looking at how users are changing their behaviors, and you know, helping build strategy for the long term, using that data knowledge. Monetization is very different. You know, obviously the top, top apex goal is have a sustainable business so that we can continue building products for our users. And so, but you know, I said this in my keynote today, it's not about making money, our mission statement is not, you know, maximize as much money as you can make. It's about building a meaningful connection between businesses, customers, users, and, you know especially in these last two or three funky, post-pandemic years, it's been such a big, an important thing to do for small businesses all over all, all around the world for users to find like goods and services and products that they care about and that they can connect to. So, you know, there is truly an connection between my engagement world and the monetization world. And you know, it's not very clear always till you go in to, like, you peel the layers. Everything we do in the ads world is also always first with users as our, you know, guiding principle. >> Yeah, you mentioned how you supported especially small businesses also during the pandemic. You touched a bit upon it in the keynote speech. Can you tell our audience what were like special or certain specific programs you implemented to support especially small businesses during these times? >> Yeah, so there are 200 million businesses on our platform. A lot of them small businesses, 10 million of them run ads. So there is a large number of like businesses on our platform who, you know use the power of social media to connect to the customers that matter to them, to like you, you know use the free products that we built. In the post-pandemic years, we built a lot of stuff very quickly when Covid first hit for business to get the word out, right? Like, they had to announce when special shopping hours existed for at-risk populations, or when certain goods and services were available versus not. We had grants, there's $100 million grant that we gave out to small businesses. Users could show sort of, you know show their support with a bunch of campaigns that we ran, and of course we continue running ads. Our ads are very effective, I guess, and, you know getting a very reliable connection with from the customer to the business. And so, you know, we've run all these studies. We support, I talked about two examples today. One of them is the largest black-owned, woman black-owned wine company, and how they needed to move to an online program and, you know, we gave them a grant, and supported them through their ads campaign and, you know, they saw 60% lift in purchases, or something like that. So, a lot of good stories, small stories, you know, on a scale of 200 million, that really sort of made me feel proud about the work we do. And you know, now more than ever before, I think people can connect so directly with businesses. You can WhatsApp them, I come from India, every business is on WhatsApp. And you can, you know, WhatsApp them, you can send them Facebook messages, and you can build this like direct connection with things that matter to you. >> We have this expectation that we can be connected anywhere. I was just at Mobile World Congress for MWC last week, where, obviously talking about connectivity. We want to be able to do any transaction, whether it's post on Facebook or call an Uber, or watch on Netflix if you're on the road, we expect that we're going to be connected. >> Yeah. >> And what we, I think a lot of us don't realize I mean, those of us in tech do, but how much data science is a facilitator of all of those interactions. >> Yeah! >> As we, Gayatree, as we talk about, like, any business, whether it is the black women-owned wine business, >> Yeah. >> great business, or a a grocer or a car dealer, everybody has to become data-driven. >> Yes. >> Because the consumer has the expectation. >> Yes. >> Talk about data science as a facilitator of just pretty much everything we are doing and conducting in our daily lives. >> Yeah, I think that's a great question. I think data science as a field wasn't really defined like maybe 15 years ago, right? So this is all in our lifetimes that we are seeing this. Even in data science today, People come from so many different backgrounds and bring their own expertise here. And I think we, you know, this conference, all of us get to define what that means and how we can bring data to do good in the world. Everything you do, as you said, there is a lot of data. Facebook has a lot of data, Meta has a lot of data, and how do we responsibly use this data? How do we use this data to make sure that we're, you know representing all diversity? You know, minorities? Like machine learning algorithms don't do well with small data, they do well with big data, but the small data matters. And how do you like, you know, bring that into algorithms? Yeah, so everything we do at Meta is very, very data-driven. I feel proud about that, to be honest, because while data gets a bad rap sometimes, having no data and making decisions in the blind is just the absolute worst thing you can do. And so, you know, we, the job as a data scientist at Facebook is to make sure that we use this data, use this responsibly, make sure that we are representing every aspect of the, you know, 3 billion users who come to our platform. Yeah, data serves all the products that we build here. >> The responsibility factor is, is huge. You know, we can't talk about AI without talking about ethics. One of the things that I was talking with Hannah and our other co-host, Tracy, about during our opening is something I just learned over the weekend. And that is that the CTO of ChatGPT is a woman. (Gayatree laughs) I didn't know that. And I thought, why isn't she getting more awareness? There's a lot of conversations with their CEO. >> Yeah. >> Everyone's using it, playing around with it. I actually asked it yesterday, "What's hot in Data Science?" (all laugh) I was like, should I have asked that to let itself in, what's hot? (Gayatree laughs) But it, I thought that was phenomenal, and we need to be talking about this more. >> Yeah. >> This is something that they're likening to the launch of the iPhone, which has transformed our lives. >> I know, it is. >> ChatGPT, and its chief technologist is a female, how great is that? >> And I don't know whether you, I don't know the stats around this, but I think CTO is even less, it's even more rare to have a woman there, like you have women CEOs because I mean, we are building upon years and years of women not choosing technical fields and not choosing STEM, and it's going to take some time, but yeah, yeah, she's a woman. Isn't it amazing? It's wonderful. >> Yes, there was a great, there's a great "Fast Company" article on her that I was looking at yesterday and I just thought, we need to do what we can to help spread, Mira Murati is her name, because what she's doing is, one of the biggest technological breakthroughs we may ever see in our lifetime. It gives me goosebumps just thinking about it. (Gayatree laughs) I also wanted to share some stats, oh, sorry, go ahead, Hannah. >> Yeah, I was going to follow up on the thing that you mentioned that we had many years with like not enough women choosing a career path in STEM and that we have to overcome this trend. What are some, like what is some advice you have like as the Vice-President Data Science? Like what