>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (bright upbeat music plays) >> Welcome back to Barcelona, everybody. You're watching theCUBE's coverage of MWC '23, my name is Dave Vellante. Just left a meeting with the CEO of Cisco, Chuck Robbins, to meet with Jeetu Patel, who's our Executive Vice President and General Manager of security and collaboration at Cisco. Good to see you. >> You never leave a meeting with Chuck Robbins to meet with Jeetu Patel. >> Well, I did. >> That's a bad idea. >> Walked right out. I said, hey, I got an interview to do, right? So, and I'm excited about this. Thanks so much for coming on. >> Thank you for having me. It's a pleasure. >> So, I mean you run such an important part of the business. I mean, obviously the collaboration business but also security. So many changes going on in the security market. Maybe we could start there. I mean, there hasn't been a ton of security talk here Jeetu, because I think it's almost assumed. It was 45 minutes into the keynote yesterday before anybody even mentioned security. >> Huh. >> Right? And so, but it's the most important topic in the enterprise IT world. And obviously is important here. So why is it you think that it's not the first topic that people mention. >> You know, it's a complicated subject area and it's intimidating. And actually that's one of the things that the industry screwed up on. Where we need to simplify security so it actually gets to be relatable for every person on the planet. But, if you think about what's happening in security, it's not just important for business it's critical infrastructure that if you had a breach, you know lives are cost now. Because hospitals could go down, your water supply could go down, your electricity could go down. And so it's one of these things that we have to take pretty seriously. And, it's 51% of all breaches happen because of negligence, not because of malicious intent. >> It's that low. Interesting. I always- >> Someone else told me the same thing, that they though it'd be higher, yeah. >> I always say bad user behavior is going to trump good security every time. >> Every single time. >> You can't beat it. But, you know, it's funny- >> Jeetu: Every single time. >> Back, the earlier part of last decade, you could see that security was becoming a board level issue. It became, it was on the agenda every quarter. And, I remember doing some research at the time, and I asked, I was interviewing Robert Gates, former Defense Secretary, and I asked him, yeah, but we're getting attacked but don't we have the best offense? Can't we have the best technology? He said, yeah but we have so much critical infrastructure the risks to United States are higher. So we have to be careful about how we use security as an offensive weapon, you know? And now you're seeing the future of war involves security and what's going on in Ukraine. It's a whole different ballgame. >> It is, and the scales always tip towards the adversary, not towards the defender, because you have to be right every single time. They have to be right once. >> Yeah. And, to the other point, about bad user behavior. It's going now beyond the board level, to it's everybody's responsibility. >> That's right. >> And everybody's sort of aware of it, everybody's been hacked. And, that's where it being such a complicated topic is problematic. >> It is, and it's actually, what got us this far will not get us to where we need to get to if we don't simplify security radically. You know? The experience has to be almost invisible. And what used to be the case was sophistication had to get to a certain level, for efficacy to go up. But now, that sophistication has turned to complexity. And there's an inverse relationship between complexity and efficacy. So the simpler you make security, the more effective it gets. And so I'll give you an example. We have this great kind of innovation we've done around passwordless, right? Everyone hates passwords. You shouldn't have passwords in 2023. But, when you get to passwordless security, not only do you reduce a whole lot of friction for the user, you actually make the system safer. And that's what you need to do, is you have to make it simpler while making it more effective. And, I think that's what the future is going to hold. >> Yeah, and CISOs tell me that they're, you know zero trust before the pandemic was like, yeah, yeah zero trust. And now it's like a mandate. >> Yeah. >> Every CISO you talk to says, yes we're implementing a zero trust architecture. And a big part of that is that, if they can confirm zero trust, they can get to market a lot faster with revenue generating or critical projects. And many projects as we know are being pushed back, >> Yeah. >> you know? 'Cause of the macro. But, projects that drive revenue and value they want to accelerate, and a zero trust confirmation allows people to rubber stamp it and go faster. >> And the whole concept of zero trust is least privileged access, right? But what we want to make sure that we get to is continuous assessment of least privileged access, not just a one time at login. >> Dave: 'Cause things change so frequently. >> So, for example, if you happen to be someone that's logged into the system and now you start doing some anomalous behavior that doesn't sound like Dave, we want to be able to intercept, not just do it at the time that you're authenticating Dave to come in. >> So you guys got a good business. I mentioned the macro before. >> Yeah. >> The big theme is consolidating redundant vendors. So a company with a portfolio like Cisco's obviously has an advantage there. You know, you guys had great earnings. Palo Alto is another company that can consolidate. Tom Gillis, great pickup. Guy's amazing, you know? >> Love Tom. >> Great respect. Just had a little webinar session with him, where he was geeking out with the analyst and so- >> Yeah, yeah. >> Learned a lot there. Now you guys have some news, at the event event with Mercedes? >> We do. >> Take us through that, and I want to get your take on hybrid work and what's happening there. But what's going on with Mercedes? >> Yeah so look, it all actually stems from the hybrid work story, which is the future is going to be hybrid, people are going to work in mixed mode. Sometimes you'll be in the office, sometimes at home, sometimes somewhere in the middle. One of the places that people are working more and more from is their cars. And connected cars are getting to be a reality. And in fact, cars sometimes become an extension of your home office. And many a times I have found myself in a parking lot, because I didn't have enough time to get home and I was in a parking lot taking a conference call. And so we've made that section easier, because we have now partnered with Mercedes. And they aren't the first partner, but they're a very important partner where we are going to have Webex available, through the connected car, natively in Mercedes. >> Ah, okay. So I could take a call, I can do it all the time. I find good service, pull over, got to take the meeting. >> Yeah. >> I don't want to be driving. I got to concentrate. >> That's right. >> You know, or sometimes, I'll have the picture on and it's not good. >> That's right. >> Okay, so it'll be through the console, and all through the internet? >> It'll be through the console. And many people ask me like, how's safety going to work over that? Because you don't want to do video calls while you're driving. Exactly right. So when you're driving, the video automatically turns off. And you'll have audio going on, just like a conference call. But the moment you stop and put it in park, you can have video turned on. >> Now, of course the whole hybrid work trend, we, seems like a long time ago but it doesn't, you know? And it's really changed the security dynamic as well, didn't it? >> It has, it has. >> I mean, immediately you had to go protect new endpoints. And those changes, I felt at the time, were permanent. And I think it's still the case, but there's an equilibrium now happening. People as they come back to the office, you see a number of companies are mandating back to work. Maybe the central offices, or the headquarters, were underfunded. So what's going on out there in terms of that balance? >> Well firstly, there's no unanimous consensus on the way that the future is going to be, except that it's going to be hybrid. And the reason I say that is some companies mandate two days a week, some companies mandate five days a week, some companies don't mandate at all. Some companies are completely remote. But whatever way you go, you want to make sure that regardless of where you're working from, people can have an inclusive experience. You know? And, when they have that experience, you want to be able to work from a managed device or an unmanaged device, from a corporate network or from a Starbucks, from on the road or stationary. And whenever you do any of those things, we want to make sure that security is always handled, and you don't have to worry about that. And so the way that we say it is the company that created the VPN, which is Cisco, is the one that's going to kill it. Because what we'll do is we'll make it simple enough so that you don't, you as a user, never have to worry about what connection you're going to use to dial in to what app. You will have one, seamless way to dial into any application, public application, private application, or directly to the internet. >> Yeah, I got a love, hate with my VPN. I mean, it's protecting me, but it's in the way a lot. >> It's going to be simple as ever. >> Do you have kids? >> I do, I have a 12 year old daughter. >> Okay, so not quite high school age yet. She will be shortly. >> No, but she's already, I'm not looking forward to high school days, because she has a very, very strong sense of debate and she wins 90% of the arguments. >> So when my kids were that age, I've got four kids, but the local high school banned Wikipedia, they can't use Wikipedia for research. Many colleges, I presume high schools as well, they're banning Chat GPT, can't use it. Now at the same time, I saw recently on Medium a Wharton school professor said he's mandating Chat GPT to teach his students how to prompt in progressively more sophisticated prompts, because the future is interacting with machines. You know, they say in five years we're all going to be interacting in some way, shape, or form with AI. Maybe we already are. What's the intersection between AI and security? >> So a couple very, very consequential things. So firstly on Chat GPT, the next generation skill is going to be to learn how to go out and have the right questions to ask, which is the prompt revolution that we see going on right now. But if you think about what's happening in security, and there's a few areas which are, firstly 3,500 hundred vendors in this space. On average, most companies have 50 to 70 vendors in security. Not a single vendor owns more than 10% of the market. You take out a couple vendors, no one owns more than 5%. Highly fractured market. That's a problem. Because it's untenable for companies to go out and manage 70 policy engines. And going out and making sure that there's no contention. So as you move forward, one of the things that Chat GPT will be really good for is it's fundamentally going to change user experiences, for how software gets built. Because rather than it being point and click, it's going to be I'm going to provide an instruction and it's going to tell me what to do in natural language. Imagine Dave, when you joined a company if someone said, hey give Dave all the permissions that he needs as a direct report to Chuck. And instantly you would get all of the permissions. And it would actually show up in a screen that says, do you approve? And if you hit approve, you're done. The interfaces of the future will get more natural language kind of dominated. The other area that you'll see is the sophistication of attacks and the surface area of attacks is increasing quite exponentially. And we no longer can handle this with human scale. You have to handle it in machine scale. So detecting breaches, making sure that you can effectively and quickly respond in real time to the breaches, and remediate those breaches, is all going to happen through AI and machine learning. >> So, I agree. I mean, just like Amazon turned the data center into an API, I think we're now going to be interfacing with technology through human language. >> That's right. >> I mean I think it's a really interesting point you're making. Now, from a security standpoint as well, I mean, the state of the art today in my email is be careful, this person's outside your organization. I'm like, yeah I know. So it's a good warning sign, but it's really not automated in any way. So two part question. One is, can AI help? You know, with the phishing, obviously it can, but the bad guys have AI too. >> Yeah. >> And they're probably going to be smarter than I am about using it. >> Yeah, and by the way, Talos is our kind of threat detection and response >> Yes. >> kind of engine. And, they had a great kind of piece that came out recently where they talked about this, where Chat GPT, there is going to be more sophistication of the folks that are the bad actors, the adversaries in using Chat GPT to have more sophisticated phishing attacks. But today it's not something that is fundamentally something that we can't handle just yet. But you still need to do the basic hygiene. That's more important. Over time, what you will see is attacks will get more bespoke. And in order, they'll get more sophisticated. And, you will need to have better mechanisms to know that this was actually not a human being writing that to you, but it was actually a machine pretending to be a human being writing something to you. And that you'll have to be more clever about it. >> Oh interesting. >> And so, you will see attacks get more bespoke and we'll have to get smarter and smarter about it. >> The other thing I wanted to ask you before we close is you're right on. I mean you take the top security vendors and they got a single digit market share. And it's like it's untenable for organizations, just far too many tools. We have a partner at ETR, they do quarterly survey research and one of the things they do is survey emerging technology companies. And when we look at in the security sector just the number of emerging technology companies that are focused on cybersecurity is as many as there are out there already. And so, there's got to be consolidation. Maybe that's through M & A. I mean, what do you think happens? Are company's going to go out of business? There's going to be a lot of M & A? You've seen a lot of companies go private. You know, the big PE companies are sucking up all these security companies and may be ready to spit 'em out and go back public. How do you see the landscape? You guys are obviously an inquisitive company. What are your thoughts on that? >> I think there will be a little bit of everything. But the biggest change that you'll see is a shift that's going to happen with an integrated platform, rather than point solution vendors. So what's going to happen is the market's going to consolidate towards very few, less than a half a dozen, integrated platforms. We believe Cisco is going to be one. Microsoft will be one. There'll be others over there. But these, this platform will essentially be able to provide a unified kind of policy engine across a multitude of different services to protect multiple different entities within the organization. And, what we found is that platform will also be something that'll provide, through APIs, the ability for third parties to be able to get their technology incorporated in, and their telemetry ingested. So we certainly intend to do that. We don't believe, we are not arrogant enough to think that every single new innovation will be built by us. When there's someone else who has built that, we want to make sure that we can ingest that telemetry as well, because the real enemy is not the competitor. The real enemy is the adversary. And we all have to get together, so that we can keep humanity safe. >> Do you think there's been enough collaboration in the industry? I mean- >> Jeetu: Not nearly enough. >> We've seen companies, security companies try to monetize private data before, instead of maybe sharing it with competitors. And so I think the industry can do better there. >> Well I think the industry can do better. And we have this concept called the security poverty line. And the security poverty line is the companies that fall below the security poverty line don't have either the influence or the resources or the know how to keep themselves safe. And when they go unsafe, everyone else that communicates with them also gets that exposure. So it is in our collective interest for all of us to make sure that we come together. And, even if Palo Alto might be a competitor of ours, we want to make sure that we invite them to say, let's make sure that we can actually exchange telemetry between our companies. And we'll continue to do that with as many companies that are out there, because actually that's better for the market, that's better for the world. >> The enemy of the enemy is my friend, kind of thing. >> That's right. >> Now, as it relates to, because you're right. I mean I, I see companies coming up, oh, we do IOT security. I'm like, okay, but what about cloud security? Do you that too? Oh no, that's somebody else. But, so that's another stove pipe. >> That's a huge, huge advantage of coming with someone like Cisco. Because we actually have the entire spectrum, and the broadest portfolio in the industry of anyone else. From the user, to the device, to the network, to the applications, we provide the entire end-to-end story for security, which then has the least amount of cracks that you can actually go out and penetrate through. The biggest challenges that happen in security is you've got way too many policy engines with way too much contention between the policies from these different systems. And eventually there's a collision course. Whereas with us, you've actually got a broad portfolio that operates as one platform. >> We were talking about the cloud guys earlier. You mentioned Microsoft. They're obviously a big competitor in the security space. >> Jeetu: But also a great partner. >> So that's right. To my opinion, the cloud has been awesome as a first line of defense if you will. But the shared responsibility model it's different for each cloud, right? So, do you feel that those guys are working together or will work together to actually improve? 'Cause I don't see that yet. >> Yeah so if you think about, this is where we feel like we have a structural advantage in this, because what does a company like Cisco become in the future? I think as the world goes multicloud and hybrid cloud, what'll end up happening is there needs to be a way, today all the CSPs provide everything from storage to computer network, to security, in their own stack. If we can abstract networking and security above them, so that we can acquire and steer any and all traffic with our service providers and steer it to any of those CSPs, and make sure that the security policy transcends those clouds, you would actually be able to have the public cloud economics without the public cloud lock-in. >> That's what we call super cloud Jeetu. It's securing the super cloud. >> Yeah. >> Hey, thanks so much for coming to theCUBE. >> Thank you for having me. >> Really appreciate you coming on our editorial program. >> Such a pleasure. >> All right, great to see you again. >> Cheers. >> All right, keep it right there. Dave Vellante with David Nicholson and Lisa Martin. We'll be back, right after this short break from MWC '23 live, in the Fira, in Barcelona. (bright music resumes) (music fades out)

(intro music) >> Good afternoon guys and gals. Welcome back to The Strip, Las Vegas. It's "theCUBE" live day four of our coverage of "AWS re:Invent". Lisa Martin, Dave Vellante. Dave, we've had some awesome conversations the last four days. I can't believe how many people are still here. The AWS ecosystem seems stronger than ever. >> Yeah, last year we really noted the ecosystem, you know, coming out of the isolation economy 'cause everybody had this old pent up demand to get together and the ecosystem, even last year, we were like, "Wow." This year's like 10x wow. >> It really is 10x wow, it feels that way. We're going to have a 10x wow conversation next. We're bringing back DataStax to "theCUBE". Please welcome Thomas Bean, it's CMO. Thomas welcome to "theCUBE". >> Thanks, thanks a lot, thanks for having me. >> Great to have you, talk to us about what's going on at DataStax, it's been a little while since we talked to you guys. >> Indeed, so DataStax, we are the realtime data company and we've always been involved in technology such as "Apache Cassandra". We actually created to support and take this, this great technology to the market. And now we're taking it, combining it with other technologies such as "Apache Pulse" for streaming to provide a realtime data cloud. Which helps our users, our customers build applications faster and help them scale without limits. So it's all about mobilizing all of this information that is going to drive the application going to create the awesome experience, when you have a customer waiting behind their mobile phone, when you need a decision to take place immediately to, that's the kind of data that we, that we provide in the cloud on any cloud, but especially with, with AWS and providing the performance that technologies like "Apache Cassandra" are known for but also with market leading unit economics. So really empowering customers to operate at speed and scale. >> Speaking of customers, nobody wants less data slower. And one of the things I think we learned in the in the pan, during the pandemic was that access to realtime data isn't nice to have anymore for any business. It is table stakes, it's competitive advantage. There's somebody right behind in the rear view mirror ready to take over. How has the business model of DataStax maybe evolved in the last couple of years with the fact that realtime data is so critical? >> Realtime data has been around for some time but it used to be really niches. You needed a lot of, a lot of people a lot of funding actually to, to implement these, these applications. So we've adapted to really democratize it, made super easy to access. Not only to start developing but also scaling. So this is why we've taken these great technologies made them serverless cloud native on the cloud so that developers could really start easily and scale. So that be on project products could be taken to the, to the market. And in terms of customers, the patterns is we've seen enterprise customers, you were talking about the pandemic, the Home Depot as an example was able to deliver curbside pickup delivery in 30 days because they were already using DataStax and could adapt their business model with a real time application that combines you were just driving by and you would get the delivery of what exactly you ordered without having to go into the the store. So they shifted their whole business model. But we also see a real strong trend about customer experiences and increasingly a lot of tech companies coming because scale means success to them and building on, on our, on our stack to, to build our applications. >> So Lisa, it's interesting. DataStax and "theCUBE" were started the same year, 2010, and that's when it was the beginning of the ascendancy of the big data era. But of course back then there was, I mean very little cloud. I mean most of it was on-prem. And so data stacks had, you know, had obviously you mentioned a number of things that you had to do to become cloud friendly. >> Thomas: Yes. >> You know, a lot of companies didn't make it, make it through. You guys just raised a bunch of dough as well last summer. And so that's been quite a transformation both architecturally, you know, bringing the customers through. I presume part of that was because you had such a great open source community, but also you have a unique value problem. Maybe you could sort of describe that a little. >> Absolutely, so the, I'll start with the open source community where we see a lot of traction at the, at the moment. We were always very involved with, with the "Apache Cassandra". But what we're seeing right now with "Apache Cassandra" is, is a lot of traction, gaining momentum. We actually, we, the open source community just won an award, did an AMA, had a, a vote from their readers about the top open source projects and "Apache Cassandra" and "Apache Pulse" are part of the top three, which is, which is great. We also run a, in collaboration with the Apache Project, the, a series of events around the, around the globe called "Cassandra Days" where we had tremendous attendance. We, some of them, we had to change venue twice because there were more people coming. A lot of students, a lot of the big users of Cassandra like Apple, Netflix who spoke at these, at these events. So we see this momentum actually picking up and that's why we're also super excited that the Linux Foundation is running the Cassandra Summit in in March in San Jose. Super happy to bring that even back with the rest of the, of the community and we have big announcements to come. "Apache Cassandra" will, will see its next version with major advances such as the support of asset transactions, which is going to make it even more suitable to more use cases. So we're bringing that scale to more applications. So a lot of momentum in terms of, in terms of the, the open source projects. And to your point about the value proposition we take this great momentum to which we contribute a lot. It's not only about taking, it's about giving as well. >> Dave: Big committers, I mean... >> Exactly big contributors. And we also have a lot of expertise, we worked with all of the members of the community, many of them being our customers. So going to the cloud, indeed there was architectural work making Cassandra cloud native putting it on Kubernetes, having the right APIs for developers to, to easily develop on top of it. But also becoming a cloud company, building customer success, our own platform engineering. We, it's interesting because actually we became like our partners in a community. We now operate Cassandra in the cloud so that all of our customers can benefit from all the power of Cassandra but really efficiently, super rapidly, and also with a, the leading unit economies as I mentioned. >> How will the, the asset compliance affect your, you know, new markets, new use cases, you know, expand your TAM, can you explain that? >> I think it will, more applications will be able to tap into the power of, of "NoSQL". Today we see a lot on the customer experience as IOT, gaming platform, a lot of SaaS companies. But now with the ability to have transactions at the database level, we can, beyond providing information, we can go even deeper into the logic of the, of the application. So it makes Cassandra and therefore Astra which is our cloud service an even more suitable database we can address, address more even in terms of the transaction that the application itself will, will support. >> What are some of the business benefits that Cassandra delivers to customers in terms of business outcomes helping businesses really transform? >> So Cassandra brings skill when you have millions of customers, when you have million of data points to go through to serve each of the customers. One of my favorite example is Priceline, who runs entirely on our cloud service. You may see one offer, but it's actually everything they know about you and everything they have to offer matched while you are refreshing your page. This is the kind of power that Cassandra provide. But the thing to say about "Apache Cassandra", it used to be also a database that was a bit hard to manage and hard to develop with. This is why as part of the cloud, we wanted to change these aspects, provide developers the API they like and need and what the application need. Making it super simple to operate and, and, and super affordable, also cost effective to, to run. So the the value to your point, it's time to market. You go faster, you don't have to worry when you choose the right database you're not going to, going to have to change horse in the middle of the river, like sixth month down the line. And you know, you have the guarantee that you're going to get the performance and also the best, the best TCO which matters a lot. I think your previous person talking was addressing it. That's also important especially in the, in a current context. >> As a managed service, you're saying, that's the enabler there, right? >> Thomas: Exactly. >> Dave: That is the model today. I mean, you have to really provide that for customers. They don't want to mess with, you know, all the plumbing, right? I mean... >> Absolutely, I don't think people want to manage databases anymore, we do that very well. We take SLAs and such and even at the developer level what they want is an API so they get all the power. All of of this powered by Cassandra, but now they get it as a, and it's as simple as using as, as an API. >> How about the ecosystem? You mentioned the show in in San Jose in March and the Linux Foundation is, is hosting that, is that correct? >> Yes, absolutely. >> And what is it, Cassandra? >> Cassandra Summit. >> Dave: Cassandra Summit >> Yep. >> What's the ecosystem like today in Cassandra, can you just sort of describe that? >> Around Cassandra, you have actually the big hyperscalers. You have also a few other companies that are supporting Cassandra like technologies. And what's interesting, and that's been a, a something we've worked on but also the "Apache Project" has worked on. Working on a lot of the adjacent technologies, the data pipelines, all of the DevOps solutions to make sure that you can actually put Cassandra as part of your way to build these products and, and build these, these applications. So the, the ecosystem keeps on, keeps on growing and actually the, the Cassandra community keeps on opening the database so that it's, it's really easy to have it connect to the rest of the, the rest environment. And we benefit from all of this in our Astra cloud service. >> So things like machine learning, governance tools that's what you would expect in the ecosystem forming around it, right? So we'll see that in March. >> Machine learning is especially a very interesting use case. We see more and more of it. We recently did a, a nice video with one of our customers called Unifour who does exactly this using also our abstract cloud service. What they provide is they analyze videos of sales calls and they help actually the sellers telling them, "Okay here's what happened here was the customer sentiment". Because they have proof that the better the sentiment is, the shorter the sell cycle is going to be. So they teach the, the sellers on how to say the right things, how to control the thing. This is machine learning applied on video. Cassandra provides I think 200 data points per second that feeds this machine learning. And we see more and more of these use cases, realtime use cases. It happens on the fly when you are on your phone, when you have a, a fraud maybe to detect and to prevent. So it is going to be more and more and we see more and more of these integration at the open source level with technologies like even "Feast" project like "Apache Feast". But also in the, in, in the partners that we're working with integrating our Cassandra and our cloud service with. >> Where are customer conversations these days, given that every company has to be a data company. They have to be able to, to democratize data, allow access to it deep into the, into the organizations. Not just IT or the data organization anymore. But are you finding that the conversations are rising up the, up the stack? Is this, is this a a C-suite priority? Is this a board level conversation? >> So that's an excellent question. We actually ran a survey this summer called "The State of the Database" where we, we asked these tech leaders, okay what's top of mind for you? And real time actually was, was really one of the top priorities. And they explained for the one that who call themselves digital leaders that for 71% of them they could correlate directly the use of realtime data, the quality of their experience or their decision making with revenue. And that's really where the discussion is. And I think it's something we can relate to as users. We don't want the, I mean if the Starbucks apps take seconds to to respond there will be a riot over there. So that's, that's something we can feel. But it really, now it's tangible in, in business terms and now then they take a look at their data strategy, are we equipped? Very often they will see, yeah, we have pockets of realtime data, but we're not really able to leverage it. >> Lisa: Yeah. >> For ML use cases, et cetera. So that's a big trend that we're seeing on one end. On the other end, what we're seeing, and it's one of the things we discussed a lot at the event is that yeah cost is important. Growth at all, at all cost does not exist. So we see a lot of push on moving a lot of the workloads to the cloud to make them scale but at the best the best cost. And we also see some organizations where like, okay let's not let a good crisis go to waste and let's accelerate our innovation not at all costs. So that we see also a lot of new projects being being pushed but reasonable, starting small and, and growing and all of this fueled by, by realtime data, so interesting. >> The other big topic amongst the, the customer community is security. >> Yep. >> I presume it's coming up a lot. What's the conversation like with DataStax? >> That's a topic we've been working on intensely since the creation of Astra less than two years ago. And we keep on reinforcing as any, any cloud provider not only our own abilities in terms of making sure that customers can manage their own keys, et cetera. But also integrating to the rest of the, of the ecosystem when some, a lot of our customers are running on AWS, how do we integrate with PrivateLink and such? We fit exactly into their security environment on AWS and they use exactly the same management tool. Because this is also what used to cost a lot in the cloud services. How much do you have to do to wire them and, and manage. And there are indeed compliance and governance challenges. So that's why making sure that it's fully connected that they have full transparency on what's happening is, is a big part of the evolution. It's always, security is always something you're working on but it's, it's a major topic for us. >> Yep, we talk about that on pretty much every event. Security, which we could dive into, but we're out of time. Last question for you. >> Thomas: Yes. >> We're talking before we went live, we're both big Formula One fans. Say DataStax has the opportunity to sponsor a team and you get the whole side pod to, to put like a phrase about DataStax on the side pod of this F1 car. (laughter) Like a billboard, what does it say? >> Billboard, because an F1 car goes pretty fast, it will be hard to, be hard to read but, "Twice the performance at half the cost, try Astra a cloud service." >> Drop the mike. Awesome, Thomas, thanks so much for joining us. >> Thank for having me. >> Pleasure having you guys on the program. For our guest, Thomas Bean and Dave Vellante, I'm Lisa Martin and you're watching "theCUBE" live from day four of our coverage. "theCUBE", the leader in live tech coverage. (outro music)

