>> Hello friends and welcome back to Fabulous Las Vegas, Nevada. We are here at AWS re:Invent in the heat of day three. Very exciting time. My name is Savannah Peterson, joined with John Furrier here on theCUBE. John, what's your, what's your big hot take from the day? Just from today. >> So right now the velocity of content is continuing to flow on theCUBE. Thank you, everyone, for watching. The security conversations. Also, the cost tuning of the cloud kind of vibe is going on. You're hearing that with the looming recession, but if you look at the show it's the bulk of the keynote time spent talking is on data and security together. So Security, Security Lake, Amazon, they continue to talk about security. This next segment's going to be awesome. We have a multi-, eight-time CUBE alumni coming back and great conversation about security. I'm looking forward to this. >> Alumni VIP, I know, it's so great. Actually, both of these guests have been on theCUBE before so please welcome Dan and Haiyan. Thank you both for being here from F5. How's the show going? You're both smiling and we're midway through day three. Good? >> It's so exciting to be here with you all and it's a great show. >> Awesome. Dan, you having a good time too? >> It's wearing me out. I'm having a great time. (laughter) >> It's okay to be honest. It's okay to be honest. It's wearing out our vocal cords for sure up here, but it is definitely a great time. Haiyan, can you tell me a little bit about F5 just in case the audience isn't familiar? >> Sure, so F5 we specialize in application delivery and security. So our mission is to deliver secure and optimize any applications, any APIs, anywhere. >> I can imagine you have a few customers in the house. >> Absolutely. >> Yeah, that's awesome. So in terms of a problem that, well an annoyance that we've all had, bots. We all want the anti-bots. You have a unique solution to this. How are you helping AWS customers with bots? Let's send it to you. >> Well we, we collect client side signals from all devices. We might study how it does floating point math or how it renders emojis. We analyze those signals and we can make a real time determination if the traffic is from a bot or not. And if it's from a bot, we could take mitigating action. And if it's not, we just forward it on to origin. So client side signals are really important. And then the second aspect of bot protection I think is understanding that bot's retool. They become more sophisticated. >> Savannah: They learn. >> They learn. >> They unfortunately learn as well. >> Exactly, yeah. So you have to have a second stage what we call retrospective analysis where you're looking over all the historical transactions, looking for anything that may have been missed by a realtime defense and then updating that stage one that real time defense to deal with the newly discovered threat. >> Let's take a step back for a second. I want to just set the table in the context for the bot conversation. Bots, automation, that's, people know like spam bots but Amazon has seen the bot networks develop. Can you scope the magnitude and the size of the problem of bots? What is the problem? And give a size of what this magnitude of this is. >> Sure, one thing that's important to realize is not all bots are bad. Okay? Some bots are good and you want to identify the automation from those bots and allow listed so you don't interfere with what they're doing. >> I can imagine that's actually tricky. >> It is, it is. Absolutely. Yeah. >> Savannah: Nuanced. >> Yeah, but the bad bots, these are the ones that are attempting credential stuffing attacks, right? They're trying username password pairs against login forms. And because of consumer habits to reuse usernames and passwords, they end up taking over a lot of accounts. But those are the bookends. There are all sorts of types of bots in between those two bookends. Some are just nuisance, like limited time offer bots. You saw some of this in the news recently with Ticketmaster. >> That's a spicy story. >> Yeah, it really is. And it's the bots that is causing that problem. They use automation to buy all these concert tickets or sneakers or you know, any limited time offer project. And then they resell those on the secondary market. And we've done analysis on some of these groups and they're making millions of dollars. It isn't something they're making like 1200 bucks on. >> I know Amazon doesn't like to talk about this but the cloud for its double edged sword that it is for all the greatness of the agility spinning up resources bots have been taking advantage of that same capability to hide, change, morph. You've seen the matrix when the bots attacked the ship. They come out of nowhere. But Amazon actually has seen the bot problem for a long time, has been working on it. Talk about that kind of evolution of how this problem's being solved. What's Amazon doing about, how do you guys help out? >> Yeah, well we have this CloudFront connector that allows all Amazon CloudFront customers to be able to leverage this technology very, very quickly. So what historically was available only to like, you know the Fortune 500 at most of the global 2000 is now available to all AWS customers who are using CloudFront just by really you can explain how do they turn it on in CloudFront? >> Yeah. So I mean CloudFront technologies like that is so essential to delivering the digital experience. So what we do is we do a integration natively. And so if your CloudFront customers and you can just use our bot defense solution by turning on, you know, that traffic. So go through our API inspection, go through our bot inspection and you can benefit from all the other efficiencies that we acquired through serving the highest and the top institutions in the world. >> So just to get this clarification, this is a super important point. You said it's native to the service. I don't have to bolt it on? Is it part of the customer experience? >> Yeah, we basically built the integration. So if you're already a CloudFront customer and you have the ability to turn on our bot solutions without having to do the integration yourself. >> Flick a switch and it's on. >> Haiyan: Totally. >> Pretty much. >> Haiyan: Yeah. >> That's how I want to get rid of all the spam in my life. We've talked a lot about the easy button. I would also like the anti-spam button if we're >> Haiyan: 100% >> Well we were talking before you came on camera that there's a potentially a solution you can sit charge. There are techniques. >> Yeah. Yeah. We were talking about the spam emails and I thought they just charge, you know 10th of a penny for every sent email. It wouldn't affect me very much. >> What's the, are people on that? You guys are on this but I mean this is never going to stop. We're going to see the underbelly of the web, the dark web continue to do it. People are harvesting past with the dark web using bots that go in test challenge credentials. I mean, it's just happening. It's never going to stop. What's, is it going to be that cat and mouse game? Are we going to see solutions? What's the, when are we going to get some >> Well it's certainly not a cat and mouse game for F5 customers because we win that battle every time. But for enterprises who are still battling the bots as a DIY project, then yes, it's just going to be a cat and mouse. They're continuing to block by IP, you know, by rate limiting. >> Right, which is so early 2000's. >> Exactly. >> If we're being honest. >> Exactly. And the attackers, by the way, the attackers are now coming from hundreds of thousands or even millions of IP addresses and some IPs are using one time. >> Yeah, I mean it seems like such an easy problem to circumnavigate. And still be able to get in. >> What are I, I, let's stick here for a second. What are some of the other trends that you're seeing in how people are defending if they're not using you or just in general? >> Yeah, maybe I'll add to to that. You know, when we think about the bot problem we also sort of zoom out and say, Hey, bot is only one part of the problem when you think about the entire digital experience the customer experiencing, right? So at F5 we actually took a more holistic sort of way to say, well it's about protecting the apps and applications and the APIs that's powering all of those. And we're thinking not only the applications APIs we're thinking the infrastructure that those API workloads are running. So one of the things we're sharing since we acquired Threat Stack, we have been busy doing integrations with our distributed cloud services and we're excited. In a couple weeks you will hear announcement of the integrated solution for our application infrastructure protection. So that's just another thing. >> On that Threat Stack, does that help with that data story too? Because it's a compliance aspect as well. >> Yeah, it helps with the telemetries, collecting more telemetries, the data story but is also think about applications and APIs. You can only be as secure as the infrastructure you're running on it, right? So the infrastructure protection is a key part of application security. And the other dimension is not only we can help with the credentials, staffing and, and things but it's actually thinking about the customer's top line. Because at the end of the day when all this inventory are being siphoned out the customer won't be happy. So how do we make sure their loyal customers have the right experience so that can improve their top line and not just sort of preventing the bots. So there's a lot of mission that we're on. >> Yeah, that surprise and delight in addition to that protection. >> 100% >> If I could talk about the evolution of an engagement with F5. We first go online, deploy the client side signals I described and take care of all the bad bots. Okay. Mitigate them. Allow list all the good bots, now you're just left with human traffic. We have other client side signals that'll identify the bad humans among the good humans and you could deal with them. And then we have additional client side signals that allow us to do silent continuous authentication of your good customers extending their sessions so they don't have to endure the friction of logging in over and over and over. >> Explain that last one again because I think that was, that's, I didn't catch that. >> Yeah. So right now we require a customer to enter in their username and password before we believe it's them. But we had a customer who a lot of their customers were struggling to log in. So we did analysis and we realized that our client side signals, you know of all those that are struggling to log in, we're confident like 40% of 'em are known good customers based on some of these signals. Like they're doing floating point math the way they always have. They're rendering emojis the way they always have all these clients that signals are the same. So why force that customer to log in again? >> Oh yeah. And that's such a frustrating user experience. >> So true. >> I actually had that thought earlier today. How many time, how much of my life am I going to spend typing my email address? Just that in itself. Then I could crawl back under the covers but >> With the biometric Mac, I forget my passwords. >> Or how about solving CAPTCHA's? How fun is that? >> How many pictures have a bus? >> I got one wrong the other day because I had to pick all the street signs. I got it wrong and I called a Russian human click farm and figured out why was I getting it wrong? And they said >> I love that you went down this rabbit hole deeply. >> You know why that's not a street sign. That's a road sign, they told me. >> That's the secret backdoor. >> Oh well yeah. >> Talk about your background because you have fascinating background coming from law enforcement and you're in this kind of role. >> He could probably tell us about our background. >> They expunge those records. I'm only kidding. >> 25, 30 years in working in local, state and federal law enforcement and intelligence among those an FBI agent and a CIA cyber operations officer. And most people are drawn to that because it's interesting >> Three letter agencies can get an eyebrow raise. >> But I'll be honest, my early, early in my career I was a beat cop and that changed my life. That really did, that taught me the importance of an education, taught me the criminal mindset. So yeah, people are drawn to the FBI and CIA background, but I really value the >> So you had a good observation eye for kind of what, how this all builds out. >> It all kind of adds up, you know, constantly fighting the bad guys, whether they're humans, bots, a security threat from a foreign nation. >> Well learning their mindset and learning what motivates them, what their objectives are. It is really important. >> Reading the signals >> You don't mind slipping into the mind of a criminal. It's a union rule. >> Right? It actually is. >> You got to put your foot and your hands in and walk through their shoes as they say. >> That's right. >> The bot networks though, I want to get into, is not it sounds like it's off the cup but they're highly organized networks. >> Dan: They are. >> Talk about the aspect of the franchises or these bots behind them, how they're financed, how they use the money that they make or ransomware, how they collect, what's the enterprise look like? >> Unfortunately, a lot of the nodes on a botnet are now just innocent victim computers using their home computers. They can subscribe to a service and agree to let their their CPU be used while they're not using it in exchange for a free VPN service, say. So now bad actors not, aren't just coming from you know, you know, rogue cloud providers who accept Bitcoin as payment, they're actually coming from residential IPs, which is making it even more difficult for the security teams to identify. It's one thing when it's coming from- >> It's spooky. I'm just sitting here kind of creeped out too. It's these unknown hosts, right? It's like being a carrier. >> You have good traffic coming from it during the day. >> Right, it appears normal. >> And then malicious traffic coming from it. >> Nefarious. >> My last question is your relationship with Amazon. I'll see security center piece of this re:Invent. It's always been day zero as they say but really it's the security data lake. A lot of gaps are being filled in the products. You kind of see that kind of filling out. Talk about the relationship with F5 and AWS. How you guys are working together, what's the status? >> We've been long-term partners and the latest release the connector for CloudFront is just one of the joint work that we did together and try to, I think, to Dan's point, how do we make those technology that was built for the very sophisticated big institutions to be available for all the CloudFront customers? So that's really what's exciting. And we also leverage a lot of the technology. You talked about the data and our entire solution are very data driven, as you know, is automation. If you don't use data, you don't use analytics, you don't use AI, it's hard to really sort of win that war. So a lot of our stuff, it's very data driven >> And the benefit to customers is what? Access? >> The customer's access, the customer's top line. We talked about, you know, like how we're really bringing better experiences at the end of the day. F5's mission is try to bring a better digital world to life. >> And it's also collaborative. We've had a lot of different stories here on on the set about companies collaborating. You're obviously collaborating and I also love that we're increasing access, not just narrowing this focus for the larger companies at scale already, but making sure that these companies starting out, a lot of the founders probably milling around on the floor right now can prevent this and ensure that user experience for their customers. throughout the course of their product development. I think it's awesome. So we have a new tradition here on theCUBE at re:Invent, and since you're alumni, I feel like you're maybe going to be a little bit better at this than some of the rookies. Not that rookies can't be great, but you're veterans. So I feel strong about this. We are looking for your 30-second Instagram reel hot take. Think of it like your sizzle of thought leadership from the show this year. So eventually eight more visits from now we can compile them into a great little highlight reel of all of your sound bites over the evolution of time. Who wants to give us their hot take first? >> Dan? >> Yeah, sure. >> Savannah: You've been elected, I mean you are an agent. A former special agent >> I guess I want everybody to know the bot problem is much worse than they think it is. We go in line and we see 98, 99% of all login traffic is from malicious bots. And so it is not a DIY project. >> 98 to 99%? That means only 1% of traffic is actually legitimate? >> That's right. >> Holy moly. >> I just want to make sure that everybody heard you say that. >> That's right. And it's very common. Didn't happen once or twice. It's happened a lot of times. And when it's not 99 it's 60 or it's 58, it's high. >> And that's costing a lot too. >> Yes, it is. And it's not just in fraud, but think about charges that >> Savannah: I think of cloud service providers >> Cost associated with transactions, you know, fraud tools >> Savannah: All of it. >> Yes. Sims, all those things. There's a lot of costs associated with that much automation. So the client side signals and multi-stage defense is what you need to deal with it. It's not a DIY project. >> Bots are not DIY. How would you like to add to that? >> It's so hard to add to that but I would say cybersecurity is a team sport and is a very data driven solution and we really need to sort of team up together and share intelligence, share, you know, all the things we know so we can be better at this. It's not a DIY project. We need to work together. >> Fantastic, Dan, Haiyan, so great to have you both back on theCUBE. We look forward to seeing you again for our next segment and I hope that the two of you have really beautiful rest of your show. Thank you all for tuning into a fantastic afternoon of coverage here from AWS re:Invent. We are live from Las Vegas, Nevada and don't worry we have more programming coming up for you later today with John Furrier. I'm Savannah Peterson. This is theCUBE, the leader in high tech coverage.

