>>Okay, welcome back everyone. This is the Cube's coverage of S reinve rein Mars. I said reinvent all my VES months away. Re Mars machine learning, automation, robotics, and space. I'm John feer, host of the cube, an exciting guest here, bringing on special guest more robot robots are welcome on the cube. We're gonna have that segment here. Mike Dooley co-founder and CEO of Labrador systems. Mike, welcome to the cube. Thanks. >>Coming on. Thank, thank you so much. Yeah. Labrador systems. We're a company is developing a new type of assistive robot for people in the home. And you know, our mission is really to help people live independently. And so we're about to show a robot that's it looks like my, what used to be in a warehouse or other places, but it's being designed to be both robust enough to operate in real world settings, help people that may be aging and using a Walker wheelchair. A cane could have early onset health conditions like Parkinson's and things like that. So >>Let me, let me set this up first, before you get into the, the demo, because I think here at re Mars, one of the things that's coming outta the show besides the cool vibe, right? Is that materials handling? Isn't the only thing you've seen with robotics. Yeah. You're seeing a lot more life industrial impact. And this is an example of one of that, isn't >>It? Yeah. We just actually got an award. It's a Joseph EGL Bergo was the first person to actually put robots in factories and automation. And in doing that, um, he set up grant for robots going beyond that, to help people live in it. So we're the first recipient of that. But yeah, I think that robots, they're not the, what you think about with Rosie yet. We're the wrong way from that, but they're, they can do really meaningful things. >>And before we get the demo, your mission hearing, what you're gonna show here is a lot of hard work and we know how hard it is. What's the mission. What's the vision. >>The mission is to help people live more independently on their own terms. Uh, we're, there's, it's an innate part of the human condition that at some point in our lives, it becomes more difficult to move ourselves or move things around it. And that is a huge impact on our independence. So when we're putting this robot in pilots, we're helping people try to regain degrees of independence, be more active deal with whatever situation they want, but under their terms and have, have control over their life. >>Okay, well, let's get into it. May I offer you a glass of water? Well, you >>Know, I have a robot that just happens to be really good at delivering things, including water. Um, we just actually pulled these out of our refrigerator on our last demo. So why don't we bring over the retriever? And so we're gonna command it to come on in. So this is a Labrador retriever. These robots have been in homes. This robot itself has been in homes, helping people do activities like this. It's able to sort of go from place to place it automatically navigates itself. Uh, just like we've been called a self-driving shelf, um, as an example, but it's meant to be very friendly, can come to a position like this could be by my armchair and it would automatically park. And then I could do something like I can pick up, okay, I want some water and maybe I want to drink it out of a cup and I can do this. And if I have a cough or something else, cough drops. My phone, all sorts of things can be in there. Um, so the purpose of the retriever is really to be this extra pair of hands, to keep things close by and move things. And it can automatically adjust to any hide or position. And if I, even if I block it like a safety, it, it >>Stops. And someone who say disabled or can't move is recovering or has some as aging or whatever the case is. This comes to them. It's autonomous in it sense. Is that that what works or yeah. Is it guided? How does >>It, it works on a series of bus stops. So the in robotics, we call those way points. But when we're talking to people, the bus stops are the places you want it to go. You have a bus stop by the front door, your kitchen sink, the refrigerator, your armchair, the laundry machine, you won't closet it. <laugh>. And with that simple metaphor, we, we train the robot in a couple hours. We create all these routes, just like a subway map. And then the robot is autonomous. So I can hit a button. I can hit my cell phone, or I can say Alexa ass lab, one to come to the kitchen. The robot will autonomously navigate through everything, go around the pets park itself. And it raises and lowers to bring things with and reach. So I'm sitting and it might lower itself down. So I can just comfortably get something at the kitchen. I, it could just go right to the level of the countertop. So it's very easy for someone that has an issue to move things with with limited, uh, challenges. >>And this really illustrates to this show again. Yeah. Talk about the impact here. Cause we're at a historic moment in robotics. >>We are. Yeah. >>What's your reaction to that? Tell your, share your vision >>On that. I've been in robotics for 25 years. Um, and I started, I actually started working actually at Lego and launching Lego Mindstorms, the end of the nineties. So I have like CEO just last night again, they gush over like you did that. Yeah. <laugh> and again, I'm pretty old school. And so we've my career. If I've been working through from toys onto like robotic floor cleaners, the algorithms that are on Roomba today came from the startup that we were all part of. We're, we're moving things to be bigger and bigger and have a bigger >>Impact. What's it feel like? I mean, cuz I mean I can see the experience and by the way, it's hardcore robotics communities out there, but now it's still mainstream. It's opening up the aperture of robotics. Yeah. It's the prime time is right now and it's an inflection point. >>Well, and it's also a point where we desperately need it. So we have incredible work for shortages <laugh> and it's not that we're, these robots are not to take people jobs away it's to do the work that people don't want to do and try to make, you know, free them up for things that are more important. Yeah. In senior care, that's the high touch we want caregivers to be helping people get outta their bed, help them safely move from place to place things that robots aren't at yet. Yeah. But for getting the garbage, for getting a drink or giving the person the freedom to say, do I wanna ask my caregiver or my spouse to do that? Or do I wanna do it myself? And so robots can be incredibly liberating experience if they're, if they're done in the right way and they're done well, >>It's a choice. It actually comes down to choice. I remember this argument way back when, oh, ATM's gonna kill the bank teller. In fact more bank tellers emerged. Right. And so there's choices come out there and, but there's still more advances to do. What is you, what do you see as milestones for the industry as you start to seeing better handling better voice activation cameras on board. I noticed some cameras in there. Yeah. So we're starting to see the, some of the smaller, faster, cheaper >>It's it's especially yeah. Faster. Cheaper is what we're after. So can we redo? So like the gyros that are on this type of robot used to be like in the tens of thousands of dollars 20 or 30 years ago. And, and then when you started seeing Roomba and the floor cleaners come out, those started what happened was basically the gyro on here that what's happening in consumer electronics, the ability for the iPhone to play, you know, the game in turn and, and do portrait and landscape. That actually is what enables all these robots that clean your floors to do very tight angles. What we're doing is this migration of consumer electronics then gets robbed and, and adopted over in that. So it's really about it's I, it's not that you're gonna see things radically change. It's just that you're gonna see more and more applications get more sophisticated and become more affordable. Our target is to bring this for a few hundred dollars a month into people's homes. Yeah. Yeah. Um, and make that economy work for as many people as >>Possible. Yeah. Mike, what a great, great illustration of great point there now on your history looking forward. Okay. Smaller Fest are cheaper. Yeah. You're gonna see a human aspect. So technology's kind of getting out of the way now you got a lot in the cloud, you got machine learning, big thing here. There's a human creative side now gonna be a big part of this. Yeah. Can you talk about like how you see that unfolding? Because again, younger people gonna come in, you got a lot more things pre-built I just saw a swaping on stage saying, oh, we, we write subroutines automatically the machine learning like, oh my God, that's so cool. Like, so more is coming for, to, for builders, right. To build what's the playbook gonna look like? How do you see the human aspect, creative crafting building? >>I, I it's, you know, it's a hard Fu future to predict. I think the issue is that humans are always gonna have to be more clever than the AI <laugh>, you know, I, I can't say that enough is that AI can solve some things and it can get smarter and smarter. You task that over and then let's work on the things that can't do. And I think that's intellectually challenging. Like, and I, and I think we have a long way to go, uh, to sort of keep on pushing that forward. So the whole mission is people get to do more interesting things with their life, more dynamic. Think about what the machines should be working on. Yeah. And then move on to the next things. >>Well, a lot of good healthcare implications. Yeah. Uh, senior living people who are themselves, >>All those are place. Yeah. >>Now that you have, um, this kind of almost a perfect storm of innovation coming, and I just think it's gonna be the beginning. You're gonna see a lot of young people come in. Yeah. And a lot of people in school now going down to the elementary school level yeah. Are really immersed in robotics. They're born with it. And certainly as they get older, what kind of disciplines do you see coming into robot? I used to be pretty clear. Yeah. Right. Nerdy, builder, builder. Now it's like what? I got Mac and rice. My code. >>Yeah. My, my co-founder and CEO has a good example. Anybody we interview, we say we really like it. If you think of yourself as an astronaut, going on to a space mission. And, and it's really appropriate being here at R Mars is that normally the astronaut has one specialty, but they have to know enough of the other skills to be able to help out. In case of an emergency robotics is so complex. There's so there's mechanical, there's electrical, there's software, they're perceptual, there's user interface, all of those Fs together. So when we're trying to do a demo and something goes wrong, I can't say why. I only do mechanical. Yeah. You got it. You really have to have a system. So I think if any system architects, people that if you're gonna, if, if you're gonna be, if mechanical is your thing, you better learn a little bit electrical and software. Yeah. If software is your thing, you better not just write code because you need to understand where you're >>Your back. Well, the old days you have to know for trend to run any instrumentation in the old days. So same kind of vibe. So what does that impact on the teamwork side? Because now I can imagine, okay, you got some general purpose knowledge, so math, science, all the disciplines, but the specialties there, I love that right now. Teamwork. Yeah. Because you, you know, I could be a generalism at some point. There's another component I'm gonna need to call my teammate for. >>Yeah. Yeah. And you have to have, yeah. So it, yeah, we're a small team, so it's a little bit easier right now, but even the technology. So like there's a, what, this is, this runs on Linux and that runs on Ross, which is a robotics operating system. The modules are, are the, are sorry, the modules, I mean redundant there, but the, the part that makes the robot go, okay, I'm gonna command it to go here. It's gonna go around it, see an obstacle. This module kicks in, even the elements become module. So that's part of how teams work is that, and, and Amazon has a rule around that is that everything has to have an API. Yeah. I have to be able to express my work and the way that somebody else can come in and talk to it in a very easy way. So you're also going away from like, sort of like the hidden code that only I touch you can't have ownership of that. You have to let your team understand how it works and let them control it and edit it. Well, >>Super exciting. Dan, first of all, great to bring robots on the cube set. Thanks to your team here. Doing that. Yeah. Um, talk about the company. Um, put a plug in, what are you guys doing? Sure. Raising money, getting more staff, more sales. We're give, give a commercial. >>Yeah. So we, we closed the seed round. So we've been around it's actually five years next month. Um, did pre-seed and then we closed the seed round that we announced back at CS. So we debuted the retriever for the first time we had it under wraps. We had it in people's homes for a year before we did that. Um, I, Amazon was one of our early investors and they actually co-led on this last round, along with our friends at iRobot. So yeah. Uh, so we've raised that we're right in the next phase of deploying this, especially going more into senior living now that that's opening up with COVID coming down and looking at helping these workforce issues where there's that crisis. So we'll be raising later this year. So we're starting to sort of do the preview for series a. We're starting to take those pre-orders for robots and for Lois. And then our goal is we're and we're actually already at the factory. So we've been converting this, these there's a version of this robot underway right now at the factory that will probably have engineering units at the end of this year. Yeah. Goal is for, uh, full production with all the supply chain issues for second half of, of next >>Year. Yeah. Well, congratulations. It's a great product. And I gotta ask you what's on the roadmap, how you see this product unfolding. What's the wishlist look like if you had all the dough in the world, what would you do next? What would you be putting on there? Sure. If you had the magic wand what's happening, >>It's a couple variables. I think it's scale. So it's driving the, this whole thing is designed to go down in cost, which improves basically accessibility. More people can afford it. The health system, Medicare, those sorts of folks. See it one. So basically get us into reduction and get us into volume is one part, I think the other ones is adding layers. I, what we, when we see our presentation and the speech we're doing tomorrow, we see this as a force multiplier for a lot of other things in healthcare. So if I bring the blood pressure cuff, like we have on the retrieval, I can be a physical reminder to take your medication, to take the, my, my readings, or we are just con having a conversation with some of our friends of Amazon is bringing an echo show to you when you want to have a conversation and take it away. >>When you don't think about that metaphor of how do I wanna live my life and what do I have control over? And then on top of it, the sensors on the robot, they're pretty sophisticated. So in my case, my mom is still around she's 91, but now in a hospital beded wheelchair could, we've seen her walking differently early, early on, and using things like Intel, real sense and, and computer vision and AI to detect things and just say to her, don't even tell anybody else, we're noticing this. Do you wanna share this with your doctor? Yeah. That's the world. I think that what we're trying to do is lay this out as version 1.0, so that when folks like us are around, it'll something like decades from now, life is so much more better for the options and choices we have. It's >>Really interesting. You know, I liked, um, kind of the theme here. There's a lot of day to day problems that people like to solve. And then there's like the new industrial problems that are emerging that are opportunities. And then there's the save the world kind of vibe. <laugh>, there's help people make things positive, right. You know, solve the climate problems, help people. And so we're kind of at this new era and it's beyond just like sustainability and, you know, bias. That's all gotta get done a new tipping point around the human aspect of >>Things. And you do it economically. I think sometimes you think that, okay, well, you're just doing this cuz you're, you're socially motivated and doesn't, you don't care how many you sell it to just so you can accomplish it. It's their link. The, the cheaper that we can make this, the more people you can impact. I think you're talking about the kids today is the work we did at Lego. In the end of the nineties, you made a, a robotics kit for 200 bucks and millions of kids. Yeah. Did that. And >>Grape pie. I mean, you had accessories to it. Make a developer friendly. >>Yeah, no, exactly. And we're getting all those requests. So I think that's the thing is like, get a new platform, learn what it's like to have this sort of capability and then let the market drive. It, let the people sort of the folks who are gonna be using it that are in a wheelchair, are dealing with Parkinson's or Ms, or other issues. What can we add to that ecosystem? So you it's, it's all about being very human centric in that. Yeah. And making the other parts of the economy make it work for them, make it so that the health system, they get an ROI on this so that, Hey, this is a good thing to put into people's homes. >>And well, I think you have the nice, attractive value proposition to investors. Obviously robotics is super cool and really relevant. Cool, cool. And relevant to me always is nice to have that. So check that, then you got the economics on price, pressure, prove the price down lower. Yeah. Open up the Tams of the market. Right. Make it more viable economically. >>Yeah, definitely. And then, and what we're having, what's driving us that wasn't around seven when we started this about four and a half years ago. Uh, my joke and I don't mean to offend them, but after doing pitching the vision of this in six months, don't be, >>Don't be afraid. We're do we, >>My, my joke. And I'm sort to see more bold about is that VCs don't think they're gonna get old. They're just gonna get rich. And so the idea is that they didn't see themselves in this position and we not Gloo and doom, you can work out, you can be active, but we're living older, longer. We are it's. My mom is born in the depression. She's been in a wheelchair for five years. She might be around for a good, another 10 or 15. And that's wonderful for her, but her need for care is really high. >>Yeah. And the pressure on the family too, there's always, there's always collateral damage on all these impacts. >>There's 53 million unpaid family caregivers in the us. Yeah. Just in the time that we've grown, been doing this, it's grown 4% a year and it's a complicated thing. And it's, it's not just the pressure on you to help your mom or dad or whoever. It's the frustration on their face when they have to always ask for that help. So it's, it's twofold. It's give them some freedom back so they can make a choice. Like my classic example is my mom wants tea. My dad's trying to watch the game. He, she asks for it. It's not hot enough. Sends it back. And that's a currency. Yeah, yeah. That she's losing and, and it's frustration as opposed to give her a choice to say, I'm gonna do this on my own. And I that's just, >>You wanna bring the computer out, do a FaceTime with the family, send it back. Or you mentioned the Alexa there's so many use cases. Oh >>No. We talked about, uh, we talked about putting like a, a device with a CA with a screen on it so she could chat and see pictures. And it says, I don't want to have this in my bedroom. That's my private space. Yeah. But if we could have the robot, bring it in when it's appropriate and take it on go the retriever that's that's >>The whole go fetch what I need right now. That's and then go lie down. Yeah. >>That's what I, I called >>Labrador. Doesn't lie down >>Actually. But well, it lowers down, it lowers down about 25 inches. That's about lying. >>Down's super exciting. And congratulations. I know, um, how passionate you are. It's obvious. Yeah. And being in the business so long, so many accomplishment you had. Yeah. But now is a whole new Dawn. A new era here. >>Yeah. Oh yeah. No, I, we just, it was real. It was on impromptu. It wasn't scheduled. There's a, a post circle on LinkedIn where all the robots got together. <laugh> you know, and they were seeing to hang out. No, and you're seeing stuff that wasn't possible. You look at this and you go, well, what's the big thing. It's a box on wheels. It's like, it wasn't possible to navigate something around the complexity of a home 10 years ago for the price we're doing. Yeah. It wasn't possible to wa have things that walk or spot that can go through construction sites. I, I think people don't realize it's it. It really is changing. And then we're, I think every five years you're gonna be seeing this more bold deployment of these things hitting our lives. It's >>It's super cool. And that's why this show's so popular. It's not obvious to mainstream, but you look at the confluence of all those forces coming together. Yeah. It's just a wonderful thing. Thanks for coming on. Appreciate >>It really, really appreciate you for this >>Time. Great success. Great demo. Mike, do cofounder, the CEO of Labrador systems. Check him out. They have the retriever, uh, future of robotics here. It's all impact all life on the planet. And more space. Two is to keep coverage here at re Mars, stay tuned for more live coverage. After this short break.

>>Everyone wants a cloud operating model. Since the introduction of the modern cloud. Last decade, the entire technology landscape has changed. We've learned a lot from the hyperscalers, especially from AWS. Now, one thing is certain in the technology business. It's so competitive. Then if a faster, better, cheaper idea comes along, the industry will move quickly to adopt it. They'll add their unique value and then they'll bring solutions to the market. And that's precisely what's happening throughout the technology industry because of cloud. And one of the best examples is Amazon's nitro. That's AWS has custom built hypervisor that delivers on the promise of more efficiently using resources and expanding things like processor, optionality for customers. It's a secret weapon for Amazon. As, as we, as we wrote last year, every infrastructure company needs something like nitro to compete. Why do we say this? Well, Wiki Bon our research arm estimates that nearly 30% of CPU cores in the data center are wasted. >>They're doing work that they weren't designed to do well, specifically offloading networking, storage, and security tasks. So if you can eliminate that waste, you can recapture dollars that drop right to the bottom line. That's why every company needs a nitro like solution. As a result of these developments, customers are rethinking networks and how they utilize precious compute resources. They can't, or won't put everything into the public cloud for many reasons. That's one of the tailwinds for tier two cloud service providers and why they're growing so fast. They give options to customers that don't want to keep investing in building out their own data centers, and they don't want to migrate all their workloads to the public cloud. So these providers and on-prem customers, they want to be more like hyperscalers, right? They want to be more agile and they do that. They're distributing, networking and security functions and pushing them closer to the applications. >>Now, at the same time, they're unifying their view of the network. So it can be less fragmented, manage more efficiently with more automation and better visibility. How are they doing this? Well, that's what we're going to talk about today. Welcome to changing the game for cloud networking made possible by pluribus networks. My name is Dave Vellante and today on this special cube presentation, John furrier, and I are going to explore these issues in detail. We'll dig into new solutions being created by pluribus and Nvidia to specifically address offloading, wasted resources, accelerating performance, isolating data, and making networks more secure all while unifying the network experience. We're going to start on the west coast and our Palo Alto studios, where John will talk to Mike of pluribus and AMI, but Donnie of Nvidia, then we'll bring on Alessandra Bobby airy of pluribus and Pete Lummus from Nvidia to take a deeper dive into the technology. And then we're gonna bring it back here to our east coast studio and get the independent analyst perspective from Bob Liberte of the enterprise strategy group. We hope you enjoy the program. Okay, let's do this over to John >>Okay. Let's kick things off. We're here at my cafe. One of the TMO and pluribus networks and NAMI by Dani VP of networking, marketing, and developer ecosystem at Nvidia. Great to have you welcome folks. >>Thank you. Thanks. >>So let's get into the, the problem situation with cloud unified network. What problems are out there? What challenges do cloud operators have Mike let's get into it. >>Yeah, it really, you know, the challenges we're looking at are for non hyperscalers that's enterprises, governments, um, tier two service providers, cloud service providers, and the first mandate for them is to become as agile as a hyperscaler. So they need to be able to deploy services and security policies. And second, they need to be able to abstract the complexity of the network and define things in software while it's accelerated in hardware. Um, really ultimately they need a single operating model everywhere. And then the second thing is they need to distribute networking and security services out to the edge of the host. Um, we're seeing a growth in cyber attacks. Um, it's, it's not slowing down. It's only getting worse and, you know, solving for this security problem across clouds is absolutely critical. And the way to do it is to move security out to the host. >>Okay. With that goal in mind, what's the pluribus vision. How does this tie together? >>Yeah. So, um, basically what we see is, uh, that this demands a new architecture and that new architecture has four tenants. The first tenant is unified and simplified cloud networks. If you look at cloud networks today, there's, there's sort of like discreet bespoke cloud networks, you know, per hypervisor, per private cloud edge cloud public cloud. Each of the public clouds have different networks that needs to be unified. You know, if we want these folks to be able to be agile, they need to be able to issue a single command or instantiate a security policy across all those locations with one command and not have to go to each one. The second is like I mentioned, distributed security, um, distributed security without compromise, extended out to the host is absolutely critical. So micro-segmentation and distributed firewalls, but it doesn't stop there. They also need pervasive visibility. >>You know, it's, it's, it's sort of like with security, you really can't see you can't protect what you can't see. So you need visibility everywhere. The problem is visibility to date has been very expensive. Folks have had to basically build a separate overlay network of taps, packet brokers, tap aggregation infrastructure that really needs to be built into this unified network I'm talking about. And the last thing is automation. All of this needs to be SDN enabled. So this is related to my comment about abstraction abstract, the complexity of all of these discreet networks, physic whatever's down there in the physical layer. Yeah. I don't want to see it. I want to abstract it. I wanted to find things in software, but I do want to leverage the power of hardware to accelerate that. So that's the fourth tenant is SDN automation. >>Mike, we've been talking on the cube a lot about this architectural shift and customers are looking at this. This is a big part of everyone who's looking at cloud operations next gen, how do we get there? How do customers get this vision realized? >>That's a great question. And I appreciate the tee up. I mean, we're, we're here today for that reason. We're introducing two things today. Um, the first is a unified cloud networking vision, and that is a vision of where pluribus is headed with our partners like Nvidia longterm. Um, and that is about, uh, deploying a common operating model, SDN enabled SDN, automated hardware, accelerated across all clouds. Um, and whether that's underlying overlay switch or server, um, hype, any hypervisor infrastructure containers, any workload doesn't matter. So that's ultimately where we want to get. And that's what we talked about earlier. Um, the first step in that vision is what we call the unified cloud fabric. And this is the next generation of our adaptive cloud fabric. Um, and what's nice about this is we're not starting from scratch. We have a, a, an award-winning adaptive cloud fabric product that is deployed globally. Um, and in particular, uh, we're very proud of the fact that it's deployed in over a hundred tier one mobile operators as the network fabric for their 4g and 5g virtualized cores. We know how to build carrier grade, uh, networking infrastructure, what we're doing now, um, to realize this next generation unified cloud fabric is we're extending from the switch to this Nvidia Bluefield to DPU. We know there's a, >>Hold that up real quick. That's a good, that's a good prop. That's the blue field and video. >>It's the Nvidia Bluefield two DPU data processing unit. And, um, uh, you know, what we're doing, uh, fundamentally is extending our SDN automated fabric, the unified cloud fabric out to the host, but it does take processing power. So we knew that we didn't want to do, we didn't want to implement that running on the CPU, which is what some other companies do because it consumes revenue generating CPU's from the application. So a DPU is a perfect way to implement this. And we knew that Nvidia was the leader with this blue field too. And so that is the first that's, that's the first step in the getting into realizing this vision. >>I mean, Nvidia has always been powering some great workloads of GPU. Now you've got DPU networking and then video is here. What is the relationship with clothes? How did that come together? Tell us the story. >>Yeah. So, you know, we've been working with pluribus for quite some time. I think the last several months was really when it came to fruition and, uh, what pluribus is trying to build and what Nvidia has. So we have, you know, this concept of a Bluefield data processing unit, which if you think about it, conceptually does really three things, offload, accelerate an isolate. So offload your workloads from your CPU to your data processing unit infrastructure workloads that is, uh, accelerate. So there's a bunch of acceleration engines. So you can run infrastructure workloads much faster than you would otherwise, and then isolation. So you have this nice security isolation between the data processing unit and your other CPU environment. And so you can run completely isolated workloads directly on the data processing unit. So we introduced this, you know, a couple of years ago, and with pluribus, you know, we've been talking to the pluribus team for quite some months now. >>And I think really the combination of what pluribus is trying to build and what they've developed around this unified cloud fabric, uh, is fits really nicely with the DPU and running that on the DPU and extending it really from your physical switch, all the way to your host environment, specifically on the data processing unit. So if you think about what's happening as you add data processing units to your environment. So every server we believe over time is going to have data processing units. So now you'll have to manage that complexity from the physical network layer to the host layer. And so what pluribus is really trying to do is extending the network fabric from the host, from the switch to the host, and really have that single pane of glass for network operators to be able to configure provision, manage all of the complexity of the network environment. >>So that's really how the partnership truly started. And so it started really with extending the network fabric, and now we're also working with them on security. So, you know, if you sort of take that concept of isolation and security isolation, what pluribus has within their fabric is the concept of micro-segmentation. And so now you can take that extended to the data processing unit and really have, um, isolated micro-segmentation workloads, whether it's bare metal cloud native environments, whether it's virtualized environments, whether it's public cloud, private cloud hybrid cloud. So it really is a magical partnership between the two companies with their unified cloud fabric running on, on the DPU. >>You know, what I love about this conversation is it reminds me of when you have these changing markets, the product gets pulled out of the market and, and you guys step up and create these new solutions. And I think this is a great example. So I have to ask you, how do you guys differentiate what sets this apart for customers with what's in it for the customer? >>Yeah. So I mentioned, you know, three things in terms of the value of what the Bluefield brings, right? There's offloading, accelerating, isolating, that's sort of the key core tenants of Bluefield. Um, so that, you know, if you sort of think about what, um, what Bluefields, what we've done, you know, in terms of the differentiation, we're really a robust platform for innovation. So we introduced Bluefield to, uh, last year, we're introducing Bluefield three, which is our next generation of Bluefields, you know, we'll have five X, the arm compute capacity. It will have 400 gig line rate acceleration, four X better crypto acceleration. So it will be remarkably better than the previous generation. And we'll continue to innovate and add, uh, chips to our portfolio every, every 18 months to two years. Um, so that's sort of one of the key areas of differentiation. The other is the, if you look at Nvidia and, and you know, what we're sort of known for is really known for our AI artificial intelligence and our artificial intelligence software, as well as our GPU. >>So you look at artificial intelligence and the combination of artificial intelligence plus data processing. This really creates the, you know, faster, more efficient, secure AI systems from the core of your data center, all the way out to the edge. And so with Nvidia, we really have these converged accelerators where we've combined the GPU, which does all your AI processing with your data processing with the DPU. So we have this convergence really nice convergence of that area. And I would say the third area is really around our developer environment. So, you know, one of the key, one of our key motivations at Nvidia is really to have our partner ecosystem, embrace our technology and build solutions around our technology. So if you look at what we've done with the DPU, with credit and an SDK, which is an open SDK called Doka, and it's an open SDK for our partners to really build and develop solutions using Bluefield and using all these accelerated libraries that we expose through Doka. And so part of our differentiation is really building this open ecosystem for our partners to take advantage and build solutions around our technology. >>You know, what's exciting is when I hear you talk, it's like you realize that there's no one general purpose network anymore. Everyone has their own super environment Supercloud or these new capabilities. They can really craft their own, I'd say, custom environment at scale with easy tools. Right. And it's all kind of, again, this is the new architecture Mike, you were talking about, how does customers run this effectively? Cost-effectively and how do people migrate? >>Yeah, I, I think that is the key question, right? So we've got this beautiful architecture. You, you know, Amazon nitro is a, is a good example of, of a smart NIC architecture that has been successfully deployed, but enterprises and serve tier two service providers and tier one service providers and governments are not Amazon, right? So they need to migrate there and they need this architecture to be cost-effective. And, and that's, that's super key. I mean, the reality is deep user moving fast, but they're not going to be, um, deployed everywhere on day one. Some servers will have DPS right away, some servers will have use and a year or two. And then there are devices that may never have DPS, right. IOT gateways, or legacy servers, even mainframes. Um, so that's the beauty of a solution that creates a fabric across both the switch and the DPU, right. >>Um, and by leveraging the Nvidia Bluefield DPU, what we really like about it is it's open. Um, and that drives, uh, cost efficiencies. And then, um, uh, you know, with this, with this, our architectural approach effectively, you get a unified solution across switch and DPU workload independent doesn't matter what hypervisor it is, integrated visibility, integrated security, and that can, uh, create tremendous cost efficiencies and, and really extract a lot of the expense from, from a capital perspective out of the network, as well as from an operational perspective, because now I have an SDN automated solution where I'm literally issuing a command to deploy a network service or to create or deploy our security policy and is deployed everywhere, automatically saving the oppor, the network operations team and the security operations team time. >>All right. So let me rewind that because that's super important. Get the unified cloud architecture, I'm the customer guy, but it's implemented, what's the value again, take, take me through the value to me. I have a unified environment. What's the value. >>Yeah. So I mean, the value is effectively, um, that, so there's a few pieces of value. The first piece of value is, um, I'm creating this clean D mark. I'm taking networking to the host. And like I mentioned, we're not running it on the CPU. So in implementations that run networking on the CPU, there's some conflict between the dev ops team who owned the server and the NetApps team who own the network because they're installing software on the, on the CPU stealing cycles from what should be revenue generating. Uh CPU's. So now by, by terminating the networking on the DPU, we click create this real clean DMARC. So the dev ops folks are happy because they don't necessarily have the skills to manage network and they don't necessarily want to spend the time managing networking. They've got their network counterparts who are also happy the NetApps team, because they