>>Hello everyone. Welcome back to the Cube Live, AWS Reinvent 2022. This is our first day of three and a half days of wall to wall coverage on the cube. Lisa Martin here with Dave Valante. Dave, it's getting louder and louder behind us. People are back. They're excited. >>You know what somebody told me today? Hm? They said that less than 15% of the audience is developers. I'm like, no way. I don't believe it. But now maybe there's a redefinition of developers because it's all about the data and it's all about the developers in my mind. And that'll never change. >>It is. And one of the things we're gonna be talking about is app modernization. As customers really navigate the journey to do that so that they can be competitive and, and meet the demands of customers. We've got an alumni back with us to talk about that. AJ Patel joins us, the SVP and GM Modern Apps and Management business group at VMware. Aj, welcome back. Thank >>You. It's always great to be here, so thank you David. Good to see >>You. Isn't great. It's great to be back in person. So the VMware Tansu team here back at Reinvent on the Flow Shore Flow show floor. There we go. Talk about some of the things that you guys are doing together, innovating with aws. >>Yeah, so it's, it's great to be back after in person after multiple years and the energy level continues to amaze me. The partnership with AWS started on the infrastructure side with VMware cloud on aws. And when with tanza, we're extending it to the application space. And the work here is really about how do you make developers productive To your earlier point, it's all about developers. It's all about getting applications in production securely, safely, continuously. And tanza is all about making that bridge between great applications being built, getting them deployed and running, running and operating at scale. And EKS is a dominant Kubernetes platform. And so the better together story of tanu and EKS is a great one for us, and we're excited to announce some sort of innovations in that area. >>Well, Tanu was so front and center at VMware Explorer. I wasn't at in, in VMware Explorer, Europe. Right. But I'm sure it was a similar kind of focus. When are customers choosing Tanu? Why are they choosing Tanu? What's, what's, what's the update since last August when >>We, you know, the market settled into three main use cases. One is all about developer productivity. You know, consistently we're all dealing with skill set gap issues. How do we make every developer productive, modern developer? And so 10 is all about enabling that develop productivity. And we can talk quite a bit about it. Second one is security's front and center and security's being shifted left right into how you build great software. How do you secure that through the entire supply chain process? And how do you run and operationalize secure at runtime? So we're hearing consistently about making secure software supply chain heart of what our solution is. And third one is, how do I run and operate the modern application at scale across any Kubernetes, across any cloud? These are the three teams that are continuing to get resonance and empowering. All of this is exciting. David is this formation of platform teams. I just finished a study with Bain Consulting doing some research for me. 40% of our organization now have some form of a central team that's responsive for, for we call platform engineering and building platforms to make developers productive. That is a big change since about two years ago even. So this is becoming mainstream and customers are really focusing on delivering in value to making developers productive. >>Now. And, and, and the other nuance that I see, and you kinda see it here in the ecosystem, but when you talk about your customers with platform engineering, they're actually building their, they're pointing their business. They gonna page outta aws, pointing their businesses to their customers, right? Becoming software companies, becoming cloud companies and really generating new forms of revenue. >>You know, the interesting thing is, some of my customers I would never have thought as leading edge are retailers. Yeah. And not your typical Starbucks that you get a great example. I have an auto parts company that's completely modernizing how they deliver point of sale all the way to the supply chain. All built on ES at scale. You're typically think of that a financial services or a telco leading the pack. But I'm seeing innovation in India. I'm seeing the innovation in AMEA coming out of there, across the board. Every industry is becoming a product company. A digital twin as we would call it. Yeah. And means they become software houses. Yeah. They behave more like you and I in this event versus a, a traditional enterprise. >>And they're building their own ecosystems and that ecosystem's generating data that's generating more value. And it's just this cycle. It's, >>It's a amazing, it's a flywheel. So innovation continues to grow. Talk about really unlocking the developer experience and delivering to them what they need to modernize apps to move as fast and quickly as they want to. >>So, you know, I think AWS coin this word undifferentiated heavy lifting. If you think of a typical developer today, how much effort does he have to put in before he can get a single line of code out in production? If you can take away all the complexity, typically security compliance is a big headache for them, right? Developer doesn't wanna worry about that. Infrastructure provisioning, getting all the configurations right, is a headache for them. Being able to understand what size of infrastructure or resource to use cost effectively. How do you run it operationally? Cuz the application team is responsible for the operational cost of the product or service. So these are the un you know, heavy lifting that developers want to get away from. So they wanna write great code, build great experiences. And we've always talked about frameworks a way to abstract with the complexity. And so for us, there's a massive opportunity to say, how do I simplify and take away all the heavy lifting to get an idea into production seamlessly, continuously, securely. >>Is that part of your partnership? Because you think about a aws, they're really not about frameworks, they're about primitives. I mean, Warner Vos even talks about that in his, in his speech, you know, but, but that makes it more challenging for developers. >>No, actually, if you look at some of their initial investments around proton and et cetera work, they're starting to do, they're recognized, you know, PS is a bad, bad word, but the outcomes a platform as a service offers is what everybody wants. Just talking to the AWS leaders, responsible area, he actually has a separate build team. He didn't know what to call the third team. He has a Kubernetes team, he has a serverless team and has a build team. And that build team is everything above Kubernetes to make the developer productive. Right. And the ecosystem to bring together to make that happen. So I think AWS is recognizing that primitives are great for the elite developers, but if they want to get the mass scale and adoption in the business, it, if you will, they're gonna have to provide richer set of building blocks and reduce the complex and partnership like ours. Make that a reality. And what I'm excited about is there's a clear gap here, and t's the best platform to kind of fill that gap. Well, >>And I, I think that, you know, they're gonna double down triple, I just wrote about this double down, triple down on the primitives. Yes. They have to have the best, you know, servers and storage and database. And I think the way they, they, I call it taping the seams is with the ecosystem. Correct. You know, and they, nobody has a, a better ecosystem. I mean, you guys are, you know, the, the postage child for the ecosystem and now this even exceeds that. But partnering up, that's how they >>Continue to, and they're looking for someone who's open, right? Yeah. Yeah. And so one of the first question is, you know, are you proprie or open? Because one of the things they're fighting against is the lock in. So they can find a friendly partner who is open source, led, you know, upstream committing to the code, delivering that innovation, and bring the ecosystem into orchestrated choreography. It's like singing a music, right? They're running a, running an application delivery team is like running a, a musical orchestra. There's so many moving parts here, right? How do you make them sing together? And so if Tan Zoo and our platform can help them sing and drive more of their services, it's only more valuable for them. And >>I think the partners would generally say, you know, AWS always talking about customer obsession. It's like becomes this bromine, you go, yeah, yeah. But I actually think in the field, the the sellers would say, yeah, we're gonna do what the customer, if that means we're gonna partner up. Yeah. And I think AWS's comp structure makes it sort >>Of, I learned today how, how incentives with marketplaces work. Yeah. And it is powerful. It's very powerful. Yeah. Right. So you line up the sales incentive, you line up the customer and the benefits, you line up bringing the ecosystem to drive business results and everybody, and so everybody wins. And which is what you're seeing here, the excitement and the crowd is really the whole, all boats are rising. Yeah. Yeah. Right, right. And it's driven by the fact that customers are getting true value out of it. >>Oh, absolutely. Tremendous value. Speaking of customers, give us an example of a customer story that you think really articulates the value of what Tanzi was delivering, especially making that developer experience far simpler. What are some of those big business outcomes that that delivers? >>You know, at Explorer we had the CIO of cvs and with their acquisition of Aetna and CVS Health, they're transforming the, the health industry. And they talked about the whole covid and then how they had to deliver the number of, you know, vaccines to u i and how quickly they had to deliver on that. It talked about Tanu and how they leverage, leverage a Tanza platform to get those new applications out and start to build that. And Ro was basically talking about his number one prior is how does he get his developers more productive? Number to priority? How does he make sure the apps are secure? Number three, priority, how does he do it cost effectively in the world? Particularly where we're heading towards where, you know, the budgets are gonna get tighter. So how do I move more dollars to innovation while I continue to drive more efficiency in my platform? And so cloud is the future. How does he make the best use of the cloud both for his developers and his operations team? Right? >>What's happening in serverless, I, in 2017, Andy Chassy was in the cube. He said if AWS or if Amazon had to build all over again, they would build in, in was using serverless. And that was a big quote. We've mined that for years. And as you were talking about developer productivity, I started writing down all the things developers have to do. Yep. With it, they gotta, they gotta build a container image. They said they gotta deploy an EC two instance. They gotta allocate memory, they gotta fence off the apps in a virtual machine. They gotta run the, you know, compute against the app goes, they gotta pay for all that. So, okay, what's your story on, what's the market asking for in terms of serverless? Because there's still some people who want control over the run time. Help us sift through that. >>And it really comes back to the application pattern or the type you're running. If it's a stateless application that you need to spin up and spin down. Serverless is awesome. Why would I wanna worry about scaling it up in, I wanna set up some SLAs, SLIs service level objectives or, or, or indicators and then let the systems bring the resources I need as I need them. That's a perfect example for serverless, right? On the other hand, if you have a, a more of a workflow type application, there's a sequence, there's state, try building an application using serverless where you had to maintain state between two, two steps in the process. Not so much fun, right? So I don't think serverless is the answer for everything, but many use cases, the scale to zero is a tremendous benefit. Events happen. You wanna process something, work is done, you quietly go away. I don't wanna shut down the server started up, I want that to happen magically. So I think there's a role of serverless. So I believe Kubernetes and servers are the new runtime platform. It's not one or the other. It's about marrying that around the application patterns. I DevOps shouldn't care about it. That's an infrastructure concern. Let me just run application, let the infrastructure manage the operations of it, whether it's serverless, whether it's Kubernetes clusters, whether it's orchestration, that's details right. I I I shouldn't worry about it. Right. >>So we shouldn't think of those as separate architectures. We should think of it as an architecture, >>The continuum in some ways Yeah. Of different application workload types. And, and that's a toolkit that the operator has at his disposal to configure and saying, where does, should that application run? Should I want control? You can run it on a, a conveyance cluster. Can I just run it on a serverless infrastructure and and leave it to the cloud provider? Do it all for me. Sure. What, what was PAs? PAs was exactly that. Yeah. Yeah. Write the code once you do the rest. Yeah. Okay. Those are just elements of that. >>And then K native is kinda in the middle, >>Right? K native is just a technology that's starting to build that capability out in a standards way to make serverless available consistently across all clouds. So I'm not building to a, a lambda or a particular, you know, technology type. I'm building it in a standard way, in a standard programming model. And infrastructure just >>Works for me on any cloud. >>The whole idea portability. Consistency. >>Right. Powerful. Yep. >>What are some of the things that, that folks can expect to learn from VMware Tan to AWS this week at the >>Show? Yeah, so there's some really great announcements. First of all, we're excited to extend our, our partnership with AWS in the area of eks. What I mean by that is we traditionally, we would manage an EKS cluster, you visibility of what's running in there, but we weren't able to manage the lifecycle With this announcement. We can give you a full management of lifecycle of S workloads. Our customers have 400 plus EKS clusters, multiple teams sharing those in a multi-tenanted way with common policy. And they wanna manage a full life cycle, including all the upstream open source component that make up Kubernetes people. That ES is the one thing, it's a collection of a lot of open, open source packages. We're making it simple to manage it consistently from a single place on the security front. We're now making tons of service mesh available in the marketplace. >>And if you look at what service MeSHs, it's an overlay. It's an abstraction. I can create an idea of a global name space that cuts across multiple VPCs. I'm, I'm hearing at Amazon's gonna make some announcements around VPC and how they stitch VPCs together. It's all moving towards this idea of abstractions. I can set policy at logical level. I don't have to worry about data security and the communication between services. These are the things we're now enabling, which are really an, and to make EKS even more productive, making enterprise grade enterprise ready. And so a lot of excitement from the EKS development teams as well to partner closely with us to make this an end to end solution for our >>Customers. Yeah. So I mean it's under chasy, it was really driving those primitives and helping developers under continuing that path, but also recognizing the need for solutions. And that's where the ecosystem comes in, >>Right? And the question is, what is that box? As you said last time, right? For the super cloud, there is a cloud infrastructure, which is becoming the new palette, but how do you make sense of the 300 plus primitives? How do you bring them together? What are the best practices, patterns? How do I manage that when something goes wrong? These are real problems that we're looking to solve. >>And if you're gonna have deeper business integration with the cloud and technology in general, you have to have that >>Abstraction. You know, one of the simple question I ask is, how do you know you're getting value from your cloud investment? That's a very hard question. What's your trade off between performance and cost? Do you know where your security, when a lock 4G happens, do you know all the open source packages you need to patch? These are very simple questions, but imagine today having to do that when everybody's doing in a bespoke manner using the set of primitives. You need a platform. The industry is shown at scale. You have to start standardizing and building a consistent way of delivering and abstracting stuff. And that's where the next stage of the cloud journey >>And, and with the economic environment, I think people are also saying, okay, how do we get more? Exactly. We're in the cloud now. How do we get more? How do we >>Value out of the cloud? >>Exactly. Totally. >>How do we transform the business? Last question, AJ for you, is, if you had a bumper sticker and you're gonna put it on your fancy car, what would it say about VMware tan zone aws? >>I would say tan accelerates apps. >>Love >>It. Thank you so much. >>Thank you. Thank you so much for joining us. >>Appreciate it. Always great to be here. >>Pleasure. Likewise. For our guest, I'm Dave Ante. I'm Lisa Martin. You're watching The Cube, the leader in emerging and enterprise tech coverage.

>>Welcome to the Cube's special program series Women of the Cloud, brought to you by aws. I'm your host, Lisa Martin. Very pleased to welcome my next guest, Courtney Kissler joins me, the SVP of tech and CTO at Zli. Courtney, welcome to the program. >>Thank you. Thanks for having me. >>Our pleasure to have you Talk to me a little bit about your career path in tech and about your role that you're doing right now. >>Yeah, so I have spent most of my career in retail. So I spent 14 years at Nordstrom, which is where I learned a lot about what it takes to be, you know, a technology leader. And really that's where I started kind of my cloud transformation journey. And then after 14 years at Nordstrom, I ended up going to Starbucks as the VP of retail technology there. And really enjoyed working at a global scale, very different, learned a lot there as well. And also had cloud transformation there too. And then I went to Nike as the VP of Digital Platform Engineering and learned a lot there as well. Different scale and very different retail situation. And two years ago, almost two years, it'll be two years in January, I joined Zuli as SVP and cto. And what I love about Zulily is, I mean, we were really born digital first. And so cloud is a big part of our ecosystem and I love that we are innovators, we are data driven, we're about experimentation, and we leverage cloud in a variety of ways. >>I love that you have an amazing pedigree background of companies that you've worked for. I can imagine all the experiences that you've had and how they've shaped you into the leader that you are today. What are some, for people that are either in maybe starting a little bit farther back in their careers or early in tech, what are some of the recommendations that you have for those that really want to grow their career and invest? What do you, what do you tell them? >>Yeah, so I would say the biggest piece of advice I can give is be a lifelong learner. The thing about technology is the technology is gonna change and evolve, and the way you respond and react to that is what's critical. And so figuring out how to be somebody who could be a problem solver and learning all the time. I, I try really hard to surround myself with people who I can learn from and really grow and, and how might I continue to engage in the technology, you know, landscape, but really make sure that I have a way to continuously learn. >>That's so important to be able to, to have the confidence to raise your hand and say, I, I wanna try something new, or I don't understand something. You know, we, I, I was reading this some stats recently. I want women in tech and I saw that women won't apply for a job, say on LinkedIn unless they meet 100% of the job requirements. Whereas men will apply if they meet only 40%. And I think more women need to know and others that you don't have to meet all those job requirements. There's so much on the job learning. You have to have the appetite, you have to have good mentors, good sponsors, but raise your hand, right. Lean into the conversation. There's amazing things that can happen as a result. >>Absolutely. And that, I love that you touched on, you know, the leaning in and also like this is an industry term, it's not mine, but I love it. Called have a personal board of directors, have people who can help you navigate and network. I would say that's one of the biggest learnings that I had throughout my career was build a community and lean on that community. And often the encouragement that you get from that will also put you in a position to be okay with not having all of the boxes checked before you pursue your next opportunity. >>Right. I love that you talked about having a personal board of directors. I wish, I wish that's advice you probably do too. That, that we had 20 years ago when we started in our careers. But it's, it's such important advice. You know, technology makes it so easy for us to connect these days, whether, you know, you're on Facebook or Instagram or LinkedIn or Twitter, leverage that network, but really create that personal board of directors, I think is sage advice for anybody at any stage in their career. I don't think you have to be a newbie to be able to, to see the value in that. I think there can be value in it along the way. I'd love to hear some of your successes where you've solved problems related to cloud in your career. Tell me about some of those. Yeah, >>Yeah, so one just, you know, brief set of context. So I started my career in security infrastructure and operations. So I was learning how to support data centers. I mean, that was part of the landscape when I started my career. And then this thing called the cloud came in to like the environment. And all of us, I think who started as infrastructure, whether you are a CIS admin or those type of traditional roles, thought, what is this gonna do to me? Like as a, as someone who's been in infrastructure for a long time. And what I really appreciated about the leadership at Nordstrom is we said, let's embrace it. Let's figure out how to learn and let's figure out what this might look like for us. And at the time we weren't, we weren't in the cloud, but we said we are going to be, you know, and a lot of companies say this, you know, cloud first. Now it's easy to say, >>Right, >>What does it look like internally? And I think there's multiple dimensions. One is, are you really designing and architecting your applications and capabilities to take advantage of the cloud? I will share that there was a lot of debate internally, and I think a lot of organizations do this where they say, avoid vendor lock in, make sure you have flexibility and it's important to be intentional about the use of cloud. Also super important to leverage the capabilities because one of the things that, you know, I believe in is that cloud can create a way for you to free up your technologists mind share to focus on things that are more strategic. And over time, cloud has become commodity. It's something that you can adopt and leverage to avoid spending time on provisioning servers and doing things that are now automated and part of the cloud offering. >>Right. >>Yeah, >>Go ahead. Sorry. >>Oh, I was gonna say, and also I think skill. So it does take a different skillset. So I do think it's important for organizations to invest and also lean on your cloud partner. So one thing I really appreciate about AWS is that there's a lot of learning and lots of ways to get certified and understand how to be successful in a cloud environment. So I think it's important to also know that, that there is another skill set that needs to be developed in order to be successful. >>What are some of the, the innovations that excite you that are coming down the pipe with respect to cloud that you may adopt at Zil? >>Yeah, that's a great question. You know, we're constantly looking at, you know, what can we, what, what can we take advantage of? And I think what I get excited about is really the ongoing innovation when it comes to data driven insights and how do you incorporate the knowledge of your customers and the broader kind of, I'll call it retail landscape, into continuing to put relevant experiences in front of your customer. And I think doing that at scale is, I mean, you can achieve it in other ways. I think a great way to achieve it is leveraging cloud and the, the scale and performance and speed to, I'll call it like speed to data insights. Like you can get so much out of that and learn. And so for me, I think it's really anything that has to do with data. >>Every company has to be a data company these days. Whether you're in retail or automotive or or manufacturing, you have to become a data driven company. You have to be able to derive those insights you talked about, you know, in the retail space, I always think, oh, I'm such a demanding consumer because I, I've been trained thanks to the cloud that I could get whatever I want, whatever I'm looking for. And these companies will start to learn me in a non-car way, hopefully, and serve up relevant personalized content that, oh yeah, that's right. I need one of those. We ex we have that expectation in our personal lives and I think we bring it into our professional lives as well. And so every company needs to be able to, to be that data company, to deliver what the end user is more and more these days, expecting that the demands are gonna be met. >>Absolutely. And I, I really appreciated what you said too about there's that innovation and expectation of your customer. There's also some really amazing innovation that can happen for your internal developer community, leveraging the cloud. There's tooling, there's data driven insights as well. Like how long is it taking us to deploy software? Well, that's a learning moment. And often cloud can help you solve for speed to delivery, having high confidence in your ability to deliver, because many of the cloud tools allow you to, you know, do a canary deployment. I'm only gonna expose this to a percentage of my customers and then I'll bring it live to everybody else. There's ways to leverage cloud technology that also makes it innovative for the internal developer. And you might even say internal customers. >>That's a great point. I always think the, the internal customer experience and the external customer experience are linked strongly hand in hand. And, and one of the things that we're seeing more and more, I think a lot this year is how influential the developer role is becoming in the, in the decisions and the technologies and what to deliver to the end user customers. So that internal experience and external experience need to be hand in hand for them both to be successful. >>Agree. And you hear a lot of movement in the industry around like platforms and developer experience and DevOps is, is an area that I'm super passionate about. And really ultimately what it is, is how are we all delivering value as fast as possible to our customers with high quality? Cuz you know, if you're doing, if you're doing speed right, you're not compromising quality. And I think this again, is a recognition of where the industry has evolved to. You can use cloud as a platform to accelerate those capabilities. >>Yes, it needs to be that accelerant really for things to be successful. I'd love to get your thoughts on over the last few years, what are some of the biggest changes that you've seen in the workforce and innovation that come to mind? >>Wow. So I think, you know, what I've seen is really the shift in, you mentioned, you know, data, every company is data driven. So the expectation of technology is to also be data driven. And I often think sometimes that many technologists think of, of technology's too complicated. Like we don't, it's too hard to be data driven. And in reality we were kind of wired for being data driven. And so infusing that into how we think about everything we do and making sure that that's part of kind of the, the DNA of the organization. I'm also a big believer in observability. So like really having good knowledge of how are the things that you're building really performing? And that's another area where cloud can help. Where you can, you can really instrument the end to end journey and have transparency to that so that your, your teams are set up for success. They can understand the help, they can see it quickly, and they can respond quickly. >>You know, I always think the horizon for technology is, is infinite. The innovation, the capabilities we need to have good strong teams, diverse teams, teams that bring in different thoughts, different perspectives, different backgrounds. My last question for you is, is on diversity in terms of the tech workforce, what are some of the things that you are seeing and maybe some advice you would give for organizations to be able to really embody diversity, equity and inclusion? >>Yeah, so I'm a big believer in, in inclusion, like I think that it often doesn't get as much focus as it should. So it's, it's very similar to a customer funnel. If you attract a bunch of diverse talent but you can't retain them, then you're gonna continue to have a challenge. So my, my focus is often on am I creating an environment where diverse talent can thrive? Now, Zuli and our broader company Q rate, like we believe passionately in diversity, equity, and inclusion, and it shows up in everything we do. So I I also think actions matter. So it's one thing to say it's important, but it's it like leaders need to demonstrate the commitment. So we've done some pretty, i, I consider to be investments in how do we continue develop our internal talent and grow diverse leaders and leaders from any position. It doesn't mean that you move into management. If we have leaders that are in engineering roles and product management roles, it's like, how do we continue to invest and also create inclusive leadership opportunities where our leaders are learning what does it look like to operate as an inclusive leader? >>So important. But to your point, action, action is so important. Sounds like you guys are doing an amazing job at Zoo Lil, not only in terms of embracing cloud first being born in the cloud, but also from a DEI perspective. I, I love that. Courtney, thank you so much for sharing your journey with us, your recommendations and thoughts. I know the audience found a lot of value in it, as did I. >>Thanks again for having me. >>Oh, my pleasure. For Courtney Kissler, I'm Lisa Martin. You're watching the Cube Special Program series, women of the Cloud, brought to you by aws. Thanks for watching.