>>You want us to >>Look at that camera? Okay. We're back in Boston, everybody. This is Dave ante for the cube, the leader in enterprise tech coverage. This is reinforce 2022 AWS's big security conference. We're here in Boston, the convention center where the cube started in 2010. Highend song is here. She's head of security and distributed cloud services at F five. And she's joined by Dan woods. Who's the global head of intelligence at F five. Great to see you again. Thanks for coming in the cube, Dan, first time I believe. Yeah. Happy to be here. All right. Good to see you guys. How's the, how's the event going for? Y'all >>It's been just fascinating to see all those, uh, new players coming in and taking security in a very holistic way. Uh, very encouraged. >>Yeah. Boston in, in July is, is good. A lot of, a lot of action to Seaport. When I was a kid, there was nothing here, couple mob restaurants and that's about it. And, uh, now it's just like a booming, >>I'm just happy to see people in, in person. Finally, is >>This your first event since? Uh, maybe my second or third. Third. Okay, >>Great. Since everything opened up and I tell you, I am done with >>Zoom. Yeah. I mean, it's very clear. People want to get back face to face. It's a whole different dynamic. I think, you know, the digital piece will continue as a compliment, but nothing beats belly to belly, as I like absolutely say. All right. Hi on let's start with you. So you guys do a, uh, security report every year. I think this is your eighth year, the app security report. Yeah. Um, I think you, you noted in this report, the growing complexity of apps and integrations, what did you, what are, what were your big takeaways this year? >>And so, like you said, this is our eighth year and we interview and talk to about 1500 of like companies and it decision makers. One of the things that's so prevalent coming out of the survey is complexity that they have to deal with, continue to increase. It's still one of the biggest headaches for all the security professionals and it professionals. And that's explainable in a way, if you look at how much digital transformation has happened in the last two years, right? It's an explosion of apps and APIs. That's powering all our digital way of working, uh, in the last two years. So it's certainly natural to, to see the complexity has doubled and tripled and, and we need to do something about it. >>And the number of tools keeps growing. The number of players keeps growing. I mean, so many really interesting, you know, they're really not startups anymore, but well funded new entrance into the marketplace. Were there any big surprises to you? You know, you're a security practitioner, you know, this space really well, anything jump out like, whoa, that surprised >>Me. Yeah. It's been an interesting discussion when we look at the results, right. You know, some of us would say, gosh, this is such a big surprise. How come people still, you know, willing to turn off security for the benefits of performance. And, and, and as a security professional, I will reflect on that. I said, it's a surprise, or is it just a mandate for all of us in security, we got to do better. And because security shouldn't be the one that prevents or add friction to what the business wants to do, right? So it's a surprise because we, how can, after all the breaches and, and then security incidents, people are still, you know, the three quarters of the, uh, interviewees said, well, you know, if we were given a choice, we'll turn off security for performance. And I think that's a call to action for all of us in security. How do we make security done in a way that's frictionless? And they don't have to worry about it. They don't have to do a trade off. And I think that's one of the things, you know, Dan in working our entire anti automation, uh, solution one is to PR protect. And the other thing is to enable. >>Yeah. You think about Dan, the, I always say the, the adversary is extremely capable. The ROI of cyber tech just keeps getting better and better. And your jobs really is to, to, to lower the ROI, right. It decrease the value, increase the cost, but you're, I mean, fishing continues to be prevalent. You're seeing relatively new technique island hopping, self forming malware. I mean, it's just mind boggling, but, but how are you seeing, you know, the attack change? You know, what what's the adversary do differently over the last, you know, several years maybe pre and post pandemic, we've got a different attack service. What are you seeing? >>Well, we're seeing a lot higher volume attacks, a lot higher volume and velocity. Mm-hmm, <affirmative> it isn't uncommon at all for us to go in line and deploy our client side signals and see, uh, the upper 90%, um, is automated, unwanted automation hitting the application. Uh, so the fact that the security teams continue to underestimate the size of the problem. That is something I see. Every time we go in into an enterprise that they underestimate the size of the problem, largely because they're relying on, on capabilities like caps, or maybe they're relying on two of a and while two of a is a very important role in security. It doesn't stop automated attacks and cap certainly doesn't stop automated >>Tax. So, okay. So you said 90% now, as high as 90% are, are automated up from where maybe dial back to give us a, a marker as to where it used to be. >>Well, less than 1% is typically what all of our customers across the F five network enjoy less than 1% of all traffick hitting origin is unwanted, but when we first go online, it is upper 90, we've seen 99% of all traffic being unwanted >>Automation. But Dan, if I dial back to say 2015, was it at that? Was it that high? That, that was automated >>Back then? Or, you know, I, I don't know if it was that high then cuz stuffing was just, you know, starting to kind take off. Right? No. Right. Um, but as pre stuffing became better and better known among the criminal elements, that's when it really took off explain the pays you're right. Crime pays >>Now. Yeah. It's unfortunate, but it's true. Yeah. Explain the capture thing. Cause sometimes as a user, like it's impossible to do the capture, you know, it's like a twister. Yeah. >>I >>Got that one wrong it's and I presume it's because capture can be solved by, by bots. >>Well, actually the bots use an API into a human click farming. So they're humans to sit around, solving captures all day long. I actually became a human capture solver for a short time just to see what the experience was like. And they put me to the training, teaching me how to solve, captures more effectively, which was fascinating, cuz I needed that training frankly. And then they tested to make sure I solve caps quickly enough. And then I had solved maybe 30 or 40 caps and I hadn't earned one penny us yet. So this is how bots are getting around caps. They just have human solve them. >>Oh, okay. Now we hear a lot at this event, you gotta turn on multifactor authentication and obviously you don't want to use just SMS based MFA, but Dan you're saying not good enough. Why explain >>That? Well, most implementations of two a is, you know, you enter in username and password and if you enter in the correct username and password, you get a text message and you enter in the code. Um, if you enter in the incorrect username and password, you're not sent to code. So the, the purpose of a credential stocking attack is to verify whether the credentials are correct. That's the purpose. And so if it's a two, a protected log in, I've done that. Admittedly, I haven't taken over the account yet, but now that I have a list of known good credentials, I could partner with somebody on the dark web who specializes in defeating two, a through social engineering or port outs or SIM swaps S so seven compromises insiders at telcos, lots of different ways to get at the, uh, two, a text message. >>So, wow, <laugh>, this is really interesting, scary discussion. So what's the answer to, to that problem. How, how have five approach >>It highend touched on it. We, we want to improve security without introducing a lot of friction. And the solution is collecting client side signals. You interrogate the users, interactions, the browser, the device, the network, the environment, and you find things that are unique that can't be spoof like how it does floating point math or how it renders emojis. Uh, this way you're able to increase security without imposing friction on, on the customer. And honestly, if I have to ever have to solve another capture again, I, I, I just, my blood is boiling over capture. I wish everyone would rip it out >>As a user. I, I second that request I had, um, technology got us into this problem. Can technology help us get out of the problem? >>It has to. Um, I, I think, uh, when you think about the world that is powering all the digital experiences and there's two things that comes to mind that apps and APIs are at the center of them. And in order to solve the problem, we need to really zero in where, you know, the epic center of the, the, uh, attack can be and, and had the max amount of impact. Right? So that's part of the reason from a F five perspective, we think of application and API security together with the multitier the defense with, you know, DDoS to bots, to the simple boss, to the most sophisticated ones. And it has to be a continuum. You don't just say, Hey, I'm gonna solve this problem in this silo. You have to really think about app and APIs. Think about the infrastructure, think about, you know, we're here at AWS and cloud native solutions and API services is all over. You. Can't just say, I only worry about one cloud. You cannot say, I only worry about VMs. You really need to think of the entire app stack. And that's part of the reason when we build our portfolio, there is web application firewall, there's API security there's bot solution. And we added, you know, application infrastructure protection coming from our acquisition for threat stack. They're actually based in Boston. Uh, so it's, it's really important to think holistically of telemetry visibility, so you can make better decisions for detection response. >>So leads me to a number of questions first. The first I wanna stay within the AWS silo for a minute. Yeah. Yeah. What do you, what's the relationship with AWS? How will you, uh, integrating, uh, partnering with AWS? Let's start there. >>Yeah, so we work with AWS really closely. Uh, a lot of our solutions actually runs on the AWS platform, uh, for part of our shape services. It's it's, uh, using AWS capabilities and thread stack is purely running on AWS. We just, uh, actually had integration, maybe I'm pre announcing something, uh, with, uh, the cloud front, with our bot solutions. So we can be adding another layer of protection for customers who are using cloud front as the w on AWS. >>Okay. So, um, you integrate, you worry about a APIs, AWS APIs and primitives, but you have business on prem, you have business, other cloud providers. How do you simplify those disparities for your customers? Do you kind of abstract all that complexity away what's F fives philosophy with regard then and creating that continuous experience across the states irrespective of physical >>Location? Yeah, I think you're spot on in terms of, we have to abstract the complexity away. The technology complexity is not gonna go away because there's always gonna be new things coming in the world become more disaggregated and they're gonna be best of brain solutions coming out. And I think it's our job to say, how do we think about policies for web application? And, you know, you're, on-prem, you're in AWS, you're in another cloud, you're in your private data center and we can certainly abstract out the policies, the rules, and to make sure it's easier for a customer to say, I want this particular use case and they push a button. It goes to all the properties, whether it's their own edge or their own data center, and whether it's using AWS, you know, cloud front as you using or web. So that is part of our adapt. Uh, we call it adaptive application. Vision is to think delivery, think security, think optimizing the entire experience together using data. You know, I come from, uh, a company that was very much around data can power so many things. And we believe in that too. >>We use a, we use a term called super cloud, which, which implies a layer that floats above the hyperscale infrastructure hides the underlying complexity of the primitives adds value on top and creates a continuous experience across clouds, maybe out to the edge even someday on prem. Is that, does that sound like, it sounds like that's your strategy and approach and you know, where are you today? And that is that, is that technically feasible today? Is it, is it a journey? Maybe you could describe >>That. Yeah. So, uh, in my title, right, you talked about a security and distribute cloud services and the distribute cloud services came from a really important acquisition. We did last year and it's about, uh, is called Wil Tara. What they brought to F five is the ability not only having lot of the SAS capabilities and delivery capabilities was a very strong infrastructure. They also kept have capability like multi-cloud networking and, you know, people can really just take our solution and say, I don't have to go learn about all the, like I think using super cloud. Yeah, yeah. Is exactly that concept is we'll do all the hard work behind the scenes. You just need to decide what application, what user experience and we'll take care of the rest. So that solutions already in the market. And of course, there's always more things we can do collect more telemetry and integrate with more solutions. So there's more insertion point and customer can have their own choice of whatever other security solution they want to put on top of that. But we already provide, you know, the entire service around web application and API services and bot solution is a big piece of that. >>So I could look at analytics across those clouds and on-prem, and actually you don't have to go to four different stove pipes to find them, is that >>Right? Yeah. And I think you'd be surprised on what you would see. Like you, you know, typically you're gonna see large amounts of unwanted automation hitting your applications. Um, it's, I, I think the reason so many security teams are, are underestimating. The size of the problem is because these attacks are coming from tens of thousands, hundreds of thousands, even millions of IP addresses. So, you know, for years, security teams have been blocking by IP and it's forced the attackers to become highly, highly distributed. So the security teams will typically identify the attack coming from the top hundred or 1500 noisiest IPS, but they missed the long tail of tens of thousands, hundreds of thousands of IPS that are only used one or two times, because, you know, over time we forced the attackers to do this. >>They're scaling. >>Yeah, they are. And, and they're coming from residential IPS now, uh, not just hosting IPS, they're coming from everywhere. >>And, and wow. I mean, I, we know that the pandemic changed the way that organization, they had to think more about network security, rethinking network security, obviously end point cloud security. But it sounds like the attackers as well, not only did they exploit that exposure, but yeah, yeah. They were working from home and then <laugh> >>The human flick farms. They're now distributor. They're all working from home. >>Now we could take advantage >>Of that when I was solving captures, you could do it on your cell phone just by walking around, solving, captures for money. >>Wow. Scary world. But we live in, thank you for helping making it a little bit safer, guys. Really appreciate you coming on the queue. >>We'll continue to work on that. And our motto is bring a better digital world to life. That's what we can set out >>To do. I love it. All right. Great. Having you guys. Thank you. And thank you for watching. Keep it right there. This is Dave ante from reinforce 2022. You're watching the cube right back after this short break.

(vibrant electronic music) >> Welcome back to SuperCloud2. You know, this event, and the Super Cloud initiative in general, it's an open industry-wide collaboration. Last August at SuperCloud22, we really honed in on the definition, which of course we've published. And there's this shared doc, which folks are still adding to and refining, in fact, just recently, Dr. Nelu Mihai added some critical points that really advanced some of the community's initial principles, and today at SuperCloud2, we're digging further into the topic with input from real world practitioners, and we're exploring that intersection of data, data mesh, and cloud, and importantly, the realities and challenges of deploying technology to drive new business capability, and I'm pleased to welcome Ash Naseer to the program. He's a Senior Director of Data Engineering at Warner Bros. Discovery. Ash, great to see you again, thanks so much for taking time with us. >> It's great to be back, these conversations are always very fun. >> I was so excited when we met last spring, I guess, so before we get started I wanted to play a clip from that conversation, it was June, it was at the Snowflake Summit in Las Vegas. And it's a comment that you made about your company but also data mesh. Guys, roll the clip. >> Yeah, so, when people think of Warner Bros., you always think of the movie studio. But we're more than that, right, I mean, you think of HBO, you think of TNT, you think of CNN. We have 30 plus brands in our portfolio, and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company, so that CNN can work at their own pace, you know, when there's election season, they can ingest their own data. And they don't have to bump up against, as an example, HBO, if Game of Thrones is goin' on. >> So-- Okay, so that's pretty interesting, so you've got these sort of different groups that have different data requirements inside of your organization. Now data mesh, it's a relatively new concept, so you're kind of ahead of the curve. So Ash, my question is, when you think about getting value from data, and how that's changed over the past decade, you've had pre-Hadoop, Hadoop, what do you see that's changed, now you got the cloud coming in, what's changed? What had to be sort of fixed? What's working now, and where do you see it going? >> Yeah, so I feel like in the last decade, we've gone through quite a maturity curve. I actually like to say that we're in the golden age of data, because the tools and technology in the data space, particularly and then broadly in the cloud, they allow us to do things that we couldn't do way back when, like you suggested, back in the Hadoop era or even before that. So there's certainly a lot of maturity, and a lot of technology that has come about. So in terms of the good, bad, and ugly, so let me kind of start with the good, right? In terms of bringing value from the data, I really feel like we're in this place where the folks that are charged with unlocking that value from the data, they're actually spending the majority of their time actually doing that. And what do I mean by that? If you think about it, 10 years ago, the data scientist was the person that was going to sort of solve all of the data problems in a company. But what happened was, companies asked these data scientists to come in and do a multitude of things. And what these data scientists found out was, they were spending most of their time on, really, data wrangling, and less on actually getting the value out of the data. And in the last decade or so, I feel like we've made the shift, and we realize that data engineering, data management, data governance, those are as important practices as data science, which is sort of getting the value out of the data. And so what that has done is, it has freed up the data scientist and the business analyst and the data analyst, and the BI expert, to really focus on how to get value out of the data, and spend less time wrangling data. So I really think that that's the good. In terms of the bad, I feel like, there's a lot of legacy data platforms out there, and I feel like there's going to be a time where we'll be in that hybrid mode. And then the ugly, I feel like, with all the data and all the technology, creates another problem of itself. Because most companies don't have arms around their data, and making sure that they know who's using the data, what they're using for, and how can the company leverage the collective intelligence. That is a bigger problem to solve today than 10 years ago. And that's where technologies like the data mesh come in. >> Yeah, so when I think of data mesh, and I say, you're an early practitioner of data mesh, you mentioned legacy technology, so the concept of data mesh is inclusive. In theory anyway, you're supposed to be including the legacy technologies. Whether it's a data lake or data warehouse or Oracle or Snowflake or whatever it is. And when you think about Jamak Dagani's principles, it's domain-centric ownership, data as product. And that creates challenges around self-serve infrastructure and automated governance, and then when you start to combine these different technologies. You got legacy, you got cloud. Everything's different. And so you have to figure out how to deal with that, so my question is, how have you dealt with that, and what role has the cloud played in solving those problems, in particular, that self-serve infrastructure, and that automated governance, and where are we in terms of solving that problem from a practitioner's standpoint? >> Yeah, I always like to say that data is a team sport, and we should sort of think of it as such, and that's, I feel like, the key of the data mesh concept, is treating it as a team sport. A lot of people ask me, they're like, "Oh hey, Ash, I've heard about this thing called data mesh. "Where can I buy one?" or, "what's the technology that I use to get a data mesh? And the reality is that there isn't one technology, you can't really buy a data mesh. It's really a way of life, it's how organizations decide to approach data, like I said, back to a team sport analogy, making sure that everyone has the seat on the table, making sure that we embrace the fact that we have a lot of data, we have a lot of data problems to solve. And the way we'll be successful is to make everyone inclusive. You know, you think about the old days, Data silos or shadow IT, some might call it. That's been around for decades. And what hasn't changed was this notion that, hey, everything needs to be sort of managed centrally. But with the cloud and with the technologies that we have today, we have the right technology and the tooling to democratize that data, and democratize not only just the access, but also sort of building building blocks and sort of taking building blocks which are relevant to your product or your business. And adding to the overall data mesh. We've got all that technology. The challenge is for us to really embrace it, and make sure that we implement it from an organizational standpoint. >> So, thinking about super cloud, there's a layer that lives above the clouds and adds value. And you think about your brands you got 30 brands, you mentioned shadow IT. If, let's say, one of those brands, HBO or TNT, whatever. They want to go, "Hey, we really like Google's analytics tools," and they maybe go off and build something, I don't know if that's even allowed, maybe it's not. But then you build this data mesh. My question is around multi-cloud, cross cloud, super cloud if you will. Is that a advantage for you as a practitioner, or does that just make things more complicated? >> I really love the idea of a multi-cloud. I think it's great, I think that it should have been the norm, not the exception, I feel like people talk about it as if it's the exception. That should have been the case. I will say, though, I feel like multi-cloud should evolve organically, so back to your point about some of these different brands, and, you know, different brands or different business units. Or even in a merger and acquisitions situation, where two different companies or multiple different companies come together with different technology stacks. You know, I feel like that's an organic evolution, and making sure that we use the concepts and the technologies around the multi-cloud to bring everyone together. That's where we need to be, and again, it talks to the fact that each of those business units and each of those groups have their own unique needs, and we need to make sure that we embrace that and we enable that, rather than stifling everything. Now where I have a little bit of a challenge with the multi-cloud is when technology leaders try to build it by design. So there's a notion there that, "Hey, you need to sort of diversify "and don't put all your eggs in one basket." And so we need to have this multi-cloud thing. I feel like that is just sort of creating more complexity where it doesn't need to be, we can all sort of simplify our lives, but where it evolves organically, absolutely, I think that's the right way to go. >> But, so Ash, if it evolves organically don't you need some kind of cloud interpreter, to create a common experience across clouds, does that exist today? What are your thoughts on that? >> There is a lot of technology that exists today, and that helps go between these different clouds, a lot of these sort of cloud agnostic technologies that you talked about, the Snowflakes and the Databricks and so forth of the world, they operate in multiple clouds, they operate in multiple regions, within a given cloud and multiple clouds. So they span all of that, and they have the tools and technology, so, I feel like the tooling is there. There does need to be more of an evolution around the tooling and I think the market's need are going to dictate that, I feel like the market is there, they're asking for it, so, there's definitely going to be that evolution, but the technology is there, I think just making sure that we embrace that and we sort of embrace that as a challenge and not try to sort of shut all of that down and box everything into one. >> What's the biggest challenge, is it governance or security? Or is it more like you're saying, adoption, cultural? >> I think it's a combination of cultural as well as governance. And so, the cultural side I've talked about, right, just making sure that we give these different teams a seat at the table, and they actually bring that technology into the mix. And we use the modern tools and technologies to make sure that everybody sort of plays nice together. That is definitely, we have ways to go there. But then, in terms of governance, that is another big problem that most companies are just starting to wrestle with. Because like I said, I mean, the data silos and shadow IT, that's been around there, right? The only difference is that we're now sort of bringing everything together in a cloud environment, the collective organization has access to that. And now we just realized, oh we have quite a data problem at our hands, so how do we sort of organize this data, make sure that the quality is there, the trust is there. When people look at that data, a lot of those questions are now coming to the forefront because everything is sort of so transparent with the cloud, right? And so I feel like, again, putting in the right processes, and the right tooling to address that is going to be critical in the next years to come. >> Is sharing data across clouds, something that is valuable to you, or even within a single cloud, being able to share data. And my question is, not just within your organization, but even outside your organization, is that something that has sort of hit your radar or is it mature or is that something that really would add value to your business? >> Data sharing is huge, and again, this is another one of those things which isn't new. You know, I remember back in the '90s, when we had to share data externally, with our partners or our vendors, they used to physically send us stacks of these tapes, or physical media on some truck. And we've evolved since then, right, I mean, it went from that to sharing files online and so forth. But data sharing as a concept and as a concept which is now very frictionless, through these different technologies that we have today, that is very new. And that is something, like I said, it's always been going on. But that needs to be really embraced more as well. We as a company heavily leverage data sharing between our own different brands and business units, that helps us make that data mesh, so that when CNN, as an example, builds their own data model based on election data and the kinds of data that they need, compare that with other data in the rest of the