can we do to make this feel more, you know, approachable and >> Yeah. >> accessible for women? >> Yeah, I, there's so much that we have done already and you know, want to continue, keep doing. Of course conferences like these were, you know and I think there are high school students here there are students from my Alma Mater's undergrad year. It's amazing to like get all these women together to get them to see what success could look like. >> Yeah. >> What being a woman leader in this space could look like. So that's, you know, that's one, at Meta I lead recruiting at Meta and we've done a bunch to sort of open up the thinking around data science and technical jobs for women. Simple things like what you write in your job description. I don't know whether you know this, or this is a story you've heard before, when you see, when you have a job description and there are like 10 things that you need to, you know be good at to apply to this job, a woman sees those 10 and says, okay, I don't meet the qualifications of one of them and she doesn't apply. And a man sees one that he meets the qualifications to and he applies. And so, you know, there's small things you can do, and just how you write your job description, what goals you set for diversity and inclusion for your own organization. We have goals, Facebook's always been pretty up there in like, you know, speaking out for diversity and Sheryl Sandberg has been our Chief Business Officer for a very long time and she's been, like, amazing at like pushing from more women. So yeah, every step of the way, I think, we made a lot of progress, to be honest. I do think women choose STEM fields a lot more than they did. When I did my Computer Science I was often one of one or two women in the Computer Science class. It takes some time to, for it to percolate all the way to like having more CTOs and CEOs, >> Yeah. >> but it's going to happen in our lifetime, and you know, three of us know this, women are going to rule the world, and it (laughs) >> Drop the mic, girl! >> And it's going to happen in our lifetime, so I'm excited about it. >> And we have responsibility in helping make that happen. You know, I'm curious, you were in STEM, you talked about Computer Science, being one of the only females. One of the things that the nadb.org data from 2022 showed, some good numbers, the number of women in technical roles is now 27.6%, I believe, so up from 25, it's up in '22, which is good, more hiring of women. >> Yeah. >> One of the biggest challenges is attrition. What keeps you motivated? >> Yeah. >> To stay what, where you are doing what you're doing, managing a family and helping to drive these experiences at Facebook that we all expect are just going to happen? >> Yeah, two things come to mind. It does take a village. You do need people around you. You know, I'm grateful for my husband. You talked about managing a family, I did the very Indian thing and my parents live with us, and they help take care of the kids. >> Right! (laughs) >> (laughs) My kids are young, six and four, and I definitely needed help over the last few years. It takes mentors, it takes other people that you look up to, who've gone through all of those same challenges and can, you know, advise you to sort of continue working in the field. I remember when my kid was born when he was six months old, I was considering quitting. And my husband's like, to be a good role model for your children, you need to continue working. Like, just being a mother is not enough. And so, you know, so that's one. You know, the village that you build around you your supporters, your mentors who keep encouraging you. Sheryl Sandberg said this to me in my second month at Facebook. She said that women drop out of technical fields, they become managers, they become sort of administrative more, in their nature of their work, and her advice was, "Don't do that, Don't stop the technical". And I think that's the other thing I'd say to a lot of women. Technical stuff is hard, but you know, keeping up with that and keeping sort of on top of it actually does help you in the long run. And it's definitely helped me in my career at Facebook. >> I think one of the things, and Hannah and I and Tracy talked about this in the open, and I think you'll agree with us, is the whole saying of you can't be what you can't see, and I like to way, "Well, you can be what you can see". That visibility, the great thing that WiDS did, of having you on the stage as a speaker this morning so people can understand, everyone, like I said, everyone knows Meta, >> Yeah. >> everyone uses Facebook. And so it's important to bring that connection, >> Yeah. >> of how data is driving the experiences, the fact that it's User First, but we need to be able to see women in positions, >> Yes. >> like you, especially with Sheryl stepping down moving on to something else, or people that are like YouTube influencers, that have no idea that the head of YouTube for a very long time, Susan Wojcicki is a woman. >> (laughs) Yes. Who pioneered streaming, and I mean how often do you are you on YouTube every day? >> Yep, every day. >> But we have to be able to see and and raise the profile of these women and learn from them and be inspired, >> Absolutely. >> to keep going and going. I like what I do, I'm making a difference here. >> Yeah, yeah, absolutely. >> And I can be the, the sponsor or the mentor for somebody down the road. >> Absolutely. >> Yeah, and then referring back to what we talked in the beginning, show that data science is so diverse and it doesn't mean if you're like in IT, you're like sitting in your dark room, >> Right. (laughs) >> coding all day, but you know, >> (laughs) Right! >> to show the different facets of this job and >> Right! >> make this appealing to women, >> Yeah. for sure. >> And I said this in my keynote too, you know, one of the things that helped me most is complimenting the data and the techniques and the algorithms with how you work with people, and you know, empathy and alignment building and leadership, strategic thinking. And I think honestly, I think women do a lot of this stuff really well. We know how to work with people and so, you know, I've seen this at Meta for sure, like, you know, all of these skills soft skills, as we call them, go a long way, and like, you know, doing the right things and having a lasting impact. And like I said, women are going to rule the world, you know, in our lifetimes. (laughs) >> Oh, I can't, I can't wait to see that happen. There's some interesting female candidates that are already throwing their hats in the ring for the next presidential election. >> Yes. >> So we'll have to see where that goes. But some of the things that are so interesting to me, here we are in California and Palo Alto, technically Stanford is its own zip code, I believe. And we're in California, we're freaking out because we've gotten so much rain, it's absolutely unprecedented. We need it, we had a massive drought, an extreme drought, technically, for many years. I've got friends that live up in Tahoe, I've been getting pictures this morning of windows that are >> (laughs) that are covered? >> Yes, actually, yes. (Gayatree laughs) That, where windows like second-story windows are covered in snow. >> Yeah. >> Climate change. >> Climate change. >> There's so much that data science is doing to power and power our understanding of climate change whether it's that, or police violence. >> Yeah. (all talk together) >> We