>> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (intro music) >> Good afternoon, everyone. Welcome back to "theCUBE's" day three coverage of MWC23. Lisa Martin here in Spain, Barcelona, Spain with Dave Nicholson. We're going to have a really interesting conversation next. We're going to really dig into MWC, it's history, where it's going, some of the controversy here. Please welcome our guests. We have Jim Harris, International Best Selling Author of "Blindsided." And Carolina Milanese is here, President and Principle Analyst of creative strategies. Welcome to "theCUBE" guys. Thank you. >> Thanks. So great to be here. >> So this is day three. 80,000 people or so. You guys have a a lot of history up at this event. Caroline, I want to start with you. Talk a little bit about that. This obviously the biggest one in, in quite a few years. People are ready to be back, but there's been some, a lot of news here, but some controversy going on. Give us the history, and your perspective on some of the news that's coming out from this week's event. >> It feels like a very different show. I don't know if I would say growing up show, because we are still talking about networks and mobility, but there's so much more now around what the networks actually empower, versus the network themselves. And a little bit of maybe that's where some of the controversy is coming from, carriers still trying to find their identity, right, of, of what their role is in all there is to do with a connected world. I go back a long way. I go back to when Mobile World Congress was called, was actually called GSM, and it was in Khan. So, you know, we went from France to Spain. But just looking at the last full Mobile World Congress here in Barcelona, in pre-pandemic to now, very different show. We went from a show that was very much focused on mobility and smartphones, to a show that was all about cars. You know, we had cars everywhere, 'cause we were talking about smart cities and connected cars, to now a show this year that is very much focused on B2B. And so a lot of companies that are here to either work with the carriers, or also talk about sustainability for instance, or enable what is the next future evolution of computing with XR and VR. >> So Jim, talk to us a little bit about your background. You, I was doing a little sleuthing on you. You're really focusing on disruptive innovation. We talk about disruption a lot in different industries. We're seeing a lot of disruption in telco. We're seeing a lot of frenemies going on. Give us your thoughts about what you're seeing at this year's event. >> Well, there's some really exciting things. I listened to the keynote from Orange's CEO, and she was complaining that 55% of the traffic on her network is from five companies. And then the CEO of Deutsche Telecom got up, and he was complaining that 60% of the traffic on his network is from six entities. So do you think they coordinated pre, pre-show? But really what they're saying is, these OTT, you know, Netflix and YouTube, they should be paying us for access. Now, this is killer funny. The front page today of the show, "Daily," the CO-CEO of Netflix says, "Hey, we make less profit than the telcos, "so you should be paying us, "not the other way around." You know, we spend half of the money we make just on developing content. So, this is really interesting. The orange CEO said, "We're not challenging net neutrality. "We don't want more taxes." But boom. So this is disruptive. Huge pressure. 67% of all mobile traffic is video, right? So it's a big hog bandwidth wise. So how are they going to do this? Now, I look at it, and the business model for the, the telcos, is really selling sim cards and smartphones. But for every dollar of revenue there, there's five plus dollars in apps, and consulting and everything else. So really, but look at how they're structured. They can't, you know, take somebody who talks to the public and sells sim cards, and turn 'em in, turn 'em in to an app developer. So how are they going to square this circle? So I see some, they're being disrupted because they're sticking to what they've historically done. >> But it's interesting because at the end of the day, the conversation that we are having right now is the conversation that we had 10 years ago, where carriers don't want to just be a dumb pipe, right? And that's what they are now returning to. They tried to be media as well, but that didn't work out for most carriers, right? It is a little bit better in the US. We've seen, you know, some success there. But, but here has been more difficult. And I think that's the, the concern, that even for the next, you know, evolution, that's the, their role. >> So how do they, how do they balance this dumb pipe idea, with the fact that if you make the toll high enough, being a dumb pipe is actually a pretty good job. You know, sit back, collect check, go to the beach, right? So where, where, where, where does this end up? >> Well, I think what's going to happen is, if you see five to 15 X the revenue on top of a pipe, you know, the hyperscalers are going to start going after the business. The consulting companies like PWC, McKinsey, the app developers, they're... So how do you engage those communities as a telco to get more revenue? I think this is a question that they really need to look at. But we tend to stick within our existing business model. I'll just give you one stat that blows me away. Uber is worth more than every taxi cab company in North America added together. And so the taxi industry owns billions in assets in cars and limousines. Uber doesn't own a single vehicle. So having a widely distributed app, is a huge multiplier on valuation. And I look to a company like Safari in Kenya, which developed M-Pesa, which Pesa means mo, it's mobile money in Swahili. And 25% of the country's GDP is facilitated by M-Pesa. And that's not even on smartphones. They're feature phones, Nokia phones. I call them dumb phones, but Nokia would call them "feature phones." >> Yeah. >> So think about that. Like 25, now transactions are very small, and the cut is tiny. But when you're facilitating 25% of a country's GDP, >> Yeah. >> Tiny, over billions of transactions is huge. But that's not the way telcos have historically thought or worked. And so M-Pesa and Safari shows the way forward. What do you think on that? >> I, I think that the experience, and what they can layer on top from a services perspective, especially in the private sector, is also important. I don't, I never believe that a carrier, given how they operate, is the best media company in the world, right? It is a very different world. But I do think that there's opportunity, first of all, to, to actually tell their story in a different way. If you're thinking about everything that a network actually empowers, there's a, there's a lot there. There's a lot that is good for us as, as society. There's a lot that is good for business. What can they do to start talking about differently about their services, and then layer on top of what they offer? A better way to actually bring together private and public network. It's not all about cellular, wifi and cellular coming together. We're talking a lot about satellite here as well. So, there's definitely more there about quality of service. Is, is there though, almost a biological inevitability that prevents companies from being able to navigate that divide? >> Hmm. >> Look at, look at when, when, when we went from high definition 720P, very exciting, 1080P, 4K. Everybody ran out and got a 4K TV. Well where was the, where was the best 4K content coming from? It wasn't, it wasn't the networks, it wasn't your cable operator, it was YouTube. It was YouTube. If you had suggested that 10 years before, that that would happen, people would think that you were crazy. Is it possible for folks who are now leading their companies, getting up on stage, and daring to say, "This content's coming over, "and I want to charge you more "for using my pipes." It's like, "Really? Is that your vision? "That's the vision that you want to share with us here?" I hear the sound of dead people walking- (laughing) when I hear comments like that. And so, you know, my students at Wharton in the CTO program, who are constantly looking at this concept of disruption, would hear that and go, "Ooh, gee, did the board hear what that person said?" I, you know, am I being too critical of people who could crush me like a bug? (laughing) >> I mean, it's better that they ask the people with money than not consumers to pay, right? 'Cause we've been through a phase where the carriers were actually asking for more money depending on critical things. Like for instance, if you're doing business email, then were going to charge you more than if you were a consumer. Or if you were watching video, they would charge you more for that. Then they understood that a consumer would walk away and go somewhere else. So they stopped doing that. But to your point, I think, and, and very much to what you focus from a disruption perspective, look at what Chat GTP and what Microsoft has been doing. Not much talk about this here at the show, which is interesting, but the idea that now as a consumer, I can ask new Bing to get me the 10 best restaurants in Barcelona, and I no longer go to Yelp, or all the other businesses where I was going to before, to get their recommendation, what happens to them? You're, you're moving away, and you're taking eyeballs away from those websites. And, and I think that, that you know, your point is exactly right. That it's, it's about how, from a revenue perspective, you are spending a lot of money to facilitate somebody else, and what's in it for you? >> Yeah. And to be clear, consumers pay for everything. >> Always. Always. (laughs) >> Taxpayers and consumers always pay for everything. So there is no, "Well, we're going to make them pay, so you don't have to pay." >> And if you are not paying, you are the product. Exactly. >> Yes. (laughing) >> Carolina, talk a little bit about what you're seeing at the event from some of the infrastructure players, the hyperscalers, obviously a lot of enterprise focus here at this event. What are some of the things that you're seeing? Are you impressed with, with their focus in telco, their focus to partner, build an ecosystem? What are you seeing? >> I'm seeing also talk about sustainability, and enabling telco to be more sustainable. You know, there, there's a couple of things that are a little bit different from the US where I live, which is that telcos in Europe, have put money into sustainability through bonds. And so they use the money that they then get from the bonds that they create, to, to supply or to fuel their innovation in sustainability. And so there's a dollar amount on sustainability. There's also an opportunity obviously from a growth perspective. And there's a risk mitigation, right? Especially in Europe, more and more you're going to be evaluated based on how sustainable you are. So there are a lot of companies here, if you're thinking about the Ciscos of the world. Dell, IBM all talking about sustainability and how to help carriers measure, and then obviously be more sustainable with their consumption and, and power. >> Going to be interesting to see where that goes over the years, as we talk to, every company we talk to at whatever show, has an ESG sustainability initiative, and only, well, many of them only want to work with other companies who have the same types of initiative. So a lot of, great that there's focus on sustainability, but hopefully we'll see more action down the road. Wanted to ask you about your book, "Blind," the name is interesting, "Blindsided." >> Well, I just want to tag on to this. >> Sure. >> One of the most exciting things for me is fast charging technology. And Shalmie, cell phone, or a smartphone maker from China, just announced yesterday, a smartphone that charges from 0 to 100% in five minutes. Now this is using GAN FEST technology. And the leader in the market is a company called Navitas. And this has profound implications. You know, it starts with the smartphone, right? But then it moves to the laptops. And then it'll move to EV's. So, as we electrify the $10 trillion a year transportation industry, there's a huge opportunity. People want charging faster. There's also a sustainability story that, to Carolina's point, that it uses less electricity. So, if we electrify the grid in order to support transportation, like the Tesla Semi's coming out, there are huge demands over a period. We need energy efficiency technologies, like this GAN FEST technology. So to me, this is humongous. And it, we only see it here in the show, in Shalmie, saying, "Five minutes." And everybody, the consumers go, "Oh, that's cool." But let's look at the bigger story, which is electrifying transportation globally. And this is going to be big. >> Yeah. And, and to, and to double click on that a little bit, to be clear, when we talk about fast charging today, typically it's taking the battery from a, not a zero state of charge, but a relatively low state of charge to 80%. >> Yep. >> Then it tapers off dramatically. And that translates into less range in an EV, less usable time on any other device, and there's that whole linkage between the power in, and the battery's ability to be charged, and how much is usable. And from a sustainability perspective, we are going to have an avalanche of batteries going into secondary use cases over time. >> They don't get tossed into landfills contrary to what people might think. >> Yep. >> In fact, they are used in a variety of ways after their primary lifespan. But that, that is, that in and of itself is a revolutionary thing. I'm interested in each of your thoughts on the China factor. Glaringly absent here, from my perspective, as sort of an Apple fanboy, where are they? Why aren't they talking about their... They must, they must feel like, "Well we just don't need to." >> We don't need to. We just don't need to. >> Absolutely. >> And then you walk around and you see these, these company names that are often anglicized, and you don't necessarily immediately associate them with China, but it's like, "Wait a minute, "that looks better than what I have, "and I'm not allowed to have access to that thing." What happens in the future there geopolitically? >> It's a pretty big question for- >> Its is. >> For a short little tech show. (Caroline laughs) But what happens as we move forward? When is the entire world going to be able to leverage in a secure way, some of the stuff that's coming out of, if they're not the largest economy in the world yet, they shortly will be. >> What's the story there? >> Well, it's interesting that you mentioned First Apple that has never had a presence at Mobile World Congress. And fun enough, I'm part of the GSMA judges for the GLOMO Awards, and last night I gave out Best Mobile Phone for last year, and it was to the iPhone4 Team Pro. and best disruptive technology, which was for the satellite function feature on, on the new iPhone. So, Apple might not be here, but they are. >> Okay. >> And, and so that's the first thing. And they are as far as being top of mind to every competitor in the smartphone market still. So a lot of the things that, even from a design perspective that you see on some of the Chinese brands, really remind you of, of Apple. What is interesting for me, is how there wouldn't be, with the exception of Samsung and Motorola, there's no one else here that is non-Chinese from a smartphone point of view. So that's in itself, is something that changed dramatically over the years, especially for somebody like me that still remember Nokia being the number one in the market. >> Huh. >> So. >> Guys, we could continue this conversation. We are unfortunately out of time. But thank you so much for joining Dave and me, talking about your perspectives on the event, the industry, the disruptive forces. It's going to be really interesting to see where it goes. 'Cause at the end of the day, it's the consumers that just want to make sure I can connect wherever I am 24 by seven, and it just needs to work. Thank you so much for your insights. >> Thank you. >> Lisa, it's been great. Dave, great. It's a pleasure. >> Our pleasure. For our guests, and for Dave Nicholson, I'm Lisa Martin. You're watching, "theCUBE," the leader in live and emerging tech coverage coming to you day three of our coverage of MWC 23. Stick around. Our next guest joins us momentarily. (outro music)

(lively music) >> Welcome back to our coverage of Cloud Native Security Con. I'm Dave Vellante, here in our Boston studio. We're connecting today, throughout the day, with Palo Alto on the ground in Seattle. And right now I'm here with Michael Foster with Red Hat. He's on the ground in Seattle. We're going to discuss the trends and containers and security and everything that's going on at the show in Seattle. Michael, good to see you, thanks for coming on. >> Good to see you, thanks for having me on. >> Lot of market momentum for Red Hat. The IBM earnings call the other day, announced OpenShift is a billion-dollar ARR. So it's quite a milestone, and it's not often, you know. It's hard enough to become a billion-dollar software company and then to have actually a billion-dollar product alongside. So congratulations on that. And let's start with the event. What's the buzz at the event? People talking about shift left, obviously supply chain security is a big topic. We've heard a little bit about or quite a bit about AI. What are you hearing on the ground? >> Yeah, so the last event I was at that I got to see you at was three months ago, with CubeCon and the talk was supply chain security. Nothing has really changed on that front, although I do think that the conversation, let's say with the tech companies versus what customers are actually looking at, is slightly different just based on the market. And, like you said, thank you for the shout-out to a billion-dollar OpenShift, and ACS is certainly excited to be part of that. We are seeing more of a consolidation, I think, especially in security. The money's still flowing into security, but people want to know what they're running. We've allowed, had some tremendous growth in the last couple years and now it's okay. Let's get a hold of the containers, the clusters that we're running, let's make sure everything's configured. They want to start implementing policies effectively and really get a feel for what's going on across all their workloads, especially with the bigger companies. I think bigger companies allow some flexibility in the security applications that they can deploy. They can have different groups that manage different ones, but in the mid to low market, you're seeing a lot of consolidation, a lot of companies that want basically one security tool to manage them all, so to speak. And I think that the features need to somewhat accommodate that. We talk supply chain, I think most people continue to care about network security, vulnerability management, shifting left and enabling developers. That's the general trend I see. Still really need to get some hands on demos and see some people that I haven't seen in a while. >> So a couple things on, 'cause, I mean, we talk about the macroeconomic climate all the time. We do a lot of survey data with our partners at ETR, and their recent data shows that in terms of cost savings, for those who are actually cutting their budgets, they're looking to consolidate redundant vendors. So, that's one form of consolidation. The other theme, of course, is there's so many tools out in the security market that consolidating tools is something that can help simplify, but then at the same time, you see opportunities open up, like IOT security. And so, you have companies that are starting up to just do that. So, there's like these countervailing trends. I often wonder, Michael, will this ever end? It's like the universe growing and tooling, what are your thoughts? >> I mean, I completely agree. It's hard to balance trying to grow the company in a time like this, at the same time while trying to secure it all, right? So you're seeing the consolidation but some of these applications and platforms need to make some promises to say, "Hey, we're going to move into this space." Right, so when you have like Red Hat who wants to come out with edge devices and help manage the IOT devices, well then, you have a security platform that can help you do that, that's built in. Then the messaging's easy. When you're trying to do that across different cloud providers and move into IOT, it becomes a little bit more challenging. And so I think that, and don't take my word for this, some of those IOT startups, you might see some purchasing in the next couple years in order to facilitate those cloud platforms to be able to expand into that area. To me it makes sense, but I don't want to hypothesize too much from the start. >> But I do, we just did our predictions post and as a security we put up the chart of candidates, and there's like dozens, and dozens, and dozens. Some that are very well funded, but I mean, you've seen some down, I mean, down rounds everywhere, but these many companies have raised over a billion dollars and it's like uh-oh, okay, so they're probably okay, maybe. But a lot of smaller firms, I mean there's just, there's too many tools in the marketplace, but it seems like there is misalignment there, you know, kind of a mismatch between, you know, what customers would like to have happen and what actually happens in the marketplace. And that just underscores, I think, the complexities in security. So I guess my question is, you know, how do you look at Cloud Native Security, and what's different from traditional security approaches? >> Okay, I mean, that's a great question, and it's something that we've been talking to customers for the last five years about. And, really, it's just a change in mindset. Containers are supposed to unleash developer speed, and if you don't have a security tool to help do that, then you're basically going to inhibit developers in some form or another. I think managing that, while also giving your security teams the ability to tell the message of we are being more secure. You know, we're limiting vulnerabilities in our cluster. We are seeing progress because containers, you know, have a shorter life cycle and there is security and speed. Having that conversation with the C-suites is a little different, especially when how they might be used to virtual machines and managing it through that. I mean, if it works, it works from a developer's standpoint. You're not taking advantage of those containers and the developer's speed, so that's the difference. Now doing that and then first challenge is making that pitch. The second challenge is making that pitch to then scale it, so you can get onboard your developers and get your containers up and running, but then as you bring in new groups, as you move over to Kubernetes or you get into more container workloads, how do you onboard your teams? How do you scale? And I tend to see a general trend of a big investment needed for about two years to make that container shift. And then the security tools come in and really blossom because once that core separation of responsibilities happens in the organization, then the security tools are able to accelerate the developer workflow and not inhibit it. >> You know, I'm glad you mentioned, you know, separation of responsibilities. We go to a lot of shows, as you know, with theCUBE, and many of them are cloud shows. And in the one hand, Cloud has, you know, obviously made the world, you know, more interesting and better in so many different ways and even security, but it's like new layers are forming. You got the cloud, you got the shared responsibility model, so the cloud is like the first line of defense. And then you got the CISO who is relying heavily on devs to, you know, the whole shift left thing. So we're asking developers to do a lot and then you're kind of behind them. I guess you have audit is like the last line of defense, but my question to you is how can software developers really ensure that cloud native tools that they're using are secure? What steps can they take to improve security and specifically what's Red Hat doing in that area? >> Yeah, well I think there's, I would actually move away from that being the developer responsibility. I think the job is the operators' and the security people. The tools to give them the ability to see. The vulnerabilities they're introducing. Let's say signing their images, actually verifying that the images that's thrown in the cloud, are the ones that they built, that can all be done and it can be done open source. So we have a DevSecOps validated pattern that Red Hat's pushed out, and it's all open source tools in the cloud native space. And you can sign your builds and verify them at runtime and make sure that you're doing that all for free as one option. But in general, I would say that the hope is that you give the developer the information to make responsible choices and that there's a dialogue between your security and operations and developer teams but security, we should not be pushing that on developer. And so I think with ACS and our tool, the goal is to get in and say, "Let's set some reasonable policies, have a conversation, let's get a security liaison." Let's say in the developer team so that we can make some changes over time. And the more we can automate that and the more we can build and have that conversation, the better that you'll, I don't say the more security clusters but I think that the more you're on your path of securing your environment. >> How much talk is there at the event about kind of recent high profile incidents? We heard, you know, Log4j, of course, was mentioned in the Keynote. Somebody, you know, I think yelled out from the audience, "We're still dealing with that." But when you think about these, you know, incidents when looking back, what lessons do you think we've learned from these events? >> Oh, I mean, I think that I would say, if you have an approach where you're managing your containers, managing the age and using containers to accelerate, so let's say no images that are older than 90 days, for example, you're going to avoid a lot of these issues. And so I think people that are still dealing with that aspect haven't set up the proper, let's say, disclosure between teams and update strategy and so on. So I don't want to, I think the Log4j, if it's still around, you know, something's missing there but in general you want to be able to respond quickly and to do that and need the tools and policies to be able to tell people how to fix that issue. I mean, the Log4j fix was seven days after, so your developers should have been well aware of that. Your security team should have been sending the messages out. And I remember even fielding all the calls, all the fires that we had to put out when that happened. But yeah. >> I thought Brian Behlendorf's, you know, talk this morning was interesting 'cause he was making an attempt to say, "Hey, here's some things that you might not be thinking about that are likely to occur." And I wonder if you could, you know, comment on them and give us your thoughts as to how the industry generally, maybe Red Hat specifically, are thinking about dealing with them. He mentioned ChatGPT or other GPT to automate Spear phishing. He said the identity problem is still not fixed. Then he talked about free riders sniffing repos essentially for known vulnerabilities that are slow to fix. He talked about regulations that might restrict shipping code. So these are things that, you know, essentially, we can, they're on the radar, but you know, we're kind of putting out, you know, yesterday's fire. What are your thoughts on those sort of potential issues that we're facing and how are you guys thinking about it? >> Yeah, that's a great question, and I think it's twofold. One, it's brought up in front of a lot of security leaders in the space for them to be aware of it because security, it's a constant battle, constant war that's being fought. ChatGPT lowers the barrier of entry for a lot of them, say, would-be hackers or people like that to understand systems and create, let's say, simple manifests to leverage Kubernetes or leverage a misconfiguration. So as the barrier drops, we as a security team in security, let's say group organization, need to be able to respond and have our own tools to be able to combat that, and we do. So a lot of it is just making sure that we shore up our barriers and that people are aware of these threats. The harder part I think is educating the public and that's why you tend to see maybe the supply chain trend be a little bit ahead of the implementation. I think they're still, for example, like S-bombs and signing an attestation. I think that's still, you know, a year, two years, away from becoming, let's say commonplace, especially in something like a production environment. Again, so, you know, stay bleeding edge, and then make sure that you're aware of these issues and we'll be constantly coming to these calls and filling you in on what we're doing and make sure that we're up to speed. >> Yeah, so I'm hearing from folks like yourself that the, you know, you think of the future of Cloud Native Security. We're going to see continued emphasis on, you know, better integration of security into the DevSecOps. You're pointing out it's really, you know, the ops piece, that runtime that we really need to shore up. You can't just put it on the shoulders of the devs. And, you know, using security focused tools and best practices. Of course you hear a lot about that and the continued drive toward automation. My question is, you know, automation, machine learning, how, where are we in that maturity cycle? How much of that is being adopted? Sometimes folks are, you know, they embrace automation but it brings, you know, unknown, unintended consequences. Are folks embracing that heavily? Are there risks associated around that, or are we kind of through that knothole in your view? >> Yeah, that's a great question. I would compare it to something like a smart home. You know, we sort of hit a wall. You can automate so much, but it has to actually be useful to your teams. So when we're going and deploying ACS and using a cloud service, like one, you know, you want something that's a service that you can easily set up. And then the other thing is you want to start in inform mode. So you can't just automate everything, even if you're doing runtime enforcement, you need to make sure that's very, very targeted to exactly what you want and then you have to be checking it because people start new workloads and people get onboarded every week or month. So it's finding that balance between policies where you can inform the developer and the operations teams and that they give them the information to act. And that worst case you can step in as a security team to stop it, you know, during the onboarding of our ACS cloud service. We have an early access program and I get on-calls, and it's not even security team, it's the operations team. It starts with the security product, you know, and sometimes it's just, "Hey, how do I, you know, set this policy so my developers will find this vulnerability like a Log4Shell and I just want to send 'em an email, right?" And these are, you know, they have the tools and they can do that. And so it's nice to see the operations take on some security. They can automate it because maybe you have a NetSec security team that doesn't know Kubernetes or containers as well. So that shared responsibility is really useful. And then just again, making that automation targeted, even though runtime enforcement is a constant thing that we talk about, the amount that we see it in the wild where people are properly setting up admission controllers and it's acting. It's, again, very targeted. Databases, cubits x, things that are basically we all know is a no-go in production. >> Thank you for that. My last question, I want to go to the, you know, the hardest part and 'cause you're talking to customers all the time and you guys are working on the hardest problems in the world. What is the hardest aspect of securing, I'm going to come back to the software supply chain, hardest aspect of securing the software supply chain from the perspective of a security pro, software engineer, developer, DevSecOps Pro, and then this part b of that is, is how are you attacking that specifically as Red Hat? >> Sure, so as a developer, it's managing vulnerabilities with updates. As an operations team, it's keeping all the cluster, because you have a bunch of different teams working in the same environment, let's say, from a security team. It's getting people to listen to you because there are a lot of things that need to be secured. And just communicating that and getting it actionable data to the people to make the decisions as hard from a C-suite. It's getting the buy-in because it's really hard to justify the dollars and cents of security when security is constantly having to have these conversations with developers. So for ACS, you know, we want to be able to give the developer those tools. We also want to build the dashboards and reporting so that people can see their vulnerabilities drop down over time. And also that they're able to respond to it quickly because really that's where the dollars and cents are made in the product. It's that a Log4Shell comes out. You get immediately notified when the feeds are updated and you have a policy in action that you can respond to it. So I can go to my CISOs and say, "Hey look, we're limiting vulnerabilities." And when this came out, the developers stopped it in production and we were able to update it with the next release. Right, like that's your bread and butter. That's the story that you want to tell. Again, it's a harder story to tell, but it's easy when you have the information to be able to justify the money that you're spending on your security tools. Hopefully that answered your question. >> It does. That was awesome. I mean, you got data, you got communication, you got the people, obviously there's skillsets, you have of course, tooling and technology is a big part of that. Michael, really appreciate you coming on the program, sharing what's happening on the ground in Seattle and can't wait to have you back. >> Yeah. Awesome. Thanks again for having me. >> Yeah, our pleasure. All right. Thanks for watching our coverage of the Cloud Native Security Con. I'm Dave Vellante. I'm in our Boston studio. We're connecting to Palo Alto. We're connecting on the ground in Seattle. Keep it right there for more coverage. Be right back. (lively music)