>>Hello. Welcome back to our super cloud 22 event. I'm John F host the cue with my co-host Dave ante. Extracting the signal from noise. We're proud to have two amazing cube alumnis here. We got Sanja Putin. Who's now the CEO of cohesive the emo Aaron who's the CTO. Co-founder also former CEO Cub alumni. The father of hyper-converged welcome back to the cube I endorsed the >>Cloud. Absolutely. Is the father. Great >>To see you guys. Thank thanks for coming on and perfect timing. The new job taking over that. The helm Mo it at cohesive big news, but part of super cloud, we wanna dig into it. Thanks for coming on. >>Thank you for having >>Us here. So first of all, we'll get into super before we get into the Supercloud. I want to just get the thoughts on the move Sanjay. We've been following your career since 2010. You've been a cube alumni from that point, we followed that your career. Why cohesive? Why now? >>Yeah, John David, thank you first and all for having us here, and it's great to be at your event. You know, when I left VMware last year, I took some time off just really primarily. I hadn't had a sabbatical in probably 18 years. I joined two boards, Phillips and sneak, and then, you know, started just invest and help entrepreneurs. Most of them were, you know, Indian Americans like me who were had great tech, were looking for the kind of go to market connections. And it was just a wonderful year to just de to unwind a bit. And along the, the way came CEO calls. And I'd asked myself, the question is the tech the best in the industry? Could you see value creation that was signi significant and you know, three, four months ago, Mohit and Carl Eschenbach and a few of the board members of cohesive called me and walk me through Mo's decision, which he'll talk about in a second. And we spent the last few months getting to know him, and he's everything you describe. He's not just the father of hyperconverge. And he wrote the Google file system, wicked smart, built a tech platform better than that second time. But we had to really kind of walk through the chemistry between us, which we did in long walks in, in, you know, discrete places so that people wouldn't find us in a Starbucks and start gossiping. So >>Why Sanjay? There you go. >>Actually, I should say it's a combination of two different decisions. The first one was to, for me to take a different role and I run the company as a CEO for, for nine years. And, you know, as a, as a technologist, I always like, you know, going deep into technology at the same time, the CEO duties require a lot of breadth, right? You're talking to customers, you're talking to partners, you're doing so much. And with the way we've been growing the with, you know, we've been fortunate, it was becoming hard to balance both. It's really also not fair to the company. Yeah. So I opted to do the depth job, you know, be the visionary, be the technologist. And that was the first decision to bring a CEO, a great CEO from outside. >>And I saw your video on the site. You said it was your decision. Yes. Go ahead. I have to ask you, cuz this is a real big transition for founders and you know, I have founder artists cuz everyone, you know, calls me that. But being the founder of a company, it's always hard to let go. I mean nine years as CEO, it's not like you had a, you had a great run. So this was it timing for you? Was it, was it a structural shift, like at super cloud, we're talking about a major shift that's happening right now in the industry. Was it a balance issue? Was it more if you wanted to get back in and in the tech >>Look, I, I also wanna answer, you know, why Sanja, but, but I'll address your question first. I always put the company first what's right for the company. Is it for me to start get stuck the co seat and try to juggle this depth and Brad simultaneously. I mean, I can stroke my ego a little bit there, but it's not good for the company. What's best for the company. You know, I'm a technologist. How about I oversee the technology part in partnership with so many great people I have in the company and I bring someone kick ass to be the CEO. And so then that was the second decision. Why Sanja when Sanjay, you know, is a very well known figure. He's managed billions of dollars of business in VMware. You know, been there, done that has, you know, some of the biggest, you know, people in the industry on his speed dial, you know, we were really fortunate to have someone like that, come in and accept the role of the CEO of cohesive. I think we can take the company to new Heights and I'm looking forward to my partnership with, with Sanja on this. >>It it's we, we called it the splash brothers and >>The, >>In the vernacular. It doesn't matter who gets the ball, whether it's step clay, we shoot. And I think if you look at some of the great partnerships, whether it was gates bomber, there, plenty of history of this, where a founder and a someone who was, it has to be complimentary skills. If I was a technologist myself and wanted to code we'd clash. Yeah. But I think this was really a match me in heaven because he, he can, I want him to keep innovating and building the best platform for today in the future. And our customers tell one customer told me, this is the best tech they've seen since VMware, 20 years ago, AWS, 10 years ago. And most recently this was a global 100 big customers. So I feel like this combination, now we have to show that it works. It's, you know, it's been three, four months. My getting to know him, you know, I'm day eight on the job, but I'm loving it. >>Well, it's a sluman model too. It's more modern example. You saw, he did it with Fred Ludy at service now. Yes. And, and of course at, at snowflake, yeah. And his book, you read his book. I dunno if you've read his book, amp it up, but app it up. And he says, I always you'll love this. Give great deference to the founder. Always show great respect. Right. And for good reason. So >>In fact, I mean you could talk to him, you actually met to >>Frank. I actually, you know, a month or so back, I actually had dinner with him in his ranch in Moana. And I posed the question. There was a number of CEOs that went there and I posed him the question. So Frank, you know, many of us, we grow being deaf guys, you know? And eventually when we take on the home of our CEO, we have to do breadth. How do you do it? And he's like, well, let me tell you, I was never a death guy. I'm a breath guy. >>I'm like, >>That's my answer. Yeah. >>So, so I >>Want the short story. So the day I got the job, I, I got a text from Frank and I said, what's your advice the first time CEO, three words, amp it up, >>Amp it up. Right? Yeah. >>And so you're always on brand, man. >>So you're an amazing operator. You've proven that time and time again at SAP, VMware, et cetera, you feel like now you, you, you wanna do both of those skills. You got the board and you got the operations cuz you look, you know, look at sloop when he's got Scarelli wherever he goes, he brings Scarelli with him as sort of the operator. How, how do you, how are you thinking >>About that? I mean it's early days, but yeah. Yeah. Small. I mean I've, you know, when I was, you know, it was 35,000 people at VMware, 80, 90,000 people at SAP, a really good run. The SAP run was 10 to 20 billion innovative products, especially in analytics and VMware six to 12 end user computing cloud. So I learned a lot. I think the company, you know, being about 2000 employees plus not to mayor tomorrow, but over the course next year I can meet everybody. Right? So first off the executive team, 10 of us, we're, we're building more and more cohesiveness if I could use that word between us, which is great, the next, you know, layers of VPs and every manager, I think that's possible. So I I'm a people person and a customer person. So I think when you take that sort of extroverted mindset, we'll bring energy to the workforce to, to retain the best and then recruit the best. >>And you know, even just the week we, we were announced that this announcement happened. Our website traffic went through the roof, the highest it's ever been, lots of resumes coming in. So, and then lots of customer engagement. So I think we'll take this, but I, I feel very good about the possibilities, because see, for me, I didn't wanna walk into the company to a company where the technology risk was high. Okay. I feel like that I can go to bed at night and the technology risk is low. This guy's gonna run a machine at the current and the future. And I'm hearing that from customers. Now, what I gotta do is get the, the amp it up part on the go to market. I know a little thing or too about >>That. You've got that down. I think the partnership is really key here. And again, nine use the CEO and then Sanja points to our super cloud trend that we've been looking at, which is there's another wave happening. There's a structural change in real time happening now, cloud one was done. We saw that transition, AWS cloud native now cloud native with an kind of operating system kind of vibe going on with on-premise hybrid edge. People say multi-cloud, but we're looking at this as an opportunity for companies like cohesive to go to the next level. So I gotta ask you guys, what do you see as structural change right now in the industry? That's disruptive. People are using cloud and scale and data to refactor their business models, change modern cases with cloud native. How are you guys looking at this next structural change that's happening right now? Yeah, >>I'll take that. So, so I'll start by saying that. Number one, data is the new oil and number two data is exploding, right? Every year data just grows like crazy managing data is becoming harder and harder. You mentioned some of those, right? There's so many cloud options available. Cloud one different vendors have different clouds. There is still on-prem there's edge infrastructure. And the number one problem that happens is our data is getting fragmented all over the place and managing so many fragments of data is getting harder and harder even within a cloud or within on-prem or within edge data is fragmented. Right? Number two, I think the hackers out there have realized that, you know, to make money, it's no longer necessary to Rob banks. They can actually see steal the data. So ransomware attacks on the rise it's become a boardroom level discussion. They say there's a ransomware attack happening every 11 seconds or so. Right? So protecting your data has become very important security data. Security has become very important. Compliance is important, right? So people are looking for data management solutions, the next gen data management platform that can really provide all this stuff. And that's what cohesive is about. >>What's the difference between data management and backup. Explain that >>Backup is just an entry point. That's one use case. I wanna draw an analogy. Let's draw an analogy to my former company, Google right? Google started by doing Google search, but is Google really just a search engine. They've built a platform that can do multiple things. You know, they might have started with search, but then they went down to roll out Google maps and Gmail and YouTube and so many other things on that platform. So similarly backups might be just the first use case, but it's really about that platform on which you can do more with the data that's next gen data management. >>But, but you am, I correct. You don't consider yourself a security company. One of your competitors is actually pivoting and in positioning themselves as a security company, I've always felt like data management, backup and recovery data protection is an adjacency to security, but those two worlds are coming together. How do you see >>It? Yeah. The way I see it is that security is part of data management. You start maybe by backing with data, but then you secure it and then you do more with that data. If you're only doing security, then you're just securing the data. You, you gotta do more with the data. So data management is much bigger. So >>It's a security is a subset of data. I mean, there you go. Big TA Sanjay. >>Well, I mean I've, and I, I, I I'd agree. And I actually, we don't get into that debate. You know, I've told the company, listen, we'll figure that out. Cuz who cares about the positioning at the bottom? My email, I say we are data management and data security company. Okay. Now what's the best word that describes three nouns, which I think we're gonna do management security and analytics. Okay. He showed me a beautiful diagram, went to his home in the course of one of these, you know, discrete conversations. And this was, I mean, he's done this before. Many, if you watch on YouTube, he showed me a picture of an ice big iceberg. And he said, listen, you know, if you look at companies like snowflake and data bricks, they're doing the management security and mostly analytics of data. That's the top of the iceberg, the stuff you see. >>But a lot of the stuff that's get backed archive is the bottom of the iceberg that you don't see. And you try to, if you try to ask a question on age data, the it guy will say, get a ticket. I'll come back with three days. I'll UNIV the data rehydrate and then you'll put it into a database. And you can think now imagine that you could do live searches analytics on, on age data that's analytics. So I think the management, the security, the analytics of, you know, if you wanna call it secondary data or backed up data or data, that's not hot and live warm, colder is a huge opportunity. Now, what do you wanna call one phrase that describes all of it. Do you call that superpower management security? Okay, whatever you wanna call it. I view it as saying, listen, let's build a platform. >>Some people call Google, a search company. People, some people call Google and information company and we just have to go and pursue every CIO and every CSO that has a management and a security and do course analytics problem. And that's what we're doing. And when I talk to the, you know, I didn't talk to all the 3000 customers, but the biggest customers and I was doing diligence. They're like this thing has got enormous potential. Okay. And we just have to now go focus, get every fortune 1000 company to pick us because this problem, even the first use case you talk back up is a little bit like, you know, razor blades and soap you've needed. You needed it 30 years ago and you'll need it for 30 years. It's just that the tools that were built in the last generation that were companies formed in 1990s, one of them I worked for years ago are aids are not built for the cloud. So I think this is a tremendous opportunity where many of those, those, those nos management security analytics will become part of what we do. And we'll come up with the right phrase for what the companies and do course >>Sanjay. So ma and Sanja. So given that given that's this Google transition, I like that example search was a data problem. They got sequenced to a broader market opportunity. What super cloud we trying to tease out is what does that change over from a data standpoint, cuz now the operating environments change has become more complex and the enterprises are savvy. Developers are savvy. Now they want, they want SAS solutions. They want freemium and expanding. They're gonna drive the operations agenda with DevOps. So what is the complexity that needs to be abstracted away? How do you see that moment? Because this is what people are talking about. They're saying security's built in, driven by developers. Developers are driving operations behavior. So what is the shift? Where do you guys see this new? Yeah. Expansive for cohesive. How do you fit into super cloud? >>So let me build up from that entry point. Maybe back up to what you're saying is the super cloud, right? Let me draw that journey. So let's say the legacy players are just doing backups. How, how sad is it that you have one silo sitting there just for peace of mind as an insurance policy and you do nothing with the data. If you have to do something with the data, you have to build another silo, you have to build another copy. You have to manage it separately. Right. So clearly that's a little bit brain damaged. Right. So, okay. So now you take a little bit of, you know, newer vendors who may take that backup platform and do a little bit more with that. Maybe they provide security, but your problem still remains. How do you do more with the data? How do you do some analytics? >>Like he's saying, right. How do you test development on that? How do you migrate the data to the cloud? How do you manage it? The data at scale? How do you do you provide a unified experience across, across multiple cloud, which you're calling the super cloud. That's where cohesive goes. So what we do, we provide a platform, right? We have tentacles in on-prem in each of the clouds. And on top of that, it looks like one platform that you manage. We have a single control plane, a UI. If you may, a single pin of glass, if, if you may, that our customers can use to manage all of it. And now it looks, starts looking like one platform. You mentioned Google, do you, when you go to, you know, kind Google search or a URL, do you really care? What happens behind the scenes mean behind the scenes? Google's built a platform that spans the whole world. No, >>But it's interesting. What's behind the scenes. It's a beautiful now. And I would say, listen, one other thing to pull on Dave, on the security part, I saw a lot of vendors this day in this space, white washing a security message on top of backup. Okay. And CSO, see through that, they'll offer warranties and guarantees or whatever, have you of X million dollars with a lot of caveats, which will never paid because it's like escape clause here. We won't pay it. Yeah. And, and what people really want is a scalable solution that works. And you know, we can match every warranty that's easy. And what I heard was this was the most scalable solution at scale. And that's why you have to approach this with a Google type mindset. I love the fact that every time you listen to sun pitch, I would, what, what I like about him, the most common word to use is scale. >>We do things at scale. So I found that him and AUR and some of the early Google people who come into the company had thought about scale. And, and even me it's like day eight. I found even the non-tech pieces of it. The processes that, you know, these guys are built for simple things in some cases were better than some of the things I saw are bigger companies I'd been used to. So we just have to continue, you know, building a scale platform with the enterprise. And then our cloud product is gonna be the simple solution for the masses. And my view of the world is there's 5,000 big companies and 5 million small companies we'll push the 5 million small companies as the cloud. Okay. Amazon's an investor in the company. AWS is a big partner. We'll talk about I'm sure knowing John's interest in that area, but that's a cloud play and that's gonna go to the cloud really fast. You not build you're in the marketplace, you're in the marketplace. I mean, maybe talk about the history of the Amazon relationship investing and all that. >>Yeah, absolutely. So in two years back late 2020, we, you know, in collaboration with AWS who also by the way is an investor now. And in cohesive, we rolled out what we call data management as a service. It's our SaaS service where we run our software in the cloud. And literally all customers have to do is just go there and sign on, right? They don't have to manage any infrastructure and stuff. What's nice is they can then combine that with, you know, software that they might have bought from cohesive. And it still looks like one platform. So what I'm trying to say is that they get a choice of the, of the way they wanna consume our software. They can consume it as a SAS service in the cloud. They can buy our software, manage it themselves, offload it to a partner on premises or what have you. But it still looks like that one platform, what you're calling a Supercloud >>Yeah. And developers are saying, they want the bag of Legos to compose their solutions. That's the Nirvana they want to get there. So that's, it has to look the same. >>Well, what is it? What we're calling a Superlo can we, can we test that for a second? So data management and service could span AWS and on-prem with the identical experience. So I guess I would call that a Supercloud I presume it's not gonna through AWS span multiple clouds, but, but >>Why not? >>Well, well interesting cuz we had this, I mean, so, okay. So we could in the future, it doesn't today. Well, >>David enough kind of pause for a second. Everything that we do there, if we do it will be customer driven. So there might be some customers I'll give you one Walmart that may want to store the data in a non AWS cloud risk cuz they're competitors. Right. So, but the control plane could still be in, in, in the way we built it, but the data might be stored somewhere else. >>What about, what about a on-prem customer? Who says, Hey, I, I like cohesive. I've now got multiple clouds. I want the identical experience across clouds. Yeah. Okay. So, so can you do that today? How do you do that today? Can we talk >>About that? Yeah. So basically think roughly about the split between the data plane and the control plane, the data plane is, you know, our cohesive clusters that could be sitting on premises that could be sitting in multiple data centers or you can run an instance of that cluster in the cloud, whichever cloud you choose. Right. That's what he was referring to as the data plane. So collectively all these clusters from the data plane, right? They stored the data, but it can all be managed using the control plane. So you still get that single image, the single experience across all clouds. And by the way, the, the, the, the cloud vendor does actually benefit because here's a customer. He mentioned a customer that may not wanna go to AWS, but when they get the data plane on a different cloud, whether it's Azure, whether it's the Google cloud, they then get data management services. Maybe they're able to replicate the data over to AWS. So AWS also gains. >>And your deployment model is you instantiate the cohesive stack on each of the regions and clouds, is that correct? And you building essentially, >>It all happens behind the scenes. That's right. You know, just like Google probably has their tentacles all over the world. We will instantiate and then make it all look like one platform. >>I mean, you should really think it's like a human body, right? The control planes, the head. Okay. And that controls everything. The data plane is large because it's a lot of the data, right? It's the rest of the body, that data plane could be wherever you want it to be. Traditionally, the part the old days was tape. Then you got disk. Now you got multiple clouds. So that's the way we think about it. And there on that piece of it will be neutral, right? We should be multi-cloud to the data plane being every single place. Cause it's customer demand. Where do you want your store data? Air gapped. On-prem no problem. We'll work with Dell. Okay. You wanna be in a particular cloud, AWS we'll work then optimized with S3 and glacier. So this is where I think the, the path to a multi-cloud or Supercloud is to be customer driven, but the control plane sits in Amazon. So >>We're blessed to have a number of, you know, technical geniuses in here. So earlier we were speaking to Ben wa deja VI, and what they do is different. They don't instantiate an individual, you know, regions. What they do is of a single global. Is there a, is there an advantage of doing it the way the cohesive does it in terms of simplicity or how do you see that? Is that a future direction for you from a technology standpoint? What are the trade offs there? >>So you want to be where the data is when you said single global, I take it that they run somewhere and the data has to go there. And in this day age, correct >>Said that. He said, you gotta move that in this >>Day and >>Age query that's, you know, across regions, look >>In this day and age with the way the data is growing, the way it is, it's hard to move around the data. It's much easier to move around the competition. And in these instances, what have you, so let the data be where it is and you manage it right there. >>So that's the advantage of instantiating in multiple regions. As you don't have to move the >>Data cost, we have the philosophy we call it. Let's bring the, the computation to the data rather than the data to >>The competition and the same security model, same governance model, same. How do you, how do you federate that? >>So it's all based on policies. You know, this overarching platform controlled by, by the control plane, you just, our customers just put in the policies and then the underlying nuts and bolts just take care >>Of, you know, it's when I first heard and start, I started watching some of his old videos, ACE really like hyperconverged brought to secondary storage. In fact, he said, oh yeah, that's great. You got it. Because I first called this idea, hyperconverged secondary storage, because the idea of him inventing hyperconverge was bringing compute to storage. It had never been done. I mean, you had the kind of big VC stuff, but these guys were the first to bring that hyperconverge at, at Nutanix. So I think this is that same idea of bringing computer storage, but now applied not to the warm data, but to the rest of the data, including a >>Lot of, what about developers? What's, what's your relationship with developers? >>Maybe you talk about the marketplace and everything >>He's yeah. And I'm, I'm curious as to do you have a PAs layer, what we call super PAs layer to create an identical developer experience across your Supercloud. I'm gonna my >>Term. So we want our customers not just to benefit from the software that we write. We also want them to benefit from, you know, software that's written by developers by third party people and so on and so forth. So we also support a marketplace on the platform where you can download apps from third party developers and run them on this platform. There's a, a number of successful apps. There's one, you know, look like I said, our entry point might be backups, but even when backups, we don't do everything. Look, for instance, we don't backup mainframes. There is a, a company we partner with, you know, and their software can run in our marketplace. And it's actually used by many, many of our financial customers. So our customers don't get, just get the benefit of what we build, but they also get the benefit of what third parties build. Another analogy I like to draw. You can tell. And front of analogy is I drew an analogy to hyperscale is like Google. Yeah. The second analogy I like to draw is that to a simple smartphone, right? A smartphone starts off by being a great phone. But beyond that, it's also a GPS player. It's a, it's a, it's a music player. It's a camera, it's a flashlight. And it also has a marketplace from where you can download apps and extend the power of that platform. >>Is that a, can we think of that as a PAs layer or no? Is it really not? You can, okay. You can say, is it purpose built for what you're the problem that you're trying to solve? >>So we, we just built APIs. Yeah. Right. We have an SDK that developers can use. And through those APIs, they get to leverage the underlying services that exist on the platform. And now developers can use that to take advantage of all that stuff. >>And it was, that was a key factor for me too. Cause I, what I, you know, I've studied all the six, seven players that sort of so-called leaders. Nobody had a developer ecosystem, nobody. Right? The old folks were built for the hardware era, but anyones were built for the cloud to it didn't have any partners were building on their platform. So I felt for me listen, and that the example of, you know, model nine rights, the name of the company that does back up. So there's, there's companies that are built on and there's a number of others. So our goal is to have a big tent, David, to everybody in the ecosystem to partner with us, to build on this platform. And, and that may take over time, but that's the way we're build >>It. And you have a metadata layer too, that has the intelligence >>To correct. It's all abstract. That that's right. So it's a combination of data and metadata. We have lots of metadata that keeps track of where the data is. You know, it allows you to index the data you can do quick searches. You can actually, you, we talking about the control plan from that >>Tracing, >>You can inject a search that'll through search throughout your multi-cloud environment, right? The super cloud that you call it. We have all that, all that goodness sounds >>Like a Supercloud John. >>Yeah. I mean, data tracing involved can trace the data lineage. >>You, you can trace the data lineage. So we, you know, provide, you know, compliance and stuff. So you can, >>All right. So my final question to wrap up, we guys, first of all, thanks for coming on. I know you're super busy, San Jose. We, we know what you're gonna do. You're gonna amp it up and, you know, knock all your numbers out. Think you always do. But what I'm interested in, what you're gonna jump into, cuz now you're gonna have the creative license to jump in to the product, the platform there has to be the next level in your mind. Can you share your thoughts on where this goes next? Love the control plane, separate out from the data plane. I think that plays well for super. How >>Much time do you have John? This guy's got, he's got a wealth. Ditis keep >>Going. Mark. Give us the most important thing you're gonna focus on. That kind of brings the super cloud and vision together. >>Yeah. Right away. I'm gonna, perhaps I, I can ion into two things. The first one is I like to call it building the, the machine, the system, right. Just to draw an analogy. Look, I draw an analogy to the us traffic system. People from all walks of life, rich, poor Democrats, Republicans, you know, different states. They all work in the, the traffic system and we drive well, right. It's a system that just works. Whereas in some other countries, you know, the system doesn't work. >>We know, >>We know a few of those. >>It's not about works. It's not about the people. It's the same people who would go from here to those countries and, and not dry. Well, so it's all about the system. So the first thing I, I have my sights on is to really strengthen the system that we have in our research development to make it a machine. I mean, it functions quite well even today, but wanna take it to the next level. Right. So that I wanna get to a point where innovation just happens in the grassroots. And it just, just like >>We automations scale optic brings all, >>Just happens without anyone overseeing it. Anyone there's no single point of bottleneck. I don't have to go take any diving catches or have you, there are people just working, you know, in a decentralized fashion and innovation just happens. Yeah. The second thing I work on of course is, you know, my heart and soul is in, you know, driving the vision, you know, the next level. And that of course is part of it. So those are the two things >>We heard from all day in our super cloud event that there's a need for an, an operating system. Yeah. Whether that's defacto standard or open. Correct. Do you see a consortium around the corner potentially to bring people together so that things could work together? Cuz there really isn't no stand there. Isn't a standards bodies. Now we have great hyperscale growth. We have on-prem we got the super cloud thing happening >>And it's a, it's kind of like what is an operating system? Operating system exposes some APIs that the applications can then use. And if you think about what we've been trying to do with the marketplace, right, we've built a huge platform and that platform is exposed through APIs. That third party developers can use. Right? And even we, when we, you know, built more and more services on top, you know, we rolled our D as we rolled out, backup as a service and a ready for thing security as a service governance, as a service, they're using those APIs. So we are building a distributor, putting systems of sorts. >>Well, congratulations on a great journey. Sanja. Congratulations on taking the hem. Thank you've got ball control. Now you're gonna be calling the ball cohesive as they say, it's, >>It's a team. It's, you know, I think I like that African phrase. If you want to go fast, you go alone. If you wanna go far, you go together. So I've always operated with the best deal. I'm so fortunate. This is to me like a dream come true because I always thought I wanted to work with a technologist that frees me up to do what I like. I mean, I started as an engineer, but that's not what I am today. Right? Yeah. So I do understand the product and this category I think is right for disruption. So I feel excited, you know, it's changing growing. Yeah. No. And it's a, it requires innovation with a cloud scale mindset and you guys have been great friends through the years. >>We'll be, we'll be watching you. >>I think it's not only disruption. It's creation. Yeah. There's a lot of white space that just hasn't been created yet. >>You're gonna have to, and you know, the proof, isn't the pudding. Yeah. You already have five of the biggest 10 financial institutions in the us and our customers. 25% of the fortune 500 users, us two of the biggest five pharmaceutical companies in the world use us. Probably, you know, some of the biggest companies, you know, the cars you have, you know, out there probably are customers. So it's already happening. >>I know you got an IPO filed confidentially. I know you can't talk numbers, but I can tell by your confidence, you're feeling good right now we are >>Feeling >>Good. Yeah. One day, one week, one month at a time. I mean, you just, you know, I like the, you know, Jeff Bezos, Andy jazzy expression, which is, it's always day one, you know, just because you've had success, even, you know, if, if a and when an IPO O makes sense, you just have to stay humble and hungry because you realize, okay, we've had a lot of success in the fortune 1000, but there's a lot of white space that hasn't picked USS yet. So let's go, yeah, there's lots of midmarket account >>Product opportunities are still, >>You know, I just stay humble and hungry and if you've got the team and then, you know, I'm really gonna be working also in the ecosystem. I think there's a lot of very good partners. So lots of ideas brew through >>The head. Okay. Well, thank you so much for coming on our super cloud event and, and, and also doubling up on the news of the new appointment and congratulations on the success guys. Coverage super cloud 22, I'm sure. Dave ante, thanks for watching. Stay tuned for more segments after this break.