had talk today on that it was amazing. >> Yes. So I want more people to know what data science is really facilitating, that impacts all of us, whether you're in a technical role or not. >> And data wins arguments. >> Yes, I love that! >> I said this is my slide today, like, you know, there's always going to be doubters and naysayers and I mean, but there's hard evidence, there's hard data like, yeah. In all of these fields, I mean the data that climate change, the data science that we have done in the environmental and climate change areas and medical, and you know, medicine professions just so much, so much more opportunity, and like, how much we can learn more about the world. >> Yeah. >> Yeah, it's a pretty exciting time to be a data scientist. >> I feel like, we're just scratching the surface. >> Yeah. >> With the potential and the global impact that we can make with data science. Gayatree, it's been so great having you on theCUBE, thank you. >> Right, >> Thank you so much, Gayatree. >> So much, I love, >> Thank you. >> I'm going to take Data WiD's arguments into my personal life. (Gayatree laughs) I was actually just, just a quick anecdote, funny story. I was listening to the radio this morning and there was a commercial from an insurance company and I guess the joke is, it's an argument between two spouses, and the the voiceover comes in and says, "Let's watch a replay". I'm like, if only they, then they got the data that helped the woman win the argument. (laughs) >> (laughs) I will warn you it doesn't always help with arguments I have with my husband. (laughs) >> Okay, I'm going to keep it in the middle of my mind. >> Yes! >> Gayatree, thank you so much. >> Thank you so much, >> for sharing, >> Thank you both for the opportunity. >> And being a great female that we can look up to, we really appreciate your insights >> Oh, likewise. >> and your time. >> Thank you. >> All right, for our guest, for Hannah Freitag, I'm Lisa Martin, live at Stanford University covering "Women in Data Science '23". Stick around, our next guest joins us in just a minute. (upbeat music) I have been in the software and technology industry for over 12 years now, so I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCUBE. (upbeat music) Being a host on theCUBE has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, "Hey, I'm really interested in this. I love talking with customers, gimme a shot, let me come into the studio and do an interview and see if we can work together". I think where I really impact theCUBE is being a female in technology. We interview a lot of females in tech, we do a lot of women in technology events and one of the things I.

(upbeat music) >> Morning guys and girls, welcome back to theCUBE's live coverage of Women in Data Science WIDS 2023 live at Stanford University. Lisa Martin here with my co-host for this segment, Tracy Zhang. We're really excited to be talking with a great female rockstar. You're going to learn a lot from her next, Jacqueline Kuo, solutions engineer at Dataiku. Welcome, Jacqueline. Great to have you. >> Thank you so much. >> Thank for being here. >> I'm so excited to be here. >> So one of the things I have to start out with, 'cause my mom Kathy Dahlia is watching, she's a New Yorker. You are a born and raised New Yorker and I learned from my mom and others. If you're born in New York no matter how long you've moved away, you are a New Yorker. There's you guys have like a secret club. (group laughs) >> I am definitely very proud of being born and raised in New York. My family immigrated to New York, New Jersey from Taiwan. So very proud Taiwanese American as well. But I absolutely love New York and I can't imagine living anywhere else. >> Yeah, yeah. >> I love it. >> So you studied, I was doing some research on you you studied mechanical engineering at MIT. >> Yes. >> That's huge. And you discovered your passion for all things data-related. You worked at IBM as an analytics consultant. Talk to us a little bit about your career path. Were you always interested in engineering STEM-related subjects from the time you were a child? >> I feel like my interests were ranging in many different things and I ended up landing in engineering, 'cause I felt like I wanted to gain a toolkit like a toolset to make some sort of change with or use my career to make some sort of change in this world. And I landed on engineering and mechanical engineering specifically, because I felt like I got to, in my undergrad do a lot of hands-on projects, learn every part of the engineering and design process to build products which is super-transferable and transferable skills sort of is like the trend in my career so far. Where after undergrad I wanted to move back to New York and mechanical engineering jobs are kind of few and fall far in between in the city. And I ended up landing at IBM doing analytics consulting, because I wanted to understand how to use data. I knew that data was really powerful and I knew that working with it could allow me to tell better stories to influence people across different industries. And that's also how I kind of landed at Dataiku to my current role, because it really does allow me to work across different industries and work on different problems that are just interesting. >> Yeah, I like the way that, how you mentioned building a toolkit when doing your studies at school. Do you think a lot of skills are still very relevant to your job at Dataiku right now? >> I think that at the core of it is just problem solving and asking questions and continuing to be curious or trying to challenge what is is currently given to you. And I think in an engineering degree you get a lot of that. >> Yeah, I'm sure. >> But I think that we've actually seen that a lot in the panels today already, that you get that through all different types of work and research and that kind of thoughtfulness comes across in all different industries too. >> Talk a little bit about some of the challenges, that data science is solving, because every company these days, whether it's an enterprise in manufacturing or a small business in retail, everybody has to be data-driven, because the end user, the end customer, whoever that is whether it's a person, an individual, a company, a B2B, expects to have a personalized custom experience and that comes from data. But you have to be able to understand that data treated properly, responsibly. Talk about some of the interesting projects that you're doing at Dataiku or maybe some that you've done in the past that are really kind of transformative across things climate change or police violence, some of the things that data science really is impacting these days. >> Yeah, absolutely. I think that what I love about coming to these conferences is that you hear about those really impactful social impact projects that I think everybody who's in data science wants to be working on. And I think at Dataiku what's great is that we do have this program called Ikig.AI where we work with nonprofits and we support them in their data and analytics projects. And so, a project I worked on was with the Clean Water, oh my goodness, the Ocean Cleanup project, Ocean Cleanup organization, which was amazing, because it was sort of outside of my day-to-day and it allowed me to work with them and help them understand better where plastic is being aggregated across the world and where it appears, whether that's on beaches or in lakes and rivers. So using data to help them better