foreign welcome back to re invent 2022 we're wrapping up four days well one evening and three solid days wall-to-wall of cube coverage I'm Dave vellante John furrier's birthday is today he's on a plane to London to go see his nephew get married his his great Sister Janet awesome family the furriers uh spanning the globe and uh and John I know you wanted to be here you're watching in Newark or you were waiting to uh to get in the plane so all the best to you happy birthday one year the Amazon PR people brought a cake out to celebrate John's birthday because he's always here at AWS re invented his birthday so I'm really pleased to have two really special guests uh former Cube host Cube Alum great wikibon contributor Stu miniman now with red hat still good to see you again great to be here Dave yeah I was here for that cake uh the twitterverse uh was uh really helping to celebrate John's birthday today and uh you know always great to be here with you and then with this you know Awesome event this week and friend of the cube of many time Cube often Cube contributor as here's a cube analyst this week as his own consultancy sarbj johal great to see you thanks for coming on good to see you Dave uh great to see you stu I'm always happy to participate in these discussions and um I enjoy the discussion every time so this is kind of cool because you know usually the last day is a getaway day and this is a getaway day but this place is still packed I mean it's I mean yeah it's definitely lighter you can at least walk and not get slammed but I subjit I'm going to start with you I I wanted to have you as the the tail end here because cause you participated in the analyst sessions you've been watching this event from from the first moment and now you've got four days of the Kool-Aid injection but you're also talking to customers developers Partners the ecosystem where do you want to go what's your big takeaways I think big takeaways that Amazon sort of innovation machine is chugging along they are I was listening to some of the accessions and when I was back to my room at nine so they're filling the holes in some areas but in some areas they're moving forward there's a lot to fix still it doesn't seem like that it seems like we are done with the cloud or The Innovation is done now we are building at the millisecond level so where do you go next there's a lot of room to grow on the storage side on the network side uh the improvements we need and and also making sure that the software which is you know which fits the hardware like there's a specialized software um sorry specialized hardware for certain software you know so there was a lot of talk around that and I attended some of those sessions where I asked the questions around like we have a specialized database for each kind of workload specialized processes processors for each kind of workload yeah the graviton section and actually the the one interesting before I forget that the arbitration was I asked that like why there are so many so many databases and IRS for the egress costs and all that stuff can you are you guys thinking about reducing that you know um the answer was no egress cost is not a big big sort of uh um show stopper for many of the customers but but the from all that sort of little discussion with with the folks sitting who build these products over there was that the plethora of choice is given to the customers to to make them feel that there's no vendor lock-in so if you are using some open source you know um soft software it can be on the you know platform side or can be database side you have database site you have that option at AWS so this is a lot there because I always thought that that AWS is the mother of all lock-ins but it's got an ecosystem and we're going to talk about exactly we'll talk about Stu what's working within AWS when you talk to customers and where are the challenges yeah I I got a comment on open source Dave of course there because I mean look we criticized to Amazon for years about their lack of contribution they've gotten better they're doing more in open source but is Amazon the mother of all lock-ins many times absolutely there's certain people inside Amazon I'm saying you know many of us talk Cloud native they're like well let's do Amazon native which means you're like full stack is things from Amazon and do things the way that we want to do things and you know I talk to a lot of customers they use more than one Cloud Dave and therefore certain things absolutely I want to Leverage The Innovation that Amazon has brought I do think we're past building all the main building blocks in many ways we are like in day two yes Amazon is fanatically customer focused and will always stay that way but you know there wasn't anything that jumped out at me last year or this year that was like Wow new category whole new way of thinking about something we're in a vocals last year Dave said you know we have over 200 services and if we listen to you the customer we'd have over two thousand his session this week actually got some great buzz from my friends in the serverless ecosystem they love some of the things tying together we're using data the next flywheel that we're going to see for the next 10 years Amazon's at the center of the cloud ecosystem in the IT world so you know there's a lot of good things here and to your point Dave the ecosystem one of the things I always look at is you know was there a booth that they're all going to be crying in their beer after Amazon made an announcement there was not a tech vendor that I saw this week that was like oh gosh there was an announcement and all of a sudden our business is gone where I did hear some rumbling is Amazon might be the next GSI to really move forward and we've seen all the gsis pushing really deep into supporting Cloud bringing workloads to the cloud and there's a little bit of rumbling as to that balance between what Amazon will do and their uh their go to market so a couple things so I think I think we all agree that a lot of the the announcements here today were taping seams right I call it and as it relates to the mother of all lock-in the reason why I say that it's it's obviously very much a pejorative compare Oracle company you know really well with Amazon's lock-in for Amazon's lock-in is about bringing this ecosystem together so that you actually have Choice Within the the house so you don't have to leave you know there's a there's a lot to eat at the table yeah you look at oracle's ecosystem it's like yeah you know oracle is oracle's ecosystem so so that is how I think they do lock in customers by incenting them not to leave because there's so much Choice Dave I agree with you a thousand I mean I'm here I'm a I'm a good partner of AWS and all of the partners here want to be successful with Amazon and Amazon is open to that it's not our way or get out which Oracle tries how much do you extract from the overall I.T budget you know are you a YouTube where you give the people that help you create a large sum of the money YouTube hasn't been all that profitable Amazon I think is doing a good balance of the ecosystem makes money you know we used to talk Dave about you know how much dollars does VMware make versus there um I think you know Amazon is a much bigger you know VMware 2.0 we used to think talk about all the time that VMware for every dollar spent on VMware licenses 15 or or 12 or 20 were spent in the ecosystem I would think the ratio is even higher here sarbji and an Oracle I would say it's I don't know yeah actually 1 to 0.5 maybe I don't know but I want to pick on your discussion about the the ecosystem the the partner ecosystem is so it's it's robust strong because it's wider I was I was not saying that there's no lock-in with with Amazon right AWS there's lock-in there's lock-in with everything there's lock-in with open source as well but but the point is that they're they're the the circle is so big you don't feel like locked in but they're playing smart as well they're bringing in the software the the platforms from the open source they're picking up those packages and saying we'll bring it in and cater that to you through AWS make it better perform better and also throw in their custom chips on top of that hey this MySQL runs better here so like what do you do I said oh Oracle because it's oracle's product if you will right so they are I think think they're filing or not slenders from their go to market strategy from their engineering and they listen to they're listening to customers like very closely and that has sort of side effects as well listening to customers creates a sprawl of services they have so many services and I criticized them last year for calling everything a new service I said don't call it a new service it's a feature of a existing service sure a lot of features a lot of features this is egress our egress costs a real problem or is it just the the on-prem guys picking at the the scab I mean what do you hear from customers so I mean Dave you know I I look at what Corey Quinn talks about all the time and Amazon charges on that are more expensive than any other Cloud the cloud providers and partly because Amazon is you know probably not a word they'd use they are dominant when it comes to the infrastructure space and therefore they do want to make it a little bit harder to do that they can get away with it um because um yeah you know we've seen some of the cloud providers have special Partnerships where you can actually you know leave and you're not going to be charged and Amazon they've been a little bit more flexible but absolutely I've heard customers say that they wish some good tunning and tongue-in-cheek stuff what else you got we lay it on us so do our players okay this year I think the focus was on the upside it's shifting gradually this was more focused on offside there were less talk of of developers from the main stage from from all sort of quadrants if you will from all Keynotes right so even Werner this morning he had a little bit for he was talking about he he was talking he he's job is to Rally up the builders right yeah so he talks about the go build right AWS pipes I thought was kind of cool then I said like I'm making glue easier I thought that was good you know I know some folks don't use that I I couldn't attend the whole session but but I heard in between right so it is really adopt or die you know I am Cloud Pro for last you know 10 years and I think it's the best model for a technology consumption right um because of economies of scale but more importantly because of division of labor because of specialization because you can't afford to hire the best security people the best you know the arm chip designers uh you can't you know there's one actually I came up with a bumper sticker you guys talked about bumper sticker I came up with that like last couple of weeks The Innovation favorite scale they have scale they have Innovation so that's where the Innovation is and it's it's not there again they actually say the market sets the price Market you as a customer don't set the price the vendor doesn't set the price Market sets the price so if somebody's complaining about their margins or egress and all that I think that's BS um yeah I I have a few more notes on the the partner if you you concur yeah Dave you know with just coming back to some of this commentary about like can Amazon actually enable something we used to call like Community clouds uh your companies like you know Goldman and NASDAQ and the like where Industries will actually be able to share data uh and you know expand the usage and you know Amazon's going to help drive that API economy forward some so it's good to see those things because you know we all know you know all of us are smarter than just any uh single company together so again some of that's open source but some of that is you know I think Amazon is is you know allowing Innovation to thrive I think the word you're looking for is super cloud there well yeah I mean it it's uh Dave if you want to go there with the super cloud because you know there's a metaphor for exactly what you described NASDAQ Goldman Sachs we you know and and you know a number of other companies that are few weeks at the Berkeley Sky Computing paper yeah you know that's a former supercloud Dave Linthicum calls it metacloud I'm not really careful I mean you know I go back to the the challenge we've been you know working at for a decade is the distributed architecture you know if you talk about AI architectures you know what lives in the cloud what lives at the edge where do we train things where do we do inferences um locations should matter a lot less Amazon you know I I didn't hear a lot about it this show but when they came out with like local zones and oh my gosh out you know all the things that Amazon is building to push out to the edge and also enabling that technology and software and the partner ecosystem helps expand that and Pull It in it's no longer you know Dave it was Hotel California all of the data eventually is going to end up in the public cloud and lock it in it's like I don't think that's going to be the case we know that there will be so much data out at the edge Amazon absolutely is super important um there some of those examples we're giving it's not necessarily multi-cloud but there's collaboration happening like in the healthcare world you know universities and hospitals can all share what they're doing uh regardless of you know where they live well Stephen Armstrong in the analyst session did say that you know we're going to talk about multi-cloud we're not going to lead with it necessarily but we are going to actually talk about it and that's different to your points too than in the fullness of time all the data will be in the cloud that's a new narrative but go ahead yeah actually Amazon is a leader in the cloud so if they push the cloud even if they don't say AWS or Amazon with it they benefit from it right and and the narrative is that way there's the proof is there right so again Innovation favorite scale there are chips which are being made for high scale their software being tweaked for high scale you as a Bank of America or for the Chrysler as a typical Enterprise you cannot afford to do those things in-house what cloud providers can I'm not saying just AWS Google cloud is there Azure guys are there and few others who are behind them and and you guys are there as well so IBM has IBM by the way congratulations to your red hat I know but IBM won the award um right you know very good partner and yeah but yeah people are dragging their feet people usually do on the change and they are in denial denial they they drag their feet and they came in IBM director feed the cave Den Dell drag their feed the cave in yeah you mean by Dragon vs cloud deniers cloud deniers right so server Huggers I call them but they they actually are sitting in Amazon Cloud Marketplace everybody is buying stuff from there the marketplace is the new model OKAY Amazon created the marketplace for b2c they are leading the marketplace of B2B as well on the technology side and other people are copying it so there are multiple marketplaces now so now actually it's like if you're in in a mobile app development there are two main platforms Android and Apple you first write the application for Apple right then for Android hex same here as a technology provider as and I I and and I actually you put your stuff to AWS first then you go anywhere else yeah they are later yeah the Enterprise app store is what we've wanted for a long time the question is is Amazon alone the Enterprise app store or are they partner of a of a larger portfolio because there's a lot of SAS companies out there uh that that play into yeah what we need well and this is what you're talking about the future but I just want to make a point about the past you talking about dragging their feet because the Cube's been following this and Stu you remember this in 2013 IBM actually you know got in a big fight with with Amazon over the CIA deal you know and it all became public judge wheeler eviscerated you know IBM and it ended up IBM ended up buying you know soft layer and then we know what happened there and it Joe Tucci thought the cloud was Mosey right so it's just amazing to see we have booksellers you know VMware called them books I wasn't not all of them are like talking about how great Partnerships they are it's amazing like you said sub GC and IBM uh with the the GSI you know Partnership of the year but what you guys were just talking about was the future and that's what I wanted to get to is because you know Amazon's been leading the way I I was listening to Werner this morning and that just reminded me of back in the days when we used to listen to IBM educate us give us a master class on system design and decoupled systems and and IO and everything else now Amazon is you know the master educator and it got me thinking how long will that last you know will they go the way of you know the other you know incumbents will they be disrupted or will they you know keep innovating maybe it's going to take 10 or 20 years I don't know yeah I mean Dave you actually you did some research I believe it was a year or so ago yeah but what will stop Amazon and the one thing that worries me a little bit um is the two Pizza teams when you have over 202 Pizza teams the amount of things that each one of those groups needs to take care of was more than any human could take care of people burn out they run out of people how many amazonians only last two or three years and then leave because it is tough I bumped into plenty of friends of mine that have been you know six ten years at Amazon and love it but it is a tough culture and they are driving werner's keynote I thought did look to from a product standpoint you could say tape over some of the seams some of those solutions to bring Beyond just a single product and bring them together and leverage data so there are some signs that they might be able to get past some of those limitations but I still worry structurally culturally there could be some challenges for Amazon to keep the momentum going especially with the global economic impact that we are likely to see in the next year bring us home I think the future side like we could talk about the vendors all day right to serve the community out there I think we should talk about how what's the future of technology consumption from the consumer side so from the supplier side just a quick note I think the only danger AWS has has that that you know Fred's going after them you know too big you know like we will break you up and that can cause some disruption there other than that I think they they have some more steam to go for a few more years at least before we start thinking about like oh this thing is falling apart or anything like that so they have a lot more they have momentum and it's continuing so okay from the I think game is on retail by the way is going to get disrupted before AWS yeah go ahead from the buyer's side I think um the the future of the sort of Technology consumption is based on the paper uh use and they actually are turning all their services to uh they are sort of becoming serverless behind the scenes right all analytics service they had one service left they they did that this year so every service is serverless so that means you pay exactly for the amount you use the compute the iops the the storage so all these three layers of course Network we talked about the egress stuff and that's a problem there because of the network design mainly because Google has a flatter design and they have lower cost so so they are actually squeezing the their their designing this their services in a way that you don't waste any resources as a buyer so for example very simple example when early earlier In This Cloud you will get a VM right in Cloud that's how we started so and you can get 20 use 20 percent of the VM 80 is getting wasted that's not happening now that that has been reduced to the most extent so now your VM grows as you grow the usage and if you go higher than the tier you picked they will charge you otherwise they will not charge you extra so that's why there's still a lot of instances like many different types you have to pick one I think the future is that those instances will go away the the instance will be formed for you on the fly so that is the future serverless all right give us bumper sticker Stu and then Serb G I'll give you my quick one and then we'll wrap yeah so just Dave to play off of sharp G and to wrap it up you actually wrote about it on your preview post for here uh serverless we're talking about how developers think about things um and you know Amazon in many ways you know is the new default server uh you know for the cloud um and containerization fits into the whole serverless Paradigm uh it's the space that I live in uh you know every day here and you know I was happy to see the last few years serverless and containers there's a blurring a line and you know subject we're still going to see VMS for a long time yeah yeah we will see that so give us give us your book Instagram my number six is innovation favorite scale that's my bumper sticker and and Amazon has that but also I I want everybody else to like the viewers to take a look at the the Google Cloud as well as well as IBM with others like maybe you have a better price to Performance there for certain workloads and by the way one vendor cannot do it alone we know that for sure the market is so big there's a lot of room for uh Red Hats of the world and and and Microsoft's the world to innovate so keep an eye on them they we need the competition actually and that's why competition Will Keep Us to a place where Market sets the price one vendor doesn't so the only only danger is if if AWS is a monopoly then I will be worried I think ecosystems are the Hallmark of a great Cloud company and Amazon's got the the biggest and baddest ecosystem and I think the other thing to watch for is Industries building on top of the cloud you mentioned the Goldman Sachs NASDAQ Capital One and Warner media these all these industries are building their own clouds and that's where the real money is going to be made in the latter half of the 2020s all right we're a wrap this is Dave Valente I want to first of all thank thanks to our great sponsors AWS for for having us here this is our 10th year at the cube AMD you know sponsoring as well the the the cube here Accenture sponsor to third set upstairs upstairs on the fifth floor all the ecosystem partners that came on the cube this week and supported our mission for free content our content is always free we try to give more to the community and we we take back so go to thecube.net and you'll see all these videos go to siliconangle com for all the news wikibon.com I publish weekly a breaking analysis series I want to thank our amazing crew here you guys we have probably 30 35 people unbelievable our awesome last session John Walls uh Paul Gillen Lisa Martin Savannah Peterson John Furrier who's on a plane we appreciate Andrew and Leonard in our ear and all of our our crew Palo Alto Boston and across the country thank you so much really appreciate it all right we are a wrap AWS re invent 2022 we'll see you in two weeks we'll see you two weeks at Palo Alto ignite back here in Vegas thanks for watching thecube the leader in Enterprise and emerging Tech coverage [Music]

>>Welcome back to NYC the Cube's coverage of Mongo DB 2022, a few thousand people here at least bigger than many people, perhaps expected, and a lot of buzz going on and we're gonna talk devs. I'm really excited to welcome back. Robbie Bellson who's the developer relations lead at Verizon and Ian Massingham. Who's the vice president of developer relations at Mongo DB Jens. Good to see you. Great >>To be here. >>Thanks having you. So Robbie, we just met a few weeks ago at the, the red hat summit in Boston and was blown away by what Verizon is doing in, in developer land. And of course, Ian, you know, Mongo it's rayon Detra is, is developers start there? Why is Mongo so developer friendly from your perspective? >>Well, it's been the ethos of MongoDB since day one. You know, back when we launched the first version of MongoDB back in 2009, we've always been about making developers lives easier. And then in 2016, we announced and released MongoDB Atlas, which is our cloud managed service for MongoDB, you know, starting with a small number of regions built on top of AWS and about 2,500 adoption events per week for MongoDB Atlas. After the first year today, MongoDB Atlas provides a managed service for MongoDB developers around the world. We're present in almost a hundred cloud regions across S DCP and Azure. And that adoption number is now running at about 25,000 developers a week. So, you know, the proof are in proof is really in the metrics. MongoDB is an incredibly popular platform for developers that wanna build data-centric applications. You just can't argue with the metrics really, >>You know, Ravi, sometimes there's an analyst who come up with these theories and one of the theories I've been spouting for a long time is that developers are gonna win the edge. And now to, to see you at Verizon building out this developer community was really exciting to me. So explain how you got this started with this journey. >>Absolutely. As you think about Verizon 5g edge or mobile edge computing portfolio, we knew from the start that developers would play a central role and not only consuming the service, but shaping the roadmap for what it means to build a 5g future. And so we started this journey back in late 20, 19 and fast forward to about a year ago with Mongo, we realized, well, wait a minute, you look at the core service offerings available at the edge. We didn't know really what to do with data. We wanted to figure it out. We wanted the vote of confidence from developers. So there I was in an apartment in Colorado racing, your open source Mongo against that in the region edge versus region, what would you see? And we saw tremendous performance improvements. It was so much faster. It's more than 40% faster for thousands and thousands of rights. And we said, well, wait a minute. There's something here. So what often starts is an organic developer, led intuition or hypothesis can really expand to a much broader go to market motion that really brings in the enterprise. And that's been our strategy from day one. Well, >>It's interesting. You talk about the performance. I, I just got off of a session talking about benchmarks in the financial services industry, you know, amazing numbers. And that's one of the hallmarks of, of Mongo is it can play in a lot of different places. So you guys both have developer relations in your title. Is that how you met some formal developer relations? >>We were a >>Program. >>Yeah, I would say that Verizon is one of the few customers that we also collaborate with on a developer relations effort. You know, it's in our mutual best interest to try to drive MongoDB consumption amongst developers using Verizon's 5g edge network and their platform. So of course we work together to help, to increase awareness of MongoDB amongst mobile developers that want to use that kind of technology. >>But so what's your story on this? >>I mean, as I, as I mentioned, everything starts with an organic developer discovery. It all started. I just cold messaged a developer advocate on Twitter and here we are at MongoDB world. It's amazing how things turn out. But one of the things that's really resonated with me as I was speaking with one of, one of your leads within your organization, they were mentioning that as Mongo DVIA developed over the years, the mantra really became, we wanna make software development easy. Yep. And that really stuck with me because from a network perspective, we wanna make networking easy. Developers are not gonna care about the internals of 5g network. In fact, they want us to abstract away those complexities so that they can focus on building their apps. So what better co-innovation opportunity than taking MongoDB, making software easy, and we make the network easy. >>So how do you think about the edge? How does you know variety? I mean, to me, you know, there's a lot of edge use cases, you know, think about the home Depot or lows. Okay, great. I can put like a little mini data center in there. That's cool. That's that's edge. Like, but when I think of Verizon, I mean, you got cell towers, you've got the far edge. How do you think about edge Robbie? >>Well, the edge is a, I believe a very ambiguous term by design. The edge is the device, the mobile device, an IOT device, right? It could be the radio towers that you mentioned. It could be in the Metro edge. The CDN, no one edge is better than the other. They're all just serving different use cases. So when we talk about the edge, we're focused on the mobile edge, which we believe is most conducive to B2B applications, a fleet of IOT devices that you can control a manufacturing plant, a fleet of ground and aerial robotics. And in doing so you can create a powerful compute mesh where you could have a private network and private mobile edge computing by way of say an AWS outpost and then public mobile edge computing by way of AWS wavelength. And why keep them separate. You could have a single compute mesh even with MongoDB. And this is something that we've been exploring. You can extend Atlas, take a cluster, leave it in the region and then use realm the mobile portfolio and spread it all across the edge. So you're creating that unified compute and data mesh together. >>So you're describing what we've been expecting is a new architecture emerging, and that's gonna probably bring new economics of new use cases, right? Where are we today in that first of all, is that a reasonable premise that this is a sort of a new architecture that's being built out and where are we in that build out? How, how do you think about the, the future of >>That? Absolutely. It's definitely early days. I think we're still trying to figure it out, but the architecture is definitely changing the idea to rip out a mobile device that was initially built and envisioned for the device and only for the device and say, well, wait a minute. Why can't it live at the edge? And ultimately become multi-tenant if that's the data volume that may be produced to each of those edge zones with hypothesis that was validated by developers that we continue to build out, but we recognize that we can't, we can't get that static. We gotta keep evolving. So one of our newest ideas as we think about, well, wait a minute, how can Mongo play in the 5g future? We started to get really clever with our 5g network APIs. And I, I think we talked about this briefly last time, 5g, programmability and network APIs have been talked about for a while, but developers haven't had a chance to really use them and our edge discovery service answering the question in this case of which database is the closest database, doesn't have to be invoked by the device anymore. You can take a thin client model and invoke it from the cloud using Atlas functions. So we're constantly permuting across the entire portfolio edge or otherwise for what it means to build at the edge. We've seen such tremendous results. >>So how does Mongo think about the edge and, and, and playing, you know, we've been wondering, okay, which database is actually gonna be positioned best for the edge? >>Well, I think if you've got an ultra low latency access network using data technology, that adds latency is probably not a great idea. So MongoDB since the very formative years of the company and product has been built with performance and scalability in mind, including things like in memory storage for the storage engine that we run as well. So really trying to match the performance characteristics of the data infrastructure with the evolution in the mobile network, I think is really fundamentally important. And that first principles build of MongoDB with performance and scalability in mind is actually really important here. >>So was that a lighter weight instance of, of Mongo or not >>Necessarily? No, not necessarily. No, no, not necessarily. We do have edge cashing with realm, the mobile databases Robbie's already mentioned, but the core database is designed from day one with those performance and scalability characteristics in mind, >>I've been playing around with this. This is kind of a, I get a lot of heat for this term, but super cloud. So super cloud, you might have data on Preem. You might have data in various clouds. You're gonna have data out at the edge. And, and you've got an abstraction that allows a developer to, to, to tap services without necessarily if, if he or she wants to go deep into the S great, but then there's a higher level of services that they can actually build for their customers. So is that a technical reality from a developer standpoint, in your view, >>We support that with the Mongo DB multi-cloud deployment model. So you can place Mongo DB, Atlas nodes in any one of the three hyperscalers that we mentioned, AWS, GCP or Azure, and you can distribute your data across nodes within a cluster that is spread across different cloud providers. So that kinds of an kind of answers the question about how you do data placement inside the MongoDB clustered environment that you run across the different providers. And then for the abstraction layer. When you say that I hear, you know, drivers ODMs the other intermediary software components that we provide to make developers more productive in manipulating data in MongoDB. This is one of the most interesting things about the technology. We're not forcing developers to learn a different dialect or language in order to interact with MongoDB. We meet them where they are by providing idiomatic interfaces to MongoDB in JavaScript in C sharp, in Python, in rust, in that in fact in 12 different pro programming languages that we support as a first party plus additional community contributed programming languages that the community have created drivers for ODMs for. So there's really that model that you've described in hypothesis exist in reality, using >>Those different Compli. It's not just a series of siloed instances in, >>In different it's the, it's the fabric essentially. Yeah. >>What, what does the Verizon developer look like? Where does that individual come from? We talked about this a little bit a few weeks ago, but I wonder if you could describe it. >>Absolutely. My view is that the Verizon or just mobile edge ecosystem in general for developers are present at this very conference. They're everywhere. They're building apps. And as Ian mentioned, those idiomatic interfaces, we need to take our network APIs, take the infrastructure that's being exposed and make sure that it's leveraging languages, frameworks, automation, tools, the likes of Terraform and beyond. We wanna meet developers where they are and build tools that are easy for them to use. And so you had talked about the super cloud. I often call it the cloud continuum. So we, we took it P abstraction by abstraction. We started with, will it work in one edge? Will it work in multiple edges, public and private? Will it work in all of the edges for a given region, public or private, will it work in multiple regions? Could it work in multi clouds? We've taken it piece by piece by piece and in doing so abstracting way, the complexity of the network, meaning developers, where they are providing those idiomatic interfaces to interact with our API. So think the edge discovery, but not in a silo within Atlas functions. So the way that we're able to converge portfolios, using tools that dev developers already use know and love just makes it that much easier. Do, >>Do you feel like I like the cloud continuum cause that's really what it is. The super cloud does the security model, how does the security model evolve with that? >>At least in the context of the mobile edge, the attack surface is a lot smaller because it's only for mobile traffic not to say that there couldn't be various configuration and human error that could be entertained by a given application experience, but it is a much more secure and also reliable environment from a failure domain perspective, there's more edge zones. So it's less conducive to a regionwide failure because there's so many more availability zones. And that goes hand in hand with security. Mm. >>Thoughts on security from your perspective, I mean, you added, you've made some announcements this week, the, the, the encryption component that you guys announced. >>Yeah. We, we issued a press release this morning about a capability called queryable encryption, which actually as we record this Mark Porter, our CTO is talking about in his keynote, and this is really the next generation of security for data stored within databases. So the trade off within field level encryption within databases has always been very hard, very, very rigid. Either you have keys stored within your database, which means that your memory, so your data is decrypted while it's resident in memory on your database engine. This allow, of course, allows you to perform query operations on that data. Or you have keys that are managed and stored in the client, which means the data is permanently OBS from the engine. And therefore you can't offload query capabilities to your data platform. You've gotta do everything in the client. So if you want 10 records, but you've got a million encrypted records, you have to pull a million encrypted records to the client, decrypt them all and see performance hit in there. Big performance hit what we've got with queryable encryption, which we announced today is the ability to keep data encrypted in memory in the engine, in the database, in the data platform, issue queries from the client, but use a technology called structural encryption to allow the database engine, to make decisions, operate queries, and find data without ever being able to see it without it ever being decrypted in the memory of the engine. So it's groundbreaking technology based on research in the field of structured encryption with a first commercial database provided to bring this to market. >>So how does the mobile edge developer think about that? I mean, you hear a lot about shifting left and not bolting on security. I mean, is this, is this an example of that? >>It certainly could be, but I think the mobile edge developer still stuck with how does this stuff even work? And I think we need to, we need to be mindful of that as we build out learning journeys. So one of my favorite moments with Mongo was an immersion day. We had hosted earlier last year where we, our, from an enterprise perspective, we're focused on BW BS, but there's nothing stopping us. You're building a B2C app based on the theme of the winner Olympics. At the time, you could take a picture of Sean White or of Nathan Chen and see that it was in fact that athlete and then overlaid on that web app was the number of medals they accrued with the little trumpeteer congratulating you for selecting that athlete. So I think it's important to build trust and drive education with developers with a more simple experience and then rapidly evolve overlaying the features that Ian just mentioned over time. >>I think one of the keys with cryptography is back to the familiar messaging for the cloud offloading heavy lifting. You actually need to make it difficult to impossible for developers to get this wrong, and you wanna make it as easy as possible for developers to deal with cryptography. And that of course is what we're trying to do with our driver technology combined with structure encryption, with query encryption. >>But Robbie, your point is lots of opportunity for education. I mean, I have to say the developers that I work with, it's, I'm, I'm in awe of how they solve problems and I, and the way they solve problems, if they don't know the answer, they figure out how to go get it. So how, how are your two communities and other communities, you know, how are they coming together to, to solve such problems and share whether it's best practices or how do I do this? >>Well, I'm not gonna lie in person. Events are a bunch of fun. And one of the easiest domain knowledge exchange opportunities, when you're all in person, you can ideate, you can whiteboard, you can brainstorm. And often those conversations are what leads to that infrastructure module that an immersion day features. And it's just amazing what in person events can do, but community groups of interest, whether it's a Twitch stream, whether it's a particular code sample, we rely heavily on digital means today to upscale the developer community, but also build on by, by means of a simple port request, introduce new features that maybe you weren't even thinking of before. >>Yeah. You know, that's a really important point because when you meet people face to face, you build a connection. And so if you ask a question, you're more likely perhaps to get an answer, or if one doesn't exist in a, in a search, you know, you, oh, Hey, we met at the, at the conference and let's collaborate on this guys. Congratulations on, on this brave new world. You're in a really interesting spot. You know, developers, developers, developers, as Steve bomber says screamed. And I was glad to see Dave was not screaming and jumping up and down on the stage like that, but, but the message still resonates. So thank you, definitely appreciate. All right, keep it right there. This is Dave ante for the cubes coverage of Mago DB world 2022 from New York city. We'll be right back.