want to control the networking. >>And now we've got this clean DMARC where the DevOps folks get the services they need and the NetApp folks get the control and agility they need. So that's a huge value. Um, the next piece of value is distributed security. This is essential. I mentioned earlier, you know, put pushing out micro-segmentation and distributed firewall, basically at the application level, right, where I create these small, small segments on an by application basis. So if a bad actor does penetrate the perimeter firewall, they're contained once they get inside. Cause the worst thing is a bad actor, penetrates a perimeter firewall and can go wherever they want and wreak havoc. Right? And so that's why this, this is so essential. Um, and the next benefit obviously is this unified networking operating model, right? Having, uh, uh, uh, an operating model across switch and server underlay and overlay, workload agnostic, making the life of the NetApps teams much easier so they can focus their time on really strategy instead of spending an afternoon, deploying a single villain, for example. >>Awesome. And I think also from my standpoint, I mean, perimeter security is pretty much, I mean, they're out there, it gets the firewall still out there exists, but pretty much they're being breached all the time, the perimeter. So you have to have this new security model. And I think the other thing that you mentioned, the separation between dev ops is cool because the infrastructure is code is about making the developers be agile and build security in from day one. So this policy aspect is, is huge. Um, new control points. I think you guys have a new architecture that enables the security to be handled more flexible. >>Right. >>That seems to be the killer feature here, >>Right? Yeah. If you look at the data processing unit, I think one of the great things about sort of this new architecture, it's really the foundation for zero trust it's. So like you talked about the perimeter is getting breached. And so now each and every compute node has to be protected. And I think that's sort of what you see with the partnership between pluribus and Nvidia is the DPU is really the foundation of zero trust. And pluribus is really building on that vision with, uh, allowing sort of micro-segmentation and being able to protect each and every compute node as well as the underlying network. >>This is super exciting. This is an illustration of how the market's evolving architectures are being reshaped and refactored for cloud scale and all this new goodness with data. So I gotta ask how you guys go into market together. Michael, start with you. What's the relationship look like in the go to market with an Nvidia? >>Sure. Um, I mean, we're, you know, we're super excited about the partnership, obviously we're here together. Um, we think we've got a really good solution for the market, so we're jointly marketing it. Um, uh, you know, obviously we appreciate that Nvidia is open. Um, that's, that's sort of in our DNA, we're about open networking. They've got other ISV who are gonna run on Bluefield too. We're probably going to run on other DPS in the, in the future, but right now, um, we're, we feel like we're partnered with the number one, uh, provider of DPS in the world and, uh, super excited about, uh, making a splash with it. >>I'm in get the hot product. >>Yeah. So Bluefield too, as I mentioned was GA last year, we're introducing, uh, well, we now also have the converged accelerator. So I talked about artificial intelligence or artificial intelligence with the Bluefield DPU, all of that put together on a converged accelerator. The nice thing there is you can either run those workloads. So if you have an artificial intelligence workload and an infrastructure workload, you can warn them separately on the same platform or you can actually use, uh, you can actually run artificial intelligence applications on the Bluefield itself. So that's what the converged accelerator really brings to the table. Uh, so that's available now. Then we have Bluefield three, which will be available late this year. And I talked about sort of, you know, uh, how much better that next generation of Bluefield is in comparison to Bluefield two. So we will see Bluefield three shipping later on this year, and then our software stack, which I talked about, which is called Doka we're on our second version are Doka one dot two. >>We're releasing Doka one dot three, uh, in about two months from now. And so that's really our open ecosystem framework. So allow you to program the Bluefields. So we have all of our acceleration libraries, um, security libraries, that's all packed into this STK called Doka. And it really gives that simplicity to our partners to be able to develop on top of Bluefield. So as we add new generations of Bluefield, you know, next, next year, we'll have, you know, another version and so on and so forth Doka is really that unified unified layer that allows, um, Bluefield to be both forwards compatible and backwards compatible. So partners only really have to think about writing to that SDK once, and then it automatically works with future generations of Bluefields. So that's sort of the nice thing around, um, around Doka. And then in terms of our go to market model, we're working with every, every major OEM. So, uh, later on this year, you'll see, you know, major server manufacturers, uh, releasing Bluefield enabled servers. So, um, more to come >>Awesome, save money, make it easier, more capabilities, more workload power. This is the future of, of cloud operations. >>Yeah. And, and, and, uh, one thing I'll add is, um, we are, um, we have a number of customers as you'll hear in the next segment, um, that are already signed up and we'll be working with us for our, uh, early field trial starting late April early may. Um, we are accepting registrations. You can go to www.pluribusnetworks.com/e F T a. If you're interested in signing up for, um, uh, being part of our field trial and providing feedback on the product, >>Awesome innovation and network. Thanks so much for sharing the news. Really appreciate it. Thanks so much. Okay. In a moment, we'll be back to look deeper in the product, the integration security zero trust use cases. You're watching the cube, the leader in enterprise tech coverage, >>Cloud networking is complex and fragmented slowing down your business. How can you simplify and unify your cloud networks to increase agility and business velocity? >>Pluribus unified cloud networking provides a unified simplify and agile network fabric across all clouds. It brings the simplicity of a public cloud operation model to private clouds, dramatically reducing complexity and improving agility, availability, and security. Now enterprises and service providers can increase their business philosophy and delight customers in the distributed multi-cloud era. We achieve this with a new approach to cloud networking, pluribus unified cloud fabric. This open vendor, independent network fabric, unifies, networking, and security across distributed clouds. The first step is extending the fabric to servers equipped with data processing units, unifying the fabric across switches and servers, and it doesn't stop there. The fabric is unified across underlay and overlay networks and across all workloads and virtualization environments. The unified cloud fabric is optimized for seamless migration to this new distributed architecture, leveraging the power of the DPU for application level micro-segmentation distributed fireball and encryption while still supporting those servers and devices that are not equipped with a DPU. Ultimately the unified cloud fabric extends seamlessly across distributed clouds, including central regional at edge private clouds and public clouds. The unified cloud fabric is a comprehensive network solution. That includes everything you need for clouds, networking built in SDN automation, distributed security without compromises, pervasive wire speed, visibility and application insight available on your choice of open networking switches and DP use all at the lowest total cost of ownership. The end result is a dramatically simplified unified cloud networking architecture that unifies your distributed clouds and frees your business to move at cloud speed, >>To learn more, visit www.pluribusnetworks.com. >>Okay. We're back I'm John ferry with the cube, and we're going to go deeper into a deep dive into unified cloud networking solution from Clovis and Nvidia. And we'll examine some of the use cases with Alessandra Burberry, VP of product management and pullovers networks and Pete Bloomberg who's director of technical marketing and video remotely guys. Thanks for coming on. Appreciate it. >>Yeah. >>So deep dive, let's get into the what and how Alexandra we heard earlier about the pluribus Nvidia partnership and the solution you're working together on what is it? >>Yeah. First let's talk about the water. What are we really integrating with the Nvidia Bluefield, the DPO technology, uh, plugable says, um, uh, there's been shipping, uh, in, uh, in volume, uh, in multiple mission critical networks. So this advisor one network operating systems, it runs today on a merchant silicone switches and effectively it's a standard open network operating system for data center. Um, and the novelty about this system that integrates a distributed control plane for, at water made effective in SDN overlay. This automation is a completely open and interoperable and extensible to other type of clouds is not enclosed them. And this is actually what we're now porting to the Nvidia DPO. >>Awesome. So how does it integrate into Nvidia hardware and specifically how has pluribus integrating its software with the Nvidia hardware? >>Yeah, I think, uh, we leverage some of the interesting properties of the Bluefield, the DPO hardware, which allows actually to integrate, uh, um, uh, our software, our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the DPU card that is completely agnostic to the hypervisor layer or OSTP layer running on, uh, on the host even more, um, uh, we can also independently manage this network, know that the switch on a Neek effectively, um, uh, managed completely independently from the host. You don't have to go through the network operating system, running on x86 to control this network node. So you throw yet the experience effectively of a top of rack for virtual machine or a top of rack for, uh, Kubernetes bots, where instead of, uh, um, if you allow me with the analogy instead of connecting a server knee directly to a switchboard, now you're connecting a VM virtual interface to a virtual interface on the switch on an ache. >>And, uh, also as part of this integration, we, uh, put a lot of effort, a lot of emphasis in, uh, accelerating the entire, uh, data plane for networking and security. So we are taking advantage of the DACA, uh, Nvidia DACA API to program the accelerators. And these accomplished two things with that. Number one, uh, you, uh, have much greater performance, much better performance. They're running the same network services on an x86 CPU. And second, this gives you the ability to free up, I would say around 20, 25% of the server capacity to be devoted either to, uh, additional workloads to run your cloud applications, or perhaps you can actually shrink the power footprint and compute footprint of your data center by 20%, if you want to run the same number of compute workloads. So great efficiencies in the overall approach, >>And this is completely independent of the server CPU, right? >>Absolutely. There is zero code from running on the x86, and this is what we think this enables a very clean demarcation between computer and network. >>So Pete, I gotta get, I gotta get you in here. We heard that, uh, the DPU is enabled cleaner separation of dev ops and net ops. Can you explain why that's important because everyone's talking DevSecOps right now, you've got net ops, net, net sec ops, this separation. Why is this clean separation important? >>Yeah, I think it's a, you know, it's a pragmatic solution in my opinion. Um, you know, we wish the world was all kind of rainbows and unicorns, but it's a little, a little messier than that. And I think a lot of the dev ops stuff and that, uh, mentality and philosophy, there's a natural fit there. Right? You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers. Well, the network has always been this other thing and the network operators have always had a very different approach to things than compute operators. And, you know, I think that we, we in the networking industry have gotten closer together, but there's still a gap there's still some distance. And I think in that distance, isn't going to be closed. And so, you know, again, it comes down to pragmatism and I think, you know, one of my favorite phrases is look good fences, make good neighbors. And that's what this is. >>Yeah. That's a great point because dev ops has become kind of the calling card for cloud, right. But dev ops is as simply infrastructure as code and infrastructure is networking, right? So if infrastructure is code, you know, you're talking about, you know, that part of the stack under the covers under the hood, if you will, this is super important distinction. And this is where the innovation is. Can you elaborate on how you see that? Because this is really where the action is right now. >>Yeah, exactly. And I think that's where, um, one from, from the policy, the security that the zero trust aspect of this, right? If you get it wrong on that network side, all of a sudden you, you can totally open up that those capabilities. And so security is part of that. But the other part is thinking about this at scale, right? So we're taking one top of rack switch and adding, you know, up to 48 servers per rack. And so that ability to automate, orchestrate and manage at scale becomes absolutely critical. >>I'll Sandra, this is really the why we're talking about here, and this is scale. And again, getting it right. If you don't get it right, you're going to be really kind of up, you know what you know, so this is a huge deal. Networking matters, security matters, automation matters, dev ops, net ops, all coming together, clean separation, um, help us understand how this joint solution with Nvidia fits into the pluribus unified cloud networking vision, because this is what people are talking about and working on right now. >>Yeah, absolutely. So I think here with this solution, we're attacking two major problems in cloud networking. One is, uh, operation of, uh, cloud networking. And the second is a distributing security services in the cloud infrastructure. First, let me talk about the first water. We really unifying. If we're unifying something, something must be at least fragmented or this jointed and the, what is this joint that is actually the network in the cloud. If you look holistically, how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers, you'll build your IP clause fabric leaf in spine typologies. This is actually a well understood the problem. I, I would say, um, there are multiple vendors, uh, uh, with, uh, um, uh, let's say similar technologies, um, very well standardized, whether you will understood, um, and almost a commodity, I would say building an IP fabric these days, but this is not the place where you deploy most of your services in the cloud, particularly from a security standpoint, two services are actually now moved into the compute layer where you actually were called builders, have to instrument the, a separate, uh, network virtualization layer, where they deploy segmentation and security closer to the workloads. >>And this is where the complication arise. These high value part of the cloud network is where you have a plethora of options that they don't talk to each other. And they are very dependent on the kind of hypervisor or compute solution you choose. Um, for example, the networking API to be between an GSXI environment or an hyper V or a Zen are completely disjointed. You have multiple orchestration layers. And when, and then when you throw in also Kubernetes in this, in this, in this type of architecture, uh, you're introducing yet another level of networking. And when Kubernetes runs on top of VMs, which is a prevalent approach, you actually just stacking up multiple networks on the compute layer that they eventually run on the physical fabric infrastructure. Those are all ships in the nights effectively, right? They operate as completely disjointed. And we're trying to attack this problem first with the notion of a unified fabric, which is independent from any workloads, whether it's this fabric spans on a switch, which can be con connected to a bare metal workload, or can span all the way inside the DPU, uh, where, um, you have, uh, your multi hypervisor compute environment. >>It's one API, one common network control plane, and one common set of segmentation services for the network. That's probably the number one, >>You know, it's interesting you, man, I hear you talking, I hear one network month, different operating models reminds me of the old serverless days. You know, there's still servers, but they call it serverless. Is there going to be a term network list? Because at the end of the day, it should be one network, not multiple operating models. This, this is a problem that you guys are working on. Is that right? I mean, I'm not, I'm just joking server listen network list, but the idea is it should be one thing. >>Yeah, it's effectively. What we're trying to do is we are trying to recompose this fragmentation in terms of network operation, across physical networking and server networking server networking is where the majority of the problems are because of the, uh, as much as you have standardized the ways of building, uh, physical networks and cloud fabrics with IP protocols and internet, you don't have that kind of, uh, uh, sort of, uh, um, um, uh, operational efficiency, uh, at the server layer. And, uh, this is what we're trying to attack first. The, with this technology, the second aspect we're trying to attack is are we distribute the security services throughout the infrastructure, more efficiently, whether it's micro-segmentation is a stateful firewall services, or even encryption. Those are all capabilities enabled by the blue field, uh, uh, the Butte technology and, uh, uh, we can actually integrate those capabilities directly into the nettle Fabrica, uh, limiting dramatically, at least for east-west traffic, the sprawl of, uh, security appliances, whether virtual or physical, that is typically the way the people today, uh, segment and secure the traffic in the cloud. >>Awesome. Pete, all kidding aside about network lists and serverless kind of fun, fun play on words there, the network is one thing it's basically distributed computing, right? So I love to get your thoughts about this distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >>Yeah, I think what's, what's beautiful and kind of what the DPU brings. That's new to this model is a completely isolated compute environment inside. So, you know, it's the, uh, yo dog, I heard you like a server, so I put a server inside your server. Uh, and so we provide, uh, you know, armed CPU's memory and network accelerators inside, and that is completely isolated from the host. So the server, the, the actual x86 host just thinks it has a regular Nick in there, but you actually have this full control plane thing. It's just like taking your top of rack switch and shoving it inside of your compute node. And so you have not only the separation, um, within the data plane, but you have this complete control plane separation. So you have this element that the network team can now control and manage, but we're taking all of the functions we used to do at the top of rack switch, and we're just shooting them now. >>And, you know, as time has gone on we've, we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, um, the other option is centralized appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that of aliens good enough, or we hope that if the excellent tunnel is good enough and we can actually apply more advanced techniques there because we can't physically, you know, financially afford that appliance to see all of the traffic. And now that we have a distributed model with this accelerator, we could do it. >>So what's the what's in it for the customer. I real quick, cause I think this is interesting point. You mentioned policy, everyone in networking knows policy is just a great thing and it adds, you hear it being talked about up the stack as well. When you start getting to orchestrating microservices and whatnot, all that good stuff going on there, containers and whatnot and modern applications. What's the benefit to the customers with this approach? Because what I heard was more scale, more edge deployment, flexibility, relative to security policies and application enablement. I mean, is that what what's the customer get out of this architecture? What's the enablement. >>It comes down to, uh, taking again the capabilities that were in that top of rack switch and asserting them down. So that makes simplicity smaller blast radiuses for failure, smaller failure domains, maintenance on the networks, and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. Um, and again, you know, we always want to kind of separate each one of those layers. So just as in say, a VX land network, my leaf and spine don't have to be tightly coupled together. I can now do this at a different layer. And so you can run a DPU with any networking in the core there. And so you get this extreme flexibility. You can start small, you can scale large. Um, you know, to me, the, the possibilities are endless. Yes, >>It's a great security control plan. Really flexibility is key. And, and also being situationally aware of any kind of threats or new vectors or whatever's happening in the network. Alessandra, this is huge upside, right? You've already identified some successes with some customers on your early field trials. What are they doing and why are they attracted to the solution? >>Yeah, I think the response from customers has been, uh, the most, uh, encouraging and, uh, exciting, uh, for, uh, for us to, uh, to sort of continue and work and develop this product. And we have actually learned a lot in the process. Um, we talked to tier two tier three cloud providers. Uh, we talked to, uh, SP um, software Tyco type of networks, uh, as well as a large enterprise customers, um, in, uh, one particular case. Um, uh, one, uh, I think, um, let me, let me call out a couple of examples here, just to give you a flavor. Uh, there is a service provider, a cloud provider, uh, in Asia who is actually managing a cloud, uh, where they are offering services based on multiple hypervisors. They are native services based on Zen, but they also are on ramp into the cloud, uh, workloads based on, uh, ESI and, uh, uh, and KVM, depending on what the customer picks from the piece on the menu. >>And they have the problem of now orchestrating through their orchestrate or integrating with the Zen center with vSphere, uh, with, uh, open stack to coordinate these multiple environments and in the process to provide security, they actually deploy virtual appliances everywhere, which has a lot of costs, complication, and eats up into the server CPU. The problem is that they saw in this technology, they call it actually game changing is actually to remove all this complexity of in a single network and distribute the micro-segmentation service directly into the fabric. And overall, they're hoping to get out of it, uh, uh, tremendous, uh, um, opics, uh, benefit and overall, um, uh, operational simplification for the cloud infrastructure. That's one potent a use case. Uh, another, uh, large enterprise customer global enterprise customer, uh, is running, uh, both ESI and hyper V in that environment. And they don't have a solution to do micro-segmentation consistently across hypervisors. >>So again, micro-segmentation is a huge driver security looks like it's a recurring theme, uh, talking to most of these customers and in the Tyco space, um, uh, we're working with a few types of customers on the CFT program, uh, where the main goal is actually to our Monet's network operation. They typically handle all the VNF search with their own homegrown DPDK stack. This is overly complex. It is frankly also as low and inefficient, and then they have a physical network to manage the, the idea of having again, one network, uh, to coordinate the provision in our cloud services between the, the take of VNF, uh, and, uh, the rest of the infrastructure, uh, is extremely powerful on top of the offloading capability of the, by the bluefin DPOs. Those are just some examples. >>That was a great use case, a lot more potential. I see that with the unified cloud networking, great stuff, feed, shout out to you guys at Nvidia had been following your success for a long time and continuing to innovate as cloud scales and pluribus here with the unified networking, kind of bring it to the next level. Great stuff. Great to have you guys on. And again, software keeps driving the innovation again, networking is just a part of it, and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in, in this, uh, new architecture and solution, uh, learn more because this is an architectural shift. People are working on this problem. They're trying to think about multiple clouds of trying to think about unification around the network and giving more security, more flexibility, uh, to their teams. How can people learn more? >>Yeah, so, uh, all Sandra and I have a talk at the upcoming Nvidia GTC conference. Um, so that's the week of March 21st through 24th. Um, you can go and register for free and video.com/at GTC. Um, you can also watch recorded sessions if you ended up watching us on YouTube a little bit after the fact. Um, and we're going to dive a little bit more into the specifics and the details and what we're providing in the solution. >>Alexandra, how can people learn more? >>Yeah, absolutely. People can go to the pluribus, a website, www boost networks.com/eft, and they can fill up the form and, uh, they will contact durables to either know more or to know more and actually to sign up for the actual early field trial program, which starts at the end of April. >>Okay. Well, we'll leave it there. Thanks. You both for joining. Appreciate it up next. You're going to hear an independent analyst perspective and review some of the research from the enterprise strategy group ESG. I'm John ferry with the >>Cube. Thanks for watching. >>Okay. We've heard from the folks at networks and Nvidia about their effort to transform cloud networking and unify bespoke infrastructure. Now let's get the perspective from an independent analyst and to do so. We welcome in ESG, senior analysts, Bob LA Liberte, Bob. Good to see you. Thanks for coming into our east coast studios. >>Oh, thanks for having me. It's great to be >>Here. Yeah. So this, this idea of unified cloud networking approach, how serious is it? What's what's driving it. >>Yeah, there's certainly a lot of drivers behind it, but probably the first and foremost is the fact that application environments are becoming a lot more distributed, right? So the, it pendulum tends to swing back and forth. And we're definitely on one that's swinging from consolidated to distributed. And so applications are being deployed in multiple private data centers, multiple public cloud locations, edge locations. And as a result of that, what you're seeing is a lot of complexity. So organizations are having to deal with this highly disparate environment. They have to secure it. They have to ensure connectivity to it and all that's driving up complexity. In fact, when we asked in one of our last surveys and last year about network complexity, more than half 54% came out and said, Hey, our network environment is now either more or significantly more complex than it used to be. >>And as a result of that, what you're seeing is it's really impacting agility. So everyone's moving to these modern application environments, distributing them across areas so they can improve agility yet it's creating more complexity. So a little bit counter to the fact and, you know, really counter to their overarching digital transformation initiatives. From what we've seen, you know, nine out of 10 organizations today are either beginning in process or have a mature digital transformation process or initiative, but their top goals, when you look at them, it probably shouldn't be a surprise. The number one goal is driving operational efficiency. So it makes sense. I've distributed my environment to create agility, but I've created a lot of complexity. So now I need these tools that are going to help me drive operational efficiency, drive better experience. >>I mean, I love how you bring in the data yesterday. Does a great job with that. Uh, questions is, is it about just unifying existing networks or is there sort of a need to rethink kind of a do-over network, how networks are built? >>Yeah, that's a, that's a really good point because certainly unifying networks helps right. Driving any kind of operational efficiency helps. But in this particular case, because we've made the transition to new application architectures and the impact that's having as well, it's really about changing and bringing in new frameworks and new network architectures to accommodate those new application architectures. And by that, what I'm talking about is the fact that these new modern application architectures, microservices, containers are driving a lot more east west traffic. So in the old days, it used to be easier in north south coming out of the server, one application per server, things like that. Right now you've got hundreds, if not thousands of microservices communicating with each other users communicating to them. So there's a lot more traffic and a lot of it's taking place within the servers themselves. The other issue that you starting to see as well from that security perspective, when we were all consolidated, we had those perimeter based legacy, you know, castle and moat security architectures, but that doesn't work anymore when the applications aren't in the castle, right. >>When everything's spread out that that no longer happens. So we're absolutely seeing, um, organizations trying to, trying to make a shift. And, and I think much, like if you think about the shift that we're seeing with all the remote workers and the sassy framework to enable a secure framework there, this it's almost the same thing. We're seeing this distributed services framework come up to support the applications better within the data centers, within the cloud data centers, so that you can drive that security closer to those applications and make sure they're, they're fully protected. Uh, and that's really driving a lot of the, you know, the zero trust stuff you hear, right? So never trust, always verify, making sure that everything is, is, is really secure micro-segmentation is another big area. So ensuring that these applications, when they're connected to each other, they're, they're fully segmented out. And that's again, because if someone does get a breach, if they are in your data center, you want to limit the blast radius, you want to limit the amount of damage that's done. So that by doing that, it really makes it a lot harder for them to see everything that's in there. >>You know, you mentioned zero trust. It used to be a buzzword, and now it's like become a mandate. And I love the mode analogy. You know, you build a moat to protect the queen and the castle, the Queens left the castles, it's just distributed. So how should we think about this, this pluribus and Nvidia solution. There's a spectrum, help us understand that you've got appliances, you've got pure software solutions. You've got what pluribus is doing with Nvidia, help us understand that. >>Yeah, absolutely. I think as organizations recognize the need to distribute their services to closer to the applications, they're trying different models. So from a legacy approach, you know, from a security perspective, they've got these centralized firewalls that they're deploying within their data centers. The hard part for that is if you want all this traffic to be secured, you're actually sending it out of the server up through the rack, usually to in different location in the data center and back. So with the need for agility, with the need for performance, right, that adds a lot of latency. Plus when you start needing to scale, that means adding more and more network connections, more and more appliances. So it can get very costly as well as impacting the performance. The other way that organizations are seeking to solve this problem is by taking the software itself and deploying it on the servers. Okay. So that's a, it's a great approach, right? It brings it really close to the applications, but the things you start running into there, there's a couple of things. One is that you start seeing that the DevOps team start taking on that networking and security responsibility, which they >>Don't want to >>Do, they don't want to do right. And the operations teams loses a little bit of visibility into that. Um, plus when you load the software onto the server, you're taking up precious CPU cycles. So if you're really wanting your applications to perform at an optimized state, having additional software on there, isn't going to, isn't going to do it. So, you know, when we think about all those types of things, right, and certainly the other side effects of that is the impact of the performance, but there's also a cost. So if you have to buy more servers because your CPU's are being utilized, right, and you have hundreds or thousands of servers, right, those costs are going to add up. So what, what Nvidia and pluribus have done by working together is to be able to take some of those services and be able to deploy them onto a smart Nick, right? >>To be able to deploy the DPU based smart SMARTNICK into the servers themselves. And then pluribus has come in and said, we're going to unify create that unified fabric across the networking space, into those networking services all the way down to the server. So the benefits of having that are pretty clear in that you're offloading that capability from the server. So your CPU's are optimized. You're saving a lot of money. You're not having to go outside of the server and go to a different rack somewhere else in the data center. So your performance is going to be optimized as well. You're not going to incur any latency hit for every trip round trip to the, to the firewall and back. So I think all those things are really important. Plus the fact that you're going to see from a, an organizational aspect, we talked about the dev ops and net ops teams. The network operations teams now can work with the security teams to establish the security policies and the networking policies. So that they've dev ops teams. Don't have to worry about that. So essentially they just create the guardrails and let the dev op team run. Cause that's what they want. They want that agility and speed. >>Yeah. Your point about CPU cycles is key. I mean, it's estimated that 25 to 30% of CPU cycles in the data center are wasted. The cores are wasted doing storage offload or, or networking or security offload. And, you know, I've said many times everybody needs a nitro like Amazon nugget, but you can't go, you can only buy Amazon nitro if you go into AWS. Right. Everybody needs a nitro. So is that how we should think about this? >>Yeah. That's a great analogy to think about this. Um, and I think I would take it a step further because it's, it's almost the opposite end of the spectrum because pluribus and video are doing this in a very open way. And so pluribus has always been a proponent of open networking. And so what they're trying to do is extend that now to these distributed services. So leverage working with Nvidia, who's also open as well, being able to bring that to bear so that organizations can not only take advantage of these distributed services, but also that unified networking fabric, that unified cloud fabric across that environment from the server across the switches, the other key piece of what pluribus is doing, because they've been doing this for a while now, and they've been doing it with the older application environments and the older server environments, they're able to provide that unified networking experience across a host of different types of servers and platforms. So you can have not only the modern application supported, but also the legacy environments, um, you know, bare metal. You could go any type of virtualization, you can run containers, et cetera. So a wide gambit of different technologies hosting those applications supported by a unified cloud fabric from pluribus. >>So what does that mean for the customer? I don't have to rip and replace my whole infrastructure, right? >>Yeah. Well, think what it does for, again, from that operational efficiency, when you're going from a legacy environment to that modern environment, it helps with the migration helps you accelerate that migration because you're not switching different management systems to accomplish that. You've got the same unified networking fabric that you've been working with to enable you to run your legacy as well as transfer over to those modern applications. Okay. >>So your people are comfortable with the skillsets, et cetera. All right. I'll give you the last word. Give us the bottom line here. >>So yeah, I think obviously with all the modern applications that are coming out, the distributed application environments, it's really posing a lot of risk on these organizations to be able to get not only security, but also visibility into those environments. And so organizations have to find solutions. As I said, at the beginning, they're looking to drive operational efficiency. So getting operational efficiency from a unified cloud networking solution, that it goes from the server across the servers to multiple different environments, right in different cloud environments is certainly going to help organizations drive that operational efficiency. It's going to help them save money for visibility, for security and even open networking. So a great opportunity for organizations, especially large enterprises, cloud providers who are trying to build that hyperscaler like environment. You mentioned the nitro card, right? This is a great way to do it with an open solution. >>Bob, thanks so much for, for coming in and sharing your insights. Appreciate it. >>You're welcome. Thanks. >>Thanks for watching the program today. Remember all these videos are available on demand@thekey.net. You can check out all the news from today@siliconangle.com and of course, pluribus networks.com many thanks diplomas for making this program possible and sponsoring the cube. This is Dave Volante. Thanks for watching. Be well, we'll see you next time.