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Welcome back to the Venetian convention center. You're watching the Cube's coverage of HPE discover 2022. The first discover live discover in three years, 2019 was the last one. The cube we were just talking about. This has been at H HP discover. Now HPE since 2011, my co-host John furrier. We're pleased to welcome Phil Maru. Who's the executive vice president and general manager of HPE Aruba. And he's joined by David Hughes, the chief product and technology officer at HPE Aruba gentleman. Welcome to the cube. Good to see you. Thank you. Thank >>You. >>Okay, so you guys talk a lot, Phil, about the intelligent edge. Yep. Okay. What do you, what do you mean by that? >>Yeah, so we, well, we're kind of focused on, is providing technology to customers that sits out at the edge and typically the edge would be, uh, any location out of the data center or out of the cloud. So for the most part, our customers would deploy our technology either in their office premises or maybe retail premises shops, uh, maybe deploying out of the home where their employees are on a factory floor. And we're really talking about technology to connect both people and devices back to, um, systems and technology throughout an organization. So, but >>I, I, you know, sometimes I call it the near edge and the far edge yeah. Near, near edge. Maybe as we saw home Depot up on the stage yesterday far, Edge's like space. Right. You're including all of that. Right. That's >>Edge. >>Yeah. And actually we, we, we, you know, we've got a broad range of technology that actually works within the data center as well. So, you know, what we are focused on is providing, uh, network technology, software and services. And, you know, for the most part, our heritage is at the edge, but it's more pervasive than that. So >>If you have the edge, you got connectivity and power, that's an edge. How much, um, is the physical world being connected now you're seeing robotics automation. Yeah. Ex and with machine learning specifically in compute, really driving a new acceleration at the edge. What you, how do you guys view that? What's your reaction? Yeah. >>I think, look, it, I think as connectivity is improving and that's both in terms of wifi connectivity, so, you know, wifi technology continues to, uh, advance and also you've got this new kind of private 5g area, just generally connectivity is becoming more pervasive and that's helping some industries that haven't previously embraced it. And I think industrial is, is one of the big ones. So, you know, historically it was difficult for kind of car manufacturers to really enable a factory floor. But now the connectivity is connectivity is better. That gives them the opportunity to be able to really change how they do things. So >>David, if you do take an outside in view, mm-hmm <affirmative>, uh, and, and, and when you talk to customers, what are they telling you and how is that informing your product strategy? >>Yeah, well, you >>Know, I think there's, there's several themes we hear. One is, you know, it's really important, better work from anywhere they wanna enable their employees, um, to get the same experience, whether they're at home or on the road or in their branch office or at headquarters. Um, you know, people are also concerned that as they deploy, deploy all of this IOT and pursuit of digital transformation, they don't want those devices to be a weak point where someone breaks into one device and moves naturally, um, across the network. So they want to have this great experience for their customers and their users, but they wanna make sure that they're not compromising security, um, in any way. And so it's about getting that balance between ease of use and, and security. That's one of the primary things we hear, >>You know, Dave, one of the things we talked about many, many years ago was when hybrid and was starting to come out multi-cloud was on the, on the table early on. Uh, we were, we were saying, Hey, the data center is just a big edge, right? I mean, if you have cloud operations and you see what's going on with GreenLake here now, the momentum hybrid cloud is cloud operations, right? An edge off data centers to a big edge on premises. And you got the edge as you have cloud operations, like say GreenLake, plugging in partners and diverse environments. You're connecting, not just branch offices that are per perimeter based. You have no perimeter and you have now other companies connecting mm-hmm <affirmative> so you got data and you got network. How do you guys see that transition as GreenLake has a very big ecosystem part of it, partners and whatnot. >>Yeah. So, you know, I think for us, um, the ecosystem of partners that we have is critical in terms of delivering what our customers need. And, you know, I think one of the really important areas is around verticals. So, um, you know, when you think about different verticals, they have similar problems, but you need to tailor the solutions. Um, to each of those, you know, we are talking a bit about devices and people. When you look at say a healthcare environment, there can be 30 devices there for each patient. And, um, so there's connecting all those devices securely, but we have partners that will help pull all of that together that may be focused on, um, you know, medical environment that may focused on stadiums. They may be focused on industrial. Um, so having partners that understand those verticals and working closely with them to deliver solutions is important in our go to market. >>So another kind of product question and related to what you just said, David, I got connectivity, speed, reliability, cost security, or maybe a missing something. But you, you said earlier, you gonna gotta balance those. How do you do that? And do you do that for the specific use cases? Like for instance, you just mentioned stadiums and 81 and how do you balance those and, and do you tailor those for the use cases? >>Yeah, well, I think it depends on the customer and different people have different views about where they need to be. So some people are, are so afraid about security. They wanna be air gapped and completely separate than the internet. That would be one extreme mm-hmm <affirmative> other people, you know, look at it and see what's happening with COVID with everyone working from home with people being able to work from Starbucks or the airport. And they're beginning to think, well, why is the branch that much different? And so what I think we are seeing is, you know, a reevaluation of how people connect to, um, the apps they're using and, uh, you know, you, you, you've probably for sure heard people talking about zero trust, talking about micro segmentation. You know, I think what we we see is that people wanna be able to build a network in a way where rather than any device being able to talk to any device or any person, which is where the internet started, we wanna build to build networks where people or devices can only talk to the destinations that are necessary for them to do their job. >>And so a lot of the technology that we are building into the network is really about making security intrinsic by limiting what can talk to what that's >>Actually micro, micro segmentations, zero trust, um, these all point to a modern, the modern network, as you say, Antonio Neri was just on the cube, talking about programmability, substrate, the words like that come to mind, what is the modern network look like? I mean, you have to be agile. You have to be programmable. You have to have security. Can you describe in your words, what does the modern network these days need to look like? How should customers think about architecting them? What are some of the table stakes and what are some of the differentiators that customers need to do to have a modern network? >>Yeah, well, you covered off a coup a few quarter, one there with clarity and so on. So let me pick one that you didn't mention. And, and I, you know, I think we are seeing, you know, a lot of interest around network as a service. And, you know, when we think about network as a service, we think about it broadly, um, you know, for consumers, we're getting more and more used to buying things as a service versus buying a thing. When you, when you get Alexa, you care about how well she answers your questions, you don't care about what CPU is or how much Ram Alexa has. And likewise with networking, people are caring about the outcomes of keeping their employees connected, keeping their, their devices and systems running. And so what for us, what NASA is all about is that shift of thinking about a network as being a collection of devices that get managed to being a framework for connectivity and running it from the point of view of those outcomes. >>And so whether, you know, it's about CapEx versus OPEX or about do it yourself, managing the network yourself versus outsourcing that, um, or it's about the, you know, Greenfield versus brownfield, each of our customers has got a different starting point, but they're all getting heading towards this destination of being able to treat their network as a service. And so that is, you know, a key area of innovation for us and whether it's big customers like home Depot that you heard about yesterday, um, where we kind of manage everything for them on a, as on a store basis, um, for connectivity, um, or, you know, the recent, um, skew based nest that we launched, which is a really scalable foundation for our partners to build nest offerings around. Um, we see this as a key part of network modernization. Yeah. >>And one of the things, again, that's great stuff. Uh, infrastructure is code, which was really kind of pioneer the DevOps movement in cloud kind of as platform level. And you got data ops now and AI at the top of the stack, we were always wondering when network as code was gonna come, uh, and where you actually have it, where it's programmable. I mean, we all know what policies do do. They're good. That's all great network as code. >>Yeah. >>And that's the concept that's like DevOps, it's like, make it work just seamlessly, just be always on. And >>Yeah. And smart, you know, people are always looking for the, for the easy button. Um, and so they want, they want things to operate easily. They want it to be easy to manage. And, you know, I actually think there's a little bit of a, um, a conflict between networkers code and the easy button, right? So it depends on the class of customers. Some customers like financials, for instance, have a huge software development organizations that are extremely capable that could, that can go with program ability that want things as code. But the majority of the, of, of the verticals that we deal with, um, don't have those big captive software organizations. And so they're really looking for automation and simplicity and they wanna outsource that problem. So in Aruba central, we have invested a lot to make it really easy for our customers to, um, get what they need, you know, is that movement of zero code. It's more like zero code. They want, they want something packaged now >>The headless networks. Yeah. Low code, no code >>Kind of thing. Yeah, that's right. And, you know, obviously for people that have the sophistication that want to, um, do the most advanced things, we have APIs. And so we support that kind of programmable way of doing things. But I'd say that that's that's, those are more specialized customers. So >>Phil, yeah. Uh, is that the strategy? I mean, David listed off a number of, of factors here is that Aruba's strategy to modernize networks to actually create the easy button through network as a service is as simple as dial tone. Is that how we >>Should think? I mean, the way I think about the strategy is I think about it as a triangle, really, along the bottom, we've got the products and services that we offer and we continue to add more products and services. We either buy companies such as silver peak a couple of years ago, or we build, uh, additional products and by, and by the way, that's in response to customers who are frustrated with some other suppliers and wanna move on mass over to, uh, companies like ourselves. So at the bottom layer of the product and services, and then the other side of the triangle one would be NAS, which we talked about, which is kind of move to buying network and as a service. And then the other side of the triangle is the platform, which for us is river central, which is part of HP GreenLake. And that's really all about, you know, kind of making it easy for customers to manage networks and Aruba central right now has got about 120,000 live customers on it. It connects to about 2 million devices and it's collecting a lot of data as well. So we anonymously collect data from all of our customers. We've got one and a half billion data points in the platform. And what we do is we let that data kind of look for anomalies and spot problems on the network before they happen for customers. >>So Aruba central predated, uh, uh, GreenLake GreenLake. Yeah. And, and so did you write to GreenLake through GreenLake APIs? How, what was the engineering work to accomplish that? >>Yeah, so really, um, Aruba central is kind of the Genesis of the GreenLake platform. So we took Aruba central and made it more generic okay. To build the GreenLake cloud platform. And you know, what we've done very recently is bring, bring Aruba into that unified infrastructure, along with storage and compute. So the same sign-on applies across all of HP's, um, products, the same way of managing licenses, managing devices. And so it provides us, uh, great foundation going forwards to, um, solve more comprehensively. Our customers automation requires. >>So, so just a quick follow. So Aruba actually was the main spring of GreenLake from the standpoint of okay. Sing, like you said, single sign on a platform that could evolve and become more, more generic. Yes. So, okay. So that was a nice little, um, bonus of the acquisition, you know, it's now the whole company >><laugh> Aruba taking over. >>Yeah. There's been a lot of work to, to, uh, you know, make it generic and, and widely applicable. Right. Yeah. Um, so, but >>You were purpose >>Built for yeah. Well it's foundational. Yes. So foundational for GreenLake, they built on top of it. Yeah. So you mentioned the data points, billions of data points. So I gotta ask you, cuz we're seeing this, um, copy more and more with machine learning, driving a lot of acceleration, cuz you can do simulations with machine learning and compute. We had Neil McDonal done earlier. He's a compute guy, you got networking. So with all this, um, these services and devices being put on and off the network humans, can't actually figure this out. You can discover what's on the network. How are you guys viewing the discovery and monitoring because there's no perimeter okay. On the network anymore. So I want to know what's out there. Um, how do you get through it? How does machine learning and AI play into this? >>Yeah. I mean, what we are trying to do is obviously flag trends for customers and say, Hey look, you know, we can either see something happening with your network. So there's a particular issue over here and we need to, I dunno, free up more capacity to solve that. Or we're looking at how their network is running and then comparing that with anonymized data from all of our other customers as well. So we're just helping find those problems. But yeah, you're right. I mean, I think it is becoming more of an issue for organizations, you know, how do you manage the network, >>But you see machine learning and AI playing a big part. >>Yeah, yeah. Yeah. I think, uh, AI massively and, and other technology advances as well that we make. So recently we, uh, also announced the availability of location awareness within our access points. And that might sound like a simple thing. But when network, when companies build out their networks, they often lose or they potentially could lose the records as to, well, where were the access points that we laid out and actually where are they not within, you know, 20 feet, but where actually are they? So we introduced kind of location, finding technology as well into our, uh, access points to make it easy for >>Customers. So Aruba one of the best, if not the best acquisition. I think that HP E has made, um, it's made by three par was, you know, good. It saved the storage business. Okay. That was more of a defensive play. Uh, but to see Aruba, it's a growth business. You guys report on it every quarter. Yeah. It's obviously a key ingredient to enable uh, uh, GreenLake and, and a that's another example, nimble was similar. We're much smaller sort of more narrow, but taking the AI ops piece and bringing it over. So it's, it was great to see HPE executing on some of its M and a as opposed to just leaving them alone and not really leveraging 'em. So guys, yeah. Congratulations really appreciate you guys coming on and explaining that. Congratulations on all the, all the great work and thanks for coming on the cube. Okay. >>Thank you guys. Yeah. Thanks for having us. >>All right, John, and I'll be back right after this short break. You're watching the cube, the leader in enterprise tech coverage from HPE Las Vegas, 2022. We'll be right back.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

>>Hey, welcome back to the cubes. Continuing coverage of AWS reinvent 2021. I'm your host, Lisa Martin. This is day two, our first full day of coverage. But day two, we have two life sets here with AWS and its ecosystem partners to remote sets over a hundred guests on the program. We're going to be talking about the next decade of cloud innovation, and I'm pleased to welcome back to cube alumni to the program. Justin Borkman is here, the co-founder and CEO of Starburst and Teresa Tung, the cloud first chief technologist at Accenture guys. Welcome back to the queue. Thank you. Thank you for having me. Good to have you back. So, so Teresa, I was doing some research on you and I see you are the most prolific prolific inventor at Accenture with over 220 patents and patent applications. That's huge. Congratulations. Thank you. Thank you. And I love your title. I think it's intriguing. I'd like to learn a little bit more about your role cloud-first chief technologist. Tell me about, >>Well, I get to think about the future of cloud and if you think about clouded powers, everything experiences in our everyday lives and our homes and our car in our stores. So pretty much I get to be cute, right? The rest of Accenture's James Bond >>And your queue. I like that. Wow. What a great analogy. Just to talk to me a little bit, I know service has been on the program before, but give me a little bit of an overview of the company, what you guys do. What were some of the gaps in the markets that you saw a few years ago and said, we have an idea to solve this? Sure. >>So Starburst offers a distributed query engine, which essentially means we're able to run SQL queries on data anywhere, uh, could be in traditional relational databases, data lakes in the cloud on-prem. And I think that was the gap that we saw was basically that people had data everywhere and really had a challenge with how they analyze that data. And, uh, my co-founders are the creators of an open source project originally called Presto now called Trino. And it's how Facebook and Netflix and Airbnb and, and a number of the internet companies run their analytics. And so our idea was basically to take that, commercialize that and make it enterprise grade for the thousands of other companies that are struggling with data management, data analytics problems. >>And that's one of the things we've seen explode during the last 22 months, among many other things is data, right? In every company. These days has to be a data company. If they're not, there's a competitor in the rear view rear view mirror, ready to come and take that place. We're going to talk about the data mesh Teresa, we're going to start with you. This is not a new car. This is a new concept. Talk to us about what a data mesh is and why organizations need to embrace this >>Approach. So there's a canonical definition about data mesh with four attributes and any data geek or data architect really resonates with them. So number one, it's really routed decentralized domain ownership. So data is not within a single line of business within a single entity within a single partner has to be across different domains. Second is publishing data as products. And so instead of these really, you know, technology solutions, data sets, data tables, really thinking about the product and who's going to use it. The third one is really around self-service infrastructure. So you want everybody to be able to use those products. And finally, number four, it's really about federated and global governance. So even though their products, you really need to make sure that you're doing the right things, but what's data money. >>We're not talking about a single tool here, right? This is more of a, an approach, a solution. >>It is a data strategy first and foremost, right? So companies, they are multi-cloud, they have many projects going on, they are on premise. So what do you do about it? And so that's the reality of the situation today, and it's first and foremost, a business strategy and framework to think about the data. And then there's a new architecture that underlines and supports that >>Just didn't talk to me about when you're having customer conversations. Obviously organizations need to have a core data strategy that runs the business. They need to be able to, to democratize really truly democratized data access across all business units. What are some of the, what are some of your customer conversations like are customers really embracing the data strategy, vision and approach? >>Yeah, well, I think as you alluded to, you know, every business is data-driven today and the pandemic, if anything has accelerated digital transformation in that move to become data-driven. So it's imperative that every business of every shape and size really put the power of data in the hands of everyone within their organization. And I think part of what's making data mesh resonates so well, is that decentralization concept that Teresa spoke about? Like, I think companies acknowledge that data is inherently decentralized. They have a lot of different database systems, different teams and data mesh is a framework for thinking about that. Then not only acknowledges that reality, but also braces it and basically says there's actually advantages to this decentralized approach. And so I think that's, what's driving the interest level in the data mesh, uh, paradigm. And it's been exciting to work with customers as they think about that strategy. And I think that, you know, essentially every company in the space is, is in transition, whether they're moving from on cloud to the prem, uh, to, uh, sorry, from on-prem to the cloud or from one cloud to another cloud or undergoing that digital transformation, they have left behind data everywhere. And so they're, they're trying to wrestle with how to grasp that. >>And there's, we know that there's so much value in data. The, the need is to be able to get it, to be able to analyze it quickly in real time. I think another thing we learned in the pandemic is it real-time is no longer a nice to have. It is essential for businesses in every organization. So Theresa let's talk about how Accenture and servers are working together to take the data mesh from a concept of framework and put this into production into execution. >>Yeah. I mean, many clients are already doing some aspect of the data mesh as I listed those four attributes. I'm sure everybody thought like I'm already doing some of this. And so a lot of that is reviewing your existing data projects and looking at it from a data product landscape we're at Amazon, right? Amazon famous for being customer obsessed. So in data, we're not always customer obsessed. We put up tables, we put up data sets, feature stores. Who's actually going to use this data. What's the value from it. And I think that's a big change. And so a lot of what we're doing is helping apply that product lens, a literal product lens and thinking about the customer. >>So what are some w you know, we often talk about outcomes, everything being outcomes focused and customers, vendors wanting to help customers deliver big outcomes, you know, cost reduction, et cetera, things like that. How, what are some of the key outcomes Theresa that the data mesh framework unlocks for organizations in any industry to be able to leverage? >>Yeah. I mean, it really depends on the product. Some of it is organizational efficiency and data-driven decisions. So just by the able to see the data, see what's happening now, that's great. But then you have so beyond the, now what the, so what the analytics, right. Both predictive prescriptive analytics. So what, so now I have all this data I can analyze and drive and predict. And then finally, the, what if, if I have this data and my partners have this data in this mesh, and I can use it, I can ask a lot of what if and, and kind of game out scenarios about what if I did things differently, all of this in a very virtualized data-driven fashion, >>Right? Well, we've been talking about being data-driven for years and years and years, but it's one thing to say that it's a whole other thing to actually be able to put that into practice and to use it, to develop new products and services, delight customers, right. And, and really achieve the competitive advantage that businesses want to have. Just so talk to me about how your customer conversations have changed in the last 22 months, as we've seen this massive acceleration of digital transformation companies initially, really trying to survive and figure out how to pivot, not once, but multiple times. How are those customer conversations changing now is as that data strategy becomes core to the survival of every business and its ability to thrive. >>Yeah. I mean, I think it's accelerated everything and, and that's been obviously good for companies like us and like Accenture, cause there's a lot of work to be done out there. Um, but I think it's a transition from a storage centric mindset to more of an analytics centric mindset. You know, I think traditionally data warehousing has been all about moving data into one central place. And, and once you get it there, then you can analyze it. But I think companies don't have the time to wait for that anymore. Right there, there's no time to build all the ETL pipelines and maintain them and get all of that data together. We need to shorten that time to insight. And that's really what we, what we've been focusing on with our, with our customers, >>Shorten that time to insight to get that value out of the data faster. Exactly. Like I said, you know, the time is no longer a nice to have. It's an absolute differentiator for folks in every business. And as, as in our consumer lives, we have this expectation that we can get whatever we want on our phone, on any device, 24 by seven. And of course now in our business lives, we're having the same expectation, but you have to be able to unlock that access to that data, to be able to do the analytics, to make the decisions based on what the data say. Are you, are you finding our total? Let's talk about a little bit about the go to market strategy. You guys go in together. Talk to me about how you're working with AWS, Theresa, we'll start with you. And then Justin we'll head over to you. Okay. >>Well, a lot of this is powered by the cloud, right? So being able to imagine a new data business to run the analytics on it and then push it out, all of that is often cloud-based. But then the great thing about data mesh it's it gives you a framework to look at and tap into multi-cloud on-prem edge data, right? Data that can't be moved because it is a private and secure has to be at the edge and on-prem so you need to have that's their data reality. And the cloud really makes this easier to do. And then with data virtualization, especially coming from the digital natives, we know it scales >>Just to talk to me about it from your perspective that the GTL. >>Yeah. So, I mean, I think, uh, data mesh is really about people process and technology. I think Theresa alluded to it as a strategy. It's, it's more than just technology. Obviously we bring some of that technology to bear by allowing customers to query the data where it lives. But the people in process side is just as important training people to kind of think about how they do data management, data analytics differently is essential thinking about how to create data as a product. That's one of the core principles that Theresa mentioned, you know, that's where I think, um, you know, folks like Accenture can be really instrumental in helping people drive that transformational change within their organization. And that's >>Hard. Transformational change is hard with, you know, the last 22 months. I've been hard on everyone for every reason. How are you facilitating? I'm curious, like to get Theresa, we'll start with you, your perspectives on how our together as servers and Accenture, with the power of AWS, helping to drive that cultural change within organizations. Because like we talked about Justin there, nobody has extra time to waste on anything these days. >>The good news is there's that imperative, right? Every business is a digital business. We found that our technology leaders, right, the top 10% investors in digital, they are outperforming are the laggards. So before pandemic, it's times to post pep devek times five, so there's a need to change. And so data is really the heart of the company. That's how you unlock your technical debt into technical wealth. And so really using cloud and technologies like Starburst and data virtualization is how we can actually do that. >>And so how do you, Justin, how does Starburst help organizations transfer that technical debt or reduce it? How does the D how does the data much help facilitate that? Because we talk about technical debt and it can, it can really add up. >>Yeah, well, a lot of people use us, uh, or think about us as an abstraction layer above the different data sources that they have. So they may have legacy data sources today. Um, then maybe they want to move off of over time, um, could be classical data, warehouses, other classical, uh, relational databases, perhaps they're moving to the cloud. And by leveraging Starburst as this abstraction, they can query the data that they have today, while in the background, moving data into the cloud or moving it into the new data stores that they want to utilize. And it sort of hides that complexity. It decouples the end user experience, the business analyst, the data scientists from where the data lives. And I think that gives people a lot of freedom and a lot of optionality. And I think, you know, the only constant is change. Um, and so creating an architecture that can stand the test of time, I think is really, really important. >>Absolutely. Speaking of change, I just saw the announcement about Starburst galaxy fully managed SAS platform now available in all three major clouds. Of course, here we are at AWS. This is a, is this a big directional shift for servers? >>It is, you know, uh, I think there's great precedent within open source enterprise software companies like Mongo DB or confluent who started with a self managed product, much the way that we did, and then moved in the direction of creating a SAS product, a cloud hosted, fully managed product that really I think, expands the market. And that's really essentially what we're doing with galaxy galaxy is designed to be as easy as possible. Um, you know, Starburst was already powerful. This makes it powerful and easy. And, uh, and, and in our view, can, can hopefully expand the market to thousands of potential customers that can now leverage this technology in a, in a faster, easier way, >>Just in sticking with you for a minute. Talk to me about kind of where you're going in, where services heading in terms of support for the data mesh architecture across industries. >>Yeah. So a couple of things that we've, we've done recently, and whether we're doing, uh, as we speak, one is, uh, we introduced a new capability. We call star gate. Now star gate is a connector between Starburst clusters. So you're going to have a Starbucks cluster, and let's say Azure service cluster in AWS, a Starbucks cluster, maybe an AWS west and AWS east. And this basically pushes the processing to where the data lives. So again, living within this construct of, uh, of decentralized data that a data mesh is all about, this allows you to do that at an even greater level of abstraction. So it doesn't even matter what cloud region the data lives in or what cloud entirely it lives in. And there are a lot of important applications for this, not only latency in terms of giving you fast, uh, ability to join across those different clouds, but also, uh, data sovereignty constraints, right? >>Um, increasingly important, especially in Europe, but increasingly everywhere. And, you know, if your data isn't Switzerland, it needs to stay in Switzerland. So starting date as a way of pushing the processing to Switzerland. So you're minimizing the data that you need to pull back to complete your analysis. And, uh, and so we think that's a big deal about, you know, kind of enabling a data mash on a, on a global scale. Um, another thing we're working on back to the point of data products is how do customers curate and create these data products and share them within their organization. And so we're investing heavily in our product to make that easier as well, because I think back to one of the things, uh, Theresa said, it's, it's really all about, uh, making this practical and finding quick wins that customers can deploy, deploy in their data mess journey, right? >>This quick wins are key. So Theresa, last question to you, where should companies go to get started today? Obviously everybody has gotten, we're still in this work from anywhere environment. Companies have tons of data, tons of sources of data, did it, infrastructure's already in place. How did they go and get started with data? >>I think they should start looking at their data projects and thinking about the best data products. I think just that mindset shift about thinking about who's this for what's the business value. And then underneath that architecture and support comes to bear. And then thinking about who are the products that your product could work better with just like any other practice partnerships, like what we have with AWS, right? Like that's a stronger together sort of thing, >>Right? So there's that kind of that cultural component that really strategic shift in thinking and on the architecture. Awesome guys, thank you so much for joining me on the program, coming back on the cube at re-invent talking about data mesh really help. You can help organizations and industry put that together and what's going on at service. We appreciate your time. Thanks again. All right. For my guests, I'm Lisa Martin, you're watching the cubes coverage of AWS reinvent 2021. The cube is the leader in global live tech coverage. We'll be right back.