(upbeat music) >> Okay, welcome back everyone, Cube coverage live on the floor in the Moscone center in San Francisco, California. I'm John Furrier host of the Cube. AWS summit 2022 is here in San Francisco, we're back in live events. Of course, Amazon summit in New York city is coming, Amazon summit this summer we'll be there as well. We've got a great guest Mike Miller, GN of AI devices at AWS always one of my favorite interviews. We've got a little prop here, we got the car, DeepRacer, very popular at the events. Mike, welcome to the Cube. Good to see you. >> Hey John, thank you for having me. It's really exciting to be back and chat with you a little bit about DeepRacer. >> Well I want to get into the prop in a second, not the prop, the product. >> Yeah. >> So DeepRacer program, you got the race track here. Just explain what it is real quick, we'll get that out of the way. >> Absolutely so, well, you know that AI, AWS is passionate about making AI and ML more accessible to developers of all skill levels. So DeepRacer is one of our tools to do that. So DeepRacer is a 3D cloud-based racing simulator, a 1/18th scale autonomously driven car and a league to add a little spicy competition into it. So developers can start with the cloud-based simulator where they're introduced to reinforcement learning which basically teaches the, our car to drive around a track through trial and error and of course you're in a virtual simulator so it's easy for it to make mistakes and restart. Then once that model is trained, it's downloaded to the car which then can drive around a track autonomously, kind of making its own way and of course we track lap time and your successful lap completions and all of that data feeds into our league to try to top the leaderboard and win prizes. >> This is the ultimate gamification tool. (chuckles) >> Absolutely >> Making it fun to learn about machine learning. All right, let's get into the car, let's get into the showcase of the car. show everyone what's going on. >> Absolutely. So this is our 1/18th scale autonomously driven car. It's built off of a monster truck chassis so you can see it's got four wheel drive, it's got steering in the front, we've got a camera on the front. So the camera is the, does the sensing to the compute board that's driven by an Intel atom a processor on the, on the vehicle, that allows it to make sense of the in front of it and then decide where it wants to drive. So you take the car, you download your trained model to it and then it races around the track. >> So the front is the camera. >> The front is the camera, that's correct. >> Okay, So... >> So it's a little bit awkward but we needed to give it plenty of room here so that I can actually see the track in front of it. >> John: It needs eyes. >> Yep. That's exactly right. >> Awesome. >> Yes. >> And so I got to buy that if I'm a developer. >> So, developers can start in two ways, they can use our virtual racing experience and so there's no hardware cost for that, but once you want the experience, the hands on racing, then the car is needed but if you come to one of our AWS summits, like here in San Francisco or anywhere else around the world we have one or more tracks set up and you can get hands on, you can bring the model that you trained at home download it to a car and see it race around the track. >> So use a car here. You guys are not renting cars, but you're letting people use the cars. >> Absolutely. >> Can I build my own car or does it have to be assembled by AWS? >> Yeah, we, we sell it as a, as a kit that's already assembled because we've got the specific compute board in there, that Intel processor and all of the software that's already built on there that knows how to drive around the track. >> That's awesome, so talk about the results. What's going on? What's the feedback from developers? Obviously it's a nerd dream, people like race cars, people love formula one now, all the racing there. IOT is always an IOT opportunity as well. >> Absolutely, and as you said, gamification, right? And so what we found and what we thought we would find was that adding in those sort of ease of learning so we make it the on-ramp to machine learning very easy. So developers of all skill levels can take advantage of this, but we also make it fun by kind of gamifying it. We have different challenges every month, we have a leader board so you can see how you rank against your peers and actually we have split our league into two, there's an open division which is more designed for novices so you'll get rewarded for just participating and then we have a pro league. So if you're one of the top performers in the open league each month, you graduate and you get to race against the big boys in the pro leagues. >> What's the purse? >> Oh, the, (John laughing) we definitely have cash and prizes that happen, both every month. We have prizes cause we do races every month and those winners of those races all get qualified to race at the championship, which of course happens in Las Vegas at re:Invent. So we bring all the winners to re:Invent and they all race against each other for the grand prize the big trophy and the, and the, and the cash prize. >> Well, you know, I'm a big fan of what you guys are doing so I'm kind of obviously biased on this whole program but you got to look at trend of what's going on in eSports and the online engagement is off the charts, are there plans to kind of make this more official and bigger? Is there traction there or is this just all part of the Amazon goodness, love that you guys give back? I mean, obviously it's got traction. >> Yeah. I mean, the thing that's interesting about eSports is the number of young people who are getting into it and what we saw over the last couple years is that, there were a lot of students who were adopting DeepRacer but there were some hurdles, you know, it wasn't really designed for them. So what we did was we made some changes and at the beginning of this year we launched a student focused DeepRacer program. So they get both free training every month, they get free educational materials and their own private league so they know students can race against other students, as part of that league. >> John: Yeah. >> So that was really our first step in kind of thinking about those users and what do we need to do to cater to their kind of unique needs? >> Tell about some of the power dynamics or the, or not power dynamics, the group dynamics around teams and individuals, can I play as an individual? Do I, do I have to be on a team? Can I do teams? How does that look? How do you think about those things? >> Yeah, absolutely. Great, great question. The primary way to compete is individually. Now we do have an offering that allows companies to use DeepRacer to excite and engage their own employees and this is where operating as a team and collaborating with your coworkers comes into play so, if, if I may there's, you know, Accenture and JPMC are a couple big customers of ours, really strong partners. >> John: Yeah. >> Who've been able to take advantage of DeepRacer to educate their workforce. So Accenture ran a 24 hour round the, round the globe race a couple years ago, encouraging their employees to collaborate and form teams to race and then this past year JPMC, had over 3000 of their builders participate over a three month period where they ran a private league and they went on to win the top two spots, first place and second place. >> John: Yeah. >> At reinvent last year. >> It reminds me the NASCAR and all these like competitions, the owners have multiple cars on the race. Do you guys at re:Invent have to start cutting people like, only two submissions or is it free for all? >> Well, you have to qualify to get to the races at re:invent so it's very, it's very cutthroat leading up to that point. We've got winners of our monthly virtual contests, the winners like of the summit races will also get invited. So it's interesting, this dynamic, you'll have some people who won virtual races, some people who won physical races, all competing together. >> And do you guys have a name for the final cup or is it like what's the, what's the final, how do you guys talk about the prizes and the... >> It's, it's the DeepRacer Championship Cup of course. >> John: Of course. (laughter) >> Big silver cup, you get to hoist it and... >> Are the names inscribed in it, is it like the Stanley cup or is it just one. >> It's a unique one, so you get to hold onto it each year. The champion gets their own version of the cup. >> It's a lot of fun. I think it's really kind of cool. What's the benefits for a student? Talk about the student ones. >> Yeah. Yeah. >> So I'm a student I'm learning machine learning, what's in it for me is a career path and the fund's obvious, I see that. >> Yeah absolutely. You know, the, for students, it's a hands on way that's a very easy on-ramp to machine learning and you know, one of the things, as I mentioned we're passionate about making it accessible to all. Well, when we mean all we were really do mean all. So, we've got a couple partners who are passionate about the same thing, right? Which is how do we, if, if AI and ML is going to transform our world and solve our most challenging problems, how can we get the right minds from all walks of life and all backgrounds to learn machine learning and get engaged? So with two of our partners, so with Udacity and with Intel we launched a $10 million AWS, AI and ML scholarship program and we built it around DeepRacer. So not only can students who are college and high school students, age 16 and over can use DeepRacer, can learn about machine learning and then get qualified to win one of several thousand scholarships. >> Any other promotions going on that people should know about? >> Yeah, one, one final one is, so we talked about enterprises like JPMC and Accenture, so we've got a promotion that we just started yesterday. So if you are an enterprise and you want to host a DeepRacer event at your company to excite your employees and get 'em collaborating more, if you have over 50 employees participating, we're going to give you up to a hundred thousand dollars in AWS credits, to offset the costs of running your DeepRacer event at your, at your company so >> That's real money. >> Yeah. Real, real, real exciting I think for companies now to pick up DeepRacer. >> So, I mean, honestly, I know Andy Jassy, I have many sports car conversations with him. He's a sports guy, he's now the CEO of Amazon, gets to go all the sporting events, NFL. I wish I could bring the Cube there but, we'll stick with with cloud for now. You got to look at the purse kind of thing. I'm interested in like the whole economic point of cause I mean, forget the learning for side for a second which is by the way awesome. This is great competition. You got leader boards, you got regional activities, you got a funneling system laddering up to the final output. >> And we've really done a decent job and, and of adding capabilities into that user experience to make it more engaging. You can see the countries that the different competitors are from, you can see how the lap times change over time, you know, we give awards as I mentioned, the two divisions now. So if you're not super competitive, we'll reward you for just participating in that open league but if you want to get competitive, we'll even better rewards monthly in the Pro League. >> Do you guys have any conversations internally like, this is getting too big, we might have to outsource it or you keep it in inside the fold? (laughter) >> We, we love DeepRacer and it's so much fun running this, >> You see where I'm going with this. You see where I'm going with this right? The Cube might want to take this over. >> Hey. >> And you know >> We're always looking for partners and sponsors who can help us make it bigger so, absolutely. >> It's a good business opportunity. I just love it. Congratulations, great stuff. What's the big learning in this, you know, as a as an executive, you look back you got GM, AI super important and, and I think it is great community, communal activity as well. What's the learning, what have you learned from this over the years besides that it's working but like what's the big takeaway? >> Yeah, I mean. We've got such a wide range of developers and builders who are customers that we need to provide a variety of opportunities for people to get hands on and there's no better way to learn a complex technology like AI and ML than getting hands on and seeing, you know, physically the result of the AI and I think that's been the biggest learning, is that just having the hands on and the sort of element of watching what it does, just light bulbs go off. When, when developers look at this and they start piecing the, the puzzle pieces together, how they can benefit. >> So I have to ask the question that might be on other peoples minds, maybe it's not, maybe I'm just thinking really dark here but gamers love to hack and they love cheat codes, they love to get, you know, get into the system, any attempts to do a little hacking to win the, the the game, have you guys, is there, you know? >> Well, well, you know, last year we, we we released an open source version of the vehicle so that people could start using it as a platform to explore and do that kind of hacking and give them an opportunity build on top of it. >> So using mods, mods modules, we can mod out on this thing. >> Yeah, absolutely. If you go to deepracer.com, we have sort of extensions page there, and you can see, somebody mounted a Nerf cannon onto the top of this, somebody built a computer vision model that could recognize you know, rodents and this thing would kind of drive to scare 'em, all kinds of fun topics. >> So it's a feature, not a bug. >> Absolutely. >> Open it up. >> Yeah. >> And also on transparency, if you have the source code out there you guys can have some review. >> Yeah. The whole idea is like, let's see what developers, >> It's really not hackable. It's not hackable. >> Yeah, I mean, for the, if you think about it when we do the races, we bring the cars ourselves, the only way a developer interacts is by giving us their trained models so... >> And you, do you guys review the models? Nothing to review, right? >> Yeah. There's nothing really to review. It's all about, you know, there, there was a model that we saw one time where the car went backwards and then went forwards across the finish line but we, we, we gently told them, well that's really not a valid way to race. >> That was kind of a hack, not really a hack. That was a hack hack. (laughter) That was just a growth hack. >> Exactly, but everybody just has a lot of fun with it across the board. >> Mike, great, thanks for coming on. Love the prop. Thanks for bringing the car on, looks great. Success every year. I want to see the purse, you know, big up to $1,000,000 you know, the masters, you know, tournament. >> Someday. (John chuckles) >> You guys.. >> Thank you for having me John. >> DeepRacer again, Fun Start has a great way to train people on machine learning, IOT device, turns into a league of its own. Great stuff for people to learn, especially students and people in companies, but the competitive juices flowing. That's what it's all about, having fun, learning. It's the Cube here in San Francisco. Stay with us for more coverage after this short break. (gentle music)

[Music] welcome everyone to thecube's presentation of the aws startup showcase data as code this is season two episode two of our ongoing series covering exciting startups in the aws ecosystem to talk about data and analytics i'm your host lisa martin i have a cube alumni here with me socket sarah the ceo and founder of nexla he's here to talk about a future of automated data engineering socket welcome back great to see you lisa thank you for having me pleasure to be here again let's dig into nexla's mission ready to use data in the hands of every user what does that mean that means that you know every organization what what are they trying to do with data they want to make use of data they want to make decisions from data they want to make data a part of their business right the challenge is that every function in an organization today needs to leverage data whether it is finance whether it is hr whether it is marketing sales or product the problem for companies is that for each of these users into each of these teams the data is not ready for them to use as it is there is a lot that goes on before the data can be in their hands and it's in the tools that they like to work with and that's where a lot of data engineering happens today i would say that is by far one of the biggest bottlenecks today for companies in accelerating their business and being you know truly data-driven so talk to me about what makes nexla unique when you're in customer conversations as every company these days in every industry has to be a data company what do you tell them about what differentiates you yeah one of the biggest challenges out there is that the variety of data that companies work with is growing tremendously you know every sas application you use becomes a data source every type of database every type of user event anything can be a source of data now it is a tremendous engineering challenge for companies to make the data usable and the biggest challenge there is people companies just cannot have enough people to write that code to make the data engineering happen and where we come in with a very unique value is how to start thinking about making this whole process much faster much more automated at the end of the day lisa time to value and time to results is by far the number one thing on top of mind for customers time to value is critical we're all thin on patients these days whether we're in our consumerizer our business lives but being able to get access to data to make intelligent decisions whether it's on something that you're going to buy or a product or service you're going to deliver is really critical give me a snapshot of some of the users of nexla yeah the users of nexla are actually across different industries one of the main one of the interesting things is that the data challenges whether you are in financial services whether you are in retail e-commerce whether you are in healthcare they are very similar is basically getting connected to all these data systems and having the data now what people do with the data is very specific to their industry so for example within the e-commerce world or retail world itself you know companies from the likes of bed bath beyond and forever 21 and poshmark which are retailers or e-commerce companies they use nexla today to bring a lot of data in uh so do delivery companies like dodash and instacart and you know so do for example logistics providers like you know narwhal or customer loyalty and customer data companies like yacht pro so across the board for example just in retail we cover a whole bunch of companies got it now let's dig into you're here to talk about the future of automated data engineering talk to me about data engineering what is it let's define it and crack it open yeah um data engineering is i would say by far one of the hottest areas of work today the one of the hardest people to hire if you're looking for one data engineering is basically um all the code you know the process and the people that is basically connecting to their system so just to give a very practical example right for um for somebody in e-commerce let's say a take-off case of door dash right it's extremely important for them to have data as to which stores have what products what is available is this something they can list for people to go and buy is this something that they can therefore deliver right this is data that changes all the time now imagine them getting data from hundreds of different merchants across the board so it is the task of data engineering to then consume that data from all these different places different formats different apis different systems and then somehow unify all the data so that it can be used by the applications that they are building so data engineering in this case becomes taking data from different places and making it useful again back to what i was talking about ready to use data it is a lot of code it's a lot of people not just that it is something that runs every single day so it means it has monitoring it has reliability um it has performance it has every aspect of engineering as we know going into it you mentioned it's a hot topic which it is but it's also really challenging to accomplish how does nexla help enable that yeah data engineering is quite interesting in that one it is difficult to implement you know the the necessary sort of pieces but it is also very repetitive at some level right i mean when you connect to say 10 systems and get data from them you know that's not the end of it you have 10 more and 10 more and 10 more and then at some point you have thousands of such you know data connectivity and data flows happening it's hard to maintain them right as well so the way nexla gets into the whole picture is looking at what can we understand about data what can we observe about the data systems what can be done from that and then start to automate certain pieces of data engineering so that we are helping those teams just accelerate a lot faster and it i would say comes down to more people being able to do these tasks rather than only very very specialized people more people being able to do the tasks more users kind of democratization of data really there can you talk to us in more detail about how naxa is automating data engineering yeah i think um you know i think this is best shared through a visual so let me walk you through that a little bit as to how we automated engineering right so if we think about data engineering three of the most core components are many parts to it but three of the most core components of that are integrating with data systems preparing and transforming data and then monitoring that right so automating data engineering happens in you know three different ways first of all connecting connecting to data is is basically about the gateway to data the ability to read and write data from different systems this is where the data journey starts but it is extremely complex because people have to write code to connect to different systems one part that we have automated is generating these connectors so that you don't have to write code for that also making them bi-directional is extremely valuable because now you can read and write from any system the second part is that the gateway the connector has read the data but how do you represent it to the user so anybody can understand it and that's where the concept of data product comes in so we also look at auto generating data products these become the common language and entity that people can understand and work with and then the third part is taking all this automation and bringing the human in the loop no automation is perfect and therefore bringing the human in the loop means that somebody who is an expert in data who can look at it and understand it can now do things which only data systems experts were able to do before so bringing in that user of data directly into the into the picture is one important part but let's not forget data challenges are very diverse and very complex so the same system also becomes accessible to the engineers who are experts in that and now both of these can work together while an engineer will come through apis and sdk and command interfaces a data user comes in through a nice no code user interface and all of these things coming together are what is accelerating back to that time to value that really everybody cares about so if i'm in marketing and i'm a data user i'm able to have a collaborative workflow with the data engineer yeah yeah for the first time that is actually possible and everybody's focuses on their expertise and their know-how so you know um somebody who for example in financial services really understands portfolio and transactions and different type of asset classes they have the data in front of them the engineers who understand the underlying real-time data feeds and those they are still involved in the loop but now they are not doing that back and forth you know as the user of data i'm not going to the engineer saying hey can you do this for me can you get the data here and that back and forth is not only time taking it's frustrating and the number one hold back right yeah that and that's time that nobody has to waste as we know for many reasons talk to me about when you look into your crystal ball which i'm sure you have one what is the future of of data engineering from nexus perspective you talked about the automation what's the future hold i think the future of data engineering becomes that we up level this at a point where um companies don't have to be slowed down for it um i think a lot of tooling is already happening the way to think about this is that here in 2022 if we think that our data challenges are you know like x they will be a thousand x in five years right i mean this complexity is just increasing very rapidly so we think that this becomes one of those fundamental layers you know and you know as i was saying maybe the last time this is like the road you know you don't feel it you just move on it you do your job you build your products deliver your services as a company this just works for you um and that's where i think the future is and that's where i think the future should be we all need to work towards that we're not there yet not there yet a lot of a lot of potential a lot of opportunity and a lot of momentum speaking of momentum i want to talk about data mesh that is a topic of a lot of excitement a lot of discussion let's unpack that yeah i think uh you know the idea that data should be democratized that people should get access to the data and it's all coming back to that sort of basic concept of scale companies can scale only when more people can do the relevant jobs without depending on each other right so the idea of data democratization has been there for a long time but you know recently in the last couple of years the concept of data mesh was introduced by zamak digani and thoughtworks and that has really caught the attention of people and the imagination of leadership as well the idea that data should be available as a product you know that democratization can happen what is the entity of the democratization that's data presented as a product that people can use and collaborate is extremely powerful um i think a lot of companies are gravitating towards that and that's why it's exciting this is promising a future that is you know possible so second speaking of data products we talked a little bit about this last time but can you really help us understand see smell touch feel what a data product is and give us that context yeah absolutely i think uh best to orient ourselves with the general thinking of how we consider something as a product right a product is something that we find ready to use for example this table that i'm using right now made out of raw materials wood metal screws somebody designed it somebody produced it and i'm using it right now when we think about data products we think about data as the raw material so for example a spreadsheet an api a database query those are the raw raw materials what is a data product is something that further enriches and enhances that entity to be much more usable ready to use right um let me illustrate that with a little bit of a visual actually and that might help okay um the idea of the data product and this is how a data product looks like in next lab for a user to write as you see the concept of a data product is something that first of all it's a logical entity this simply means that it's not a new copy of data just like containers or logical compute units you know these data products are logical entities but they represent data in the same consistent fashion regardless of where the data comes from what format it is in they provide the user the idea of what the structure of data is what the sample data looks like what the characteristics of data are it allows people to have some documentation around it what does the data mean what do these attributes you know mean and how to interpret them how to validate that data something that users often know in an industry how is my data looking like well this value can never be negative because it's a price for example right um then the ability to take these data products that you know we automate by generating as i was mentioning earlier automatically creating these data products taking these data products to create new data products now that's something that's very unique about data you could take data off about an order for a from a company and say well the order data has an order id and a user id but i need to look up shipping address so i can combine user and order data to get that information in one place so you know creating new data products giving people access hey i've designed a data product i think you'll find it useful you can go use that as it is you don't have to go from scratch so all of those things together make a data product something that people can find ready to use again and this is this is also usable by the again that example where i'm in marketing uh or i'm in sales this is available to me as a general user as a general user in the tool of your choice so you can say oh no i am most familiar with using data in a spreadsheet i would like it there or i prefer my data in a tableau or a looker to visualize it and you can have it there so these data products give multiple interfaces for the end user to make use of it got it i like it you're meeting the user where they are with relevant data that helps them understand so much more contextually i'm curious when you're in customer conversations customers that come to you saying saka we need to build the data mesh how is nexl relevant they're how what is your conversation like yeah when people want to build a data mesh they're really looking for how their organization will scale into the future uh there are multiple components to building a data mesh there's a tooling part of it the technology portion there are people and processes right i mean unless you train people in certain processes and say hey when you build a data product you know make sure you have taken care of privacy or compliance to certain rules or who do you give access to is something you have to follow some rules about so we provide the technology component of it and then the people and process is something that companies you know then as they adopt and do that right so the concept of data product becomes core to building the data mesh having governance on it uh having all this be self-serve it's an essential part of that so that's where we come into the picture as a as a technology component to the whole story and working to deliver on that mission to getting data in the hands of every user you mentioned i want to dig into in the last few minutes here that we have uh the target audience you mentioned a few by name big names customers that nexla has you i heard retail i heard e-commerce i think i heard logistics but talk to me about the target customer for nexla any verticals in particular or any company's sizes in particular as well yeah you know the one of the top three banks in the country is a big user of nexla as part of their data stack uh we actually sit as part of their enterprise-wide ai platform providing data to their data scientists um we're not allowed to share their name unfortunately but um you know there are multiple other companies in asset management area for example they work with a lot of data in markets portfolio and so on um the leading medical devices companies using nexla data scientists there are using data coming in real time or streaming for medical devices to train and um and combine that with other data to do sort of clinical trial related research that they do um we have you know the companies for example linkedin is an excellent customer linkedin is by far the largest social network um their marketing team leverages nexla to bring data from different type of systems together as well um you know so are companies in education space like nerdy is a public company that uses nexla for you know student enrollment education data as they collaborate with school districts for example um you know there are companies across the board in marketing live brand you know for example uses nexla so we are um we are you know from who uses nexla is today mostly mid to large to very large enterprises today leverage nexla as a very critical component and often mission critical data for which they leverage us do you see that changing anytime soon as every company these days has to be a data company we expect that as consumers whether it's my grocery store um or my local coffee shop that they've got to use data to deliver me that personalized experience do you see the target audience kind of shifting down to more into mid-market smb space for next level oh yeah absolutely look we started the journey of the company with the thinking that the most complex data challenges exist in the large enterprise and if we can make it no code self-serve easy to use for them we can bring the same high-end technology to everybody and this is exactly why we recently launched in the amazon marketplace so anybody can go there get access to nexla and start to use it and you will see more and more of that happen where we will be bringing even some free versions of our product available so you're absolutely right every company needs to leverage data and i think people are getting much better at it you know especially in the last couple of years i've seen that teams have become much more sophisticated yes even if you are a coffee shop and you're running campaigns you know getting people yelp reviews and so on this data that you can use and understand better your demographic your customer and run your business better so one day yes we will absolutely be in the hands of every single person here a lot more opportunity to delight a lot more consumers and customers socket thank you so much for joining me on the program during the startup showcase you did a great job of helping us understand the future of automated data engineering we appreciate your insights thank you so much lisa it's a pleasure talking to you likewise for soccer sarah i'm lisa martin you're watching thecube's coverage of the aws startup showcase season two episode two stick around more great content coming up from the cube the leader in hybrid tech event coverage [Music]

>>Welcome to this exclusive discussion. IBM, the next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond. My name is Dave Volante from the Cuban today, we're going to take a deep dive into some of the most important trends impacting the life sciences industry in the next 60 minutes. Yeah, of course. We're going to hear how IBM is utilizing Watson and some really important in life impacting ways, but we'll also bring in real world perspectives from industry and the independent analyst view to better understand how technology and data are changing the nature of precision medicine. Now, the pandemic has created a new reality for everyone, but especially for life sciences companies, one where digital transformation is no longer an option, but a necessity. Now the upside is the events of the past 22 months have presented an accelerated opportunity for innovation technology and real world data are coming together and being applied to support life science, industry trends and improve drug discovery, clinical development, and treatment commercialization throughout the product life cycle cycle. Now I'd like to introduce our esteemed panel. Let me first introduce Lorraine Marshawn, who is general manager of life sciences at IBM Watson health. Lorraine leads the organization dedicated to improving clinical development research, showing greater treatment value in getting treatments to patients faster with differentiated solutions. Welcome Lorraine. Great to see you. >>Dr. Namita LeMay is the research vice-president of IDC, where she leads the life sciences R and D strategy and technology program, which provides research based advisory and consulting services as well as market analysis. The loan to meta thanks for joining us today. And our third panelist is Greg Cunningham. Who's the director of the RWE center of excellence at Eli Lilly and company. Welcome, Greg, you guys are doing some great work. Thanks for being here. Thanks >>Dave. >>Now today's panelists are very passionate about their work. If you'd like to ask them a question, please add it to the chat box located near the bottom of your screen, and we'll do our best to answer them all at the end of the panel. Let's get started. Okay, Greg, and then Lorraine and meta feel free to chime in after one of the game-changers that you're seeing, which are advancing precision medicine. And how do you see this evolving in 2022 and into the next decade? >>I'll give my answer from a life science research perspective. The game changer I see in advancing precision medicine is moving from doing research using kind of a single gene mutation or kind of a single to look at to doing this research using combinations of genes and the potential that this brings is to bring better drug targets forward, but also get the best product to a patient faster. Um, I can give, uh, an example how I see it playing out in the last decade. Non-oncology real-world evidence. We've seen an evolution in precision medicine as we've built out the patient record. Um, as we've done that, uh, the marketplace has evolved rapidly, uh, with, particularly for electronic medical record data and genomic data. And we were pretty happy to get our hands on electronic medical record data in the early days. And then later the genetic test results were combined with this data and we could do research looking at a single mutation leading to better patient outcomes. But I think where we're going to evolve in 2022 and beyond is with genetic testing, growing and oncology, providing us more data about that patient. More genes to look at, uh, researchers can look at groups of genes to analyze, to look at that complex combination of gene mutations. And I think it'll open the door for things like using artificial intelligence to help researchers plow through the complex number of permutations. When you think about all those genes you can look at in combination, right? Lorraine yes. Data and machine intelligence coming together, anything you would add. >>Yeah. Thank you very much. Well, I think that Greg's response really sets us up nicely, particularly when we think about the ability to utilize real-world data in the farm industry across a number of use cases from discovery to development to commercial, and, you know, in particular, I think with real world data and the comments that Greg just made about clinical EMR data linked with genetic or genomic data, a real area of interest in one that, uh, Watson health in particular is focused on the idea of being able to create a data exchange so that we can bring together claims clinical EMR data, genomics data, increasingly wearables and data directly from patients in order to create a digital health record that we like to call an intelligent patient health record that basically gives us the digital equivalent of a real life patient. And these can be used in use cases in randomized controlled clinical trials for synthetic control arms or natural history. They can be used in order to track patients' response to drugs and look at outcomes after they've been on various therapies as, as Greg is speaking to. And so I think that, you know, the promise of data and technology, the AI that we can apply on that is really helping us advance, getting therapies to market faster, with better information, lower sample sizes, and just a much more efficient way to do drug development and to track and monitor outcomes in patients. >>Great. Thank you for that now to meta, when I joined IDC many, many years ago, I really didn't know much about the industry that I was covering, but it's great to see you as a former practitioner now bringing in your views. What do you see as the big game-changers? >>So, um, I would, I would agree with what both Lorraine and Greg said. Um, but one thing that I'd just like to call out is that, you know, everyone's talking about big data, the volume of data is growing. It's growing exponentially actually about, I think 30% of data that exists today is healthcare data. And it's growing at a rate of 36%. That's huge, but then it's not just about the big, it's also about the broad, I think, um, you know, I think great points that, uh, Lorraine and Greg brought out that it's, it's not just specifically genomic data, it's multi omic data. And it's also about things like medical history, social determinants of health, behavioral data. Um, and why, because when you're talking about precision medicine and we know that we moved away from the, the terminology of personalized to position, because you want to talk about disease stratification and you can, it's really about convergence. >>Um, if you look at a recent JAMA paper in 2021, only 1% of EHS actually included genomic data. So you really need to have that ability to look at data holistically and IDC prediction is seeing that investments in AI to fuel in silico, silicone drug discovery will double by 20, 24, but how are you actually going to integrate all the different types of data? Just look at, for example, diabetes, you're on type two diabetes, 40 to 70% of it is genetically inherited and you have over 500 different, uh, genetic low side, which could be involved in playing into causing diabetes. So the earlier strategy, when you are looking at, you know, genetic risk scoring was really single trait. Now it's transitioning to multi rate. And when you say multi trade, you really need to get that integrated view that converging for you to, to be able to drive a precision medicine strategy. So to me, it's a very interesting contrast on one side, you're really trying to make it specific and focused towards an individual. And on the other side, you really have to go wider and bigger as well. >>Uh, great. I mean, the technology is enabling that convergence and the conditions are almost mandating it. Let's talk about some more about data that the data exchange and building an intelligent health record, as it relates to precision medicine, how will the interoperability of real-world data, you know, create that more cohesive picture for the, for the patient maybe Greg, you want to start, or anybody else wants to chime in? >>I think, um, the, the exciting thing from, from my perspective is the potential to gain access to data. You may be weren't aware of an exchange in implies that, uh, some kind of cataloging, so I can see, uh, maybe things that might, I just had no idea and, uh, bringing my own data and maybe linking data. These are concepts that I think are starting to take off in our field, but it, it really opens up those avenues to when you, you were talking about data, the robustness and richness volume isn't, uh, the only thing is Namita said, I think really getting to a rich high-quality data and, and an exchange offers a far bigger, uh, range for all of us to, to use, to get our work done. >>Yeah. And I think, um, just to chime, chime into that, uh, response from Greg, you know, what we hear increasingly, and