(serene music) >> From around the globe, it's theCUBE, with digital coverage of IBM Think 2021, brought to you by IBM. >> Hey, welcome to theCUBE's coverage of IBM Think, the digital event experience. I'm your host, Lisa Martin, welcoming back to the program one of our CUBE alumn. Dominique Dubois joins me. She's the Global Strategy and Offerings Executive in the Business Transformation Services of IBM. Dominique, it's great to talk to you again. >> Hi Lisa, great to be with you today. >> So we're going to be talking about the theme of this interview. It's going to be the ROI of AI for business. We've been talking about AI emerging technologies for a long time now. We've also seen a massive change in the world. I'd love to talk to you about how organizations are adopting these emerging technologies to really help transform their businesses. And one of the things that you've talked about in the past, is that there's these different elements of AI for business. One of them is trust, right, the second is ease of use, and then there's this importance of data in all of these important emerging technologies that require so much data. How do those elements of AI come together to help IBM's clients be able to deliver the products and services that their customers are depending on? >> Yeah. Thank you, Lisa. So, when we look at AI and AI solutions with our clients, I think how that comes together is in the way in which we don't look at AI, or AI application solution, independently, right. We're looking at it and we're applying it within our customer's operations with respect to the work that it's going to do, with respect to the part of the operations and the workflow and the function that it sits in, right. So the idea around trust and ease of use and the data that can be leveraged in order to kind of create that AI and allow that AI to be self-learning and continue to add value really is fundamental around how we design and how we implement it within the workflow itself. And how we are working with the employees, with the actual humans, that are going to be touching that AI, right, to help them with new skills that are required to work with AI, to help them with what we call the new ways of working, right, 'cause it's that adoption that really is critical to get the use of AI in enterprises at scale. >> That adoption that you just mentioned, that's critical. That can be kind of table stakes. But what we've seen in the last year is that we've all had to pivot, multiple times, and be reactionary, or reactive, to so many things out of our control. I'm curious what you've seen in the last year in terms of the appetite for adoption on the employees front. Are they more willing to go, all right, we've got to change the way we do things, and it's probably going to be, some of these are going to be permanent? >> Yeah. Lisa, we've absolutely seen a huge rise in the adoption, right, or in the openness, the mindset. Let's just call it the mindset, right. It's more of an open mindset around the use of technology, the use of technology that might be AI backed or AI based, and the willingness to, and I will say, the willingness to try is really then what starts that journey of trust, right. And we're seeing that open up in spades. >> That is absolutely critical. It's just the willingness, being open-minded enough to go, all right, we've got to do this, so we've got to think about this. We don't really have any other choices here. Things are changing pretty quickly. So talk to me, in this last year of change, we've seen massive disruptions and some silver linings for sure, but I'd love to know what IBM and the state of Rhode Island have done together in its challenging time. >> Yeah, so, really interesting partnership that we started with the state of Rhode Island. Obviously, I think this year, there's been lots of things. One of them has been speed, so everything that we had to do has been with haste, right, with urgency. And that's no different than what we did with the state of Rhode Island. The governor there, Gina Raimondo, she took very swift action, right, when the pandemic started. And one of the actions she took was to partner with private firms, such as IBM and others, to really help get her economy back open. And that required a lot of things. One of them, as you mentioned, trust, right, was a major part of what the governor there needed with her citizenships, with her citizens, excuse me, in order to be able to open back up the economy, right. And so, a key pillar of her program, and with our partnership, was around the AI-backed solutions that we brought to the state of Rhode Island, so inclusive of contact tracing, inclusive of work that we had provided around AI-based analytics that allowed really the governor to speak to citizens with hard facts quickly, almost real time, right, and start to build that trust, but also competence, and competence was the main, one of the main things that was required during this pandemic time. And so, there were, through this, the AI-based solutions that we provided, which were, there were many pillars, we were able to help Rhode Island not only open their economy, but they were one of the only states that had their schools open in the fall, and as a parent, I always see that as a litmus, if you will, of how our state is doing, right. And so they opened in the fall, and they, as far as I know, have stayed open. And I think part of that was from the AI-based contact tracing, the AI-backed virtual, sorry, AI analytics, the analytics suite around infections and predictions and what we were able to provide the governor in order to make swift decisions and take action. >> That's really impressive. That's one of the challenges I've had living in California, is you (mumbles) you are going to be data-driven than actually be data-driven, but the technology, living in Silicon Valley, the technology is there to be able to facilitate that, yet there was such a disconnect, and I think that's, you bring up the word confidence, and customers need confidence, citizens need confidence, knowing that what we've seen in the last year has shown in a lot of examples that real time isn't a nice-to-have anymore, it's a requirement. I mean, this is clearly life-and-death situations. That's a great example of how a state came to IBM to partner and say, how can we actually leverage emerging technologies like AI to really and truly make real-time data-driven decisions that affect every single person in our state. >> Mm-hmm. Absolutely, absolutely! Really, really, I think, a great example of the public-private partnerships that are really popping up now, more and more so because of that sense of urgency and that need to build greater ecosystems to create better solutions. >> So that's a great example in healthcare, one that our government in public health, and I think everybody, it will resonate with everybody here, but you've also done some really interesting work that I want to talk about with AI-driven insights into supply chain. We've also seen massive changes to supply chain, and so many organizations having to figure out, whether they were brick-and-mortar only, changing that, or really leveraging technology to figure out where do we need to be distributing products and services, where do we need to be investing. Talk to me about Bestseller India, and what it is that you guys have done there with intelligent workflows to really help them transform their supply chain. >> Yeah, Bestseller India, really great, hugely successful fashion forward company in India, and that term fashion forward always is mind boggling to me because basically, these are clothing retailers who go from runway to store within a matter of days, couple of weeks, which always is just hugely impressive, right, just what goes into that. And when you think about what happens in a supply chain to be able to do that, the requirements around demand forecasting, what quantities, of what style, what design, to what stores, and you think about the India market, which is notoriously a difficult market, lots of micro-segments, and so very difficult to serve. And then you couple that what's been happening from an environmental sustainability perspective, right. I think every industry has been looking more about how they can be more environmentally sustainable, and the clothing industry is no different. And when, and there is a lot of impact, right, so a stat that really has hit home with me, right: 20% of all the clothes that are made globally goes unsold. That's all a lot of clothing, that's a lot of material, and that's a lot of environmental product that goes into creating it. And so, Bestseller India really took it to heart to become not only more environmentally sustainable, but to help itself and be digitally ready for things like the pandemic that ultimately hit. And they were in a really good position. And we worked with them to create something called Fabric AI. So Fabric AI is India's only, first and only, AI-based platform that drives their supply chain, so it drives not only their decisions on what design should they manufacture, but it also helps to improve the entire workflow of what we call design to store. And the AI-based solution is really revolutionary, right, within India, but I think it's pretty revolutionary globally, right, globally as well. And it delivered really big impact, so, reductions in the cost, right, 15-plus reduction in cost. It helped their top line, so they saw a 5% plus top line, but it also reduced their unsold inventory by 5% and more, right. They're continuing to focus on that environmental sustainability that I think is a really important part of their DNA, right, the Bestseller India's DNA. >> And it's one that so many companies and other industries can learn from. I was reading in that case study on Bestseller India on the IBM website that I think it was 40 liters of water to make a cotton shirt. And to your point about the percentage of clothing that actually goes unsold and ends up in landfills, you see there the opportunity for AI to unlock the visibility that companies in any industry need to determine what is the demand that we should be filling, where should it be distributed, where should we not be distributing things. And so I think it was an interesting kind of impetus that Bestseller India had about one of their retail lines or brands was dropping in revenue, but they had been able to apply this technology to other areas of the business and make a pretty big impact. >> Yeah, absolutely. So they had been been very fortunate to have 11 years of growth, right, in all of their brands. And then one of their brands kind of hit headwinds, but the CIO and head of supply chain at that time really had the foresight to be able to say, you know what, we're hitting a problem, one of our brands, but this really is indicative of a more systemic problem. And that problem was lack of transparency, lack of data-driven, predictive, and automation to be able to drive a more effective and efficient kind of supply chain in the end, so, really had the forethought to dive into that and fix it. >> Yeah. And now talk to me about IBM Garage Band, and how's that, how did that help in this particular case? >> Yeah. So, in order to do this, right, it was, they had no use of AI, no use of automation, at the time that we started this. And so to really not only design and build and execute on Fabric AI, but to actually focus on the adoption, right, of AI within the business, we really needed to bring together the leaders across many lines of businesses, IT and HR, right. And when you think about pulling all of these different units together, we used our IBM Garage approach, which really is, there are many attributes and many facets of the IBM Garage, but I think one of the great results of using our IBM Garage approach is being able to pull from across all those different businesses, all of which may have some different objectives, right, they're coming from a different lens, from a different space, and pulling them together around one focus mission, which for here was Fabric AI. And we were able to actually design and build this in less than six months, which I think is pretty dramatic and pretty incredible from a speed and acceleration perspective. But I think even more so was the adoption, was the way in which we had, through all of it, already been working with the employees 'cause it's really touched almost every part of Bestseller India, so really being able to work with them and all the employees to make sure that they were ready for these new ways of working, that they had the right skills, that they had the right perspective, and that it was going to be adopted. >> That, we, if we unpack that, if we had time, that can be a whole separate conversation because the important, the most important thing about adoption is the cultures of these different business units have to come together. You said you rolled this out in a very short period of time, but you also were taking the focus on the employees. They need to understand the value in it. why they should be adopting it. And changing that culture, that's a whole other separate conversation, but that's an, that's a very interesting and very challenging thing to do. I wish we had more time to talk about that one. >> Yeah. It really is an, that the approach of bringing everyone together, it makes it just very dynamic, which is what's needed when you have all of those different lenses coming together, so, yeah. >> It is, 'cause you get a little bit of thought diversity as well when we're using AI. Well, Dominic, thank you for joining me today. Talked to me about what you guys are doing with many different types of customers, how you're helping them to integrate emerging technologies to really transform their business and their culture. We appreciate your time. >> Well, thank you, Lisa. Thanks >> For Dominique Dubois, I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think, the digital event. (upbeat music)

>> Voice Over: Data is at the heart of transformation, and the change every company needs to succeed. But it takes more than new technology. It's about teams, talent and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you, it's time to lead the way, it's time for thought leaders. (soft upbeat music) >> Welcome to Thought.Leaders a digital event brought to you by ThoughtSpot, my name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers, and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not, ThoughtSpot is disrupting analytics, by using search and machine intelligence to simplify data analysis and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology but leadership, a mindset and a culture, that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action? And today we're going to hear from experienced leaders who are transforming their organizations with data, insights, and creating digital first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, chief data strategy officer of the ThoughtSpot is Cindi Howson, Cindi is an analytics and BI expert with 20 plus years experience, and the author of Successful Business Intelligence: Unlock the Value of BI & Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics Magic Quadrant. In early last year, she joined ThoughtSpot to help CEOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi great to see you, welcome to the show. >> Thank you Dave, nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair Hello Sudheesh, how are you doing today? >> I'm well, good to talk to you again. >> That's great to see you, thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course to our audience, and what they're going to learn today. (upbeat music) >> Thanks Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been you know, cooped up in our homes, I know that the vendors like us, we have amped up our sort of effort to reach out to you with, invites for events like this. So we are getting very more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one, that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time, we want to make sure that we value your time, then this is going to be used. Number two, we want to put you in touch with industry leaders and thought leaders, generally good people, that you want to hang around with long after this event is over. And number three, as we plan through this, you know we are living through these difficult times we want this event to be more of an uplifting and inspiring event too. Now, the challenge is how do you do that with the team being change agents, because teens and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, changes sort of like, if you've ever done bungee jumping, and it's like standing on the edges, waiting to make that one more step you know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step today. Change requires a lot of courage, and when we are talking about data and analytics, which is already like such a hard topic not necessarily an uplifting and positive conversation most businesses, it is somewhat scary, change becomes all the more difficult. Ultimately change requires courage, courage to first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that you know, maybe I don't have the power to make the change that the company needs, sometimes they feel like I don't have the skills, sometimes they may feel that I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations when it comes to data and insights that you talked about. You know, that are people in the company who are going to have the data because they know how to manage the data, how to inquire and extract, they know how to speak data, they have the skills to do that. But they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is the silo of people with the answers, and there is a silo of people with the questions, and there is gap, this sort of silos are standing in the way of making that necessary change that we all know the business needs. And the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process but sometimes no matter how big the company is or how small the company is you may need to bring some external stimuli to start the domino of the positive changes that are necessary. The group of people that we are brought in, the four people, including Cindi that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to dress the rope, that you will be safe and you're going to have fun, you will have that exhilarating feeling of jumping for a bungee jump, all four of them are exceptional, but my owner is to introduce Michelle. And she's our first speaker, Michelle I am very happy after watching our presentation and reading your bio that there are no country vital worldwide competition for cool parents, because she will beat all of us. Because when her children were small, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age where they like football and NFL, guess what? She's the CIO of NFL, what a cool mom. I am extremely excited to see what she's going to talk about. I've seen this slides, a bunch of amazing pictures, I'm looking to see the context behind it, I'm very thrilled to make that client so far, Michelle, I'm looking forward to her talk next. Welcome Michelle, it's over to you. (soft upbeat music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one, and I thought this is about as close as I'm ever going to get. So I want to talk to you about quarterbacking our digital revolution using insights data, and of course as you said, leadership. First a little bit about myself, a little background as I said, I always wanted to play football, and this is something that I wanted to do since I was a child, but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines, and a female official on the field. I'm a lifelong fan and student of the game of football, I grew up in the South, you can tell from the accent and in the South is like a religion and you pick sides. I chose Auburn University working in the Athletic Department, so I'm testament to you can start the journey can be long it took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well, not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football you know, this is a really big rivalry. And when you choose sides, your family is divided, so it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands. Delivering memories and amazing experiences that delight from Universal Studios, Disney to my current position as CIO of the NFL. In this job I'm very privileged to have the opportunity to work with the team, that gets to bring America's game to millions of people around the world. Often I'm asked to talk about how to create amazing experiences for fans, guests, or customers. But today I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event every game, every awesome moment is execution, precise repeatable execution. And most of my career has been behind the scenes, doing just that, assembling teams to execute these plans, and the key way that companies operate at these exceptional levels, is making good decisions, the right decisions at the right time and based upon data, so that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves. And it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kinds of world-class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney, in the 90s I was at Disney, leading a project called destination Disney, which it's a data project, it was a data project, but it was CRM before CRM was even cool. And then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today, like the magic band, just these magical express. My career at Disney began in finance, but Disney was very good about rotating you around, and it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team, asking for data more and more data. And I learned that all of that valuable data was locked up in our systems, all of our point of sales systems, our reservation systems, our operation systems, and so I became a shadow IT person in marketing, ultimately leading to moving into IT, and I haven't looked back since. In the early 2000s I was at Universal Studios Theme Park as their CIO, preparing for and launching the wizarding world of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wine shop. As today at the NFL, I am constantly challenged to do leading edge technologies using things like sensors, AI, machine learning, and all new communication strategies, and using data to drive everything from player performance, contracts to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contract tracing devices joined with testing data. Talk about data, actually enabling your business without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First RingCentral, it's a cloud based unified communications platform, and collaboration with video message and phone, all in one solution in the cloud. And Quotient Technologies, whose product is actually data. The tagline at quotient is the result in knowing. I think that's really important, because not all of us are data companies, where your product is actually data. But we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about, as thought leaders in your companies. First just hit on it is change, how to be a champion and a driver of change. Second, how to use data to drive performance for your company, and measure performance of your company. Third, how companies now require intense collaboration to operate, and finally, how much of this is accomplished through solid data-driven decisions. First let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it, and thankfully for the most part knock on wood we were prepared for it. But this year everyone's cheese was moved, all the people in the back rooms, IT, data architects and others, were suddenly called to the forefront. Because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, the 2020 Draft. We went from planning, a large event in Las Vegas under the bright lights red carpet stage to smaller events in club facilities. And then ultimately to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements. And we only had a few weeks to figure it out. I found myself for the first time being in the live broadcast event space, talking about bungee dress jumping, this is really what it felt like. It was one in which no one felt comfortable, because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky but it ended up being Oh, so rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at this level, highest level. As an example, the NFL has always measured performance obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact, those with the best stats, usually win the games. The NFL has always recorded stats, since the beginning of time, here at the NFL a little this year as our 100 and first year and athletes ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us, is both how much more we can measure, and the immediacy with which it can be measured. And I'm sure in your business, it's the same, the amount of data you must have has got to have quadrupled recently and how fast you need it and how quickly you need to analyze it, is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to a next level, it's powered by Amazon Web Services, and we gathered this data real time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast, and of course it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns speed, matchups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that we'll gather more and more information about player's performance as it relates to their health and safety. The third trend is really I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes it's important to think about for those of you that are IT professionals and developers, you know more than 10 years ago, agile practices began sweeping companies or small teams would work together rapidly in a very flexible, adaptive and innovative way, and it proved to be transformational. However today, of course, that is no longer just small teams the next big wave of change, and we've seen it through this pandemic is that it's the whole enterprise that must collaborate and be agile. If I look back on my career when I was at Disney, we owned everything 100%, we made a decision, we implemented it, we were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy in from the top down, you got the people from the bottom up to do it, and you executed. At Universal, we were a joint venture, our attractions and entertainment was licensed, our hotels were owned and managed by other third parties. So influence and collaboration and how to share across companies became very important. And now here I am at the NFL and even the bigger ecosystem. We have 32 clubs that are all separate businesses 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved centralized control has gotten less and less and has been replaced by intense collaboration not only within your own company, but across companies. The ability to work in a collaborative way across businesses and even other companies that has been a big key to my success in my career. I believe this whole vertical integration and big top down decision making is going by the wayside in favor of ecosystems that require cooperation, yet competition to coexist. I mean the NFL is a great example of what we call coopertition, which is cooperation and competition. When in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough, you must be able to turn it to insights, partnerships between technology teams who usually hold the keys to the raw data, and business units who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with first of all making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave, and drive, don't do the ride along program, it's very important to drive, driving can be high risk but it's also high reward. Embracing the uncertainty of what will happen, is how you become brave, get more and more comfortable with uncertainty be calm and let data be your map on your journey, thanks. >> Michelle, thank you so much. So you and I share a love of data, and a love of football. You said you want to be the quarterback, I'm more an old wine person. (Michelle laughing) >> Well, then I can do my job without you. >> Great, and I'm getting the feeling now you know, Sudheesh is talking about bungee jumping. My boat is when we're past this pandemic, we both take them to the Delaware Water Gap and we do the cliff jumping. >> That sounds good, I'll watch. >> You'll watch, okay, so Michelle, you have so many stakeholders when you're trying to prioritize the different voices, you have the players, you have the owners you have the league, as you mentioned to the broadcasters your, your partners here and football mamas like myself. How do you prioritize when there's so many different stakeholders that you need to satisfy? I think balancing across stakeholders starts with aligning on a mission. And if you spend a lot of time understanding where everyone's coming from, and you can find the common thread ties them all together you sort of do get them to naturally prioritize their work, and I think that's very important. So for us at the NFL, and even at Disney, it was our core values and our core purpose is so well known, and when anything challenges that we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent. And that means listening to every single stakeholder even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic and having a mission and understanding it, is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling. So I thank you for your metership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. (soft upbeat music) >> So we're going to take a hard pivot now and go from football to Chernobyl, Chernobyl, what went wrong? 1986, as the reactors were melting down they had the data to say, this is going to be catastrophic and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone," which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure the additional thousands getting cancer, and 20,000 years before the ground around there and even be inhabited again, This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with, and this is why I want you to focus on having fostering a data-driven culture. I don't want you to be a laggard, I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, isn't really two sides of the same coin, real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology, and recently a CDO said to me, "You know Cindi, I actually think this is two sides of the same coin. One reflects the other, what do you think?" Let me walk you through this, so let's take a laggard. What is the technology look like? Is it based on 1990s BI and reporting largely parameterized reports on-premises data warehouses, or not even that operational reports, at best one enterprise data warehouse very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to. Or is there also a culture of fear, afraid of failure, resistance to change complacency and sometimes that complacency it's not because people are lazy, it's because they've been so beaten down every time a new idea is presented. It's like, no we're measured on least cost to serve. So politics and distrust, whether it's between business and IT or individual stakeholders is the norm. So data is hoarded, let's contrast that with a leader, a data and analytics leader, what is their technology look like? Augmented analytics, search and AI-driven insights not on-premises, but in the cloud and maybe multiple clouds. And the data is not in one place, but it's in a data lake, and in a data warehouse, a logical data warehouse. The collaboration is being a newer methods whether it's Slack or teams allowing for that real time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust, there is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals, whether it's the best fan experience and player safety in the NFL or best serving your customers. It's innovative and collaborative. There's none of this, oh, well, I didn't invent that, I'm not going to look at that. There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas to fail fast, and they're energized, knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact what we like to call the new decision makers. Or really the frontline workers. So Harvard business review partnered with us to develop this study to say, just how important is this? They've been working at BI and analytics as an industry for more than 20 years. Why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager a warehouse manager, a financial services advisor. 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools, the sad reality only 20% of organizations are actually doing this, these are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state of the art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets really just taking data out of ERP systems that were also on-premises, and state of the art was maybe getting a management report, an operational report. Over time visual based data discovery vendors, disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data sometimes coming from a data warehouse, the current state of the art though, Gartner calls it augmented analytics, at ThoughtSpot, we call it search and AI-driven analytics. And this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses, and I think this is an important point. Oftentimes you, the data and analytics leaders, will look at these two components separately, but you have to look at the BI and analytics tier in lockstep with your data architectures to really get to the granular insights, and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot I'll just show you what this looks like, instead of somebody's hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom getting to a visualization that then can be pinned to an existing Pinboard that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non analyst to create themselves. Modernizing the data and analytics portfolio is hard, because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years, now it's maybe three years, and the time to maturity has also accelerated. So you have these different components the search and AI tier, the data science tier, data preparation and virtualization. But I would also say equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI-driven insights. Competitors have followed suit, but be careful if you look at products like Power BI or SAP Analytics Cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift or Azure Synapse or Google BigQuery, they do not. They require you to move it into a smaller in memory engine. So it's important how well these new products inter operate. The pace of change, it's acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI, and that is roughly three times the prediction they had just a couple years ago. So let's talk about the real world impact of culture. And if you've read any of my books or used any of the maturity models out there whether the Gartner IT score that I worked on, or the data warehousing institute also has a maturity model. We talk about these five pillars to really become data-driven, as Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources. It's the talent, the people, the technology, and also the processes, and often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders you have told me now culture is absolutely so important. And so we've pulled it out as a separate pillar, and in fact, in polls that we've done in these events, look at how much more important culture is, as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is, and let's take an example of where you can have great data but if you don't have the right culture there's devastating impacts. And I will say, I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data, that said, "Hey, we're not doing good cross selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts, facing billions in fines, change in leadership, that even the CEO attributed to a toxic sales culture, and they're trying to fix this. But even recently there's been additional employee backlash saying that culture has not changed. Let's contrast that with some positive examples, Medtronic a worldwide company in 150 countries around the world, they may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes you know, this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients, they took the bold move of making their IP for ventilators publicly available, that is the power of a positive culture. Or Verizon, a major telecom organization, looking at late payments of their customers, and even though the US federal government said "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, he said, "You know what? We will spend the time upskilling our people giving them the time to learn more about the future of work, the skills and data and analytics," for 20,000 of their employees, rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions, bring in a change agent identify the relevance, or I like to call it WIIFM, and organize for collaboration. So the CDO whatever your title is, chief analytics officer chief digital officer, you are the most important change agent. And this is where you will hear, that oftentimes a change agent has to come from outside the organization. So this is where, for example in Europe, you have the CDO of Just Eat takeout food delivery organization, coming from the airline industry or in Australia, National Australian Bank, taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in disrupt, it's a hard job. As one of you said to me, it often feels like Sisyphus, I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM, what is in it for me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline as well as those analysts, as well as the executives. So if we're talking about players in the NFL they want to perform better, and they want to stay safe. That is why data matters to them. If we're talking about financial services this may be a wealth management advisor, okay, we could say commissions, but it's really helping people have their dreams come true whether it's putting their children through college, or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers, you asked them about data, they'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better that is WIIFM. And sometimes we spend so much time talking the technology, we forget what is the value we're trying to deliver with it. And we forget the impact on the people that it does require change. In fact, the Harvard Business Review Study, found that 44% said lack of change management is the biggest barrier to leveraging both new technology but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI Competency Center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model, centralized for economies of scale, that could be the common data, but then in bed, these evangelists, these analysts of the future, within every business unit, every functional domain, and as you see this top bar, all models are possible but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time, because data is helping organizations better navigate a tough economy lock in the customer loyalty, and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at thought leaders, and next I'm pleased to introduce our first change agent Thomas Mazzaferro, chief data officer of Western Union, and before joining Western Union, Tom made his mark at HSBC and JP Morgan Chase spearheading digital innovation in technology operations, risk compliance, and retail banking. Tom, thank you so much for joining us today. (soft upbeat music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable, different business teams and technology teams into the future. As we look across our data ecosystems and our platforms and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive over the shift from a data standpoint, into the future. That includes being able to have the right information with the right quality of data at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that, as part of that partnership, and it's how we've looked to integrated into our overall business as a whole. We've looked at how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go on to google.com or you go on to Bing, or go to Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us as the same thing, but in the business world. So using ThoughtSpot and other AI capability is allowed us to actually enable our overall business teams in our company, to actually have our information at our fingertips. So rather than having to go and talk to someone or an engineer to go pull information or pull data, we actually can have the end users or the business executives, right? Search for what they need, what they want, at the exact time that action needed, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on the journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology or our (indistinct) environments, and as we move that we've actually picked to our cloud providers going to AWS and GCP. We've also adopted Snowflake to really drive into organize our information and our data, then drive these new solutions and capabilities forward. So big portion of us though is culture, so how do we engage with the business teams and bring the IT teams together to really drive these holistic end to end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven, this is the key. If you can really start to provide answers to business questions before they're even being asked, and to predict based upon different economic trends or different trends in your business, what does is be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization. And as part of that, it's really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions, or partnerships into the future. These are really some of the keys that become crucial as you move forward right into this new age, especially with COVID, with COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating, and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities, and those solutions forward. As we go through this journey, both of my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only a celebrating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes both on the platform standpoint, tools, but also what our customers want, what do our customers need, and how do we then surface them with our information, with our data, with our platform, with our products and our services, to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization such as how do you use your data to support the current business lines. But how do you actually use your information your data, to actually better support your customers better support your business, better support your employees, your operations teams and so forth, and really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon, thank you. >> Tom, that was great, thanks so much. Now I'm going to have to brag on you for a second, as a change agent you've come in disrupted, and how long have you been at Western Union? >> Only nine months, I just started this year, but there'd be some great opportunities and big changes, and we have a lot more to go, but we're really driving things forward in partnership with our business teams, and our colleagues to support those customers forward. >> Tom, thank you so much that was wonderful. And now I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe, and he is a serial change agent. Most recently with Schneider Electric, but even going back to Sam's Club, Gustavo welcome. (soft upbeat music) >> So hi everyone my name is Gustavo Canton and thank you so much Cindi for the intro. As you mentioned, doing transformations is a you know, high effort, high reward situation. I have empowerment in transformation and I have led many transformations. And what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today, is that you need to be bold to evolve. And so in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started barriers or opportunities as I see it, the value of AI, and also how do you communicate, especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are nontraditional sometimes. And so how do we get started? So I think the answer to that is, you have to start for you, yourself as a leader and stay tuned. And by that, I mean you need to understand not only what is happening in your function or your field, but you have to be very into what is happening in society, socioeconomically speaking, wellbeing, you know, the common example is a great example. And for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential, for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be you know, stay in tune and have the skillset and the courage. But for me personally, to be honest to have this courage is not about not being afraid. You're always afraid when you're making big changes and your swimming upstream. But what gives me the courage is the empathy part, like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business, and what the leaders are trying to do, what I do it thinking about the mission of how do I make change for the bigger, you know workforce so the bigger good, despite the fact that this might have a perhaps implication, so my own self interest in my career, right? Because you have to have that courage sometimes to make choices, that are not well seeing politically speaking what are the right thing to do, and you have to push through it. So the bottom line for me is that, I don't think they're transforming fast enough. And the reality is I speak with a lot of leaders and we have seen stories in the past, and what they show is that if you look at the four main barriers, that are basically keeping us behind budget, inability to add, cultural issues, politics, and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, this topic about culture is actually gaining more and more traction, and in 2018, there was a story from HBR and it was for about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand, and are aware that we need to transform, commit to the transformation and set us deadline to say, "Hey, in two years, we're going to make this happen, what do we need to do to empower and enable these search engines to make it happen?" You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So I'll give you samples of some of the roadblocks that I went through, as I think the intro information most recently as Cindi mentioned in Schneider. There are three main areas, legacy mindset, and what that means is that we've been doing this in a specific way for a long time, and here is how we have been successful. We're working the past is not going to work now, the opportunity there is that there is a lot of leaders who have a digital mindset, and their up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people you know, three to five years for them to develop, because the world is going to in a way that is super fast. The second area and this is specifically to implementation of AI is very interesting to me, because just example that I have with ThoughtSpot, right? We went to an implementation and a lot of the way the IT team functions, so the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, your opportunity here is that you need to really find what success look like, in my case, I want the user experience of our workforce to be the same as your experience you have at home. It's a very simple concept, and so we need to think about how do we gain that user experience with this augmented analytics tools, and then work backwards to have the right talent, processes and technology to enable that. And finally, and obviously with COVID a lot of pressure in organizations and companies to do more with less, and the solution that most leaders I see are taking is to just minimize cost sometimes and cut budget. We have to do the opposite, we have to actually invest some growth areas, but do it by business question. Don't do it by function, if you actually invest in these kind of solutions, if you actually invest on developing your talent, your leadership, to see more digitally, if you actually invest on fixing your data platform is not just an incremental cost, it's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work in working very hard but it's not efficiency, and it's not working in the way that you might want to work. So there is a lot of opportunity there, and you just to put it into some perspective, there have been some studies in the past about you know, how do we kind of measure the impact of data? And obviously this is going to vary by organization, maturity there's going to be a lot of factors. I've been in companies who have very clean, good data to work with, and I think with companies that we have to start basically from scratch. So it all depends on your maturity level, but in this study what I think is interesting is, they try to put a tagline or attack price to what is a cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work, when you have data that is flawed as opposed to have imperfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be a $100. But now let's say you have any percent perfect data and 20% flow data, by using this assumption that flow data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100, this just for you to really think about as a CIO, CTO, you know CSRO, CEO, are we really paying attention and really closing the gaps that we have on our infrastructure? If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this? Or how do I break through some of these challenges or some of these barriers, right? I think the key is I am in analytics, I know statistics obviously, and love modeling and you know, data and optimization theory and all that stuff, that's what I can do analytics, but now as a leader and as a change agent, I need to speak about value, and in this case, for example for Schneider, there was this tagline coffee of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that I understood what kind of language to use, how to connect it to the overall strategy and basically how to bring in the right leaders, because you need to, you know, focus on the leaders that you're going to make the most progress. You know, again, low effort, high value, you need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution, and finally you need to make it super simple for the you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics, I pulled up, it was actually launched in July of this year. And we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many manufacturers, but one thing that is really important is as you bring along your audience on this, you know, you're going from Excel, you know in some cases or Tableau to other tools like you know, ThoughtSpot, you need to really explain them, what is the difference, and how these two can truly replace some of the spreadsheets or some of the views that you might have on these other kind of tools. Again, Tableau, I think it's a really good tool, there are other many tools that you might have in your toolkit. But in my case, personally I feel that you need to have one portal going back to seeing these points that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory, and I will tell you why, because it took a lot of effort for us to get to these stations. Like I said it's been years for us to kind of lay the foundation, get the leadership and chasing culture, so people can understand why you truly need to invest what I meant analytics. And so what I'm showing here is an example of how do we use basically, you know a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics, hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week per employee save on average, user experience or ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot we were able to achieve five hours, per week per employee savings. I used to experience for 4.3 out of five, and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications obviously the operations things and the users, in HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize this kind of effort takes a lot of energy, you are a change agent, you need to have a courage to make these decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these very souls for this organization, and that gave me the confidence to know that the work has been done, and we are now in a different stage for the organization. And so for me it safe to say, thank you for everybody who has believed obviously in our vision, everybody who has believed in, you know, the word that we were trying to do and to make the life for, you know workforce or customers that are in community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation, and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream you know, what would mentors what people in this industry that can help you out and guide you on this kind of a transformation is not easy to do is high effort but is well worth it. And with that said, I hope you are well and it's been a pleasure talking to you, talk to you soon, take care. >> Thank you Gustavo, that was amazing. All right, let's go to the panel. (soft upbeat music) >> I think we can all agree how valuable it is to hear from practitioners, and I want to thank the panel for sharing their knowledge with the community, and one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time, and it is critical to have support from the top, why? Because it directs the middle, and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard, is that you all prioritize database decision making in your organizations, and you combine two of your most valuable assets to do that, and create leverage, employees on the front lines, and of course the data. That was rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID's broken everything. And it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo let's start with you if I'm an aspiring change agent, and let's say I'm a budding data leader. What do I need to start doing? What habits do I need to create for long lasting success? >> I think curiosity is very important. You need to be, like I say, in tune to what is happening not only in your specific field, like I have a passion for analytics, I can do this for 50 years plus, but I think you need to understand wellbeing other areas across not only a specific business as you know, I come from, you know, Sam's Club Walmart retail, I mean energy management technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to use lean continuous improvement that's just going to take you so far. What you have to do is and that's what I tried to do is I try to go into areas, businesses and transformations that make me, you know stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions organizations, and do these change management and decisions mindset as required for these kinds of efforts. >> Thank you for that is inspiring and Cindi, you love data, and the data is pretty clear that diversity is a good business, but I wonder if you can add your perspectives to this conversation. >> Yeah, so Michelle has a new fan here because she has found her voice, I'm still working on finding mine. And it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment. But why I think diversity matters more now than ever before, and this is by gender, by race, by age, by just different ways of working and thinking is because as we automate things with AI, if we do not have diverse teams looking at the data and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority, you are finding your voice, having a seat at the table and just believing in the impact of your work has never been more important. And as Michelle said more possible >> Great perspectives thank you, Tom, I want to go to you. I mean, I feel like everybody in our businesses in some way, shape or form become a COVID expert but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth actually you know, in a digital business over the last 12 months really, even in celebration, right? Once COVID hit, we really saw that in the 200 countries and territories that we operate in today and service our customers and today, that there's been a huge need, right? To send money, to support family, to support friends and loved ones across the world. And as part of that, you know, we are very honored to support those customers that we across all the centers today. But as part of that celebration, we need to make sure that we had the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did celebrate some of our plans on digital to help support that overall growth coming in, and to support our customers going forward. Because there were these times during this pandemic, right? This is the most important time, and we need to support those that we love and those that we care about. And in doing that, it's one of those ways is actually by sending money to them, support them financially. And that's where really are part of that our services come into play that, you know, I really support those families. So it was really a great opportunity for us to really support and really bring some of our products to this level, and supporting our business going forward. >> Awesome, thank you. Now I want to come back to Gustavo, Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much and doing things with data or the technology that was just maybe too bold, maybe you felt like at some point it was failing, or you pushing your people too hard, can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization I ask the question, Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right? It forces us to remove silos and collaborate in a faster way, so to me it was an opportunity to actually integrate with other areas and drive decisions faster. But make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing and you need to be okay with that. Sometimes you need to be okay with tension, or you need to be okay, you know debating points or making repetitive business cases onto people connect with the decision because you understand, and you are seeing that, hey, the CEO is making a one, two year, you know, efficiency goal, the only way for us to really do more with less is for us to continue this path. We cannot just stay with the status quo, we need to find a way to accelerate transformation... >> How about you Tom, we were talking earlier was Sudheesh had said about that bungee jumping moment, what can you share? >> Yeah you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right? That's what I tell my team is that you need to feel comfortable being uncomfortable. I mean, that we have to be able to basically scale, right? Expand and support that the ever changing needs the marketplace and industry and our customers today and that pace of change that's happening, right? And what customers are asking for, and the competition the marketplace, it's only going to accelerate. So as part of that, you know, as we look at what how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan into align, to drive the actual transformation, so that you can scale even faster into the future. So as part of that, so we're putting in place here, right? Is how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> We're definitely out of our comfort zones, but we're getting comfortable with it. So, Cindi, last question, you've worked with hundreds of organizations, and I got to believe that you know, some of the advice you gave when you were at Gartner, which is pre COVID, maybe sometimes clients didn't always act on it. You know, they're not on my watch for whatever variety of reasons, but it's being forced on them now, but knowing what you know now that you know, we're all in this isolation economy how would you say that advice has changed, has it changed? What's your number one action and recommendation today? >> Yeah well, first off, Tom just freaked me out. What do you mean this is the slowest ever? Even six months ago, I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more, very aware of the power in politics and how to bring people along in a way that they are comfortable, and now I think it's, you know what? You can't get comfortable. In fact, we know that the organizations that were already in the cloud, have been able to respond and pivot faster. So if you really want to survive as Tom and Gustavo said, get used to being uncomfortable, the power and politics are going to happen. Break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy as Michelle said, and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where's Sudheesh going to go on bungee jumping? (all chuckling) >> That's fantastic discussion really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things, whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise wide digital transformation, not just as I said before lip service. And sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tremendous results. Yeah, what does that mean getting it right? Everybody's trying to get it right. My biggest takeaway today, is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions that can drive you revenue, cut costs, speed, access to critical care, whatever the mission is of your organization. Data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh please bring us home. >> Thank you, thank you Dave, thank you theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I had from all four of our distinguished speakers. First, Michelle, I was simply put it, she said it really well, that is be brave and drive. Don't go for a drive along, that is such an important point. Often times, you know that I think that you have to do to make the positive change that you want to see happen. But you wait for someone else to do it, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding the importance of finding your voice, taking that chair, whether it's available or not and making sure that your ideas, your voices are heard and if it requires some force then apply that force, make sure your ideas are good. Gustavo talked about the importance of building consensus, not going at things all alone sometimes building the importance of building the courtroom. And that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom instead of a single take away, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in, and they were able to make the change that is necessary through this difficult time. So in a matter of months, if they could do it, anyone could. The second thing I want to do is to leave you with a takeaway that is I would like you to go to thoughtspot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to thoughtspot.com/beyond, our global user conferences happening in this December, we would love to have you join us. It's again, virtual, you can join from anywhere, we are expecting anywhere from five to 10,000 people, and we would love to have you join and see what we would have been up to since the last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing, you'll be sharing things that you have been working to release something that will come out next year. And also some of the crazy ideas for engineers I've been cooking up. All of those things will be available for you at ThoughtSpot Beyond, thank you, thank you so much.

(speaks in foreign language) >> Narrator: Data is at the heart of transformation and the change every company needs to succeed, but it takes more than new technology. It's about teams, talent, and cultural change. Empowering everyone on the front lines to make decisions, all at the speed of digital. The transformation starts with you. It's time to lead the way, it's time for thought leaders. >> Welcome to Thought Leaders, a digital event brought to you by ThoughtSpot. My name is Dave Vellante. The purpose of this day is to bring industry leaders and experts together to really try and understand the important issues around digital transformation. We have an amazing lineup of speakers and our goal is to provide you with some best practices that you can bring back and apply to your organization. Look, data is plentiful, but insights are not. ThoughtSpot is disrupting analytics by using search and machine intelligence to simplify data analysis, and really empower anyone with fast access to relevant data. But in the last 150 days, we've had more questions than answers. Creating an organization that puts data and insights at their core, requires not only modern technology, but leadership, a mindset and a culture that people often refer to as data-driven. What does that mean? How can we equip our teams with data and fast access to quality information that can turn insights into action. And today, we're going to hear from experienced leaders, who are transforming their organizations with data, insights and creating digital-first cultures. But before we introduce our speakers, I'm joined today by two of my co-hosts from ThoughtSpot. First, Chief Data Strategy Officer for ThoughtSpot is Cindi Hausen. Cindi is an analytics and BI expert with 20 plus years experience and the author of Successful Business Intelligence Unlock The Value of BI and Big Data. Cindi was previously the lead analyst at Gartner for the data and analytics magic quadrant. And early last year, she joined ThoughtSpot to help CDOs and their teams understand how best to leverage analytics and AI for digital transformation. Cindi, great to see you, welcome to the show. >> Thank you, Dave. Nice to join you virtually. >> Now our second cohost and friend of theCUBE is ThoughtSpot CEO Sudheesh Nair. Hello Sudheesh, how are you doing today? >> I am well Dave, it's good to talk to you again. >> It's great to see you. Thanks so much for being here. Now Sudheesh, please share with us why this discussion is so important to your customers and of course, to our audience and what they're going to learn today? (gentle music) >> Thanks, Dave, I wish you were there to introduce me into every room that I walk into because you have such an amazing way of doing it. It makes me feel also good. Look, since we have all been cooped up in our homes, I know that the vendors like us, we have amped up our, you know, sort of effort to reach out to you with invites for events like this. So we are getting way more invites for events like this than ever before. So when we started planning for this, we had three clear goals that we wanted to accomplish. And our first one that when you finish this and walk away, we want to make sure that you don't feel like it was a waste of time. We want to make sure that we value your time, and this is going to be useful. Number two, we want to put you in touch with industry leaders and thought leaders, and generally good people that you want to hang around with long after this event is over. And number three, as we plan through this, you know, we are living through these difficult times, we want an event to be, this event to be more of an uplifting and inspiring event too. Now, the challenge is, how do you do that with the team being change agents? Because change and as much as we romanticize it, it is not one of those uplifting things that everyone wants to do or likes to do. The way I think of it, change is sort of like, if you've ever done bungee jumping. You know, it's like standing on the edges, waiting to make that one more step. You know, all you have to do is take that one step and gravity will do the rest, but that is the hardest step to take. Change requires a lot of courage and when we are talking about data and analytics, which is already like such a hard topic, not necessarily an uplifting and positive conversation, in most businesses it is somewhat scary. Change becomes all the more difficult. Ultimately change requires courage. Courage to to, first of all, challenge the status quo. People sometimes are afraid to challenge the status quo because they are thinking that, "You know, maybe I don't have the power to make the change that the company needs. Sometimes I feel like I don't have the skills." Sometimes they may feel that, I'm probably not the right person to do it. Or sometimes the lack of courage manifest itself as the inability to sort of break the silos that are formed within the organizations, when it comes to data and insights that you talked about. You know, there are people in the company, who are going to hog the data because they know how to manage the data, how to inquire and extract. They know how to speak data, they have the skills to do that, but they are not the group of people who have sort of the knowledge, the experience of the business to ask the right questions off the data. So there is this silo of people with the answers and there is a silo of people with the questions, and there is gap. These sort of silos are standing in the way of making that necessary change that we all I know the business needs, and the last change to sort of bring an external force sometimes. It could be a tool, it could be a platform, it could be a person, it could be a process, but sometimes no matter how big the company is or how small the company is. You may need to bring some external stimuli to start that domino of the positive changes that are necessary. The group of people that we have brought in, the four people, including Cindi, that you will hear from today are really good at practically telling you how to make that step, how to step off that edge, how to trust the rope that you will be safe and you're going to have fun. You will have that exhilarating feeling of jumping for a bungee jump. All four of them are exceptional, but my honor is to introduce Michelle and she's our first speaker. Michelle, I am very happy after watching her presentation and reading her bio, that there are no country vital worldwide competition for cool patents, because she will beat all of us because when her children were small, you know, they were probably into Harry Potter and Disney and she was managing a business and leading change there. And then as her kids grew up and got to that age, where they like football and NFL, guess what? She's the CIO of NFL. What a cool mom. I am extremely excited to see what she's going to talk about. I've seen the slides with a bunch of amazing pictures, I'm looking to see the context behind it. I'm very thrilled to make the acquaintance of Michelle. I'm looking forward to her talk next. Welcome Michelle. It's over to you. (gentle music) >> I'm delighted to be with you all today to talk about thought leadership. And I'm so excited that you asked me to join you because today I get to be a quarterback. I always wanted to be one. This is about as close as I'm ever going to get. So, I want to talk to you about quarterbacking our digital revolution using insights, data and of course, as you said, leadership. First, a little bit about myself, a little background. As I said, I always wanted to play football and this is something that I wanted to do since I was a child but when I grew up, girls didn't get to play football. I'm so happy that that's changing and girls are now doing all kinds of things that they didn't get to do before. Just this past weekend on an NFL field, we had a female coach on two sidelines and a female official on the field. I'm a lifelong fan and student of the game of football. I grew up in the South. You can tell from the accent and in the South football is like a religion and you pick sides. I chose Auburn University working in the athletic department, so I'm testament. Till you can start, a journey can be long. It took me many, many years to make it into professional sports. I graduated in 1987 and my little brother, well not actually not so little, he played offensive line for the Alabama Crimson Tide. And for those of you who know SEC football, you know this is a really big rivalry, and when you choose sides your family is divided. So it's kind of fun for me to always tell the story that my dad knew his kid would make it to the NFL, he just bet on the wrong one. My career has been about bringing people together for memorable moments at some of America's most iconic brands, delivering memories and amazing experiences that delight. From Universal Studios, Disney, to my current position as CIO of the NFL. In this job, I'm very privileged to have the opportunity to work with a team that gets to bring America's game to millions of people around the world. Often, I'm asked to talk about how to create amazing experiences for fans, guests or customers. But today, I really wanted to focus on something different and talk to you about being behind the scenes and backstage. Because behind every event, every game, every awesome moment, is execution. Precise, repeatable execution and most of my career has been behind the scenes doing just that. Assembling teams to execute these plans and the key way that companies operate at these exceptional levels is making good decisions, the right decisions, at the right time and based upon data. So that you can translate the data into intelligence and be a data-driven culture. Using data and intelligence is an important way that world-class companies do differentiate themselves, and it's the lifeblood of collaboration and innovation. Teams that are working on delivering these kind of world class experiences are often seeking out and leveraging next generation technologies and finding new ways to work. I've been fortunate to work across three decades of emerging experiences, which each required emerging technologies to execute. A little bit first about Disney. In '90s I was at Disney leading a project called Destination Disney, which it's a data project. It was a data project, but it was CRM before CRM was even cool and then certainly before anything like a data-driven culture was ever brought up. But way back then we were creating a digital backbone that enabled many technologies for the things that you see today. Like the MagicBand, Disney's Magical Express. My career at Disney began in finance, but Disney was very good about rotating you around. And it was during one of these rotations that I became very passionate about data. I kind of became a pain in the butt to the IT team asking for data, more and more data. And I learned that all of that valuable data was locked up in our systems. All of our point of sales systems, our reservation systems, our operation systems. And so I became a shadow IT person in marketing, ultimately, leading to moving into IT and I haven't looked back since. In the early 2000s, I was at Universal Studio's theme park as their CIO preparing for and launching the Wizarding World of Harry Potter. Bringing one of history's most memorable characters to life required many new technologies and a lot of data. Our data and technologies were embedded into the rides and attractions. I mean, how do you really think a wand selects you at a wand shop. As today at the NFL, I am constantly challenged to do leading edge technologies, using things like sensors, AI, machine learning and all new communication strategies, and using data to drive everything, from player performance, contracts, to where we build new stadiums and hold events. With this year being the most challenging, yet rewarding year in my career at the NFL. In the middle of a global pandemic, the way we are executing on our season is leveraging data from contact tracing devices joined with testing data. Talk about data actually enabling your business. Without it we wouldn't be having a season right now. I'm also on the board of directors of two public companies, where data and collaboration are paramount. First, RingCentral, it's a cloud based unified communications platform and collaboration with video message and phone, all-in-one solution in the cloud and Quotient Technologies, whose product is actually data. The tagline at Quotient is The Result in Knowing. I think that's really important because not all of us are data companies, where your product is actually data, but we should operate more like your product is data. I'd also like to talk to you about four areas of things to think about as thought leaders in your companies. First, just hit on it, is change. how to be a champion and a driver of change. Second, how to use data to drive performance for your company and measure performance of your company. Third, how companies now require intense collaboration to operate and finally, how much of this is accomplished through solid data-driven decisions. First, let's hit on change. I mean, it's evident today more than ever, that we are in an environment of extreme change. I mean, we've all been at this for years and as technologists we've known it, believed it, lived it. And thankfully, for the most part, knock on wood, we were prepared for it. But this year everyone's cheese was moved. All the people in the back rooms, IT, data architects and others were suddenly called to the forefront because a global pandemic has turned out to be the thing that is driving intense change in how people work and analyze their business. On March 13th, we closed our office at the NFL in the middle of preparing for one of our biggest events, our kickoff event, The 2020 Draft. We went from planning a large event in Las Vegas under the bright lights, red carpet stage, to smaller events in club facilities. And then ultimately, to one where everyone coaches, GMs, prospects and even our commissioner were at home in their basements and we only had a few weeks to figure it out. I found myself for the first time, being in the live broadcast event space. Talking about bungee jumping, this is really what it felt like. It was one in which no one felt comfortable because it had not been done before. But leading through this, I stepped up, but it was very scary, it was certainly very risky, but it ended up being also rewarding when we did it. And as a result of this, some things will change forever. Second, managing performance. I mean, data should inform how you're doing and how to get your company to perform at its level, highest level. As an example, the NFL has always measured performance, obviously, and it is one of the purest examples of how performance directly impacts outcome. I mean, you can see performance on the field, you can see points being scored and stats, and you immediately know that impact. Those with the best stats usually win the games. The NFL has always recorded stats. Since the beginning of time here at the NFL a little... This year is our 101st year and athlete's ultimate success as a player has also always been greatly impacted by his stats. But what has changed for us is both how much more we can measure and the immediacy with which it can be measured and I'm sure in your business it's the same. The amount of data you must have has got to have quadrupled recently. And how fast do you need it and how quickly you need to analyze it is so important. And it's very important to break the silos between the keys to the data and the use of the data. Our next generation stats platform is taking data to the next level. It's powered by Amazon Web Services and we gather this data, real-time from sensors that are on players' bodies. We gather it in real time, analyze it, display it online and on broadcast. And of course, it's used to prepare week to week in addition to what is a normal coaching plan would be. We can now analyze, visualize, route patterns, speed, match-ups, et cetera, so much faster than ever before. We're continuing to roll out sensors too, that will gather more and more information about a player's performance as it relates to their health and safety. The third trend is really, I think it's a big part of what we're feeling today and that is intense collaboration. And just for sort of historical purposes, it's important to think about, for those of you that are IT professionals and developers, you know, more than 10 years ago agile practices began sweeping companies. Where small teams would work together rapidly in a very flexible, adaptive and innovative way and it proved to be transformational. However today, of course that is no longer just small teams, the next big wave of change and we've seen it through this pandemic, is that it's the whole enterprise that must collaborate and be agile. If I look back on my career, when I was at Disney, we owned everything 100%. We made a decision, we implemented it. We were a collaborative culture but it was much easier to push change because you own the whole decision. If there was buy-in from the top down, you got the people from the bottom up to do it and you executed. At Universal, we were a joint venture. Our attractions and entertainment was licensed. Our hotels were owned and managed by other third parties, so influence and collaboration, and how to share across companies became very important. And now here I am at the NFL an even the bigger ecosystem. We have 32 clubs that are all separate businesses, 31 different stadiums that are owned by a variety of people. We have licensees, we have sponsors, we have broadcast partners. So it seems that as my career has evolved, centralized control has gotten less and less and has been replaced by intense collaboration, not only within your own company but across companies. The ability to work in a collaborative way across businesses and even other companies, that has been a big key to my success in my career. I believe this whole vertical integration and big top-down decision-making is going by the wayside in favor of ecosystems that require cooperation, yet competition to co-exist. I mean, the NFL is a great example of what we call co-oppetition, which is cooperation and competition. We're in competition with each other, but we cooperate to make the company the best it can be. And at the heart of these items really are data-driven decisions and culture. Data on its own isn't good enough. You must be able to turn it to insights. Partnerships between technology teams who usually hold the keys to the raw data and business units, who have the knowledge to build the right decision models is key. If you're not already involved in this linkage, you should be, data mining isn't new for sure. The availability of data is quadrupling and it's everywhere. How do you know what to even look at? How do you know where to begin? How do you know what questions to ask? It's by using the tools that are available for visualization and analytics and knitting together strategies of the company. So it begins with, first of all, making sure you do understand the strategy of the company. So in closing, just to wrap up a bit, many of you joined today, looking for thought leadership on how to be a change agent, a change champion, and how to lead through transformation. Some final thoughts are be brave and drive. Don't do the ride along program, it's very important to drive. Driving can be high risk, but it's also high reward. Embracing the uncertainty of what will happen is how you become brave. Get more and more comfortable with uncertainty, be calm and let data be your map on your journey. Thanks. >> Michelle, thank you so much. So you and I share a love of data and a love of football. You said you want to be the quarterback. I'm more an a line person. >> Well, then I can't do my job without you. >> Great and I'm getting the feeling now, you know, Sudheesh is talking about bungee jumping. My vote is when we're past this pandemic, we both take him to the Delaware Water Gap and we do the cliff jumping. >> Oh that sounds good, I'll watch your watch. >> Yeah, you'll watch, okay. So Michelle, you have so many stakeholders, when you're trying to prioritize the different voices you have the players, you have the owners, you have the league, as you mentioned, the broadcasters, your partners here and football mamas like myself. How do you prioritize when there are so many different stakeholders that you need to satisfy? >> I think balancing across stakeholders starts with aligning on a mission and if you spend a lot of time understanding where everyone's coming from, and you can find the common thread that ties them all together. You sort of do get them to naturally prioritize their work and I think that's very important. So for us at the NFL and even at Disney, it was our core values and our core purpose is so well known and when anything challenges that, we're able to sort of lay that out. But as a change agent, you have to be very empathetic, and I would say empathy is probably your strongest skill if you're a change agent and that means listening to every single stakeholder. Even when they're yelling at you, even when they're telling you your technology doesn't work and you know that it's user error, or even when someone is just emotional about what's happening to them and that they're not comfortable with it. So I think being empathetic, and having a mission, and understanding it is sort of how I prioritize and balance. >> Yeah, empathy, a very popular word this year. I can imagine those coaches and owners yelling, so thank you for your leadership here. So Michelle, I look forward to discussing this more with our other customers and disruptors joining us in a little bit. >> (gentle music) So we're going to take a hard pivot now and go from football to Chernobyl. Chernobyl, what went wrong? 