company, sports, entertainment, and so forth and so on. Everyone has their unique data, but that data sharing capability brings it together wherever there is a need. So you think about having a Tiger Woods documentary, as an example, on HBO Max and making sure that you reach the audiences that are interested in golf and interested in sports and so forth, right? That all comes through the magic of data sharing, so, it's really critical, internally, for us. And then externally as well, because just understanding how our products are doing on our partners' networks and different distribution channels, that's important, and then just understanding how our consumers are consuming it off properties, right, I mean, we have brands that transcend just the screen, right? We have a lot of physical merchandise that you can buy in the store. So again, understanding who's buying the Batman action figures after the Batman movie was released, that's another critical insight. So it all gets enabled through data sharing, and something we rely heavily on. >> So I wanted to get your perspective on this. So I feel like the nirvana of data mesh is if I want to use Google BigQuery, an Oracle database, or a Microsoft database, or Snowflake, Databricks, Amazon, whatever. That that's a node on the mesh. And in the perfect world, you can share that data, it can be governed, I don't think we're quite there today, so. But within a platform, maybe it's within Google or within Amazon or within Snowflake or Databricks. If you're in that world, maybe even Oracle. You actually can do some levels of data sharing, maybe greater with some than others. Do you mandate as an organization that you have to use this particular data platform, or are you saying "Hey, we are architecting a data mesh for the future "where we believe the technology will support that," or maybe you've invented some technology that supports that today, can you help us understand that? >> Yeah, I always feel like mandate is a strong area, and it breeds the shadow IT and the data silos. So we don't mandate, we do make sure that there's a consistent set of governance rules, policies, and tooling that's there, so that everyone is on the same page. However, at the same time our focus is really operating in a federated way, that's been our solution, right? Is to make sure that we work within a common set of tooling, which may be different technologies, which in some cases may be different clouds. Although we're not that multi-cloud. So what we're trying to do is making sure that everyone who has that technology already built, as long as it sort of follows certain standards, it's modern, it has the capabilities that will eventually allow us to be successful and eventually allow for that data sharing, amongst those different nodes, as you put it. As long as that's the case, and as long as there's a governance layer, a master governance layer, where we know where all that data is and who has access to what and we can sort of be really confident about the quality of the data, as long as that case, our approach to that is really that federated approach. >> Sorry, did I hear you correctly, you're not multi-cloud today? >> Yeah, that's correct. There are certain spots where we use that, but by and large, we rely on a particular cloud, and that's just been, like I said, it's been the evolution, it was our evolution. We decided early on to focus on a single cloud, and that's the direction we've been going in. >> So, do you want to go to a multi-cloud, or, you mentioned organic before, if a business unit wants to go there, as long as they're adhering to those standards that you put out, maybe recommendations, that that's okay? I guess my question is, does that bring benefit to your business that you'd like to tap, or do you feel like it's not necessary? >> I'll go back to the point of, if it happens organically, we're going to be open about it. Obviously we'll have to look at every situations, not all clouds are created equal as well, so there's a number of different considerations. But by and large, when it happens organically, the key is time to value, right? How do you quickly bring those technologies in, as long as you could share the data, they're interconnected, they're secured, they're governed, we are confident on the quality, as long as those principles are met, we could definitely go in that direction. But by and large, we're sort of evolving in a singular direction, but even within a singular cloud, we're a global company. And we have audiences around the world, so making sure that even within a single cloud, those different regions interoperate as one, that's a bigger challenge that we're having to solve as well. >> Last question is kind of to the future of data and cloud and how it's going to evolve, do you see a day when companies like yours are increasingly going to be offering data, their software, services, and becoming more of a technology company, sort of pointing your tooling and your proprietary knowledge at the external world, as an opportunity, as a business opportunity? >> That's a very interesting concept, and I know companies have done that, and some of them have been extremely successful, I mean, Amazon is the biggest example that comes to mind, right-- >> Yeah. >> When they launched AWS, something that they had that expertise they had internally, and they offered it to the world as a product. But by and large, I think it's going to be far and few between, especially, it's going to be focused on companies that have technology as their DNA, or almost like in the technology sector, building technology. Most other companies have different markets that they are addressing. And in my opinion, a lot of these companies, what they're trying to do is really focus on the problems that we can solve for ourselves, I think there are more problems than we have people and expertise. So my guess is that most large companies, they're going to focus on solving their own problems. A few, like I said, more tech-focused companies, that would want to be in that business, would probably branch out, but by and large, I think companies will continue to focus on serving their customers and serving their own business. >> Alright, Ash, we're going to leave it there, Ash Naseer. Thank you so much for your perspectives, it was great to see you, I'm sure we'll see you face-to-face later on this year. >> This is great, thank you for having me. >> Ah, you're welcome, alright. Keep it right there for more great content from SuperCloud2. We'll be right back. (gentle percussive music)

>>Welcome back to the cubes coverage of women in data science, 2022. We've been live all day at Stanford at the Arriaga alumni center. Lisa Martin, John furrier joins me next, trying to, to cure your FOMO that you have. >>I love this events. My favorite events is 2015. We've been coming, growing community over 60 countries, 500 ambassadors and growing so many members. Widths has become a global phenomenon. And it's so exciting to be part of just being part of the ride. Judy and Karen, the team have been amazing partners and it's been fun to watch the progression and international women's day is tomorrow. And just the overall environment's changed a lot since then. It's gotten better. I'm still a lot more work to do, but we're getting the word out, but this year seems different. It seems more like a tipping point is happening and real-time cultural change. A lot of problems. COVID pulled forward. A lot of things, there's a war going on in Europe. It's just really weird time. And it's just seems like it's a tipping point. >>I think that's what we felt today was that it was a tipping point. There was a lot of our guests on the program that are first time with attendees. So in seven, just seven short years, this is the seventh annual width it's gone from this one day technical conference to this global movement, as you talked about. And I think that we definitely felt that women of all ages and men that are here as well understand we're at that tipping point and what needs to be done next to push it over the edge. >>Well, I'm super excited that you are able to do all the amazing interviews. I watched some of them online. I had to come by and, and join the team because I have FOMO. I love doing the interviews, but they're including me. I'm happy to be included, but I got to ask you, I mean, what was different this year? Because it was interesting. It's a hybrid event. It's in part, they didn't have it in person last year, right? So it's hybrid. I showed the streams where everywhere good interviews, what was some of the highlights? >>Just a very inspiring stories of women who really this morning's conversation that I got to hear before I came to set was about mentors and sponsors and how important it is for women of any age and anybody really to build their own personal board of directors with mentors and sponsors. And they were very clear in the difference between a mentor and a sponsor and John something. I didn't understand the difference between the two until a few years ago. I think it was at a VMware event and it really surprised me that I have mentors do ask sponsors. And so that was a discussion that everybody on this onset talked about. >>It was interesting. We're doing also the international women's day tomorrow, big 24 interviews, including the winds of content, as well as global women leaders around the world and to new J Randori, who runs all of AWS, Amy are your maps. And she told me the same thing. She's like, there's too many mentors, not enough sponsors. And she said that out loud. I felt, wow. That was a defining moment because he or she is so impressive. Worked at McKinsey, okay. Was an operator in, in running businesses. Now she heads up AWS saying out loud, we have too many mentors, this get down to business and get sponsors. And I asked her the same thing and she said, sponsors, create opportunities. Mentors, give feedback. And mentors go both ways. And she said, my S my teenage son is a mentor to me for some of the cool new stuff, but ventures can go both ways. Sponsors is specifically about opportunities, and I'm like, I felt like that comment hit home. >>It's so important, but it's also important to teach girls. And especially the there's younger girls here this year, there's high school and middle, I think even middle school girls here, how to have the confidence to, to find those mentors, those sponsors and cultivate those relationships. That's a whole, those are skills that are incredibly important, as important as it is to understand AI data science, machine learning. It's to be able to, to have the confidence in a capability to create that and find those sponsors to help you unlock those opportunities. >>You know, I feel lucky to do the interviews, certainly being included as a male, but you've been doing a lot of the interviews as females and females. I got to ask you what was the biggest, because every story is different. Some people will it's about taking charge of their career. Sometimes it's maybe doing something different. What some of the story themes did you see in your interviews out there? What were some of the, the coverings personal? Yeah. >>Yeah. A lot of, a lot of the guests had stem backgrounds and were interested in the stem studies from when they were quite young and had strong family backgrounds that helps to nurture that. I >>Also heard that role models. Yes, >>Exactly, exactly. A strong family backgrounds. I did talk to a few women who come from different backgrounds, like international business and, but loved data and wanted to be able to apply that and really learn data analytics and understand data science and understand the opportunities that, that it brings. And also some of the challenges there. Everybody had an inspiring story. >>Yeah. It's interesting. One of the senior women I interviewed, she was from Singapore and she fled India during a bombing war and then ended up getting her PhD. Now she's in space and weld and all that stuff. And she said, we're now living in nerd, native environment, me and the younger generation they're nerds. And I, you know, were at Stanford dirt nation. Of course we're Stanford, it's nerd nerd nation here. But her point is, is that everything's digital now. So the younger generation, they're not necessarily looking for programmers, certainly coding. Great. But if you're not into coding, you can still solve society problems. There's plenty of jobs that are open for the first time that weren't around years ago, which means there's problems that are new to that need new minds and new, fresh perspectives. So I thought that aperture of surface area of opportunities to contribute in women in tech is not just coding. No, and that was a huge, >>That was, and we also, this morning, I got to hear, and we've talked about, we talked with several of the women before the event about data science in healthcare, data science, in transportation equity. That was a new thing for me, John, that I didn't know, I didn't, I never thought about transient equity and transportation or lack thereof. And so w what this conference showed, I think this year is that the it's not just coding, but it's every industry. As we know, every company is a data company. Every company is a tech company. If they're not, they're not going to be here for a long. So the opportunities for women is the door is just blown. >>And I said, from my interviews, it's a data problem. That's our line. We always say in the cube, people who know our program programming, we say that, but it actually, when we get the data on the pipeline and the pipeline, it has data points where the ages of drop-off of girls and young women is 12 to 14 and 16 to 18, where the drop-off is significant. So attack the pipelining problem is one that I heard a lot of. And the other one that comes out a lot, it's kind of common sense, and it's talked about it, but it's nuanced, but it became very elevated this year in the breaking, the bias theme, which was role models are huge. So seeing powerful women in leadership positions is really a focus and that's inspires people and they can see themselves. And so I think when people see role models of women and, and folks on in positions, not just coded, but even at the executive suite huge focus. So I think that's going to be a next step function in my mind. That's that's, if I had to predict the trend, it would be you see a lot more role modeling, flexing that big time. >>Good that's definitely needed. You know, we, we often used to say she can't be what she can't see, but one of the interviews that I had said, she can be what she can see. And I loved the pivot on that because it put a positive light, but to your point, there needs to be more female role models that, that girls can look up to. So they can see, I can do this. Like she's doing leading, you know, YouTube, for example, or Sheryl Sandberg of Facebook. We need more of these role models to show the tremendous amount of opportunities that are there, and to help those, not just the younger girls, those even that are maybe more mature find that confidence to build. >>And I think that was another king that came out role models from family members, dad, or a relative, or someone that could see was a big one. The other common thread was, yeah. I tend to break stuff and like to put it together. So at a young age, they kind of realized that they were kind of nerdy and they like to do stuff very engineering, but mind is where math or science. And that was interesting. Sally eaves from in the UK brought this up, she's a professor and does cyber policy. She said, it's a stems gray, but put the arts in there, make it steam. So steam and stem are in two acronyms. Stem is, is obviously the technical, but adding arts because of the creativity needs, we need creativity and problem solving with technical. Yes. So it's not just stem it's theme. We've heard that before, but not as much this year, it's amplified big >>Time. Sally's great. I had the chance to interview her in the last couple of months. And you, you bring up creativity, which is an incredibly important point. You know, there are the, obviously the hard skills, the technical skills that are needed, but there's also creativity. Curiosity being curious to ask a question, there's probably many questions that we haven't even thought to ask yet. So encouraging that curiosity, that natural curiosity is as important as maybe someone say as the actual technical knowledge, >>What was the biggest thing you saw this year? If you zoom out and you look at the forest from the trees, what was the big observation for you this year? >>I think it's the growth of woods. We've decided seven years. It's now in 60 countries, 200 events, 500 ambassadors, probably 500 plus. And the number of people that I had on the program, John, that this is their first woods. So just the fact that it's growing, we, we we've seen it for years, but I think we really saw a lot of the fresh faces and heard from them today had stories of how they got involved and how they met Margo, how she found them. I had a younger Alon who'd just graduated from Harvard back in the spring. So maybe not even a year ago, working at Skydio, doing drone work and had a great perspective on why it's important to have women in the drone industry, the opportunities Jones for good. And it was just nice to hear that fresh perspective. And also to S to hear the women who are new to woods, get it immediately. You walk into the Arriaga alumni center in the morning and you feel the energy and the support and that it was just perpetuated year after year. >>Yeah, it's awesome. I think one of the things I think it was reflecting on this morning was how many women we've interviewed in our cube alumni database now. And we yet are massing quite the database of really amazing people and there's more coming in. So that was kind of on a personal kind of reflection on the cube and what we've been working on together. All of us, the other thing that jumped out at me was the international aspect this year. It just seems like there's a community of tribal vibe where it's not just the tech industry, you know, saying rod, rod, it's a complete call to arms around more stories, tell your story. Yes. More enthusiasm outside of the corporate kind of swim lanes into like more of, Hey, let's get the stories out there. And the catalyst from an interview turned into follow up on LinkedIn, just a lot more like viral network effect so much more this year than ever before. So, you know, we just got to get the stories. >>Absolutely. And I think people given what we've been through the last two years are just really hungry for that. In-person collaboration, the opportunity to see more leadership to get inspired and any level of their career. I think the women here this today have had that opportunity and it's been overwhelmingly positive as you can imagine as it is every year. But I agree. I think it's been more international and definitely much more focused on teaching some of the other skills, the confidence, the creativity, the curiosity. >>Well, Lisa, as of right now, it's March 8th in Japan. So today, officially is kicking off right now. It's kicking off international women's day, March 8th, and the cube has a four region portal that we're going to make open, thanks to the sponsors with widths and Stanford and AWS supporting our mission. We're going to have Latin America, AMIA Asia Pacific and north America content pumping on the cube all day today, tomorrow. >>Exactly. And we've had such great conversations. I really enjoyed talking to the women. I always, I love hearing the stories as you talked about, we need more stories to make it personal, to humanize it, to learn from these people who either had some of them had linear paths, but a lot of emergency zig-zaggy, as you would say. And I always find that so interesting to understand how they got to where they are. Was it zig-zaggy, was it zig-zaggy intentionally? Yes. Some of the women that I talked to had very intentional pivots in their career to get them where they are, but I still thought that story was a very, >>And I like how you're here at Stanford university with winds the day before international Wednesday, technically now in Asia, it's starting, this is going to be a yearly trend. This is season one episode, one of the cube covering international women's day, and then every day for the rest of the year, right? >>What were some of your takeaways from some of the international women's day conversations that you had? >>Number one thing was community. The number one vibe was besides the message of more roles or available role models are important. You don't have to be a coder, but community was inherently the fabric of every conversation. The people were high energy, highly knowledgeable about on being on point around the core issue. It wasn't really politicized was much more of about this is really goodness and real examples of force multipliers of diversity, inclusion and equity, when, what works together as a competitive advantage. And, you know, as a student of business, that is a real change. I think, you know, the people who do it are going to have a competitive advantage. So community competitive advantage and just, and just overall break that bias through the mentoring and the sponsorships. >>And we've had a lot of great conversations about, I loved the theme of international women's day, this year breaking the bias. I asked everybody that I spoke with for international women's day and for width. What does that mean to you? And where are we on that journey? And everyone had a really insightful stories to share about where we are with that in their opinions, in their fields industries. Why, and ultimately, I think the general theme was we have the awareness now that we need, we have the awareness from an equity perspective, that's absolutely needed. We have to start there, shine the light on it so that the bias can be broken and opportunities for everybody can just proliferate >>Global community is going to rise and it's going to tend to rise. The tide is rising. It's going to get better and better. It was a fun year this year. And I think it was relief that COVID kind of going out, people getting back into physical events has been, been really, really great. >>Yep, absolutely. So, John, I, I appreciate all the opportunities that you've given me as a female anchor on the show. International women's day coverage was fantastic. Widths 2022 coming to an end was fantastic. Look forward to next year. >>Well, Margo, Judy and Karen who put this together, had a vision and that vision was right and it was this working and when it gets going, it has escape, velocity unstoppable. >>It's a rocket ship. That's a rocket. I love that. I love to be part of John. Thanks for joining me on the wrap. We want to thank you for watching the cubes coverage of international women's day. The women's showcase as well as women in data science, 2022. We'll see you next time.

>>Welcome everyone to the cubes coverage of women in data science conference width 2022. Live from Stanford at the Arriaga alumni center. I'm Lisa Martin. My next guest is joined me. Sharon Hutchins is here the VP and chief of AI plus data operations at Intuit Sharon. Welcome. Thank you. >>Excited to >>Be here. This is your first woods, very first but into it in words. >>That's right. Intuitively it's goes way back. I'm relatively new to the organization, but Intuit has been a long time sponsor of woods, and we love this organization. We have a great alignment with our goals, which has a passion and commitment to advancing women in technology and data science. And we have the same goal added to it. We are at 30% women in technology with the goal of hitting 37% by 2024. And I know that widths has a great goal of 30 by 30, so that's awesome. >>30 by 30. And here we are around, I think it's still less than 25% of stem positions are filled by women. But obviously you're ahead of that on Intuit congratulate. >>I think we're ahead of that. And I think part of the reason why we're ahead of that is because we've got great programs at Intuit to support women. One of our key programs is tech women at Intuit. And so it's an internal initiative where we focus on attracting, retaining and advancing women. So it's a great way for women across technology to support one another. Sure. You've heard of the term there's power in the pack, and we believe that when we connect women, we can help elevate their voices, which elevates our business and elevates our products. >>It does. In fact, there's some stats I was looking at recently that just showed if there was even 30% females at the executive level, how much more profitable organizations can be in how much higher performance they can have. So the data is there that suggests this is a really smart business decision to be making. >>Absolutely absolutely the data is, is no lie. I see it firsthand in my own business. And in fact, at Intuit, we've got a broader initiative around diversity and inclusion. It's led from the top. We have set goals across the company and we hold ourselves accountable because we know that if there are more women at the table and more diversity at the table, all around, we make better business decisions. And if you look at our product suite, which is a terrible tax, QuickBooks, mint, credit, karma, and MailChimp, we've got a diverse customer base of a hundred thousand, sorry, a hundred million customers. And so it's a lot of diversity in our customer base and we want a lot of diversity in the company. >>Fantastic. That there's such a dedicated effort to it. You just came in here from the career panel. Talk to me about that. What were some of the key things that were discussed? Yeah, >>I have my notebook open here because there were so many great takeaways from actually just from the day in general. I'm just so at, at the types of issues that women are tackling across different industries, they're tackling bias. And we know that bias is corrected when women are at the table, but from a career perspective, some of the things that were mentioned from the panel is the fact that women need to own their own careers and they need to actively manage their careers. And there's only so much your manager can do and should do. You've got to be in the driver's seat, driving your own career. One of the things that we've done at Intuit as we've implemented sort of a self promoting process. So twice a year during our promotion period, either your manager can nominate you for a promotion or you can self promote. So it's all about you creating a portfolio of all of your great work. And of course, you know, managers are very supportive of the process and support, you know, women and, and all technologists in crafting their portfolios for a fair chance at promotion. And so we just believe that if you take bias out of a career progression, you can close that fair and equitable gap that we see sometimes across industries with compensation. >>This is, that would be great if we can ever get there. One of the things that's nice about woods, I think it was last year or the year before they opened it up to high school students. So it was so nice walking in this morning, seeing the young, fresh faces, the mature faces, but you bring up a great point of women need to be their own mini to create their own personal board of directors and really be able to, to be at the helm of their career. Do you, did you find that the audience is receptive to that? Do they have the confidence to be able to do that? >>Yeah, absolutely. And, and that was a point that was raised a couple of times this morning, there were women who talked about having great mentors, but it is more important to have a board of your personal board of directors than one mentor, because you've got to make sure that you sort of tackle all aspects of your career life. And you know, it's not all about the technology, a good portion of how you spend your time and where you spend your time is collaborating and negotiating and communicating across the company. And so that's very important. And so that was a key message that folks shared this morning. >>That's good. That's incredibly important. I wish we had more time. You've got to run to the airport. Sharon, it's been a pleasure to have you on the program. Thank you for sharing what Intuit and woods are doing together, your involvement and some of the great messages, inspiring messages from the career panel. >>Exactly. And for all of the young expiring high school students. Yes. We want them to check out into it. www.intuit.com, careers, >>Intuit.com. Is it slash careers slash careers slash careers perfectly. I'm an Intuit customer. I will say. Awesome. It's been a pleasure talking to you. Thank you, Sharon. Bye-bye for Sharon Hutchins. I'm Lisa Martin. You're watching the cubes coverage of women in data science, 2022.