it's pretty pervasive across the industry right now, because this ability to create an exchange or the intelligent, uh, patient health record, these are new ideas, you know, they're still rather nascent and it always is the operating model. Uh, that, that is the, uh, the difficult challenge here. And certainly that is the case. So we do have data in various silos. Uh, they're in patient claims, they're in electronic medical records, they might be in labs, images, genetic files on your smartphone. And so one of the challenges with this interoperability is being able to tap into these various sources of data, trying to identify quality data, as Greg has said, and the meta is underscoring as well. Uh, we've gotta be able to get to the depth of data that's really meaningful to us, but then we have to have technology that allows us to pull this data together. >>First of all, it has to be de-identified because of security and patient related needs. And then we've gotta be able to link it so that you can create that likeness in terms of the record, it has to be what we call cleaned or curated so that you get the noise and all the missing this out of it, that's a big step. And then it needs to be enriched, which means that the various components that are going to be meaningful, you know, again, are brought together so that you can create that cohort of patients, that individual patient record that now is useful in so many instances across farm, again, from development, all the way through commercial. So the idea of this exchange is to enable that exact process that I just described to have a, a place, a platform where various entities can bring their data in order to have it linked and integrated and cleaned and enriched so that they get something that is a package like a data package that they can actually use. >>And it's easy to plug into their, into their studies or into their use cases. And I think a really important component of this is that it's gotta be a place where various third parties can feel comfortable bringing their data together in order to match it with other third parties. That is a, a real value, uh, that the industry is increasingly saying would be important to them is, is the ability to bring in those third-party data sets and be able to link them and create these, these various data products. So that's really the idea of the data exchange is that you can benefit from accessing data, as Greg mentioned in catalogs that maybe are across these various silos so that you can do the kind of work that you need. And that we take a lot of the hard work out of it. I like to give an example. >>We spoke with one of our clients at one of the large pharma companies. And, uh, I think he expressed it very well. He said, what I'd like to do is have like a complete dataset of lupus. Lupus is an autoimmune condition. And I've just like to have like the quintessential lupus dataset that I can use to run any number of use cases across it. You know, whether it's looking at my phase one trial, whether it's selecting patients and enriching for later stage trials, whether it's understanding patient responses to different therapies as I designed my studies. And so, you know, this idea of adding in therapeutic area indication, specific data sets and being able to create that for the industry in the meta mentioned, being able to do that, for example, in diabetes, that's how pharma clients need to have their needs met is through taking the hard workout, bringing the data together, having it very therapeutically enriched so that they can use it very easily. >>Thank you for that detail and the meta. I mean, you can't do this with humans at scale in technology of all the things that Lorraine was talking about, the enrichment, the provenance, the quality, and of course, it's got to be governed. You've got to protect the privacy privacy humans just can't do all that at massive scale. Can it really tech that's where technology comes in? Doesn't it and automation. >>Absolutely. >>I, couldn't more, I think the biggest, you know, whether you talk about precision medicine or you talk about decentralized trials, I think there's been a lot of hype around these terms, but what is really important to remember is technology is the game changer and bringing all that data together is really going to be the key enabler. So multimodal data integration, looking at things like security or federated learning, or also when you're talking about leveraging AI, you're not talking about things like bias or other aspects around that are, are critical components that need to be addressed. I think the industry is, uh, it's partly, still trying to figure out the right use cases. So it's one part is getting together the data, but also getting together the right data. Um, I think data interoperability is going to be the absolute game changer for enabling this. Uh, but yes, um, absolutely. I can, I can really couldn't agree more with what Lorraine just said, that it's bringing all those different aspects of data together to really drive that precision medicine strategy. >>Excellent. Hey Greg, let's talk about protocols decentralized clinical trials. You know, they're not new to life silences, but, but the adoption of DCTs is of course sped up due to the pandemic we've had to make trade-offs obviously, and the risk is clearly worth it, but you're going to continue to be a primary approach as we enter 2022. What are the opportunities that you see to improve? How DCTs are designed and executed? >>I see a couple opportunities to improve in this area. The first is, uh, back to technology. The infrastructure around clinical trials has, has evolved over the years. Uh, but now you're talking about moving away from kind of site focus to the patient focus. Uh, so with that, you have to build out a new set of tools that would help. So for example, one would be novel trial, recruitment, and screening, you know, how do you, how do you find patients and how do you screen them to see if are they, are they really a fit for, for this protocol? Another example, uh, very important documents that we have to get is, uh, you know, the e-consent that someone's says, yes, I'm, well, I understand this study and I'm willing to do it, have to do that in a more remote way than, than we've done in the past. >>Um, the exciting area, I think, is the use of, uh, eco, uh, E-Pro where we capture data from the patient using apps, devices, sensors. And I think all of these capabilities will bring a new way of, of getting data faster, uh, in, in this kind of model. But the exciting thing from, uh, our perspective at Lily is it's going to bring more data about the patient from the patient, not just from the healthcare provider side, it's going to bring real data from these apps, devices and sensors. The second thing I think is using real-world data to identify patients, to also improve protocols. We run scenarios today, looking at what's the impact. If you change a cut point on a, a lab or a biomarker to see how that would affect, uh, potential enrollment of patients. So it, it definitely the real-world data can be used to, to make decisions, you know, how you improve these protocols. >>But the thing that we've been at the challenge we've been after that this probably offers the biggest is using real-world data to identify patients as we move away from large academic centers that we've used for years as our sites. Um, you can maybe get more patients who are from the rural areas of our countries or not near these large, uh, uh, academic centers. And we think it'll bring a little more diversity to the population, uh, who who's, uh, eligible, but also we have their data, so we can see if they really fit the criteria and the probability they are a fit for the trial is much higher than >>Right. Lorraine. I mean, your clients must be really pushing you to help them improve DCTs what are you seeing in the field? >>Yes, in fact, we just attended the inaugural meeting of the de-central trials research Alliance in, uh, in Boston about two weeks ago where, uh, all of the industry came together, pharma companies, uh, consulting vendors, just everyone who's been in this industry working to help define de-central trials and, um, think through what its potential is. Think through various models in order to enable it, because again, a nascent concept that I think COVID has spurred into action. Um, but it is important to take a look at the definition of DCT. I think there are those entities that describe it as accessing data directly from the patient. I think that is a component of it, but I think it's much broader than that. To me, it's about really looking at workflows and processes of bringing data in from various remote locations and enabling the whole ecosystem to work much more effectively along the data continuum. >>So a DCT is all around being able to make a site more effective, whether it's being able to administer a tele visit or the way that they're getting data into the electronic data captures. So I think we have to take a look at the, the workflows and the operating models for enabling de-central trials and a lot of what we're doing with our own technology. Greg mentioned the idea of electronic consent of being able to do electronic patient reported outcomes, other collection of data directly from the patient wearables tele-health. So these are all data acquisition, methodologies, and technologies that, that we are enabling in order to get the best of the data into the electronic data capture system. So edit can be put together and processed and submitted to the FDA for regulatory use for clinical trial type submission. So we're working on that. I think the other thing that's happening is the ability to be much more flexible and be able to have more cloud-based storage allows you to be much more inter-operable to allow API APIs in order to bring in the various types of data. >>So we're really looking at technology that can make us much more fluid and flexible and accommodating to all the ways that people live and work and manage their health, because we have to reflect that in the way we collect those data types. So that's a lot of what we're, what we're focused on. And in talking with our clients, we spend also a lot of time trying to understand along the, let's say de-central clinical trials continuum, you know, w where are they? And I know Namita is going to talk a little bit about research that they've done in terms of that adoption curve, but because COVID sort of forced us into being able to collect data in more remote fashion in order to allow some of these clinical trials to continue during COVID when a lot of them had to stop. What we want to make sure is that we understand and can codify some of those best practices and that we can help our clients enable that because the worst thing that would happen would be to have made some of that progress in that direction. >>But then when COVID is over to go back to the old ways of doing things and not bring some of those best practices forward, and we actually hear from some of our clients in the pharma industry, that they worry about that as well, because we don't yet have a system for operationalizing a de-central trial. And so we really have to think about the protocol it's designed, the indication, the types of patients, what makes sense to decentralize, what makes sense to still continue to collect data in a more traditional fashion. So we're spending a lot of time advising and consulting with our patients, as well as, I mean, with our clients, as well as CRS, um, on what the best model is in terms of their, their portfolio of studies. And I think that's a really important aspect of trying to accelerate the adoption is making sure that what we're doing is fit for purpose, just because you can use technology doesn't mean you should, it really still does require human beings to think about the problem and solve them in a very practical way. >>Great, thank you for that. Lorraine. I want to pick up on some things that Lorraine was just saying. And then back to what Greg was saying about, uh, uh, DCTs becoming more patient centric, you had a prediction or IDC, did I presume your fingerprints were on it? Uh, that by 20 25, 70 5% of trials will be patient-centric decentralized clinical trials, 90% will be hybrid. So maybe you could help us understand that relationship and what types of innovations are going to be needed to support that evolution of DCT. >>Thanks, Dave. Yeah. Um, you know, sorry, I, I certainly believe that, uh, you know, uh, Lorraine was pointing out of bringing up a very important point. It's about being able to continue what you have learned in over the past two years, I feel this, you know, it was not really a digital revolution. It was an attitude. The revolution that this industry underwent, um, technology existed just as clinical trials exist as drugs exist, but there was a proof of concept that technology works that this model is working. So I think that what, for example, telehealth, um, did for, for healthcare, you know, transition from, from care, anywhere care, anytime, anywhere, and even becoming predictive. That's what the decentralized clinical trials model is doing for clinical trials today. Great points again, that you have to really look at where it's being applied. You just can't randomly apply it across clinical trials. >>And this is where the industry is maturing the complexity. Um, you know, some people think decentralized trials are very simple. You just go and implement these centralized clinical trials, but it's not that simple as it it's being able to define, which are the right technologies for that specific, um, therapeutic area for that specific phase of the study. It's being also a very important point is bringing in the patient's voice into the process. Hey, I had my first telehealth visit sometime last year and I was absolutely thrilled about it. I said, no time wasted. I mean, everything's done in half an hour, but not all patients want that. Some want to consider going back and you, again, need to customize your de-centralized trials model to, to the, to the type of patient population, the demographics that you're dealing with. So there are multiple factors. Um, also stepping back, you know, Lorraine mentioned they're consulting with, uh, with their clients, advising them. >>And I think a lot of, um, a lot of companies are still evolving in their maturity in DCTs though. There's a lot of boys about it. Not everyone is very mature in it. So it's, I think it, one thing everyone's kind of agreeing with is yes, we want to do it, but it's really about how do we go about it? How do we make this a flexible and scalable modern model? How do we integrate the patient's voice into the process? What are the KPIs that we define the key performance indicators that we define? Do we have a playbook to implement this model to make it a scalable model? And, you know, finally, I think what organizations really need to look at is kind of developing a de-centralized mature maturity scoring model, so that I assess where I am today and use that playbook to define, how am I going to move down the line to me reach the next level of maturity. Those were some of my thoughts. Right? >>Excellent. And now remember you, if you have any questions, use the chat box below to submit those questions. We have some questions coming in from the audience. >>At one point to that, I think one common thread between the earlier discussion around precision medicine and around decentralized trials really is data interoperability. It is going to be a big game changer to, to enable both of these pieces. Sorry. Thanks, Dave. >>Yeah. Thank you. Yeah. So again, put your questions in the chat box. I'm actually going to go to one of the questions from the audience. I get some other questions as well, but when you think about all the new data types that are coming in from social media, omics wearables. So the question is with greater access to these new types of data, what trends are you seeing from pharma device as far as developing capabilities to effectively manage and analyze these novel data types? Is there anything that you guys are seeing, um, that you can share in terms of best practice or advice >>I'll offer up? One thing, I think the interoperability isn't quite there today. So, so what's that mean you can take some of those data sources. You mentioned, uh, some Omix data with, uh, some health claims data and it's the, we spend too much time and in our space putting data to gather the behind the scenes, I think the stat is 80% of the time is assembling the data 20% analyzing. And we've had conversations here at Lilly about how do we get to 80% of the time is doing analysis. And it really requires us to think, take a step back and think about when you create a, uh, a health record, you really have to be, have the same plugins so that, you know, data can be put together very easily, like Lorraine mentioned earlier. And that comes back to investing in as an industry and standards so that, you know, you have some of data standard, we all can agree upon. And then those plugs get a lot easier and we can spend our time figuring out how to make, uh, people's lives better with healthcare analysis versus putting data together, which is not a lot of fun behind the scenes. >>Other thoughts on, um, on, on how to take advantage of sort of novel data coming from things like devices in the nose that you guys are seeing. >>I could jump in there on your end. Did you want to go ahead? Okay. So, uh, I mean, I think there's huge value that's being seen, uh, in leveraging those multiple data types. I think one area you're seeing is the growth of prescription digital therapeutics and, um, using those to support, uh, you know, things like behavioral health issues and a lot of other critical conditions it's really taking you again, it is interlinking real-world data cause it's really taking you to the patient's home. Um, and it's, it's, there's a lot of patients in the city out here cause you can really monitor the patient real-time um, without the patient having coming, you know, coming and doing a site visit once in say four weeks or six weeks. So, um, I, and, uh, for example, uh, suicidal behavior and just to take an example, if you can predict well in advance, based on those behavioral parameters, that this is likely to trigger that, uh, the value of it is enormous. Um, again, I think, uh, Greg made a valid point about the industry still trying to deal with resolving the data interoperability issue. And there are so many players that are coming in the industry right now. There are really few that have the maturity and the capability to address these challenges and provide intelligence solutions. >>Yeah. Maybe I'll just, uh, go ahead and, uh, and chime into Nikita's last comment there. I think that's what we're seeing as well. And it's very common, you know, from an innovation standpoint that you have, uh, a nascent industry or a nascent innovation sort of situation that we have right now where it's very fragmented. You have a lot of small players, you have some larger entrenched players that have the capability, um, to help to solve the interoperability challenge, the standards challenge. I mean, I think IBM Watson health is certainly one of the entities that has that ability and is taking a stand in the industry, uh, in order to, to help lead in that way. Others are too. And, uh, but with, with all of the small companies that are trying to find interesting and creative ways to gather that data, it does create a very fragmented, uh, type of environment and ecosystem that we're in. >>And I think as we mature, as we do come forward with the KPIs, the operating models, um, because you know, the devil's in the detail in terms of the operating models, it's really exciting to talk these trends and think about the future state. But as Greg pointed out, if you're spending 80% of your time just under the hood, you know, trying to get the engine, all the spark plugs to line up, um, that's, that's just hard grunt work that has to be done. So I think that's where we need to be focused. And I think bringing all the data in from these disparate tools, you know, that's fine, we need, uh, a platform or the API APIs that can enable that. But I think as we, as we progress, we'll see more consolidation, uh, more standards coming into play, solving the interoperability types of challenges. >>And, um, so I think that's where we should, we should focus on what it's going to take and in three years to really codify this and make it, so it's a, it's a well hum humming machine. And, you know, I do know having also been in pharma that, uh, there's a very pilot oriented approach to this thing, which I think is really healthy. I think large pharma companies tend to place a lot of bets with different programs on different tools and technologies, to some extent to see what's gonna stick and, you know, kind of with an innovation mindset. And I think that's good. I think that's kind of part of the process of figuring out what is going to work and, and helping us when we get to that point of consolidating our model and the technologies going forward. So I think all of the efforts today are definitely driving us to something that feels much more codified in the next three to five years. >>Excellent. We have another question from the audience it's sort of related to the theme of this discussion, given the FDA's recent guidance on using claims and electronic health records, data to support regulatory decision-making what advancements do you think we can expect with regards to regulatory use of real-world data in the coming years? It's kind of a two-parter so maybe you guys can collaborate on this one. What role that, and then what role do you think industry plays in influencing innovation within the regulatory space? >>All right. Well, it looks like you've stumped the panel there. Uh, Dave, >>It's okay to take some time to think about it, right? You want me to repeat it? You guys, >>I, you know, I I'm sure that the group is going to chime into this. I, so the FDA has issued a guidance. Um, it's just, it's, it's exactly that the FDA issues guidances and says that, you know, it's aware and supportive of the fact that we need to be using real-world data. We need to create the interoperability, the standards, the ways to make sure that we can include it in regulatory submissions and the like, um, and, and I sort of think about it akin to the critical path initiative, probably, I don't know, 10 or 12 years ago in pharma, uh, when the FDA also embrace this idea of the critical path and being able to allow more in silico modeling of clinical trial, design and development. And it really took the industry a good 10 years, um, you know, before they were able to actually adopt and apply and take that sort of guidance or openness from the FDA and actually apply it in a way that started to influence the way clinical trials were designed or the in silico modeling. >>So I think the second part of the question is really important because while I think the FDA is saying, yes, we recognize it's important. Uh, we want to be able to encourage and support it. You know, when you look for example, at synthetic control arms, right? The use of real-world data in regulatory submissions over the last five or six years, all of the use cases have been in oncology. I think there've been about maybe somewhere between eight to 10 submissions. And I think only one actually was a successful submission, uh, in all those situations, the real-world data arm of that oncology trial that synthetic control arm was actually rejected by the FDA because of lack of completeness or, you know, equalness in terms of the data. So the FDA is not going to tell us how to do this. So I think the second part of the question, which is what's the role of industry, it's absolutely on industry in order to figure out exactly what we're talking about, how do we figure out the interoperability, how do we apply the standards? >>How do we ensure good quality data? How do we enrich it and create the cohort that is going to be equivalent to the patient in the real world, uh, in the end that would otherwise be in the clinical trial and how do we create something that the FDA can agree with? And we'll certainly we'll want to work with the FDA in order to figure out this model. And I think companies are already doing that, but I think that the onus is going to be on industry in order to figure out how you actually operationalize this and make it real. >>Excellent. Thank you. Um, question on what's the most common misconception that clinical research stakeholders with sites or participants, et cetera might have about DCTs? >>Um, I could jump in there. Right. So, sure. So, um, I think in terms of misconceptions, um, I think the communist misconceptions that sites are going away forever, which I do not think is really happening today. Then the second, second part of it is that, um, I think also the perspective that patients are potentially neglected because they're moving away. So we'll pay when I, when I, what I mean by that neglected, perhaps it was not the appropriate term, but the fact that, uh, will patients will, will, will patient engagement continue, will retention be strong since the patients are not interacting in person with the investigator quite as much. Um, so site retention and patient retention or engagement from both perspectives, I think remains a concern. Um, but actually if you look at, uh, look at, uh, assessments that have been done, I think patients are more than happy. >>Majority of the patients have been really happy about, about the new model. And in fact, sites are, seem to increase, have increased investments in technology by 50% to support this kind of a model. So, and the last thing is that, you know, decentralized trials is a great model and it can be applied to every possible clinical trial. And in another couple of weeks, the whole industry will be implementing only decentralized trials. I think we are far away from that. It's just not something that you would implement across every trial. And we discussed that already. So you have to find the right use cases for that. So I think those were some of the key misconceptions I'd say in the industry right now. Yeah. >>Yeah. And I would add that the misconception I hear the most about is, uh, the, the similar to what Namita said about the sites and healthcare professionals, not being involved to the level that they are today. Uh, when I mentioned earlier in our conversation about being excited about capturing more data, uh, from the patient that was always in context of, in addition to, you know, healthcare professional opinion, because I think both of them bring that enrichment and a broader perspective of that patient experience, whatever disease they're faced with. So I, I think some people think is just an all internet trial with just someone, uh, putting out there their own perspective. And, and it's, it's a combination of both to, to deliver a robust data set. >>Yeah. Maybe I'll just comment on, it reminds me of probably 10 or 15 years ago, maybe even more when, um, really remote monitoring was enabled, right? So you didn't have to have the study coordinator traveled to the investigative site in order to check the temperature of the freezer and make sure that patient records were being completed appropriately because they could have a remote visit and they could, they could send the data in a via electronic data and do the monitoring visit, you know, in real time, just the way we're having this kind of communication here. And there was just so much fear that you were going to replace or supplant the personal relationship between the sites between the study coordinators that you were going to, you know, have to supplant the role of the monitor, which was always a very important role in clinical trials. >>And I think people that really want to do embrace the technology and the advantages that it provided quickly saw that what it allowed was the monitor to do higher value work, you know, instead of going in and checking the temperature on a freezer, when they did have their visit, they were able to sit and have a quality discussion for example, about how patient recruitment was going or what was coming up in terms of the consent. And so it created a much more high touch, high quality type of interaction between the monitor and the investigative site. And I think we should be looking for the same advantages from DCT. We shouldn't fear it. We shouldn't think that it's going to supplant the site or the investigator or the relationship. It's our job to figure out where the technology fits and clinical sciences always got to be high touch combined with high-tech, but the high touch has to lead. And so getting that balance right? And so that's going to happen here as well. We will figure out other high value work, meaningful work for the site staff to do while they let the technology take care of the lower quality work, if you will, or the lower value work, >>That's not an, or it's an, and, and you're talking about the higher value work. And it, it leads me to something that Greg said earlier about the 80, 20, 80% is assembly. 20% is actually doing the analysis and that's not unique to, to, to life sciences, but, but sort of question is it's an organizational question in terms of how we think about data and how we approach data in the future. So Bamyan historically big data in life sciences in any industry really is required highly centralized and specialized teams to do things that the rain was talking about, the enrichment, the provenance, the data quality, the governance, the PR highly hyper specialized teams to do that. And they serve different constituencies. You know, not necessarily with that, with, with context, they're just kind of data people. Um, so they have responsibility for doing all those things. Greg, for instance, within literally, are you seeing a move to, to, to democratize data access? We've talked about data interoperability, part of that state of sharing, um, that kind of breaks that centralized hold, or is that just too far in the future? It's too risky in this industry? >>Uh, it's actually happening now. Uh, it's a great point. We, we try to classify what people can do. And, uh, the example would be you give someone who's less analytically qualified, uh, give them a dashboard, let them interact with the data, let them better understand, uh, what, what we're seeing out in the real world. Uh, there's a middle user, someone who you could give them, they can do some analysis with the tool. And the nice thing with that is you have some guardrails around that and you keep them in their lane, but it allows them to do some of their work without having to go ask those centralized experts that, that you mentioned their precious resources. And that's the third group is those, uh, highly analytical folks that can, can really deliver, uh, just value beyond. But when they're doing all those other things, uh, it really hinders them from doing what we've been talking about is the high value stuff. So we've, we've kind of split into those. We look at people using data in one of those three lanes and it, and it has helped I think, uh, us better not try to make a one fit solution for, for how we deliver data and analytic tools for people. Right. >>Okay. I mean, DCT hot topic with the, the, the audience here. Another question, um, what capabilities do sponsors and CRS need to develop in-house to pivot toward DCT? >>Should I jump in here? Yeah, I mean, um, I think, you know, when, when we speak about DCTs and when I speak with, uh, folks around in the industry, I, it takes me back to the days of risk-based monitoring. When it was first being implemented, it was a huge organizational change from the conventional monitoring models to centralize monitoring and risk-based monitoring, it needs a mental reset. It needs as Lorraine had pointed out a little while ago, restructuring workflows, re redefining processes. And I think that is one big piece. That is, I think the first piece, when, you know, when you're implementing a new model, I think organizational change management is a big piece of it because you are disturbing existing structures, existing methods. So getting that buy-in across the organization towards the new model, seeing what the value add in it. And where do you personally fit into that story? >>How do your workflows change, or how was your role impacted? I think without that this industry will struggle. So I see organizations, I think, first trying to work on that piece to build that in. And then of course, I also want to step back for the second to the, uh, to the point that you brought out about data democratization. And I think Greg Greg gave an excellent point, uh, input about how it's happening in the industry. But I would also say that the data democratization really empowerment of, of, of the stakeholders also includes the sites, the investigators. So what is the level of access to data that you know, that they have now, and is it, uh, as well as patients? So see increasingly more and more companies trying to provide access to patients finally, it's their data. So why shouldn't they have some insights to it, right. So access to patients and, uh, you know, the 80, 20 part of it. Uh, yes, he's absolutely right that, uh, we want to see that flip from, uh, 20%, um, you know, focusing on, on actually integrating the data 80% of analytics, but the real future will be coming in when actually the 20 and 18 has gone. And you actually have analysts the insights out on a silver platter. That's kind of wishful thinking, some of the industries is getting there in small pieces, but yeah, then that's just why I should, why we share >>Great points. >>And I think that we're, we're there in terms that like, I really appreciate the point around democratizing the data and giving the patient access ownership and control over their own data. I mean, you know, we see the health portals that are now available for patients to view their own records, images, and labs, and claims and EMR. We have blockchain technology, which is really critical here in terms of the patient, being able to pull all of their own data together, you know, in the blockchain and immutable record that they can own and control if they want to use that to transact clinical trial types of opportunities based on their data, they can, or other real world scenarios. But if they want to just manage their own data because they're traveling and if they're in a risky health situation, they've got their own record of their health, their health history, uh, which can avoid, you know, medical errors occurring. So, you know, even going beyond life sciences, I think this idea of democratizing data is just good for health. It's just good for people. And we definitely have the technology that can make it a reality. Now >>You're here. We have just about 10 minutes left and now of course, now all the questions are rolling in like crazy from the crowd. Would it be curious to know if there would be any comments from the panel on cost comparison analysis between traditional clinical trials in DCTs and how could the outcome effect the implementation of DCTs any sort of high-level framework you can share? >>I would say these are still early days to, to drive that analysis because I think many companies are, um, are still in the early stages of implementation. They've done a couple of trials. The other part of it that's important to keep in mind is, um, is for organizations it's, they're at a stage of, uh, of being on the learning curve. So when you're, you're calculating the cost efficiencies, if ideally you should have had two stakeholders involved, you could have potentially 20 stakeholders involved because everyone's trying to learn the process and see how it's going to be implemented. So, um, I don't think, and the third part of it, I think is organizations are still defining their KPIs. How do you measure it? What do you measure? So, um, and even still plugging in the pieces of technology that they need to fit in, who are they partnering with? >>What are the pieces of technology they're implementing? So I don't think there is a clear cut as answered at this stage. I think as you scale this model, the efficiencies will be seen. It's like any new technology or any new solution that's implemented in the first stages. It's always a little more complex and in fact sometimes costs extra. But as, as you start scaling it, as you establish your workflows, as you streamline it, the cost efficiencies will start becoming evident. That's why the industry is moving there. And I think that's how it turned out on the long run. >>Yeah. Just make it maybe out a comment. If you don't mind, the clinical trials are, have traditionally been costed are budgeted is on a per patient basis. And so, you know, based on the difficulty of the therapeutic area to recruit a rare oncology or neuromuscular disease, there's an average that it costs in order to find that patient and then execute the various procedures throughout the clinical trial on that patient. And so the difficulty of reaching the patient and then the complexity of the trial has led to what we might call a per patient stipend, which is just the metric that we use to sort of figure out what the average cost of a trial will be. So I think to point, we're going to have to see where the ability to adjust workflows, get to patients faster, collect data more easily in order to make the burden on the site, less onerous. I think once we start to see that work eases up because of technology, then I think we'll start to see those cost equations change. But I think right now the system isn't designed in order to really measure the economic benefit of de-central models. And I think we're going to have to sort of figure out what that looks like as we go along and since it's patient oriented right now, we'll have to say, well, you know, how does that work, ease up? And to those costs actually come down and then >>Just scale, it's going to be more, more clear as the media was saying, next question from the audiences, it's kind of a best fit question. You all have touched on this, but let me just ask it is what examples in which, in which phases suit DCT in its current form, be it fully DCT or hybrid models, none of our horses for courses question. >>Well, I think it's kind of, uh, it's, it's it's has its efficiencies, obviously on the later phases, then the absolute early phase trials, those are not the ideal models for DCTs I would say so. And again, the logic is also the fact that, you know, when you're, you're going into the later phase trials, the volume of number of patients is increasing considerably to the point that Lorraine brought up about access to the patients about patient selection. The fact, I think what one should look at is really the advantages that it brings in, in terms of, you know, patient access in terms of patient diversity, which is a big piece that, um, the cities are enabling. So, um, if you, if, if you, if you look at the spectrum of, of these advantages and, and just to step back for a moment, if you, if you're looking at costs, like you're looking at things like remote site monitoring, um, is, is a big, big plus, right? >>I mean, uh, site monitoring alone accounts for around a third of the trial costs. So there are so many pieces that fall in together. The challenge actually that comes when you're in defining DCTs and there are, as Rick pointed out multiple definitions of DCTs that are existing, uh, you know, in the industry right now, whether you're talking of what Detroit is doing, or you're talking about acro or Citi or others. But the point is it's a continuum, it's a continuum of different pieces that have been woven together. And so how do you decide which pieces you're plugging in and how does that impact the total cost or the solution that you're implementing? >>Great, thank you. Last question we have in the audience, excuse me. What changes have you seen? Are there others that you can share from the FDA EU APAC, regulators and supporting DCTs precision medicine for approval processes, anything you guys would highlight that we should be aware of? >>Um, I could quickly just add that. I think, um, I'm just publishing a report on de-centralized clinical trials should be published shortly, uh, perspective on that. But I would say that right now, um, there, there was a, in the FDA agenda, there was a plan for a decentralized clinical trials guidance, as far as I'm aware, one has not yet been published. There have been significant guidances that have been published both by email and by, uh, the FDA that, um, you know, around the implementation of clinical trials during the COVID pandemic, which incorporate various technology pieces, which support the DCD model. Um, but I, and again, I think one of the reasons why it's not easy to publish a well-defined guidance on that is because there are so many moving pieces in it. I think it's the Danish, uh, regulatory agency, which has per se published a guidance and revised it as well on decentralized clinical trials. >>Right. Okay. Uh, we're pretty much out of time, but I, I wonder Lorraine, if you could give us some, some final thoughts and bring us home things that we should be watching or how you see the future. >>Well, I think first of all, let me, let me thank the panel. Uh, we really appreciate Greg from Lily and the meta from IDC bringing their perspectives to this conversation. And, uh, I hope that the audience has enjoyed the, uh, the discussion that we've had around the future state of real world data as, as well as DCT. And I think, you know, some of the themes that we've talked about, number one, I think we have a vision and I think we have the right strategies in terms of the future promise of real-world data in any number of different applications. We certainly have talked about the promise of DCT to be more efficient, to get us closer to the patient. I think that what we have to focus on is how we come together as an industry to really work through these very vexing operational issues, because those are always the things that hang us up and whether it's clinical research or whether it's later stage, uh, applications of data. >>We, the healthcare system is still very fragmented, particularly in the us. Um, it's still very, state-based, uh, you know, different states can have different kinds of, uh, of, of cultures and geographic, uh, delineations. And so I think that, you know, figuring out a way that we can sort of harmonize and bring all of the data together, bring some of the models together. I think that's what you need to look to us to do both industry consulting organizations, such as IBM Watson health. And we are, you know, through DTRA and, and other, uh, consortia and different bodies. I think we're all identifying what the challenges are in terms of making this a reality and working systematically on those. >>It's always a pleasure to work with such great panelists. Thank you, Lorraine Marshawn, Dr. Namita LeMay, and Greg Cunningham really appreciate your participation today and your insights. The next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond has been brought to you by IBM in the cube. You're a global leader in high tech coverage. And while this discussion has concluded, the conversation continues. So please take a moment to answer a few questions about today's panel on behalf of the entire IBM life sciences team and the cube decks for your time and your feedback. And we'll see you next time.