>>mhm >>Hello welcome to this cube conversation here in Palo alto California and john for with the cube we had a great conversation around the rise of the cloud and the massive opportunities and challenges around analytics data ai suggestion. Air ceo of thought spot is here with me for conversation. Great to see you. Welcome back to the cube. How are you? >>Well john it is so good to be back. I wish that we could do one of those massive set up that you have and do this face to face but zoom is not bad. >>You guys are doing very well. We have been covering you guys been covering the progress um great technology enabled business. You're on the wave of this cloud analytics you're seeing, we've seen massive changes and structural changes for the better. It's a tailwind for anyone in the cloud data business. And you also on the backdrop of all that the Covid and now the covid is looking at coming out of covid with growth strategies. People are building modern or modernizing their infrastructure and data is not just a department, it's everywhere. You guys are in the middle of this. Take us through what's the update on thought spot. What are you guys doing? What do you see the market right now? Honestly, delta variants coming coming strong but we think will be out of this soon. Where where are >>we look I think it all starts with the users like you said the consumers are demanding more and more from the business they are interacting with. You're no longer happy with being served like uh I'm gonna put you all in a bucket and then Delaware services to you. Everyone's like look look at me, I have likes and dislikes that is probably going to be different from someone that you think are similar to me. So unless you get to know me and deliver bespoke services to me, I'm gonna go somewhere else who does that And the call that the way you do that is through the data that I'm giving to you. So the worst thing you can do is to take my data and still treat me like an average and numbers and what's happening with the cloud is that it is now possible and it wasn't okay. So I grew up in India where newspapers will always have stock market summary on like one full page full of takers and prices and the way it used to work is that you wake up in the morning you look at the newspaper, I don't know if you have had the same thing and then you call your broker is based on in place of that. Can you imagine doing that now? I mean the information is at your fingertips. Hurricane IDa either is actually going to enter in Louisiana somewhere. What good is it? Yesterday morning state on this morning state if I'm trying to make a decision on whether I should pack my stuff and move away or you know finding to from home depot supply chain manager. I shouldn't figure out what should I be doing for Louisiana in the next two days, this is all about the information that's available to you. If you plan to use it and deliver better services for your consumer cloud makes it possible. >>You know, it's interesting you mentioned that the old way things were it seems so slow, then you got the 15 minute quotes, then there's now a real time. Everything has to be real time. And clearly there's two major things happening at the same time which makes exciting the business model and the competitive advantages for leaders and business to use data is critical but also on the developer side where apps are being developed if you don't have the data access, the machine learning won't work well. So as machine learning becomes really courted driving ai this modern analytics cloud product that you guys announced brings to bear kind of two major lifts the developer app modernization as well as competitive advantage for the companies that need to deploy this. So you guys have announced this modern approach analytics cloud, so to speak. What are some of the challenges that companies are having? Because you gotta, if you hit both of those you're gonna right a lot of value. What are some of the challenges for people who want to do this modern cloud? >>I think the challenge is basically all inside in the company. If you ask companies why are they failing to modernize? They will point to what's inside, it's not outside the technology is there the stack is the vendors are there, It is sometimes lack of courage at the leadership level which is a huge problem. I'll give an example. Uh, we have recently announced what we call thoughts part everywhere, which is our way of looking at how to modernize and bring the data inside that you're looking forward to where you are because Lord knows we all have enough apps on our Octa or a single sign on. The last thing you need is one more how no matter how good it is, they don't want to log into yet under their tool, whether it's thought spot or not. But the insights that you are talking about needs to be there when you need. And the difference is uh, the fundamental approach of data analytics was built on embedded model. You know what we are proposing is what we call data apps. So the difference between data apps and the typical dashboard being embedded into your analytics model is sort of like think of it. Uh newspapers telephones and the gap in between. So there is newspapers radio that is walkie talkie and telephone. They're all different and newspapers get printed and it comes to you and you read in the morning, you can talk back to it, you can drag and drop, you can change it right walkie talkies on the other hand, you know, you could have one conversation then come back to that. Whereas phone, you can have true direction conversation? They're all different if you think of embedding it is sort of like the newspaper, the information that you can't talk back. So somebody resembling something that came out monday, you're going to a board meeting on Wednesday and you look at that and make decisions. That is not enough in the new world, you just can't do that. It's not about what a lot of tools can actually answer what the real magic the real value for customers are unlocked when you ask three subsequent questions and answer them and they will come down to when you hear what you have to know. So what? Right and then what if and then the last is what next Imagine you can answer those three questions every business person every time no matter how powerful the dashboard is, they will always have the next question. What? So what? Okay the business customers are turning so what is it good, is it bad? Is it normal or the next question is like now what what do I do with it two, the ability to take all these three questions so what and what a fun. Now what? That requires true interactivity, you know, start with an intent and with an action and that is what we are actually proposing with the data apps which is only possible if you're sitting on top of a snowflake or red shift kind of really powerful and massive cloud data warehouse where the data comes and moves with agility. >>So how has this cloud data model rewritten the rules of business? Because what you're bringing up is essentially now full interactivity really getting in, getting questions that are iterating and building on context to each other. But with all this massive cloud data, people are really excited by this. How is it changing business than the rules of business? >>Yeah. So think about, I mean topical things like there is a hurricane able to enter, hit the cost of the United States. It's a moving target. No one knows exactly where it is going to be. There is only 15 models from here. 10, 10 models from Europe that's going to predict which way it's going to take every millimeter change in that map is going to have significant consequences for lives and resources and money. Right. This is true for every business. What cloud does this? Uh you have your proprietary data for example, let's say you're a bank and you have proprietary data, you're launching a new product And the propriety data was 2025 extremely valuable. But what what's not proprietary but what is available to you? Which could make that data so much more relevant if you layer them on top census data, this was a census here. The census data is updated. Do you not want that vaccination leader? We clearly know that purchasing power parity will vary based on vaccinations and county by county. But is that enough? You need to have street by street is county data enough. If you're going to open startup, Mr Starbucks? No, you probably want to know much more granular data. You wanna know traffic. Is the traffic picking up business usually an office space where people are not coming to office or is it more of a shopping mall where people are still showing all of these data is out there for you? What cloud is making it possible? Unlike the old era where you know, your data is an SFP oracle or carry later in your data center, it's available for you with a matter of clicks. What thought sport modern analytics. Cloud is a simple thing. We are the front end to bring all of this data and make sense of it. You can sit on top of any cloud data and then interact with a complete sort of freedom without compromising on security, compliance or relevance. And what happens is the analysts, the people who are responsible for bringing the data and then making sure that it is secure and delivered. They are no longer doing incremental in chart updates and dashboard updates. What they're doing is solving business problems, business people there freely interacting and making bigger decisions. That actually adds value to their consumers. This is what your customers are looking for, your users are looking for and if you're not doing it, your competitor will do that. So this is why cloud is not a choice for you. It's not an option for you. It is the only way and if you fail to take that back the other way is taking the world out of a cliff. >>Yeah, that's I love it. But I want to get this uh topic of thoughts about anywhere, but I want to just close out on this whole idea of modern cloud scale analytics. What technology under the hood do you guys see that customers should pay attention to with thought spot and in general because the scale there. So is it just machine learning? We hear data lakes, you know, you know different configurations of that. Machine learning is always thrown around like a buzzword. What new technology capability should every executive by your customer look for when it comes to really doing analytics, modern in the cloud >>analytics has to be near real time, Which means what two things speed at scale, make sure it's complex, it can deal with complexity in data structure. Data complexity is a huge problem. Now imagine doing that at scale and then delivering with performance. That means you have to rethink Look Tableau grew out of excellent worksheets that is the market leader, it is a $40 billion dollar market with the largest company having only a billion dollars in revenue. This is a massive place where the problems need to be solved differently. So the underlying technology to me are like I said, these three things, number one cannot handle the cloud scale, you will have hundreds of billions of rows of data that you brought. But when you talk about social media sentiment of customers, analysis of traffic and weather patterns, all of these publicly available valuable data. We're talking trillions of rows of data. So that is scale. Now imagine complexity. So financial sector for example, there is health care where you know some data is visible, some data is not visible, some some is public assumption not or you have to take credit data and let it on top of your marketing data. So it becomes more complex. And the last is when you answer ask a question, can you deliver with absolute confidence that you're giving the right answer With extremely high performance and to do that you have to rebuild the entire staff. You cannot take your, you know, stack that was built in 1990s and so now we can do search So search that is built for these three things with the machine learning and ai essentially helping at every step of the way so that you're not throwing all this inside directly to a human, throw it to a i engine and the ai engine curates what is relevant to you, showing it to you. And then based on your interaction with that inside, I improve my own logic so that the next interaction, the next situation is going to be significantly better. My point is you cannot take a triple a map and then try to act like this google maps. One is built presuming and zoom out and learn from you. The other one is built to give you rich information but doesn't talk back. So the staff has to be fundamentally rebuilt for the club. That's what he's doing. >>I love I love to buy direction. I love the interactivity. This topic of thought spot everywhere, which you mentioned at the beginning of this conversation, you mentioned data apps which by the way I love that concept. I want to do a drill down on that. Uh I saw data marketplace is coming somewhat working but I think it's going to get it better. I love that idea of an app um, and using as developers but you also mentioned embedded analytics. You made a comment about that. So I gotta ask you what's the difference between data apps and embedded analytics? >>Embedded analytics means that uh you know the dashboards that you love but the one that doesn't talk back to you is going to be available inside the app that you built for your other So if a supply chain app that was built by let's say accenture inside that you haven't had your dashboard without logging into tablet. Great. But what you do, what's the big deal? It is the same thing. My point is like I said every time a business user sees a chart. The questions are going to come up. The next 10 question is where the values on earth for example on Yelp imagine if you will piece about I'm hungry. I want to find a restaurant and it says go to this burrito place. It doesn't work like that. It's not good enough. The reason why yell towards is because I start with an intent. I'm hungry. Okay show me all restaurants. Okay I haven't had about it for a while. Let me see the photos. Let me read the reviews. Let me see if my friends have eaten, let me see some menu. Can I walk there? I do all of this but just what underneath it. There is a rich set of data that probably helped have their own secret source and reviews and then you have google map powering some of them. But I don't care all of that is coming together to deliver a seamless experience that satisfies my hunger. Which will be very different from if you use the same map at the same place you might go to an italian place. I go to bed right. That is the power of a data app in business people are still sitting with this. I am hungry. I gotta eat burrito. That's not how it should be in the new world. A business user should have the freedom to add exactly what the customers require looking for and solve that problem without delay. That means every application should be power and enriched with the data where you can interact and customized. That is not something that enterprise customers are actually used to and to do that you need like I said a I and search powering like the google map underneath it, but you need an app like a yelp like app, that's what we deliver. So for example, uh just last week we delivered a service now app on snowflake. You know, it just changes the game. You are thinking about customer cases. You're a large company, you have support coming from Philippines and India some places the quality is good. Some places bad dashboards are not good enough saying that okay, 17% of our customers are unhappy but we are good. That's not the world we live in. That is the tyranny of >>average, >>17% were unhappy. You got to solve for them. >>You mentioned snowflake and they had their earnings. David and I were commenting about how some of the analysts got it all wrong. And you bring up a really good point that kind of highlights the real trend. Not so much how many new customers they got. But there do what customers are doing more. Right? So, so what's happening is that you're starting to see with data apps, it does imply Softwares in there because it's it's application. So the software wrapping around data. This is interesting because people that are using the snowflakes of the world and thought spot your software and your platform, they're doing more with data. So it's not so much. I use snowflake, I use snowflake now I'm going to do more with it. That's the scale kicking. So this is an opportunity to look at that more equation. How do you talk >>with >>when you see that? Because that's the real thing is like, okay, that's I bought software as a service. But what's the more that's happening? What do you see >>that is such an important point? Even I haven't thought about it that john but you're absolutely right. That is sometimes people think of snowflake is taking care of it and no. Yeah, yes, Sarah later used to store once and zeros and they're moving it into club. That is not the point. Like I said, marketplace as an example when you are opening it up for for example, bringing the entire world's data with one click accessible to you securely. That is something you couldn't do on number two. You can have like 100 suppliers and all of a sudden you can now take a single copy of data and then make it available to all of them without actually creating multiple copies and control it differently. That's not something without cloudy, potentially could do. So things like that are fundamentally different. It is much more than like one plus one equals two. It is one plus one is 33. Like our view is that when you are re platform ng like that, you have to think from customer first. What does the customer do? The customer care that you meant from Entre into cloud or event from Teradata snowflake. No, they will care if their lives are better. Are they able to get better services are able to get it faster. That's what it is. So to me it is very simple. The destiny of an insight or data information is action, right? Imagine you're driving a car and if your car updates the gas tank every monday morning, imagine how you know, stressful your life will be for the whole week. I have to wait until next monday wanting to figure out what, whether I have enough gas or not, that's not the new world, that information is there, you need to have it real time and act on it. If you go through the Tesla you realize now that you know, I'm never worried about mileage because it is going to take me to the supercharger because it knows what I need to get to, it knows how long it is going to be, how bad the traffic is. It is synthesizing all of that to give me peace of mind. >>So this is a great >>conversation. That's a >>great question. It's a great conversation because it's really kind of brings in kind of what's happening, you see successful companies that are working with cloud scale and data like you're talking about, it's you get in there, you get the data, the data apps and all of a sudden you hit it, you hit the value equation and it's like almost like discovering oil all of a sudden you have a gusher and then people just see massive increase in value. It's not like the outcome, it's kind of there, you've got to kind of get in there and this is the scale piece and you see people having strategies to do that, they say okay we're gonna get in there, we're going to use the data to iterate but also watch the data learn where's that value, This is that more trend and and there's a successful of the developing. So I have to ask you when you, when you talk about people and culture, um that's not the way it used to be, used to be like okay I'm buying an outcome. I deployed some software mechanisms and at the end of the day there's some value there. Maybe I write it off maybe I, you know, overtime charges and some accounting thing. All changed the culture and the people in charge now are transforming the management techniques. What do you see as a successful mindset for a customer as they managed through these new paradigms and new new success formulas. >>I see a fork in leadership when it comes to courage. There are people with the spine and there are people without the spine and the ones with the spine are absolutely killing it. They are unafraid. They are not saying, look, I'm just going to stick with the incumbents that I've known for the last 20 years. Look, I used to drive a Toyota forever because I love the Toyota. And then you know after Nutanix IPO went to Lexus still Toyota because it's reliable. I don't, I'm not a huge card person. It works. But guess what? I knew they were missing Patrick and I care about the environment. I don't want to keep pushing hydrocarbons out there. It's not politics. I just don't like burning stuff into the earth atmosphere. So when Tesla came out, it's not like I love the quality I don't personally like alone mask, you know after that Thailand fiasco of cave rescue and all of that. But I can clearly see that Toyota is not going to catch up to Tesla in the next 10 years. And guess what? My loyalty is much more to doing the right thing for my family and to the world. And I switched this is what business leaders need to know. They can't simply say, well, tabloid as search to. They're not as good as thought sports. We'll just stick with them because they have done with us. That's what weak leaders do and customers suffer for that. What I see like the last two weeks ago when I was in new york. I met with them. A business leader for one of the largest banks in the world with 25,000 people reporting to him. The person walks into the room wearing shorts and t shirts uh, and was so full of energy and so full of excitement. I thought I'm going to learn from him and he was asking questions about how we do our business in bed and learning from me. I was humbled, I was flawed and I realized that's what a modern business leader looks like. Even if it is one of the largest and oldest banks in the world, that's the kind of people are making big difference and it doesn't matter how all the companies, how old their data is they have mainframes or not. I hear this excuses all the type of er, mainframes, we can't move, we have COBOL going on. And guess what? You keep talking about that and hear leaders like him are going to transform those companies And next thing you know, there are some of the most modern companies in the world. >>Well certainly they, we know that they don't have any innovation strategy or any kind of R and D or anything going on that could be caught flat footed in the companies that didn't have that going on, didn't have the spine or the, the, the vision to, to at least try the cloud before Covid when Covid hit, those companies are really either going out of business or they're hurting the people who were in the cloud really move their teams into the cloud quicker to take advantage of uh, the environment that they had to. So this became a skill issue. So, so this is a big deal. This is a big deal. And having the right skills are people skilled, it will be a, I both be running everything for them. What is your take on that? >>This is an important question. You can't just say you got to do more things or new things and not take care of all things. You know, there's only 89, 10 hours so you can work in their uh, analysts in the Atlantic species constantly if your analysts are sitting there and making incremental dashboards and reports change every day and then backlog is growing for 56 days and the users are unhappy because you're not getting answers and then you ask them to go to new things. It's just not going to be enough and you can hire your way out of it. You have to make sure that if you say that I have 20 100 x product already, I don't want 21st guess what? Sometimes to be five products, you need to probably go to 21 you got to do new things to actually take away the gunk off the old and in that context, the re skilling starts with unburdening, unburdening of menial task, unburned routine task. There is nothing more frustrating than making reports and dashboards that people don't even use And 90% of the time analysts, they're amazing experiences completely wasted when they're making incremental change to tabloid reports. I kind of believe thought spot and self service on top of cloud data takes away all of that without compromising security and then you invest the experienced people. Business experience is so critical. So don't just go and hire university students and say, okay, they'll go come and quote everything the experience that they have in knowing what the business is about and what it matters to their users, that domain experience and then uplevel them res kill them and then bring fresh energy to challenge that and then make sure there is a culture that allows that to happen. These three things. That's why I said leadership is not just about hiring event of firing another, it's about cultivating a culture and living that value by saying, look if I am wrong, call me, call me out in public because I want to show you how I deal with conflict. So this is I love this thing because when I see these large companies where they're making these massive changes so fast, it inspires you to say you know what if they can do it, anyone can do it. But then I also see if the top leadership is not aligned to that. They are just trying to retire without the stock tanking too much and let me just get through two more years. The entire company suffers. >>So that's great to chat with you got great energy, love your business, love the energy, love the focus. Um it's a new wave you're on. It's a big wave um and it's it's relevant, it's cool and relevant and it's the modern way and people have to have a spine to be successful if not for the faint of heart, but the rewards are there if you get this right. This is what I I love about this new environment. Um so I gotta ask you just to kind of close it out. How would you plug the company for the folks watching that might want to engage with you guys. What's the elevator pitch? What's the positioning? How would you describe thought spot in a bumper sticker or in a positioning statement. Take a minute to talk about that. >>Remember martin Anderson said that software is eating the world, I think it is now time to update that data is eating everything including software. If you don't have a way to turn data into bespoke action for your customers. Guess what? Your customers are gonna go somewhere where they that's happening right? You may not be in the data business but the data company is going to take your business. Thought spot is very simple. We want to be the friend tent for all cloud data when it comes to structured because that's where business value numbers is world satisfaction and dissatisfaction for reduces allying it is important to move data to action and thought Spot is the pioneer in doing that through search and I >>I really think you guys want something very powerful. Looking forward to chatting with you at the upcoming eight of a startup showcase. I think data is a developer mindset. It's an app, it's part of everything. It will. Everyone's a data company, everyone is a media company. Data is everything you guys are on something really big and people got a program it with it, make experiences whether it's simple scripts, point and click. That is a new kind of developer out there. You guys are tapping into it. Great stuff. Thank >>you for coming on. Thank you john it's good to talk to you. >>Okay. It's a cube conversation here in Palo alto California were remote. We're virtual. That's the cube virtual. I'm sean for your host. Thanks for watching. Mhm. Mhm

>>Good afternoon, I'm wanting or evening depending >>On where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is Mooney castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurances, maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry >>In there. So >>Recently what we have been seeing, and it's actually accelerated as a result of their recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customer's risks, lotta via commercial, a client or a personal person, personal insurance risk in a dynamic and a bespoke way. And what I mean with that is in a dynamic way, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and they're changing per person. They're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in depth capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendor is global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help account for that. >>And this is a bit a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress insurers make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models. And then, and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing certain events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, Watts next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fatherless or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. >>So you will see that on the reporting side, in the beginning, we start with braids, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as the descriptions move into some sort of an unstructured data environments, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the outer side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. Um, but I'm not Whitely integrated yet. So I will walk through some use cases around specifically. So think about sensors, wearables, you know, sense of some people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or a risk risk inspector or engineer to every building. You know, insurers now fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies all done out at pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different states, Dara. Some of them aren't managed and controlled by you. Some audits you get from third parties and we'll talk about Della medics in a little bit. It's one of the use cases, the move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it, eat it in an operational environment for processing what in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close. And it will come in as a, as a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on all the different regulations that, that we are subject to. So we do that in the cloud era share data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrid, most insurers run hybrid models, which are part of that data on premise and part of the data and use cases and workloads in the cloud. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you through some of the use cases that we've seen in action recently that showcases how this >>Work in real life. First one >>Is to seize that group plus Cloudera, um, uh, full disclosure is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be Dutch is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any ensure customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need us in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs >>And results. >>The other one that I wanted to quickly mention is Octo as most of you know, Optune is a very, very large telemedics provider, telematics data provider globally speaking with Cloudera for quite some time, this one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points and really want to doing as the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance telematics programs, right to pay as you drive B when you drive the, how you drive. And this whole telemedics part of insurance is actually growing very fast too in, in, still in solidified proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. >>So they become preventative and predictive too. So now you got to the program staff being me as a dry for seeing Monique you're hopping in the car for two hours. Now, maybe it's time to take a break. Um, we see that there's a Starbucks coming up on the right or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. It's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial commercial enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are and, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly move to other use cases or deployed into other use cases to help price risks better, better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance whisk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other, any, any insurable entity, right? The police physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, you know, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as the last thing I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.