understand that. I feel like from a day-to-day though, we, in terms of our customers, they're really looking at very basic problems with data. And I say basic, not to diminish it, but really just to kind of say that it's high impact, but basic problems around how do they forecast sales better? That's a really kind of, sort of basic problem, but it's actually super-complex and really impactful for people, for companies when it comes to forecasting how much headcount they need to have in the next year or how much inventory to have if they're retail. And all of those are going to, especially for smaller companies, make a huge impact on whether they make profit or not. And so, what's great about working at Dataiku is you get to work on these high-impact projects and oftentimes I think from my perspective, I work as a solutions engineer on the commercial team. So it's just, we work generally with smaller customers and sometimes talking to them, me talking to them is like their first introduction to what data science is and what they can do with that data. And sort of using our platform to show them what the possibilities are and help them build a strategy around how they can implement data in their day-to-day. >> What's the difference? You were a data scientist by title and function, now you're a solutions engineer. Talk about the ascendancy into that and also some of the things that you and Tracy will talk about as those transferable, those transportable skills that probably maybe you learned in engineering, you brought data science now you're bringing to solutions engineering. >> Yeah, absolutely. So data science, I love working with data. I love getting in the weeds of things and I love, oftentimes that means debugging things or looking line by line at your code and trying to make it better. I found that on in the data science role, while those things I really loved, sometimes it also meant that I didn't, couldn't see or didn't have visibility into the broader picture of well like, well why are we doing this project? And who is it impacting? And because oftentimes your day-to-day is very much in the weeds. And so, I moved into sales or solutions engineering at Dataiku to get that perspective, because what a sales engineer does is support the sale from a technical perspective. And so, you really truly understand well, what is the customer looking for and what is going to influence them to make a purchase? And how do you tell the story of the impact of data? Because oftentimes they need to quantify well, if I purchase a software like Dataiku then I'm able to build this project and make this X impact on the business. And that is really powerful. That's where the storytelling comes in and that I feel like a lot of what we've been hearing today about connecting data with people who can actually do something with that data. That's really the bridge that we as sales engineers are trying to connect in that sales process. >> It's all about connectivity, isn't it? >> Yeah, definitely. We were talking about this earlier that it's about making impact and it's about people who we are analyzing data is like influencing. And I saw that one of the keywords or one of the biggest thing at Dataiku is everyday AI, so I wanted to just ask, could you please talk more about how does that weave into the problem solving and then day-to-day making an impact process? >> Yes, so I started working on Dataiku around three years ago and I fell in love with the product itself. The product that we have is we allow for people with different backgrounds. If you're coming from a data analyst background, data science, data engineering, maybe you are more of like a business subject matter expert, to all work in one unified central platform, one user interface. And why that's powerful is that when you're working with data, it's not just that data scientist working on their own and their own computer coding. We've heard today that it's all about connecting the data scientists with those business people, with maybe the data engineers and IT people who are actually going to put that model into production or other folks. And so, they all use different languages. Data scientists might use Python and R, your business people are using PowerPoint and Excel, everyone's using different tools. How do we bring them all in one place so that you can have conversations faster? So the business people can understand exactly what you're building with the data and can get their hands on that data and that model prediction faster. So that's what Dataiku does. That's the product that we have. And I completely forgot your question, 'cause I got so invested in talking about this. Oh, everyday AI. Yeah, so the goal of of Dataiku is really to allow for those maybe less technical people with less traditional data science backgrounds. Maybe they're data experts and they understand the data really well and they've been working in SQL for all their career. Maybe they're just subject matter experts and want to get more into working with data. We allow those people to do that through our no and low-code tools within our platform. Platform is very visual as well. And so, I've seen a lot of people learn data science, learn machine learning by working in the tool itself. And that's sort of, that's where everyday AI comes in, 'cause we truly believe that there are a lot of, there's a lot of unutilized expertise out there that we can bring in. And if we did give them access to data, imagine what we could do in the kind of work that they can do and become empowered basically with that. >> Yeah, we're just scratching the surface. I find data science so fascinating, especially when you talk about some of the real world applications, police violence, health inequities, climate change. Here we are in California and I don't know if you know, we're experiencing an atmospheric river again tomorrow. Californians and the rain- >> Storm is coming. >> We are not good... And I'm a native Californian, but we all know about climate change. People probably don't associate all of the data that is helping us understand it, make decisions based on what's coming what's happened in the past. I just find that so fascinating. But I really think we're truly at the beginning of really understanding the impact that being data-driven can actually mean whether you are investigating climate change or police violence or health inequities or your a grocery store that needs to become data-driven, because your consumer is expecting a personalized relevant experience. I want you to offer me up things that I know I was doing online grocery shopping, yesterday, I just got back from Europe and I was so thankful that my grocer is data-driven, because they made the process so easy for me. And but we have that expectation as consumers that it's going to be that easy, it's going to be that personalized. And what a lot of folks don't understand is the data the democratization of data, the AI that's helping make that a possibility that makes our lives easier. >> Yeah, I love that point around data is everywhere and the more we have, the actually the more access we actually are providing. 