1986, as the reactors were melting down, they had the data to say, "This is going to be catastrophic," and yet the culture said, "No, we're perfect, hide it. Don't dare tell anyone." Which meant they went ahead and had celebrations in Kiev. Even though that increased the exposure, additional thousands getting cancer and 20,000 years before the ground around there can even be inhabited again. This is how powerful and detrimental a negative culture, a culture that is unable to confront the brutal facts that hides data. This is what we have to contend with and this is why I want you to focus on having, fostering a data-driven culture. I don't want you to be a laggard. I want you to be a leader in using data to drive your digital transformation. So I'll talk about culture and technology, is it really two sides of the same coin? Real-world impacts and then some best practices you can use to disrupt and innovate your culture. Now, oftentimes I would talk about culture and I talk about technology. And recently a CDO said to me, "You know, Cindi, I actually think this is two sides of the same coin, one reflects the other." What do you think? Let me walk you through this. So let's take a laggard. What does the technology look like? Is it based on 1990s BI and reporting, largely parametrized reports, on-premises data warehouses, or not even that operational reports. At best one enterprise data warehouse, very slow moving and collaboration is only email. What does that culture tell you? Maybe there's a lack of leadership to change, to do the hard work that Sudheesh referred to, or is there also a culture of fear, afraid of failure, resistance to change, complacency. And sometimes that complacency, it's not because people are lazy. It's because they've been so beaten down every time a new idea is presented. It's like, "No, we're measured on least to serve." So politics and distrust, whether it's between business and IT or individual stakeholders is the norm, so data is hoarded. Let's contrast that with the leader, a data and analytics leader, what does their technology look like? Augmented analytics, search and AI driven insights, not on-premises but in the cloud and maybe multiple clouds. And the data is not in one place but it's in a data lake and in a data warehouse, a logical data warehouse. The collaboration is via newer methods, whether it's Slack or Teams, allowing for that real-time decisioning or investigating a particular data point. So what is the culture in the leaders? It's transparent and trust. There is a trust that data will not be used to punish, that there is an ability to confront the bad news. It's innovation, valuing innovation in pursuit of the company goals. Whether it's the best fan experience and player safety in the NFL or best serving your customers, it's innovative and collaborative. There's none of this, "Oh, well, I didn't invent that. I'm not going to look at that." There's still pride of ownership, but it's collaborating to get to a better place faster. And people feel empowered to present new ideas, to fail fast and they're energized knowing that they're using the best technology and innovating at the pace that business requires. So data is democratized and democratized, not just for power users or analysts, but really at the point of impact, what we like to call the new decision-makers or really the frontline workers. So Harvard Business Review partnered with us to develop this study to say, "Just how important is this? We've been working at BI and analytics as an industry for more than 20 years, why is it not at the front lines? Whether it's a doctor, a nurse, a coach, a supply chain manager, a warehouse manager, a financial services advisor." 87% said they would be more successful if frontline workers were empowered with data-driven insights, but they recognize they need new technology to be able to do that. It's not about learning hard tools. The sad reality only 20% of organizations are actually doing this. These are the data-driven leaders. So this is the culture and technology, how did we get here? It's because state-of-the-art keeps changing. So the first generation BI and analytics platforms were deployed on-premises, on small datasets, really just taking data out of ERP systems that were also on-premises and state-of-the-art was maybe getting a management report, an operational report. Over time, visual based data discovery vendors disrupted these traditional BI vendors, empowering now analysts to create visualizations with the flexibility on a desktop, sometimes larger data, sometimes coming from a data warehouse. The current state-of-the-art though, Gartner calls it augmented analytics. At ThoughtSpot, we call it search and AI driven analytics, and this was pioneered for large scale data sets, whether it's on-premises or leveraging the cloud data warehouses. And I think this is an important point, oftentimes you, the data and analytics leaders, will look at these two components separately. But you have to look at the BI and analytics tier in lock-step with your data architectures to really get to the granular insights and to leverage the capabilities of AI. Now, if you've never seen ThoughtSpot, I'll just show you what this looks like. Instead of somebody hard coding a report, it's typing in search keywords and very robust keywords contains rank, top, bottom, getting to a visual visualization that then can be pinned to an existing pin board that might also contain insights generated by an AI engine. So it's easy enough for that new decision maker, the business user, the non-analyst to create themselves. Modernizing the data and analytics portfolio is hard because the pace of change has accelerated. You used to be able to create an investment, place a bet for maybe 10 years. A few years ago, that time horizon was five years. Now, it's maybe three years and the time to maturity has also accelerated. So you have these different components, the search and AI tier, the data science tier, data preparation and virtualization but I would also say, equally important is the cloud data warehouse. And pay attention to how well these analytics tools can unlock the value in these cloud data warehouses. So ThoughtSpot was the first to market with search and AI driven insights. Competitors have followed suit, but be careful, if you look at products like Power BI or SAP analytics cloud, they might demo well, but do they let you get to all the data without moving it in products like Snowflake, Amazon Redshift, or Azure Synapse, or Google BigQuery, they do not. They require you to move it into a smaller in-memory engine. So it's important how well these new products inter-operate. The pace of change, its acceleration, Gartner recently predicted that by 2022, 65% of analytical queries will be generated using search or NLP or even AI and that is roughly three times the prediction they had just a couple of years ago. So let's talk about the real world impact of culture and if you've read any of my books or used any of the maturity models out there, whether the Gartner IT Score that I worked on or the Data Warehousing Institute also has a maturity model. We talk about these five pillars to really become data-driven. As Michelle spoke about, it's focusing on the business outcomes, leveraging all the data, including new data sources, it's the talent, the people, the technology and also the processes. And often when I would talk about the people in the talent, I would lump the culture as part of that. But in the last year, as I've traveled the world and done these digital events for thought leaders. You have told me now culture is absolutely so important, and so we've pulled it out as a separate pillar. And in fact, in polls that we've done in these events, look at how much more important culture is as a barrier to becoming data-driven. It's three times as important as any of these other pillars. That's how critical it is. And let's take an example of where you can have great data, but if you don't have the right culture, there's devastating impacts. And I will say I have been a loyal customer of Wells Fargo for more than 20 years, but look at what happened in the face of negative news with data. It said, "Hey, we're not doing good cross-selling, customers do not have both a checking account and a credit card and a savings account and a mortgage." They opened fake accounts facing billions in fines, change in leadership that even the CEO attributed to a toxic sales culture and they're trying to fix this, but even recently there's been additional employee backlash saying the culture has not changed. Let's contrast that with some positive examples. Medtronic, a worldwide company in 150 countries around the world. They may not be a household name to you, but if you have a loved one or yourself, you have a pacemaker, spinal implant, diabetes, you know this brand. And at the start of COVID when they knew their business would be slowing down, because hospitals would only be able to take care of COVID patients. They took the bold move of making their IP for ventilators publicly available. That is the power of a positive culture. Or Verizon, a major telecom organization looking at late payments of their customers and even though the U.S. Federal Government said, "Well, you can't turn them off." They said, "We'll extend that even beyond the mandated guidelines," and facing a slow down in the business because of the tough economy, They said, "You know what? We will spend the time upskilling our people, giving them the time to learn more about the future of work, the skills and data and analytics for 20,000 of their employees rather than furloughing them. That is the power of a positive culture. So how can you transform your culture to the best in class? I'll give you three suggestions. Bring in a change agent, identify the relevance or I like to call it WIIFM and organize for collaboration. So the CDO, whatever your title is, Chief Analytics Officer, Chief Digital Officer, you are the most important change agent. And this is where you will hear that oftentimes a change agent has to come from outside the organization. So this is where, for example, in Europe you have the CDO of Just Eat, a takeout food delivery organization coming from the airline industry or in Australia, National Australian Bank taking a CDO within the same sector from TD Bank going to NAB. So these change agents come in, disrupt. It's a hard job. As one of you said to me, it often feels like. I make one step forward and I get knocked down again, I get pushed back. It is not for the faint of heart, but it's the most important part of your job. The other thing I'll talk about is WIIFM What's In It For Me? And this is really about understanding the motivation, the relevance that data has for everyone on the frontline, as well as those analysts, as well as the executives. So, if we're talking about players in the NFL, they want to perform better and they want to stay safe. That is why data matters to them. If we're talking about financial services, this may be a wealth management advisor. Okay, we could say commissions, but it's really helping people have their dreams come true, whether it's putting their children through college or being able to retire without having to work multiple jobs still into your 70s or 80s. For the teachers, teachers you ask them about data. They'll say, "We don't need that, I care about the student." So if you can use data to help a student perform better, that is WIIFM and sometimes we spend so much time talking the technology, we forget, what is the value we're trying to deliver with this? And we forget the impact on the people that it does require change. In fact, the Harvard Business Review study found that 44% said lack of change management is the biggest barrier to leveraging both new technology, but also being empowered to act on those data-driven insights. The third point, organize for collaboration. This does require diversity of thought, but also bringing the technology, the data and the business people together. Now there's not a single one size fits all model for data and analytics. At one point in time, even having a BICC, a BI competency center was considered state of the art. Now for the biggest impact, what I recommend is that you have a federated model centralized for economies of scale. That could be the common data, but then embed these evangelists, these analysts of the future within every business unit, every functional domain. And as you see this top bar, all models are possible, but the hybrid model has the most impact, the most leaders. So as we look ahead to the months ahead, to the year ahead, an exciting time because data is helping organizations better navigate a tough economy, lock in the customer loyalty and I look forward to seeing how you foster that culture that's collaborative with empathy and bring the best of technology, leveraging the cloud, all your data. So thank you for joining us at Thought Leaders. And next, I'm pleased to introduce our first change agent, Tom Mazzaferro Chief Data Officer of Western Union and before joining Western Union, Tom made his Mark at HSBC and JP Morgan Chase spearheading digital innovation in technology, operations, risk compliance and retail banking. Tom, thank you so much for joining us today. (gentle music) >> Very happy to be here and looking forward to talking to all of you today. So as we look to move organizations to a data-driven capability into the future, there is a lot that needs to be done on the data side, but also how does data connect and enable different business teams and the technology teams into the future? As we look across our data ecosystems and our platforms, and how we modernize that to the cloud in the future, it all needs to basically work together, right? To really be able to drive an organization from a data standpoint, into the future. That includes being able to have the right information with the right quality of data, at the right time to drive informed business decisions, to drive the business forward. As part of that, we actually have partnered with ThoughtSpot to actually bring in the technology to help us drive that. As part of that partnership and it's how we've looked to integrate it into our overall business as a whole. We've looked at, how do we make sure that our business and our professional lives, right? Are enabled in the same ways as our personal lives. So for example, in your personal lives, when you want to go and find something out, what do you do? You go onto google.com or you go onto Bing or you go onto Yahoo and you search for what you want, search to find an answer. ThoughtSpot for us is the same thing, but in the business world. So using ThoughtSpot and other AI capability is it's allowed us to actually enable our overall business teams in our company to actually have our information at our fingertips. So rather than having to go and talk to someone, or an engineer to go pull information or pull data. We actually can have the end users or the business executives, right. Search for what they need, what they want, at the exact time that they actually need it, to go and drive the business forward. This is truly one of those transformational things that we've put in place. On top of that, we are on a journey to modernize our larger ecosystem as a whole. That includes modernizing our underlying data warehouses, our technology, our... The local environments and as we move that, we've actually picked two of our cloud providers going to AWS and to GCP. We've also adopted Snowflake to really drive and to organize our information and our data, then drive these new solutions and capabilities forward. So a big portion of it though is culture. So how do we engage with the business teams and bring the IT teams together, to really help to drive these holistic end-to-end solutions and capabilities, to really support the actual business into the future. That's one of the keys here, as we look to modernize and to really enhance our organizations to become data-driven. This is the key. If you can really start to provide answers to business questions before they're even being asked and to predict based upon different economic trends or different trends in your business, what decisions need to be made and actually provide those answers to the business teams before they're even asking for it. That is really becoming a data-driven organization and as part of that, it really then enables the business to act quickly and take advantage of opportunities as they come in based upon industries, based upon markets, based upon products, solutions or partnerships into the future. These are really some of the keys that become crucial as you move forward, right, into this new age, Especially with COVID. With COVID now taking place across the world, right? Many of these markets, many of these digital transformations are celebrating and are changing rapidly to accommodate and to support customers in these very difficult times. As part of that, you need to make sure you have the right underlying foundation, ecosystems and solutions to really drive those capabilities and those solutions forward. As we go through this journey, both in my career but also each of your careers into the future, right? It also needs to evolve, right? Technology has changed so drastically in the last 10 years, and that change is only accelerating. So as part of that, you have to make sure that you stay up to speed, up to date with new technology changes, both on the platform standpoint, tools, but also what do our customers want, what do our customers need and how do we then service them with our information, with our data, with our platform, and with our products and our services to meet those needs and to really support and service those customers into the future. This is all around becoming a more data-driven organization, such as how do you use your data to support your current business lines, but how do you actually use your information and your data to actually better support your customers, better support your business, better support your employees, your operations teams and so forth. And really creating that full integration in that ecosystem is really when you start to get large dividends from these investments into the future. With that being said, I hope you enjoyed the segment on how to become and how to drive a data-driven organization, and looking forward to talking to you again soon. Thank you. >> Tom, that was great. Thanks so much and now going to have to drag on you for a second. As a change agent you've come in, disrupted and how long have you been at Western Union? >> Only nine months, so just started this year, but there have been some great opportunities to integrate changes and we have a lot more to go, but we're really driving things forward in partnership with our business teams and our colleagues to support those customers going forward. >> Tom, thank you so much. That was wonderful. And now, I'm excited to introduce you to Gustavo Canton, a change agent that I've had the pleasure of working with meeting in Europe and he is a serial change agent. Most recently with Schneider Electric but even going back to Sam's Clubs. Gustavo, welcome. (gentle music) >> So, hey everyone, my name is Gustavo Canton and thank you so much, Cindi, for the intro. As you mentioned, doing transformations is, you know, a high reward situation. I have been part of many transformations and I have led many transformations. And, what I can tell you is that it's really hard to predict the future, but if you have a North Star and you know where you're going, the one thing that I want you to take away from this discussion today is that you need to be bold to evolve. And so, in today, I'm going to be talking about culture and data, and I'm going to break this down in four areas. How do we get started, barriers or opportunities as I see it, the value of AI and also, how you communicate. Especially now in the workforce of today with so many different generations, you need to make sure that you are communicating in ways that are non-traditional sometimes. And so, how do we get started? So, I think the answer to that is you have to start for you yourself as a leader and stay tuned. And by that, I mean, you need to understand, not only what is happening in your function or your field, but you have to be very in tune what is happening in society socioeconomically speaking, wellbeing. You know, the common example is a great example and for me personally, it's an opportunity because the number one core value that I have is wellbeing. I believe that for human potential for customers and communities to grow, wellbeing should be at the center of every decision. And as somebody mentioned, it's great to be, you know, stay in tune and have the skillset and the courage. But for me personally, to be honest, to have this courage is not about not being afraid. You're always afraid when you're making big changes and you're swimming upstream, but what gives me the courage is the empathy part. Like I think empathy is a huge component because every time I go into an organization or a function, I try to listen very attentively to the needs of the business and what the leaders are trying to do. But I do it thinking about the mission of, how do I make change for the bigger workforce or the bigger good despite the fact that this might have perhaps implication for my own self interest in my career. Right? Because you have to have that courage sometimes to make choices that are not well seen, politically speaking, but are the right thing to do and you have to push through it. So the bottom line for me is that, I don't think we're they're transforming fast enough. And the reality is, I speak with a lot of leaders and we have seen stories in the past and what they show is that, if you look at the four main barriers that are basically keeping us behind budget, inability to act, cultural issues, politics and lack of alignment, those are the top four. But the interesting thing is that as Cindi has mentioned, these topic about culture is actually gaining more and more traction. And in 2018, there was a story from HBR and it was about 45%. I believe today, it's about 55%, 60% of respondents say that this is the main area that we need to focus on. So again, for all those leaders and all the executives who understand and are aware that we need to transform, commit to the transformation and set a deadline to say, "Hey, in two years we're going to make this happen. What do we need to do, to empower and enable these change agents to make it happen? You need to make the tough choices. And so to me, when I speak about being bold is about making the right choices now. So, I'll give you examples of some of the roadblocks that I went through as I've been doing transformations, most recently, as Cindi mentioned in Schneider. There are three main areas, legacy mindset and what that means is that, we've been doing this in a specific way for a long time and here is how we have been successful. What worked in the past is not going to work now. The opportunity there is that there is a lot of leaders, who have a digital mindset and they're up and coming leaders that are perhaps not yet fully developed. We need to mentor those leaders and take bets on some of these talents, including young talent. We cannot be thinking in the past and just wait for people, you know, three to five years for them to develop because the world is going in a way that is super-fast. The second area and this is specifically to implementation of AI. It's very interesting to me because just the example that I have with ThoughtSpot, right? We went on implementation and a lot of the way the IT team functions or the leaders look at technology, they look at it from the prism of the prior or success criteria for the traditional BIs, and that's not going to work. Again, the opportunity here is that you need to redefine what success look like. In my case, I want the user experience of our workforce to be the same user experience you have at home. It's a very simple concept and so we need to think about, how do we gain that user experience with these augmented analytics tools and then work backwards to have the right talent, processes, and technology to enable that. And finally and obviously with COVID, a lot of pressure in organizations and companies to do more with less. And the solution that most leaders I see are taking is to just minimize costs sometimes and cut budget. We have to do the opposite. We have to actually invest on growth areas, but do it by business question. Don't do it by function. If you actually invest in these kind of solutions, if you actually invest on developing your talent and your leadership to see more digitally, if you actually invest on fixing your data platform, it's not just an incremental cost. It's actually this investment is going to offset all those hidden costs and inefficiencies that you have on your system, because people are doing a lot of work and working very hard but it's not efficient and it's not working in the way that you might want to work. So there is a lot of opportunity there and just to put in terms of perspective, there have been some studies in the past about, you know, how do we kind of measure the impact of data? And obviously, this is going to vary by organization maturity, there's going to be a lot of factors. I've been in companies who have very clean, good data to work with and I've been with companies that we have to start basically from scratch. So it all depends on your maturity level. But in this study, what I think is interesting is they try to put a tagline or a tag price to what is the cost of incomplete data. So in this case, it's about 10 times as much to complete a unit of work when you have data that is flawed as opposed to having perfect data. So let me put that just in perspective, just as an example, right? Imagine you are trying to do something and you have to do 100 things in a project, and each time you do something, it's going to cost you a dollar. So if you have perfect data, the total cost of that project might be $100. But now let's say you have 80% perfect data and 20% flawed data. By using this assumption that flawed data is 10 times as costly as perfect data, your total costs now becomes $280 as opposed to $100. This just for you to really think about as a CIO, CTO, you know CHRO, CEO, "Are we really paying attention and really closing the gaps that we have on our data infrastructure?" If we don't do that, it's hard sometimes to see the snowball effect or to measure the overall impact, but as you can tell, the price tag goes up very, very quickly. So now, if I were to say, how do I communicate this or how do I break through some of these challenges or some of these barriers, right? I think the key is, I am in analytics, I know statistics obviously and love modeling, and, you know, data and optimization theory, and all that stuff. That's what I came to analytics, but now as a leader and as a change agent, I need to speak about value and in this case, for example, for Schneider. There was this tagline, make the most of your energy. So the number one thing that they were asking from the analytics team was actually efficiency, which to me was very interesting. But once I understood that, I understood what kind of language to use, how to connect it to the overall strategy and basically, how to bring in the right leaders because you need to, you know, focus on the leaders that you're going to make the most progress, you know. Again, low effort, high value. You need to make sure you centralize all the data as you can, you need to bring in some kind of augmented analytics, you know, solution. And finally, you need to make it super-simple for the, you know, in this case, I was working with the HR teams and other areas, so they can have access to one portal. They don't have to be confused and looking for 10 different places to find information. I think if you can actually have those four foundational pillars, obviously under the guise of having a data-driven culture, that's when you can actually make the impact. So in our case, it was about three years total transformation, but it was two years for this component of augmented analytics. It took about two years to talk to, you know, IT, get leadership support, find the budgeting, you know, get everybody on board, make sure the success criteria was correct. And we call this initiative, the people analytics portal. It was actually launched in July of this year and we were very excited and the audience was very excited to do this. In this case, we did our pilot in North America for many, many, many factors but one thing that is really important is as you bring along your audience on this, you know. You're going from Excel, you know, in some cases or Tableu to other tools like, you know, ThoughtSpot. You need to really explain them what is the difference and how this tool can truly replace some of the spreadsheets or some of the views that you might have on these other kinds of tools. Again, Tableau, I think it's a really good tool. There are other many tools that you might have in your toolkit but in my case, personally, I feel that you need to have one portal. Going back to Cindi's points, that really truly enable the end user. And I feel that this is the right solution for us, right? And I will show you some of the findings that we had in the pilot in the last two months. So this was a huge victory and I will tell you why, because it took a lot of effort for us to get to this stage and like I said, it's been years for us to kind of lay the foundation, get the leadership, initiating culture so people can understand, why you truly need to invest on augmented analytics. And so, what I'm showing here is an example of how do we use basically, you know, a tool to capturing video, the qualitative findings that we had, plus the quantitative insights that we have. So in this case, our preliminary results based on our ambition for three main metrics. Hours saved, user experience and adoption. So for hours saved, our ambition was to have 10 hours per week for employee to save on average. User experience, our ambition was 4.5 and adoption 80%. In just two months, two months and a half of the pilot, we were able to achieve five hours per week per employee savings, a user experience for 4.3 out of five and adoption of 60%. Really, really amazing work. But again, it takes a lot of collaboration for us to get to the stage from IT, legal, communications, obviously the operations things and the users. In HR safety and other areas that might be basically stakeholders in this whole process. So just to summarize, this kind of effort takes a lot of energy. You are a change agent, you need to have courage to make this decision and understand that, I feel that in this day and age with all this disruption happening, we don't have a choice. We have to take the risk, right? And in this case, I feel a lot of satisfaction in how we were able to gain all these great resource for this organization and that give me the confident to know that the work has been done and we are now in a different stage for the organization. And so for me, it's just to say, thank you for everybody who has belief, obviously in our vision, everybody who has belief in, you know, the work that we were trying to do and to make the life of our, you know, workforce or customers and community better. As you can tell, there is a lot of effort, there is a lot of collaboration that is needed to do something like this. In the end, I feel very satisfied with the accomplishments of this transformation and I just want to tell for you, if you are going right now in a moment that you feel that you have to swim upstream, you know, work with mentors, work with people in the industry that can help you out and guide you on this kind of transformation. It's not easy to do, it's high effort, but it's well worth it. And with that said, I hope you are well and it's been a pleasure talking to you. Talk to you soon. Take care. >> Thank you, Gustavo. That was amazing. All right, let's go to the panel. (light music) Now I think we can all agree how valuable it is to hear from practitioners and I want to thank the panel for sharing their knowledge with the community. Now one common challenge that I heard you all talk about was bringing your leadership and your teams along on the journey with you. We talk about this all the time and it is critical to have support from the top. Why? Because it directs the middle and then it enables bottoms up innovation effects from the cultural transformation that you guys all talked about. It seems like another common theme we heard is that you all prioritize database decision making in your organizations. And you combine two of your most valuable assets to do that and create leverage, employees on the front lines, and of course the data. Now as as you rightly pointed out, Tom, the pandemic has accelerated the need for really leaning into this. You know, the old saying, if it ain't broke, don't fix it, well COVID has broken everything and it's great to hear from our experts, you know, how to move forward, so let's get right into it. So Gustavo, let's start with you. If I'm an aspiring change agent and let's say I'm a budding data leader, what do I need to start doing? What habits do I need to create for long-lasting success? >> I think curiosity is very important. You need to be, like I said, in tune to what is happening, not only in your specific field, like I have a passion for analytics, I've been doing it for 50 years plus, but I think you need to understand wellbeing of the areas across not only a specific business. As you know, I come from, you know, Sam's Club, Walmart retail. I've been in energy management, technology. So you have to try to push yourself and basically go out of your comfort zone. I mean, if you are staying in your comfort zone and you want to just continuous improvement, that's just going to take you so far. What you have to do is, and that's what I try to do, is I try to go into areas, businesses and transformations, that make me, you know, stretch and develop as a leader. That's what I'm looking to do, so I can help transform the functions, organizations, and do