>> Narrator: From around the globe, It's theCUBE! Covering Fortinet Security Summit, brought to you by Fortinet. >> And welcome to the cube coverage here at the PGA champion-- Fortinet championship, where we're going to be here for Napa valley coverage of Fortinet's, the championships security summit, going on Fortinet, sponsoring the PGA, but a great guest Merritt Baer, who's the principal in the office of the CISO at Amazon web services. Great to see you. Thanks for coming on. >> Merritt: Thank you for having me. It's good to be here. >> So Fortinet, uh, big brand now, sponsoring the PGA. Pretty impressive that they're getting out there with the golf. It's very enterprise focused, a lot of action. A lot of customers here. >> Merritt: It seems like it, for sure. >> Bold move. Amazon, Amazon web services has become the gold standard in terms of cloud computing, seeing DevOps people refactoring. You've seen the rise of companies like Snowflake building on Amazon. People are moving not only to the cloud, but they're refactoring their business and security is top of mind for everyone. And obviously cybersecurity threats that Fortinet helps cover, you guys are partnering with them, is huge. What is your state of the union for cyber? What's the current situation with the threat landscape? Obviously there's no perimeter in the cloud. More end points are coming on board. The Edge is here. 5G, wavelength with outpost, a lot happening. >> That was a long question, but I'll, I'll try. So I think, you know, as always business in innovation is the driver. And security needs to be woven into that. And so I think increasingly we're seeing security not be a no shop, but be an enabler. And especially in cloud, when we're talking about the way that you do DevOps with security, I know folks don't like the term DevSecOps, but you know, to be able to do agile methodology and be able to do the short sprints that are really agile and, and innovative where you can-- So instead of nine months or whatever, nine week timelines, we're talking about short sprints that allow you to elastically scale up and down and be able to innovate really creatively. And to do that, you need to weave in your security because there's no like, okay, you pass go, you collect $200. Security is not an after the fact. So I think as part of that, of course the perimeter is dead, long live the perimeter, right? It does matter. And we can talk about that a little bit. You know, the term zero trust is really hot right now. We can dig into that if that's of interest. But I think part of this is just the business is kind of growing up. And as you alluded to we're at the start of what I think is an S curve that is just at the beginning. >> You know, I was really looking forward to Reinforced this year. It was got canceled last year, but the first inaugural event was in Boston. I remember covering that. This year it was virtual, but the keynote Steven gave was interesting, security hubs at the center of it. And I want to ask you, because I need you to share your view on how security's changed with the cloud, because there's now new things that are there to take advantage of if you're a business or an enterprise, yeah on premises, there's a standard operating procedure. You have the perimeter, et cetera. That's not there anymore, but with the cloud, there's a new, there's new ways to protect and security hub is one. What are some of the new things that cloud enables for security? >> Well, so just to clarify, like perimeters exist logically just like they do physically. So, you know, a VPC for example, would be a logical perimeter and that is very relevant, or a VPN. Now we're talking about a lot of remote work during COVID, for example. But one of the things that I think folks are really interested with Security Hub is just having that broad visibility and one of the beauties of cloud is that, you get this tactile sense of your estate and you can reason about it. So for example, when you're looking at identity and access management, you can look at something like access analyzer that will under the hood be running on a tool that our, our group came up with that is like reasoning about the permissions, because you're talking about software layers, you're talking about computer layer reasoning about security. And so another example is in inspector. We have a tool that will tell you without sending a single packet over the network, what your network reach ability is. There's just like this ability to do infrastructure as code that then allows you to do security as code. And then that allows for ephemeral and immutable infrastructures so that you could, for example, get back to a known good state. That being said, you know, you kill a, your web server gets popped and you kill it and you spin up a new one. You haven't solved your problem, right? You need to have some kind of awareness of networking and how principals work. But at the same time, there's a lot of beauties about cloud that you inherit from a security perspective to be able to work in those top layers. And that's of course the premise of cloud. >> Yeah, infrastructure as code, you mentioned that, it's awesome. And the program ability of it with, with server-less functions, you're starting to see new ways now to spin up resources. How is that changing the paradigm and creating opportunities for better security? Is it, is it more microservices? Is it, is, are there new things that people can do differently now that they didn't have a year ago or two years ago? Because you're starting to see things like server-less functions are very popular. >> So yes, and yes, I think that it is augmenting the way that we're doing business, but it's especially augmenting the way we do security in terms of automation. So server-less, under the hood, whether it's CloudWatch events or config rules, they are all a Lambda function. So that's the same thing that powers your Alexa at home. These are server-less functions and they're really simple. You can program them, you can find them on GitHub, but they are-- one way to really scale your enterprise is to have a lot of automation in place so that you put those decisions in ahead of time. So your gray area of human decision making is scaled down. So you've got, you know, what you know to be allowable, what you know to be not allowable. And then you increasingly kind of whittled down that center into things that really are novel, truly novel or high stakes or both. But the focus on automation is a little bit of a trope for us. We at Amazon like to talk about mechanisms, good intentions are not enough. If it's not someone's job, it's a hope and hope is not a plan, you know, but creating the actual, you know, computerized version of making it be done iteratively. And I think that is the key to scaling a security chain because as we all know, things can't be manual for long, or you won't be able to grow. >> I love the AWS reference. Mechanisms, one way doors, raising the bar. These are all kind of internal Amazon, but I got to ask you about the Edge. Okay. There's a lot of action going on with 5G and wavelength. Okay, and what's interesting is if the Edge becomes so much more robust, how do you guys see that security from a security posture standpoint? What should people be thinking about? Because certainly it's just a distributed Edge point. What's the security posture, How should we be thinking about Edge? >> You know, Edge is a kind of catch all, right, we're talking about Internet of Things. We're talking about points of contact. And a lot of times I think we focus so much on the confidentiality and integrity, but the availability is hugely important when we're talking about security. So one of the things that excites me is that we have so many points of contact and so many availability points at the Edge that actually, so for example, in DynamoDB, the more times you put a call on it, the more available it is because it's fresher, you've already been refreshing it, there are so many elements of this, and our core compute platform, EC2, all runs on Nitro, which is our, our custom hardware. And it's really fascinating, the availability benefits there. Like the best patching is a patching you don't have to do. And there are so many elements that are just so core to that Greengrass, you know, which is running on FreeRTOS, which has an open source software, for example, is, you know, one element of zero trust in play. And there are so many ways that we can talk about this in different incarnations. And of course that speaks to like the breadth and depth of the industries that use cloud. We're talking about automotive, we're talking about manufacturing and agriculture, and there are so many interesting use cases for the ways that we will use IOT. >> Yeah. It's interesting, you mentioned Nitro. we also got Annapurna acquisition years ago. You got latency at the Edge. You can handle low latency, high volume compute with the data. That's pretty powerful. It's a paradigm shift. That's a new dynamic. It's pretty compelling, these new architectures, most people are scratching their heads going, "okay, how do I do this, like what do I do?" >> No, you're right. So it is a security inheritance that we are extremely calculated about our hardware supply chain. And we build our own custom hardware. We build our own custom Silicon. Like, this is not a question. And you're right in that one of the things, one of the north stars that we have is that the security properties of our engineering infrastructure are built in. So there just is no button for it to be insecure. You know, like that is deliberate. And there are elements of the ways that nature works from it running, you know, with zero downtime, being able to be patched running. There are so many elements of it that are inherently security benefits that folks inherit as a product. >> Right. Well, we're here at the security summit. What are you excited for today? What's the conversations you're having here at the Fortinet security summit. >> Well, it's awesome to just meet folks and connect outside. It's beautiful outside today. I'm going to be giving a talk on securing the cloud journey and kind of that growth and moving to infrastructure as code and security as code. I'm excited about the opportunity to learn a little bit more about how folks are managing their hybrid environments, because of course, you know, I think sometimes folks perceive AWS as being like this city on a hill where we get it all right. We struggle with the same things. We empathize with the same security work. And we work on that, you know, as a principal in the office of the CISO, I spend a lot of my time on how we do security and then a lot of my time talking to customers and that empathy back and forth is really crucial. >> Yeah. And you've got to be on the bleeding edge and have the empathy. I can't help but notice your AWS crypto shirt. Tell me about the crypto, what's going on there. NFT's coming out, is there a S3 bucket at NFT now, I mean. (both laughing) >> Cryptography never goes out of style. >> I know, I'm just, I couldn't help-- We'll go back to the pyramids on that one. Yeah, no, this is not a, an advertisement for cryptocurrency. It is, I'm a fangirl of the AWS crypto team. And as a result of wearing their shirts, occasionally they send me more shirts. And I can't argue with that. >> Well, love, love, love the crypto. I'm big fan of crypto, I think crypto is awesome. Defi is amazing. New applications are going to come out. We think it's going to be pretty compelling, again, let's get today right. (laughing) >> Well, I don't think it's about like, so cryptocurrency is just like one small iteration of what we're really talking about, which is the idea that math resolves, and the idea that you can have value in your resolution that the math should resolve. And I think that is a fundamental principle and end-to-end encryption, I believe is a universal human right. >> Merritt, thank you for coming on the cube. Great, great to have you on. Thanks for sharing that awesome insight. Thanks for coming on. >> Merritt: Thank you. >> Appreciate it. Okay. CUBE coverage here in Napa valley, our remote set for Fortinet's security cybersecurity summit here as part of their PGA golf Pro-Am tournament happening here in Napa valley. I'm John Furrier. Thanks for watching.

>>As we've been reporting, the pandemic has called CSOs to really shift their spending priorities towards securing remote workers. Almost overnight. Zero trust has gone from buzzword to mandate. What's more as we wrote in our recent cybersecurity breaking analysis, not only Maseca pro secured increasingly distributed workforce, but now they have to be wary of software updates in the digital supply chain, including the very patches designed to protect them against cyber attacks. Hello everyone. And welcome to this Q conversation. My name is Dave Vellante and I'm pleased to welcome Derek manky. Who's chief security insights, and global threat alliances for four guard labs with fresh data from its global threat landscape report. Derek. Welcome. Great to see you. >>Thanks so much for, for the invitation to speak. It's always a pleasure. Multicover yeah, >>You're welcome. So first I wonder if you could explain for the audience, what is for guard labs and what's its relationship to fortunate? >>Right. So 40 grand labs is, is our global sockets, our global threat intelligence operation center. It never sleeps, and this is the beat. Um, you know, it's, it's been here since inception at port in it. So it's it's 20, 21 years in the making, since Fortinet was founded, uh, we have built this in-house, uh, so we don't go yum technology. We built everything from the ground up, including creating our own training programs for our, our analysts. We're following malware, following exploits. We even have a unique program that I created back in 2006 to ethical hacking program. And it's a zero-day research. So we try to meet the hackers, the bad guys to their game. And we of course do that responsibly to work with vendors, to close schools and create virtual patches. Um, and, but, you know, so it's, it's everything from, uh, customer protection first and foremost, to following, uh, the threat landscape and cyber. It's very important to understand who they are, what they're doing, who they're, uh, what they're targeting, what tools are they using? >>Yeah, that's great. Some serious DNA and skills in that group. And it's, it's critical because like you said, you can, you can minimize the spread of those malware very, very quickly. So what, what now you have, uh, the global threat landscape report. We're going to talk about that, but what exactly is that? >>Right? So this a global threat landscape report, it's a summary of, uh, all, all the data that we collect over a period of time. So we released this, that biannually two times a year. Um, cyber crime is changing very fast, as you can imagine. So, uh, while we do release security blogs, and, uh, what we call threat signals for breaking security events, we have a lot of other vehicles to release threat intelligence, but this threat landscape report is truly global. It looks at all of our global data. So we have over 5 million censorship worldwide in 40 guard labs, we're processing. I know it seems like a very large amount, but North of a hundred billion, uh, threat events in just one day. And we have to take the task of taking all of that data and put that onto scale for half a year and compile that into something, um, that is, uh, the, you know, that that's digestible. That's a, a very tough task, as you can imagine, so that, you know, we have to work with a huge technologies back to machine learning and artificial intelligence automation. And of course our analyst view to do that. >>Yeah. So this year, of course, there's like the every year is a battle, but this year was an extra battle. Can you explain what you saw in terms of the hacker dynamics over the past? Let's say 12 months. I know you do this twice a year, but what trends did you see evolving throughout the year and what have you seen with the way that attackers have exploited this expanded attack surface outside of corporate network? >>Yeah, it was quite interesting last year. It certainly was not normal. Like we all say, um, and that was no exception for cybersecurity. You know, if we look at cyber criminals and how they pivoted and adapted to the scrap threat landscape, cyber cyber criminals are always trying to take advantage of the weakest link of the chain. They're trying to always prey off here and ride waves of global trends and themes. We've seen this before in, uh, natural disasters as an example, you know, um, trying to do charity kind of scams and campaigns. And they're usually limited to a region where that incident happened and they usually live about two to three weeks, maybe a month at the most. And then they'll move on to the next to the next trip. That's braking, of course, because COVID is so global and dominant. Um, we saw attacks coming in from, uh, well over 40 different languages as an example, um, in regions all across the world that wasn't lasting two to three weeks and it lasted for the better part of a year. >>And of course, what they're, they're using this as a vehicle, right? Not preying on the fear. They're doing everything from initial lockdown, uh, fishing. We were as COVID-19 movers to, um, uh, lay off notices then to phase one, reopenings all the way up to fast forward to where we are today with vaccine rollover development. So there's always that new flavor and theme that they were rolling out, but because it was so successful for them, they were able to, they didn't have to innovate too much, right. They didn't have to expand and shifted to new to new trends. And themes are really developed on new rats families as an example, or a new sophisticated malware. That was the first half of the year and the second half of the year. Um, of course people started to experience COVID fatigue, right? Um, people started to become, we did a lot of education around this. >>People started to become more aware of this threat. And so, um, cyber criminals have started to, um, as we expected, started to become more sophisticated with their attacks. We saw an expansion in different ransomware families. We saw more of a shift of focus on, on, um, uh, you know, targeting the digital supply chain as an example. And so that, that was, that was really towards Q4. Uh, so it, it was a long lived lead year with success on the Google themes, um, targeting healthcare as an example, a lot of, um, a lot of the organizations that were, you know, really in a vulnerable position, I would say >>So, okay. I want to clarify something because my assumption was that they actually did really increase the sophistication, but it sounds like that was kind of a first half trends. Not only did they have to adapt and not have to, but they adapt it to these new vulnerabilities. Uh, my sense was that when you talk about the digital supply chain, that that was a fairly sophisticated attack. Am I, am I getting that right? That they did their sort of their, their, their increased sophistication in the first half, and then they sort of deployed it, did it, uh, w what actually happened there from your data? >>Well, if we look at, so generally there's two types of attacks that we look at, we look at the, uh, the premeditated sophisticated attacks that can have, um, you know, a lot of ramp up work on their end, a lot of time developing the, the, the, the weaponization phase. So developing, uh, the exploits of the sophisticated malware that they're gonna use for the campaign reconnaissance, understanding the targets, where platforms are developed, um, the blueprinting that DNA of, of, of the supply chain, those take time. Um, in fact years, even if we look back to, um, uh, 10 plus years ago with the Stuxnet attacks, as an example that was on, uh, nuclear centrifuges, um, and that, that had four different zero-day weapons at the time. That was very sophisticated, that took over two years to develop as an example. So some of these can take years of time to develop, but they're, they're, uh, very specific in terms of the targets are going to go after obviously the ROI from their end. >>Uh, the other type of attack that we see is as ongoing, um, these broad, wide sweeping attacks, and the reality for those ones is they don't unfortunately need to be too sophisticated. And those ones were the ones I was talking about that were really just playing on the cool, the deem, and they still do today with the vaccine road and development. Uh, but, but it's really because they're just playing on, on, um, you know, social engineering, um, using, uh, topical themes. And in fact, the weapons they're using these vulnerabilities are from our research data. And this was highlighted actually the first pop landscape before last year, uh, on average were two to three years old. So we're not talking about fresh vulnerabilities. You've got to patch right away. I mean, these are things that should have been patched two years ago, but they're still unfortunately having success with that. >>So you mentioned stuck next Stuxnet as the former sort of example, of one of the types of attacks that you see. And I always felt like that was a watershed moment. One of the most sophisticated, if not the most sophisticated attack that we'd ever seen. When I talk to CSOs about the recent government hack, they, they, they suggest I infer maybe they don't suggest it. I infer that it was of similar sophistication. It was maybe thousands of people working on this for years and years and years. Is that, is that accurate or not necessarily? >>Yeah, there's definitely a, there's definitely some comparisons there. Uh, you know, one of the largest things is, uh, both attacks used digital circuits certificate personation, so they're digitally signed. So, you know, of course that whole technology using cryptography is designed by design, uh, to say that, you know, this piece of software installed in your system, hassles certificate is coming from the source. It's legitimate. Of course, if that's compromised, that's all out of the window. And, um, yeah, this is what we saw in both attacks. In fact, you know, stocks in that they also had digitally designed, uh, certificates that were compromised. So when it gets to that level of students or, uh, sophistication, that means definitely that there's a target that there has been usually months of, of, uh, homework done by cyber criminals, for reconnaissance to be able to weaponize that. >>W w what did you see with respect to ransomware? What were the trends there over the past 12 months? I've heard some data and it's pretty scary, but what did you see? >>Yeah, so we're actually, ransomware is always the thorn in our side, and it's going to continue to be so, um, you know, in fact, uh, ransomware is not a new itself. It was actually first created in 1989, and they demanded ransom payments through snail mail. This was to appeal a box, obviously that, that, that didn't take off. Wasn't a successful on the internet was porn at the time. But if you look at it now, of course, over the last 10 years, really, that's where it ran. The ransomware model has been, uh, you know, lucrative, right? I mean, it's been, um, using, uh, by force encrypting data on systems, so that users had to, if they were forced to pay the ransom because they wanted access to their data back data was the target currency for ransomware. That's shifted now. And that's actually been a big pivotal over the last year or so, because again, before it was this let's cast a wide net, in fact, as many people as we can random, um, and try to see if we can hold some of their data for ransom. >>Some people that data may be valuable, it may not be valuable. Um, and that model still exists. Uh, and we see that, but really the big shift that we saw last year and the threat landscape before it was a shift to targeted rats. So again, the sophistication is starting to rise because they're not just going out to random data. They're going out to data that they know is valuable to large organizations, and they're taking that a step further now. So there's various ransomware families. We saw that have now reverted to extortion and blackmail, right? So they're taking that data, encrypting it and saying, unless you pay us as large sum of money, we're going to release this to the public or sell it to a buyer on the dark web. And of course you can imagine the amount of, um, you know, damages that can happen from that. The other thing we're seeing is, is a target of going to revenue services, right? So if they can cripple networks, it's essentially a denial of service. They know that the company is going to be bleeding, you know, X, millions of dollars a day, so they can demand Y million dollars of ransom payments, and that's effectively what's happening. So it's, again, becoming more targeted, uh, and more sophisticated. And unfortunately the ransom is going up. >>So they go to where the money is. And of course your job is to, it's a lower the ROI for them, a constant challenge. Um, we talked about some of the attack vectors, uh, that you saw this year that, that cyber criminals are targeting. I wonder if, if, you know, given the work from home, if things like IOT devices and cameras and, you know, thermostats, uh, with 75% of the work force at home, is this infrastructure more vulnerable? I guess, of course it is. But what did you see there in terms of attacks on those devices? >>Yeah, so, uh, um, uh, you know, unfortunately the attack surface as we call it, uh, so the amount of target points is expanding. It's not shifting, it's expanding. We still see, um, I saw, I mentioned earlier vulnerabilities from two years ago that are being used in some cases, you know, over the holidays where e-commerce means we saw e-commerce heavily under attack in e-commerce has spikes since last summer, right. It's been a huge amount of traffic increase everybody's shopping from home. And, uh, those vulnerabilities going after a shopping cart, plugins, as an example, are five to six years old. So we still have this theme of old vulnerabilities are still new in a sense being attacked, but we're also now seeing this complication of, yeah, as you said, IOT, uh, B roll out everywhere, the really quick shift to work from home. Uh, we really have to treat this as if you guys, as the, uh, distributed branch model for enterprise, right. >>And it's really now the secure branch. How do we take, um, um, you know, any of these devices on, on those networks and secure them, uh, because yeah, if you look at the, what we highlighted in our landscape report and the top 10 attacks that we're seeing, so hacking attacks hacking in tabs, this is who our IPS triggers. You know, we're seeing attempts to go after IOT devices. Uh, right now they're mostly, uh, favoring, uh, well in terms of targets, um, consumer grade routers. Uh, but they're also looking at, um, uh, DVR devices as an example for, uh, you know, home entertainment systems, uh, network attached storage as well, and IP security cameras, um, some of the newer devices, uh, what, the quote unquote smart devices that are now on, you know, virtual assistance and home networks. Uh, we actually released a predictions piece at the end of last year as well. So this is what we call the new intelligent edge. And that's what I think is we're really going to see this year in terms of what's ahead. Um, cause we always have to look ahead and prepare for that. But yeah, right now, unfortunately, the story is, all of this is still happening. IOT is being targeted. Of course they're being targeted because they're easy targets. Um, it's like for cybercriminals, it's like shooting fish in a barrel. There's not just one, but there's multiple vulnerabilities, security holes associated with these devices, easy entry points into networks. >>I mean, it's, um, I mean, attackers they're, they're highly capable. They're organized, they're well-funded they move fast, they're they're agile, uh, and they follow the money. As we were saying, uh, you, you mentioned, you know, co vaccines and, you know, big pharma healthcare, uh, where >>Did you see advanced, persistent >>Threat groups really targeting? Were there any patterns that emerged in terms of other industry types or organizations being targeted? >>Yeah. So just to be clear again, when we talk about AP teams, um, uh, advanced, specific correct group, the groups themselves they're targeting, these are usually the more sophisticated groups, of course. So going back to that theme, these are usually the target, the, um, the premeditated targeted attacks usually points to nation state. Um, sometimes of course there's overlap. They can be affiliated with cyber crime, cyber crime, uh, uh, groups are typically, um, looking at some other targets for ROI, uh, bio there's there's a blend, right? So as an example, if we're looking at the, uh, apt groups I had last year, absolutely. Number one I would say would be healthcare. Healthcare was one of those, and it's, it's, it's, uh, you know, very unfortunate, but obviously with the shift that was happening at a pop up medical facilities, there's a big, a rush to change networks, uh, for a good cause of course, but with that game, um, you know, uh, security holes and concerns the targets and, and that's what we saw IPT groups targeting was going after those and, and ransomware and the cyber crime shrine followed as well. Right? Because if you can follow, uh, those critical networks and crippled them on from cybercriminals point of view, you can, you can expect them to pay the ransom because they think that they need to buy in order to, um, get those systems back online. Uh, in fact, last year or two, unfortunately we saw the first, um, uh, death that was caused because of a denial of service attack in healthcare, right. Facilities were weren't available because of the cyber attack. Patients had to be diverted and didn't make it on the way. >>All right. Jericho, sufficiently bummed out. So maybe in the time remaining, we can talk about remediation strategies. You know, we know there's no silver bullet in security. Uh, but what approaches are you recommending for organizations? How are you consulting with folks? >>Sure. Yeah. So a couple of things, um, good news is there's a lot that we can do about this, right? And, um, and, and basic measures go a long way. So a couple of things just to get out of the way I call it housekeeping, cyber hygiene, but it's always worth reminding. So when we talk about keeping security patches up to date, we always have to talk about that because that is reality as et cetera, these, these vulnerabilities that are still being successful are five to six years old in some cases, the majority two years old. Um, so being able to do that, manage that from an organization's point of view, really treat the new work from home. I don't like to call it a work from home. So the reality is it's work from anywhere a lot of the times for some people. So really treat that as, as the, um, as a secure branch, uh, methodology, doing things like segmentations on network, secure wifi access, multi-factor authentication is a huge muscle, right? >>So using multi-factor authentication because passwords are dead, um, using things like, uh, XDR. So Xers is a combination of detection and response for end points. This is a mass centralized management thing, right? So, uh, endpoint detection and response, as an example, those are all, uh, you know, good security things. So of course having security inspection, that that's what we do. So good threat intelligence baked into your security solution. That's supported by labs angles. So, uh, that's, uh, you know, uh, antivirus, intrusion prevention, web filtering, sandbox, and so forth, but then it gets that that's the security stack beyond that it gets into the end user, right? Everybody has a responsibility. This is that supply chain. We talked about. The supply chain is, is, is a target for attackers attackers have their own supply chain as well. And we're also part of that supply chain, right? The end users where we're constantly fished for social engineering. So using phishing campaigns against employees to better do training and awareness is always recommended to, um, so that's what we can do, obviously that's, what's recommended to secure, uh, via the endpoints in the secure branch there's things we're also doing in the industry, um, to fight back against that with prime as well. >>Well, I, I want to actually talk about that and talk about ecosystems and collaboration, because while you have competitors, you all want the same thing. You, SecOps teams are like superheroes in my book. I mean, they're trying to save the world from the bad guys. And I remember I was talking to Robert Gates on the cube a couple of years ago, a former defense secretary. And I said, yeah, but don't, we have like the best security people and can't we go on the offensive and weaponize that ourselves. Of course, there's examples of that. Us. Government's pretty good at it, even though they won't admit it. But his answer to me was, yeah, we gotta be careful because we have a lot more to lose than many countries. So I thought that was pretty interesting, but how do you collaborate with whether it's the U S government or other governments or other other competitors even, or your ecosystem? Maybe you could talk about that a little bit. >>Yeah. Th th this is what, this is what makes me tick. I love working with industry. I've actually built programs for 15 years of collaboration in the industry. Um, so, you know, we, we need, I always say we can't win this war alone. You actually hit on this point earlier, you talked about following and trying to disrupt the ROI of cybercriminals. Absolutely. That is our target, right. We're always looking at how we can disrupt their business model. Uh, and, and in order, there's obviously a lot of different ways to do that, right? So a couple of things we do is resiliency. That's what we just talked about increasing the security stack so that they go knocking on someone else's door. But beyond that, uh, it comes down to private, private sector collaborations. So, uh, we, we, uh, co-founder of the cyber threat Alliance in 2014 as an example, this was our fierce competitors coming in to work with us to share intelligence, because like you said, um, competitors in the space, but we need to work together to do the better fight. >>And so this is a Venn diagram. What's compared notes, let's team up, uh, when there's a breaking attack and make sure that we have the intelligence so that we can still remain competitive on the technology stack to gradation the solutions themselves. Uh, but let's, let's level the playing field here because cybercriminals moved out, uh, you know, um, uh, that, that there's no borders and they move with great agility. So, uh, that's one thing we do in the private private sector. Uh, there's also, uh, public private sector relationships, right? So we're working with Interpol as an example, Interfor project gateway, and that's when we find attribution. So it's not just the, what are these people doing like infrastructure, but who, who are they, where are they operating? What, what events tools are they creating? We've actually worked on cases that are led down to, um, uh, warrants and arrests, you know, and in some cases, one case with a $60 million business email compromise fraud scam, the great news is if you look at the industry as a whole, uh, over the last three to four months has been for take downs, a motet net Walker, uh, um, there's also IE Gregor, uh, recently as well too. >>And, and Ian Gregor they're actually going in and arresting the affiliates. So not just the CEO or the King, kind of these organizations, but the people who are distributing the ransomware themselves. And that was a unprecedented step, really important. So you really start to paint a picture of this, again, supply chain, this ecosystem of cyber criminals and how we can hit them, where it hurts on all angles. I've most recently, um, I've been heavily involved with the world economic forum. Uh, so I'm, co-author of a report from last year of the partnership on cyber crime. And, uh, this is really not just the pro uh, private, private sector, but the private and public sector working together. We know a lot about cybercriminals. We can't arrest them. Uh, we can't take servers offline from the data centers, but working together, we can have that whole, you know, that holistic effect. >>Great. Thank you for that, Derek. What if people want, want to go deeper? Uh, I know you guys mentioned that you do blogs, but are there other resources that, that they can tap? Yeah, absolutely. So, >>Uh, everything you can see is on our threat research blog on, uh, so 40 net blog, it's under expired research. We also put out, uh, playbooks, w we're doing blah, this is more for the, um, the heroes as he called them the security operation centers. Uh, we're doing playbooks on the aggressors. And so this is a playbook on the offense, on the offense. What are they up to? How are they doing that? That's on 40 guard.com. Uh, we also release, uh, threat signals there. So, um, we typically release, uh, about 50 of those a year, and those are all, um, our, our insights and views into specific attacks that are now >>Well, Derek Mackie, thanks so much for joining us today. And thanks for the work that you and your teams do. Very important. >>Thanks. It's yeah, it's a pleasure. And, uh, rest assured we will still be there 24 seven, three 65. >>Good to know. Good to know. And thank you for watching everybody. This is Dave Volante for the cube. We'll see you next time.