>>Live from Stanford university on your host. Lisa Martin. My next guest joins me with many I, our corporate vice president at Microsoft, Rick Minnie. It's great to have you on the program. Thank you for having me. Tell me a little bit about your background. So you run Microsoft advertising, engineering organizations. You also manage a multi-billion dollar marketplace globally. Yes. Big responsibilities. >>A little bit >>About you and your role at Microsoft. >>So basically online advertising, you know, funds a lot of the consumer services like search, you know, feeds. And so I run all of the online advertising pieces. And so my team is a combination of machine learning in theory, software engineers, online services. So you think of you think of what needs to happen for running an online advertising ecosystem? That's billions of dollars. I have all these people on my team when I get to work with these fantastic people. So that's my >>Roles. We have a really diverse team. >>Yes. My background itself is in AI. So my PhD was in language modeling and natural language processing. That's how I got into the space. And then I did, you know, machine learning. Then I did some auctions and then I'd, you know, I basically have touched almost all pieces of the puzzle. So from, I appreciate what's required to run a business the size. And so from that perspective, you know, yeah, it is a lot of diverse people, but at the same time, I feel like I know what they do >>Right then interdisciplinary collaboration must be incredibly important and >>Powerful. It is. I mean, for machine learning engineer or machine learning scientists to be successful, when you're running a production system, they have to really appreciate what constraints are there, you know, required online. So you have to look at how much CPU you use, how much memory you need, how fast can your model inference run with your model. And so they have to work very closely with the soft, soft engineering field. But at the same time, the software engineering guys need to know that their job is not to constrain the machine learning scientists. So, you know, as the models get larger, they have to get more creative. Right. And if that balance is right, then you get a really ambitious product. If that balance is not right, then you end up with a very small micro micro system. And so my job is to really make sure that the team is really ambitious in their thinking, not always liking, pushing the borders of what can be done. >>I like that pushing the borders of what can be done. You know, we, we often, when we talk about roles in, in stammered technology, we've talked about the hard skills, but the soft skills you've mentioned creativity. I always think creativity and curiosity are two soft skills that are really important in data science and AI. Talk to me about what your thoughts are. There >>Definitely creativity, because a lot of the problems that you, you know, when you're in school, the problems you face are very theoretical problems. And when you go into the industry and you realize that you need to solve a problem using the theory you learned, then you have to either start making different kinds of assumptions or realize that some assumptions just can be made because life is messy and online. You know, users are messy. They don't all interact with your system the same way. So you get creative in what can be solved. And then what needs to be controlled and folks who can't figure that piece out, they try to solve everything using machine learning, and they become a perfectionist, but nothing ever gets done then. So you need this balance and, and creativity plays a huge role in that space. And collaboration is you're always working with a diverse group of people. So explaining the problem space to someone who's selling your product, say someone is, you know, you build this automated bidding engine and they have to take this full mouth full and sell it to a customer. You've got to give them the terminology to use, tell, explain to them what are the benefits if somebody uses that. So I, I feel people who can empathize with the fact that this has to be explained, do a lot better when they're working in a product system, you know, bringing machine learning to a production system. >>Right. There's a lot of enablement >>There. Yes, exactly. Yeah. Yeah. >>Were you always interested in, in stem and engineering and AIS from when you were small? >>Somewhat? I mean, I've been, I got to my college degree. I was very certain by that point I wanted to be an engineer and my path to AI was kind of weird because I didn't really want to do computer science. So I ended up doing electrical engineering, but in my last year I did a project on speech recognition and I got introduced to computer programming. That was my first introduction to computer programming at the end of it, I knew I was going to work in the space. And so I came to the U S with less than three or four months of a computer engineering background. You know, I barely knew how to code. I had done some statistics, but not nearly enough to be in machine learning. And, but I landed in a good place. And I came to be in Boston university and I landed in a great lab. And I learned everything on my feet in that lab. I do feel like from that point onwards, I have always been interested and I'm never satisfied with just being interested in what's hot right now. I really want to know what can be solved later in the future. So that combination, I think, you know, really keeps me always learning, growing, and I'm never happy with just what's being done. >>Right? Yeah. We here, we've been hearing a lot about that today at weds. Just the tremendous opportunities that are here, the opportunities for data science, for good drones, for good data science and AI in healthcare and in public transportation. For example, you've been involved in with winds from the beginning. So you've gotten to see this small movement grow into this global really kind of is a >>Phenomenon. It is, >>It's a movement. Yes. You talk to me about your involvement with winds from the beginning and some of the things that you're helping them do. And now, >>So I, I first met Karen and marble initially when I was trying to get students from ICME to apply for roles in Microsoft. I really thought they had the right mix of applied and research mindset and the skill sets that were coming out of ICME rock solid in their math and theoretical foundations. So that's how I got to know them. And then they were just thinking about bids at that point in time. And so I said, you know, how can I help? And so I think I've been a keynote speaker, Pam list run a workshop. And then I got involved with the woods high school volunteer effort. And I'd say, that's the most rewarding piece of my visit involvement. And so I've been with them every year. I never Ms. Woods. I'm always here. And I think it is, you know, Grace Hopper was the technology conference for women and, and it's, it's, it's an awesome conference. I mean, it's amazing to sit next to so many women engineers, but data science was a part of it, but not a critical part of it. And so having this conference, that's completely focused on data science and making it accessible. The talks are accessible, making it more personable to, to all the invitees here. I think it creates a great community. So for me, I think it's, I hope they can run this and grow this for >>Yeah. Over 200 online events this year in 60 countries, they're aiming to reach a hundred thousand people annually. It's, it's grown dramatically in a short time period. Yes, >>Absolutely. Yeah. It hasn't been that long. It hasn't been that long and every year they add something new to the table. So for this year, I mean last year I thought the high schoolers, they started bringing in the high schoolers and this year again, I thought the high school. >>Yeah, >>Exactly. And I think the mix of getting data science from across a diversity, because a lot of the conferences are very focused. Like, you know, they, they will be the focused on healthcare and data science or pure AI or pure machine learning. This conference has a mix of a lot of different elements. And so attendees get to see how it's something is being used in healthcare and how something is being used in recommendations. And I think that diversity is really valuable. >>Oh, it's hugely valuable that the thought diversity is this is probably the conference where I discovered what thought diversity was if only a few years ago and the power and the opportunities that it can unlock for people everywhere for businesses in any industry. Yes. >>I want to kind of play off one of the things you said before, you know, data science for good, the, the incredible part of data sciences, you can do good wherever you are with data science. So take online advertising, you know, we build products for all advertisers, but we quickly figured out that are really large advertisers. They have their own data science teams and they are optimizing and, you know, creating new ads and making sure the best ads are serving at all times. They have figured out, you know, they have machine learning pipelines, so they are really doing their best already. But then there's this whole tale of small advertisers who just don't have the wherewithal or the knowledge to do any of that. Now, can you make data, use data science and your machine learning models and make it accessible for that long table? Pretty much any product you build, you will have the symptom of heavy users and then the tail users. And can you create an experience that is as valuable for those tailored users as it is for the heavy users. So data science for good exists, whatever problem you're solving, basically, >>That's nice to hear. And so you're going to be participating in some of the closing remarks today. What are some of the pearls of wisdom that you're going to enlighten the audience with today? >>Well, I mean the first thing I, to tell this audiences that they need to participate, you know, in whatever they shaped form, they need to participate in this movement of getting more women into stem and into data science. And my reasoning is, you know, I joined the lab and my professor was a woman and she was very strong scientists, very strong engineer. And that one story was enough to convince me that I belong. And if you can imagine that we create thousands of these stories, this is how you create that feeling of inclusion, where people feel like they belong. Yeah. Look, just look at those other 50 people here, those other a hundred stories here. This is how you create that movement. And so the first thing I want the audience to do is participate, come back, volunteer, you know, submit papers for keynote speeches, you know, be a part of this movement. >>So that's one. And then the second is I want them to be ambitious. So I don't want them to just read a book and apply the theory. I really want them to think about what problem are they solving and could they have solved it in the, in the scale manner that it can be solved. So I'll give a few examples and problems and I'll throw them out there as well. So for instance, experimentation, one of the big breakthroughs that happened in a lot of these large companies in data science is experimentation. You can AB experiment pretty much anything. You know, we can, Google has this famous paper where they talk about how they experimented with thousands of different blues just to get the right blue. And so experimentation has been evolving and data scientists are figuring out that if they can figure out interactions between experiments, you can actually run multiple experiments on the same user. >>So at any given time, you may be subject to four or five different experiments. Now, can we now scale that to infinity so that you can actually run as many experiments as you want questions like these, you shouldn't stop with just saying, oh, I know how AB experimentation works. The question you should be asking is how many such experiments can I run? How do I scale the system? As one of the keynote speakers initially talked about the unasked questions. And I think that's what I want to leave this audience with that don't stop at, you know, answering the questions that you're asked or solving the problems. You know, of you think about the problems you haven't solved your blind spots, you know, those blind spots and that I think I want ambitious data scientists. And so that's the message I want to give this audience. >>I can feel your energy when you say that. And you're involved with, with, with Stanford program for middle school and high school girls. If we look at the data and we see, there's still only about a quarter of stem positions are filled by females, what do you see? Do you see an inspiring group of young women in those middle school and high school girls that, that you see we're, we're on trend to start increasing that percentage. >>So I had a high schooler who just went, you know, she, she, she just, she's at UCLA now shout out to her and she, but she just went through high school. And what I realized is it's the same problem of not having enough stories around you, not having enough people around you that are all echoing the sentiment for, Hey, I love math. A lot of girls just don't talk about us. Yeah. And so I think the reason I want to start in middle school and high school is I think the momentum needs to start there. Yes. Because they get to college. And actually you heard my story. I didn't know any programming until I came here and I had already finished my four years of college and I still figured it out. Right. But a lot of women lose confidence to change fields after four years of college. >>Yes. And so if you don't catch them in early and you're catching them late, then you need to give them this boost of confidence or give them that ramp up time to learn, to figure out, like, I have a few people who are joining me from pure math nowadays. And these kids, these kids come in and within six months they're off and running. So, you know, in the interview phase, people might say, oh, they don't have any coding skills. Six months later, if you interview them, they pick up coding skills. Yeah. And so if you can get them started early on, I think, you know, they don't have this crisis of confidence of moving, changing fields. That's why I feel, and I don't think we are there yet, to be honest, I don't think yet. I think >>You still think there are plenty of girls being told. Now you can't do computer science. No, you can't do physics. No, you can't do math. >>Actually. They are denying it to themselves in many cases because they say, Hey, I go to physics class and there are two boys, two girls out of 50 boys. And I don't think girls are in, you know, you get the stereotype that maybe girls are not interested in physics. And it's not about, Hey, as a girl, I'm doing really well in physics. Maybe I should take this as my career. So I do feel we need to create more resounding stories in the area. And then I think we'll drum up that momentum. That's >>A great point. More stories, more and names to success here so that she can be what she can see exactly what many it's been great having you on the program. Thank you for joining me and sharing your background and some of the pearls of wisdom that you're gonna be dropping on the audience shortly today. We appreciate your insights. Thank you. My pleasure. Who Rick, Minnie, I are. I'm Lisa Martin. You're watching the cubes coverage weds 2022. We'll be right back after a short break.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante by the dip has been been an effective strategy since the market bottomed in early march last year the approach has been especially successful in tech and even more so for those tech names that one were well positioned for the forced march to digital i sometimes call it i.e remote work online commerce data centric platforms and certain cyber security plays and two already had the cloud figured out the question on investors minds is where to go from here should you avoid some of the high flyers that are richly valued with eye-popping multiples or should you continue to buy the dip and if so which companies that capitalized on the trends from last year will see permanent shifts in spending patterns that make them a solid long-term play hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we shine the spotlight on three companies that may be candidates for a buy the dip strategy and it's our pleasure to welcome in ivana delevco who's the chief investment officer and founder of spear alpha a new research-centric etf focused on industrial technology ivana is a long-time equity analyst with a background in both long and short investing ivana welcome to the program thanks so much for coming on thanks for having me david yeah it's really our pleasure i i want to start with your etf and give the folks a bit more background about you first you know we gotta let people know i'm not an investment pro i'm not an advisor i don't make stock recommendations i don't sell investments so you got to do your own research i have a lot of data so happy to share it but you got to understand your own risks you of course yvonne on the other hand you do offer investment services and so people before investing got to carefully review all the available available investment docs understand what you're getting into before you invest now with that out of the way ivana i have some stats up here on this slide your spear you're a newly launched female lead firm that does deep research into the supply chain we're going to talk about that you try to uncover as i understand it under-appreciated industrial tech firms and some really pretty cool areas that we list here but tell us a little bit more about your background and your etf so thanks for having me david my background is in industrial research and industrial technology investments i've spent the past 15 years covering this space and what we've seen over the past five years is technology changes that are really driving fundamental shifts in industrial manufacturing processes so whether this is 5g connectivity innovation in the software stack increasing compute speeds all of these are major technological advancements that are impacting uh traditional manufacturers so what we try to do is assess speak to these firms and assess who is at the leading and who is at the lagging end of this digital transformation and we're trying to assess what vendors they're using what processes they're implementing and that is how we generate most of our investment ideas okay great and and we show on the bottom of of this sort of intro slide if you will uh so one of the processes that you use and one of the things that that is notable a lot of people compare you uh to kathy woods are investments when you came out uh i think you use a different process i mean maybe there are some similarities in terms of disruption but at the bottom of this slide it shows a mckinsey sort of graphic that that i think informs people as to how you really dig into the supply chain from a research standpoint is that right absolutely so for us it's all about understanding the supply chain going deep in the supply chain and gather data points from primary sources that we can then translate into investment opportunities so if you look at this mckinsey graph uh you will see that there is a lot of opportunity to for these companies to transform themselves both on the front end which means better revenue better products and on their operation side which means lower cost whether it's through better operations or through better processes on the the back end so what we do is we will speak to a traditional manufacturing company and ask them okay well what do you use for better product development and they will give us the name of the firms and give us an assessment of what's the differences between the competitors why they like one versus the other so then we're gonna take the data and we will put it into our financial model and we'll understand the broader market for it um the addressable market the market share that the company has and will project the growth so for these higher growth stocks that that you cover the main alpha generation uh potential here is to understand what the amount of growth these companies will generate over the next 10 to 20 years so it's really all about projecting growth in the next three years in the next five years and where will growth ultimately settle in in the next 10 to 20 years love it we're gonna have a fun conversation because today we're going to get into your thesis for cooper snowflake and z scalar we're going to bring in some of our own data some of our data from etr and and why you think these companies may be candidates for long-term growth and and be buy the dip stock so to do that i hacked up this little comparison slide we're showing here i do this for context our audience knows i'm not a cfa or a valuation expert but we like to do simple comparisons just to give people context and a sense of relative size growth and valuation and so this chart attempts to do that so what i did is i took the most recent quarterly revenue for cooper snowflake and z scalar multiplied it by four to get a run rate we included servicenow in the table just for baseline reference because bill mcdermott as we've reported aspires to make service now the next great enterprise software company alongside with salesforce and oracle and some of the others and and all these companies that we list here that through the three here they aspire to do so in their own domain so we're displaying the market cap from friday morning september 10th we calculated a revenue run rate multiple and we show the quarterly revenue growth and what this data does is gives you a sense of the three companies they're well on their way to a billion dollars in revenue it underscores the relationship between revenue growth and valuation snowflake being the poster child for that dynamic savannah i know you do much more detailed financial analysis but let's talk about these companies in order maybe start with koopa they just crushed their quarter i mean they blew away consensus on the top line what else about the company do you like and why is it on your by the dip list so just to back up david on valuation these companies investors either directly or indirectly value on a dcf basis and what happened at the beginning of the year as interest rates started increasing people started freaking out and once you plug in 100 basis points higher interest rate in your dcf model you get significant price downside so that really drove a lot of the pullback at the beginning of the year right now where we stand today interest rates haven't really moved all that significantly off the bot of the bottom they're still around the same levels maybe a little bit higher but those are not the types of moves that are going to drive significant downside in this stock so as things have stabilized here a lot of these opportunities look pretty attractive on that basis so koopa specifically came out of our um if you go back to that uh the chart of like where the opportunities lie in um in across the manufacturing uh um enterprise koopa is really focused on business pen management so they're really trying to help companies reduce their cost uh and they're a leader in the space uh they're unique uh unique in that they're cloud-based so the feedback we've been hearing from from our companies that use it jetblue uses it train technologies uses it the feedback we've been hearing is that they love the ease of implementation so it's very easy to implement and it drives real savings um savings for these companies so we see in our dcf model we see multiple years of this 30 40 percent growth and that's really driving our price target yeah and we can i can confirm that i mean i mean just anecdotally you know you know we serve a lot of the technology community and many of our clients are saying hey okay you know when you go to do invoicing or whatever you work with procurement it's koopa you know this is some ariba that's kind of the legacy which is sap we'll talk about that a little later but let's talk about snowflake um you know snowflake we've been tracking them very closely we know the management there we've watched them through their last two companies now here and have been following that company early on since since really 2015. tell us why you like snowflake um and and maybe why you think it can continue its rapid growth thanks david so first of all i need to compliment you on your research on the company on the technology side so where we come in is more from understanding where our companies can use soft snowflake and where snowflake can add value so what we've been hearing from our companies is the challenge that they're facing is that everybody's moving to the cloud but it's not as simple as just send your data to the cloud and call aws and they're gonna generate more revenue for your solve your cost problem so what we've been hearing is that companies need to find tools that are easy to use where they can use their own domain expertise and just plug and play so um ansys is one of the companies we covered the dust simulation they've found snowflake to be an extremely useful tool in sales lead generation and within sales crm systems have been around for a while and they're they've really been implemented but analyzing sales numbers is something that is new to this company some some of our companies don't even know what their sales are even when they look back after the quarter is closed so tools like this help um companies do easy analytics and therefore drive revenue and cost savings growth so we see really big runway for for this company and i think the most misunderstood part about it is that people view it as a warehousing data warehousing play while this is all about compute and the company does a good job separating the two and what our their customers like or like the companies that we cover like about it is that it can lower their compute costs um and make it much easier much more easily manageable for them great and we're going to talk about more about each of these companies but let's talk about z-scaler a bit i mean z-scaler is a company we've been very excited about and identified them kind of early on they've definitely benefited from the move to cloud generally and specifically the remote work uh situation with the cyber threats etc but tell us why you like z-scaler so interestingly z-scaler um we like the broader security space um the broader cyber security space and interestingly our companies are not yet spending to the level that is commensurate with the increase in attack rate so we think this is a trend that is really going to accelerate as we go forward um my own board 20 of the time on the last board meeting was spent on cyber security what we're doing and this is a pretty simple operation that that we're running here so you can imagine for a large enterprise with thousands of people all around the world um needing to be on a single simple system z-scaler really fits well here very easy to implement several of our industrial companies use it siemens uses it ge uses it and they've had great great experience with it excellent i just want to take a quick look at how some of these names have performed over the last year and and what if anything this data tells us this is a chart comparing the past 12 months performance of of those four companies uh that we just talked about and we added in you know servicenow z scalar as you can see has outperformed the other despite your commentary on discounted cash flow snowflake is underperformed really precisely for the reasons that you mentioned not to mention the fact that it was pretty highly valued and you can see relative to the nas but it's creeping back lately after very strong earnings even though the stock dropped after it beat earnings because the street wants the cfo to say to guide even higher than maybe as mike scarpelli feels is prudent and you can see cooper has also underperformed relatively speaking i mean it absolutely destroyed consensus this week the stock went up but it's been off with the the weaker market this week i know you like to take a longer term view but but anything you would add here yeah so interestingly both z-scaler and koopa were in the camp of as we went into earnings expectations were already pretty high because few of their competitors reported very strong results so this scalar yesterday their revenue growth was was pretty strong the stock is down today uh and the reason is because people were kind of caught up a little bit in the noise of this quarter growth is 57 last quarter it was 60 like is this a deceleration we don't see it as that at all and the company brought up one point that i thought was extremely interesting which is as their deal sizes are getting larger it takes a little longer time for them to see the revenue come through so it takes a little bit of time to for you to see it into from billings into into revenue same thing with cooper very strong earnings report but i think expectations were already pretty high going into it uh given the service now and um and anna plan as well reported strong results so i think it's all about positioning so we love these setups where you can buy the deep in on this opportunity where like people get caught up in um short-term noise and and it creates good entry points excellent i i want to bring in some data from our partner etr and see if you have any comments ivana so what we're showing here is a two-dimensional chart we like to show this uh very frequently it's based on a survey of between a thousand and fifteen hundred chief information officers and technology buyers every quarter this is from their most recent july survey the vertical axis shows net score which is a measure of spending momentum i mean this it measures the net percentage of customers in the survey that are spending more on a particular product or platform in other words it essentially subtracts the percentage of customers spending less from those spending more which yields a net score it's more granular than that but basically that's what it does the horizontal axis is market share or pervasiveness in the data set it's not revenue market share like you get from idc it's it's a mention market share and now that red dotted line at the 40 percent mark on the vertical represents an elevated level in other words anything above 40 percent we consider notable and we've plotted our three by the dip companies and included some of their competitors for context and you can see we added salesforce servicenow and oracle and that orange ellipse because they're some of the bigger names in the software business so let's take these in alphabetical order ivana starting with koopa in the blue you can see we plotted them next to sap's ariba and you can see cooper has stronger spending momentum but not as much presence in the market so to me my influence is oh that's an opportunity for them to steal share more modern technology you know more facile and of course oracle has products in this space but the oracle dot includes all oracle products not just the procurement stuff but uh maybe your thoughts on this absolutely i love this