'cause now compute is cheaper, data is literally everywhere, you can get access to it very easily. And so, I feel like more people are just getting themselves involved and that's, I mean this whole conference around just bringing more women into this industry and more people with different backgrounds from minority groups so that we get their thoughts, their opinions into the work is so important and it's becoming a lot easier with all of the technology and tools just being open source being easier to access, being cheaper. And that I feel really hopeful about in this field. >> That's good. Hope is good, isn't it? >> Yes, that's all we need. But yeah, I'm glad to see that we're working towards that direction. I'm excited to see what lies in the future. >> We've been talking about numbers of women, percentages of women in technical roles for years and we've seen it hover around 25%. I was looking at some, I need to AnitaB.org stats from 2022 was just looking at this yesterday and the numbers are going up. I think the number was 26, 27.6% of women in technical roles. So we're seeing a growth there especially over pre-pandemic levels. Definitely the biggest challenge that still seems to be one of the biggest that remains is attrition. I would love to get your advice on what would you tell your younger self or the previous prior generation in terms of having the confidence and the courage to pursue engineering, pursue data science, pursue a technical role, and also stay in that role so you can be one of those females on stage that we saw today? >> Yeah, that's the goal right there one day. I think it's really about finding other people to lift and mentor and support you. And I talked to a bunch of people today who just found this conference through Googling it, and the fact that organizations like this exist really do help, because those are the people who are going to understand the struggles you're going through as a woman in this industry, which can get tough, but it gets easier when you have a community to share that with and to support you. And I do want to definitely give a plug to the WIDS@Dataiku team. >> Talk to us about that. >> Yeah, I was so fortunate to be a WIDS ambassador last year and again this year with Dataiku and I was here last year as well with Dataiku, but we have grown the WIDS effort so much over the last few years. So the first year we had two events in New York and also in London. Our Dataiku's global. So this year we additionally have one in the west coast out here in SF and another one in Singapore which is incredible to involve that team. But what I love is that everyone is really passionate about just getting more women involved in this industry. But then also what I find fortunate too at Dataiku is that we have a strong female, just a lot of women. >> Good. >> Yeah. >> A lot of women working as data scientists, solutions engineer and sales and all across the company who even if they aren't doing data work in a day-to-day, they are super-involved and excited to get more women in the technical field. And so. that's like our Empower group internally that hosts events and I feel like it's a really nice safe space for all of us to speak about challenges that we encounter and feel like we're not alone in that we have a support system to make it better. So I think from a nutrition standpoint every organization should have a female ERG to just support one another. >> Absolutely. There's so much value in a network in the community. I was talking to somebody who I'm blanking on this may have been in Barcelona last week, talking about a stat that showed that a really high percentage, 78% of people couldn't identify a female role model in technology. Of course, Sheryl Sandberg's been one of our role models and I thought a lot of people know Sheryl who's leaving or has left. And then a whole, YouTube influencers that have no idea that the CEO of YouTube for years has been a woman, who has- >> And she came last year to speak at WIDS. >> Did she? >> Yeah. >> Oh, I missed that. It must have been, we were probably filming. But we need more, we need to be, and it sounds like Dataiku was doing a great job of this. Tracy, we've talked about this earlier today. We need to see what we can be. And it sounds like Dataiku was pioneering that with that ERG program that you talked about. And I completely agree with you. That should be a standard program everywhere and women should feel empowered to raise their hand ask a question, or really embrace, "I'm interested in engineering, I'm interested in data science." Then maybe there's not a lot of women in classes. That's okay. Be the pioneer, be that next Sheryl Sandberg or the CTO of ChatGPT, Mira Murati, who's a female. We need more people that we can see and lean into that and embrace it. I think you're going to be one of them. >> I think so too. Just so that young girls like me like other who's so in school, can see, can look up to you and be like, "She's my role model and I want to be like her. And I know that there's someone to listen to me and to support me if I have any questions in this field." So yeah. >> Yeah, I mean that's how I feel about literally everyone that I'm surrounded by here. I find that you find role models and people to look up to in every conversation whenever I'm speaking with another woman in tech, because there's a journey that has had happen for you to get to that place. So it's incredible, this community. >> It is incredible. WIDS is a movement we're so proud of at theCUBE to have been a part of it since the very beginning, since 2015, I've been covering it since 2017. It's always one of my favorite events. It's so inspiring and it just goes to show the power that data can have, the influence, but also just that we're at the beginning of uncovering so much. Jacqueline's been such a pleasure having you on theCUBE. Thank you. >> Thank you. >> For sharing your story, sharing with us what Dataiku was doing and keep going. More power to you girl. We're going to see you up on that stage one of these years. >> Thank you so much. Thank you guys. >> Our pleasure. >> Our pleasure. >> For our guests and Tracy Zhang, this is Lisa Martin, you're watching theCUBE live at WIDS '23. #EmbraceEquity is this year's International Women's Day theme. Stick around, our next guest joins us in just a minute. (upbeat music)

(ambient music) >> 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 WiDS 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 sata journalism program, master's program, at Stanford. So great to have you guys. >> So excited to be here. >> So data journalism's so interesting. Tracy, tell us a little bit about you, what you're interested in, and then Hannah we'll have you do the same thing. >> Yeah >> Yeah, definitely. I definitely think data journalism is very interesting, and in fact, I think, 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, like you said, I'm in this 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 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 story to end up in a research paper, which is only very limited 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 make it comprehensive and accessible for a broader audience. So then I found this program at Stanford and I was like, okay, that's the perfect transition from political science to journalism, and to use data to 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. >> Tracy: Definitely. >> Not everyone was in STEM as a young kid or studied computer science. Maybe some are engineering, maybe some are are philosophy or economic, it's so interesting. And 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, maybe in the financial business, or in health, as you mentioned, the variety of fields, it's very powerful, powerful tool to use. >> It's a very power, oh, go ahead Tracy. >> No, definitely. I just wanted to build off of that. It's important to put a face on data. So a dataset 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 or 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. 