the change management, the essential mindset that's required for this kind of effort. >> Well, thank you for that. That is inspiring and Cindi you love data and the data is pretty clear that diversity is a good business, but I wonder if you can, you know, add your perspectives to this conversation? >> Yeah, so Michelle has a new fan here because she has found her voice. I'm still working on finding mine and it's interesting because I was raised by my dad, a single dad, so he did teach me how to work in a predominantly male environment, but why I think diversity matters more now than ever before and this is by gender, by race, by age, by just different ways of working and thinking, is because as we automate things with AI, if we do not have diverse teams looking at the data, and the models, and how they're applied, we risk having bias at scale. So this is why I think I don't care what type of minority you are, finding your voice, having a seat at the table and just believing in the impact of your work has never been more important and as Michelle said, more possible. >> Great perspectives, thank you. Tom, I want to go to you. So, I mean, I feel like everybody in our businesses is in some way, shape, or form become a COVID expert, but what's been the impact of the pandemic on your organization's digital transformation plans? >> We've seen a massive growth, actually, in our digital business over the last 12 months really, even acceleration, right, once COVID hit. We really saw that in the 200 countries and territories that we operate in today and service our customers in today, that there's been a huge need, right, to send money to support family, to support friends, and to support loved ones across the world. And as part of that we are very honored to be able to support those customers that, across all the centers today, but as part of the acceleration, we need to make sure that we have the right architecture and the right platforms to basically scale, right? To basically support and provide the right kind of security for our customers going forward. So as part of that, we did do some pivots and we did accelerate some of our plans on digital to help support that overall growth coming in and to support our customers going forward, because during these times, during this pandemic, right, this is the most important time and we need to support those that we love and those that we care about. And doing that some of those ways is actually by sending money to them, support them financially. And that's where really our products and our services come into play that, you know, and really support those families. So, it was really a great opportunity for us to really support and really bring some of our products to the next level and supporting our business going forward. >> Awesome, thank you. Now, I want to come back to Gustavo. Tom, I'd love for you to chime in too. Did you guys ever think like you were pushing the envelope too much in doing things with data or the technology that it was just maybe too bold, maybe you felt like at some point it was failing, or you're pushing your people too hard? Can you share that experience and how you got through it? >> Yeah, the way I look at it is, you know, again, whenever I go to an organization, I ask the question, "Hey, how fast you would like to conform?" And, you know, based on the agreements on the leadership and the vision that we want to take place, I take decisions and I collaborate in a specific way. Now, in the case of COVID, for example, right, it forces us to remove silos and collaborate in a faster way. So to me, it was an opportunity to actually integrate with other areas and drive decisions faster, but make no mistake about it, when you are doing a transformation, you are obviously trying to do things faster than sometimes people are comfortable doing, and you need to be okay with that. Sometimes you need to be okay with tension or you need to be okay, you know, debating points or making repetitive business cases until people connect with the decision because you understand and you are seeing that, "Hey, the CEO is making a one, two year, you know, efficiency goal. The only way for us to really do more with less is for us to continue this path. We can not just stay with the status quo, we need to find a way to accelerate the transformation." That's the way I see it. >> How about Utah, we were talking earlier with Sudheesh and Cindi about that bungee jumping moment. What can you share? >> Yeah, you know, I think you hit upon it. Right now, the pace of change will be the slowest pace that you see for the rest of your career. So as part of that, right, this is what I tell my team, is that you need to be, you need to feel comfortable being uncomfortable. Meaning that we have to be able to basically scale, right? Expand and support the ever changing needs in the marketplace and industry and our customers today, and that pace of change that's happening, right? And what customers are asking for and the competition in the marketplace, it's only going to accelerate. So as part of that, you know, as you look at how you're operating today in your current business model, right? Things are only going to get faster. So you have to plan and to align and to drive the actual transformation, so that you can scale even faster into the future. So it's part of that, that's what we're putting in place here, right? It's how do we create that underlying framework and foundation that allows the organization to basically continue to scale and evolve into the future? >> Yeah, we're definitely out of our comfort zones, but we're getting comfortable with it. So Cindi, last question, you've worked with hundreds of organizations and I got to believe that, you know, some of the advice you gave when you were at Gartner, which was pre-COVID, maybe sometimes clients didn't always act on it. You know, not my watch or for whatever, variety of reasons, but it's being forced on them now. But knowing what you know now that, you know, we're all in this isolation economy, how would you say that advice has changed? Has it changed? What's your number one action and recommendation today? >> Yeah, well first off, Tom, just freaked me out. What do you mean, this is the slowest ever? Even six months ago I was saying the pace of change in data and analytics is frenetic. So, but I think you're right, Tom, the business and the technology together is forcing this change. Now, Dave, to answer your question, I would say the one bit of advice, maybe I was a little more very aware of the power in politics and how to bring people along in a way that they are comfortable and now I think it's, you know what, you can't get comfortable. In fact, we know that the organizations that were already in the cloud have been able to respond and pivot faster. So, if you really want to survive, as Tom and Gustavo said, get used to being uncomfortable. The power and politics are going to happen, break the rules, get used to that and be bold. Do not be afraid to tell somebody they're wrong and they're not moving fast enough. I do think you have to do that with empathy, as Michelle said and Gustavo, I think that's one of the key words today besides the bungee jumping. So I want to know where Sudheesh is going to go bungee jumping. (all chuckling) >> Guys, fantastic discussion, really. Thanks again to all the panelists and the guests, it was really a pleasure speaking with you today. Really, virtually all of the leaders that I've spoken to in theCUBE program recently, they tell me that the pandemic is accelerating so many things. Whether it's new ways to work, we heard about new security models and obviously the need for cloud. I mean, all of these things are driving true enterprise-wide digital transformation, not just as I said before, lip service. You know, sometimes we minimize the importance and the challenge of building culture and in making this transformation possible. But when it's done right, the right culture is going to deliver tournament results. You know, what does that mean? Getting it right. Everybody's trying to get it right. My biggest takeaway today is it means making data part of the DNA of your organization. And that means making it accessible to the people in your organization that are empowered to make decisions, decisions that can drive new revenue, cut costs, speed access to critical care, whatever the mission is of your organization, data can create insights and informed decisions that drive value. Okay, let's bring back Sudheesh and wrap things up. Sudheesh, please bring us home. >> Thank you, thank you, Dave. Thank you, theCUBE team, and thanks goes to all of our customers and partners who joined us, and thanks to all of you for spending the time with us. I want to do three quick things and then close it off. The first thing is I want to summarize the key takeaways that I heard from all four of our distinguished speakers. First, Michelle, I will simply put it, she said it really well. That is be brave and drive, don't go for a drive alone. That is such an important point. Often times, you know the right thing that you have to do to make the positive change that you want to see happen, but you wait for someone else to do it, not just, why not you? Why don't you be the one making that change happen? That's the thing that I picked up from Michelle's talk. Cindi talked about finding, the importance of finding your voice. Taking that chair, whether it's available or not, and making sure that your ideas, your voice is heard and if it requires some force, then apply that force. Make sure your ideas are heard. Gustavo talked about the importance of building consensus, not going at things all alone sometimes. The importance of building the quorum, and that is critical because if you want the changes to last, you want to make sure that the organization is fully behind it. Tom, instead of a single takeaway, what I was inspired by is the fact that a company that is 170 years old, 170 years old, 200 companies and 200 countries they're operating in and they were able to make the change that is necessary through this difficult time in a matter of months. If they could do it, anyone could. The second thing I want to do is to leave you with a takeaway, that is I would like you to go to ThoughtSpot.com/nfl because our team has made an app for NFL on Snowflake. I think you will find this interesting now that you are inspired and excited because of Michelle's talk. And the last thing is, please go to ThoughtSpot.com/beyond. Our global user conference is happening in this December. We would love to have you join us, it's, again, virtual, you can join from anywhere. We are expecting anywhere from five to 10,000 people and we would love to have you join and see what we've been up to since last year. We have a lot of amazing things in store for you, our customers, our partners, our collaborators, they will be coming and sharing. We'll be sharing things that we have been working to release, something that will come out next year. And also some of the crazy ideas our engineers have been cooking up. All of those things will be available for you at ThoughtSpot Beyond. Thank you, thank you so much.

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to again sunset legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, This is John Furrier here inside theCUBE studios we serve 85 of the Fortune 100, over half the global 2000. for an update with Informatica's digital conference. The reason why customers come back and speak on our behalf Take a look at it, check it out online. and literally thousands of customers speak on our behalf, Join the community. Be part of those thousands of customers that they have, it's humbling, is because we have the best and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. intelligent data platform in the market. I'm John Furrier, thanks for watching. And we also understand our customers aren't buying software. (soft music) They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight.

(upbeat music) >> From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're in our Palo Alto studios today. We're having a CUBE Conversation around a really interesting topic. It's applied AI, applied machine learning. You know, we hear a lot about artificial intelligence and machine learning in kind of the generic sense, but I think really, where we're going to see a lot of the activity is when that's applied to specific solutions and specific applications. And we're really excited to have our next guest. He's applying AI and machine learning in a really interesting and important space. So joining us from San Francisco is Bryton Shang. He's the founder and CEO of Aquabyte. Bryton great to see you. >> Yeah, Jeff. Great to be here. >> I can't believe it's been almost a year since we met at a Kosta Noah event. I looked it up June of last year. Wow, how time flies. But before we get into it, give everyone just kind of the quick overview of what you guys are up to at Aquabyte. >> Aquabyte's a company, we're building software to be able to help fish farmers. It's computer vision and machine learning software based on a camera that takes pictures of a fish in a fish pen, analyzes those images and helps the farmer understand the health of the fish, the weight of the fish, how much to feed and generally better manage their farms. >> It's such a great story. So for those people that haven't seen it, I encourage you to jump on the internet and look up the AWS special that Werner did on Aquabyte last year. It's a really nice piece, really gets into the technology and a lot of the fun part of the story. I really enjoyed it and you know, congratulations to you for getting featured in that AWS piece. But let's go to how did you get here? I mean, you're really interesting guy. You're a multiple company founder coming out of Princeton, in most of your startup role, your startups are all about, Applied Mathematics and Statistics but you've been in everything from finance and trading to looking at cells in the context of Cancer. How did you get to Aquabyte? Was it the technology? And then you found a cool solution? Or did you hear about, you know, an interesting problem and you thought, you know, I have just the trick to help attack that problem. >> Well, so I had studied Operations Research and Financial Engineering at Princeton, which I guess we would call nowadays, like modern day machine learning and data science. So that was something as you mentioned, first I'd apply it to algorithmic trading, and then got on to more general applications of computer vision for example, in cancer detection. The idea to apply machine learning talk to aquaculture, came from a number of different sources. One was from a previous co-founder who had been doing some investigation in the fish farming space, had a business school classmate who owned a fish farm. And also growing up in Ithaca, New York near to Cornell I had a family friend who is a professor of aquaculture. And really just to learn about fish farming and overfishing and the idea that over half the fish we eat nowadays are coming from fish farms and that you could use machine learning and computer vision to make these farms more efficient. That being very interesting and compelling. >> So it's really interesting. One of the things that jumped out from me when I watched the piece with Werner was the amazing efficiency on the feed to protein output in fish farming. I had no idea that it was so high, it's basically approaching one to one really interesting opportunity. And I had no idea to that, as you said over 50% of the world's seafood that's consumed was commercially farmed. So really a giant opportunity and so great space to be in a lot of environmental impacts. So but how did you decide to find an entree? We know where to find an entree for machine learning to make a big impact in this industry. >> So it came from a couple different angles. First, there's been applications of machine learning computer vision and other industries that served as good parallels where we're using cameras to be able to take images and then use computer vision to derive insight from those images. For example, just take aquaculture where you're using cameras to spray weeds to understand crop yield. And so there's good parallels and other industries. aquaculture specifically, I was also looking at what was coming out in the machine learning literature in terms of using cameras to size fish. And so the idea that you could use cameras to size fish was very interesting because then you can use that to figure out growth rates and feeding. And as I developed my idea, it really became clear that you could use computer vision and machine learning to do a wide range of things at the farm and so, it started with this idea about using cameras to size fish and then it became monitoring health and sea lice and parasites and then ultimately, all the aspects of the farm that you would want to manage. >> And correct me for wrong, but do you guys identify individual fish within the population within that big net and then you're basically tracking individuals and then aggregating that to see the health of the whole population. >> That's right, the spot pattern on the fish is unique and we have an algorithm that's able to use that to determine each individual fish via the spot pattern. >> Wow. And then how long once, once you kind of got together with the farmers to really start to say, wow, we can use this application for, as you said, worrying about lice and disease control and oh wow, we can use this application to measure growth. So now we know the health of the environment or wow, now we know the size so we can impact our harvest depending on what our customers are looking for. I assume there's all kinds of ways you can slice and dice the data that comes out of the system into actual information that can be applied in lots of different ways. >> Right So I started the company back in 2017. And if you think about aquaculture, it's actually a hugely international industry 99% outside the US, and within aquaculture, very quickly zeroed in on salmon farming, and specifically salmon farming in Norway. Norway produces about half of the world's farmed salmon and ended up going there for a conference Aqua Nor August of 2017 and whilst there had my idea and a prototype for sizing the fish with a camera, but then also realized in Norway they have recently passed regulations around counting sea lice on the fish so this is parasite that attaches to the fish and is regulated and pretty much every country that grows fish in the ocean and farmers asked me then, okay, if you could use the camera to size fish, can you also count sea lice? And can you also detect the appetite? And then it just turned into this more platform approach where this single camera could do a wide variety of application. >> That's awesome. And I'm just curious to get your take on, the acceptance and really the excitement around, you know, kind of application of machine learning in this computer vision in terms of the digital transformation of commercial fish farming, because once it sounds like once they discovered the power of this thing, they very quickly saw lots of different applications, and I assume continue to see kind of new applications to apply this to transform their business. >> Right, I would say fish farming itself is already fairly highly mechanized. So you're dealing with fairly rough conditions in the ocean. And a lot of the equipment there is already mechanized. So you have automatic feeders, you have feeding systems. That said, there isn't too much computer vision machine learning in the industry. Today, a lot of that is fairly new to the farmers. That said they were open to trying out the technology, especially when it helps save labor at the farm. And it's something that they have familiarity with, with some of the applications for example, with Tesla with their autopilot and other examples that you could point to in common day use. >> That's interesting that you brought up Tesla, I was going to say that the Tesla had an autonomous driving day presentation. I don't know, it's probably been a year or so now but really long in-depth presentations by some of his key technical people around the microprocessor and AI and machine learning and a whole thing about computer vision. And, you know, there's this great debate about, can you can you have an autonomous car without Lidar and I love the great quote from that thing was you "Lions don't have Lidar "and they chase down gazelles all day long." So, we can do a lot with our vision. I'm curious, some of the specific challenges within working in your environment within working in water and working with all kinds of crazy light conditions. It's funny on that Tesla, they talked about really some of the more challenging environments being like a tunnel, inside of a tunnel with wet pavement. So, kind of reflections and these kind of metric conditions that make it much harder. What are some of the special challenges you guys had to overcome? And how much, is it really the technology? Or is it really being done in the software and the algorithms and the analyzing or is it basically a bunch of pixel dots? >> Right. The basic technology is based on similar, it's a serial camera that takes images of the fish. Now, a lot of the special challenges we deal with relate to the underwater domain. So underwater, you're dealing with a rough environment, there could be particles in the water, specularity some reflections underwater, you're dealing with practical challenges such as algae, but even the behavior of the fish, are they swimming by the camera? Or do you want to position your camera in the pen. Also, water itself has interesting optical properties. So the deeper you go, it affects the wavelength that's hitting the camera. And also you have specialized optics where the focal length and other aspects of the optics are affected underwater. And so a lot of the specific expertise we've developed is understanding how to sense properly underwater. Some of that is handled by the mechanical design. A lot of it is also handled by the software, where on the camera we have GPUs that are processing the images and using deep learning computer vision algorithms to identify fish parts and sea lice and other aspects of the fish. >> It's crazy, and how many fish are in one you know, individuals are in one of these nets. >> So single pen can have as much as 100,000. Where actually in one pen, which is I think it's the largest salmon farm in Norway based on an oil rig called the ocean farm where they have 2 million fish in a single pen. >> 2 million fish, and you're in that one. >> Right, yes. >> And you've identified all 2 million fish or do you work on some sampling? Or how do you make sure every fish eventually swims by the camera? Or does the camera move around inside that population? That's an amazing amount of fish. >> So I think we'll eventually get to the point where we can identify every single fish in the pen and use that to track individual health and growth. Well we practice what we use the individual recognition algorithm the deal is to de-duplicate fish. So a common question we get asked is okay, what if the same fish swims by the camera twice, and so it's used to de-duplicate fish But I think eventually you'd be able to survey the entire population. >> That's crazy. So where do you guys go next Bryton, again you've brought your analytical brain to a number of problems. Do you see kind of expanding the use within the fish industry and kind of a vertical player? Do you see really a horizontal play in different parts of agriculture and beyond to apply some of the techniques and the IP that you guys have built up so far? >> Well, starting with Norwegian salmon, we want to bring this to other countries around the world for other species. So we've expanded to our second species, which is a rainbow trout. We also are, starting with computer vision are building this very interesting data set which we can use to enable other applications. Eventually, we'll get to the point where that data allows us to run fully autonomous fish farms. Right now the limitations of fish farming is that it needs to be close to the shore. So you can have people go to the farms. And once you have fully autonomous fish farms, then you can have fish farms in the open ocean, fish farms on land. And with the world being 70% water, we're only producing about 5% of the protein from the oceans. And so it presents a massive opportunity for us to be able to increase the amount of world's demand for protein. Also given that we're running out of land to grow crops. >> Wow, that's amazing. We're only getting 5% of our food protein out of the ocean at this stage? >> Right, right. >> That is crazy. I thought it would be much higher than that. Well, certainly a really cool opportunity and, a kind of a really awesome little documentary by Werner and the team, definitely go watch it if you haven't seen it. So I just give you the last word as you've been in this industry and really seen kind of the transformative potential of something like computer vision in commercial fishing and who would have even thought that, six or seven years ago? How does that help you kind of think forward, kind of the opportunity really to use these types of applications like computer vision and machine learning to advance something so important, like food creation for our world. >> I think there's definitely a lot of opportunities to be able to use machine learning computer vision, similar technologies to help make these industries a lot more efficient. Also a lot more environmentally sustainable. I'd say something like this industry, like aquaculture, it's not so apparent just if you're in the valley, and even in the US just because 99% of it happens outside the US and so to be able to be familiar with the industry to know that it exists and to build applications itself is a bit of a challenge. I would say that is changing. One of the things that actually came out a couple weeks ago was an executive order to actually start kick starting offshore aquaculture in the US. So it is starting in the US. But more generally, I do think there's a massive opportunity to be able to apply machine and computer vision in new industries that previously haven't been addressed. >> Yeah, that's great. And I just love how you got kind of a single source of data, but really the information that you can apply and the applications you can apply are actually quite broad. It's a super use case. Well, Bryton, thanks for spending a few minutes. I've really enjoyed the story. Congratulations on your funding rounds and your continued success. >> Thanks, and really appreciate to be on and yeah, hope to continue to help bring the world more sustainable seafood. >> Absolutely. Well, thanks a lot Bryton. So he's Bryton and I'm Jeff. You're watching theCUBE. We'll see you next time, thanks for watching. (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Everyone, welcome to the special CUBE Conversation here in the Palo Alto studios of theCUBE. We have our quarantine crew and we are here getting all the stories and all the top news, information from experts and thought leaders in the industry. And we're here for a special interview as part of Informatica's digital, virtual event happening. We have Tracey Newell who's the president of Informatica, a CUBE alumni. Great to have you on remotely. Normally you're here in person, but we're in person. Thanks for coming on. >> (laughs) It's great to be here, John. We're virtually together. Happy to spend time together. >> Yeah, and we were in a really tough crisis situation with COVID-19, had a lot of discussions around strategies of how to manage it, get through it, and grow beyond it. But business needs to go on, and this has been the theme. You got to kind of stabilize your base, move forward. But a lot of people are looking at either retrenching and rethinking with coming out of this on the other side. You guys have a digital, virtual event happening where you still got to get the word out. You are the president of Informatica. You guys have a value proposition that is core to the future. It's data and it's been something that we've talked about for years on theCUBE around data's value. And now, this is now apparent to everybody in this COVID crisis. You're talking to customers all the time. What are they thinking? It's not just an industry inside baseball, kind of inside the ropes conversation. This is now mainstream. What are you hearing from your customers? >> Yeah, so it's certainly been interesting times. Digital transformation, has been a CEO on boardroom discussion now for several years and customers have known for a while that the key to having a real strong transformation is data. They've got to have high-quality data to make the right decisions. And what I've been hearing from clients, I've spent a lot of time over the last six to eight weeks while we are in the midst of this situation, talking to customers that are thriving, that are retailers quickly trying to stand up e-commerce sites because their customers are trying to reach them virtually, and they're just not equipped for that. And so data's key when it comes to e-commerce, of course. And yet, there's other customers that know that they do have to re-imagine, they have to re-plan, they have to re-organize coming out of this situation. And even though some of these clients have been hit pretty hard economically, they're all saying data is the most important thing to make sure that they make the right decisions and the right calls. So literally, CDO for a Fortune 100 manufacturer said data is more important today than it was 60 days ago 'cause we've got to make the right decisions. >> It's interesting, we were joking on theCUBE just last week around the term virtualization, which was kind of VMware invented, and that enabled Amazon to be a cloud, right? So without virtualization, all of that value wouldn't have been realized and that whole wave. But now when you think about virtual living, which we're all kind of doing, this interview here is an illustration of that, the virtualization of life and companies is now happening. So when we come out of this, it's going to be a hybrid world (laughs). People are going to not ignore what just happened, they're going to see the benefits. E-commerce, to your point, has grown in the past eight weeks faster than it has grown in the past 10 years. I just saw a stat come out. So now we believe that the world is going to be accelerated on this digital side quickly, not just the talking point. But as we go physical and hybrid, this is going to be a double-down situation. So what are the challenges in that? Because obviously, it's a complex world digital, it's not easy, you don't just video stream. And it's community, it's data (laughs). What are the challenges? What are the core challenges that customers have to solve to execute through this new reality? >> Yeah, so many customers are, as I said, rethinking and re-planning. There's a large oil and energy company where the CIO said, "I want to be data center free over the last few years." And we're talking about, "Why is that?" And this move to cloud is simply accelerating given the current situation that people are in, and why is that? Well, we're certain they're trying to improve analytics. They're trying to innovate, and they're doing an outstanding job. And yet at the same time, every time they can sunset one of those legacy applications that's sitting on premise, they can save millions and millions if not tens or hundreds of millions of dollars as they start to exit the data center. So we see a huge move to cloud. It's complex because they have to make sure, again, a large insurance company said, "We're sunsetting our cloud data warehouse, our data lake, "and by the way, we're using that to close our books "every quarter, so we can't get this wrong." And so from our standpoint, we built most of the on-premise data warehouse and data lakes. We're pretty good at this stuff. And we're very focused on helping our clients here. >> It's interesting, you're going to see a lot of core thinking around what's important going forward and doubling down around it. I just did an interview for a developer audience and I asked, "What's the reality "that you think comes out of this?" And the answer was microservices and cloud native and automation is here to stay. It's definitely been validated. There's really no debate there. You guys have had this intelligent and automation fabric product in the environment out there, is one of the value propositions of Informatica. How does that fit into all this? And can you give some examples of customers and/or prospects that take advantage of this and how it relates to being positioned to help going forward? >> Great question. So we believe that automation and AI is critical for clients to have a data-driven strategy because data is everywhere, it's fragmented. But you can't solve this by sheer muscle. You got to have AI and machine learning underlying everything that you're doing around your data strategy. So our strategy has been simple for a long time. If you buy one-for-one family category Informatica, we believe that you should choose the best-of-breed. And Gartner thinks that we're best-of-breed in all categories that we play in. But if you have a second or third product, you should get the benefits of AI and machine learning. Examples would include the American Medical Association. They're clearly such an important client to serve these days. They're using our data quality, our data integration, and our master data management tools to ensure that they have privacy but also accurate data at the same time. >> It's interesting the at scale problem that we're seeing and the current environment we were just talking about earlier is exposes the value of data because we're lurking at home. This is an edge on the network (laughs). There's still data being processed, you need security. So the complexity now doesn't change the need for governance and compliance. All these things are still available. So it seems that the game is still the same, but yet now more complexity's been surfaced from this. What's your thoughts on this? You've been talking to customers pre-COVID, pre-pandemic. And now you're going to be doing during and post. There's more complexity but the game doesn't change. You still got to do all these things. >> The importance of making sure you have a holistic data strategy is more important now than ever before. Again, when I talk to clients, some as we've mentioned with e-commerce, they're saying, "I've got to have a 360 degree view "of my customers, my partners, my suppliers." CFOs want a 360 degree view of their supply chain so they can do better vendor management than ever before. And yet, at the same time as we mentioned, they're trying to modernize their data as they move to cloud and improve analytics. And of course, you can't accomplish either one of those objectives if you don't have a strong governance strategy. So this concept of an intelligent data platform is really resonating with clients. I had a large GSI in our briefing center back when we were doing that a few months ago, and they said, "You know, gosh, "we would need 20 companies to do what you do." And that you've got to have a platform play, and it's all got to be backed through AI and machine learning to make sure you're making the best decisions. >> You know, platform business is not for the faint of heart. And I've looked at, and we've built platforms certainly on theCUBE on a small scale. But the difference between a tool and a platform are two different things. Platforms enable change and create value. You create more value than you deliver for the partner that's building on top of that, seems to be the tenet of platforms. Whether it's cybersecurity or data, this has just been a ton of tools, right (laughs)? So you got a tool for this, you got a tool for that. So this has been one of those things, again, we've talked with them and you guys were on theCUBE many years about in this big data world. As you move to a platform, what are some of the analytic challenges that the customers need to be thinking about to solve? Because you're starting to see the bifurcation of a nice-to-have versus core. The analytics 360, you mentioned business 360. Hey, who doesn't want a 360 degree view of their business? But is it a nice-to-have or is it critical? So these are the kind of conversations I would love to get your thoughts on, Tracey. Nice-to-haves versus critical, and what are the key problems to solve for analytics? >> Yeah, so when you think about analytics, really, frankly, any decision that clients are making right now, you got to make sure that this is truly the most important. That it's got a business case behind it, and it's the most important place to be spending your dollars these days. What I'm seeing with clients, just last week, a large airline, you can imagine, they invested heavily in data governance and data privacy because they know that it's important to have an analytical and clear view to who are their customers, and how do they make sure they protect the privacy of the customers while they build on their loyalty program? We just, last week, saw a large auto manufacturer, again, investing heavily in this area of data governance and privacy. One of my favorite stories came from a CDO who's in oil and energy. Again, another industry making tough choices right now. And they said, "I want my data "to be like pouring myself a glass of water." And I looked at him, I said, "What does that mean?" And she goes, "Well, if you go pour yourself a glass of water, you don't curate the water, "test the water, and prep the water." And of course, that's what all these expensive data scientists are doing. They're spending all their time trying to understand the data. And so CFOs are getting tired of two reports showing up on their desk to answer one question and the reports say something else. Which one do you believe? You've got to have a trusted and really strong analytical approach to making the decisions that clients are going to be forced to make coming out of this situation and the data's integrity has never been more important. >> I love the water example because it's really a lot of flow. You've got fast flowing data. You've got real relevance, maybe slow data but it's relevant. You've got clean data, you've got dirty data. I mean, thinking about the old database days, cleansing data, it's a term. Data wrangling, totally makes sense. This is the outcome that they want. They just want to have the applications sides dealing with the data as fast as possible, most relevant. So it is like water. But to make that happen, you got to have the processing (laughs) behind the curtain. This is the hard part. Can you just illustrate some thinking around how you guys help do that? Because, okay, you've got a platform. But if you're making the water clean and flowing on tap if you will, what goes on to make that happen? Take me through the pitch there, what do you guys do? >> Yeah, so we think every enterprise in the future is going to want to invest in a data marketplace. And so what we announced in December as part of our governance solution, which again, is tied into the entire intelligent data platform on all that we do, for us to helping customers to modernize their products with master data management. We're heavily invested in cloud native solutions with all the major hyper-scalers. And then combined with our governance solutions, we've announced a data marketplace where the very business friendly application that the data scientists can use. They don't