chart i think that's your spot on this would be the same way i would interpret the chart where um increased spending momentum is is a sign of the company providing products that people like and we we expect to see cooper's share grow market share grow over time as well so let's come back to the chart and i want to i want to really point out the green ellipse this is the data zone if you will uh and we're like a broken record on this program with snowflake has performed unbelievably well in net score and spending momentum every quarter the dtr has captured enough end sample in its survey holding near or above 80 percent its net score consistently is has been up there and we've plotted data bricks in that zone it's been expected right that data bricks is going to do an ipo this year late last month company raised 1.6 billion in a private round so i guess that was either a strategy to delay the ipo or raise a bunch more cash and give late investors a low risk bite at the apple you know pre-ipo as we saw with snowflake last year what we didn't plot here are some of snowflake's biggest competitors ivana who also happen to be their partners most notably the big cloud players all who have their own database offerings aws microsoft and google now you've said snowflake is much more than a database company i wonder if you could add some color here yeah that's a very good point david uh basically the the driver of the thesis in snowflake is all about acceleration and spending and what we are seeing is the customers that are signed up on their platform today they're not even spending they're probably spending less than five percent of what they can ultimately spend on this product and the reason is because they don't yet know what the ultimate applications are for this right so you're gonna start with putting the data in a format you can use and you need to come up with use cases or how are you actually going to use this data so back to the example that i gave with answers the first use case that they found was trying to optimize leads there could be like 100 other use cases and they're coming up with with those on a daily basis so i would expect um this score to keep keep uh keep up pretty high or or go even higher as we as people figure out how they can use this product you know the buy-the-dip thesis on snowflake was great last quarter because the stock pulled back after they announced earnings and when we reported we said you know mike the the company see well cleveland research came out remember they got the dip on that and we looked at the data and we said mike scarpelli said that you know we're going to probably as a percentage of overall customers decelerate the net net new logos but we're going deeper into the customer base and that's exactly what's happening with with snowflake but okay let's bring up the slide again last but not least the z scaler we love z scalar we named z scaler in 2019 as an emerging four-star security company along with crowdstrike and octa and we said these three should be on your radar and as you see we've plotted z scalar with octa who with its it's its recent move into to converging identity and governance uh it gets kind of interesting uh we plotted them with palo alto as well another cyber security player that we've covered extensively we love octa in addition to z-scaler we great respect for palo alto and you'll note all of them are over that 40 percent line these are disruptors they're benefiting well not so much palo alto they're more legacy but the the other two are benefiting from that shift to work from home cloud security modern tech stack uh the acquisition that octa-made of of of auth0 and again z scalar cloud security getting rid of a lot of hardware uh really has a huge tailwind at its back if on a zscaler you know they've benefited from the huge my cloud migration trend what are your thoughts on the company so i actually love all three companies that are there right and the point is people are just going to spend more money whether you are on the cloud of the cloud the data centers need more security as well so i think there is a strong case to be made for all three with this scaler the upside is that it's just very easy to use very easy to implement and if you're somebody that is just setting up infrastructure on the cloud there is no reason for you to call any other competitor right with palo alto the case there is that if you have an established um security platfor if you're on their security platform the databa on the data center side uh they they did introduce through several acquisitions a pretty attractive cloud offering as well so they've been gaining share as well in the space and and the company does look pretty attractive on valiation basis so for us cyber security is really all about rising tide lifts all boats here right so you can have a pure play like this scaler uh that benefits from the cloud but even somebody like palo alto is pretty well positioned um to benefit yeah we think so too over a year ago we reported on the valuation divergence between palo alto and fortinet fortinet was doing a better job moving to the cloud and obviously serves more of a mid-market space palo alto had some go-to-market execution challenges we said at the time they're going to get through those and when we talk to chief information security officers palo alto is like the gold standard they're the thought leader they want to work with them but at the same time they also want to participate in some of these you know modern cloud stacks so i we agree there's plenty of room for all three um just to add a bit more color and drill into the spending data a little bit more this slide here takes that net score and shows the progression since january 2019 and you can see a snowflake just incredible in terms of its ability to maintain that elevated net score as we talked about and the table on the insert it shows you the number of responses and all three of these companies have been getting more mentions over time but snowflake and z scale are now both well over 100 n in the survey each quarter and the other notable piece here and this is really important you can see all three are coming out of the isolation economy with the spending uptick nice upticks shown in the most recent survey so that's again another positive but i want to close ivana with kind of making the bull and bear case and have you address really the risks to the buy the dip scenario so look there are a lot of reasons to like these companies we talked about them cooper they've got earnings momentum you know management on the call side had very strong end market demand this the stock you know has underperformed the nasdaq you know this year snowflake and zscaler they also have momentum snowflake get this enormous tam uh although they were punished for not putting a hard number on it which is ridiculous in my opinion i mean the thing is it's huge um the investors were just kind of you know wanting a little binky baby blanket but they all have modern tech in the cloud and really importantly this shows in the etr surveys you know the momentum that they have so very high retention is the other point i wanted to make the very very low churn of these companies however cooper's management despite the blowout quarter they gave kind of underwhelming guidance they've cited headwinds uh they've with the the the lamisoft uh migration to their cloud platform snowflake is kind of like price to perfection so maybe that's an advantage because every every little negative news is going to going to cause the company to dip but it's you know it's pretty high value because salutman and scarpelli everybody expects them to surpass what happened at servicenow which was a rocket ship and it could be all argued that all three are richly priced and overvalued so but ivana you're looking out as you said a couple of years three years maybe even five years how do you think about the potential downside risks in in your by the dip scenario you buy every dip you looking for bigger dips or what's your framework there so what we try to do is really look every quarter the company reports is there something that's driving fundamental change to the story or is it a one-off situation where people are just misunderstanding what the company is reporting so in the case we kind of addressed some of the earnings that that were reported but with koopa we think the man that management is guiding conservatively as they should so we're not very concerned about their ability to execute on on the guidance and and to exceed the guidance with snowflake price to perfection that's never a good idea to avoid a stock uh because it just shows that there is the company is doing a great job executing right so um we are looking for reports like the cleveland report where they would be like negative on the stock and that would be an entry point uh for us so broadly we apply by the deep philosophy but not not if something fundamentally changes in the story and none of these three are showing any signs of fundamental change okay we're going to leave it right there thanks to my guest today ivana tremendous having you would love to have you back great to see you thank you david and def you definitely want to check out sprx and the spear etf now remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you do is search breaking analysis podcasts you can always connect with me on twitter i'm at d vallante or email me at david.vellante at siliconangle.com love the comments on linkedin don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

(upbeat music) >> Hello and welcome to this Cube conversation. I'm John Furrier, your host of theCube here in Palo Alto, California. We're here in 2021 as we get through the pandemic and vaccine on the horizon all around the world. It's great to welcome Sandy Carter, Vice President of Partners and Programs with Amazon Web Services. Sandy, great to see you. I wanted to check in with you for a couple of reasons. One is just get a take on the landscape of the marketplace as well as you've got some always good programs going on. You're in the middle of all the action. Great to see you. >> Nice to see you too, John. Thanks for having me. >> So one of the things that's come out of this COVID and as we get ready to come out of the pandemic you starting to see some patterns emerging, and that is cloud and cloud-native technologies and SAS and the new platforming and refactoring using cloud has created an opportunity for companies. Your partner group within public sector and beyond is just completely exploding and value creation. Changing the world's society is now accelerated. We've covered that in the past, certainly in detail last year at re:Invent. Now more than ever it's more important. You're doing some pretty cutting things. What's your update here for us? >> Well, John, we're really excited because you know the heartbeat of countries of the United States globally are small and medium businesses. So today we're really excited to launch Think Big for Small Business. It's a program that helps accelerate public sector serving small and diverse partners. So you know that these small and medium businesses are just the engine for inclusive growth and strategy. We talked about some stats today, but according to the World Bank, smaller medium business accounts for 98% of all companies, they contribute a 50% of the GDP, two-thirds of the employment opportunities, and the fastest growing areas are in minority owned businesses, women, black owned, brown owned, veteran owned, aborigine, ethnic minorities who are just vital to the economic role. And so today this program enables us as AWS to support this partner group to overcome the challenges that they're seeing today in their business with some benefits specifically targeted for them from AWS. >> Can I ask you what was the driver behind this? Obviously, we're seeing the pandemic and you can't look at on the TV or in the news without seeing the impact that small businesses had. So I can almost imagine that might be some motivation, but what is some of the conversations that you're having? Why this program? Why think Big for Small Business pilot experience that you're launch? >> Well, it's really interesting. The COVID obviously plays a role here because COVID hit small and medium businesses harder, but we also, you know, part of Amazon is working backwards from the customers. So we collected feedback from small businesses on their experience in working with us. They all want to work with us. And essentially they told us that they need a little bit more help, a little bit more push around programmatic benefits. So we listened to them to see what was happening. In addition, AWS grew up with a startup community. That's how we grew up. And so we wanted to also reflect our heritage and our commitment to these partners who represent such a heartbeat of many different economies. That was really the main driver. And today we had, John, one of our follow the sun. So we're doing sessions in Latin America, Canada, the US, APJ, Europe. And if you had heard these partners today it was just such a great story of how we were able to help them and help them grow. >> One of the cultural changes that we've been reporting on SiliconANGLE, you're seeing it all over the world is the shift in who's adopting, who's starting businesses. And you're seeing, you mentioned minority owned businesses but it goes beyond that. Now you have complete diverse set entrepreneurial activity. And cloud has generated this democratization wave. You starting to see businesses highly accelerated. I mean, more than ever, I've never seen in the entrepreneurial equation the ability to start, get started and get to success, get to some measurable MVP, minimal viable product, and then ultimately to success faster than ever before. This has opened up the doors to anyone to be an entrepreneur. And so this brings up the conversation of equality in entrepreneurship. I know this is close to your heart. Share your thoughts on this big trend. >> Yeah, and that's why this program it's not just a great I think achievement for AWS, but it's very personal to the entire public sector team. If you look at entrepreneurs like, Lisa Burnett, she's the President and Managing Director of DLZP. They are a female owned minority owned business from Texas. And as you listen to her story about equity, she has this amazing business, migrating Oracle workloads over to AWS, but as she started growing she needed help understanding a little bit more about what AWS could bring to the table, how we could help her, what go to market strategies we could bring, and so that equalizer was this program. She was part of our pilot. We also had John Wieler on. He is the Vice President of Biz Dev from IMT out of Canada. And he is focused on government for Canada. And as a small business, he said today something that was so impactful, he goes, "Amazon never asked me if I'm a small business. They now treat me like I'm big. I feel like I'm one of the big guys and that enables me grow even bigger." And we also talked today to Juan Pablo De Rosa. He's the CEO of Technogi. And it's a small business in Mexico. And what do they do? They do migrations. They just migrate legacy workloads over. And again, back to that equality point you made, how cool was it that here's this company in Mexico, and they're doing all these migrations and we can help them even be more successful and to drive more jobs in the region. It's a very equalizing program and something that we're very proud of. >> You know what I love about your job and I love talking to you about this (Sandy laughs) because it's so much fun. You have a global perspective. It's not just United States. There's a global perspective. This event you're having this morning that you kicked off with is not just in the US, it's a follow the sun kind of a community. You got quite the global community developing there, Sandy. Can you share some insight behind the curtain, behind AWS, how this is developing? How you're handling it? What you're doing to nurture and grow that community that really wants to engage with you because you are making them feel big because (laughs) that's what cloud does. It makes them punch above their weight class and innovate. >> Yeah, that's very correct. >> This is the core thesis of Amazon. So you've got a community developing, how are you handling it? How are you building it? How are you nurturing it? What are your thoughts? >> You know what, John? You're so insightful because that's actually the goal of this program. We want to help these partners. We want to help them grow. But our ultimate goal is to build that small and medium business community that is based on AWS. In fact, at re:Invent this year, we were able to talk about MST which is based out of Malaysia, as well as cloud prime based out of Korea. And just by talking about it, those two CEOs reached out to each other from Korea and Malaysia and started talking. And then we today introduced folks from Mexico, and Canada, and the US, and Bulgaria. And so, we really pride ourselves on facilitating that community. Our dream here, our vision here is that we would build that small business community to be much more scalable but starting out by making those connections, having that mentoring that will be built in together, doing community meetings that advisory meetings together. We piloted this program in 2020. We already have 37 partners. And they told me as I met with them, they already feel like this small and medium business community or family. Family was the word they used, I think, moving forward. So you nailed it. That's the goal here is to create that community where people can share their thoughts and mentor each other. >> And it's on the ground floor too. It's just beginning. I think it's going to be so much larger. And to piggyback off that I want to also point out and highlight and get your reaction to is the success that you've been having and Amazon Web Services in general but mainly in the public sector side with the public private partnership. You're seeing this theme emerge really been a big way. I've been enclose to it and hosting and being interviewing a lot of folks at that, your customers whether it's cybersecurity in space, the Mars partnership that you guys just got on Mars with partnerships. So it's a global and interstellar soon to be huge everywhere. But this is a big discussion because as from cybersecurity, geopolitical to space, you have this partnership with public private because you can't do it alone. The public markets, the public sector cannot do it alone. And it pretty much everyone's agreeing to that. So this dynamic of public sector and partnering private public is a pretty big deal. Unpack that for us real quickly. >> Yeah, it really is a big deal. And in fact, we've worked with several companies. I'll just use one sector. Public Safety and Disaster Response. We just announced the competency at re:Invent for our tech partners. And what we found is that when communities are facing a disaster, it really is government or the public sector plus the private sector. We had many solutions where citizens are providing data that helps the government manage a disaster or manage or help in a public safety scenario to things like simple things you would think, but in one country they were looking at bicycle routes and discovered that certain bicycle routes there were more crashes. And so one of our partners decided to have the community provide the data. And so as they were collecting that data, putting in the data lake in AWS, the community or the private sector was providing the data that enabled the application, our Public Sector Partner application to identify places where bicycle accidents happen most often. And I love the story, John, because the CEO of the partner told me that they measured their results in terms of ELO, I'm sorry, ROL, Return on Lives not ROI, because they save so many lives just from that simple application. >> Yeah, and the data's all there. You just saw on the news, Tiger Woods got into a car accident and survived. And as it turns out to your point that's a curve in the road where a lot of accidents happen. And if that data was available that could have been telegraphed right into the car itself and slow down, kind of like almost a prevention. So he just an example of just all the innovation possibilities that are abound out there. >> And that's why we love our small businesses and startups too, John. They are driving that innovation. The startups are driving that innovation and we're able to then open access to that innovation to governments, agencies, healthcare providers, space. You mentioned Mars. One of our partners MAXR helped them with the robotics. So it's just a really cool experience where you can open up that innovation, help create new jobs through these small businesses and help them be successful. There's really nothing, nothing better. >> Can I ask you- >> Small, small is beautiful. >> Can I asked you a personal question on this been Mars thing? >> Yeah. >> What's it like at Amazon Web Services now because that was such a cool mission. I saw Teresa Carlson, had a post on the internet and LinkedIn as well as her blog post. You had posted a picture of me and you had thumbs were taking an old picture from in real life. Space is cool, Mars in particular, everyone's fixated on it. Pretty big accomplishment. What's it like at Amazon? People high five in each other pretty giddy, what's happening? >> Oh yeah. The thing about Amazon is people come here to change the world. That's what we want to do. We want to have an impact on history. We want to help make history. And we do it all on behalf of our customers. We're innovating on behalf of our customers. And so, I think we get excited when our customers are successful, when our partners are successful, which is why I'm so excited right now, John, because we did that session this morning, and as I listened to Juan Pablo Dela Rosa, and just all the partners, Lisa, John, and just to hear them say, "You helped us," that's what makes us giddy. And that's what makes us excited. So it could be something as big as Mars. We went to Mars but it's also doing something for small businesses as well. It runs the spectrum that really drives us and fuels that energy. And of course, we've got great leadership as you know, because you get to talk to Andy. Andy is such a great leader. He motivates and he inspires us as well to do more on behalf of our customer. >> Yeah, you guys are very customer focused and innovative which is really the kind of the secret sauce. I love the fact that small medium sized business can also be part of the solutions. And I truly believe that, and why I wanted us to promote and amplify what you're working on today is because the small medium size enterprise and business is the heart of the recovery on a global scale. So important and having the resources to do that, and doing it easily and consuming the cloud so that they can apply the value. It's going to change lives. I think the thing that people aren't really talking much about right now, is that the small medium size businesses will be the road to recovery. >> I agree with you. And I love this program because it does promote diversity, something that Amazon is very much focused on. It's global, so it has that global reach and it supports small business, and therefore the recovery that you talked about. So it is I think an amazing emphasis on all the things that really matter now. During COVID, John, we learned about what really matters, and this program focuses on those things and helping others. >> Well, great to see you. I know you're super busy. Thanks for coming on and sharing the update, and certainly talking about the small mid size business program. I'm sure you're busy getting ready to give the awards out to the winners this year. Looking forward to seeing that come up soon. >> Great. Thank you, John. And don't forget if you are a small and medium business partner 'cause this program is specifically for partners, check out Think Big for Small Business. >> Think Big for Small Business. Sandy Carter, here on theCube, sharing our insight, of course all the updates from the worldwide public sector partner program, doing great things. I'm John Furrier for theCube. Thanks for watching. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, husband. Welcome back to the cubes. Live coverage of AWS reinvent 2020. We're not in person this year. We're virtual This is the Cube Virtual. I'm John for your host of the Cube. Roger Barker, the General Manager AWS Robotics and Autonomous Service. And a lot of other cool stuff was on last year. Always. Speed Racer. You got the machines. Now you have real time Robotics hitting, hitting seen Andy Jassy laid out a huge vision and and data points and announcements around Industrial this I o t it's kind of coming together. Roger, great to see you. And thanks for coming on. I want to dig in and get your perspective. Thanks for joining the Cube. >>Good to be here with you again today. >>Alright, so give us your take on the announcements yesterday and how that relates to the work that you're doing on the robotic side at a w s. And where where does this go from? You know, fun to real world to societal impact. Take us through. What? You how you see that vision? >>Yeah, sure. So we continue to see the story of how processing is moving to the edge and cloud services, or augmenting that processing at the edge with unique and new services. And he talked about five new industrial machine learning services yesterday, which are very relevant to exactly what we're trying to do with AWS robot maker. Um, a couple of them monitor on, which is for equipment monitoring for anomalies. And it's a whole solution, from an edge device to a gateway to a service. But we also heard about look out for equipment, which is if a customer already has their own censors. It's a service that can actually back up that that sensor on their on the device to actually get identify anomalies or potential failures. And we saw look out for video, which allows customers to actually use their camera and and build a service to detect anomalies and potential failures. When A. W s robot maker, we have Ross Cloud Service extensions, which allow developers to connect their robot to these services and so increasingly, that combination of being able to put sensors and processing at the edge, connecting it back with the cloud where you could do intelligent processing and understand what's going on out in the environment. So those were exciting announcements. And that story is going to continue to unfold with new services. New sensors we can put on our robots to again intelligently process the data and control these robots and industrial settings. >>You know, this brings up a great point. And, you know, I wasn't kidding. Was saying fun to real world. I mean, this is what's happening. Um, the use cases air different. You look at you mentioned, um, you know, monitor on lookout. But those depend Panorama appliance. You had computer vision, machine learning. I mean, these are all new, cool, relevant use cases, but they're not like static. It's not like you're going to see them. Just one thing is like the edge has very diverse and sometimes mostly purpose built for the edge piece. So it's not like you could build a product. Okay, fits everywhere. Talk about that dynamic and why the robotics piece has to be agile. And what do you guys doing to make that workable? Because, you know, you want purpose built. The purpose built implies supply chain years. in advance. It implies slow and you know, how do you get the trust? How do you get the security? Take us through that, please. >>So to your point, um, no single service is going to solve all problems, which is why AWS has has released a number of just primitives. Just think about Kinesis video or Aiken. Stream my raw video from an edge device and build my own machine learning model in the cloud with sage maker that will process that. Or I could use recognition. So we give customers these basic building blocks. But we also think about working customer backward. What is the finished solution that we could give a customer that just works out of the box? And the new services we heard about we heard about yesterday were exactly in that latter category. Their purpose built. They're ready to be used or trained for developers to use and and with very little customization that necessary. Um, but the point is, is that is that these customers that are working these environments, the business questions change all the time, and so they need actually re program a robot on the fly, for example, with a new mission to address the new business need that just arose is a dynamic, which we've been very tuned into since we first started with a device robo maker. We have a feature for a fleet management, which allows a developer to choose any robot that's out in their fleet and take the software stack a new software stack tested in simulation and then redeploy it to that robot so it changes its mission. And this is a This is a dialogue we've been seeing coming up over the last year, where roboticists are starting to educate their company that a robot is a device that could be dynamically program. At any point in time, they contest their application and simulation while the robots out in the field verify it's gonna work correctly and simulation and then change the mission for that robot. Dynamically. One of my customers they're working with Woods Hole Institute is sending autonomous underwater robots out into the ocean to monitor wind farms, and they realized the mission may change may change based on what they find out. If the wind farm with the equipment with their autonomous robot, the robot itself may encounter an issue and that ability because they do have connective ity to change the mission dynamically. First Testament, of course, in simulation is completely changing the game for how they think about robots no longer a static program at once, and have to bring it back in the shop to re program it. It's now just this dynamic entity that could test and modify it any time. >>You know, I'm old enough to know how hard that really is to pull off. And this highlights really kind of how exciting this is, E. I mean, just think about the idea of hardware being dynamically updated with software in real time and or near real time with new stacks. I mean, just that's just unheard of, you know, because purpose built has always been kind of you. Lock it in, you deploy it. You send the tech out there this kind of break fixed kind of mindset. Let's changes everything, whether it's space or underwater. You've been seeing everything. It's software defined, software operated model, so I have to ask you First of all, that's super awesome. Anyway, what's this like for the new generation? Because Andy talked on stage and in in my one On one way I had with him. He talked about, um, and referring to land in some of these new things. There's a new generation of developer. So you gotta look at these young kids coming out of school to them. They don't understand what how hard this is. They just look at it as lingua frank with software defined stuff. So can you share some of the cutting edge things that are coming out of these