>>As we've been reporting, the pandemic has called CSOs to really shift their spending priorities towards securing remote workers. Almost overnight. Zero trust has gone from buzzword to mandate. What's more as we wrote in our recent cybersecurity breaking analysis, not only Maseca pro secured increasingly distributed workforce, but now they have to be wary of software updates in the digital supply chain, including the very patches designed to protect them against cyber attacks. Hello everyone. And welcome to this Q conversation. My name is Dave Vellante and I'm pleased to welcome Derek manky. Who's chief security insights, and global threat alliances for four guard labs with fresh data from its global threat landscape report. Derek. Welcome. Great to see you. >>Thanks so much for, for the invitation to speak. It's always a pleasure. Multicover yeah, >>You're welcome. So first I wonder if you could explain for the audience, what is for guard labs and what's its relationship to fortunate? >>Right. So 40 grand labs is, is our global sockets, our global threat intelligence operation center. It never sleeps, and this is the beat. Um, you know, it's, it's been here since inception at port in it. So it's it's 20, 21 years in the making, since Fortinet was founded, uh, we have built this in-house, uh, so we don't go yum technology. We built everything from the ground up, including creating our own training programs for our, our analysts. We're following malware, following exploits. We even have a unique program that I created back in 2006 to ethical hacking program. And it's a zero-day research. So we try to meet the hackers, the bad guys to their game. And we of course do that responsibly to work with vendors, to close schools and create virtual patches. Um, and, but, you know, so it's, it's everything from, uh, customer protection first and foremost, to following, uh, the threat landscape and cyber. It's very important to understand who they are, what they're doing, who they're, uh, what they're targeting, what tools are they using? >>Yeah, that's great. Some serious DNA and skills in that group. And it's, it's critical because like you said, you can, you can minimize the spread of those malware very, very quickly. So what, what now you have, uh, the global threat landscape report. We're going to talk about that, but what exactly is that? >>Right? So this a global threat landscape report, it's a summary of, uh, all, all the data that we collect over a period of time. So we released this, that biannually two times a year. Um, cyber crime is changing very fast, as you can imagine. So, uh, while we do release security blogs, and, uh, what we call threat signals for breaking security events, we have a lot of other vehicles to release threat intelligence, but this threat landscape report is truly global. It looks at all of our global data. So we have over 5 million censorship worldwide in 40 guard labs, we're processing. I know it seems like a very large amount, but North of a hundred billion, uh, threat events in just one day. And we have to take the task of taking all of that data and put that onto scale for half a year and compile that into something, um, that is, uh, the, you know, that that's digestible. That's a, a very tough task, as you can imagine, so that, you know, we have to work with a huge technologies back to machine learning and artificial intelligence automation. And of course our analyst view to do that. >>Yeah. So this year, of course, there's like the every year is a battle, but this year was an extra battle. Can you explain what you saw in terms of the hacker dynamics over the past? Let's say 12 months. I know you do this twice a year, but what trends did you see evolving throughout the year and what have you seen with the way that attackers have exploited this expanded attack surface outside of corporate network? >>Yeah, it was quite interesting last year. It certainly was not normal. Like we all say, um, and that was no exception for cybersecurity. You know, if we look at cyber criminals and how they pivoted and adapted to the scrap threat landscape, cyber cyber criminals are always trying to take advantage of the weakest link of the chain. They're trying to always prey off here and ride waves of global trends and themes. We've seen this before in, uh, natural disasters as an example, you know, um, trying to do charity kind of scams and campaigns. And they're usually limited to a region where that incident happened and they usually live about two to three weeks, maybe a month at the most. And then they'll move on to the next to the next trip. That's braking, of course, because COVID is so global and dominant. Um, we saw attacks coming in from, uh, well over 40 different languages as an example, um, in regions all across the world that wasn't lasting two to three weeks and it lasted for the better part of a year. >>And of course, what they're, they're using this as a vehicle, right? Not preying on the fear. They're doing everything from initial lockdown, uh, fishing. We were as COVID-19 movers to, um, uh, lay off notices then to phase one, reopenings all the way up to fast forward to where we are today with vaccine rollover development. So there's always that new flavor and theme that they were rolling out, but because it was so successful for them, they were able to, they didn't have to innovate too much, right. They didn't have to expand and shifted to new to new trends. And themes are really developed on new rats families as an example, or a new sophisticated malware. That was the first half of the year and the second half of the year. Um, of course people started to experience COVID fatigue, right? Um, people started to become, we did a lot of education around this. >>People started to become more aware of this threat. And so, um, cyber criminals have started to, um, as we expected, started to become more sophisticated with their attacks. We saw an expansion in different ransomware families. We saw more of a shift of focus on, on, um, uh, you know, targeting the digital supply chain as an example. And so that, that was, that was really towards Q4. Uh, so it, it was a long lived lead year with success on the Google themes, um, targeting healthcare as an example, a lot of, um, a lot of the organizations that were, you know, really in a vulnerable position, I would say >>So, okay. I want to clarify something because my assumption was that they actually did really increase the sophistication, but it sounds like that was kind of a first half trends. Not only did they have to adapt and not have to, but they adapt it to these new vulnerabilities. Uh, my sense was that when you talk about the digital supply chain, that that was a fairly sophisticated attack. Am I, am I getting that right? That they did their sort of their, their, their increased sophistication in the first half, and then they sort of deployed it, did it, uh, w what actually happened there from your data? >>Well, if we look at, so generally there's two types of attacks that we look at, we look at the, uh, the premeditated sophisticated attacks that can have, um, you know, a lot of ramp up work on their end, a lot of time developing the, the, the, the weaponization phase. So developing, uh, the exploits of the sophisticated malware that they're gonna use for the campaign reconnaissance, understanding the targets, where platforms are developed, um, the blueprinting that DNA of, of, of the supply chain, those take time. Um, in fact years, even if we look back to, um, uh, 10 plus years ago with the Stuxnet attacks, as an example that was on, uh, nuclear centrifuges, um, and that, that had four different zero-day weapons at the time. That was very sophisticated, that took over two years to develop as an example. So some of these can take years of time to develop, but they're, they're, uh, very specific in terms of the targets are going to go after obviously the ROI from their end. >>Uh, the other type of attack that we see is as ongoing, um, these broad, wide sweeping attacks, and the reality for those ones is they don't unfortunately need to be too sophisticated. And those ones were the ones I was talking about that were really just playing on the cool, the deem, and they still do today with the vaccine road and development. Uh, but, but it's really because they're just playing on, on, um, you know, social engineering, um, using, uh, topical themes. And in fact, the weapons they're using these vulnerabilities are from our research data. And this was highlighted actually the first pop landscape before last year, uh, on average were two to three years old. So we're not talking about fresh vulnerabilities. You've got to patch right away. I mean, these are things that should have been patched two years ago, but they're still unfortunately having success with that. >>So you mentioned stuck next Stuxnet as the former sort of example, of one of the types of attacks that you see. And I always felt like that was a watershed moment. One of the most sophisticated, if not the most sophisticated attack that we'd ever seen. When I talk to CSOs about the recent government hack, they, they, they suggest I infer maybe they don't suggest it. I infer that it was of similar sophistication. It was maybe thousands of people working on this for years and years and years. Is that, is that accurate or not necessarily? >>Yeah, there's definitely a, there's definitely some comparisons there. Uh, you know, one of the largest things is, uh, both attacks used digital circuits certificate personation, so they're digitally signed. So, you know, of course that whole technology using cryptography is designed by design, uh, to say that, you know, this piece of software installed in your system, hassles certificate is coming from the source. It's legitimate. Of course, if that's compromised, that's all out of the window. And, um, yeah, this is what we saw in both attacks. In fact, you know, stocks in that they also had digitally designed, uh, certificates that were compromised. So when it gets to that level of students or, uh, sophistication, that means definitely that there's a target that there has been usually months of, of, uh, homework done by cyber criminals, for reconnaissance to be able to weaponize that. >>W w what did you see with respect to ransomware? What were the trends there over the past 12 months? I've heard some data and it's pretty scary, but what did you see? >>Yeah, so we're actually, ransomware is always the thorn in our side, and it's going to continue to be so, um, you know, in fact, uh, ransomware is not a new itself. It was actually first created in 1989, and they demanded ransom payments through snail mail. This was to appeal a box, obviously that, that, that didn't take off. Wasn't a successful on the internet was porn at the time. But if you look at it now, of course, over the last 10 years, really, that's where it ran. The ransomware model has been, uh, you know, lucrative, right? I mean, it's been, um, using, uh, by force encrypting data on systems, so that users had to, if they were forced to pay the ransom because they wanted access to their data back data was the target currency for ransomware. That's shifted now. And that's actually been a big pivotal over the last year or so, because again, before it was this let's cast a wide net, in fact, as many people as we can random, um, and try to see if we can hold some of their data for ransom. >>Some people that data may be valuable, it may not be valuable. Um, and that model still exists. Uh, and we see that, but really the big shift that we saw last year and the threat landscape before it was a shift to targeted rats. So again, the sophistication is starting to rise because they're not just going out to random data. They're going out to data that they know is valuable to large organizations, and they're taking that a step further now. So there's various ransomware families. We saw that have now reverted to extortion and blackmail, right? So they're taking that data, encrypting it and saying, unless you pay us as large sum of money, we're going to release this to the public or sell it to a buyer on the dark web. And of course you can imagine the amount of, um, you know, damages that can happen from that. The other thing we're seeing is, is a target of going to revenue services, right? So if they can cripple networks, it's essentially a denial of service. They know that the company is going to be bleeding, you know, X, millions of dollars a day, so they can demand Y million dollars of ransom payments, and that's effectively what's happening. So it's, again, becoming more targeted, uh, and more sophisticated. And unfortunately the ransom is going up. >>So they go to where the money is. And of course your job is to, it's a lower the ROI for them, a constant challenge. Um, we talked about some of the attack vectors, uh, that you saw this year that, that cyber criminals are targeting. I wonder if, if, you know, given the work from home, if things like IOT devices and cameras and, you know, thermostats, uh, with 75% of the work force at home, is this infrastructure more vulnerable? I guess, of course it is. But what did you see there in terms of attacks on those devices? >>Yeah, so, uh, um, uh, you know, unfortunately the attack surface as we call it, uh, so the amount of target points is expanding. It's not shifting, it's expanding. We still see, um, I saw, I mentioned earlier vulnerabilities from two years ago that are being used in some cases, you know, over the holidays where e-commerce means we saw e-commerce heavily under attack in e-commerce has spikes since last summer, right. It's been a huge amount of traffic increase everybody's shopping from home. And, uh, those vulnerabilities going after a shopping cart, plugins, as an example, are five to six years old. So we still have this theme of old vulnerabilities are still new in a sense being attacked, but we're also now seeing this complication of, yeah, as you said, IOT, uh, B roll out everywhere, the really quick shift to work from home. Uh, we really have to treat this as if you guys, as the, uh, distributed branch model for enterprise, right. >>And it's really now the secure branch. How do we take, um, um, you know, any of these devices on, on those networks and secure them, uh, because yeah, if you look at the, what we highlighted in our landscape report and the top 10 attacks that we're seeing, so hacking attacks hacking in tabs, this is who our IPS triggers. You know, we're seeing attempts to go after IOT devices. Uh, right now they're mostly, uh, favoring, uh, well in terms of targets, um, consumer grade routers. Uh, but they're also looking at, um, uh, DVR devices as an example for, uh, you know, home entertainment systems, uh, network attached storage as well, and IP security cameras, um, some of the newer devices, uh, what, the quote unquote smart devices that are now on, you know, virtual assistance and home networks. Uh, we actually released a predictions piece at the end of last year as well. So this is what we call the new intelligent edge. And that's what I think is we're really going to see this year in terms of what's ahead. Um, cause we always have to look ahead and prepare for that. But yeah, right now, unfortunately, the story is, all of this is still happening. IOT is being targeted. Of course they're being targeted because they're easy targets. Um, it's like for cybercriminals, it's like shooting fish in a barrel. There's not just one, but there's multiple vulnerabilities, security holes associated with these devices, easy entry points into networks. >>I mean, it's, um, I mean, attackers they're, they're highly capable. They're organized, they're well-funded they move fast, they're they're agile, uh, and they follow the money. As we were saying, uh, you, you mentioned, you know, co vaccines and, you know, big pharma healthcare, uh, where >>Did you see advanced, persistent >>Threat groups really targeting? Were there any patterns that emerged in terms of other industry types or organizations being targeted? >>Yeah. So just to be clear again, when we talk about AP teams, um, uh, advanced, specific correct group, the groups themselves they're targeting, these are usually the more sophisticated groups, of course. So going back to that theme, these are usually the target, the, um, the premeditated targeted attacks usually points to nation state. Um, sometimes of course there's overlap. They can be affiliated with cyber crime, cyber crime, uh, uh, groups are typically, um, looking at some other targets for ROI, uh, bio there's there's a blend, right? So as an example, if we're looking at the, uh, apt groups I had last year, absolutely. Number one I would say would be healthcare. Healthcare was one of those, and it's, it's, it's, uh, you know, very unfortunate, but obviously with the shift that was happening at a pop up medical facilities, there's a big, a rush to change networks, uh, for a good cause of course, but with that game, um, you know, uh, security holes and concerns the targets and, and that's what we saw IPT groups targeting was going after those and, and ransomware and the cyber crime shrine followed as well. Right? Because if you can follow, uh, those critical networks and crippled them on from cybercriminals point of view, you can, you can expect them to pay the ransom because they think that they need to buy in order to, um, get those systems back online. Uh, in fact, last year or two, unfortunately we saw the first, um, uh, death that was caused because of a denial of service attack in healthcare, right. Facilities were weren't available because of the cyber attack. Patients had to be diverted and didn't make it on the way. >>All right. Jericho, sufficiently bummed out. So maybe in the time remaining, we can talk about remediation strategies. You know, we know there's no silver bullet in security. Uh, but what approaches are you recommending for organizations? How are you consulting with folks? >>Sure. Yeah. So a couple of things, um, good news is there's a lot that we can do about this, right? And, um, and, and basic measures go a long way. So a couple of things just to get out of the way I call it housekeeping, cyber hygiene, but it's always worth reminding. So when we talk about keeping security patches up to date, we always have to talk about that because that is reality as et cetera, these, these vulnerabilities that are still being successful are five to six years old in some cases, the majority two years old. Um, so being able to do that, manage that from an organization's point of view, really treat the new work from home. I don't like to call it a work from home. So the reality is it's work from anywhere a lot of the times for some people. So really treat that as, as the, um, as a secure branch, uh, methodology, doing things like segmentations on network, secure wifi access, multi-factor authentication is a huge muscle, right? >>So using multi-factor authentication because passwords are dead, um, using things like, uh, XDR. So Xers is a combination of detection and response for end points. This is a mass centralized management thing, right? So, uh, endpoint detection and response, as an example, those are all, uh, you know, good security things. So of course having security inspection, that that's what we do. So good threat intelligence baked into your security solution. That's supported by labs angles. So, uh, that's, uh, you know, uh, antivirus, intrusion prevention, web filtering, sandbox, and so forth, but then it gets that that's the security stack beyond that it gets into the end user, right? Everybody has a responsibility. This is that supply chain. We talked about. The supply chain is, is, is a target for attackers attackers have their own supply chain as well. And we're also part of that supply chain, right? The end users where we're constantly fished for social engineering. So using phishing campaigns against employees to better do training and awareness is always recommended to, um, so that's what we can do, obviously that's, what's recommended to secure, uh, via the endpoints in the secure branch there's things we're also doing in the industry, um, to fight back against that with prime as well. >>Well, I, I want to actually talk about that and talk about ecosystems and collaboration, because while you have competitors, you all want the same thing. You, SecOps teams are like superheroes in my book. I mean, they're trying to save the world from the bad guys. And I remember I was talking to Robert Gates on the cube a couple of years ago, a former defense secretary. And I said, yeah, but don't, we have like the best security people and can't we go on the offensive and weaponize that ourselves. Of course, there's examples of that. Us. Government's pretty good at it, even though they won't admit it. But his answer to me was, yeah, we gotta be careful because we have a lot more to lose than many countries. So I thought that was pretty interesting, but how do you collaborate with whether it's the U S government or other governments or other other competitors even, or your ecosystem? Maybe you could talk about that a little bit. >>Yeah. Th th this is what, this is what makes me tick. I love working with industry. I've actually built programs for 15 years of collaboration in the industry. Um, so, you know, we, we need, I always say we can't win this war alone. You actually hit on this point earlier, you talked about following and trying to disrupt the ROI of cybercriminals. Absolutely. That is our target, right. We're always looking at how we can disrupt their business model. Uh, and, and in order, there's obviously a lot of different ways to do that, right? So a couple of things we do is resiliency. That's what we just talked about increasing the security stack so that they go knocking on someone else's door. But beyond that, uh, it comes down to private, private sector collaborations. So, uh, we, we, uh, co-founder of the cyber threat Alliance in 2014 as an example, this was our fierce competitors coming in to work with us to share intelligence, because like you said, um, competitors in the space, but we need to work together to do the better fight. >>And so this is a Venn diagram. What's compared notes, let's team up, uh, when there's a breaking attack and make sure that we have the intelligence so that we can still remain competitive on the technology stack to gradation the solutions themselves. Uh, but let's, let's level the playing field here because cybercriminals moved out, uh, you know, um, uh, that, that there's no borders and they move with great agility. So, uh, that's one thing we do in the private private sector. Uh, there's also, uh, public private sector relationships, right? So we're working with Interpol as an example, Interfor project gateway, and that's when we find attribution. So it's not just the, what are these people doing like infrastructure, but who, who are they, where are they operating? What, what events tools are they creating? We've actually worked on cases that are led down to, um, uh, warrants and arrests, you know, and in some cases, one case with a $60 million business email compromise fraud scam, the great news is if you look at the industry as a whole, uh, over the last three to four months has been for take downs, a motet net Walker, uh, um, there's also IE Gregor, uh, recently as well too. >>And, and Ian Gregor they're actually going in and arresting the affiliates. So not just the CEO or the King, kind of these organizations, but the people who are distributing the ransomware themselves. And that was a unprecedented step, really important. So you really start to paint a picture of this, again, supply chain, this ecosystem of cyber criminals and how we can hit them, where it hurts on all angles. I've most recently, um, I've been heavily involved with the world economic forum. Uh, so I'm, co-author of a report from last year of the partnership on cyber crime. And, uh, this is really not just the pro uh, private, private sector, but the private and public sector working together. We know a lot about cybercriminals. We can't arrest them. Uh, we can't take servers offline from the data centers, but working together, we can have that whole, you know, that holistic effect. >>Great. Thank you for that, Derek. What if people want, want to go deeper? Uh, I know you guys mentioned that you do blogs, but are there other resources that, that they can tap? Yeah, absolutely. So, >>Uh, everything you can see is on our threat research blog on, uh, so 40 net blog, it's under expired research. We also put out, uh, playbooks, w we're doing blah, this is more for the, um, the heroes as he called them the security operation centers. Uh, we're doing playbooks on the aggressors. And so this is a playbook on the offense, on the offense. What are they up to? How are they doing that? That's on 40 guard.com. Uh, we also release, uh, threat signals there. So, um, we typically release, uh, about 50 of those a year, and those are all, um, our, our insights and views into specific attacks that are now >>Well, Derek Mackie, thanks so much for joining us today. And thanks for the work that you and your teams do. Very important. >>Thanks. It's yeah, it's a pleasure. And, uh, rest assured we will still be there 24 seven, three 65. >>Good to know. Good to know. And thank you for watching everybody. This is Dave Volante for the cube. We'll see you next time.

>>From around the globe, it's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Oh, welcome back to the cube. As we continue our coverage of AWS reinvent 2020, you know, I know you're familiar with Moneyball, the movie, Brad Pitt, starting as Billy Bean, the Oakland A's general manager, where the A's were all over data, right. With the Billy Bean approach, it was a very, uh, data driven approach to building his team and a very successful team. Well, AWS is taking that to an extraordinary level and with us to talk about that as Matt Hearst, who was the head of global sports marketing and communications at AWS and Matt, thanks for joining us here on the queue. >>John is my pleasure. Thanks so much for having me. You >>Bet. Um, now we've already heard from a couple of folks, NFL folks, uh, at re-invent, uh, about the virtual draft. Um, but for those of our viewers who maybe aren't up to speed on that, or having a chance to see, uh, what those folks had to say, uh, let's just talk about that as an opener, um, about your involvement with the NFL and particularly with, with the draft and, and what that announcement was all about. >>Sure. We, we saw, we've seen a great evolution with our work with the NFL over the past few years. And you mentioned during the infrastructure keynote where Michelle McKenna who's, the CIO for the NFL talks about how they were able to stage the 2020 virtual draft, which was the NFL is much most watched ever, uh, you know, over 55 million viewers over three days and how they were unable to do it without the help and the power of AWS, you know, utilizing AWS is reliability, scalability, security, and network connectivity, where they were able to manage thousands of live feeds to flow to the internet and go to ESPN, to airline. Um, but additionally, Jennifer LinkedIn, who's the SVP of player health and innovation at the NFL spoke during the machine learning keynote during reinvent. And she talked about how we're working with the NFL, uh, to co-develop the digital athlete, which is a computer simulation model of a football player that can replicate infinite scenarios in a game environment to help better foster and understanding of how to treat and rehabilitate injuries in the short term and in the long-term in the future, ultimately prevent, prevent and predict injuries. >>And they're using machine learning to be able to do that. So there's, those are just a couple of examples of, uh, what the NFL talked about during re-invent at a couple of keynotes, but we've seen this work with the NFL really evolve over the past few years, you know, starting with next gen stats. Those are the advanced statistics that, uh, brings a new level of entertainment to football fans. And what we really like to do, uh, with the NFL is to excite, educate, and innovate. And those stats really bring fans closer to the game to allow the broadcasters to go a little bit deeper, to educate the fans better. And we've seen some of those come to life through some of our ads, uh, featuring Deshaun Watson, Christian McCaffrey, um, these visually compelling statistics that, that come to life on screen. Um, and it's not just the NFL. AWS is doing this with some of the top sports leagues around the world, you know, powering F1 insights, Buddhist league, and match facts, six nations, rugby match stats, all of which utilize AWS technology to uncover advanced stats and really help educate and engage fans around the world in the sports that they love. >>Let's talk about that engagement with your different partners then, because you just touched on it. This is a wide array of avenues that you're exploring. You're in football, you're in soccer, you're in sailing, uh, you're uh, racing formula one and NASCAR, for example, all very different animals, right? In terms of their statistics and their data and of their fan interest, what fans ultimately want. So, um, maybe on a holistic basis first, how are you, uh, kind of filtering through your partner's needs and their fans needs and your capabilities and providing that kind of merger of capabilities with desires >>Sports, uh, for AWS and for Amazon are no different than any other industry. And we work backwards from the customer and what their needs are. You know, when we look at the sports partners and customers that we work with and why they're looking to AWS to help innovate and transform their sports, it's really the innovative technologies like machine learning, artificial intelligence, high performance computing, internet of things, for example, that are really transforming the sports world and some of the best teams and leagues that we've talked about, that you touched on, you know, formula one, NASCAR, NFL, Buena, Sligo, six nations, rugby, and so on and so forth are using AWS to really improve the athlete and the team performance transform how fans view and engage with sports and deliver these real-time advanced statistics to give fans, uh, more of that excitement that we're talking about. >>Let me give you a couple of examples on some of these innovative technologies that our customers are using. So the Seattle Seahawks, I built a data Lake on AWS to use it for talent, evaluation and acquisition to improve player health and recovery times, and also for their game planning. And another example is, you know, formula and we talk about the F1 insights, those advanced statistics, but they're also using AWS high-performance computing that helped develop the next generation race car, which will be introduced in the 2022 season. And by using AWS F1 was able to reduce the average time to run simulations by 70% to improve the car's aerodynamics, reducing the downforce loss and create more wheel to wheel racing, to bring about more excitement on the track. And a third example, similar to, uh, F1 using HPC is any of those team UK. So they compete in the America's cup, which is the oldest trophy in international sports. And endosteum UK is using an HPC environment running on Amazon, easy to spot instances to design its boat for the upcoming competition. And they're depending on this computational power on AWS needing 2000 to 3000 simulations to design the dimension of just a single boat. Um, and so the power of the cloud and the power of the AWS innovative technologies are really helping, uh, these teams and leagues and sports organizations around the world transform their sport. >>Well, let's go back. Uh, you mentioned the Seahawks, um, just as, uh, an example of maybe, uh, the kind of insights that that you're providing. Uh, let's pretend I'm there, there's an outstanding running back and his name's Matt Hearst and, uh, and he's at a, you know, a college let's just pretend in California someplace. Um, what kind of inputs, uh, are you now helping them? Uh, and what kind of insights are you trying to, are you helping them glean from those inputs that maybe they didn't have before? And how are they actually applying that then in terms of their player acquisition and thinking about draft, right player development, deciding whether Matt Hertz is a good fit for them, maybe John Wallace is a good fit for them. Um, but what are the kinds of, of, uh, what's that process look like? >>So the way that the Seahawks have built the data Lake, they built it on AWFs to really, as you talk about this talent, evaluation and acquisition, to understand how a player, you know, for example, a John Walls could fit into their scheme, you know, that, that taking this data and putting it in the data Lake and figuring out how it fits into their schemes is really important because you could find out that maybe you played, uh, two different positions in high school or college, and then that could transform into, into the schematics that they're running. Um, and try to find, I don't want to say a diamond in the rough, but maybe somebody that could fit better into their scheme than, uh, maybe the analysts or others could figure out. And that's all based on the power of data that they're using, not only for the talent evaluation and acquisition, but for game planning as well. >>And so the Seahawks building that data Lake is just one of those examples. Um, you know, when, when you talk about a player, health and safety, as well, just using the NFL as the example, too, with that digital athlete, working with them to co-develop that for that composite NFL player, um, where they're able to run those infinite scenarios to ultimately predict and prevent injury and using Amazon SageMaker and AWS machine learning to do so, it's super important, obviously with the Seahawks, for the future of that organization and the success that they, that they see and continue to see, and also for the future of football with the NFL, >>You know, um, Roger Goodell talks about innovation in the national football league. We hear other commissioners talking about the same thing. It's kind of a very popular buzz word right now is, is leagues look to, uh, ways to broaden their, their technological footprint in innovative ways. Again, popular to say, how exactly though, do you see AWS role in that with the national football league, for example, again, or maybe any other league in terms of inspiring innovation and getting them to perhaps look at things differently through different prisms than they might have before? >>I think, again, it's, it's working backwards from the customer and understanding their needs, right? We couldn't have predicted at the beginning of 2020, uh, that, you know, the NFL draft will be virtual. And so working closely with the NFL, how do we bring that to life? How do we make that successful, um, you know, working backwards from the NFL saying, Hey, we'd love to utilize your technology to improve Clare health and safety. How are we able to do that? Right. And using machine learning to do so. So the pace of innovation, these innovative technologies are very important, not only for us, but also for these, uh, leagues and teams that we work with, you know, using F1 is another example. Um, we talked about HPC and how they were able to, uh, run these simulations in the cloud to improve, uh, the race car and redesign the race car for the upcoming seasons. >>But, uh, F1 is also using Amazon SageMaker, um, to develop new F1 insights, to bring fans closer to the action on the track, and really understand through technology, these split-second decisions that these drivers are taking in every lap, every turn, when to pit, when not to pit things of that nature and using the power of the cloud and machine learning to really bring that to life. And one example of that, that we introduced this year with, with F1 was, um, the fastest driver insight and working F1, worked with the Amazon machine learning solutions lab to bring that to life and use a data-driven approach to determine the fastest driver, uh, over the last 40 years, relying on the years of historical data that they store in S3 and the ML algorithms that, that built between AWS and F1 data scientists to produce this result. So John, you and I could sit here and argue, you know, like, like two guys that really love F1 and say, I think Michael Schumacher is the fastest drivers. It's Lewis, Hamilton. Who's great. Well, it turned out it was a arts incentive, you know, and Schumacher was second. And, um, Hamilton's third and it's the power of this data and the technology that brings this to life. So we could still have a fun argument as fans around this, but we actually have a data-driven results through that to say, Hey, this is actually how it, how it ranked based on how everything works. >>You know, this being such a strange year, right? With COVID, uh, being rampant and, and the major influence that it has been in every walk of global life, but certainly in the American sports. Um, how has that factored into, in terms of the kinds of services that you're looking to provide or to help your partners provide in order to increase that fan engagement? Because as you've pointed out, ultimately at the end of the day, it's, it's about the consumer, right? The fan, and giving them info, they need at the time they want it, that they find useful. Um, but has this year been, um, put a different point on that for you? Just because so many eyeballs have been on the screen and not necessarily in person >>Yeah. T 20, 20 as, you know, a year, unlike any other, um, you know, in our lifetimes and hopefully going forward, you know, it's, it's not like that. Um, but we're able to understand that we can still bring fans closer to the sports that they love and working with, uh, these leagues, you know, we talk about NFL draft, but with formula one, we, uh, in the month of may developed the F1 Pro-Am deep racer event that featured F1 driver, uh, Daniel Ricardo, and test driver TA Sianna Calderon in this deep racer league and deep racers, a one 18th scale, fully autonomous car, um, that uses reinforcement learning, learning a type of machine learning. And so we had actual F1 driver and test driver racing against developers from all over the world. And technology is really playing a role in that evolution of F1. Um, but also giving fans a chance to go head to head against the Daniel Ricardo, which I don't know that anyone else could ever say that. >>Yeah, I raced against an F1 driver for head to head, you know, and doing that in the month of may really brought forth, not only an appreciation, I think for the drivers that were involved on the machine learning and the technology involved, but also for the developers on these split second decisions, these drivers have to make through an event like that. You know, it was, it was great and well received. And the drivers had a lot of fun there. Um, you know, and that is the national basketball association. The NBA played in the bubble, uh, down in Orlando, Florida, and we work with second spectrum. They run on AWS. And second spectrum is the official optical provider of the NBA and they provide Clippers court vision. So, uh, it's a mobile live streaming experience for LA Clippers fans that uses artificial intelligence and machine learning to visualize data through on-screen graphic overlays. >>And second spectrum was able to rely on, uh, AWS is reliability, connectivity, scalability, and move all of their equipment to the bubble in Orlando and still produce a great experience for the fans, um, by reducing any latency tied to video and data processing, um, they needed that low latency to encode and compress the media to transfer an edit with the overlays in seconds without losing quality. And they were able to rely on AWS to do that. So a couple of examples that even though 2020 was, uh, was a little different than we all expected it to be, um, of how we worked closely with our sports partners to still deliver, uh, an exceptional fan experience. >>So, um, I mean, first off you have probably the coolest job at AWS. I think it's so, uh, congratulations. I mean, it's just, it's fascinating. What's on your want to do less than in terms of 20, 21 and beyond and about what you don't do now, or, or what you would like to do better down the road, any one area in particular that you're looking at, >>You know, our, our strategy in sports is no different than any other industry. We want to work backwards from our customers to help solve business problems through innovation. Um, and I know we've talked about the NFL a few times, but taking them for, for another example, with the NFL draft, improving player health and safety, working closely with them, we're able to help the NFL advance the game both on and off the field. And that's how we look at doing that with all of our sports partners and really helping them transform their sport, uh, through our innovative technologies. And we're doing this in a variety of ways, uh, with a bunch of engaging content that people can really enjoy with the sports that they love, whether it's, you know, quick explainer videos, um, that are short two minute or less videos explaining what these insights are, these advanced stats. >>So when you see them on the screening and say, Oh yeah, I understand what that is at a, at a conceptual level or having blog posts from a will, Carlin who, uh, has a long storied history in six nations and in rugby or Rob Smedley, along story history and F1 writing blog posts to give fans deeper perspective as subject matter experts, or even for those that want to go deeper under the hood. We've worked with our teams to take a deeper look@howsomeofthesecometolifedetailingthetechnologyjourneyoftheseadvancedstatsthroughsomedeepdiveblogsandallofthiscanbefoundataws.com slash sports. So a lot of great rich content for, uh, for people to dig into >>Great stuff, indeed. Um, congratulations to you and your team, because you really are enriching the fan experience, which I am. One of, you know, hundreds of millions are enjoying that. So thanks for that great work. And we wish you all the continued success down the road here in 2021 and beyond. Thanks, Matt. Thanks so much, Sean.