have to be data engineers or data wranglers. And yet, it's also a place where people can go to have a clean and trusted view. It's all backed by machine learning and AI so that data scientists can see, you know, where did this data pull from? Based upon, you know, you asked this question, then you might also want to look over here to get a different answer to your question. Understand, what's been certified, who certified the solution? All those questions. We always say you can ask the internet anything. How come you can't ask your own company anything and trust the information? And that's what we've announced with our governance solutions, then the clean enterprise data marketplace. >> I love data value. Both have been close to my heart from day one. Maybe back when theCUBE started in 2010 when Hadoop hit the scene, we saw the value of data. I always felt it was going to be part of the applications. And now more than ever, these kinds of things like trust, real time, and being programmable. I mean, when I start thinking about automation, you're really talking about programmability, right? So you got to have the efficiencies. I think you guys have got a really interesting value proposition there. Great stuff. >> Yeah, well, your example on Hadoop and Big Data, we're seeing a repeat in history again. When everyone built the on-premise data warehouses and data lake, they used Informatica to automate and to build at scale. And then we did it again when people moved to Big Data and they started investing in Hadoop and Cloudera and Hortonworks, now Cloudera, of course. We helped to accelerate that automation, and that's exactly what we're doing again in cloud. So most CIOs are trying to gain some legacy applications, and the faster you can speed data ingestion at scale, but also understand data quality and data integrity at the same time so that you don't move your on-premise data, data swamp into the cloud, that's expensive. We can really help to look at this holistically and solve these problems for customers faster. >> Well, Tracey, it's great to see you. I wish we could be there in person, but there's no personal event. You've got a virtual digital event happening. It's going to be ongoing which is digital. So it's 365 days a year more ongoing. Take a minute to talk to your customers that are out there since we have you on camera. Let's automate the value proposition. What's the update on Informatica? What's the pitch to your customers and prospects? What's new with Informatica? Why Informatica? Your core value proposition and why they should work with you. >> Yeah, so we've been serving our customers for 25 years. And the reason why we have such loyalty, we serve 85 of the Fortune 100, over half the global 2000. The reason why customers come back and speak on our behalf and literally thousands of customers speak on our behalf, it's humbling, is because we have the best intelligent data platform in the market. And we also understand our customers aren't buying software. They're buying a business outcome. And we have more people in customer success to enable customers to be successful in all of these journeys we've talked about today. And so I'd like to encourage everyone to attend CLAIREview, which is our new conference series, kicks off on May 20th. CLAIRE is our AI engine, is a Netflix-like experience where you can learn more about all the areas where we can help you in the items we've discussed today. So for clients that are looking to save money by sunsetting legacy apps, we can help accelerate your move to the cloud, improve analytics while you also build a data governance strategy and culture into your environment. So really excited about it, John. I mean, it will be an ongoing series so that based on what you learn and what you like, we'll recommend future sessions for you to help you be successful coming out of this current situation. >> Tracey, thanks for that great insight. One final personal question I want to ask you. I've been following you guys for a long time, and we've had you on theCUBE many times. You've been a seasoned veteran in the industry. You've seen cycles of innovation. You've seen the ups and downs over the years. You've been on boards, you've been a leader, a senior leader. What do you talk about with your friends and peers when you look at this current inflection point? As there's the candid conversations are happening, it's really an opportunity, but also there are serious challenges. As a leader, how should leaders be thinking about getting through this? What's your personal view? You've seen many cycles. You've see many waves. This wave coming is going to be big. This change is certainly going to create an uptick, we believe, exponentially a step function transformation. What's your view? What are some of the conversations that you're having with your friends, peers around what to do? >> Yeah, so I think in any situation like the one that we're in, it's important first and foremost to take care of the employees, take care of the customers, take care of the short term needs. That's critical. And yet at the same time in parallel, to be thinking longer term because there is an opportunity when you go through a situation like this to regroup and to think about, what will be the key markets that come back the fastest? What will be your differentiation, your company's differentiation so that you come out of this when the market does start to rebound and really thriving. So it's always this constant balance of how you deal with the short-term and the realities that we're in because people are making some tough decisions. And yet at the same time, make sure that you're very clear on your long-term strategy so that you can come out of this swinging. >> Great advice. That's a masterclass right there. Thank you for sharing that. Of course, check out Informatica's CLAIREview event. Of course, the digital events are always online. Check them out. Tracey, thanks for your time and thanks for that insight and update, appreciate it. >> Yeah, great to be here, John. Look forward to seeing you in person soon. >> Okay, take care. This is John Furrier here inside theCUBE studios for an update with Informatica's digital conference. Take a look at it, check it out online. Join the community. Be part of those thousands of customers that they have, and check it out, give them feedback. Again, we're remote, we're virtual. It's a virtual CUBE. I'm John Furrier, thanks for watching. (soft music)

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>>law from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back. Everyone's two cubes. Live coverage I'm John for with the Cube were here reinvent date, too, as it winds down Walter Wall interviews two sets here. We want to think Intel, big sponsor of this, said we without Intel, we wouldn't have this great content. They support our mission at the Q. We really appreciate it. We're here and strengthen the signal the noise on our seventh reinvent of the eight years that they've been here. We've been documenting history, and we got a great panel lined up here. They got Sebastian to holler Who's the CEO? Sale Drone. Henry Stalls, Stool The VP of Science and Technology and Bowery Farming. Great use case around the food supply and Janet his era space weather scientists at NASA. The Kilo Physics division. We got a great lineup here. Great panel. Welcome to the Cube. Thanks for coming. Thank you. Okay. We'll start with you, Jen. And you're doing some super cool space exploration. You're looking at super storms in space. What's your story? >>Yeah, I work at NASA and NASA has in its mandate to understand how to protect life on Earth and in space from events like space, weather and other things. And I'm working with Amazon right now to understand how storms in space get amplified into super storms in space, which now people understand, can have major impacts on infrastructures head earth like power grits. >>So there's impact. >>There's a >>guy's measuring that, not like a supernova critical thing like >>that >>of, like, practical space. >>Actually, the idea that the perception of the world of the other risks of space weather changed dramatically in 1989 when Superstorm actually caused the collapse of a power grid in Canada and the currents flowing in the ground from the storm entered the power grid and it collapsed in 90 seconds. It couldn't even intervene. >>Wow, some serious issues. We want to get into the machine learning and how you guys are applying. But let's get through here, and we're doing some pretty cool stuff that's really important. Mission. Food supply and global food supply something that you're doing. What I think it might explain. >>Yeah, Bowery were growing food for a better future by revolutionizing agriculture. And to do that, we're building these ah network of large warehouse scale indoor farms where we go all sorts of produce indoors 365 days a year, using zero pesticides using hydroponic systems and led technology. So it's really exciting. And at the core of it is some technology we call the Bowery operating system, which is how we leverage software hardware in a I tow, operate and learn from our farm. >>I'm looking forward to digging into that Sebastian sale drone. You're doing some stuff you're sailing around the world. You got nice chance that you now tell your story. >>Sadly, no way. Use wind powered robots to study the 20% of the planet that's currently really data scarce. And that's the oceans on. So we measure things like biomass, which is how many fish down in the ocean. We measure the input of energy, which impacts weather and climate. We mapped the seabed on. We do all kinds of different tasks which are very, very expensive to do with few ships >>and to report now that climate change is on everyone's agenda, understanding potentially blind spots. Super important, right? >>That's what I'm trying to, You know, this whole question of if it's a question of what? When and what and how much. And so, you know, the ice is melting, the Gulf Stream is changing, and Nina is wrecking havoc. But we just do not understand this because we just don't have the data. In city, we use satellites where they have very low resolution. They cannot see through the water where you ships. No, has 16 ships he in the U. S. So we have to do better. We have to translate this into a big data problem. So that's what we're doing. We have 1000 sale drones on our plan with 100 water right now. And so we're trying to instrument old oceans all the time, >>you know, and data scales your friend because you don't want more data. Yes. Talk about what you're working on. What kind of a I in machine learning are you doing? You just gathering day. Then you're pumping it up to the cloud via satellites or what's going on there? >>One of the one of the use cases trying to understand you know who's out there. What are they doing? Another doing anything illegal. So to do this, you need to use cameras and look at the horizon and detect. You know whether you have vessels. And if those vessels are not transmitting the position, it means that they're trying to stay hidden on the ocean. And so we use machine learning and I that we train on on AWS to try to understand what where those things are. It's hard enough on land at sea. It's very hard because every pixel is moving. You have waves. The horizon is moving, the skies moving, the ship is moving. And so trying to solve this problem is a completely new thing that's called maritime domain awareness on, and it's something that has never been done before. >>And what's the current status of the project? >>So wave been live for about four years now we have 100 sail drones were building one a day towards the goal of having 1000 which we covered all the planet in a six by six degrees squares on. We are operationally active in the Arctic in the tropical Pacific. In the Atlantic. We just circumnavigated Antarctica, So it's the thing. That's really it's out there. But it's very far from from from land, >>So the spirit of cloud and agility static buoy goes away. You want to put the sale drones out there to gather and move around and capture. >>That's what the buoy is. You know, a massive steel thing, which has a full mile long cable, and it's it's headed to the silo in a fix stations one point and the ocean goes by. You having and robots means that you can go where you know something interesting is happening where you have a hurricane where you might have an atmospheric river where you might have a natural catastrophe or man made catastrophe. So this intelligence of the platform is really important in the navigation. That platform requires intelligence. And on the other side, getting 1000 times more data allows you to understand things better, just like Michael is doing. >>It isn't a non profit of four profit venture. >>It's a for profit company. So we said raw data a fraction of the cost of existing solution to try to create this kind of transformative impact on understanding what's happening >>that's super exciting for all the maritime folks out there because I love the ocean myself. Henry, you you're tackling real big mission. How using technology. I can almost imagine the instrumentation must be off the charts. What's your opportunity? Looked like? A tech perspective >>s o The level of control we have in our farms is really unparalleled. Weaken tune Just about every parameter that goes into growing our plans from temperature humidity Co Two light intensity day night cycles list keeps going on. And so to do Maur with fewer resource is to grow Maurin our farms. We're doing something called science a scale where we can pull different levers and make changes to recipes in real time. And we're using a I tow, understand the impact that those changes have and to guide us going from millions of different permutations. Trillions of permutations, really too. The perfect outdone >>converging. You jittery? Look at the product outcome. You circle that dated back is all on Amazon >>way. Do operate on Amazon. Yeah, and we're using deep learning technology to analyze pictures that come from cameras all over our farms. So we actually have eyes on every single crop that grows in our facilities and So we process those, learn from the data and and funnel that back into the >>like, Maybe put more light on this or do that kind of make a just a conditions. Is that that thing? That's >>exactly it. And we grow lots of different types of plants. We grow butter, head lettuce, romaine, kale, spinach, arugula, basil, cilantro. So there's a lot of different things we grow, and each of them require different, different little tweaks here and there. Toe produced over the best tasting and most nutritious product. >>That's cool, Janet Space. Lastly, on one inspection, we're gonna live on Mars someday. So you might be a weather forecaster for what route to take to Mars. But right now, the practical matter is Israel correlation between these storms. What kind of data problem are you looking at? What is the machine learning? What are some of the cool things you're working on? >>It? We have a big date, a problem because storms of that magnitude are very rare. So it's hard for us to find enough data to train a I we can't actually train a we have to use, you know, learning that doesn't require us to train it, but we've decided to take the approach that these super storms are like anomalies on the normal weather patterns. So we're trying to use the kind of a I that you used to detect anomalies like people who are trying to break into to do bank fraud or, you know, do a Web server tax. We use that same kind of software to tryto identify anomalies that are the space weather and look at the patterns between sort of a normal, more of a normal storm and a space with a huge space weather event to see how they patterns. Comparing how you're amplifying the regular storm into this big Superstorm activity. >>So it sounds like you have to be prepared for identifying the anomaly. See you looking at anomalies to figure out where the anomaly might be ready to be ready to get the anomaly. >>Yeah, you look at the background, and then what sticks out of the background that doesn't look like the background is is identified as the anomaly. And that's the storms that air happening, which are quite rare, >>all three of you guys to do some real cutting edge cool projects. I guess my question would be for the folks that are putting their toe in the water for machine learning. They tend to be new use cases like what you guys are doing, whether it's just a company tryingto read, factor themselves or we become reborn in the cloud ran legacy stuff. When you hear it, Amazon reinvent. This is the big question for these folks that are here. You guys are on the front end of a really cool projects. What's your advice that the people are trying to get in that mindset? >>So I think I think you know the way the way to think about this is if you're good at something and if you think you have the solution for something, how can you make that a 1,000,000 times more efficient? And so the problem is, there's just not enough capacity in the world, usually to treat data sets that a 1,000,000 times larger. And this is where machine learning should be thought about it as an extension of what humans really good at using a pair of eyes, ears or whatever or the sense. And so in our case. For example, counting fish acoustician, train acoustician, look at sonar data and understand schools of fish and can recognize them. And by using this knowledge base, we can train machines to do this on a much grander scale. And when you're doing a much grander scale, you derive. Ah, holding tight to >>your point is that humans are critical. I'm the process. So scaling the human capabilities and maybe filling in another scale issues or >>that's what a machine learning is. It's the greatest enabler of our time. It enables us to do things which are impossible to do before because we just didn't have enough people to do them at scale. >>AKI is being able to ask questions, right? And so if you have the questions to ask, you can apply this technology in a way that's never really been before possible. >>You're Jake. >>Yeah, I am actually someone who didn't know anything about a Ira ml when I started. I'm on. I'm a research scientist. That space weather. So coming into this, I'm working with E m L Solutions Lab here and putting a I experts with with experts and space brother we're getting we're doing things that are gonna give us new advances. I mean, We're already seeing things we didn't know before. So I think that if you partner with people who really have strong a I knowledge, you can use your knowledge of science to really get to the really important issues. >>Okay, I have to ask the final lightning round question. What is the coolest thing that you've done with your project that you've either observed implemented? That is super cool. Super cool. What's the coolest thing >>well in in terms of us were using anomaly detection to identify storms and in the first round through it actually identified every single Superstorm, which was not the major super storms, but it did. But it also started identifying other anomalous events, and when you went looked at him, they were anomalous events. So we're seeing things. It's picking out the weird things that are happening in space weather. It's kind of exciting and interesting. >>I worked for a day with you. I would love to just leave these anomalies every what's the coolest thing that you've seen or done with your project? >>I think the fact that we've built our own custom hardware own camera systems, uh, and that we feed those through algorithms that tell us something about what's happening minute by minute with plans as they grow to see pictures of plants minute by minute, they dance and it's truly it's It's remarkable. >>Wow! Fascinating Machin >>We've counted every single fish on the West Coast, the United States, every single air from Canada to Mexico. I thought I >>was pretty >>good. I didn't think it was possible. >>Very cool. But what's the number? >>Yeah, If I could tell you, I would. But I'm not allowed to tell you the jam. >>And you know where the salmon are, where they're running all that good stuff. Awesome. Well, congratulations, You guys doing some amazing work is pioneering a great example of just what's coming. And I love this angle of making larger human impact using technology. Where you guys a shaping technology for good things. Really, really exciting. Thanks for coming on, John Kerry. We're here live in Vegas for re invent 2019. Stay with more coverage. Day three coming tomorrow back with more After this break, when a fake intel for making it all happened presented by Intel Without their sponsorship, we wouldn't be able to bring this great content. Thanks for watching

>> Live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas. Lisa Martin with John Ferrier. You're watching the Cube live at Del Technologies World twenty nineteen. This is our second full day of Double Cube set coverage. We've got a couple of we got a really cool conversation coming up for you. We've got Robbie Pender County, one of our alumni on the cue back as VP product management server solutions. Robbie, Welcome back. >> Thank you, Lisa. Much appreciated. >> And you brought some Hollywood? Yes, Glenn Glenn er, president of Sony Innovation Studios. Glenn and welcome to the Cube. >> Thank you very much. It's great to be here. >> So you are love this intersection of Hollywood and technology. But you're a filmmaker. >> Yeah, I have been filming movies for many years. I started off making motion pictures for many years. Executive produced him and oversaw production for them at one of our movie labels called Screen Gems, which is part of Sony Pictures. >> Wait a tremendous amount of evolution of the creative process being really fueled by technology and vice versa. Sony Innovation Studios is not quite one year old. This is a really exciting venture. Tell us about that and and what the The impetus was to start this company. >> You know that the genesis for it was based out of necessity because I looked at a nice Well, you know, I love making movies were doing it for a long time. And the challenge of making good pictures is resource is and you never get enough money. Believe or not, you never get enough money and never get enough time. That's everybody's issue, particularly time management. And I thought, Well, you know, we got a pretty good technology company behind us. What if we looked inward towards technology to help us find solutions? And so innovation studios is born out of that idea on what was exciting about it was to know that we had, uh, invited partners to the game right here with Del so that we could make movies and television shows and commercials and even enterprise solutions leaning into state of the art and cutting edge technology. >> And what some of the work private you guys envision coming out this mission you mentioned commercials TV. Is it going to be like an artist's studio actor actress in ball is take us through what this is going to look like. How does it get billed out? >> I lean into my career as a producer. To answer that one and say is going to enable that's one of the greatest things about being a producer is enabling stories, uh, inspiring ideas to be green lit that may not have been able to be done so before. And there's a key reason why we can't do that, because one of our key technologies is what we call the volumetric image acquisition. That's a lot of words. You probably say. What the heck is that? But a volumetric image acquisition is our ability to capture a real world, this analog world and digitize it, bring it into our servers using the power of Del and then live in that new environment, which is now a virtual sets. And that virtual set is made out of billions and trillions in quadrillions of points, much like the matter around us. And that's a difference because many people use pixels, which is interpretation of like we're using points which is representative of the world around us, so it's a whole revolutionary way of looking at it. But what it allows us to do is actually film in it in a thirty K moving volume. >> It's like a monster green screen for the world. Been away >> in a way, you're you're you're interaction around it because you have peril X, so these cameras could be photographing us. And for all you know, we may not be here. Could be at stage seven at Innovation Studios and not physically here, but you couldn't tell the >> difference. This is like cloud computing. We talking check world, you don't the provisional these resource is you just get what you want. This is Hollywood looking at the artistry, enabling faster, more agile storytelling. You don't need to go set up a town and go get the permit. All the all the heavy lifting you're shooting in this new digital realm. >> That's right. Exactly. Now I love going on location on There's a lot to celebrate about going on location, but we can always get to that location. Think of all the locations that we want to be in that air >> base off limits. Both space, the one I >> haven't been, uh, but but on said I've been I've walked on virtual moons and I've walked on set moons. But what if we did a volumetric image acquisition of someone set off the moon? Now we have that, and then we can walk around it. Or what if there's a great club, a nightclub? This says guys and wanted to shoot here. But we have performances Monday night, Tuesday night, Wednesday night there. You know they have a job. What? We grabbed that image acquired it. And then you could be there anytime you want. >> Robbie, we could go for an hour here. This is just a great comic. I >> completely agree with >> you. The Cube. You could You could sponsor a cube in this new world. We could run the Q twenty four seven is absolutely >> right. And we don't even have >> to talk about the relationship with Dale because on Del Technologies, because you're enabling new capabilities. New kind of artistry, just totally cool. Want to get back to the second? But you guys were involved. What's your role? How do you get involved? Tell the story about your >> John. I mean, first and foremost one of the things didn't Glendon mention is he's actually got about fifty movies to his credit. So the guy actually knows this stuff. So which is absolutely fantastic. So we said, How do you go take coverage to the next level? So what else is better than trying to work something out, wherein we together between what Glenn and Esteem does at the Sony Innovation Labs for Studio Sorry. And as in Dead Technologies could do is to try and actually stretch the boundaries of our technology to a next tent that when he talks about kazillion bytes of data right one followed by harmony, our zeros. We have to be able to process the data quickly. We have to be able to go out and do their rendering. We probably have to go out and do whatever is needed to make a high quality movie, and that, I think, in a way, is actually giving us an opportunity to go back and test the boundaries of their technology. They're building, which we believe this is the first of its kind in the media industry. If we can go learn together from this experience, we can actually go ahead and do other things in other industries do. Maybe. And we were just talking about how we could also take this. He's got his labs here in Los Angeles, were thinking maybe one of the next things we do based on the learning to get. We probably could take it to other parts of the world. And if we are successful, we might even take it to other industries. What if we could go do something to help in this field of medicine? >> It's just thinking that, right? Yes. Think >> about it. Lisa, John. I mean, it's phenomenal. I mean, this is something Michael always talks about is how do we as del technologies help in progress in the human kind? And if this is something that we can learn from, I think it's going to be phenomenal. >> I think I think that's so interesting. Not only is that a good angle for Del Technologies, the thing that strikes me is the access to artist trees, voices, new voices that may be missed in the prop the vetting process the old way. But, you know, you got to know where we're going. No, in the venture, cobble way seen this with democratization of seed labs and incubators where, if you can create access to the story, tells on the artists we're gonna have one more exposure to people might have missed. But also as things change, like whether it's Ray Ray beaming and streaming we saw in the gaming side to volumetric or volumetric things, you're gonna have a better canvas, more paint brushes on the creative side and more action. Is that the mission to get AC Get those artists in there? Is it? Is that part of the core mission submission? Because you're going to be essentially incubating new opportunities really fast. >> It's, uh, it's very important to me. Personally. I know it speaks of the values of both Sony and L. I like to call it the democratization of storytelling. You know, I've been very blessed again, a Hollywood producer, and we maybe curate a certain kind of movie, a certain kind of experience. But there's so many voices around the world that need to be hurt, and there are so many stories that otherwise can't be enabled. Imagine a story that perhaps is >> a unique special voice but requires distance. It requires five disparate locations. Perhaps it's in London Piccadilly Circus and in Times Square. And perhaps it's overto Abu Dhabi on DH Libya somewhere because that's part of the story. We can now collapse geography and bring those locations to a central place and allow a story to be told that may not otherwise have been able to be created. And that's vital to the fabric of storytelling. Worldwide >> is going to change the creative process to You don't have to have that waterfall kind of mentality like we don't talk about intact. You're totally distributed content, decentralized, potentially the creative process going change with all the tools and also the visual tools. >> That's right. It's >> almost becoming unlimited. >> You want it to be unlimited. You want the human spirit to be unlimited. You want to be able to elevate people on. That's the great thing about what we're trying to achieve and will achieve. >> It is your right. I mean, it is interesting, you know, we were just talking about this too. We're in, you know, as an example, shock tank. Yes, right. I mean, they obviously did it the filming and stuff, and then they don't have the access, let's say to the right studio, but The fact is, there had all this done on DH. No, they had all the rendering. They had the captured already done. You could now go out and do your chute without having all the space you needed. >> That's right. In the case of Shark Tank, which shoots a Sony Pictures studios, they knew they had a real estate issue. The fact of the matter is, there's a limited amount of sound stages around the world. They needed to sound stages and only had access to one. So we went in and we did a volumetric image acquisition of their exit interview stage. They're set. And then when it came time to shoot the second half a season ten, one hundred contestants went into a virtual set and were filmed in that set. And the funny thing is, one of the guys in the truck you know how you have the camera trucks and, you know, off offstage, he leaned into the mike. Is that you guys, could you move that plant a couple inches to the left and somebody said, Uh, I don't think we can do it right now, he said. We're on a movie lot. You could move a plant. They said, No, it's physically not there. We're on innovation studios goes Oh, that's right. It's virtual mind. >> So he was fooled. >> He was pulled. In a way, we're >> being hashing it out within a team. When we heard about some of the things you know Glenn and Team are doing is think about this. If you have to teach people when we are running short of doctors, right? Yeah, if you could. With this technology and the learnings that come from here, if you could go have an expert surgeon do surgery once you're captured, it would be nice. Just imagine, to take that learning, go to the new surgeons of the future and trained them and so they can get into the act without actually doing it. So my point in all this is this is where I think we can take technology, that next level where we can not only learn from one specific industry, but we could potentially put it to human good in terms of what we could to and not only preparing the next of doctors, but also take it to the next level. >> This was a great theme to Michael Dell put out there about these new kinds of use case is that the time is now to do before. Maybe you couldn't get there with technology, but maybe aspirational, eh? Let's do it. I could see that. Glenn, I want to ask you specifically. The time is now. This is all kind of coming together. Timing's pretty good. It's only gonna get better. It's gonna be good. Tech, Tech mojo Coming for the creative side. Where were we before? Because I could almost imagine this is not a new vision for you. Probably seen it now that this house here now what was it like before for, um and compare contrast where you were a few years ago, maybe decades. Now what's different? Why? Why is this so important? >> You know, for me, there's a fundamental change in how we can create content and how we can tell stories. It used to be the two most expensive words in the movie TV industry were what if today that the most important words to me or what if Because what if we could collapse geography? What if we could empower a new story? Technology is at a place where if we can dream it. Chances are we can make it a reality. We're changing the dynamics of how we may content. He used to be lights, action, camera. I think it's now lights, action, compute power action, you know, is that kind of difference. >> That is an amazing vision. I think society now has opportunities to kind of take that from distance learning to distance connections, the distance sharing experiences, whether it's immersion, virtual analog face the face. I could really be powerful. Yeah, >> and this is not even a year old. >> That's right. >> So if you look at your your launch, you said, I think let june fourth twenty eighteen. What? Where do you go from here? I mean, like we said, this is like, unlimited possibilities. But besides putting Robbie in the movie, naturally, Yes, of course I have >> a star here >> who video. >> So I got to say he's got star power. >> What's what. The next year? Exactly. >> Very exciting. I will say we have shark tank Thie Advanced Imaging Society gives an award for being the first volume metric set ever put out on the airwaves. Uh, for that television show was a great honor. Uh, we have already captured, uh, men in black. We captured a fifty thousand square foot stage that had the men in black headquarters has been used for commercials to market the film that comes out this June. We have captured sets where television >> shows and in the in hopes that they got a second season and one television show called up and said, Guys, we got the second season so they don't have to go back to what was a very expensive set and a beautiful set >> Way captured that set. It reminds me of a story of productions and a friend of mine said, which is every year. The greatest gift I have is building a beautiful set and and to me, the biggest challenges. When I say, remember that sent you built four years ago. I need that again. Now you can go >> toe hard, replicate the exact set, you capture it digitally. It lives. >> That's exactly it. >> And this is amazing. I mean, I'd love to do a cube set into do ah, like a simulcasts. Virtually. >> So. This is the next thing John and Lisa. You guys could be sitting anywhere going forward. We don't have to be really sitting here you could be doing. What do you have to do? And, you know, you got everything rendered >> captured. We don't have to come to Vegas twenty times a year. >> We billed upset once >> You want to see you here believing that So I'LL take that >> visual is a really beautiful thing. So if we can with hologram just seeing people doing conscious. But Hollywood Frank Zappa just did a concert hologram concert, but bringing real people and from communities around the world where the localization diversity right into a content mixture is just so powerful. >> Actually, you said something very interesting, John, which is one of the other teams to which is, if you have a globally connected society and he wanted try and personalize it to that particular nation ethnicity group. You can do that easily now because you can probably pop in actors from the local area with the same city. Yeah, think about it. >> It's surely right. >> There's a cascade of transformations that that this is going Teo to generate. I mean just thinking of how different even acting schools and drama schools will be well, teaching people how to behave in these virtual environments, right? >> How to immerse themselves in these environments. And we have tricks up our sleeves that Khun put the actor in that moment through projection mapping and the other techniques that allow filmmakers and actors to actually understand the world. They're about to stepped in rather than a green screen and saying, OK, there's going to be a creature over here is gonna be blue Water Falls over there will actually be able to see that environment because that environment will exist before they step on the stage. >> Well, great job the Dale Partnership On my final question, Glenn free since you're awesome and got a great vision so smart, experienced, I've been really thinking a lot about how visualization and artistry are coming together and how disciplines silo disciplines like music. They do great music, but they're not translating to the graphics. It was just some about Ray tracing and the impact with GP use for immersive experiences, which was seeing on the client side of the house. It del So you got the back and stuff, but you metrics. And so, as artist trees, the next generation come up. This is now a link between the visual that audio, the storytelling. It's not a siloed. >> It is not >> your I want to get your vision on. How do you see this playing out and your advice for young artists? That might be, you know, looked as country. What do you know? That's not how we do it. >> Well, the beautiful thing is that there are new ways to tell stories. You know, Hollywood has evolved over the last century. If you look at the studios and still exist, they have all evolved, and that's why they do exist. Great storytellers evolved. We tell stories differently, so long as we can emotionally relate to the story that's being told. I say Do it in your own voice. The cinematic power is among us. We're blessed that when we look back, we have that shared experience, whether it's animate from Japan or traditional animation from Walt Disney, everybody shares a similar history. Now it's opportunity to author our new stories and we can do that and physical assets and volumetric assets and weakened blend the real and the unreal. With the compute power. The world is our oyster. >> Wow, >> What a nice >> trap right there. >> Exactly that is, um I dropped the transformation of Hollywood. What? And it's really think the tip of the iceberg. Unlimited story potential. Thank you, Glenn. Thank you. This has been a fascinating cannot wait to hear, See and feel and touch What's next for Sony Animation studios With your technology power We appreciate your time. >> Yeah, Thank you. Thank you both of >> our pleasure for John Farrier. I'm Lisa Martin. You're watching the Cube lie from Del Technologies World twenty nineteen We've just wrapped up Day two we'LL see you tomorrow.