new new the new talent or the new developers? Uh, I'm sure the creativity is off the charts. Can you share some cool, um, use cases? Share your perspective? >>Absolutely. I think there's a couple of interesting cases to look at. One is, you know, roboticists historically have thought about all the processing on the robot. And if you say cloud and cloud service, they just couldn't fathom that reality that all the processing has cannot has to be, you know, could be moved off of the robot. Now you're seeing developers who are looking at the cloud services that we're launching and our cloud service extensions, which give you a secure connection to the cloud from your robot. They're starting to realize they can actually move some of that processing off the robot that could lower the bomb or the building materials, the cost of the robot. And they can have this dynamic programming surface in the cloud that they can program and change the behavior of the robot. So that's a dialogue we've seen coming over the last couple years, that rethinking of where the software should live. What makes sense to run on the robot? And what should we push out to the cloud? Let alone the fact that if you're aggregating information from hundreds of robots, you can actually build machine learning models that actually identify mistakes a single robot might make across the fleet and actually use that insight to actually retrain the models. Push new applications down, pushing machine learning models down. That is a completely different mindset. It's almost like introducing distributed computing to roboticists that you actually think this fabric of robots and another, more recent trend we're seeing that were listening very closely to customers is the ability to use simulation and machine learning, specifically reinforcement. Learning for a robot actually try different tasks up because simulations have gotten so realistic with the physics engines and the rendering quality that is almost nearly realistic for a camera. The physics are actually real world physics, so that you can put a simulation of your robot into a three D simulated world and allow it to bumble around and make mistakes while trying to perform the task that you frankly don't know how to write the code for it so complex and through reinforcement, learning, giving rewards signals if it does something right or punishment or negative rewards signals. If it does something wrong, the machine learning algorithm will learn to perform navigation and manipulation tasks, which again the programmer simply didn't have to write a line of code for other than creating the right simulation in the right set of trials >>so that it's like reversing the debugging protocol. It's like, Hey, do the simulations. The code writes itself. Debug it on the front end. It rights itself rather than writing code, compiling it, debugging it, working through the use cases. I mean, it's pretty different. >>It is. It's really a new persona. When we started out, not only are you taking that roboticist persona and again introduced him to the cloud services and distributed computing what you're seeing machine learning scientists with robotics experience is actually rising. Is a new developer persona that we have to pay attention to him. We're talking to right now about what they what they need from our service. >>Well, Roger, I get I'm getting tight on time here. I want one final question before we break. How does someone get involved with Amazon? And I'll see you know, whether it's robotics and new areas like space, which is verging, there's a lot of action, a lot of interest. Um, how does someone engaged with Amazon to get involved, Whether I'm a student or whether I'm a professional, I want a code. What's what's the absolutely, >>absolutely, so certainly reinvent. We have several sessions that reinvent on AWS robo maker. Our team is there, presenting and talking about our road map and how people can get engaged. There is, of course, the remarks conference, which will be happening next year, hopefully to get engaged. Our team is active in the Ross Open Source Community and Ross Industrial, which is happening in Europe later in December but also happens in the Americas, where were present giving demos and getting hands on tutorials. We're also very active in the academic research in education arena. In fact, we just released open source curriculum that any developer could get access to on Get Hub for Robotics and Ross, as well as how to use robo maker that's freely available. Eso There's a number of touch points and, of course, I'd be welcome to a field. Any request for people to learn more or just engage with our team? >>Arthur Parker, general manager. It is robotics and also the Autonomous Systems Group at AWS Amazon Web services. Great stuff, and this is really awesome insight. Also, you know it za candy For the developers, it's the new generation of people who are going to get put their teeth into some new science and some new problems to solve. With software again, distributed computing meets robotics and hardware, and it's an opportunity to change the world literally. >>It is an exciting space. It's still Day one and robotics, and we look forward to seeing the car customers do with our service. >>Great stuff, of course. The Cube loves this country. Love robots. We love autonomous. We love space programming all this stuff, totally cutting edge cloud computing, changing the game at many levels with the digital transformation just a cube. Thanks for watching

yeah so that talks sort of stemmed out of the TED talk that I gave on owning your body is data and it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions and so ways that you can use like air temperature data or your heart rate data or what is this data say over time what does it say about your body's health and really challenging the audience to get excited about looking at that data we have so many devices that collect data automatically for us and often we don't pause long enough to actually look at that historical data and so that was what the talk was about today like here's what you can find when you actually sit down and look at that data what's the most important data you think people should be collecting about themselves well definitely not your weight it depends you know I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body is fertile when you're ovulating it can so that information could give women during that time period really critical information but in general I think it's just a matter of being aware of of how your body is changing so for some people maybe it's your blood pressure or your blood sugar you have high blood pressure or high blood sugar those things become really critical to keep an eye on and and I really encourage people whatever data they take to be active in the understanding of an interpretation of the data so it's not like if you take this data you'll be healthier you know you live to a hundred it's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking so I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that foundation is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than math or computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection so I think one of the key things about the ethics panel here at woods this morning was that first of all it started the day which is a good sign if it shouldn't be a separate topic of discussion we need this conversation about values about what we're trying to build for who were trying to protect how we're trying to recognize individual human agency that has to be built in throughout data science so it's a good start to have a panel about it at the beginning of the conference but I'm hopeful that the rest of the conversation will not leave it behind we talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control data scientists are building data sets and algorithmic forms of analyses that are both of those two things are just in coded sets of values and if you try to have a conversation about that at just the math level you're gonna miss the social level you're gonna miss the fact that that's humanity you're talking about so it needs to really be integrated throughout the process talking about the values of what you're manipulating and the values of the world that you're releasing these tools into yeah so into it we are a champion of gender life diversity and also all sorts of diversity and when we first learned about wig we said we need to be a champion of the women in data science conference because for me personally oftentimes when I'm in a room going over technical details I'm often the only woman and not just I'm often the only woman executive and so part of the sponsorship is to create this community of women very technical women in this field to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science so first of all having doing we which should I believe in the vision that we are working towards which is really creating you can mount an economic opportunity for every member of the global workforce and if you're kind of starting from that and thinking about that is our sort of the the the the axiom that we're working towards and I thinking about how you can do that and obviously the sort of the table stake or just the the the the fundamental saying that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data there are members in trust with us right so how can we do that we have some early effort in using and developing differential privacy as a technique for us to do a lot better ways regarding preserving their privacy as really leveraging the data and but also at the same time it doesn't end there right because you're thinking about creating opportunity it's not just about its preserve the privacy but also when we are leveraging the data how can we leverage the data in a way that is able to create opportunity in a fair way so so there is also a lot of effort that we're having with regarding how can we do that and what does fairness mean what are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop products the way that we're thinking about responsible design and the way that we build our algorithms the way that we measure in every single dimension you

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Hi! And welcome to the Cube. I'm your host, Sonia, to guard. And we're live at Stanford University covering Woods Women in Data Science Conference The fifth annual one And joining us today is Daphne Koller, who is the co founder who sorry is the CEO and founder of In Citro that Daphne. Welcome to the Cube. >>Nice to be here, Sonia. Thank you for having me. So >>tell us a little bit about in Citro how you how you got founded and more about your >>role. So I've been working in the intersection of machine learning and biology and health for quite a while, and it was always a bit of an interesting journey and that the data sets were quite small and limited. We're now in a different world where there's tools that are allowing us to create massive biological data sense that I think can help us solve really significant societal problems. And one of those problems that I think is really important is drug discovery and development, where despite many important advancements, the costs just keep going up and up and up. And the question is, can we use machine learning to solve that problem >>better? And you talk about this more in your keynote, so give us a few highlights of what you talked about. So in the last, you can think of >>drug discovery development in the last 50 to 70 years as being a bit of a glass half full glass, half empty. The glass half full is the fact that there's diseases that used to be a death sentence or of sentenced, a lifelong of pain and suffering that >>are now >>addressed by some of the modern day medicines. And I think that's absolutely amazing. The >>other side of >>it is that the cost of developing new drugs has been growing exponentially and what's come to be known as the Rooms law being the inverse of Moore's law, which is the one we're all familiar with because the number of drugs approved per 1,000,000,000 U. S. Dollars just keeps going down exponentially. So the question is, can we change that curve? >>And you talked in your keynote about the interdisciplinary culture to tell us more about that? I think in >>order to address some of the critical problems that we're facing. One needs to really build a culture of people who work together at from different disciplines, each bringing their own insights and their own ideas into the mix. So and in Citro, we actually have a company. That's half life scientists, many of whom are producing data for the purpose of driving machine learning models and the other Halford machine learning people in data scientists who are working on those. But it's not a handoff where one group produces that they then the other one consumes and interpreted. But really, they start from the very beginning to understand. What are the problems that one could solve together? How do you design the experiment? How do you build the model and how do you derive insights from that that can help us make better medicines for people? >>And, um, I also wanted to ask you the you co founded coursera, so tell us a little bit more about that platform. So I found that >>coursera as a result of work that I've been doing at Stanford, working on how technology can make education better and more accessible. This was a project that I did here, number of my colleagues as well. And at some point in the fall of 2011 there was an experiment of Let's take some of the content that we've been we've been developing within within Stanford and put it out there for people to just benefit from, and we didn't know what would happen. Would it be a few 1000 people, but within a matter of weeks with minimal advertising Other than one New York Times article that went viral, we had 100,000 people in each of those courses. And that was a moment in time where, you know, we looked at it at this and said, Can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world? And so I ended up taking a what was supposed to be to really absence from Stanford to go and co found coursera, and I thought I'd go back after two years, but the But at the end of that two year period, the there was just so much more to be done and so much more impact that we could bring to people all over the world, people of both genders, people of different social economic status, every single country around the world. We just felt like this was something that I couldn't not dio. >>And how did you Why did you decide to go from an educational platform to then going into machine learning and biomedicine? >>So I've been doing Corsair for about five years in 2016 and the company was on a great trajectory. But it's primarily >>a >>a content company, and around me, machine learning was transforming the world, and I wanted to come back and be part of that. And when I looked around, I saw machine learning being applied to e commerce and the natural language and to self driving cars. But there really wasn't a lot of impact being made on the life science area. I wanted to be part of making that happen, partly because I felt like coming back to your earlier comment that in order to really have that impact, you need to have someone who speaks both languages. And while there's a new generation of researchers who are bilingual in biology and machine learning, there's still a small group in there, very few of those in kind of my age cohort and I thought that I would be able to have a real impact by bullying company in the space. >>So it sounds like your background is pretty varied. What advice would you give to women who are just starting college now who may be interested in the similar field? Would you tell them they have to major in math? Or or do you think that maybe, like there's some other majors that may be influential as well? I think >>there is a lot of ways to get into data science. Math is one of them. But there's also statistics or physics. And I would say that especially for the field that I'm currently in, which is at the intersection of machine learning data science on the one hand, and biology and health on the other one can, um, get there from biology or medicine as well. But what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in, because that foundation is a really strong one. There is ah lot of people out there who are basically lightweight consumers of data science, and they don't really understand how the methods that they're deploying, how they work and that limits thumb in their ability to advance the field and come up with new methods that are better suited, perhaps, of the problems of their tackling. So I think it's totally fine. And in fact, there's a lot of value to coming into data science from fields other than now third computer science. But I think taking courses in those fields, even while you're majoring in whatever field you're interested in, is going to make you a much better person who lives at that intersection. >>And how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies >>that are very strongly R and D, focused like like in Citro and others? Having a technical co founder is absolutely essential because it's fine to have and understanding of whatever the user needs and so on and come from the business side of it. And a lot of companies have a business co founder. But not understanding what the technology can actually do is highly limiting because you end up hallucinating. Oh, if we could only do this and that would be great. But you can't and people end up often times making ridiculous promises about what's technology will or will not do because they just don't understand where the land mines sit. And, um, and where you're going to hit reels, obstacles in the path. So I think it's really important to have a strong technical foundation in these companies. >>And that being said, Where do you see in Teacher in the future? And how do you see it solving, Say, Nash, that you talked about in your keynote. >>So we hope that in Citro will be a fully integrated drug discovery and development company that is based on a completely different foundation than a traditional pharma company where they grew up. In the old approach of that is very much a bespoke scientific um, analysis of the biology of different diseases and then going after targets are ways of dealing with the disease that are driven by human intuition. Where I think we have the opportunity to go today is to build a very data driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that accord with people's preconceptions of what matters and what doesn't. And so hopefully we'll be able to overtime create enough data and applying machine learning to address key bottlenecks in the drug discovery development process that we can bring better drugs to people, and we can do it faster and hopefully it much lower cost. >>That's great. And you also mention in your keynote that you think the 20 twenties is like a digital biology era, so tell us more about that. So I think if >>you look, if you take a historical perspective on science and think back, you realize that there's periods in history where one discipline has made a tremendous amount of progress in relatively short amount of time because of a new technology or a new way of looking at things in the 18 seventies, that discipline was chemistry with the understanding of the periodic table, and that you actually couldn't turn lead into gold in the 19 hundreds. That was physics with understanding the connection between matter and energy in between space and time. In the 19 fifties that was computing where silicon chips were suddenly able to perform calculations that up until that point, only people have been able to >>dio. And then in 19 nineties, >>there was an interesting bifurcation. One was three era of data, which is related to computing but also involves elements, statistics and optimization of neuroscience. And the other one was quantitative biology. In which file do you move from a descriptive signs of taxonomy izing phenomenon to really probing and measuring biology in a very detailed on high throughput way, using techniques like micro arrays that measure the activity of 20,000 genes at once, or the human genome sequencing of the human genome and many others. But >>these two fields kind of >>evolved in parallel, and what I think is coming now, 30 years later, is the convergence of those two fields into one field that I like to think of a digital biology where we are able using the tools that have and continue to be developed, measure biology, an entirely new levels of detail, of fidelity of scale. We can use the techniques of machine learning and data signs to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do. And that will have implications and bio materials in energy and the environment in agriculture. And I think also in human health. And it's a incredibly exciting space toe to be in right now, because just so much is happening in the opportunities to make a difference and make the world a better place or just so large. >>That sounds awesome. Stephanie. Thank you for your insight. And thanks for being on the Cube. Thank you. I'm Sonia. Taqueria. Thanks for watching. Stay tuned for more. Okay? Great. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in Data Science 2020. Brought to you by Silicon Angle Media >>and welcome to the Cube. I'm your host Sonia category, and we're live at Stanford University, covering the fifth annual Woods Women in Data Science conference. Joining us today is Tilapia Williams, who's the associate professor of mathematics at Harvey Mudd College and host of Nova Wonders at PBS to leave a welcome to the Cappy to be here. Thanks for having me. So you have a lot of rules. So let's first tell us about being an associate professor at Harvey Mudd. >>Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun in the math department, but I'm a statistician by training, so I teach a lot of courses and statistics and data science and things like that. >>Very cool. And you're also a host of API s show called Novo Wonders. >>Yeah, that came about a couple of years ago. Folks at PBS reached out they had seen my Ted talk, and they said, Hey, it looks like you could be fund host of this science documentary shows So, Nova Wonders, is a six episode Siri's. It kind of takes viewers on a journey of what the cutting edge questions and science are. Um, so I got to host the show with a couple other co host and really think about like, you know, what are what are the animals saying? And so we've got some really fun episodes to do. What's the universe made of? Was one of them what's living inside of us. That was definitely a gross win. Todo figure out all the different micro organisms that live inside our body. So, yeah, it's been funded in hopes that show as well. >>And you talk about data science and AI and all that stuff on >>Yeah. Oh, yeah, yeah, one of the episodes. Can we build a Brain was dealt with a lot of data, big data and artificial intelligence, and you know, how good can we get? How good can computers get and really sort of compared to what we see in the movies? We're a long way away from that, but it seems like you know we're getting better every year, building technology that is truly intelligent, >>and you gave a talk today about mining for your own personal data. So give us some highlights from your talk. Yeah, >>so that talks sort of stemmed out of the Ted talk that I gave on owning your body's data. And it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions on DSO ways that you can use, like your temperature data or your heart rate. Dina. Or what is data say over time? What does it say about your body's health and really challenging the audience to get excited about looking at that data? We have so many devices that collect data automatically for us, and often we don't pause on enough to actually look at that historical data. And so that was what the talk was about today, like, here's what you can find when you actually sit down and look at that data. >>What's the most important data you think people should be collecting about themselves? >>Well, definitely not. Your weight is. I don't >>want to know what that >>is. Um, it depends, you know, I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body's fertile. When you're ovulating, it can. So that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar. You have high blood pressure or high blood sugar. Those things become really critical to keep an eye on. And, um, and I really encourage people whatever data they take, too, the active in the understanding of an interpretation of the data. It's not like if you take this data, you'll be healthy radio. You live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. So >>absolutely put putting people in charge of their >>own bodies. That's >>right. >>And actually speaking about that in your Ted talk, you mentioned how you were. Your doctor told you to have a C section and you looked at the data and he said, No, I'm gonna have this baby naturally. So tell us more about that. >>Yes, you should always listen to your medical pressures. But in this case, I will say that it was It was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that, like, I have a PhD in statistics and I know data, he was really kind of objectively with the on call doctor at the time, looking at the data >>and talking about it. >>And this doctor was this is his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person would have only looked at this one chart and was it was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say, like, let me tell you more about you know, my body and this is pregnancy number three like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, Here's my advice. We just need to do this. I'm like, >>Oh, >>you know, and so is gently as possible. I tried to really share that data. Um, and then it got to the point where it was sort of like either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, Well, to sign the waiver that cost quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so, yeah, >>I think >>that at the time, >>But, >>you know, with that caveat that you should listen to what, your doctors >>Yeah. I mean, there's really interesting, like, what's the boundary between, Like what the numbers tell you and what professional >>tells me Because I don't have an MD. Right. And so, you know, I'm cautious not to overstep that, but I felt like in that case, the doctor wasn't really even considering the data that I was bringing. Um, I was we were actually induced with our first son, but again, that was more of a conversation, more of a dialogue. Here's what's happening here is what we're concerned about and the data to really back it up. And so I felt like in that case, like Yeah, I'm happy to go with your suggestion, but I could number three. It was just like, No, this isn't really >>great. Um, so you also wrote a book called Power In Numbers. The Rebel Women of Mathematics. So what inspired you to write this book? And what do you hope readers take away from it? >>A couple different things. I remember when I saw the movie hidden figures. And, um, I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fun connection toe, you know, having worked at NASA. And, um, when this movie came out and I'm sitting there watching it and I'm, like ball in just crying, like I didn't know that there were black women who worked at NASA like, before me, you know, um and so it felt it felt it was just so transformative for me to see these stories just sort of unfold. And I thought, like, Well, why didn't I learn about these women growing up? Like imagine, Had I known about Katherine Johnsons of the world? Maybe that would have really inspired Not just me, but, you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say, like, there have been women who've done mathematics, um, and statistics and data science for years, and they're women who are doing it now. So a lot of the about 1/3 of the book are women who were still here and, like, active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some things like, Oh my gosh, >>women in math >>It is really like a very picturesque book of showing this beautiful images of the women and their mathematics and their work. And yes, I'm really proud of it. >>That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African American women, especially who are part of this industry. So what advice would you give to those women who who feel like they don't belong. >>Yeah, well, a they really do belong. Um, and I think it's also incumbent of people in the industry to sort of recognize ways that they could be advocate for women, and especially for women of color, because often it takes someone who's already at the table to invite other people to the table. And I can't just walk up like move over, get out the way I'm here now. But really being thoughtful about who's not representative, how do we get those voices here? And so I think the onus is often mawr on. People who occupy those spaces are ready to think about how they can be more intentional in bringing diversity in other spaces >>and going back to your talk a little bit. Um uh, how how should people use their data? >>Yeah, so I mean, I think, um, the ways that we've used our data, um, have been to change our lifestyle practices. And so, for example, when I first got a Fitbit, um, it wasn't really that I was like, Oh, I have a goal. It was just like I want something to keep track of my steps And then I look at him and I feel like, Oh, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, Okay, let me see if I hit 10 stuff, you know, 10,000 >>steps in a day or >>and so, in some ways, having the data allows you to set goals. Some people come in knowing, like, I've got this goal. I want to hit it. But for me, it was just sort of like, um and so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a gold up, restore it, and it also gives me that historical information to see like, Oh, this is how far I've come. Like I can't stop there, you know, >>that's a great social impact. >>That's right. Yeah, absolutely. >>and, um, Do you think that so in terms of, like, a security and privacy point of view, like if you're recording all your personal data on these devices, how do you navigate that? >>Yeah, that's a tough one. I mean, because you are giving up that data privacy. Um, I usually make sure that the data that I'm allowing access to this sort of data that I wouldn't care if it got published on the cover of you know, the New York Times. Maybe I wouldn't want everyone to see what my weight is, but, um, and so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data as opposed to, say, my DNA. Like I'm not going to do a DNA test. You know, I don't want my data to be mapped it out there for the world. Um, but I think that that's increasingly become a concern because people are giving access to of their information to different companies. It's not clear how companies would use that information, so if they're using my data to build a product will make a product better. You know we don't see any world from that way. We don't have the benefit of it, but they have access to our data. And so I think in terms of data, privacy and data ethics, there's a huge conversation