>>Good morning or good afternoon or good evening, depending on where you are and welcome to this session, reduce claims, fraud, we're data, very excited to have you all here. My name is Winnie castling and I'm Cloudera as managing director for the insurance vertical. First and foremost, we want to let you know that we know insurance. We have done it for a long time. Collectively, personally, I've done it for over 30 years. And, you know, as a proof of that, we want to let you know that we insure, we insure as well as we do data management work for the top global companies in the world, in north America, over property casualty, general insurance health, and, um, life and annuities. But besides that, we also take care of the data needs for some smaller insurance companies and specialty companies. So if you're not one of the huge Glomar conglomerates in the world, you are still perfectly fine with us. >>So >>Why are we having this topic today? Really digital claims and digital claims management is accelerating. And that's based on a couple of things. First and foremost, customers are asking for it. Customers are used to doing their work more digitally over the last descending year or two. And secondly, with the last year or almost two, by now with the changes that we made in our work processes and in society at large around cuvettes, uh, both regulators, as well as companies have enabled digital processing and the digital journey to a degree that they've never done before. Now that had some really good impacts for claims handling. It did meant that customers were more satisfied. They felt they have more control over their processes in the cloud and the claims experience. It also reduced in a lot of cases, both in commercial lines, as well as in personal lines, the, um, the, the time periods that it took to settle on a claim. However, um, the more digital you go, it, it opened up more access points for fraud, illicit activities. So unfortunately we saw indicators of fraud and fraud attempts, you know, creeping up over the last time period. So we thought it was a good moment to look at, you know, some use cases and some approaches insurers can take to manage that even better than they already >>Are. >>And this is how we plan to do that. And this is how we see this in action. On the left side, you see progress of data analytics and data utilization, um, around, in this case, we're talking about claims fraud, but it's a generic picture. And really what it means is most companies that start with data affords pretty much start around data warehousing and we eliminate analytics and all around BI and reporting, which pretty much is understanding what we know, right? The data that we already have utilizing data to understand better what we know already. Now, when we move to the middle blue collar, we get into different types of analytics. We get into exploratory data science, we get to predictions and we start getting in the space of describing what we can learn from what we know, but also start moving slowly into predicting. So first of all, learn and gather insights of what we already know, and then start augmenting with that with other data sets and other findings, so that we can start predicting for the future, what might happen. >>And that's the point where we get to AI, artificial intelligence and machine learning, which will help us predict which of our situations and claims are most likely to have a potential fraud or abuse scenario attached to it. So that's the path that insurers and other companies take in their data management and analytics environments. Now, if you look at the right side of this light, you see data complexity per use cases in this case in fraud. So the bubbles represent the types of data that are being used, or the specific faces that we discussed on the left side. So for reporting, we used a TPA data, policy verification, um, claims file staff data, that it tends to be heavily structured and already within the company itself. And when you go to the middle to the more descriptive basis, you start getting into unstructured data, you see a lot of instructor texts there, and we do a use case around that later. >>And this really enables us to better understand what the scenarios are that we're looking at and where the risks are around. In our example today, fraud, abuse and issues of resources. And then the more you go to the upper right corner, you see the outside of the baseball field, people refer to it, you see new unstructured data sources that are being used. You tend to see the more complex use cases. And we're looking at picture analysis, we're looking at voice analysis there. We're looking at geolocation. That's quite often the first one we look at. So this slide actually shows you the progress and the path in complexity and in utilization of data and analytical tool sets to manage data fraud, fraud, use cases, optimally. >>Now how we do that and how we look at at a Cloudera is actually not as complicated as, as this slight might want to, um, to, to give you an impression. So let's start at the left side at the left side, you see the enterprise data, which is data that you as an organization have, or that you have access to. It doesn't have to be internal data, but quite often it is now that data goes into a data journey, right? It gets collected first. It gets manipulated and engineered so that people can do something with it. It gets stored something, you know, people need to have access to it. And then they get into analytical capabilities who are inside gathering and utilization. Now, especially for insurance companies that all needs to be underpinned by a very, very strong security and governance, uh, environment. Because if not the most regulated industry in the world, insurance is awfully close. >>And if it's not the most regulated one, it's a close second. So it's critically important that insurers know, um, where the data is, who has access to it for Rodriguez, uh, what is being used for so terms like lineage, transparency are crucial, crucially important for insurance. And we manage that in the shared data experience. So it goes over the whole Cloudera platform and every application or tool or experience you use would include Dao. And on the right side, you see the use cases that tend to be deployed around claims and claims fraud, claims, fraud management. So over the last year or so, we've seen a lot of use cases around upcoding people get one treatment or one fix on a car, but it gets coded as a more expensive one. That's a fraud scenario, right? We see also the more classical fraud things and we see anti money laundering. So those are the types of use cases on the right side that we are supporting, um, on the platform, uh, around, um, claims fraud. >>And this is an example of how that actually looks like now, this is a one that it's actually a live one of, uh, a company that had, um, claims that dealt with health situations and being killers. So that obviously is relevant for health insurers, but you also see it in, um, in auto claims and counterclaims, right, you know, accidents. There are a lot of different claims scenarios that have health risks associated with it. And what we did in this one is we joined tables in a complex schema. So we have to look at the claimant, the physician, the hospital, all the providers that are involved procedures that are being deployed. Medically medicines has been utilized to uncover the full picture. Now that is a hard effort in itself, just for one claim and one scenario. But if you want to see if people are abusing, for example, painkillers in this scenario, you need to do that over every instant that is member. >>This claimant has, you know, with different doctors, with different hospitals, with different pharmacies or whatever that classically it's a very complicated and complex, um, the and costly data operation. So nowadays that tends to be done by graph databases, right? So you put fraud rings within a graph database and walk the graph. And if you look at it here in batch, you can see that in this case, that is a member that was shopping around for being killers and went through different systems and different providers to get, um, multiple of the same big LR stat. You know, obviously we don't know what he or she did with it, but that's not the intent of the system. And that was actually a fraud and abuse case. >>So I want to share some customer success stories and recent, uh, AML and fraud use cases. And we have a couple of them and I'm not going to go in an awful lot of detail, um, about them because we have some time to spend on one of them immediately after this. But one of them for example, is voice analytics, which is a really interesting one. And on the baseball slide that I showed you earlier, that would be a right upper corner one. And what happened there is that an insurance company utilized the, uh, the voice records they got from the customer service people to try to predict which one were potentially fraud list. And they did it in two ways. They look at actually the contents of what was being said. So they looked at certain words that were being used certain trigger words, but they also were looking at tone of voice pitch of voice, uh, speed of talking. >>So they try to see trends there and hear trends that would, um, that would bring them for a potential bad situation. Now good and bad news of this proof of concept was it's. We learned that it's very difficult just because every human is different to get an indicator for bad behavior out of the pitch or the tone or the voice, you know, or those types of nonverbal communication in voice. But we did learn that it was easier to, to predict if a specific conversation needed to be transferred to somebody else based on emotion. You know, obviously as we all understand life and health situations tend to come with emotions, or so people either got very sad or they got very angry or so the proof of concept didn't really get us to a firm understanding of potential driverless situation, but it did get us to a much better understanding of workflow around, um, claims escalation, um, in customer service to route people, to the right person, depending on what they need. >>And that specific time, another really interesting one was around social media, geo open source, all sorts of data that we put together. And we linked to the second one that I listed on slide here that was an on-prem deployment. And that was actually an analysis that regulators were asking for in a couple of countries, uh, for anti money laundering scams, because there were some plots out there that networks of criminals would all buy the low value policies, surrendered them a couple of years later. And in that way, God criminal money into the regular amount of monetary system whitewashed the money and this needed some very specific and very, very complex link analysis because there were fairly large networks of criminals that all needed to be tied together, um, with the actions, with the policies to figure out where potential pain points were. And that also obviously included ecosystems, such as lawyers, administrative offices, all the other things, no, but most, you know, exciting. >>I think that we see happening at the moment and we, we, you know, our partner, if analytics just went live with this with a large insurer, is that by looking at different types that insurers already have, um, unstructured data, um, um, their claims nodes, um, repour its claims, filings, um, statements, voice records, augmented with information that they have access to, but that's not their ours such as geo information obituary, social media Boyd on the cloud. And we can analyze claims much more effectively and efficiently for fraud and litigation and alpha before. And the first results over the last year or two showcasing a significant degree is significant degrees in claims expenses and, um, and an increase at the right moment of what a right amount in claims payments, which is obviously a good thing for insurers. Right? So having said all of that, I really would like to give Sri Ramaswami, the CEO of infinite Lytics, the opportunity to walk you through this use case and actually show you how this looks like in real life. So Sheree, here >>You go. So >>Insurers often ask us this question, can AI help insurance companies, lower loss expenses, litigation, and help manage reserves better? We all know that insurance industry is majority. Majority of it is unstructured data. Can AI analyze all of this historically and look for patterns and trends to help workflows and improve process efficiencies. This is exactly why we brought together industry experts at infill lyrics to create the industries where very first pre-trained and prebuilt insights engine called Charlie, Charlie basically summarizes all of the data structured and unstructured. And when I say unstructured, I go back to what money basically traded. You know, it is including documents, reports, third-party, um, it reports and investigation, uh, interviews, statements, claim notes included as well at any third party enrichment that we can legally get our hands on anything that helps the adjudicate, the claims better. That is all something that we can include as part of the analysis. And what Charlie does is takes all of this data and very neatly summarizes all of this. After the analysis into insights within our dashboard, our proprietary naturally language processing semantic models adds the explanation to our predictions and insights, which is the key element that makes all of our insights >>Actually. So >>Let's just get into, um, standing what these steps are and how Charlie can help, um, you know, with the insights from the historical patterns in this case. So when the claim comes in, it comes with a lot of unstructured data and documents that the, uh, the claims operations team have to utilize to adjudicate, to understand and adjudicate the claim in an efficient manner. You are looking at a lot of documents, correspondences reports, third party reports, and also statements that are recorded within the claim notes. What Charlie basically does is crunches all, all of this data removes the noise from that and brings together five key elements, locations, texts, sentiments, entities, and timelines in the next step. >>In the next step, we are basically utilizing Charlie's built-in proprietary, natural language processing models to semantically understand and interpret all of that information and bring together those key elements into curated insights. And the way we do that is by building knowledge, graphs, and ontologies and dictionaries that can help understand the domain language and convert them into insights and predictions that we can display on the dash. Cool. And if you look at what has been presented in the dashboard, these are KPIs and metrics that are very interesting for a management staff or even the operations. So the management team can basically look at the dashboard and start with the summarized data and start to then dig deeper into each of the problematic areas and look at patterns at that point. And these patterns that we learn from not only from what the system can provide, but also from the historic data can help understand and uncover some of these patterns in the newer claims that are coming in so important to learn from the historic learnings and apply those learnings in the new claims that are coming in. >>Let's just take a very quick example of what this is going to look like a claims manager. So here the claims manager discovers from the summarized information that there are some problems in the claims that basically have an attorney involved. They have not even gone into litigation and they still are, you know, I'm experiencing a very large, um, average amount of claim loss when they compare to the benchmark. So this is where the manager wants to dig deeper and understand the patterns behind it from the historic data. And this has to look at the wealth of information that is sitting in the unstructured data. So Charlie basically pulls together all these topics and summarizes these topics that are very specific to certain losses combined with entities and timelines and sentiments, and very quickly be able to show to the manager where the problematic areas are and what are those patterns leading to high, severe claims, whether it's litigation or whether it's just high, severe indemnity payments. >>And this is where the managers can adjust their workflows based on what we can predict using those patterns that we have learned and predict the new claims, the operations team can also leverage Charlie's deep level insights, claim level insights, uh, in the form of red flags, alerts and recommendations. They can also be trained using these recommendations and the operations team can mitigate the claims much more effectively and proactively using these kind of deep level insights that need to look at unstructured data. So at the, at the end, I would like to say that it is possible for us to achieve financial benefits, leveraging artificial intelligence platforms like Charlie and help the insurers learn from their historic data and being able to apply that to the new claims, to work, to adjust their workflows efficiently. >>Thank you very much for you. That was very enlightening as always. And it's great to see that actually, some of the technology that we all work so hard on together, uh, comes to fruition in, in cost savings and efficiencies and, and help insurers manage potential bad situations, such as claims fraud batter, right? So to close this session out as a next step, we would really urge you to a Sasha available data sources and advanced or predictive fraud prevention capabilities aligned with your digital initiatives to digital initiatives that we all embarked on over the last year are creating a lot of new data that we can use to learn more. So that's a great thing. If you need to learn more at one to learn more about Cloudera and our insurance work and our insurance efforts, um, you to call me, uh, I'm very excited to talk about this forever. So if you want to give me a call or find a place to meet when that's possible again, and schedule a meeting with us, and again, we love insurance. We'll gladly talk to anyone until they say in parts of the United States, the cows come home about it. And we're dad. I want to thank you all for attending this session and hanging in there with us for about half an hour. And I hope you have a wonderful rest of the day. >>Good afternoon, I'm wanting or evening depending on where you are and welcome to this breakout session around insurance, improve underwriting with better insights. >>So first and >>Foremost, let's summarize very quickly, um, who we're with and what we're talking about today. My name is goonie castling, and I'm the managing director at Cloudera for the insurance vertical. And we have a sizeable presence in insurance. We have been working with insurance companies for a long time now, over 10 years, which in terms of insurance, it's maybe not that long, but for technology, it really is. And we're working with, as you can see some of the largest companies in the world and in the continents of the world. However, we also do a significant amount of work with smaller insurance companies, especially around specialty exposures and the regionals, the mutuals in property, casualty, general insurance, life, annuity, and health. So we have a vast experience of working with insurers. And, um, we'd like to talk a little bit today about what we're seeing recently in the underwriting space and what we can do to support the insurance industry in there. >>So >>Recently what we have been seeing, and it's actually accelerated as a result of the recent pandemic that we all have been going through. We see that insurers are putting even more emphasis on accounting for every individual customers with lotta be a commercial clients or a personal person, personal insurance risk in a dynamic and a B spoke way. And what I mean with that is in a dynamic, it means that risks and risk assessments change very regularly, right? Companies go into different business situations. People behave differently. Risks are changing all the time and the changing per person they're not changing the narrow generically my risk at a certain point of time in travel, for example, it might be very different than any of your risks, right? So what technology has started to enable is underwrite and assess those risks at those very specific individual levels. And you can see that insurers are investing in that capability. The value of, um, artificial intelligence and underwriting is growing dramatically. As you see from some of those quotes here and also risks that were historically very difficult to assess such as networks, uh, vendors, global supply chains, um, works workers' compensation that has a lot of moving parts to it all the time and anything that deals with rapidly changing risks, exposures and people, and businesses have been supported more and more by technology such as ours to help, uh, gone for that. >>And this is a bit of a difficult slide. So bear with me for a second here. What this slide shows specifically for underwriting is how data-driven insights help manage underwriting. And what you see on the left side of this slide is the progress in make in analytical capabilities. And quite often the first steps are around reporting and that tends to be run from a data warehouse, operational data store, Starsky, Matt, um, data, uh, models and reporting really is, uh, quite often as a BI function, of course, a business intelligence function. And it really, you know, at a regular basis informs the company of what has been taken place now in the second phase, the middle dark, the middle color blue. The next step that is shore stage is to get into descriptive analytics. And what descriptive analytics really do is they try to describe what we're learning in reporting. >>So we're seeing sorts and events and sorts and findings and sorts of numbers and certain trends happening in reporting. And in the descriptive phase, we describe what this means and you know why this is happening. And then ultimately, and this is the holy grill, the end goal we like to get through predictive analytics. So we like to try to predict what is going to happen, uh, which risk is a good one to underwrite, you know, watch next policy, a customer might need or wants water claims as we discuss it. And not a session today, uh, might become fraud or lists or a which one we can move straight through because they're not supposed to be any issues with it, both on the underwriting and the claims side. So that's where every insurer is shooting for right now. But most of them are not there yet. >>Totally. Right. So on the right side of this slide specifically for underwriting, we would, we like to show what types of data generally are being used in use cases around underwriting, in the different faces of maturity and analytics that I just described. So you will see that on the reporting side, in the beginning, we start with rates, information, quotes, information, submission information, bounding information. Um, then if you go to the descriptive phase, we start to add risk engineering information, risk reports, um, schedules of assets on the commercial side, because some are profiles, uh, as a descriptions, move into some sort of an unstructured data environment, um, notes, diaries, claims notes, underwriting notes, risk engineering notes, transcripts of customer service calls, and then totally to the other side of this baseball field looking slide, right? You will see the relatively new data sources that can add tremendous value. >>Um, but I'm not Whitely integrated yet. So I will walk through some use cases around these specifically. So think about sensors, wearables, you know, sensors on people's bodies, sensors, moving assets for transportation, drone images for underwriting. It's not necessary anymore to send, uh, an inspection person and inspector or risk, risk inspector or engineer to every building, you know, be insurers now, fly drones over it, to look at the roofs, et cetera, photos. You know, we see it a lot in claims first notice of loss, but we also see it for underwriting purposes that policies out there. Now that pretty much say sent me pictures of your five most valuable assets in your home and we'll price your home and all its contents for you. So we start seeing more and more movements towards those, as I mentioned earlier, dynamic and bespoke types of underwriting. >>So this is how Cloudera supports those initiatives. So on the left side, you see data coming into your insurance company. There are all sorts of different data. There are, some of them are managed and controlled by you. Some orders you get from third parties, and we'll talk about Della medics in a little bit. It's one of the use cases. They move into the data life cycle, the data journey. So the data is coming into your organization. You collected, you store it, you make it ready for utilization. You plop it either in an operational environment for processing or in an analytical environment for analysis. And then you close on the loop and adjusted from the beginning if necessary, no specifically for insurance, which is if not the most regulated industry in the world it's coming awfully close, and it will come in as a, a very admirable second or third. >>Um, it's critically important that that data is controlled and managed in the correct way on the old, the different regulations that, that we are subject to. So we do that in the cloud era Sharon's data experiment experience, which is where we make sure that the data is accessed by the right people. And that we always can track who did watch to any point in time to that data. Um, and that's all part of the Cloudera data platform. Now that whole environment that we run on premise as well as in the cloud or in multiple clouds or in hybrids, most insurers run hybrid models, which are part of the data on premise and part of the data and use cases and workloads in the clouds. We support enterprise use cases around on the writing in risk selection, individualized pricing, digital submissions, quote processing, the whole quote, quote bound process, digitally fraud and compliance evaluations and network analysis around, um, service providers. So I want to walk you to some of the use cases that we've seen in action recently that showcases how this work in real life. >>First one >>Is to seize that group plus Cloudera, um, uh, full disclosure. This is obviously for the people that know a Dutch health insurer. I did not pick the one because I happen to be dodged is just happens to be a fantastic use case and what they were struggling with as many, many insurance companies is that they had a legacy infrastructure that made it very difficult to combine data sets and get a full view of the customer and its needs. Um, as any insurer, customer demands and needs are rapidly changing competition is changing. So C-SAT decided that they needed to do something about it. And they built a data platform on Cloudera that helps them do a couple of things. It helps them support customers better or proactively. So they got really good in pinging customers on what potential steps they need to take to improve on their health in a preventative way. >>But also they sped up rapidly their, uh, approvals of medical procedures, et cetera. And so that was the original intent, right? It's like serve the customers better or retain the customers, make sure what they have the right access to the right services when they need it in a proactive way. As a side effect of this, um, data platform. They also got much better in, um, preventing and predicting fraud and abuse, which is, um, the topic of the other session we're running today. So it really was a good success and they're very happy with it. And they're actually starting to see a significant uptick in their customer service, KPIs and results. The other one that I wanted to quickly mention is Octo. As most of you know, Optune is a very, very large telemedics provider, telematics data provider globally. It's been with Cloudera for quite some time. >>This one I want to showcase because it showcases what we can do with data in mass amounts. So for Octo, we, um, analyze on Cloudera 5 million connected cars, ongoing with 11 billion data points. And really what they're doing is the creating the algorithms and the models and insurers use to, um, to, um, run, um, tell them insurance, telematics programs made to pay as you drive pay when you drive, pay, how you drive. And this whole telemedics part of insurance is actually growing very fast too, in, in, still in sort of a proof of concept mini projects, kind of initiatives. But, um, what we're succeeding is that companies are starting to offer more and more services around it. So they become preventative and predictive too. So now you got to the program staff being me as a driver saying, Monique, you're hopping in the car for two hours. >>Now, maybe it's time you take a break. Um, we see that there's a Starbucks coming up on the ride or any coffee shop. That's part of a bigger chain. Uh, we know because you have that app on your phone, that you are a Starbucks user. So if you stop there, we'll give you a 50 cents discount on your regular coffee. So we start seeing these types of programs coming through to, again, keep people safe and keep cars safe, but primarily of course the people in it, and those are the types of use cases that we start seeing in that telematic space. >>This looks more complicated than it is. So bear with me for a second. This is a commercial example because we see a data work. A lot of data were going on in commercial insurance. It's not Leah personal insurance thing. Commercial is near and dear to my heart. That's where I started. I actually, for a long time, worked in global energy insurance. So what this one wheelie explains is how we can use sensors on people's outfits and people's clothes to manage risks and underwrite risks better. So there are programs now for manufacturing companies and for oil and gas, where the people that work in those places are having sensors as part of their work outfits. And it does a couple of things. It helps in workers' comp underwriting and claims because you can actually see where people are moving, what they are doing, how long they're working. >>Some of them even tracks some very basic health-related information like blood pressure and heartbeat and stuff like that, temperature. Um, so those are all good things. The other thing that had to us, it helps, um, it helps collect data on the specific risks and exposures. Again, we're getting more and more to individual underwriting or individual risk underwriting, who insurance companies that, that ensure these, these, um, commercial, commercial, um, enterprises. So they started giving discounts if the workers were sensors and ultimately if there is an unfortunate event and it like a big accident or big loss, it helps, uh, first responders very quickly identify where those workers are. And, and, and if, and how they're moving, which is all very important to figure out who to help first in case something bad happens. Right? So these are the type of data that quite often got implements in one specific use case, and then get broadly moved to other use cases or deployed into other use cases to help price risks, betters better, and keep, you know, risks, better control, manage, and provide preventative care. Right? >>So these were some of the use cases that we run in the underwriting space that are very excited to talk about. So as a next step, what we would like you to do is considered opportunities in your own companies to advance risk assessment specific to your individual customer's need. And again, customers can be people they can be enterprises to can be other any, any insurable entity, right? The please physical dera.com solutions insurance, where you will find all our documentation assets and thought leadership around the topic. And if you ever want to chat about this, please give me a call or schedule a meeting with us. I get very passionate about this topic. I'll gladly talk to you forever. If you happen to be based in the us and you ever need somebody to filibuster on insurance, please give me a call. I'll easily fit 24 hours on this one. Um, so please schedule a call with me. I promise to keep it short. So thank you very much for joining this session. And as a last thing, I would like to remind all of you read our blogs, read our tweets. We'd our thought leadership around insurance. And as we all know, insurance is sexy.