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, since 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, today, March 8th, and around event- >> Tracy: A big holiday. >> 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? 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 a, I dunno, 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 WiDS is great about. >> WiDS is so fantastic for that network effect, as you talked about. It's always such, as I was telling you about before we went live, I've covered five or six WiDS for theCUBE, and it's always such a day of positivity, it's a day of 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 it, not just to tech. Because we're talking and we saw Meta was a keynote who's going to come to talk with Hannah and me in a little bit, we see Total Energies on the program today, we see Microsoft, Intuit, 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, 'cause 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 conference like this will just show us how these women are great in their fields. They're great data scientists that are bringing great insight to the company and even to the social good as well. So yeah, I think that's very important just 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, 'cause 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, that 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. >> Hannah: Yeah. >> Tracy: Yeah. >> So that expectation that everyone has is all about data, though we don't necessarily think about it like that. >> Hannah: Exactly. >> Tracy: Exactly. >> But it's all about the data that, the past data, the data science, as well as the realtime 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. >> Yeah. 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 and 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 a vision in mind, I think that's really important to drive you. >> Having those mentors and sponsors, 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 but I didn't understand the difference between a mentor and a sponsor. And then it got me thinking, who are my sponsors? And I started going through LinkedIn, oh, 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 'cause I'm not sure if you know this, ChatGPT, exploding, I was on it yesterday looking at- >> Everyone talking about it. >> 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. >> Tracy: Yes. >> Hannah: Yeah. >> So I said, Oh, I'm so sorry. But everyone's talking about ChatGPT, it is the most advanced AI chatbot 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 ChatGPT is a woman? >> Tracy: I did not know, but I learned that. >> I learned that a couple 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 ChatGPT, 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 completely had no idea. >> I didn't either. I saw it on LinkedIn over the weekend and I thought, I have to talk about that because it's so important when we talk about some of the trends, other trends from AnitaB.org, I talked about some of those positive trends. Overall hiring has 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 slowly. 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 AnitaB.org, more than doubled. So we're seeing women getting into the field and dropping out for various reasons. And so that's still an extent concern that we have. What do you think would motivate you to stick around if you were in a technical role? Same question for you in a minute. >> Right, you were talking about how we see an increase especially in the intern level for women. And I think if, I don't know, this is a great for a start point for pushing the momentum to start growth, pushing the needle rightwards. But I think if we can see more increase in the upper level, the women representation in the upper level too, maybe that's definitely a big goal and something we should work towards to. >> Lisa: Absolutely. >> But if there's more representation up in the CTO position, like in the managing level, I think that will definitely be a great factor to keep women in data science. >> I was looking at some trends, sorry, Hannah, forgetting what this 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, 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. We think of, if you ask, name a female executive that you'd recognize, everyone would probably say Sheryl 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. >> Tracy: Exactly. >> 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. >> Hannah: Exactly. >> 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 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 or 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. >> Absolutely. >> How to balance work and life. 'Cause we never ask that. We just ask the woman. >> No, they just get it done, probably because there's a woman on the other end whose making it happen. >> Exactly. 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 slight 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, theCUBE- >> Tracy: And look at you now. >> Look at me now. And theCUBE, the guys Dave Vellante and John Furrier are two of those people that are sponsors of mine. But 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. 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 going to have great conversations today, I'm so excited to be co-hosting with both of you. You're going to be inspired, you're going 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. >> Exactly. >> 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 we can make data science even more equitable and 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. (ambient music)

(upbeat music) >> Hey everyone, welcome to this event: Build Your Cloud Center of Excellence. I'm your host, Lisa Martin, and I have two guests here with me today to talk about the hybrid cloud, the multi-cloud trends, and specifically the complexity. While we know these trends provide agility and flexibility for customers, they also bring in complexity. And this session is going to focus on exploring that with RBI and HitachiVantara. Please welcome my guests, Adithya Sastry the SVP of Digital Solutions at HitachiVantara and Werner Mayer, head of group core IT and head of group data at RBI International. Guys, welcome to the program. >> Thank you Lisa. Werner, nice to see you again. >> Great to see you both. >> And Werner, we're going to start with you. Talk about RBI. Tell the audience a little bit about what the business is and then we're going to get into your cloud transformation journey over the last couple of years. >> Yes, thank you. So Raiffeisen Bank International is international working banking groups. So our core markets are Central Eastern European, Central Eastern Europe and Austria. And we are serving around 50 million clients in this market. So we active in 13 markets. >> Got it. Talk to me, Werner about the cloud transformation journey that RBI has been on over the last couple of years and some of the complexities that you've experienced as you've launched it. >> Sure. Thank you for the question. So in 2020, we decided that we have to renew our IT strategy. And the aim of the strategy was to change the organization in a way that it