to have around that. We're only kind >>of at the beginning of understanding what that is. Yeah, >>well, thank you so much for being on the Cube. Really having you here. Thank you. Thanks. So I'm Sonia to Gary. Thanks so much for watching the cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah, and welcome to the Cube. I'm your host Sonia Category and we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is new Sha Ajami, who's the director of urban water policy for Stanford. You should welcome to the Cube. Thank you for having me. Absolutely. So tell us a little bit about your role. So >>I directed around water policy program at Stanford. We focused on building solutions for resilient cities to try to use data science and also the mathematical models to better understand how water use is changing and how we can build a future cities and infrastructure to address the needs of the people in the US, in California and across the world. >>That's great. And you're gonna give a talk today about how to build water security using big data. So give us a preview of your talk. >>Sure. So the 20th century water infrastructure model was very much of a >>top down model, >>so we built solutions or infrastructure to bring water to people, but people were not part of the loop. They were not the way that they behaved their decision making process. What they used, how they use it wasn't necessarily part of the process and the assume. There's enough water out there to bring water to people, and they can do whatever they want with it. So what we're trying to do is you want to change this paradigm and try to make it more bottom up at to engage people's decision making process and the uncertainty associated with that as part of the infrastructure planning process. Until I'll be talking, I'll talk a little bit about that. >>And where is the most water usage coming from? So, >>interestingly enough, in developed world, especially in the in the western United States, 50% of our water is used outdoors for grass and outdoor spacing, which we don't necessarily are dependent on. Our lives depend on it. I'll talk about the statistics and my talk, but grass is the biggest club you're going in the US while you're not really needing it for food consumption and also uses four times more water >>than than >>corn, which is which is a lot of water. And in California alone, if you just think about some of the spaces that we have grass or green spaces, we have our doors in the in. The in the malls are institutional buildings or different outdoor spaces. We have some of that water. If we can save, it can provide water for about a 1,000,000 or two million people a year. So that's a lot of water that we can be able to we can save and use, or you are actually a repurpose for needs that you really half. >>So does that also boil down to like people of watering their own lawns? Or is the problem for a much bigger grass message? >>Actually, interestingly enough, that's only 10% of that water out the water use. The rest of it is actually the residential water use, which is what you and I, the grass you and I have in our backyard and watering it so that water is even more than that amount that I mentioned. So we use a lot of water outdoors and again. Some of these green spaces are important for community building for making sure everybody has access to green spaces and people. Kids can play soccer or play outdoors, but really our individual lawns and outdoor spaces. If there are not really a native you know landscaping, it's not something that views enough to justify the amount of water you use for that purpose. >>So taking longer showers and all the stuff is very minimal compared to no, not >>at all. Sure, those are also very, very important. That's another 50% of our water. They're using that urban areas. It is important to be mindful the baby wash dishes. Maybe take shower the baby brush rt. They're not wasting water while you're doing that. And a lot of other individual decisions that we make that can impact water use on a daily basis. >>Right, So So tell us a little bit more about right now in California, We just had a dry February was the 1st 150 years, and you know, this is a huge issue for cities, agriculture and for potential wildfires. So tell us about your opinion about that. So, >>um, the 20th century's infrastructure model I mentioned at the beginning One of the flaws in that system is that it assumes that we will have enough snow in the mountains that would melt during the spring and summer time and would provide us water. The problem is, climate change has really, really impacted that assumption, and now you're not getting as much snow, which is comes back to the fact that this February we have not received any snow. We're still in the winter and we have spring weather and we don't really have much snow on the mountain. Which means that's going to impact the amount of water we have for summer and spring time this year. We had a great last year. We got enough water in our reservoirs, which means that you can potentially make it through. But then you have consecutive years that are dry and they don't receive a lot of water precipitation in form of snow or rain. That will become a very problematic issue to meet future water demands in California. >>And do you think this issue is along with not having enough rainfall, but also about how we store water, or do you think there should be a change in that policy? >>Sure, I think that it definitely has something also in the way we store water and be definitely you're in the 21st century. We have different problems and challenges. It's good to think about alternative ways off a storing water, including using groundwater sources. Groundwater as a way off, storing excess water or moving water around faster and making sure we use every drop of water that falls on the ground and also protecting our water supplies from contamination or pollution. >>And you see it's ever going to desalination or to get clean water. So, interestingly >>enough, I think desalination definitely has worth in other parts of the world, and then they have. Then you have smaller population or you have already tapped out of all the other options that are available to you. Desalination is expensive. Solution costs a lot of money to build this infrastructure and also again depends on you know, this centralized approach that we will build something and provide resources to people from from that location. So it's very costly to build this kind of solutions. I think for for California we still have plenty of water that we can save and repurpose, I would say, and also we still can do recycling and reuse. We can capture our stone water and reuse it, so there's so many other, cheaper, more accessible options available before you go ahead and build a desalination plants >>and you're gonna be talking about sustainable water resource management. So tell us a little bit more about that, too. So the thing with >>water mismanagement and occasionally I use also the word like building resilient water. Future is all about diversifying our water supply and being mindful of how they use our water, every drop of water that use its degraded on. It needs to be cleaned up and put back in the environment, so it always starts from the bottom. The more you save, the less impact you have on the environment. The second thing is you want to make sure every trouble wanted have used. We can use it as many times possible and not make it not not. Take it, use it, lose its right away, but actually be able to use it multiple times for different purposes. Another point that's very important, as actually majority of the water they've used on a daily basis is it doesn't need to be extremely clean drinking water quality. For example, if you tell someone that you're flushing down our toilets. Drinkable water would surprise you that we would spend this much time and resources and money and energy to clean that water to flush it down the toilet video using it. So So basically rethinking the way we built this infrastructure model is very important, being able to tailor water to the needs that we have and also being mindful of Have you use that resource? >>So is your research focus mainly on California or the local community? We actually >>are solutions that we built on our California focus. Actually, we try to build solutions that can be easily applied to different places. Having said that, because you're working from the bottom up, wavy approach water from the bottom up, you need to have a local collaboration and local perspective to bring to their to this picture on. A lot of our collaborators have been so far in California, we have had data from them. We were able to sort of demonstrate some of the assumptions we had in California. But we work actually all over the world. We have collaborators in Europe in Asia and they're all trying to do the same thing that we dio on. You're trying to sort of collaborate with them on some of the projects in other parts of the world. >>That's awesome. So going forward, what do you hope to see with sustainable water management? So, to >>be honest with you, I would often we think about technology as a way that would solve all our problems and move us out of the challenges we have. I would say technology is great, but we need to really rethink the way we manager resource is on the institutions that we have on there. We manage our data and information that we have. And I really hope that became revolutionized that part of the water sector and disrupt that part because as we disrupt this institutional part >>on the >>system, provide more system level thinking to the water sector, I'm hoping that that would change the way we manage our water and then actually opens up space for some of these technologies to come into play as >>we go forward. That's awesome. So before we leave here, you're originally from Tehran. Um and and now you're in this data science industry. What would you say to a kid who's abroad, who wants to maybe move here and have a career in data science? >>I would say Study hard, Don't let anything to disk or do you know we're all equal? Our brains are all made the same way. Doesn't matter what's on the surface. So, um so I and encourage all the girls study hard and not get discouraged and fail as many times as you can, because failing is an opportunity to become more resilient and learn how to grow. And, um and I have, and I really hope to see more girls and women in this in these engineering and stem fields, to be more active on, become more prominent. >>Have you seen a large growth within the past few years? Definitely, >>the conversation is definitely there, and there are a lot more women, and I love how Margot and her team are sort of trying to highlight the number of people who are out there. And working on these issues because that demonstrates that the field wasn't necessarily empty was just not not highlighted as much. So for sure, it's very encouraging to see how much growth you have seen over the years for sure >>you shed. Thank you so much. It's really inspiring all the work you do. Thank you for having me. So no, Absolutely nice to meet you. I'm Senator Gary. Thanks for watching the Cube and stay tuned for more. Yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. Yeah. >>Hi. And welcome to the Cube. I'm your host Sonia Category. And we're live at Stanford University for the fifth annual Woods Women in Data Science Conference. Joining us today is none. Ho, the director of data Science at Intuit None. Welcome to the Cube. >>Thank you for having me here, so yeah, >>so tell us a little bit about your role at Intuit. So I leave the >>applied Machine Learning teams for our QuickBooks product lines and also for our customer success organization within my team. We do applied machine learning. So what? We specialize in building machine learning products and delivering them into our products for >>our users. Great. Today. Today you're giving a talk. You talked about how organizations want to achieve greater flexibility, speed and cost efficiencies on. And you're giving it a technical vision. Talk today about data science in the cloud world. So what should data scientists know about data science in a cloud world? >>Well, I'll just give you a little bit of a preview into my talk later because I don't want to spoil anything. Yeah, but I think one of the most important things being a data scientist in a cloud world is that you have to fundamentally change the way you work a lot of a start on our laptops or a server and do our work. But when you move to the cloud, it's like all bets are off. All the limiters are off. And so how do you fully take advantage of that? How do you change your workflow? What are some of the things that are available to you that you may not know about? And in addition to that, some some things that you have to rewire in your brain to operate in this new environment. And I'm going to share some experiences that I learned firsthand and also from my team in into its cloud migration over the past six years. >>That's great. Excited to hear that on DSO you were getting into it into it has sponsored Woods for many years now. Last year we spoke with could be the San Juan from Intuit. So tell us about this Intuit's sponsorship. Yeah, >>so into it. We are a champion of gender diversity and also all sorts of diversity. And when we first learned about which we said, We need to be a champion of the women in data science conference because for me personally, often times when I'm in a room, um, going over technical details I'm often the only woman and not just I'm often the only woman executive and so part of the sponsorship is to create this community of women, very technical women in this field, to share our work together to build this community and also to show the great diversity of work that's going on across the field of data science. >>And so Intuit has always been really great for embracing diversity. Tell us a little bit about about bad experience, about being part of Intuit and also about the tech women part. Yeah, >>so one of the things that into it that I really appreciate is we have employees groups around specific interests, and one of those employees groups is tech women at Intuit and Tech women at Intuit. The goal is to create a community of women who can provide coaching, mentorship, technical development, leadership development and I think one of the unique things about it is that it's not just focused on the technical development side, but on helping women develop into leadership positions. For me, When I first started out, there were very few women in executive positions in our field and data science is a brand new field, and so it takes time to get there. Now that I'm on the other side, one of the things that I want to do is be able to give back and coach the next generation. And so the tech women at Intuit Group allows me to do that through a very strong mentorship program that matches me and early career mentees across multiple different fields so that I can provide that coaching in that leadership development >>and speaking about like diversity. In the opening address, we heard that diversity creates perspectives, and it also takes away bias. So why gender diversity is so important into it, and how does it help take away that bias? Yeah, >>so one of the important things that I think a lot of people don't realize is when you go and you build your products, you bring in a lot of biases and how you build the product and ultimately the people who use your products are the general population for us. We serve consumer, small businesses and self employed. And if you take a look at the diversity of our customers, it mirrors the general population. And so when you think about building products, you need to bring in those diverse perspectives so you could build the best products possible because of people who are using those products come from a diverse background as well, >>right? And so now at Intuit like instead of going from a desktop based application, we're at a cloud based application, which is a big part of your talk. How do you use data Teoh for a B testing and why is it important? >>Yeah, a B testing That is a personal passion of mine, actually, because as a scientist, what we like to do is run a lot of experiments and say, Okay, what is the best thing out there so that ultimately, when you ship a new product or feature, you send the best thing possible that's verified by data, and you know exactly how users are going to react to it. When we were on desktop, they made it incredibly difficult because those were back in the days. And I don't know if you remember those put back in the days when you had a floppy disk, right or even a CD ROM's. That's how we shipped our products. And so all the changes that you wanted to make had to be contained. In the end, you really only ship it once per year. So if there's any type of testing that we did, we're bringing our users and have them use our products a little bit and then say Okay, we know exactly what we need to dio ship that out. So you only get one chance now that we're in the cloud. What that allows us to do is to test continuously via a B, testing every new feature that comes out. We have a champion Challenger model, and we can say Okay, the new version that we're shipping out is this much better than the previous one. We know it performs in this way, and then we got to make the decision. Is this the best thing to do for a customer? And so you turn what was once a one time process, a one time change management process. So one that's distributed throughout the entire year and at any one time we're running hundreds of tests to make sure that we're shipping exactly the best things for our customers. >>That's awesome. Um, so, um, what advice would you give to the next generation of women who are interested in stem but maybe feel like, Oh, I might be the only woman. I don't know if I should do this. Yeah, I think that the biggest >>thing for me was finding men's ownership, and initially, when I was very early career and even when I was doing my graduate studies for me, a mentor with someone who was in my field. But when I first joined into it, an executive in another group who is a female, said, Hey, I'd like to take your side, provide you some feedback, and this is some coaching I want to give you, And that was when I realized you don't actually need to have that person be in your field to actually guide you through to the next up. And so, for women who are going through their journey and early on, I recommend finding a mentor who is at a stage where you want to go, regardless of which field there in, because everybody has diverse perspectives and things that they can teach you as you go along. >>And how do you think Woods is helping women feel like they can do data science and be a part of the community? Yeah, I think >>what you'll see in the program today is a huge diversity of our speakers, our Panelists through all different stages of their career and all different fields. And so what we get to see is not only the time baseline of women who are in their PhDs all the way to very, very well established women. The provost of Stanford University was here today, which is amazing to see someone at the very top of the career who's been around the block. But the other thing is also the diversity and fields. When you think about data science, a lot of us think about just the tech industry. But you see it in healthcare. You see it in academia and there's a scene that wide diversity of where data science and where women who are practicing data science come from. I think it's really empowering because you can see yourself in the representation does matter quite a bit. >>Absolutely. And where do you see data science going forward? >>Oh, that is a, uh, tough and interesting question, actually. And I think that in the current environment today, we could talk about where it could go wrong or where it could actually open the doors. And for me, I'm an eternal optimist on one of the things that I think is really, really exciting for the future is we're getting to a stage where we're building models, not just for the general population. We have enough data and we have enough compute where we can build a model. Taylor just for you, for all of your life's on for me. I think that that is really, really powerful because we can build exactly the right solution to help our customers and our users succeed. Specifically, me working in the personal friend, Small business finance lease. That means I can hope that cupcake shop owner actually manage her cash flow and help her succeed to me that I think that's really powerful. And that's where data science is headed. >>None. Thank you so much for being on the Cube and thank you for your insight. Thank you so much. I'm so sorry. Thanks for watching the Cube. Stay tuned for more. Yeah, Yeah, yeah, yeah, yeah, yeah.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah. Hi. And welcome to the Cube. I'm your host, Sonia Atari. And we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is Lillian Kearse. Keo, who's the Insights manager at Spotify. Slowly and welcome to the Cube. Thank you so much for having me. So tell us a little bit about your role at a Spotify. >>Yeah, So I'm actually one of the few insights managers in the personalization team. Um, and within my little group, we think about data and algorithms that help power the larger personalization experiences throughout Spotify. So, from your limits to discover weekly to your year and wrap stories to your experience on home and the search results, that's >>awesome. Can you tell us a little bit more about the personalization? Um, team? >>Yes. We actually have a variety of different product areas that come together to form the personalization mission, which is the mission is like the term that we use for a big department at Spotify, and we collaborate across different product areas to understand what are the foundational data sets and the foundational machine learning tools that are needed to be able to create features that a user can actually experience in the app? >>Great. Um, and so you're going to be on the career panel today? How do you feel about that? I'm >>really excited. Yeah, Yeah, the would seem is in a great job of bringing together Diverse is very, uh, it's overused term. Sometimes they're a very diverse group of people with lots of different types of experiences, which I think is core. So how I think about data science, it's a wide definition. And so I think it's great to show younger and mid career women all of the different career paths that we can all take. >>And what advice would you would you give to? Women were coming out of college right now about data science. >>Yeah, so my my big advice is to follow your interests. So there's so many different types of data science problems. You don't have to just go into a title that says data scientists or a team that says Data scientist, You can follow your interest into your data science. Use your data science skills in ways that might require a lot of collaboration or mixed methods, or work within a team where there are different types of different different types of expertise coming together to work on problems. >>And speaking of mixed methods, insights is a team that's a mixed methods research groups. So tell us more about that. Yes, I >>personally manage a data scientist, Um, user researcher and the three of us collaborate highly together across their disciplines. We also collaborate across research science, the research science team right into the product and engineering teams that are actually delivering the different products that users get to see. So it's highly collaborative, and the idea is to understand the problem. Space deeply together, be able to understand. What is it that we're trying to even just form in our head is like the need that a user work and human and user human has, um, in bringing in research from research scientists and the product side to be able to understand those needs and then actually have insights that another human, you know, a product owner you can really think through and understand the current space and like the product opportunities >>and to understand that user insight do use a B testing. >>We use a lot of >>a B testing, so that's core to how we think about our users at Spotify. So we use a lot of a B testing. We do a lot of offline experiments to understand the potential consequences or impact that certain interventions can have. But I think a B testing, you know, there's so much to learn about best practices there and where you're talking about a team that does foundational data and foundational features. You also have to think about unintended or second order effects of algorithmic a B test. So it's been just like a huge area of learning in a huge area of just very interesting outcomes. And like every test that we run, we learn a lot about not just the individual thing. We're testing with just the process overall. >>And, um, what are some features of Spotify that customers really love anything? Anything >>that's like we know use a daily mix people absolutely love every time that I make a new friend and I saw them what they work on there like I was just listening to my daily makes this morning discover weekly for people who really want >>to stay, >>you know, open to new music is also very popular. But I think the one that really takes it is any of the end of year wrapped campaigns that we have just the nostalgia that people have, even just for the last year. But in 2019 we were actually able to do 10 years, and that amount of nostalgia just went through the roof like people were just like, Oh my goodness, you captured the time that I broke up with that, you >>know, the 1st 5 years ago, or just like when I discovered that I love Taylor Swift, even though I didn't think I like their or something like that, you know? >>Are there any surprises or interesting stories that you have about, um, interesting user experiences? Yeah. >>I mean, I could give I >>can give you an example from my experience. So recently, A few a few months ago, I was scrolling through my home feed, and I noticed that one of the highly rated things for me was women in >>country, and I was like, Oh, that's kind of weird. I don't consider >>myself a country fan, right? And I was like having this moment where I went through this path of Wait, That's weird. Why would Why would this recommend? Why would the home screen recommend women in country, country music to me? And then when I click through it, um, it would show you a little bit of information about it because it had, you know, Dolly Parton. It had Margo Price and it had the high women and those were all artistes. And I've been listening to a lot, but I just had not formed an identity as a country music. And then I click through It was like, Oh, this is a great play list and I listen to it and it got me to the point where I was realizing I really actually do like country music when the stories were centered around women, that it was really fun to discover other artists that I wouldn't have otherwise jumped into as well. Based on the fact that I love the story writing and the song, writing these other country acts that >>so quickly discovered that so you have a degree in industrial mathematics, went to a liberal arts college on purpose because you want to try out different classes. So how is that diversity of education really helped >>you in your Yes, in my undergrad is from Smith College, which is a liberal arts school, very strong liberal arts foundation. And when I went to visit, one of the math professors that I met told me that he, you know, he considers studying math, not just to make you better at math, but that it makes you a better thinker. And you can take in much more information and sort of question assumptions and try to build a foundation for what? The problem that you're trying to think through is. And I just found that extremely interesting. And I also, you know, I haven't undeclared major in Latin American studies, and I studied like neuroscience and quantum physics for non experts and film class and all of these other things that I don't know if I would have had the same opportunity at a more technical school, and I just found it really challenging and satisfying to be able to push myself to think in different ways. I even took a poetry writing class I did not write good poetry, but the experience really stuck with me because it was about pushing myself outside of my own boundaries. >>And would you recommend having this kind of like diverse education to young women now who are looking >>and I absolutely love it? I mean, I think, you know, there's some people believe that instead of thinking about steam, we should be talking instead of thinking about stem. Rather, we should be talking about steam, which adds the arts education in there, and liberal arts is one of them. And I think that now, in these conversations that we have about biases in data and ML and AI and understanding, fairness and accountability, accountability bitterly, it's a hardware. Apparently, I think that a strong, uh, cross disciplinary collaborative and even on an individual level, cross disciplinary education is really the only way that we're gonna be able to make those connections to understand what kind of second order effects for having based on the decisions of parameters for a model. In a local sense, we're optimizing and doing a great job. But what are the global consequences of those decisions? And I think that that kind of interdisciplinary approach to education as an individual and collaboration as a team is really the only way. >>And speaking about bias. Earlier, we heard that diversity is great because it brings out new perspectives, and it also helps to reduce that unfair bias. So how it Spotify have you managed? Or has Spotify managed to create a more diverse team? >>Yeah, so I mean, it starts with recruiting. It starts with what kind of messaging we put out there, and there's a great team that thinks about that exclusively. And they're really pushing all of us as managers. As I seizes leaders to really think about the decisions in the way that we talk about things and all of these micro decisions that we make and how that creates an inclusive environments, it's not just about diversity. It's also about making people feel like this is where they should be. On a personal level, you know, I talk a lot with younger folks and people who are trying to just figure out what their place is in technology, whether it be because they come from a different culture, >>there are, >>you know, they might be gender, non binary. They might be women who feel like there is in a place for them. It's really about, You know, the things that I think about is because you're different. Your voice is needed even more. You know, like your voice matters and we need to figure out. And I always ask, How can I highlight your voice more? You know, how can I help? I have a tiny, tiny bit of power and influence. You know, more than some other folks. How can I help other people acquire that as well? >>Lilian, thank you so much for your insight. Thank you for being on the Cube. Thank you. I'm your host, Sonia today. Ari. Thank you for watching and stay tuned for more. Yeah, yeah.