(upbeat music) >> We just compressed about a decade of online commerce progress into about 13 or 14 months. So now we're going to look at how one retailer navigated through the pandemic and what the future of their business looks like. And with me is Alan Jensen who is the chief information officer and senior vice president of the Salling Group. Hello, Alan, how are you? >> Fine, thank you. >> Good to see you. Look, you know, when I look at the hundred year history plus of your company, I mean, it's a marked by transformations and some of them are quite dramatic. So you're Denmark's largest retailer. I wonder if you could share a little bit more about the company its history and how it continues to improve the customer experience while at the same time keeping costs under control so vital in your business. >> Yeah, the company founded approximately 100 year ago with a department store in Denmark. And I think in the 60s we founded the first supermarket in Denmark with the self-service and combined textile and food in the same store. And in the beginning 70s, we found that the first hypermarket in Denmark and then the discounter came from Germany early in 1980 and we started a discount chain. And so we are actually building department store in hypermarket in supermarket, and in the discount sector. And today we are more than 1,500 stores in three different countries in Denmark, Poland, and Germany. And especially for the Danish market we have a approximately 38% market share and it is the leader. We have over the last 10 years developed further into online first in non-food and now in food with home delivery with Clayton Calais. And we have done some acquisition in the convenience with me box solutions to our customers. And we have today also some restaurant burger chain and we are running the Starbucks in Denmark. So you can see a full plate of different opportunities for our customer in especially Denmark. >> It's an awesome story. And of course the founder's name is still on the masthead. What a great legacy. Now, of course the pandemic has forced many changes quite dramatic including the behaviors of retail customers. Maybe you could talk a little bit about how your digital transformation at the Salling Group prepared you for this shift in consumption patterns and any other challenges that you faced. >> I think luckily as for some of the you can say the coati solution in 19 we just roll out using our computers. We are direct access, so you can work from anywhere whether you are traveling from home and so on. We introduced a new age from delivery model and we just finalized the rolling out teams in January, February 20. And that was some very strong thing for suddenly moving all our employees from office to home and more or less overnight we succeed continuing our work and for IT We have not missed any deadline or task for the business in 2020. So I think that was a pretty awesome to see. And for the business, of course the pandemic changed a lot as the change in customer behavior, more or less overnight with plus 50, 80% on the online solution forced us to do some different priorities as we were looking at food home delivery and originally expected to start rolling out in 2022 but took a fast decision in April last year to launch immediately. And we have been developing that over the last eight months and has been live for the last three months now in the market. So you can say the pandemic really front-loaded some of our strategic actions for two to three years. >> What's that saying? Luck is the by-product of great planning and preparation. So let's talk about... what happened? >> And when you are in a company with some strong financial situation that you can move immediately with investment when you take such a decision, then it's really failing yeah. >> Right, awesome. Two-part question. Talk about how you leverage data to support the Solent group's mission and you know drive value for customers. And maybe you could talk about some of the challenges you face with just the amount of data, the speed of data, et cetera. >> Yeah, I said data is everything when you are in retail, as retail is detail as you need to monitor your operation down to each store each department. And if you can say, we have challenged that data is just growing rapidly as a year by year it's growing more and more because you're able to be more detailed, you're able to capture more data. And for a company like ours we need to be updated every morning as our fully updated sales for all unit department single skew selling in the stores is updated three o'clock in the night and send out to all top management and our managers all over the company. It's actually 8,000 reports going out before six o'clock every day in the morning. We have introduced a loyalty program and we are capturing a lot of data on customer behavior. What is their preferred of us? What is their preferred time in the week for buying different things. And all these data is now used to personalize our offers to our value customers. So we can be exactly hitting the best time and convert it to sales. Data is also now used for what we call intelligent price reductions so instead of just reducing prices with 50% if it's a close to running out of date now the system automatically calculate whether a store has just enough to finish with full price before end of day, or actually have too much and need to maybe reduce by 80% before. So being able to sell. So these automated solutions build on data is bringing efficiency into our operation. >> Wow, you make it sound easy. These are non-trivial items. So congratulations on that. I wonder if we could close HPE was kind enough to introduce us, tell us a little bit about the infrastructure of the solutions you're using how they differentiate you in the market. And I'm interested in you know why HPE what distinguishes them, why they choose there. >> When you look out a lot is looking at moving data to the cloud, but we still believe that due to performance, due to the availability, more or less on demand, we still don't see the cloud strong enough for Salling Group capturing all our data. We have been quite successfully having one data truth across the whole company and having one just one single BI solution and having that huge amount of data. I think we have one of the 10 largest sub business warehouses global. And on the other hand we also want to be agile and want to scale when needed. So getting close to a cloud solution, we saw it be GreenLake as a solution, getting close to the cloud but still being on-prem and could deliver what we need to have fast performance on data, but still in a high quality and still very secure for us to run. >> Great, thank you for that. Alan thanks so much for your time really appreciate your insights and congratulations on the progress and best of luck in the future. >> Thank you. >> All right, keep it right there. We have tons more content coming. You're watching Accelerating Next from HPE. (upbeat music)

from around the globe it's thecube covering space and cyber security symposium 2020 hosted by cal poly hello everyone welcome to the space and cyber security symposium 2020 hosted by cal poly where the intersection of space and security are coming together i'm john furrier your host with thecube here in california i want to welcome our featured guest lieutenant general john f thompson with the united states space force approach to cyber security that's the topic of this session and of course he's the commander of the space and missile system center in los angeles air force base also heading up space force general thank you for coming on really appreciate you kicking this off welcome to the symposium hey so uh thank you very much john for that very kind introduction also uh very much thank you to cal poly uh for this opportunity to speak to this audience today also a special shout out to one of the organizers uh dustin brun for all of his work uh helping uh get us uh to this point uh ladies and gentlemen as uh as uh john mentioned uh i'm jt thompson uh i lead the 6 000 men and women of the united states space forces space and missile system center which is headquartered here at los angeles air force base in el segundo if you're not quite sure where that's at it's about a mile and a half from lax this is our main operating location but we do have a number of other operating locations around the country with about 500 people at kirtland air force base in albuquerque new mexico uh and about another 500 people on the front range of the rockies uh between colorado springs and uh and denver plus a smattering of other much smaller operating locations nationwide uh we're responsible for uh acquiring developing and sustaining the united states space force's critical space assets that includes the satellites in the space layer and also on the ground layer our ground segments to operate those satellites and we also are in charge of procuring launch services for the u.s space force and a number of our critical mission partners across the uh department of defense and the intelligence community um just as a couple of examples of some of the things we do if you're unfamiliar with our work we developed and currently sustained the 31 satellite gps constellation that satellite constellation while originally intended to help with global navigation those gps signals have provided trillions of dollars in unanticipated value to the global economy uh over the past three decades i mean gps is everywhere i think everybody realizes that agriculture banking the stock market the airline industry uh separate and distinct navigation systems it's really pervasive across both the capabilities for our department of defense and capabilities for our economy and and individuals billions of individuals across our country and the planet some of the other work we do for instance in the communications sector uh secure communications satellites that we design and build that link america's sons and daughters serving in the military around the world and really enable real-time support and comms for our deployed forces and those of our allies we also acquire uh infrared missile warning satellites uh that monitor the planet for missile launches and provide advanced warning uh to the u.s homeland and to our allies uh in case some of those missile launches are uh nefarious um on a note that's probably a lot closer to home maybe a lot closer to home than many of us want to think about here in the state of california in 2018 smc jumped through a bunch of red tape and bureaucracy uh to partner with the u.s forest service during the two of the largest wildfires in the state's history the camp and woolsey fires in northern california as those fires spread out of control we created processes on the fly to share data from our missile warning satellites those are satellites that are systems that are purpose built to see heat sources from thousands of miles above the planet and we collaborated with the us forest service so that firefighters on the ground uh could track those fires more in real time and better forecast fires and where they were spreading thereby saving lives and and property by identifying hot spots and flare-ups for firefighters that data that we were able to working with our contractors pass to the u.s forest service and authorities here in california was passed in less than an hour as it was collected to get it into the hands of the emergency responders the first responders as quickly as possible and doing that in an hour greatly surpassed what was available from some of the other assets in the airborne and ground-based fire spotters it was really instrumental in fighting those fires and stopping their spread we've continued uh that involvement in recent years using multiple systems to support firefighters across the western u.s this fall as they battled numerous wildfires that unfortunately continue working together with the u.s forest service and with other partners uh we like to make uh we like to think that we made a difference here but there's still a lot more work to go and i think that we should always be asking ourselves uh what else can space data be used for and how can we more rapidly get that space data to uh stakeholders so that they can use it for for purposes of good if you will how else can we protect our nation how else can we protect our friends and allies um i think a major component of the of the discussion that we will have throughout this conference is that the space landscape has changed rapidly and continues to change rapidly um just over the past few years uh john and i were talking before we went live here and 80 nations now have uh space programs 80 nearly 80 space faring nations on the planet um if you just look at one mission area that uh the department of defense is interested in and that's small launch there are currently over a hundred different small launch companies uh within the u.s industrial base vying for commercial dod and civil uh payload capabilities uh mostly to low earth orbit it's it's just truly a remarkable time if you factor in those things like artificial intelligence and machine learning um where we're revolutionary revolutionizing really uh the ways that we generate process and use data i mean it's really remarkable in 2016 so if you think about this four years ago uh nasa estimated that there were 28 terabytes of information transiting their space network each day and that was four years ago um uh obviously we've got a lot of desire to work with a lot of the people in the audience of this congress or in this conference uh we need to work with big thinkers like many of you to answer questions on how best we apply data analytics to extract value and meaning from that data we need new generations of thinkers to help apply cutting edge edge theories of data mining cyber behaviorism and internet of things 2.0 it's just truly a remarkable time uh to be in the space business and the cyber aspects of the states of the space business are truly truly daunting and important to uh to all of us um integrating cyber security into our space systems both commercial and government is a mandate um it's no longer just a nice to have as the us space force and department of the air force leadership has said many times over the past couple of years space is becoming congested and contested and that contested aspect means that we've got to focus on cyber security uh in the same way that the banking industry and cyber commerce focus on uh cyber security day in and day out the value of the data and services provided is really directly tied to the integrity and availability of that data and services from the space layer from the ground control segments associated with it and this value is not just military it's also economic and it's not just american it's also a value for the entire world particularly particularly our allies as we all depend upon space and space systems your neighbors and friends here in california that are employed at the space and missile system center uh work with network defenders we work with our commercial contractors and our systems developers um our international allies and partners to try and build as secure and resilient systems as we can from the ground up that keep the global commons of space free and open for exploration and for commerce um as john and i were talking earlier before we came online there's an aspect of cyber security for space systems especially for some of our legacy systems that's more how do we bolt this on because we fielded those space systems a number of years ago and the the challenges of cyber security in the space domain have grown so we have a part that we have to worry about bolting it on but then we have to worry about building it in as we as we field new systems and build in a flexibility that that realizes that the cyber threat or the cyber security landscape will evolve over time it's not just going to be stagnant there will always be new vulnerabilities and new threat vectors that we always have to look at look uh as secretary barrett who is our secretary of the air force likes to say most americans use space before they have their first cup of coffee in the morning the american way of life really depends on space and as part of the united states space force we work with defense leaders our congress joint and international military teammates and industry to ensure american leadership in space i really thank you for this opportunity to address the audience today john and thanks so much to cal poly for letting me be one of the speakers at this event i really look forward to this for uh several months and so with that i look forward to your questions as we kind of move along here general thank you very much for the awesome uh introductory statement uh for the folks watching on the stream brigadier general carthan is going to be in the chat answering any questions feel free to chat away he's the vice commander of space and missile systems center he'll be available um a couple comments from your keynote before i get to my questions because it just jumped in my head you mentioned the benefits of say space but the fires in california we're living that here that's really real time that's a benefit you also mentioned the ability for more people launching payloads into space and i only imagine moore's law smaller faster cheaper applies to rockets too so i'm imagining you have the benefits of space and you have now more potential objects flying out sanctioned and maybe unsanctioned so you know is it going to be more rules around that i mean this is an interesting question because it's exciting space force but for all the good there is potentially bad out there yeah so i i john i think the uh i think the basics of your question is as space becomes more congested and contested is there a need for more international norms of how satellites fly in space what kind of basic features satellites have to perhaps deorbit themselves what kind of basic protections does do all satellites should all satellites be afforded as part of a peaceful global commons of space i think those are all fantastic questions and i know that u.s and many uh allied policy makers are looking very very hard at those kinds of questions in terms of what are the norms of behavior and how we uh you know how how we field and field is the military term but you know how we uh populate uh using civil or uh commercial terms uh that space layer at different altitudes uh low earth orbit mid mid-earth orbit geosynchronous earth orbit different kinds of orbits uh what the kind of mission areas we accomplish from space that's all things that need to be definitely taken into account as uh as the place gets a little bit not a little bit as the place gets increasingly more popular day in and day out well i'm super excited for space force i know that a new generation of young folks are really interested in it's an emerging changing great space the focus here at this conference is space and cyber security intersection i'd like to get your thoughts on the approach that space force is taking to cyber security and how it impacts our national goals here in the united states yeah yeah so that's a that's a great question john let me let me talk about in two uh two basic ways but number one is and and i know um some people in the audience this might make them a little bit uncomfortable but i have to talk about the threat right um and then relative to that threat i really have to talk about the importance of uh of cyber and specifically cyber security as it relates to that threat um the threats that we face um really represent a new era of warfare and that new era of warfare involves both space and cyber uh we've seen a lot of action in recent months uh from certain countries notably china and russia uh that have threatened what i referred to earlier as the peaceful global commons of space for example uh it through many unclassified sources and media sources everybody should understand that um uh the russians have been testing on orbit uh anti-satellite capabilities it's been very clear if you were following just the week before last the department of defense released its uh 2020 military and security developments involving the people's republic of china um uh and uh it was very clear that china is developing asats electronic jammers directed energy weapons and most relevant to today's discussion offensive cyber uh capabilities there are kinetic threats uh that are very very easy to see but a cyber attack against a critical uh command and control site or against a particular spacecraft could be just as devastating to the system and our war fighters in the case of gps and important to note that that gps system also impacts many civilians who are dependent upon those systems from a first response perspective and emergency services a cyber attack against a ground control site could cause operators to lose control of a spacecraft or an attacker could feed spoofed data to a system to mislead operators so that they send emergency services personnel to the to the wrong address right attacks on spacecraft on orbit whether directly via a network of intrusion or enabled through malware introduced during the systems production uh while we're building the satellite can [ __ ] or corrupt the data denial of service type attacks on our global networks obviously would disrupt our data flow and interfere with ongoing operations and satellite control i mean if gps went down i you know i hesitate to say it this way because we might elicit some screams from the audience but if gps went down a starbucks wouldn't be able to handle your mobile order uber drivers wouldn't be able to find you and domino's certainly certainly wouldn't be able to get there in 30 minutes or less right so with a little bit of tongue-in-cheek there from a military operations perspective it's dead serious um uh we have become accustomed in the commercial world to threats like lance ransomware and malware and those things have unfortunately become commonplace in commercial terrestrial networks and computer systems however what we're seeing is that our adversaries with the increased competition in space these same techniques are being retooled if you will to use against our national security space systems uh day in and day out um as i said during my opening remarks on the importance of cyber the value of these systems is directly tied to their integrity if commanders in the field uh firefighters in california or baristas in in starbucks can't trust the data they see they're receiving then that really harms their decision-making capabilities one of the big trends we've recently seen is the mood move towards proliferated leo uh uh constellations obviously uh spacex's uh starlink uh on the commercial side and on the military side the work that darpa and my organization smc are doing on blackjack and casino as well as some space transport layer constellation work that the space development agency is designing are all really really important types of mesh network systems that will revolutionize how we plan and field warfighting systems and commercial communications and internet providing systems but they're also heavily reliant on cyber security uh we've got to make sure that they are secured to avoid an accident or international damage uh loss of control of these constellations really could be catastrophic from both a mission perspective or from uh you know satellites tumbling out of low earth orbit perspective another trend is introductions in artificial intelligence and machine learning on board spacecraft or at the edge our satellites are really not so much hardware systems with a little software anymore in the commercial sector and in the defense sector they're basically flying boxes full of software right and we need to ensure the data that we're getting out of those flying boxes full of software are helping us base our decisions on accurate data and algorithms govern governing the right actions and that those uh that those systems are impervious to the extent possible uh to nefarious uh modifications so in summation a cyber security is vital element of everything in our national security space goals and i would argue for our national uh goals uh writ large including uh economic and information uh uh dimensions uh the space force leadership at all levels uh from uh some of the brand new second lieutenants that general raymond uh swore into the space force this morning uh ceremonially from the uh air force association's air space and cyberspace conference uh to the various highest levels general raymond uh general d t thompson myself and a number of other senior leaders in this enterprise we've got to make sure that we're all working together to keep cyber security at the forefront of our space systems because it they absolutely depend on it you know you mentioned uh hardware software threats opportunities challenges i want to ask you because you you got me thinking of the minute there around infrastructure i mean we've heard critical infrastructure you know grids here on on earth you're talking about critical infrastructure a redefinition of what critical infrastructure is an extension of what we have so i'd love to get your thoughts about space force's view of that critical infrastructure vis-a-vis the threat vectors because you know the term threat vectors has been kicked around in the cyber space oh yeah threat vectors they're always increasing the surface area well if the surface area is from space it's an unlimited surface area so you got different vectors so you got new critical infrastructure developing real time really fast and you got an expanded threat vector landscape putting that in perspective for the folks that aren't really inside the ropes on these critical issues how would you explain this and how would you talk about those two things well so i tell you um i just like um uh just like uh i'm sure people in the security side or the cyber security side of the business in the banking industry feel they feel like it's uh all possible threat vectors represent a dramatic and protect potentially existential threat to all of the dollars that they have in the banking system to the financial sector on the department of defense side we've got to have sort of the same mindset um that threat vector from to and through space against critical space systems ground segments the launch enterprise or transportation uh to orbit and the various different uh domains within uh within space itself like i mentioned before uh leo mio and geo-based satellites with different orbits all of the different mission areas that are accomplished from space that i mentioned earlier some that i didn't mention like weather tactical or wide band communications uh various new features of space control all of those are things that we have to worry about from a cyber security uh threat perspective and it's a it's a daunting challenge right now right yeah it's awesome and one of the things we've been following on the hardware side here in the on the ground is the supply chain we've seen you know malware being you know really put into really obscure hardware who manufactures it as being outsourced obviously government has restrictions but with the private sector uh you mentioned china and and the us kind of working together across these these peaceful areas but you got to look at the supply chain how does the supply chain the security aspect impact the mission of the u.s space force yeah yeah so so um how about another um just in terms of an example another kind of california-based historical example right um the very first u.s satellite uh explorer one was built by uh the jet propulsion uh laboratory folks uh not far from here in el segundo up in uh up in pasadena um that satellite when it was first built in the late 50s uh weighed a little bit over 30 pounds and i'm sure that each and every part was custom made and definitely made by u.s companies fast forward to today the global supply chain is so tightly coupled and frankly many industries are so specialized almost specialized regionally around the planet we focus every day to guarantee the integrity of every component that we put in our space systems is absolutely critical to the operations of those satellites and we're dependent upon them but it becomes more difficult and more difficult to understand the the heritage if you will of some of the parts that are used the thousands of parts that are used in some of our satellites that are literally school bus sized right the space industry especially uh national security space sector um uh is relatively small compared to other commercial industries and we're moving to towards using more and more parts uh from non-us companies uh cyber security and cyber awareness have to be baked in from the beginning if we're going to be using parts that maybe we don't necessarily um understand 100 percent like an explorer one uh the the lineage of that particular part the environmental difficulties in space are well known the radiation environment the temperature extremes the vacuum those require specialized component and the us military is not the only uh customer in that space in fact we're definitely not the dominant customer uh in space anymore all those factors require us along with our other government partners and many different commercial space organizations to keep a very close eye on our supply chains from a quality perspective a security perspective and availability um there's open source reporting on supply training intrusions from um many different breaches of commercial retailers to the infectious spread of uh you know compromised patches if you will and our adversaries are aware of these techniques as i mentioned earlier with other forms of attack considering our supply chains and development networks really becomes fair game for our adversaries so we have to uh take that threat seriously um between the government and industry sectors here in the u.s we're also working with our industry partners to enact stronger defenses and assess our own vulnerabilities last fall we completed an extensive review of all of our major contracts here at space and missile system center to determine the levels of cyber security requirements we've implemented across our portfolio and it sounds really kind of you know businessy geeky if you will you know hey we looked at our contracts to make sure that we had the right clauses in our contracts to address cyber security as dynamically as we possibly could and so we found ourselves having to add new language to our contracts to require system developers to implement some more advanced uh protective measures in this evolving cyber security environment so that data handling and supply chain perspective uh protections um from contract inception to launch and operations were taken into account uh cyber security really is a key performance parameter for us now it's as important as the the mission performance of the system it's as important as cost it's as important as schedule because if we deliver the perfect system on time and on cost uh it can perform that missile warning or that communications mis mission perfectly but it's not cyber secure if it doesn't have cyber protections built into it or the ability to implement mitigations against cyber uh threats then we've essentially fielded a shoe box in space that doesn't do the k the the war fighter or the nation uh any good um supply chain risk management is a is a major challenge for us uh we're doing a lot to coordinate with our industry partners uh we're all facing it head on uh to try and build secure and trusted components uh that keep our confidence as leaders firefighters and baristas uh as the case may be uh but it is a challenge and we're trying to rise to that challenge you know this so exciting this new area because it really touches everything you know talk about geeking out on on the tech the hardware the systems but also you put your kind of mba hat on you go what's the roi of the extra development and how you how things get built because the always the exciting thing for space geeks is like you're building cool stuff people love it's it's exciting but you still have to build and cyber security has proven that security has to be baked in from the beginning and be thought as a system architecture so you're still building things which means you've got to acquire things you got to acquire parts you got to acquire build software and and sustain it how is security impacting the acquisition and the sustainment of these systems for space yeah from initial development uh through planning for the acquisition design development fielding or production fielding and sustainment it impacts all aspects of of the life cycle john uh we simply especially from the concept of baking in cyber security uh we can't wait until something is built and then try and figure out how to make it cyber secure so we've moved way further uh towards working side by side with our system developers to strengthen cyber security from the very beginning of a system's development cyber security and the resilience associated with it really have to be treated as a key system attribute as i mentioned earlier equivalent with data rates or other metrics of performance we like to talk in uh in the space world about uh mission assurance and mission assurance has always you know sort of taken us as we as we technically geek out right mission assurance has always taken us to the will this system work in space right can it work in a vacuum can it work in you know as it