can react and adapt fast to the future challenges. So one of the important pillars for us was that we are adapting fast also for new technologies. And this was core pillar in our strategy. So we're searching for technologies which are fit in to our HR transformation. And we found that the cloud and the public cloud environment fits to this venture. So we tested that. We are building up also the competent centers for that and also established the group cloud platform for that. Because our invoice to onboard our international group with the 13 units to this group cloud platform. So that means we have a lot to do to hardening the platforms in terms of security to put in. We have standard for that. We have to introduce large scale programs to train hundreds of engineers. We tested the approach, We convinced the top management and we implemented this, this program. So one of the highlights was, of course, also the the safeguarding of the Ukraine, let's say, banking environment. So we had to lift and shift the complete bank in three months. And it shows that let's say our platforms works. And let's say the approach is proven that we can scale it over the group. >> That's a big challenge. A lot of complexity especially with some of the global things going on. Adithya, these challenges are, are not unique to RBI. A lot of your customers are facing challenges with complexity around cloud management, cloud ops. What can you unpack was the real issue is here? >> Yeah, Lisa, absolutely. And you know, before I answer your question, I do want to, you know, just say a couple of things about Raiffeisen Bank. And you know, we've had the pleasure of working with them for about a year, a little bit more than a year now. And, and, and the way they approach the cloud transformation journey is - should be a template for a lot of the organizations in terms of the preparation in terms of understanding, you know. How other companies have done it and what are the pitfalls. What's worked, and really what's the recipe for their, you know, journey, right? Which is very unique because, you know, you look at you know, being present across 30 different countries within central and eastern Europe as Werner said. And the complexities of dealing with local regulations, GDPR and all these other issues that come with it, right? And not to mention the language variation from country to country. So, you know, phenomenal story there. The journey and the journey still goes, right Werner? It's not complete yet. But Lisa, to your question, you know. When we look at, you know, the complexities of this transformation, that most modern enterprises are going through. It's not very unique, right? What is unique for a Raiffeisen Bank is - has been the preparation. As you get into this journey of moving workloads to cloud, be it refactoring, modernizing, migrating, etc. One of the things that really is often overlooked is: "Are my applications and data workloads resilient on the cloud?" Meaning  how is the performance? Are they just running or are they performing with high availability to meet your customers goals? Is it scalable? And are my cost in line with what I projected when I moved prep. >> Because that's one of the areas we are seeing where you know, what enterprises projected from a cost savings to what they're realizing a year and a half into the journey is a pretty big delta, right? And, and, and a lot of it is dependent on are the cloud - are the applications and the workloads cloud, designed for the cloud? Or are they designed for on-prem which you just move to the cloud. >> So Werner, it sounds like what Adithya said is a compliment to, to you guys and the team at RBI in terms of this being a template for managing complexity. Give us, Werner, your perspective in terms of modern cloud ops. What's in? What's out? What is it that customers really need to be focusing on to be successful? >> Thanks for the compliment, Lisa. And I think this is a great relationship also in the journey. Topic is, is, is a - is a complex program where a lot of things have to fit together. But it was mentioning the resilience. The course, we call it finops, security operations and so on have to come together and have to work on spot. At the end, it's also, let's say, how we are able enabling our teams and how we are ramping out the skills of our teams to deal with these multidimensional, let's say environments. And this is something what we spend a lot of time in order to prepare, but also to bring up the people on a certain level that they can operate at. Because card guard handling is, is different than before. Because beforehand you have central operations team. They do everything for you. But in this world let's say we are also putting the responsibility of the run component of the absent to the - in the tribes and the application teams. And they have to do much more than before. On the other hand, we have first central rules. We have monitoring functions. We have support functions on that in order to best support them in their journey. So this is a hybrid between, let's say, what the teams have to do with the responsibility in the teams, but also with the central functions which are supporting them. And everything have to work together and goes hand in - right, to go hand-in-hand. >> Yeah. Yeah. And if, if I could just add Lisa really quick and and Werner hit the nail on the head, right? Because you cannot look at cloud operation the way we have traditionally looked at managed services. That's the key thing, right? You cannot, you know, traditional managed services you had L1, L2, L3 and then it goes into some sort of a vacuum and then all of a sudden somebody calls you at some point, right? >> Werner: Exactly. >> And it really has flipped, right? To, to Werner's point. And Werner hit that name on the head because you really have to understand. Bring an engineering led approach to make sure that the problems, you know, when you see an issue that you have some level of automation in terms of problem isolation. And then the problem is routed the right individual ie the application engineering team or the data engineering team for resolution in a rapid manner. Right? I think that the key - >> Yes. A very important point with that is said, yeah. So you cannot traditional transport let's say, the operation model what you have now into the cloud because this will not work, yeah. And finally at the end you will not benefit on the technology possibilities there. So super important point. My vision in the cloud and this is also something what we are working on is a sort of zero-ops environment, yeah? Because we're ultimately dealing with the automatization technologies and so on, you can that much - to much more compared to the traditional environment and the benefit of the cloud is: You can test it. You can give it feedback when it is not working, yeah? So it's a completely different operating model. What we try to establish in the cloud environment. >> So really what this seems like guys is is quite a delicate balance that you're solving for. Not the only delicate balance but Werner sticking with you. Talk to us about some of the challenges that you've had around cloud cost management in particular. Help us understand that. >> Thanks for the question. So in principle, we are doing very well on the cost side, surprisingly. And we also started the cloud journey that is said this is not the cost case. Because as I said before, let's say one of the pillars in