>>from around the globe. It's the Cube with digital coverage of workplace next made possible by Hewlett Packard Enterprise. >>Hello, everyone. We're here covering workplace next on the Cube For years, you know, we've talked about new ways to work, and it was great thought exercise. And then overnight the pandemic heightened the challenges of creating an effective work force. Most of the executives that we talked to in our survey say that productivity actually has improved since the work from Home Mandate was initiative. But, you know, we're talking not just about productivity, but the well being of our associates and managing the unknown. We're going to shift gears a little bit now. We've heard some interesting real world examples of how organizations are dealing with the rapid change in workplace, and we've heard about some lessons to take into the future. But now we're going to get more practical and look at some of the tools that are available to help you navigate. The changes that we've been discussing and with me to talk about these trends related to the future of work are are are Qadoura, who's the vice president of worldwide sales and go to market for Green Lake at HP Sadat Malik is the VP of I O t and Intelligent Edge at HP and Satish Yarra Valley is the global cloud and infrastructure practice Head at Whip Probe guys welcomes. Good to see you. Thanks for coming on. >>Thanks for having us. >>You're very welcome. Let me start with Sadat. You're coming from Austin, Texas here. So thank you. Stay crazy. As they say in Austin, for the uninitiated, maybe you could talk a little bit about h p E point. Next. It's a strategic component of H p. E. And maybe tell us a little bit about those services. >>Thank you so much for taking the time today. Appreciate everybody's participation here. So absolutely so point Next is HP Services on. This is the 23,000 strong organization globally spread out, and we have a very strong ecosystem of partners that be leveraged to deliver services to our customers. Um, our organization differentiates itself in the market by focusing on digital digital transformation journeys for our customers. For customers looking toe move to a different way off, engaging with its customers, transforming the way its employees work, figuring out a different way off producing the products that it sells to. His customers are changing the way it operationalize these things. For example, moving to the cloud going to a hybrid model, we help them achieve any of these four transformation outcomes. So point next job is toe point. What is next in this digital transformation journey and then partner with our customers to make that happen? So that's what we do. >>Thank you for that. I mean, obviously, you're gonna be seeing a lot of activity around workplace with shift from work from home, changes in the network changes in security. I mean the whole deal. What are some of your top takeaways that you can share with our audience? >>Yeah, they're >>so a lot has been happening in the workplace arena lately. So this is not new, right? This is not something that all of a sudden side happening when Kobe 19 hit, uh, the digital workplace was already transforming before over 19 happened. What over 19 has done is that it has massively accelerated the pace at which this change was happening. So, for example, right remote work was already there before over 19. But now everybody is working remotely so, in many ways, the solution that we have for remote work. They have been strained to appoint, never seen before. Networks that support these remote work environments have been pushed to their limits. Security was already there, right? So security was a critical piece off any off the thinking, any of the frameworks that we had. But now security is pivotal and central. Any discussion that we're having about the workplace environment data is being generated all across the all across the environment that we operated, right? So it's no longer being generated. One place being stored. Another. It's all over the place now. So what Kobe, 19 has done is that the transformation that was already underway in the digital workplace, it has taken that and accelerated it massive. The key take away for me is right that we have to make sure that when we're working with our customers, our clients, we don't just look at the technology aspect of things. We have to look at all the other aspect as well the people in the process aspect off this environment. It is critical that we don't assume that just because the technology is there to address these challenges that I just mentioned. Our people and our processes would be able to handle that as well. We need to bring everybody along. Everybody has different needs, and we need to be able to cater to those needs effectively. So that's my biggest take away. Make sure that the process and the people aspect of things was hand in glove with the technology that we were able to bring to bear here. >>Got it. Thank you. So, ah, let's go to San Francisco, bringing our war to the conversation. You're one of your areas of focus is is HP Green Lake. You guys were early on with the as a service model. Clearly, we've seen Mawr interest in cloud and cloud like models. I wonder if you could just start by sharing. What's Green Lake all about? Where does it fit into this whole workplace? Next, Uh, conversation that we're having? >>Yeah, absolutely. Um HP Green lake effectively is the cloud that comes to your data center to your Coehlo or to your edge, right? We saw with Public Cloud. The public cloud brought a ton of innovations, um, into the sort of hyper scale model. Now, with HP. What we've done is we've said, Look, customers need this level of innovation and this level of, you know, pay as you go economics the, you know, management layer the automation layer not just in a public cloud environment, but also in our customers data center or to the other potential edges or Coehlo scenarios. And what we've done is we've brought together Asada just mentioned the best of our point next services our software management layer as well as H. P. E s rich portfolio of hardware to come together to create that cloud experience. Um, of course, we can't do this without the rich ecosystem around us as well. And so everything from you know, some of our big S I partners like we bro, who also have the virtual desktop expertise or virtual desk that then come together to start helping us launch some of these new workloads supported cloud services such as D. D i eso for my perspective, v. D. I is the most important topic for a lot of our customers right now, especially in sectors like financial services, um, advanced engineering scenarios and health care where they need access to those, uh to their data centers in a very secure way and in a highly cost optimized way as well. >>Well, okay. Thank you. And then let's let's bring in, uh, petition talk a little bit about the ecosystem. I mean, we're pro. That's really kind of your wheelhouse. We've been talking a lot on the cube about moving from an industry of point products to platforms and now ecosystem innovation, Uh, are are mentioned VD I we saw that exploding eso teach. Maybe you could weigh in here and and share with us what you're seeing in the market and specifically around ecosystem. >>As we all know, the pandemic has redefined the way we collaborate to support this collaboration. We have set up huge campuses and office infrastructure In summary, our industry has centralized approach. Now, the very premise of the centralization bringing people together for work has changed. This evolving workspace dynamics have triggered the agency to reimagine the workspace strategy. CEO, CEO S and C H R ose are all coming together to redefine the business process and find new ways off engaging with customers and employees as organizations embrace work from home for the foreseeable future. Customer need to create secure by design workspaces for remote working environments. With the pro virtual disk platform, we can help create such seamless distal workspaces and enable customers to connect, collaborate and communicate with ease from anywhere securely. They're consistent user experience. Through this platform led approach, we are able to utter the market demands which are focused on business outcomes. >>Okay, and this is the specifics of this hard news that you're talking about Video on demand and Citrix coming together with your ecosystem. H p E were pro and again, the many partners that you work with is that correct? >>Well, actually, Dave, we see a strong playoff ecosystem partners coming together to achieve transformative business outcomes. As Arbor said earlier, HP and Wipro have long standing partnership, and today's announcement around HP Green Lake is an extension off this collaboration, where we provide leverage HP Green Leg Andre Pro, which elders platform to offer video as a service in a paper user model. Our aim is to enable customers fast track there. It is still works based transformation efforts by eliminating the need to support upfront capital investments and old provisioning costs while allowing customers to enjoy the benefit off compromise, control, security and compliance. Together, we have implemented our solution across various industry segments and deliver exceptional customer experiences by helping customer businesses in their workspace. Transformation journeys by defining their workspace strategy with an intelligent, platform led approach that enables responsiveness, scalability and resilience. It's known that Wipro is recognized as a global leader in the distal workspace and video I, with HP being a technology leader, enabling us with high level of program ability on integration capabilities. We see tremendous potential to jointly address the industry challenges as we move forward. >>Excellent. Uh, sad. I wanna come back to you. We talk a lot about the digital business, the mandate for digital business, especially with the pandemic. Let's talk about data. Earlier this year, HP announced the number of solutions that used data to help organizations work more productively safely. You know, the gamut talk about data and the importance of data and what you guys were doing there specifically, >>Yeah, that's a great question. So that is fundamental to everything that we're doing in the workplace arena, right? So from a technology perspective that provides us with the wherewithal to be able to make all the changes that we want to make happen for the people in the process side of things. So the journey that we've been on this past year is a very interesting one. Let me share with the audience a little bit of what's been going on on the ground with our customers. Um, what's what's been happening in the field? So when the when Kobe 19 hit right, a lot of our customers were subjected to these shutdown, which were very pervasive, and they had to stop their operations. In many cases, they had to send their employees home. So at that point, HB stepped in the point. Next organization stepped in and helped these customers set up remote work out options, which allowed them to keep their businesses going while they handle these shutdowns. Fast forward. Six months and the shutdown. We're starting to get lifted and our customers were coming back to us and saying to us that Hey, we would now like to get a least a portion off our workforce back to the normal place of work. But we're concerned that if we do that, it's gonna jeopardize their safety because off the infection concerned that were there. So what we did was that we built a cities or five solutions using various types of video analytics and data analysis analysis technologies that allowed these customers to make that move. So these five solutions, uh, let me walk, walk our customers and our clients and audience through those. The first two of these solutions are touchless entry and fever detection. So this is the access control off your premise, right? So to make sure that whoever is entering the building that's in a safe manner and any infection concerned, we stop it at the very get go once the employees inside the workplace, the next thing that we have is a set of two solutions. What one is social distance tracing and tracking, and the other one is workplace alerting. What these two solutions do is that they use video analytics and data technology is to figure out if there is a concern with employees adhering to the various guidelines that are in place on alerting the employees and the employers if there is any infringement happening which could risk overall environment. Finally, we realized right that irrespective off how much technology and process we put in place. Not everybody will be able to come into the normal place of work. So what we have done is that the first solution that we have is augmented reality and visual remote guidance. This solution uses a our technologies allow. People were on site to take advantage of the expertise that resides offsite to undertake complex task task, which could be as complex as overhauling a machine on ah factory floor using augmented reality where somebody off site who's an expert in that machine is helping somebody on site data has become central to a lot of the things that we do. But as I said, technology is one aspect of things. So ultimately the people process technology continuum has to come together to make these solutions real for our customers. >>Thank you, Arwa. We just have just about 30 seconds left and I wonder if you could close on. We're talking about cloud hybrid. Uh, everybody's talking about hybrid. We're talking about the hybrid workplace. What do you see for the for the future over the next 2345 years? >>Absolutely. And I think you're right, Dave. It is, ah, hybrid world. It's a multi cloud world. Ultimately, what our customers want is the choice and the flexibility to bring in the capabilities that drive the business outcomes that they need to support. And that has multiple dimensions, right? It's making sure that they are minimizing their egress costs, right. And many of our on Prem solutions do give them that flexibility. It is the paper use economics that we talked about. It is about our collective capability as an ecosystem to come together. You know, with Citrix and NVIDIA with R s I partner we pro and the rich heritage of HP es services as well as hardware to bring together these solutions that are fully managed on behalf of our customers so that they can focus their staff their i t capabilities on the products and services they need to deliver to their customers. >>Awesome. Guys, I wish we had more time. We got to go day volonte for the cube. Keep it right there. Lots of great more content coming your way. >>Yeah,

>>Hello. Everyone were here it the Data Cloud summit and we had a real treat for you. I call it the CMO Power Panel. We're gonna explore how data is transforming marketing, branding and promotion, and with me, a three phenomenal marketing pros and chief marketing officers. Denise Person is the CMO Snowflakes Kat Holden of Thought spot and Laura Langdon of Whip pro Folks. Great to see you. Thanks so much for coming on the Cube. >>Great to be with you, David. >>Awesome. Denise, let's let's start with you. I want to talk about the role and the changing role of the CMO. It's changed a lot, you know, sports, of course, with all this data, but I wonder what you're experiencing and can you share us share with us? Why marketing, especially, is being impacted by data? >>Well, data is really what has helped us marketers turn ourselves into revenue drivers instead instead of call centers, and that's definitely a much better place to be. We come today measure things that were never possible before. What a person most excited about is the rial time access to data we have today. In the past, we used to get stale reports, you know, weeks after a marketing program was over. Today we get data in real time as our campaigns are up and running. And this is really what enables us to make those riel time adjustments to our investments in real time. And that is really have a profound impact on the results were having. And also today, you know, more than ever, adaptability is truly the superpower or marketing today and day. That's really what allows us to adapt to our customers preferences in real time. And that's really critical at this time. >>That's interesting what you say because, you know, in tough times used to be okay. Sales and engineering put a brick wall around those and you know the name it. Marketing, Say Okay, cut. But now it's like you go to marketing and say, Okay, what's the data say? How do we have to pivot and Scott? I wonder what of data and cloud really brought to the modern marketer that you might not have had before this modern era? >>Well, it Z this era. I don't think there's ever been a better time to be a marketer than there is right now, and The primary reason is that we have access to data and insights like we've never had, and I'm not exaggerating. When I say that I have 100 times more access to data, then I had a decade. It's just phenomenal when you look at the power of cloud search, ai These new consumer experiences for analytics. We can do things in seconds that used to take days and so it's B comments did he said. Ah, superpower for us toe. Have access to so much data and it's, you know, Kobe has been hard. Ah, lot of our marketing teams who've never worked harder, making this pivot from the physical world to the virtual world. But there, you know, at least we're working, and three other part of it is that digital she's created this phenomenal opportunity for us because the beauty of digital and digital transformation is that everything now is trackable, which makes it measurable and means that we can actually get insights that we can act on in a smarter way. And you know, it's worth giving an example. If you just look at this show right, like this event that we're doing in a physical world, all of you watching at home, you'd be in front of us in a room and we'd be able to know if you're in the room, right? We tracking the scanners when you walked in. But that's basically it. At that point, we don't really get a good sense for how much you like what we're saying. Uh, maybe you filled out a survey, but only 5 to 10% of people ever do that in the digital world. We know how long you stick around, and as a result, like it's easy people could just with the click, you know, change the channel. And so the bar for content has gone way up as we do these events. But we know how long people are sticking around. And that's what's so special about it. You know Denise and her team as the host of this show, they're going to know how long people watch this segment and that knowing is powerful. I mean, it's simple as using a product like thought spot. You could just ask a question. How many you know, what's the average view? Time by session and boom and sharp pops up. You're gonna know what's working, what's not. And that's something that you could take and act on in the future. And that's what our That's what customers were doing. So you know, snowflake in the spot that we share a customer with Lulu and they're tracking programs. So what people are watching at home, how long they're watching what they're watching next, and they're able to do that in a super granular way and improve their content as a result. And that's the power of this new world we live in. Uh, that's made the cloud and data so accessible to folks like us. >>Well, thank you for that. And I want to come back to that notion to understand how you're bringing data into your marketing office. But I wanna bring Laura and Laura were pro You guys partner with a lot of brands, a lot of companies around the world. I mean, thousands of partners, obviously snowflake and thought spot are, too. How are you using data to optimize these co marketing relationships? You know specifically, what are the trends that you're seeing around around things like customer experience? >>So, you know, we used data for all of our marketing decisions our own as well as with our partners. And I think what's really been interesting about partner marketing data is we can we can feed that back to our sales team, right? So it's very directional for them as well in their efforts moving forward. So I think that's a place where specifically to partners, it's really powerful. We can also use our collective data to go out to customers to better effect. And then, you know, regarding these trends, we just did a survey on the state of the intelligent enterprise. We we interviewed 300 companies, US and UK, and there were three Interesting. I thought statistics relevant to this, um, Onley 22% of the companies that we interviewed felt that their marketing was where it needed to be from an automation standpoint. So lots of room for us to grow right. Lots of space for us to play. And 61% of them believed that it was critical that they implement this technology to become a more intelligent enterprise. But when they ranked readiness by function, marketing came in six right, so H R R and D finance were all ahead of marketing. It was followed by sales, you know, And then the final data point that I think was interesting was 40% of those agreed that while the technology was the most important thing, that thought leadership was critical, you know? And I think that's where marketers really could bring. You know, our tried and true experience to bear and merging with this technology. >>Great. Thank you. So so did he say I've been getting the Kool Aid injection this week around Data Cloud have been pushing people, But now that I have the CMO in front of me, I wanna ask about the data cloud and what it means specifically for the customers. And what are some of the learnings? Maybe that you've experienced that can support some of the things that that Laura and Scott were just discussing. >>Yeah. Scott said before, right, he had 100 times more data than he ever has before. And that's again, if you look at all the companies we talked to around the world, it's not about the amount of data that they have. That is the problem is the ability to access that data that data for most companies is trapped across Silas across the organization. It's It's in data applications, systems of records. Some of that data sits with your partners that you want access, and that's really what the data clouds camps in. Data Cloud is really mobilizing that data for you. It brings all that data together for you in one place so you can finally access that data and really provide ubiquitous access to that data to everyone in your organization that needs it and can truly unlock the value off that data. And from a marketing perspective, I mean, we are responsible for the customer experience, you know, we provide to our customers. And if you have access toe all the data on your customers, that's when you have that customer 3 60 that we've all been talking about for so many years. And if you have all that data, you can truly, you know, look at their, you know, buying behaviors, put all those dots together and create those exceptional customer experiences. You can do things such as the retailers do in terms of personal decision, for instance, rights and those are the type of experiences in our customers are expecting today. They are expecting a 100% personalized experience for them all the time. And if you don't have all the data, you can't really put those experiences together at scale. And that is really where the data cloud comes in again. The data cloud is not only about mobilizing your own data within your enterprise. It's also about having access to data from your partners or extending access to your own data in a secure way to your partners within your ecosystems. >>Yeah, So I'm glad you mentioned a couple of things. I've been writing about this a lot, and particularly the 3 60 that we would dying for but haven't really been able to tap. I didn't call it the Data Cloud. I don't have a marketing gene. I had another sort of boring name for it, but I think there's, you know, similar vectors there. So I appreciate that. Scott, I wanna come back to this notion of building data DNA in your marketing, you know, fluency on and how you put data at the core of your marketing ops. I've been working with a lot of folks in banking and manufacturing and other industries that air that are struggling to do this. How are you doing it? What are some of the challenges that you can share and maybe some advice for your peers out there? >>Yeah, sure, it's, um Well, you brought up this concept of data fluency and it zone important one. And there's been a lot of talking industry about data literacy and being able to read data. But I think it's more important to be able to speak data to be fluent. And as marketers, we're all storytellers. And when you combine data with storytelling, magic happens. And so getting a data fluency is a great goal for us toe have for all of the people in our companies. And to get to that end, I think one of the things that's happening is that people are hiring wrong and they're thinking about it. They're making some mistakes. And so a couple of things come to mind when, especially when I look at marketing teams that I'm familiar with, they're hiring a lot of data analysts and data scientists, and those folks are amazing and every team needs them. Uh, but if you go to big on that, you do yourself a disservice. The second key thing is that you're basically giving your front lines, focus your marketing managers or people on the front lines. An excuse not to get involved data. And I think that's a big mistake because it used to be really hard. But with the technologies available to us now, these new consumer like experiences for Data Analytics, anybody can do it. And so we as leaders have to encourage them to do it. And I'll give you just a you know, an example. You know, I've got about 32 people on my marketing team, and I don't have any data analysts on my team across our entire company. We have a couple of analysts and a couple of data engineers, and what's happening is the world is changing where those folks, their enablers, they architect the system, they bring in the different status forces they use. Technologies like snowflake has been so great at making it easier for people. The folks technology together, and they get data out of it quickly. But they're pulling it together, and then we'll simple things like, Hey, I just want to see this weekly instead of monthly. You don't need to waste your expensive data science talent. Gartner puts a stand out there that 50% of data scientists are doing basic visualization work. That's not a good use of their time. You The products are >>easy >>enough now that everyday marketing managers could do that. And when you have a marketing manager come to you and say, You know, I just figured out this this campaign, which looks great on the surface, is doing poorly. From our perspective, that's a magic moment. And so we all need to coach our teams to get there. And I would say, you know, lead by example, give them an opportunity Thio access data and turn it into a story that's really powerful. And then, lastly, praised people who do it, use it as something to celebrate inside our companies is a great way to kind of get this initiative. >>E love it. You're talking about democratizing data, making it self service. People feel ownership, you know, Laura did. He starts talking about the ecosystem, and you're kind of the ecosystem pro here. How does the ecosystem help marketers succeed? Maybe you could talk about the power of many versus the resource of of one. >>Sure, you know, I think it's a it's a game changer and it will continue to be. And I think it's really the next level for marketers to harness this. This power that's out there and use it. You know, it's something that's important to us. But it's also something we're starting to see our customers demand, you know, we went from a one size fits all solution, Thio. They want to bring the best in class to their organization. We all need to be really agile and flexible right now. And I think this ecosystem allows that, you know, you think about the power of a snow plate snowflake mining data for you, and then a thought spot really giving you the dashboard toe, have what you want. And then, of course, um, implementation partner like a whip Roh coming in and really being able to plug in whatever else you need, um, to deliver. And, uh, I think it's really super powerful. And I think it gives us, you know, it just gives us so much to play with. And so much room to grow is market. >>Thank you. Did he say why don't you bring us home? We're almost out of time here, but marketing, art, science both. What do you thoughts? >>Definite? Both. I think that's exciting. Part about marketing. It is a balancing act between art and science. Clearly, it's problem or science today than it used to be. But the art part is really about inspiring change. It's about changing people's people's behavior and challenging the status quo, right? That's the art part. The science part. That's about making the right decisions all the time, right? Making sure we are truly investing in what's gonna drive revenue for us. >>Guys, thanks so much for coming on the Cube. Great discussion. Really appreciate it. Thank you for watching everybody. We're here at the data clouds summit. A lot of great content, so keep it right there. We'll be right back right after this short break.

(bright music) >> Announcer: From around the globe, it's theCUBE with digital coverage of Dell Technologies World Digital Experience. Brought to you by Dell Technologies. >> Hello, and welcome back to theCUBE's virtual coverage of Dell Technologies World Digital Experience. I'm John Furrier, your host of theCUBE here for this interview. We're not face to face this year, we're remote because of the pandemic. We've got a great guest, CUBE alumni, John Roese who's the Global Chief Technology Officer at Dell Technologies. John, great to see you. Thank you for remoting in from New Hampshire. Thanks for your time and thanks for coming on. >> Oh, glad to be here. Glad to be here from New Hampshire. The travel is a lot easier this way so-- >> It's been an interesting time. What a year it's been with the pandemic, the good, bad, and the ugly has been playing out. But if you look at the role of technology, the big theme this year at Dell Technologies World is the digital transformation acceleration. Everyone is kind of talking about that, but when you unpack the technology side of it, you're seeing a technology enablement theme that is just unprecedented from an acceleration standpoint. COVID has forced people to look at things that they never had to look at before. Disruption to business models and business systems like working at home. (Furrier laughs) Who would have forecasted that kind of disruption. Workloads changing, workforces working differently with in the mid of things. So an absolute exposure to the core issues and challenges that need to be worked on and double down on. And some cases, projects that might not have been as a priority. So you have all of this going on, customers really trying to double down on the things that are working, the things they need to fix, so they can come out of the pandemic with a growth strategy with modern apps, with cloud and hybrid and multicloud. This has been a huge forcing function. I'd love to get your first reaction to that big wave. >> Yeah, no, no, I think as a technologist, sometimes you can see the future maybe a little clearer than the business people can. Because there's one thing about technology, it either is, or it isn't. Either is code or hardware and real or it's marketing. And we knew the technology evolution was occurring, we knew the multicloud world was real, we knew that machine intelligence was real. And we've been working on this for maybe decades. But prior to COVID, many of these areas were still considered risky or speculative. And people couldn't quite grok exactly why they wanted a machine doing work on their behalf or why they might want an AI to be a participant in their collaboration sessions or why they might want an autonomous vehicle at all. And we were talking about how many people autonomous vehicles that were going to kill as opposed to how many that we're going to help. Then we had COVID. And suddenly we realized that the fragility of our physical world and the need for digital is much higher. And so it's actually opened up an enormous accelerant on people's willingness to embrace new technologies. And so whether it's a predictable acceleration of machine intelligence or autonomous systems, or this realization that the cloud world is actually more than one answer, there's multiple clouds working together. Because if you try to do a digital transformation acceleration, you realize that it's not one problem. It's many, many problems all working together, and then you discover that, hey, some of these can be solved with cloud one and some can be solving with cloud two, and some of them you want to do in your own infrastructure, in a private cloud, and some might belong at the edge. And then suddenly you come to this conclusion that, hey, having strategy has to deal with this system as a system. And so across the board, COVID has been an interesting catalyst to get people to really think practically about the technology available to them and how they might be able to take advantage of it quicker. And that's a mixed blessing for us technologists because they want things sooner, and that means we have to do more engineering. But at the same time, open-minded consumers of technology are very helpful in digital transformations. >> Well, I want to unpack that rethinking with COVID and post COVID. I mean, everything is going to come down to before COVID and after COVID world. I think it's going to be the demarcation that's going to be looked at historically. Before we get into that though, I want to get your thoughts on some of the key pillars of these transformational technologies in play today. Last year at Dell World, when we were physically face to face, we were laying out on theCUBE and in our analysis, the Dell Technologies has got an end to end view. You saw a little bit at VMworld this year, the Project Monterey, is looking much more systematically across the board. You mentioned systems as consequences. The reaction of changes. But lay out for us the key areas, the key pillars of the transformational technologies that customers need to look at now to drive the digital path. >> Yeah, we cast a very wide net. We look at literally thousands of technologies, we organize them and we try to understand and predict which ones are going to matter. And it turns out that over the last couple of years, we figured out there's really six, what I'll call expanding technology areas that are actually probably likely to be necessary for almost any digital transformation. And they aren't exactly what people have been doing historically. So in no particular order, and they may sound obvious, but when you think about your future, it's very likely all six of these are going to touch you. The first is, the obvious one of being able to develop and deliver a multicloud. The cloud journey is by no means done. We are at like the second inning of a nine inning game, maybe even earlier. We have barely created the multiple cloud world, much less the true multicloud world, and then really exploiting and automating has work to be done. But that's a strategic area for us and everybody to navigate forward. In parallel to that, what we realize is that multiple cloud is no longer just present in data centers and public clouds, it's actually existing in the real world. So this idea of edge, the reconstituting of IT out in the real world to deliver the real time behavior necessary to actually serve what we predict will be about 70% of the world's data that will happen outside of data centers. The third is 5G. And that's a very specific technology, and I have a long telco background. I was the CTO of one of the largest telecom companies in the world and I was involved in 2G, 3G and 4G. (Furrier chuckles) 5G is not another G. It is not just faster 4G. It does that, but with things like massive machine type communication with having a million sensorized devices in a kilometer or ultra reliable, low latency communication. The ability to get preferential services to critical streams of data across the infrastructure, mobile edge compute, putting the edge IT out into the cellular environment. And the fact that it's built in the cloud and IT era. So it's programmable, software defined. 5G is going to go from being an outside of the IT discussion to being the fabric inside the IT discussion. And so I will bet that anybody who has people in the real world and that they're trying to deliver a digital experience, will have to take advantage of the capabilities of 5G to do it right. But super strategic important area for Dell and for our industry. Continuing on, we have the data world, the data management world. It's funny, we've been doing data as an industry for a very long time, but the world we were in was the data at rest world, databases, data lakes, traditional applications. And that's great. It still matters, but this new world of data in motion is beginning. And what that means is the data is now moving into pipelines. We're not moving it somewhere and then figuring it out, we're figuring it out as the data flows across this multicloud environment. And that requires an entirely different tool, chain, architecture and infrastructure. But it's incredibly important because it's actually the thing that powers most digital transformation if they're real time. In parallel to that, number five on the list is AI and machine learning. And we have a controversial view on this. We don't view AI as purely a technology. It clearly is a technology, but what we really think customers should think about it as is as a new class of user. Because AIs are actually some of the most aggressive producers and consumers of data and consumers of IT infrastructure. We actually estimate that within the next four or five years, the majority of IT capacity in an enterprise environment will actually be consumed at the behest of the machine learning algorithm or an AI system than a traditional application or person. And all you have to do is do one AI project to understand that I'm correct, because they are just massive demand drivers for your infrastructure, but they have massive return on that demand. They give you things you can't do without them. And then last on the list is this area of security. And to be candid, we have really messed up this area as an industry. We have a security product for every problem, we have proliferation of security technologies. And to make matters worse, we now operate most enterprises on the assumption the bad guys are already inside and we're doing things to prevent them from causing harm. Now, if that's all it is, we really lost this one. So we have an obligation to reverse this trend, to start moving back to embedding the security and the infrastructure with intrinsic security, with zero trust models, with things like SASSY, which is basically creating new models of the edge security paradigm to be more agile and software defined. But most importantly, we have to pull it all together and say, "You know what we're really measuring is the trustworthiness "of the systems we work with, "not the individual components." So this elevation of security to trust is going to be a big journey for all of us. And every one of those six are individual areas, but when you combine them, they actually describe the foundation of a digital transformation. And so it's important for people to be aware of them, it's important for companies like Dell to be very active in all of them, because ultimately what you have today, plus those six properly executed, is the digital transformation outcome that most people are heading towards. >> You just packed it all six pillars into one soundbite. That was awesome. Great insight there. One of the things that's interesting, you mentioned AI. I love that piece around AI being a consumer. They are a consumer of data, they're also a consumer of what used to be handled by either systems or humans. That's interesting. 5G is another one. Pat Gelsinger has said at VMworld that 5G, and when I interviewed him he said 5G is a business app, not a consumer app. Yet, if you look at the recent iPhone announcement by Apple, iPhone 12, and iPhone 12 Pro, 5G is at the center of that announcement. But they're taking it from a different perspective. That's a real world application. They've got the watch, they have new chips in their devices, huge advantage. It's not just bandwidth. And remember the original iPhone launch with 3G if you remember. That made the iPhone. Some are saying if it didn't have the 3G or 2G and 3G, I think it was 3G in the first iPhone. 