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to the special. Keep conversation here in Palo Alto, California. I'm John for a co host of the Cube. Were Arsenal was a C I S O. C. So for Microsoft also corporate vice President, Chief information security. Thanks for joining me today. >> Thank you. >> Appreciate it. Thanks. So you have a really big job. You're a warrior in the industry, security is the hardest job on the planet. >> And hang in sight >> of every skirt. Officer is so hard. Tell us about the role of Microsoft. You have overlooked the entire thing. You report to the board, give us an overview of what >> happens. Yeah. I >> mean, it's you know, obviously we're pretty busy. Ah, in this world we have today with a lot of adversaries going on, an operational issues happening. And so I have responsibility. Accountability for obviously protecting Microsoft assets are customer assets. And then ah, And for me, with the trend also responsibility for business continuity Disaster recovery company >> on the sea. So job has been evolving. We're talking before the camera came on that it's coming to CEO CF roll years ago involved to a business leader. Where is the sea? So roll now in your industry is our is a formal title is it establishes their clear lines of reporting. How's it evolved? What's the current state of the market in terms of the sea? So it's roll? >> Yeah, the role is involved. A lot. Like you said, I think like the CIA or twenty years ago, you know, start from the back room of the front room and I think the, you know, one of things I look at in the role is it's really made it before things. There's technical architecture, there's business enablement. There's operational expert excellence. And then there's risk management and the older ah, what does find the right word? But the early see so model was really about the technical architecture. Today. It's really a blend of those four things. How do you enable your business to move forward? How do you take calculated risks or manage risks? And then how do you do it really effectively and efficiently, which is really a new suit and you look at them. You'LL see people evolving to those four functions. >> And who's your boss? Would you report to >> I report to a gentleman by the name of a curtain. Little Benny on DH. He is the chief digital officer, which would be a combination of Seo did officer and transformation as well as all of Microsoft corporate strategy >> and this broad board visibility, actually in security. >> Yeah, you >> guys, how is Microsoft evolved? You've been with the company for a long time >> in the >> old days ahead perimeters, and we talk about on the Cube all the time. When a criminalist environment. Now there's no perimeter. Yeah, the world's changed. How is Microsoft evolved? Its its view on security Has it evolved from central groups to decentralize? How is it how how was it managed? What's the what's the current state of the art for security organization? >> Well, I think that, you know, you raise a good point, though things have changed. And so in this idea, where there is this, you know, perimeter and you demanded everything through the network that was great. But in a client to cloak cloud world, we have today with mobile devices and proliferation or cloud services, and I ot the model just doesn't work anymore. So we sort of simplified it down into Well, we should go with this, you know, people calls your trust, I refer to It is just don't talk to strangers. But the idea being is this really so simplified, which is you've got to have a good identity, strong identity to participate. You have to have managed in healthy device to participate, to talk to, ah, Microsoft Asset. And then you have to have data in telemetry that surrounds that all the time. And so you basically have a trust, trust and then verify model between those three things. And that's really the fundamental. It's really that simple. >> David Lava as Pascal senior with twenty twelve when he was M. C before he was the C E O. V M. Where he said, You know his security do over and he was like, Yes, it's going to be a do over its opportunity. What's your thoughts on that perspective? Has there been a do over? Is it to do over our people looking at security and a whole new way? What's your thoughts? >> Yeah, I mean, I've been around security for a long time, and it's there's obviously changes in Massa nations that happened obviously, at Microsoft. At one point we had a security division. I was the CTO in that division, and we really thought the better way to do it was make security baked in all the products that we do. Everything has security baked in. And so we step back and really change the way we thought about it. To make it easier for developers for end users for admin, that is just a holistic part of the experience. So again, the technology really should disappear. If you really want to be affected, I think >> don't make it a happy thought. Make it baked in from Day one on new product development and new opportunity. >> Yeah, basically, shift the whole thing left. Put it right in from the beginning. And so then, therefore, it's a better experience for everyone using it. >> So one of things we've observed over the past ten years of doing the Cube when do first rolled up with scene, you know, big data role of date has been critical, and I think one of the things that's interesting is, as you get data into the system, you can use day that contextually and look at the contextual behavioral data. It's really is create some visibility into things you, Meyer may not have seen before. Your thoughts and reaction to the concept of leveraging data because you guys get a lot of data. How do you leverage the data? What's the view of data? New data will make things different. Different perspectives creates more visibility. Is that the right view? What's your thoughts on the role of Data World Data plays? >> Well, they're gonna say, You know, we had this idea. There's identity, there's device. And then there's the data telemetry. That platform becomes everything we do, what there's just security and are anomalous behavior like you were talking about. It is how do we improve the user experience all the way through? And so we use it to the service health indicator as well. I think the one thing we've learned, though, is I was building where the biggest data repositories your head for some time. Like we look at about a six point five trillion different security events a day in any given day, and so sort of. How do you filter through that? Manage? That's pretty amazing, says six point five trillion >> per day >> events per day as >> coming into Microsoft's >> that we run through the >> ecosystem your systems. Your computers? >> Yeah. About thirty five hundred people. Reason over that. So you can Certainly the math. You need us. Um, pretty good. Pretty good technology to make it work effectively for you and efficiently >> at RC A Heard a quote on the floor and on the q kind of echoing the same sentiment is you can't hire your way to success in this market is just not enough people qualified and jobs available to handle the volume and the velocity of the data coming in. Automation plays a critical role. Your reaction to that comment thoughts on? >> Well, I think I think the cure there, John, those when you talk about the volume of the data because there's what we used to call speeds and feeds, right? How big is it? And I used to get great network data so I can share a little because we've talked, like from the nineties or whatever period that were there. Like the network was everything, but it turns out much like a diverse workforce creates the best products. It turns out diverse data is more important than speeds and feeds. So, for example, authentication data map to, you know, email data map to end point data map. TEO SERVICE DATA Soon you're hosting, you know, the number of customers. We are like financial sector data vs Healthcare Data. And so it's the ability Teo actually do correlation across that diverse set of data that really differentiates it. So X is an example. We update one point two billion devices every single month. We do six hundred thirty billion authentications every single month. And so the ability to start correlating those things and movement give us a set of insights to protect people like we never had before. >> That's interesting telemetry you're getting in the marketplace. Plus, you have the systems to bring it in >> a pressure pressure coming just realized. And this all with this consent we don't do without consent, we would never do without consent. >> Of course, you guys have the terms of service. You guys do a good job on that, But I think the point that I'm seeing there is that you guys are Microsoft. Microsoft got a lot of access. Get a lot of stuff out there. How does an enterprise move to that divers model because they will have email, obviously. But they have devices. So you guys are kind of operating? I would say tear one of the level of that environment cause you're Microsoft. I'm sure the big scale players to that. I'm just an enterprising I'm a bank or I'm an insurance company or I'm in oil and gas, Whatever the vertical. Maybe. What do I do if I'm the sea? So they're So what does that mean, Diversity? How should they? >> Well, I think they have a diverse set of data as well. Also, if they participate, you know, even in our platform today, we you know, we have this thing called the security graph, which is an FBI people can tap into and tap into the same graph that I use and so they can use that same graph particular for them. They can use our security experts to help them with that if they don't have the all the resource and staff to go do that. So we provide both both models for that to happen, and I think that's why a unique perspective I should think should remind myself of which is we should have these three things. We have a really good security operations group we have. I think that makes us pretty unique that people can leverage. We build this stuff into the product, which I think is good. But then the partnership, the other partners who play in the graph, it's not just us. So there's lots of people who play on that as well. >> So like to ask you two lines of questions. Wanting on the internal complex is that organizations will have on the external complexity and realities of threats and coming in. How do they? How do you balance that out? What's your vision on that? Because, you know, actually, there's technology, his culture and people, you know in those gaps and capabilities on on all three. Yeah, internally just getting the culture right and then dealing with the external. How does a C so about his company's balance? Those realities? >> Well, I think you raised a really good point, which is how do you move the culture for? That's a big conversation We always have. And that was sort of, you know, it's interesting because the the one side we have thirty five hundred people who have security title in their job, But there's over one hundred thousand people who every day part of their job is doing security, making sure they'LL understand that and know that is a key part we should reinforce everyday on DSO. But I think balancing it is, is for me. It's actually simplifying just a set of priorities because there's no shortage of, you know, vendors who play in the space. There's no shortage of things you can read about. And so for us it was just simplifying it down and getting it. That simplifies simplified view of these are the three things we're going to go do we build onerous platform to prioritize relative to threat, and then and then we ensure we're building quality products. Those five things make it happen. >> I'd like to get your thoughts on common You have again Before I came on camera around how you guys view simplification terminal. You know, you guys have a lot of countries, the board level, and then also you made a common around trust of security and you an analogy around putting that drops in a bucket. So first talk about the simplification, how you guys simplifying it and why? Why is that important? >> You think we supply two things one was just supplying the message to people understood the identity of the device and making sure everything is emitting the right telemetry. The second part that was like for us but a Z to be illustrative security passwords like we started with this technology thing and we're going to do to FAA. We had cards and we had readers and oh, my God, we go talk to a user. We say we're going to put two FAA everywhere and you could just see recoil and please, >> no. And then >> just a simple change of being vision letters. And how about this? We're just going to get rid of passwords then People loved like they're super excited about it. And so, you know, we moved to this idea of, you know, we always said this know something, know something new, how something have something like a card And they said, What about just be something and be done with it? And so, you know, we built a lot of the capability natively into the product into windows, obviously, but I supported energies environment. So I you know, I support a lot of Mac clinics and IOS and Android as well So you've read it. Both models you could use by or you could use your device. >> That's that. That's that seems to be a trend. Actually, See that with phones as well as this. Who you are is the password and why is the support? Because Is it because of these abuses? Just easy to program? What's the thought process? >> I think there's two things that make it super helpful for us. One is when you do the biometric model. Well, first of all, to your point, the the user experience is so much better. Like we walk up to a device and it just comes on. So there's no typing this in No miss typing my password. And, you know, we talked earlier, and that was the most popular passwords in Seattle with Seahawks two thousand seventeen. You can guess why, but it would meet the complexity requirements. And so the idea is, just eliminate all that altogether. You walk up machine, recognize you, and you're often running s o. The user experience is great, but plus it's Actually the entropy is harder in the biometric, which makes it harder for people to break it, but also more importantly, it's bound locally to the device. You can't run it from somewhere else. And that's the big thing that I think people misunderstanding that scenario, which is you have to be local to that. To me, that's a >> great example of rethinking the security paradigm. Exactly. Let's talk about trust and security. You you have an opinion on this. I want to get your thoughts, the difference between trust and security so they go hand in hand at the same time. They could be confused. Your thoughts on this >> well being. You can have great trust. You can, so you can have great security. But you generally and you would hope that would equate like a direct correlation to trust. But it's not. You need to you build trust. I think our CEO said it best a long time ago. You put one bucket of water, one bucket. Sorry, one truffle water in the bucket every time. And that's how you build trust. Over time, my teenager will tell you that, and then you kick it over and you put it on the floor. So you have to. It's always this ratcheting up bar that builds trust. >> They doing great you got a bucket of water, you got a lot of trust, that one breach. It's over right, >> and you've got to go rebuild it and you've got to start all over again. And so key, obviously, is not to have that happen. But then, that's why we make sure you have operational rigor and >> great example that just totally is looking Facebook. Great. They have massive great security. What really went down this past week, but still the trust factor on just some of the other or societal questions? >> Yeah, >> and that something Do it. >> Security. Yeah, I think that's a large part of making sure you know you're being true. That's what I said before about, you know, we make sure we have consent. We're transparent about how we do the things we do, and that's probably the best ways to build trust. >> Okay, so you guys have been successful in Microsoft, just kind of tight the company for second to your role. It's pretty well documented that the stock prices at an all time high. So if Donatella Cube alumni, by the way, has been on the cue before he he took over and clear he didn't pivot. He just said we'd go in the cloud. And so the great moves, he don't eat a lot of great stuff. Open source from open compute to over the source. And this ship has turned and everything's going great. But that cheering the cloud has been great for the company. So I gotta ask you, as you guys move to the cloud, the impact to your businesses multi fold one products, ecosystem suppliers. All these things are changing. How has security role in the sea? So position been impact that what have you guys done? How does that impact security in general? Thoughts? >> Yeah, I think we obviously were like any other enterprise we had thousands of online are thousands of line of business applications, and we did a transformation, and we took a method logical approach with risk management. And we said, Okay, well, this thirty percent we should just get rid of and decommission these. We should, you know, optimize and just lifting shifting application. That cloud was okay, but it turns out there's massive benefit there, like for elasticity. Think of things that quarterly reporting or and you'll surveys or things like that where you could just dynamically grow and shrink your platform, which was awesome linear scale that we never had Cause those events I talk about would require re architectures. Separate function now becomes linear. And so I think there is a lot of things from a security perspective I could do in a much more efficient must wear a fish. In fact, they're then I had to have done it before, but also much more effective. I just have compute capability. Didn't have I have signal I didn't have. And so we had to wrap her head around that right and and figure out how to really leverage that. And to be honest, get the point. We're exploited because you were the MySpace. I have disaster and continent and business. This is processed stuff. And so, you know, everyone build dark fiber, big data centers, storage, active, active. And now when you use a platform is a service like on that kind of azure. You could just click a Bach and say, I want this thing to replicate. It also feeds your >> most diverse data and getting the data into the system that you throw a bunch of computer at that scale. So What diverse data? How does that impact the good guys and the bad guys? That doesn't tip the scales? Because if you have divers date and you have his ability, it's a race for who has the most data because more data diversity increases the aperture and our visibility into events. >> Yeah, I you >> know, I should be careful. I feel like I always This's a job. You always feel like you're treading water and trying to trying to stay ahead. But I think that, um, I think for the first time in my tenure do this. I feel there's an asymmetry that benefits. They're good guys in this case because of the fact that your ability to reason over large sets of data like that and is computed data intensive and it will be much harder for them like they could generally use encryption were effectively than some organization because the one the many relationship that happens in that scenario. But in the data center you can't. So at least for now, I feel like there's a tip This. The scales have tipped a bit for the >> guy that you're right on that one. I think it's good observation I think that industry inside look at the activity around, from new fund adventures to overall activity on the analytics side. Clearly, the data edge is going to be an advantage. I think that's a great point. Okay, that's how about the explosion of devices we're seeing now. An explosion of pipe enabled devices, Internet of things to the edge. Operational technologies are out there that in factory floors, everything being I P enables, kind of reminds me of the old days. Were Internet population you'd never uses on the Internet is growing, and >> that costs a lot >> of change in value, creation and opportunities devices. Air coming on both physical and software enabled at a massive rate is causing a lot of change in the industry. Certainly from a security posture standpoint, you have more surface area, but they're still in opportunity to either help on the do over, but also create value your thoughts on this exploding device a landscape, >> I think your Boston background. So Metcalfe's law was the value the net because the number of the nodes on the network squared right, and so it was a tense to still be true, and it continues to grow. I think there's a huge value and the device is there. I mean, if you look at the things we could do today, whether it's this watch or you know your smartphone or your smart home or whatever it is, it's just it's pretty unprecedented the capabilities and not just in those, but even in emerging markets where you see the things people are doing with, you know, with phones and Lauren phones that you just didn't have access to from information, you know, democratization of information and analysis. I think it's fantastic. I do think, though, on the devices there's a set of devices that don't have the same capabilities as some of the more markets, so they don't have encryption capability. They don't have some of those things. And, you know, one of Microsoft's responses to that was everything. Has an M see you in it, right? And so we, you know, without your spirit, we created our own emcee. That did give you the ability to update it, to secure, to run it and manage it. And I think that's one of the things we're doing to try to help, which is to start making these I, O. T or Smart devices, but at a very low cost point that still gives you the ability because the farm would not be healed Update, which we learn an O. T. Is that over time new techniques happen And you I can't update the system >> from That's getting down to the product level with security and also having the data great threats. So final final talk Tracking one today with you on this, your warrior in the industry, I said earlier. See, so is a hard job you're constantly dealing with compliance to, you know, current attacks, new vector, new strains of malware. And it's all over the map. You got it. You got got the inbound coming in and you got to deal with all that the blocking and tackling of the organization. >> What do you What do >> you finding as best practice? What's the what if some of the things on the cso's checklist that you're constantly worried about and or investing in what some of >> the yeah, >> the day to day take us through the day to day life >> of visited a lot? Yeah, it >> starts with not a Leslie. That's the first thing you have to get used to, but I think the you know again, like I said, there's risk Manager. Just prioritize your center. This is different for every company like for us. You know, hackers don't break and they just log in. And so identity still is one of the top things. People have to go work on him. You know, get rid of passwords is good for the user, but good for the system. We see a lot in supply chain going on right now. Obviously, you mentioned in the Cambridge Analytical Analytics where we had that issue. It's just down the supply chain. And when you look at not just third party but forthe party fifth party supply and just the time it takes to respond is longer. So that's something that we need to continue to work on. And then I think you know that those are some of the other big thing that was again about this. How do you become effective and efficient and how you managed that supply chain like, You know, I've been on a mission for three years to reduce my number of suppliers by about fifty percent, and there's still lots of work to do there, but it's just getting better leverage from the supplier I have, as well as taking on new capability or things that we maybe providing natively. But at the end of the day, if you have one system that could do what four systems going Teo going back to the war for talent, having people, no forces and versus one system, it's just way better for official use of talent. And and obviously, simplicity is the is the friend of security. Where is entropy is not, >> and also you mentioned quality data diversity it is you're into. But also there's also quality date of you have quality and diverse data. You could have a nice, nice mechanism to get machine learning going well, but that's kind of complex, because in the thie modes of security breaches, you got pre breached in breech post breach. All have different data characteristics all flowing together, so you can't just throw that answer across as a prism across the problem sets correct. This is super important, kind of fundamentally, >> yeah, but I think I >> would I would. The way I would characterize those is it's honestly, well, better lessons. I think I learned was living how to understand. Talk with CFO, and I really think we're just two things. There's technical debt that we're all working on. Everybody has. And then there's future proofing the company. And so we have a set of efforts that go onto like Red Team. Another actually think like bad people break them before they break you, you know, break it yourself and then go work on it. And so we're always balancing how much we're spending on the technical, that cleanup, you know, modernizing systems and things that are more capable. And then also the future proofing. If you're seeing things coming around the corner like cryptography and and other other element >> by chain blockchain, my supply chain is another good, great mechanism. So you constantly testing and R and D also practical mechanisms. >> And there in the red team's, which are the teams that attacking pen everything, which is again, break yourself first on this super super helpful for us >> well bred. You've seen a lot of ways of innovation have been involved in multiple ways computer industry client server all through the through the days, so feel. No, I feel good about this you know, because it reminds me and put me for broken the business together. But this is the interesting point I want to get to is there's a lot of younger Si SOS coming in, and a lot of young talent is being attractive. Security has kind of a game revived to it. You know, most people, my friends, at a security expert, they're all gamers. They love game, and now the thrill of it. It's exciting, but it's also challenging. Young people coming might not have experience. You have lessons you've learned. Share some thoughts over the years that scar either scar tissue or best practices share some advice. Some of the younger folks coming in breaking into the business of, you know, current situation. What you learned over the years it's Apple Apple. But now the industry. >> Yeah, sadly, I'd probably say it's no different than a lot of the general advice I would have in the space, which is there's you value experience. But it turns out I value enthusiasm and passion more here so you can teach about anybody whose passion enthusiastic and smart anything they want. So we get great data people and make them great security people, and we have people of a passion like you know, this person. It's his mission is to limit all passwords everywhere and like that passion. Take your passion and driver wherever you need to go do. And I >> think the nice >> thing about security is it is something that is technically complex. Human sociology complex, right? Like you said, changing culture. And it affects everything we do, whether it's enterprise, small, medium business, large international, it's actually a pretty It's a fasten, if you like hard problem. If you're a puzzle person, it's a great It's a great profession >> to me. I like how you said Puzzle. That's I think that's exactly it. They also bring up a good point. I want to get your thoughts on quickly. Is the talent gap is is really not about getting just computer science majors? It's bigger than that. In fact, I've heard many experts say, and you don't have to be a computer scientist. You could be a lot of cross disciplines. So is there a formula or industry or profession, a college degree? Or is it doesn't matter. It's just smart person >> again. It depends if your job's a hundred percent. Security is one thing, but like what we're trying to do is make not we don't have security for developers you want have developed to understand oppa security and what they build is an example on DSO. Same with administrators and other components. I do think again I would say the passion thing is a key piece for us, but But there's all aspects of the profession, like the risk managers air, you know, on the actuarial side. Then there's math people I had one of my favorite people was working on his phD and maladaptive behavior, and he was super valuable for helping us understand what actually makes things stick when you're trying to train their educate people. And what doesn't make that stick anthropologist or super helpful in this field like anthropologist, Really? Yeah, anthropologist are great in this field. So yeah, >> and sociology, too, you mentioned. That would think that's a big fact because you've got human aspect interests, human piece of it. You have society impact, so that's really not really one thing. It's really cross section, depending upon where you want to sit in the spectrum of opportunity, >> knowing it gives us a chance to really hire like we hire a big thing for us has been hard earlier in career and building time because it's just not all available. But then also you, well, you know, hire from military from law enforcement from people returning back. It's been actually, it's been a really fascinating thing from a management perspective that I didn't expect when I did. The role on has been fantastic. >> The mission. Personal question. Final question. What's getting you excited these days? I mean, honestly, you had a very challenging job and you have got attend all the big board meetings, but the risk management compliance. There's a lot of stuff going on, but it's a lot >> of >> technology fund in here to a lot of hard problems to solve. What's getting you excited? What what trends or things in the industry gets you excited? >> Well, I'm hopeful we're making progress on the bad guys, which I think is exciting. But honestly, this idea the you know, a long history of studying safety when I did this and I would love to see security become the air bags of the technology industry, right? It's just always there on new president. But you don't even know it's there until you need it. And I think that getting to that vision would be awesome. >> And then really kind of helping move the trust equation to a whole other level reputation. New data sets so data, bits of data business. >> It's total data business >> breath. Thanks for coming on the Q. Appreciate your insights, but also no see. So the chief information security officer at Microsoft, also corporate vice president here inside the Cuban Palo Alto. This is cute conversations. I'm John Career. Thanks for watching. >> Thank you.