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the Cube. I'm Lisa Martin Live at the Stanford Ari Aga Alone, My Center for the Fourth Annual Women and Data Science Conference with twenty nineteen and were joined by a very special guest, Liza Donnelly, cartoonist for The New Yorker. But Liza, you are a visual journalists, visual journalism. You're here live, drawing a lot of the things that are going on. It would. You were just at the Oscars at the Grammys. Your work is so unique, so descriptive. Tell us a little bit our audience about what is visual journalism? >> Well, I suppose a lot of us define it different ways. But I did find it is somebody who I am, somebody who goes to events, either political or social, cultural and draw what I see. I'm not a court reporter. I'm I'm an Impressionist. I give people a feeling that they're they're with me from what? By what I draw what I see, how I draw it, and and it's I don't usually put any editorializing in those visual drawings, but my perspective is sort of a certain kind of approach. >> So you're bringing your viewers along this journey in almost real time. When people see people might be most failure with New Yorker your illustrations there. But folks that are watching the Woods event lie that engaging with that tell us a little bit about the importance of using the illustrations to bring them on this journey as if they were here. >> Well, you know, I send the drawings out immediately, do them on my iPad and I send them out on social media almost immediately, so as I do that so that people can see them immediately. So they feel like they're there, and it's a way to draw attention to whatever it is I'm drawing. Because on the Internet, there's so many words in so many photographs, people see a drawing by other stream that like, Wait, what's that? And I'm a thumb stopper, in other words, so it's. It gives people different perspective on what's going on. And I think that my background is a cartoonist for The New Yorker for forty years. Informs these drawings in an indirect background kind of way, because I have been watching culture have been watching politics for a very long time, so it gives me a, you know, a new attitude or a way to look at what's going on, >> right? And so you you call these illustrations, not cartoons. >> I do call the cartoons cartoons. Okay, we'll do the cartoons for the for >> The New Yorker and some other magazines, and those have a caption, and they often are supposed to be funny, or at least cultural commentary. I do political cartoons for medium, and those also have it have a point of view, are a caption. But the's this visual journalism like I'm doing here is more like reportage. It's more like this is what's happening here. You might be interested in seeing what people are talking about, what they're doing and I do behind the scenes to I don't just do like the Oscars. I'll do the stars if I could get them. And on the red crime on the red carpet, it's really cool. If I catch them, I'll draw them. And then But then I also do the people taking out the trash, the guy painting, you know, painting the sideboard or the counterman, things like that. So I try to give a sense of what it's like to be there. >> So you really kind of telling a story from different perspectives. Yes, right. Yeah. And so the role of I'd love to understand you mentioned being with the New Yorker for very long time and loved. You understand from your perspective, the evolution of cartoons and the impact they can make in in our society, in politics and economics. Tell us a little bit about some of the impacts that you've seen evolve over the last few decades. >> Well, I've written about >> that. I'm also a writer. I've written about that for a very sites. Did a commentary on op ed for The New York Times about the Charlie Hebdo's murders a couple years ago because we know cartoons can be very controversial. Yes and problematic Nick. And that's been true through the course of the history of our country, and I'm sure in England and other countries as well. But it's compounded. Now because of the Internet. I think cartoons could be misunderstood that could be used as weapons. People are gonna be talking about this next week at the South by Southwest. I'm talking about political cartoons and what what their impact has been in the past and how, >> how they, how they create an impact now >> and why that is, and how we could use it to the to our to good effect. You know, not a divisive tool, which I think is a problem that we're dealing with right now in our culture is everybody's so divided and so opinionated and so hateful towards each other. Can we use cartoons? Not to perpetuate that, but to make things better in some way. >> And that's kind of the theme of Wits, Women and Data Science Conference. You know, we're talking Teo and listening Teo at the live event here at Stanford and all of those around the world. It's really strong leaders and data sign. So we think of data science on DH, the technical skills. But data is generated. We generate tons of it as people, right with whatever we're buying, what we're watching on Netflix. But we're listening to on Spotify, etcetera. There's this data trail that we're all leaving, and we know you talked about using cartoons for good. Same conversations that we have on the data side, about being able to use data for good for cancer research, for example, rather than exposing and being malicious, that's interesting. Parallel that you've seen over the years that there is a lot of potential here. Tell me a little bit about the appetite in. Maybe we'll say the millennials and the younger generations for cartoons as a tool for positive the spread of positive social news and not fake news. >> Well, there. I know that >> there's more and more cartoons on the Internet now. A lot of Web comics and cartoonists are young. Cartoonists are using the Internet effectively, too. Put out their ideas. In fact, I when the Internet hit, I was mid career right, and it just took off and helped me become Mohr more well known just by leveraging the Internet. No, because I love it. You know, I love Communicate. It's >> actually it's really an extension >> of what I did as a child learning to draw, communicate with people. I was shy. I don't want to talk. The Internet is just a matter of for me. It's like a dialogue with people on DH. That's how I look at it, and I I think this new generation is really trying to find ways to use these tools in a good way. I think there's a whole new, you know, the kids in their >> twenties. I think they're trying >> to make a better world, are working on it, and that's exciting. >> You talk about communication and how you used your artistic skills from the time you were a child to communicate. Being shy. We also talk about communication in the context of events like the women, the data science, where it isn't just enough to be ableto understand and have the technical acumen to evaluate complex, messy data sets. But the communication piece kind of go back, Teo sort of basic human scaled, being able to communicate effectively. This is what I think the data say and why, and here's what we can do with it. So I think it's interesting that you're here at this event. That has a lot of parallels with communication with using a tool or information for the betterment off a little bit about how you got involved with women in data science. >> Well, I met Margot Garretson >> about five years ago, and through a mutual friend, we met in Iceland. All places >> like it's conference >> about women's rights. It was, it was the Icelandic women are so powerful anyway. We met there, really, to be good friends, and she invited me to come live, draw her new conference at the time. I think she had one year of it, and I thought, data science, OK, >> did you even know what >> that Wass? Yeah, kind of. But I didn't think I didn't see my connection. But I thought, Well, it's about women's rights and >> I'm a big part of my interest in what I want to do with my work is promote equal rights for women around the world. And so I thought, this this sounds terrific. Plus, it's global, and I do a lot of work globally to help them and help freedom of speech as well. So it seemed to be a great fit on DH and and it seems even more to be a good fit in that. It's a way to get the information out there in a visual way because people will hear that word data, and they like they probably just >> start. Yeah, zero because >> they see it connected with a cartoon or drawing it humanizes it for them a little bit. And if I could do that, that's great. And that's what's also fun is that I thought about this today was drawing the speakers, and I'm drawing one of the speakers. I forget her name right now, but I thought and I put it out on the Internet. There were no words on there, but it was just a woman speaker talking about really very technical data science. I put on the Internet with the caption on the tweet and I thought, People, it's it's it's just a constant reminder to people that women are doing this. And it's not a silly not like writing a long essay about why women should be in data signs and why they are and why they're important. But they're doing great things. But if you see it, it resonates a little bit more quickly and more forcefully. >> Absolutely. And it aligns with what we hear and say a lot of we can't be what we can't see. >> That's right. Yeah, that's a saying right where you said that. >> Yes. I'm not sure I'd love to take credit for it. Sure >> would be if she can see it, she could be it. That's another >> thing. That a young girl, she's my drawing of a professor talking on stage. Maybe she'll think about it. >> Absolutely. So in the last few seconds here, can you just give us a little bit of an idea of how you actually What What inspires you when you're seeing someone give a talk like you mentioned about maybe an esoteric or a very technical top? What do you normally look for? That's that Ah ha moment that you want to capture in ten minutes. >> Well, I try to capture that person's essence. I'm not a caricaturist. I don't pretend to be, but I draw >> a likeness of them, and they're the full body is the best body language. You know, they're just tick yah late ing. And then oftentimes I try to capture a sentence that they're saying that has has more universal appeal that somehow brings like a not like a layman into the subject A little bit. If I can find that sentence in what they're saying, I'll put that you have the speech balloon will be saying that. But I just try to capture the person best. I can >> do anything if you compare two wins. Twenty eighteen. Here we are a year later. Even more people here, the live event, even more people engaging and think Margo's that about twenty thousand live today. One hundred thousand over. I think the one hundred thirty plus regional with events, anything that you hear, see or feel that's even more exciting this year than last year. >> Um, well, I do. I do feel the >> the increase in numbers. I can feel it. There's there soon be more people here I don't true, but the senior more young people here, what else is it is it is a buzz. I think there's a >> There's an energy >> is an energy. Not that there wasn't there last. The last I've >> done three years now. It's been there, but there's a certain excitement right now. I think more women are stepping into this field of being recognized for doing so. >> And it's great that you're able Tio, reach, help wigs, reach an even bigger audience and tell this story with your illustrations in a more visual way, way also. Thank you so much, Liza, for taking some time. Must daughter by the Cuban talked to us. It's an honor to meet you And you. I love your drawings. >> Thank you so much. You >> want to thank you for watching the Cube? I'm Lisa Martin Live at the fourth annual Women and Data Science Conference at Stanford's took around. Be right back with my next guests.

>> Live from Stanford University. It's the Cube covering global Women in Data Science conference brought to you by Silicon Angle media. >> Welcome back to the key. We air live at Stanford University for the fourth annual Women in Data Science Conference. The Cube has had the pleasure of being here all four years on I'm welcoming Back to the Cube, one of our distinguished alumni Janet George, the fellow chief data officer, scientists, big data and cognitive computing at Western Digital. Janet, it's great to see you. Thank you. Thank you so much. So I mentioned yes. Fourth, Annie will women in data science. And it's been, I think I met you here a couple of years ago, and we look at the impact. It had a chance to speak with Margo Garrett's in a about an hour ago, one of the co founders of Woods saying, We're expecting twenty thousand people to be engaging today with the Livestream. There are wigs events in one hundred and fifty locations this year, fifty plus countries expecting about one hundred thousand people to engage the attention. The focus that they have on data science and the opportunities that it has is really palpable. Tell us a little bit about Western Digital's continued sponsorship and what makes this important to you? >> So Western distal has recently transformed itself as a company, and we are a data driven company, so we are very much data infrastructure company, and I think that this momentum off A is phenomenal. It's just it's a foundational shift in the way we do business, and this foundational shift is just gaining tremendous momentum. Businesses are realizing that they're going to be in two categories the have and have not. And in order to be in the half category, you have started to embrace a You've got to start to embrace data. You've got to start to embrace scale and you've got to be in the transformation process. You have to transform yourself to put yourself in a competitive position. And that's why Vest Initial is here, where the leaders in storage worldwide and we'd like to be at the heart of their data is. >> So how has Western Digital transform? Because if we look at the evolution of a I and I know you're give you're on a panel tan, you're also giving a breakout on deep learning. But some of the importance it's not just the technical expertise. There's other really important skills. Communication, collaboration, empathy. How has Western digital transformed to really, I guess, maybe transform the human capital to be able to really become broad enough to be ableto tow harness. Aye, aye, for good. >> So we're not just a company that focuses on business for a We're doing a number of initiatives One of the initiatives were doing is a I for good, and we're doing data for good. This is related to working with the U. N. We've been focusing on trying to figure out how climate change the data that impacts climate change, collecting data and providing infrastructure to store massive amounts of species data in the environment that we've never actually collected before. So climate change is a huge area for us. Education is a huge area for us. Diversity is a huge area for us. We're using all of these areas as launching pad for data for good and trying to use data to better mankind and use a eye to better mankind. >> One of the things that is going on at this year's with second annual data fun. And when you talk about data for good, I think this year's Predictive Analytics Challenge was to look at satellite imagery to train the model to evaluate which images air likely tohave oil palm plantations. And we know that there's a tremendous social impact that palm oil and oil palm plantations in that can can impact, such as I think in Borneo and eighty percent reduction in the Oregon ten population. So it's interesting that they're also taking this opportunity to look at data for good. And how can they look at predictive Analytics to understand how to reduce deforestation like you talked about climate and the impact in the potential that a I and data for good have is astronomical? >> That's right. We could not build predictive models. We didn't have the data to put predictive boats predictive models. Now we have the data to put put out massively predictive models that can help us understand what change would look like twenty five years from now and then take corrective action. So we know carbon emissions are causing very significant damage to our environment. And there's something we can do about it. Data is helping us do that. We have the infrastructure, economies of scale. We can build massive platforms that can store this data, and then we can. Alan, it's the state at scale. We have enough technology now to adapt to our ecosystem, to look at disappearing grillers, you know, to look at disappearing insects, to look at just equal system that be living, how, how the ecosystem is going to survive and be better in the next ten years. There's a >> tremendous amount of power that data for good has, when often times whether the Cube is that technology conferences or events like this. The word trust issues yes, a lot in some pretty significant ways. And we often hear that data is not just the life blood of an organization, whether it's in just industry or academia. To have that trust is essential without it. That's right. No, go. >> That's right. So the data we have to be able to be discriminated. That's where the trust comes into factor, right? Because you can create a very good eh? I'm odder, or you can create a bad air more so a lot depends on who is creating the modern. The authorship of the model the creator of the modern is pretty significant to what the model actually does. Now we're getting a lot of this new area ofthe eyes coming in, which is the adversarial neural networks. And these areas are really just springing up because it can be creators to stop and block bad that's being done in the world next. So, for example, if you have malicious attacks on your website or hear militias, data collection on that data is being used against you. These adversarial networks and had built the trust in the data and in the so that is a whole new effort that has started in the latest world, which is >> critical because you mentioned everybody. I think, regardless of what generation you're in that's on. The planet today is aware of cybersecurity issues, whether it's H vac systems with DDOS attacks or it's ah baby boomer, who was part of the fifty million Facebook users whose data was used without their knowledge. It's becoming, I won't say accepted, but very much commonplace, Yes, so training the A I to be used for good is one thing. But I'm curious in terms of the potential that individuals have. What are your thoughts on some of these practices or concepts that we're hearing about data scientists taking something like a Hippocratic oath to start owning accountability for the data that they're working with. I'm just curious. What's >> more, I have a strong opinion on this because I think that data scientists are hugely responsible for what they are creating. We need a diversity of data scientists to have multiple models that are completely divorce, and we have to be very responsible when we start to create. Creators are by default, have to be responsible for their creation. Now where we get into tricky areas off, then you are the human auto or the creator ofthe Anay I model. And now the marshal has self created because it a self learned who owns the patent, who owns the copyright to those when I becomes the creator and whether it's malicious or non malicious right. And that's also ownership for the data scientist. So the group of people that are responsible for creating the environment, creating the morals the question comes into how do we protect the authors, the uses, the producers and the new creators off the original piece of art? Because at the end of the day, when you think about algorithms and I, it's just art its creation and you can use the creation for good or bad. And as the creation recreates itself like a learning on its own with massive amounts of data after an original data scientist has created the model well, how we how to be a confident. So that's a very interesting area that we haven't even touched upon because now the laws have to change. Policies have to change, but we can't stop innovation. Innovation has to go, and at the same time we have to be responsible about what we innovate >> and where do you think we are? Is a society in terms of catching As you mentioned, we can't. We have to continue innovation. Where are we A society and society and starting to understand the different principles of practices that have to be implemented in order for proper management of data, too. Enable innovation to continue at the pace that it needs. >> June. I would say that UK and other countries that kind of better than us, US is still catching up. But we're having great conversations. This is very important, right? We're debating the issues. We're coming together as a community. We're having so many discussions with experts. I'm sitting in so many panels contributing as an Aye aye expert in what we're creating. What? We see its scale when we deploy an aye aye, modern in production. What have we seen as the longevity of that? A marker in a business setting in a non business setting. How does the I perform and were now able to see sustained performance of the model? So let's say you deploy and am are in production. You're able inform yourself watching the sustained performance of that a model and how it is behaving, how it is learning how it's growing, what is its track record. And this knowledge is to come back and be part of discussions and part of being informed so we can change the regulations and be prepared for where this is going. Otherwise will be surprised. And I think that we have started a lot of discussions. The community's air coming together. The experts are coming together. So this is very good news. >> Theologian is's there? The moment of Edward is building. These conversations are happening. >> Yes, and policy makers are actively participating. This is very good for us because we don't want innovators to innovate without the participation of policymakers. We want the policymakers hand in hand with the innovators to lead the charter. So we have the checks and balances in place, and we feel safe because safety is so important. We need psychological safety for anything we do even to have a conversation. We need psychological safety. So imagine having a >> I >> systems run our lives without having that psychological safety. That's bad news for all of us, right? And so we really need to focus on the trust. And we need to focus on our ability to trust the data or a right to help us trust the data or surface the issues that are causing the trust. >> Janet, what a pleasure to have you back on the Cube. I wish we had more time to keep talking, but it's I can't wait till we talk to you next year because what you guys are doing and also your pact, true passion for data science for trust and a I for good is palpable. So thank you so much for carving out some time to stop by the program. Thank you. It's my pleasure. We want to thank you for watching the Cuba and Lisa Martin live at Stanford for the fourth annual Women in Data Science conference. We back after a short break.