the strategy strategy was the enablement of technology to the benefit of customer solutions to be adaptive, to be faster. But at the end it turned out that let's say with giving the responsibility of the operation to the dedicated team, they found they - they were working much closer to the cost, and let's say monitoring the cost, then we headed into traditional environments, yeah? I also saw some examples in the group where sort of gamification of the cost were going on. To say who can save more To say who can save more and make more much more out of that what you have in the cloud. And at the end we see that in minimum the cost are balance to the traditional environments in the data centers. But we also saw that let's say, the cost were brought down much more than before. So at the beginning we were relative conservative with the assumptions, yeah? But it turns out that we are really getting the benefit. The things are getting faster and also the costs are going down. And we see this in real cases. >> Yeah. And, and, and Lisa, if I could add something really quick, right? Because - There's been a mad rush to the cloud, right? Everybody kind of, it was, you know, the buzz the buzz was let's get to the cloud. We'll start to realize all these savings. And all of a sudden, everything kind of magically gets better, right? And what we have seen is also, you know, companies or customers or enterprises that have started this journey about 5, 6 years ago and are about, you know, a few years into it. What we are realizing is the cloud costs have increased significantly to what their projections were early on. And the way they're trying to address the cloud cost is by creating a FinOps organization that's looking at, you know, the cost of cloud from a structure standpoint and support as a reactive measure. Saying, "Hey if we move from Azure or one provider to another is there any benefit? If we move certain applications from the cloud back to on-prem, is there any benefit?" When in fact, one of the things that we have noticed really is: The problem needs to shift left to the engineering teams. Because if you are designing the applications and the systems the right way to begin with, then you can manage the data cost issues or the cost overruns, right? So you design for the cloud as opposed to designing and then looking at how do we optimize cloud. >> So Adithya, you talked about the RBI use case as really kind of a template but also some of the challenges with respect to hybrid and multi-cloud are kind of like a chicken and egg scenario. Talk to us kind of like overall about how Hitachi is really helping customers address these challenges and maximize the benefits to get the flexibility to get the agility so that they can deliver what their end user customers are expecting. >> Yeah, yeah. So, so one of the things we are doing, Lisa, when we work with customers, is really trying to understand, you know, look at their entire portfolio of applications, right? And, and look at what the intent of the applications is between customer facing, external customer, internal customer, high availability, production, etc., right? And then we go through a methodology called E3 which is envision, enable and execute. Which is really envision what the end stage should be regardless of what the environment is, right? And then we enable, which is really kind of go through a proof of value to move a few workloads, to modernize, rearchitect, replatform, etc. And look at the benefit of that application on its destination. If it's a cloud - if it's a cloud service provider or if it's another data center, whatever it may be, right? And finally, you know, once we've proven the value and the benefit and and say and kind of monetize the, you know realize the value of it from an agility, from a cost, from security and resilience, etc. Then we go through the execution, which was look we look at the entire portfolio, the entire landscape. And we go through a very disciplined manner working with our customers to roadmap it. And then we execute in a very deliberate manner where you can see value every 2-3 months. Because gone the days when you can do things as a science project that took 2-3 years, right? We, we - Everyone wants to see value, want to see - wants to see progress, and most importantly we want to see cost benefit and agility sooner than later. >> Those are incredibly important outcomes. You guys have done a great job explaining what you're doing together. This sounds like a great relationship. All right, so my last question to both of you is: "If I'm a customer and I'm planning a cloud transformation for my company, what are the two things you want me to remember and consider as I plan this? Werner, we'll start with you. >> I would pick up two things, yeah? The first one is: When you are organizing your company in HR way, then cloud is the HR technology for the HR transformation. Because HR teams needs HR technology. And the second important thing is, what I would say is: Cloud is a large scale and fast moving technology enabler to the company. So if your company is going forward to say: Technology is their enabler tool from a future business then cloud can support this journey. >> Excellent. I'm going to walk away with those. And Adithya, same question to you. I'm a, I'm a customer. I'm at an organization. I'm planning a cloud transformation. Top two things you want me to walk away with. >> Yeah. And I think Werner kind of actually touched on that in the second one, which is: it's not a tech, just an IT or a technology initiative. It is a business initiative, right? Because ultimately what you do from this cloud journey should drive, you know, should lead into business transformation or help your business grow top line or drive margin expansion, etc. So couple of things I would say, right? One is, you know, get Being and prioritize. Work with your business owners, with, you know with the cross-functional team not just the technology team. That's one. The second thing is: as the technology team or the IT team shepherds this journey, you know, keep everyone informed and engaged as you go through this journey. Because as you go through moving workloads modernizing workload, there is an impact to, you know receivables through omnichannel experiences the way customers interact and transact with you, right? And that comes with making making sure your businesses are aware your business stakeholders are aware. So in turn the end customers are aware. So you know, it's not a one and done from an engagement, it's a journey. And bring in the right experts. Talk to people who've done it, done this before, who have kind of stepped in all the pitfalls so you don't have to, right? That's the key. >> That's great advice. That's great advice for anything in life, I think. You talk about the collaboration, the importance of the business and the technology folks coming together. It really has to be - It's a delicate balance as we said before but it really has to be a holistic collaborative approach. Guys, thank you so much for joining me talking through what HitachiVantara and RBI are doing together. It sounds like you're well into this journey and it sounds like it's going quite well. We thank you so much for your insights and your perspectives. >> Thank you, Lisa. Werner, thank you again. >> Good stuff guys. For my guests, I'm Lisa Martin. Thank you so much for watching our event: Build Your Cloud Center of Excellence. (upbeat music)