3G, it would have not been as successful. So again, Apple is endorsing 5G. Gelsinger talks about it as a business app. Double down on that, because I think 5G will highlight some of the COVID issues because people are working at home. They're on the go. They want to do video conferencing. Maybe they want to do this programmable. Unpack the importance of 5G as an enabler and as an IT component. >> Yeah. As I mentioned, 5G isn't just about enhanced mobile broadband which is faster YouTube. It's about much more than that. And because of that combination of technologies, it becomes the connective tissue for almost every digital transformation. So our view by the way, just to give you the Dell official position, we actually view that the 5G or the telecom industry is going through three phases around 5G. The first phase has already happened. It was an early deployment of 5G using traditional technology. It was just 5G as an extension of the 4G environment. That's great, it's out there. There's a phase that we're in right now, which I call the geopolitical phase, where all of a sudden, everybody from companies to countries to industries have realized this is really important. And we have to figure out how to make sure we have a secure source of supply that is based on the best technology. And that has created an interest by people like Dell and VMware and Microsoft, and many other companies to say, "Wait a minute. "This isn't just a telecom thing. "This is, as Pat said a business system. "This is part of the core of all digital." And so that's pulled people like Dell and others more aggressively into the telecom world in this middle phase. But what really is happening is the third phase. And the third phase is a recasting of the architecture of telecom to make it much more like the cloud and IT world. To separate hardware from software, to implemented software defined principles, to putting machine interfaces, to treat it like a cloud and IT system architecturally. And that's where things like OpenRAN, integrated open networks, and these new initiatives are coming into play. All of that from Dell perspective is fantastic because what it says is the telecom world is heading towards companies like us. And so, as you may know, we set up a brand new telecom business at scale up here to our other businesses this year. We already are doing billions of dollars in telecom, but now we believe we should be playing a meaningful tier-one role in this modern telecom ecosystem. It will be a team sport. There's lots of other players we have to work with. But because of the breadth of applications of 5G. And whether it's again, an iPhone with 5G is great to do YouTube, but it's incredibly powerful if you run your business applications on there, and what you want to actually deliver is an immersive augmented experience. So without 5G, it will be very hard to do that. So it becomes a new and improved client. We announced a Latitude 9,000 Series, and we're one of the first to put out a 5G enabled laptop. In certain parts of the world, we're now starting to ship these. Well again, when you have access to millimeter wave and gigabit speed capacity, you can do some really interesting things on that device, more oriented towards what we call collaborative computing which the client device and the adjacent infrastructure have so much bandwidth between them, that they look like one system. And they can share the burden of augmented reality, of data processing, of AI processing all in the real time domain. Carry that a little further, and when we get into the areas like healthcare transformation or educational transformation. What we realize immediately is reach is everything. You want to have a premium broadband experience, and you need a better system to do that. But really the thing that has to happen is not just a Zoom call, but an immersive experience in which a combination of low bandwidth, always on sensors are able to send their data streams back. But also, if you want to have a more immersive experience to really exploit your health situation, being able to do it with holography and other tools, which require a lot more bandwidth is critical. So no matter where you go in a digital transformation in the real world that has real people and things out in the real world involved in it, the digital fabric for connectivity is critical. And you suddenly realize the current architecture's pre-5G aren't sufficient. And so 5G becomes this linchpin to basically make sure that the client and the cloud and the data center all have a framework that they can actually work together without, let's call it a buffering resistance between them called the network. Imagine if the network was an enabler, not an impediment. >> Yeah, I think you're on point here. I think this is really teases out to me the next-gen business transformation, digital transformation because if you think about what you just talked about, connective tissue, linchpin with 5G, data as a driver, multicloud, the six pillars you laid out, and you mentioned systems, connective tissue systems. I mean, you're basically talking tech under the hood like operating system mindset. These systems design are interesting. If you put the pieces together, you can create business value. Not so much speeds and feeds, business value. You mentioned telco cloud. I find that fascinating. I've been saying on theCUBE for years, and I think it's finally playing out. I want to get your reactions of this is, this rise of the specialty cloud. I called it tier-one on the power law kind of the second wave of cloud. Look at Snowflake. They went public. Biggest IPO in the history of the New York Stock Exchange of Wall Street, second to VMware. They built on Amazon. (Furrier laughs) Okay. You have the telco cloud, we have theCUBE cloud, we have the media cloud. So you're seeing businesses looking at the cloud as a business model opportunity, not just buying gear to run something faster, right? So you're getting at something here where it's real benefits are now materializing and are now visible. First of all, do you agree with that? I'm sure you do. I'd love to get your thoughts on that. And if you do, how do companies put this together? Because you need software, you got to have the power source with cloud. What's your reaction to that? >> Absolutely. I think, now obviously there are many clouds. We have some mega clouds out there and then we have lots of other specialty clouds. And by the way, sometimes you remember we view cloud as an operating model, an experience, a way to present an IT service. How it's implemented is less important than what it looks like to the user. Your example of Snowflake. I don't view Snowflake as AWS. I view Snowflake as a storage business. (Furrier chuckles) >> It's a business. >> It's a business cloud. I mean, they could lift it up and move it onto another cloud infrastructure and still be Snowflake. So, as we look forward, we do see more of the consumables that we're going to use and digital transformation appearing as these cloud services. Sometimes they're SaaS cloud, sometimes they're an infrastructure cloud, sometimes they're a private cloud. One of the most interesting ones though that we see that hasn't happened yet is the edge clouds that are going to form. Edge is different. It's in the real time domain, it's distributed. If you do it at scale, it might look like massive amounts of capacity, but it isn't infinite in one place. Public cloud is infinite capacity all in one place. An edge cloud is infinite capacity distributed across 50,000 points of presence at which each of them has a finite amount of capacity. And the other difference though, is that edge clouds tend to live in the real time domain. So 30 millisecond round trip latency. Well, the reason this one's exciting to me is that when you think about what happened at the software and business model innovation, when for instance public clouds and even co-location became more accessible, companies who had this idea that needed a very large capacity of infrastructure that could be consumed as a service suddenly came into existence. Salesforce.com go through the laundry list. But all of those examples were non-real time functions because the clouds they were built on were non-real time clouds if you take them in the end to end, in the system perspective. We know that there are going to be both from the telecom operators and from cloud providers and co-location providers, and even enterprises, a proliferation of infrastructure out in the real time domain called edges. And those are going to be organized and delivered as cloud services. They're going to be pools of flexible elastic capacity. What excites me is suddenly we're going to spawn a level of innovation, where people who had this great idea that they needed to access cloud light capacity, but they ran into the problem that the capacity was too far away from the time domain they needed to operate. And we've already seen some examples of this in AR and VR. Autonomous vehicles require a real time cloud near the car, which doesn't exist yet. When we think about things like smart cities and smart factories, they really need to have that cloud capacity in the time domain that matters if they want to be a real time control system. And so, I don't know exactly what the innovation is going to be, but when you see a new capability show up, in this case, it's inevitable that we're going to see pools of elastic, consumable capacity in the real time environment as edges start to form. It's going to spawn another innovation cycle that could be as big as what happened in the public cloud environment for non-real time. >> Well, I think that's a great point in time series. Databases for one would be one instant innovation. You mentioned data, data management, time is valuable to the latency and this maybe not viable after if you're a car, right? So you pass them. So again, all different concepts. And the one thing that, first of all, I agree with you on this whole cloud thing. A nice edge cloud is going to develop nicely. But the question there is it's going to be software defined, agreed. Security, data, you've got databases, you've got software operated. You mentioned security being broken, and security product for every problem. And you want to bake it in, intrinsic or whatever you call it these days. How do you get the security model? Because you've got access. Do you federate that? How do you build in security at that level? Whether it's a space satellite or a moving vehicle, the edge is the edge. So what's your thoughts on security as you're looking at this mobility, this agility is horizontally scalable distributed system. What's the security paradigm? >> Well the first thing, it has nothing to do with security, but impacts your security outcome in a meaningful way when you talk about the edge. And that is, we have got to stop getting confused that an edge is a single monolithic thing. And we have got to start understanding that an edge is actually a combination of two things. It is a platform that will provide the capacity and a workload that will do the job, the code. And today, what we find is many people are advocating for edges are actually delivering an end to end stack that includes bespoke hardware, its infrastructure, and the workloads and capabilities. If that happens, we end up with 1,000 black boxes that all do one thing, which doesn't make any sense out in the real world. So the minute you shift to what the edge is really going to be, which is a combination of edge platforms and edge workloads, you start your journey towards a better security model. First thing that happens is you can secure and make a high integrity the edge platform. You can make sure that that platform has a hardware to trust, that it operates potentially in a zero trust model, that it has survivability and resiliency, but it doesn't really care what's running on it as much as it has to be stable. Now if you get that one right, now at least you have a stable platform between your public and private environments and the edge. At the workload level though, now you have to think about, well, edge workloads actually should not be bloated. They should not be extremely large scale because there's not enough capacity at the edge. So concepts like SASSY is a good example, which is one of the analyst firms that coined that term. But I like the concept, which is, hey, what if at the edge you're delivering the workload, but the workload is protected by a bunch of cloud-oriented security services that effectively are presented as part of the service chain? So you don't have to have your own firewall built into every workload because you're in an edge architecture, you can use virtual firewalling that's coming to you as a software service, or you can use the SDN, the service chain it into the networking path, and then you can provide deep packet inspection and other services. It all goes back to this idea that, when you deal with the edge, first and foremost, you have to have a reliable stable platform to guarantee a robust foundation. And that is an infrastructure security problem. But then you have to basically deal with the security problems of the workload in a different way than you do it in a data center. In a data center, you have infinite computing. You can put all kinds of appendages on your code, and it's fine because there's just more compute next to you. In the edge, we have to keep the code pure. It has to be an analytics engine or an AI engine for systems control in a factory. And the security services actually have to be a function of the end to end path. More likely delivered as software services slightly upstream. That architectural shift is not something people have figured out yet. But if we get it right, now we actually have a modern, zero trust distributed, software defined, service changeable, dynamic security architecture, which is a much better approach to an intrinsic security than trying to just hard-code the security into the workload and tie it to the platform which never has worked. So we're going to have to have a pretty big rethink to get through this. But for me, it's pretty clear what we have to do. >> Now I'd say that's good observation. Great insight. I'll just double down and ask a followup on that. I get that. I see where you're going with that software defined, software operated service. I love the SASSY concept. We've covered it. But the edge is still purpose-built devices. I mean, we've talked about an iPhone, and you're talking about a watch, you're talking about a space module, whatever it is at the edge on a tower, it could be a radio. I mean, whatever it is, you seem to have purpose-built hardware. You mentioned this root of trust. That'll kind of never kind of go away. You're going to have that. What's your thoughts on that as someone who realizes I got to harden the edge, at least from a hardware standpoint, but I want to be enabled for self-defined. I don't want to have a product be purpose-built and then be obsolete in a year. Because that's again the challenge of supply chain management, building hardware. What's your thoughts on that? >> Yeah. Our edge strategy, we double click a little bit is different than the strategy to build for a data center. We want consistency between them, but there's actually five areas of edge that actually are specific to it. The first is the hardware platform itself. Edge hardware platforms are different than the platforms you put in data centers, whether it be a client or the infrastructure underneath it. And so we're already building hardened devices and devices that are optimized for power and cooling and space constraints in that environment. The second is the runtime on that system is likely to be different. Today we use the V Cloud Foundation where that works very well, but as you get smaller and smaller and further away, you have to miniaturize and reduce the footprint. The control plane, we would like to make that consistent. We are using Tanzu and Dell Technologies Cloud Platform to extend out to the edge. And we think that having a consistent control plan is important, but the way you adapt something like Tanzu from the edge is different because it's in a different place. The fourth is life cycle, which is really about how you secure, how you deploy, how you deal with day two operations. There's no IT person out at the edge, so you're not in a data center. So you have to automate those systems and deal with them in a different way. And then lastly, the way you package an edge solution and deliver it is much different than the way you build a data center. You actually don't want to deal with those four things I just described as individual snowflakes. You want them packaged and delivered as an outcome. And that's why more and more of the edge platform offerings are really cloudlets or they're a platform that you can use to extend your IT capacity without having to think about Kubernetes versus VMs versus other things. It's just part of the infrastructure. So all of that tells us that edge is different enough, that the way you designed for it, the way you implement it, and even the life cycle, it has to take into account that it's not in a data center. The trick is to then turn that into an extended multicloud where the control plane is consistent, or when you push code into production with Kubernetes, you can choose to land that container in a data center or push it out to the edge. So you have both a system consistency goal, but also the specialization of the edge environment. Everything from hardware, to control plane, to lifecycle, that's the reality of how these things have to be built. >> That's a great point. It's a systems architecture, whether you're looking at from the bottoms up component level to top down kind of policy and or software defined. So great insight. I wish we had more time. I'd love to get you back and talk about data. We were talking before you came on camera about data. But quickly before we go, your thoughts on AI and the consequences of AI. AI is a consumer. I love that insight. Totally agree. Certainly it's an application. Technology is kind of horizontal. It can be vertically specialized with data. What's your thoughts on how AI can be better for society and some of the unintended consequences that we manage that. >> Yeah, I'm an optimist. I actually, we've worked with enough AI systems for long enough to see the benefit. Every one of Dell's products today has machine intelligence inside of it. So we can exceed the potential of its hardware and software without it. It's a very powerful tool. And it does things that human beings just simply can't do. I truly believe that it's the catalyst for the next wave of business process functionality, of new innovation. So it's definitely not something to stay away from. That being said, we don't know exactly how it can go wrong. And we know that there are examples where corrupted or bad bias data could influence it and have a bad outcome. And there are an infinite set of problems to go solve with AI, but there are ones that are a little dangerous to go pursue if you're not sure. And so our advice to customers today is, look, you do not need to build The Terminator to get advantage from AI. You can do something much simpler. In fact, in most enterprise context, we believe that the best path is go look at your existing business processes, where there is a decision that's made by a human being, and it's an inefficient decision. And if you can locate those points where a supply chain decision or an engineering decision or a testing decision is done by human beings poorly, and you can use machine intelligence to improve it by five or 10%, you will get a significant material impact on your business if you go after the right processes. At Dell we're doing a ton of AI and machine learning in our supply chain. Why is that important? Well, we happen to have the largest tech supply chain in the world. If we improve it by 1%, it's a gigantic impact on the company. And so our advice to people is you don't have to build man autonomous car. You don't have to build The Terminator. You can apply it much more tactically in spaces that are much safer. Even in the HR examples, we tell our HR people, "Hey, use it for things like performance management "and simplifying the processing of data. "Don't use it to hire a bot." That's a little dangerous right now. Because you might inadvertently introduce racism or sexism into that, and we still have some work to do there. So it's a very large surface area. Go where the safe areas are. It'll keep you busy for the next several years, improving your business in dramatic ways. And as we improve the technology for bias correction and management of AI systems and fault tolerance and simplicity, then go after the hard one. So this is a great one. Go after the easy stuff. You'll get a big benefit and you won't take the risk. >> You get the low hanging fruit learn, iterate through it. I'm glad you guys are using machine learning and AI in the supply chain. Make sure it's secure, big issue. I know you guys were on top of it and have a great operation there. John, great to have you on. John Roese, the Global Chief Technology Officer at Dell Technologies. Great to have you on. Take a minute to close out the last minute here. What's the most important story from Dell Technologies World this year? I know it's virtual. It's not face to face. But beyond that, what's the big takeaway in your mind, if you could share one point, what would it be for the folks watching? >> Yeah, I think the biggest point is something we talked about, which is we are in a period of digital transformation acceleration. COVID is bad, but it woke us up to the possibilities and the need for digital transformation. And so if you were on the fence or if you're moving slowly and now you have an opportunity to move fast. However, moving fast is hard if you try to do it by yourself. And so we've structured Dell, we've the six big areas we're focused on. They only have one purpose, it's to build the modern infrastructure platforms to enable digital transformation to happen faster. And my advice to people is, great. You're moving faster. Pick your partners well. Choose the people that you want to go on the journey with. And we think we're well positioned for that. And you will have much better progress if you take a broad view of the technology ecosystem and you've lightened up the appropriate partnerships with the people that can help you get there. And the outcome is a successful digital leader just is going to handle things like COVID and ease disruption better than a digital laggard. And we now have the data to prove that. So it's all about digital acceleration is the punchline. >> Well great to have you on. Great segment, great insight. And thank you for sharing the six pillars and the conversation. Super relevant on what's going on to create new business value, new opportunities for businesses and society. I'm John Furrier with theCUBE. Thanks for watching. (bright music)

>> Narrator: From around the globe its the Cube, covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody this is Dave Vellante and welcome back to the Cube's coverage of HPE's Discover 2020 the Virtual Experience the Cube. The Cube has been virtualized We like to say Am very happy to welcome in Neil McDonalds, he's the General Manager for Compute at HPE. Great to see you again Neil, wish we were face to face, but this will have to do. >> Very well, it's great to see you Dave. Next time we'll do this face to face. >> Next time we have hopefully next year. We'll see how things are going, but I hope you're safe and your family's all good and I say it's good to talk to you, you know we've talked before many times you know, it's interesting just to know the whole parlance in our industry is changing even you know Compute in your title, and no longer do we think about it as just sort of servers or a box you guys are moving to this as a service notion, really it's kind of fundamental or, poignant that we see this really entering this next decade. It's not going to be the same as last decade, is it? >> No, I think our customers are increasingly looking at delivering outcomes to their customers in their lines of business, and Compute can take many forms to do that and it's exciting to see the evolution and the technologies that we're delivering and the consumption models that our customers are increasingly taking advantage of such as GreenLake. >> Yes so Antonio obviously in his Keynote made a big deal in housing previous Keynotes about GreenLake, a lot of themes on you know, the cloud economy and as a service, I wonder if you could share with our audience, you know what are the critical aspects that we should know really around GreenLake? >> Well, GreenLake is growing tremendously for us we have around a thousand customers, delivering infrastructure through the GreenLake offerings and that's backed by 5,000 people in the company around the world who are tuning an optimizing and taking care of that infrastructure for those customers. There's billions of dollars of total contract value under GreenLake right now, and it's accelerating in the current climate because really what GreenLake is all about is flexibility. The flexibility to scale up, to scale down, the ability to pay as you use the infrastructure, which in the current environment, is incredibly helpful for conserving cash and boosting both operational flexibility with the technology, but also financial flexibility, in our customer's operations. The other big advantage of course at GreenLake is it frees up talent most companies are in the world of challenges in freeing up their talent to work on really impactful business transformation initiatives, we've seen in the last couple of quarters, an even greater acceleration of digital transformation work for example and if all of your talent is tied up in managing the existing infrastructure, then that's a drain on your ability to transform and in some industries even survive right now, so GreenLake can help with all of those elements and, with all of the pressure from COVID, it's actually becoming even more consumed, by more and more customers around the world it's- >> Yeah right I mean that definitely ties into the whole as a service conversation as well I mean to your point, you know, digital transformation you know, the last couple of years has really accelerated, but I feel yeah, I feel like in the last 90 days, it's accelerated more than it has in the last three years, because if you weren't digital, you really had no way to do business and as a service has really played into that so I wonder if you could talk about yours as a service, you know, posture and thinking. >> Well you're absolutely right Dave organizations that had not already embarked on a digital transformation, have rapidly learned in our current situation that it's not an optional activity. Those that were already on that path are having to move faster, and those that weren't are having to develop those strategies very rapidly in order to transform their business and to survive. And the really new thing about GreenLake and the other service offerings that we provide in that context is how it can accelerate the deployment. Many companies for example, have had to deal with VDI deployments in order to enable many more of their workforce to be productive when they can't be in the office or in the facility and a solution like GreenLake can really help enable very rapid deployment and build up but not just VDI many other workloads in high performance Compute or in SAP HANA for example, are all areas that we're bringing value to customers through that kind of as a service offering. Yeah, a couple of examples Nokia software is using GreenLake to accelerate their research and development as they drive the leadership and the 5G revolution, and they're doing that at a fraction of the cost of the public cloud. We've got Zanotti, which has built a private cloud for artificial intelligence and HPC is being used to develop the next generation of autonomous software for cars. And finally, we've got also Portion from Arctic who have built a fully managed hybrid cloud environment to accelerate all the application development without having to bear the traditional costs of an over-provisioned complex infrastructure. So all of our customers are relying on that because Compute and Innovation is just at the core of the digital transformations that everybody is embarked on as they modernize their businesses right now and it's exciting to be able to be part of that and to be able to do there, to help. >> So of course in the tech business innovation is the you know the main spring of growth and change, which is constant in our industry and I have a panel this week with Doctor Go talking about swarm learning in AI, and that's some organic innovation that HPE is doing, but as well, you've done some, M&A as well. Recently, you guys announced and we covered it a pretty major investment in Pensando Systems. I wonder if you could talk a little bit about what, that means to the Compute business specifically in, HPE customers generally. >> So that partnership with Pensando was really exciting, and it's great to see the momentum that its building in delivering value to our customers, at the end of the day we've been successful with Pensando in building that momentum in very highly regulated industries and the value that is really intrinsic to Pensando is the simplifying of the network architecture. Traditionally, when you would manage an enterprise network environment, you would create centralized devices for services like load balancing or firewalls and other security functionality and all the traffic in the data center would be going back and forth, tromboning across the infrastructure as you sought to secure your underlying Compute. The beauty of the Pensando technology is that we actually push that functionality all the way out to the edge at the server so whether those servers are in a data center, whether they're in a colocation facility, whether they're on the edge, we can deliver all of that security service that would traditionally be required in centralized expensive, complex, unique devices that were specific to each individual purpose, and essentially make that a software defined set of services running in each node of your infrastructure, which means that as you scale your infrastructure, you don't have a bottleneck. You're just scaling that security capability with the scaling of your computer infrastructure. It takes traffic off your core networks, which gives you some benefits there, but fundamentally it's about a much more scalable, responsive cost-efficient approach to managing the security of the traffic in your networks and securing the Compute end points within your infrastructure. And it's really exciting to see that being picked up, in financial services and healthcare, and other segments that have you know, very high standards, with respect to security and infrastructure management, which is a great complement to the technology from Pensando and the partnership that we have with Pensando and HPE. >> And it's compact too we should share with our audience it's basically a card, that you stick inside of a server correct Neil? >> That's exactly right. Pensando's PCIe card together with HPE servers, puts that security functionality in the server, exactly where your data is being processed and the power of that is several fold, it avoids the tromboning that we talked about back across the whole network every time you've got to go to a centralized security appliance, it eliminates those complex single purpose appliances from the infrastructure, and that of course means that the failure domain is much smaller cause your failure demands a single server, but it also means that as you scale your infrastructure, your security infrastructure scales with the servers. So you have a much simpler network architecture, and as I say, that's being delivered in environments with very high standards for security, which is a really a great endorsement of the Pensando technology and the partnership that HPE and Pensando will have in bringing that technology to market for our customers. >> So if I understand it correctly, the Pensando is qualified for Pro-Lite, Appollo and in Edgelines. My question is, so if I'm one of those customers today, what's in it for me? Are they sort of hopping on this for existing infrastructure, or is it part of, sort of new digital initiatives, I wonder if you could explain. >> So if you were looking to build out infrastructure for the future, then you would ask yourself, why would you continue to carry forward legacy architectures in your network with these very expensive custom appliances for each security function? Why not embrace a software defined approach that pushes that to the edge of your network whether the edge are in course or are actually out on the edge or in your data centers, you can have that security functionality embedded within your Compute infrastructure, taking advantage of Pensandos technologies. >> So obviously things have changed is specifically in the security space, people are talking about this work from home, and this remote access being a permanent or even a quasi-permanent situation. So I wonder if we could talk about the edge and specifically where Aruba fits in the edge, how Pensando compliments. What's HPE's vision with regard to how this evolves and maybe how it's been supercharged with the COVID pandemic. >> So we're very fortunate to have the Aruba intelligent edge technology in the HPE portfolio. And the power of that technology is its focus on the analysis of data and the development of solutions at the site of the data generated. Increasingly the data volumes are such that they're going to have to be dealt with at the edge and given that, you need to be building edge infrastructure that is capable enough and secure enough for that to be the case. And so we've got a great compliment between the, intelligent edge technology within the Aruba portfolio, with all of the incredible management capabilities that are in those platforms combined with technologies like Pensando and our HPE Compute platforms, bring the ability to build a very cohesive, secure, scalable infrastructure that tackles the challenges of having to do this computer at the edge, but still being able to do it in both a secure and easily managed way and that's the power of the combination of Aruba, HPE Compute and Pensando. >> Well, with the expanded threat surface with people working from home organizations are obviously very concerned about compliance, and being able to enforce consistent policies across this sort of new network, so I think what you're talking about is it's very important that you have a cohesive system from a security standpoint you're not just bolting on some solution at the tail end, your comments. >> Well security, always depends on all the links in the chain and one of the most critical links in the chain is the security of the actual Compute itself. And within the HPE compliant platforms, we've done a lot of work to build very differentiated and exclusive capability with our hardware, a Silicon Root of Trust, which is built directly into Silicon. And that enables us to ensure the integrity of the entire boot chain on the security of the platform, drones up in ways that can't be done with some of the other hardware approaches that are prevalent in the industry, and that's actually brought some benefit, in financial terms to our customers because of the certifications that are enabled in the, Cyber Catalyst designations that we've earned for the platforms. >> So we also know from listening to your announcements with Pensando just observing security in general, that this notion of micro-segmentation is very important being able to have increased granularity as opposed to kind of a blob, maybe you could explain why that's important you know, the so what behind micro-segmentation if you will. >> Well it's all about minimizing the threat perimeter on any given device and if you can minimize the vectors through which your infrastructure will interact on the network, then you can provide additional layers of security and that's the power of having your security functionality right down at the edge, because you can have a security processor sitting right in the server and providing great security of the node level you're no longer relying on the network management and getting all of that right and you also have much greater flexibility because you can easily in a software defined environment, push the policies that are relevant for the individual pieces of infrastructure in an automated policy driven way, rather than having to rely on someone in network security, getting the manual configuration of that infrastructure, correct to protect the individual notes. And if you take that kind of approach, and you embed that kind of technology in servers, which are fundamentally robust in terms of security because of the Silicon Root of Trust that we've embedded across our platform portfolio whether that's Pro-line or Synergy or BladeSystem or Edgeline, you get a tremendous combination, as a result of these technologies, and as I mentioned, the being Cyber Catalyst designation is a proof point of that. Last year there we're over 150 security products, put forward for the Sovereign Capitalist designation, and the only a handful were actually awarded I think 17, of which two were HPE Compute and Aruba. And the power of is that many organizations are not having to deal with insurance for Cybersecurity events. And the Catalyst designation can actually lead to lower premiums for the choice of the infrastructure that you've made to such as HPE Compute, has actually enabled you to have a lower cost of insuring your organization against cybersecurity issues, because infrastructure matters and the choice of infrastructure with the right innovation in it is a really critical choice for organizations moving forward in security and in so many other ways. >> Yeah, you mentioned a lot of things there software defined, that's going to enable automation and scale, you talked about the perimeter you know, the perimeter of the traditional moat around the castle that's gone the perimeter, there is no perimeter anymore, it's everywhere so that whole you know, weakest link in the chain and the chain of events. And then the other thing you talked about was the layers you know very important when you're talking to security practitioners you know, building layers in so all of this really is factoring in security in particular, is factoring into customer buying decisions. Isn't it? >> Well security is incredibly important for so many of our customers across many industries. And having the ability to meet those security needs head on is really critical. We've been very successful in leveraging these technologies for many customers in many different industries, you know, one example is we've recently won multiple deals with the Defense Intelligence Systems Agency, who you will imagine have very high standards for security, worth hundreds of millions of dollars of that infrastructure so there's a great endorsement, from the customer set who are taking advantage of these technologies and finding that they deliver great benefits for them in the operational security of their infrastructure. >> Yeah what if I could ask you a question on the edge. I mean, as somebody who is you know, with a company that is really at the heart of technology, and I'm sure you're constantly looking at new companies, M&A you know et cetera, you know inventing tech, but I want to ask you about the architectures for the edge and just in thinking about a lot of data at the edge, not all the data is going to come back to the data center or the cloud, there's going to be a lot of AI influencing going on in real time or near real time. Do you guys see different architectures emerging to support that edge? I mean from a Compute standpoint or is it going to be traditional architectures that support that. >> It's clearly an evolving architectural approach because for the longest time, infrastructure was built with some kind of hub you know, whether or not some data center or in the cloud, around all of the devices at the edge would be essentially calling home, so edge devices historically have been very focused on connectivity on acquisition of data, and then sending that data back for some kind of processing and action at some centralized location. And the reality is that given the amount of data being generated at the edge now given the capability even of the most modern networks, it's simply not possible to be moving those kinds of data volumes all the way back to some remote processing environment, and then communicating a decision for action all the way back up to the edge. First of all, the networks kind of handle the volume data's involved if every device in the world was doing that, and secondly, the latencies are too slow. They're not fast enough in order to be able to take the action needed at the edge. So that means that you have to countenance systems at the edge that are not actually storing data, that are not actually computing upon data, and in a lot of edge systems historically, they would evolve from very proprietary, very vertically integrated systems to Brax PC controller based systems with some form of IP connectivity back to, some central processing environment. And the reality is that if you build your infrastructure that way, you finish up with a very unmanageable fleet, you finish up with a very fragmented, disjointed infrastructure and our perspective is that companies that are going to be successful in the future have to think themselves as an edge to cloud approach. They have to be pursuing this in a way that views, the edge, the data center, and the cloud as part of an integrated continuum, which enables the movement of data when needed you heard about the swarm learning that you talked about with my colleague Doctor Go, where there's a balance of what is computed, where in the infrastructure, and so many other examples, but you need to be able to move Compute to where the data is, and you need to be able to do that efficiently with a unified approach to the architecture. And that's where assets like the HPE Data Fabric come into play, which enable that kind of unification across the different locations of equipment. It also means you need to think differently about the actual building blocks themselves, in a lot of edge environments, if you take a Classic 19 interact mode Compute device, that was originally designed for the data center it's simply not the right kind of infrastructure. So that's why we have offerings like the Edgeline portfolio and the HPE products there, because they're designed to operate in those environments with different environmentals than you find the data center with different interfaces to systems of action and systems of control, than you'd typically find in a data center environment yet still bringing many of the security benefits and the manageability benefits that we've talked about earlier in our conversation today Dave. So it's definitely going to be an evolving, a new architectural approach at the edge, and companies that are thoughtful about their choice of infrastructure, are going to be much more successful than those that take a more incremental approach, and we were excited to be there to help our customers on that journey. >> Yeah Neil it's a very exciting time I mean you know, much of the innovation in the last decade was found inside the data center and in your world a lot of times you know, inside the server itself but what you're describing is this, end-to-end system across the network and that systems view, and then there's going to be a ton of innovation there and we're very excited for you thanks so much for coming on the Cube it was great to see you again. >> It is great to be here and we're just excited to be here to help our customers, and giving them the best volume for the workloads whether that's taking advantage of GreenLake, taking advantage of the innovative security technologies that we've talked about, or being the edge to cloud platform as a service company that can help our customers transform in this distributed world from the edge to the data center to the cloud. Thanks for having me Dave. >> You very welcome, awesome summary and its always good to see you Neil. Thank you for watching everybody this David Vellante, for the Cube our coverage of the HPE Discover 2020 Virtual Experience, will be right back to the short break. (soft upbeat music)