as it uh you know transfers through uh the van allen radiation belt or through the the um the southern hemisphere's electromagnetic anomaly right will it work out in space and now from a resiliency perspective yeah it has to work in space it's got to be functional in space but it's also got to be resistant to these cyber security threats it's it's not just i think uh general dt thompson quoted this term it's not just widget assurance anymore it's mission assurance um uh how does that satellite uh operator that ground control segment operate while under attack so let me break your question a little bit uh just for purposes of discussion into into really two parts uh cyber uh for cyber security for systems that are new and cyber security uh for systems that are in sustainment or kind of old and legacy um obviously there's cyber vulnerabilities that threaten both and we really have to employ different strategies for for defense of of each one for new systems uh we're desperately trying to implement across the department of defense in particular in the space world a kind of a devsecops methodology and practice to delivering software faster and with greater security for our space systems here at smc we have a program called enterprise ground services which is a tool kit basically a collection of tools for common command and control of different satellite systems egs as we call it has an integrated suite for defensive cyber capabilities network operators can use these tools to gain unprecedented insight to data flows and to monitor space network traffic for anomalies or other potential indicators of of bad behavior malicious behavior if you will um uh it's rudimentary at this point but because we're using devsecops and that incremental development approach as we scale it it just becomes more and more capable you know every every product increment that we field here at uh at uh la air force base uh uh we have the united space space forces west coast software factory which we've dubbed kobayashi maru they're using those agile devops uh software development practices uh to deliver uh space awareness software uh to the combined space operations center uh affectionately called the csp that c-spock is just down the road uh from cal poly uh there in san luis obispo at vandenberg air force base they've securely linked the c-spock with other space operation centers around the planet our allies australia canada and the uk uh we're partnering with all of them to enable secure and enhanced combined space operations so lots of new stuff going on as we bake in new development uh capabilities for our our space systems but as i mentioned earlier we've got large constellations on satellite of satellites on orbit right now some of them are well in excess of a decade or more old on orbit and so the design aspects of those satellites are several decades old and so but we still have to worry about them because they're critical to our space capabilities um we've been working with an air force materiel command organization uh called crows which stands for the cyber resiliency office for uh weapon systems to assess all of those legacy platforms from a cyber security perspective and develop defensive strategies and potential hardware and software upgrades to those systems to better enable them to to live through this increasingly cyber security uh concerned era that we currently live in our industry partners have been critical to to both of those different avenues both new systems and legacy systems we're working closely with them to defend and upgrade uh national assets and develop the capabilities to do similar with uh with new national assets coming online the vulnerabilities of our space systems really kind of threaten the way we've done business in the past both militarily and in the case of gps economically the impacts of that cyber security risk are clear in our acquisition and sustainment processes but i've got to tell you it that as the threat vectors change as the vulnerabilities change we've got to be nimble enough agile enough to be able to bounce back and forth we can't just say uh many people in the audience are probably familiar with the rmf or the risk management framework approach to um to reviewing uh the cyber security of a system we can't have program managers and engineers just accomplish an rmf on a system and then hey high five we're all good uh it's a journey not a destination that's cyber security and it's a constant battle rhythm throughout a weapon systems life cycle not just a single event i want to get to this commercial business needs and your needs on the next question but before i go there you mentioned the agile and i see that clearly because when you have accelerated innovation cycles you've got to be faster and we saw this in the computer industry mainframes mini computers and then when you started getting beyond me when the internet hit and pcs came out you saw the big enterprises the banks and and government start to work with startups it used to be a joke in the entrepreneurial circles is that you know there's no way if you're a startup you're ever going to get a contract with a big business enterprise now that used to be for public sector and certainly uh for you guys so as you see startups out there and there's acquisition involved i'm sure would love to love to have a contract with space force there's an roi calculation where if it's in space and you have a sustainment view edit software you might have a new kind of business model that could be attractive to startups could you share your thoughts on the folks who want to be a supplier to you uh whether they're a startup or an existing business that wants to be agile but they might not be that big company we are john that's a fantastic question we are desperately trying to reach out to to those new space advocates to those startups to those um what we sometimes refer to within the department of defense those non-traditional uh defense contractors a couple of things just for uh thinking purposes on some of the things that we're trying to highlight um uh three years ago we created here at uh space and missile system center uh the space enterprise consortium uh to provide a platform uh a contractual vehicle really to enable us to rapidly prototype uh development of space systems and to collaborate uh between the u.s space force uh traditional defense contractors non-traditional vendors like startups and even some academic institutions uh spec as we call it space enterprise consortium uses a specialized contracting tool to get contracts uh awarded quickly many in the audience may be familiar with other transaction agreements and that's what spec is based on and so far in just three years spec has awarded 75 different uh prototyping contracts worth over 800 million dollars with a 36 reduction in time to award and because it's a consortium based competition for um for these kinds of prototyping efforts the barrier to entry for small and non-traditional for startups even for academic institutions to be able to compete for these kinds of prototypings is really lowered right um uh these types of partnerships uh that we've been working through on spec uh have really helped us work with smaller companies who might not have the background or expertise in dealing with the government or in working with cyber security uh for their systems both their developmental systems and the systems that they're designing and trying to build we want to provide ways for companies large and small to partner together and support um uh kind of mutually beneficial uh relationships between all um recently uh at the annual air force association uh conference that i mentioned earlier i moderated a panel with several space industry leaders uh all from big traditional defense contractors by the way and they all stressed the importance of building bridges and partnerships uh between major contractors in the defense industry and new entrants uh and that helps us capture the benefits of speed and agility that come with small companies and startups as well as the expertise and specialized skill sets of some of those uh larger contractors uh that we rely on day in and day out advanced cyber security protections and utilization of secure facilities are just a couple of things that i think we could be prioritizing more so in those collaborations as i mentioned earlier the spec has been very successful in awarding a number of different prototyping contracts and large dollar values and it's just going to get better right there's over 400 members of the space enterprise consortium 80 of them are non-traditional kinds of vendors and we just love working with them another thing that many people in the audience may be familiar with in terms of our outreach to innovators uh if you will and innovators that include uh cyber security experts is our space pitch day events right so we held our first event last november in san francisco uh where we awarded over a two-day period about 46 million dollars to 30 different companies um that had potentially game-changing ideas these were phase two small business innovative research efforts uh that we awarded with cash on the spot uh we're planning on holding our second space pitch day in the spring of 2021. uh we're planning on doing it right here in los angeles uh covent 19 environment permitting um and we think that these are you know fantastic uh uh venues for identifying and working with high-speed startups startups and small businesses who are interested in uh really truly partnering with the us air force it's a as i said before it's a really exciting time to be a part of this business uh and working with the innovation economy uh is something that the department of defense uh really needs to do in that um the innovation that we used to think was ours you know that 80 percent of the industrial-based innovation that came from the department of defense uh the the script has been flipped there and so now more than 70 percent uh particularly in space innovation uh comes from the commercial sector not from uh not from the defense business itself and so um that's a tsunami of uh investment and a tsunami of uh capability and i need to figure out how to get my surfboard out and ride it you know what i mean yeah i mean it's one of those things where the flip the script has been flipped but it's exciting because it's impacting everything are you talking about systems architecture you're talking about software you're talking about a business model you talk about devsecops from a technical perspective but now you have a business model innovation all the theaters of uh are exploding in innovation technical business personnel this brings up the workforce challenge you've got the cyber needs for the u.s space force there's probably a great roi model for new kinds of software development that could be priced into contracts that's a entrepreneurial innovation you got the the business model theater you've got the personnel how does the industry adopt and change you guys are clearly driving this how does the industry adjust to you yeah so um i think a great way to answer that question is to just talk about the kind of people that we're trying to prioritize in the u.s space force from a from an acquisition perspective and in this particular case from a from a cyber security perspective as i mentioned earlier it's the most exciting time to be in space programs uh really since the days of apollo um uh you know just to put it in terms that you know maybe have an impact with the audience uh from 1957 until today approximately 9 000 satellites uh have been launched from the various space faring countries around the planet uh less than two thousand of those nine thousand are still up on orbit and operational and yet in the new space regime um players like spacex have plans to launch you know 12 000 satellites for some of their constellations alone it really is a remarkable time in terms of innovation and fielding of space capabilities and all of those space capabilities whether they're commercial civil or defense are going to require appropriate cyber security uh protections it's just a really exciting time uh to be working in stuff like this and so uh folks like the folks in this audience who have a passion about space and a passion about cyber security are just the kind of people that we want to work with because we need to make sure our systems are are secure and resilient we need folks that have technical and computing expertise engineering skills to be able to design cybersecure systems that can detect and mitigate attacks uh but we also as you alluded to we need people that have that business and um you know business acumen human networking background so that we can launch the startups and work with the non-traditional businesses uh help to bring them on board help to secure both their data and our data and uh and and make sure our processes and systems are are free as much as possible from uh uh from attack um for preparation for for audience members who are young and maybe thinking about getting into this uh trade space um you gotta be smart on digital networking uh you gotta understand basic internet protocols concepts uh programming languages uh database design uh learn what you can from penetration or vulnerability testing and and uh risk assessment i will tell you this and i don't think he will i know he will not mind me telling you this but you've got to be a lifelong learner and so two years ago i'm at home one evening and i get a phone call on my cell phone and it's my boss the commander of air force space command uh general j raymond who is now currently the chief of space operations and he is on temporary duty flying overseas he lands where he's going and he first thing he does when he lands is he calls me and he goes jt um while i was traveling um i noticed that there were e-books available on the commercial airliner i was traveling on and there was an e-book on something called scrumming and agile devsecops and i read it have you read it um and i said no sir but if you tell me what the title of the book is i will read it and so i got to go to my staff meeting um you know the very next week the next time we had a staff meeting and tell everybody in the stab meeting hey if the four star and the three star can read the book about scrumming then i'm pretty sure all of you around this table and all our lieutenants and our captains our gs13s all of our government employees can get smart on uh the scrumming development process and interestingly as another side i had a telephone call with him last year during the holidays where he was trying to take some leave and i said sir what are you up to today are you are you you know making eggnog for the event tonight or whatever and the chief of space operations told me no i'm trying to teach myself python i'm at lesson two and it's not going so well but i'm i'm gonna figure this out and so that kind of thing if the chief of staff or the you know the the the chief of space operations can prioritize scrumming and python language and innovation in his daily schedule then we're definitely looking for other people who can do that and we'll just say lower levels of rank uh throughout our entire space force enterprise um look i i we don't need to need people that can code a satellite from scratch but we need to know we need to have people that have a basic grasp of the programming basics and cyber security requirements and that can turn those things into into meaningful actions obviously in the space domain things like basic physics and orbital mechanics are also important uh space is not an intuitive uh domain so under understanding how things survive uh on orbit is really critical to making the right design and operational decisions and you know i know there's probably a lot because of this conference i know there's a probably a whole lot of high-speed cyber security experts out in the audience and i need those people in the u.s space force the the country is counting on it but i wouldn't discount having people that are just cyber aware or cyber savvy right i have contracting officers and logisticians and program managers and they don't have to be high-end cyber security experts but they have to be aware enough about it to be able to implement cyber security protections um into our space system so the skill set is is really really broad um our adversaries are pouring billions of dollars into uh define designing uh and fielding offensive and destructive space cyber security weapons right they've repeatedly shown really a blatant disregard of safety and international norms for good behavior on orbit and the cyber security aspects of our space systems is really a key battleground going forward so that we can maintain that as i mentioned before peaceful uh global commons of space we really need all hands on deck if you're interested in helping in uniform if you're interested in helping uh not in uniform uh but as a government employee a commercial or civil employee to help us make cyber security more important uh or more cape more able to be developed for our space systems then we'd really love to uh to work with you or have you on the team to build that safe and secure future for our space systems lieutenant general john thompson great insight thank you for sharing all that awesome stories too and motivation for the young next generation the united states space force approach of cyber security really amazing talk thank you for your time final parting question is as you look out and you had your magic wand what's your view for the next few years in terms of things that we could accomplish it's a super exciting time what do you hope for so um um first of all john thanks to you and and thanks to cal poly uh for the invitation and and thanks to everybody for uh for their interest in cyber security especially as it relates to space systems that's here at the conference um uh there's a quote and i'll read it here uh from uh bernard schriever who was the uh the founder if you will uh a legend in uh dod space the founder of the western development division which was a predecessor organization to space and missile systems center general shrever i think captures the essence of what how we see the next couple of years the world has an ample supply of people who can always come up with a dozen good reasons why new ideas will not work and should not be tried but the people who produce progress are breed apart they have the imagination the courage and the persistence to find solutions and so i think if you're hoping that the next few years of space innovation and cyber security innovation are going to be a pony ride at the county fair then perhaps you should look for another line of work because i think the next few years in space and cyber security innovation are going to be more like a rodeo um and a very dynamic rodeo as it goes it is a an awesome privilege to be part of this ecosystem it's really an honor for me to um to be able to play some small role uh in the space ecosystem and trying to improve it uh while i'm trying to improve the chances of uh of the united states of america in a uh in a space war fighting uh uh environment um and so i thank all of you for uh participating today and for this little bit of time that you've allowed me to share with you thank you sir thank you for your leadership and thank you for the for the time for this awesome event space and cyber security symposium 2020 i'm john furrier on behalf of cal poly thanks for watching [Music]

>> Narrator: From around the globe. It's theCUBE covering space in cybersecurity symposium 2020 hosted by Cal Poly. >> Hello, everyone. Welcome to the space and cybersecurity symposium, 2020 hosted by Cal Poly where the intersection of space and security are coming together. I'm John Furrier, your host with theCUBE here in California. I want to welcome our featured guest, Lieutenant General, John F. Thompson with the United States Space Force approach to cybersecurity. That's the topic of this session. And of course he's the commander of the space and missile system center in Los Angeles Air Force Base. Also heading up Space Force. General, thank you for coming on. I really appreciate to you kicking this off. Welcome to the symposium. >> Hey, so thank you very much, John, for that very kind introduction. Also very much thank you to Cal Poly for this opportunity to speak to this audience today. Also a special shout out to one of the organizers, Dustin Debrun, for all of his work, helping get us to this point. Ladies and gentlemen as a John mentioned, I'm JT Thompson. I lead the 6,000 men and women of the United States Space Force's Space and Missile System Center, which is headquartered here at Los Angeles Air Force Base and El Segundo. If you're not quite sure where that's at, it's about a mile and a half from LAX. This is our main operating location, but we do have a number of other operating locations around the country. We're about 500 people at Kirtland Air Force Base in Albuquerque, New Mexico, and an about another 500 people on the front range of the Rockies between Colorado Springs and Denver plus a smattering of other much smaller operating locations nationwide. We're responsible for acquiring, developing and sustaining the United States Space Force's, critical space assets. That includes the satellites in the space layer and also on the ground layer our ground segments to operate those satellites. And we also are in charge of procuring launch services for the US Space Force and a number of our critical mission partners across the Department of Defense and the intelligence community. Just as a couple of examples of some of the things we do, if you're unfamiliar with our work we developed and currently sustain the 31 satellite GPS constellation that satellite constellation, while originally intended to help with global navigation, those GPS signals have provided trillions of dollars in unanticipated value to the global economy over the past three decades. GPS is everywhere. I think everybody realizes that. Agriculture, banking, the stock market, the airline industry, separate and distinct navigation systems. It's really pervasive across both capabilities for our Department of Defense and capabilities for our economy and individuals, billions of individuals across our country and the planet. Some of the other work we do for instance, in the communications sector, secure communications satellites that we designed and build that link America's sons and daughters serving in the military around the world and really enable real time support and comms for our deployed forces. And those of our allies. We also acquire infrared missile warning satellites that monitor the planet for missile launches that provide advanced warning to the US Homeland and to our allies in case some of those missile launches are nefarious. On a note, that's probably a lot closer to home, maybe a lot closer to home than many of us want to think about here in the state of California. In 2018, SMC jumped through a bunch of red tape and bureaucracy to partner with the US Forest Service during two of the largest wildfires in the state's history, the Camp and Woolsey fires in Northern California. As those fires spread out of control, we created processes on the fly to share data from our missile warning satellites. Those are satellites that are systems that are purpose built to see heat sources from thousands of miles above the planet. And we collaborated with the US Forest Service so that firefighters on the ground could track those fires more in real time and better forecast fires and where they were spreading, thereby saving lives and property by identifying hotspots and flareups for firefighters. That data that we were able to working with our contractors pass to the US Forest Service and authorities here in California, was passed in less than an hour as it was collected to get it into the hands of the emergency responders, the first responders as quickly as possible and doing that in an hour greatly surpassed what was available from some of the other assets in the airborne and ground-based fire spotters. It was really instrumental in fighting those fires and stopping their spread. We've continued that involvement in recent years, using multiple systems to support firefighters across the Western US this fall, as they battled numerous wildfires that unfortunately continue. Working together with the US Forest Service and with other partners we'd like to think that we've made a difference here, but there's still a lot more work to go. And I think that we should always be asking ourselves what else can space data be used for and how can we more rapidly get that space data to stakeholders so that they can use it for purposes of good, if you will. How else can we protect our nation? How else can we protect our friends and allies? I think a major component of the discussion that we will have throughout this conference is that the space landscape has changed rapidly and continues to change rapidly. Just over the past few years, John and I were talking before we went live here and 80 nations now have space programs. Nearly 80 space faring nations on the planet. If you just look at one mission area that the Department of Defense is interested in, and that's small launch, there are currently over 100 different small launch companies within the US industrial base vying for commercial DoD and civil payload capabilities, mostly to lower earth orbit. It's truly a remarkable time. If you factor in those things like artificial intelligence and machine learning, where we're revolutionizing really, the ways that we generate process and use data. It's really remarkable. In 2016, so if you think about this four years ago, NASA estimated that there were 28 terabytes of information transiting their space network each day. And that was four years ago. Obviously we've got a lot of desire to work with a lot of the people in the audience in this conference, we need to work with big thinkers, like many of you to answer questions on how best we apply data analytics to extract value and meaning from that data. We need new generations of thinkers to help apply cutting edge theories of data mining, cyber behaviorism, and Internet of Things 2.0, it's just truly a remarkable time to be in the space business and the cyber aspects of the space business are truly, truly daunting and important to all of us. Integrating cyber security into our space systems, both commercial and government is a mandate. it's no longer just a nice to have as the US Space Force and Department of the Air Force leadership has said many times over the past couple of years, space is becoming congested and contested. And that contested aspect means that we've got to focus on cyber security in the same way that the banking industry and cyber commerce focus on cybersecurity day in and day out. The value of the data and services provided is really directly tied to the integrity and availability of that data and services from the space layer, from the ground control segments associated with it. And this value is not just military, it's also economic and it's not just American, it's also a value for the entire world, particularly our allies, as we all depend upon space and space systems. Your neighbors and friends here in California that are employed at the space and missile system center work with network defenders. We work with our commercial contractors and our systems developers, our international allies and partners to try and build as secure and resilient systems as we can from the ground up that keep the global comments of space free and open for exploration and for commerce as John and I were talking earlier, before we came online, there's an aspect of cybersecurity for space systems, especially for some of our legacy systems, that's more, how do we bolt this on? Cause we fielded those space systems a number of years ago, and the challenges of cybersecurity in the space domain have grown. So we have a part that we have to worry about, bolting it on, but then we have to worry about building it in as we field new systems and build in a flexibility that realizes that the cyber threat or the cybersecurity landscape will evolve over time. It's not just going to be stagnant. There will always be new vulnerabilities and new threat vectors that we all have to look at. Look, as Secretary Barrett, who is our secretary of the air force likes to say most Americans use space before they have their first cup of coffee in the morning. The American way of life really depends on space. And as part of the United States Space Force, we work with defense leaders, our Congress joint, and international military teammates and industry to ensure American leadership in space. I really thank you for this opportunity to address the audience today, John, and thanks so much to Cal Poly for letting me be one of the speakers at this event. I've really looked forward to this for several months. And so with that, I look forward to your questions as we kind of move along here. >> General, thank you very much for those awesome introductory statement. For the folks watching on the stream, Brigadier General Carthan's going to be in the chat, answering any questions, feel free to chat away. He's the vice commander of Space and Missile System Center, he'll be available. A couple of comments from your keynote before I get to my questions. Cause it just jumped into my head. You mentioned the benefits of say space with the fires in California. We're living that here. That's really realtime. That's a benefit. You also mentioned the ability for more people launching payloads into space. I'm only imagined Moore's law smaller, faster, cheaper applies to rockets too. So I'm imagining you have the benefits of space and you have now more potential objects flying out sanctioned and maybe unsanctioned. So is it going to be more rules around that? This is an interesting question cause it's exciting Space Force, but for all the good there is potentially bad out there. >> Yeah. So John, I think the basics of your question is as space becomes more congested and contested, is there a need for more international norms of how satellites fly in space? What kind of basic features satellites have to perhaps de orbit themselves? What kind of basic protections should all satellites be afforded as part of a peaceful global commons of space? I think those are all fantastic questions. And I know that US and many allied policy makers are looking very, very hard at those kinds of questions in terms of what are the norms of behavior and how we field, and field as the military term. But how we populate using civil or commercial terms that space layer at different altitudes, lower earth orbit, mid earth orbit, geosynchronous earth orbit, different kinds of orbits, what the kind of mission areas we accomplished from space. That's all things that need to be definitely taken into account as the place gets a little bit, not a little bit as the place gets increasingly more popular day in and day out. >> I'm super excited for Space Force. I know that a new generation of young folks are really interested in it's an emerging, changing great space. The focus here at this conference is space and cybersecurity, the intersection. I'd like to get your thoughts on the approach that a space force is taking to cybersecurity and how it impacts our national goals here in the United States. >> Yeah. So that's a great question John, let me talk about it in two basic ways. At number one is an and I know some people in the audience, this might make them a little bit uncomfortable, but I have to talk about the threat. And then relative to that threat, I really have to talk about the importance of cyber and specifically cyber security, as it relates to that threat. The threats that we face really represented a new era of warfare and that new era of warfare involves both space and cyber. We've seen a lot of action in recent months from certain countries, notably China and Russia that have threatened what I referred to earlier as the peaceful global commons of space. For example, it threw many unclassified sources and media sources. Everybody should understand that the Russians have been testing on orbit anti-satellite capabilities. It's been very clear if you were following just the week before last, the Department of Defense released its 2020 military and security developments involving the People's Republic of China. And it was very clear that China is developing ASATs, electronic jammers, directed energy weapons, and most relevant to today's discussion, offensive cyber capabilities. There are kinetic threats that are very, very easy to see, but a cyber attack against a critical command and control site or against a particular spacecraft could be just as devastating to the system and our war fighters in the case of GPS and important to note that that GPS system also impacts many civilians who are dependent on those systems from a first response perspective and emergency services, a cyber attack against a ground control site could cause operators to lose control of a spacecraft or an attacker could feed spoofed data to assist them to mislead operators so that they sent emergency services personnel to the wrong address. Attacks on spacecraft on orbit, whether directly via a network intrusion or enabled through malware introduced during the system's production while we're building the satellite can cripple or corrupt the data. Denial-of-service type attacks on our global networks obviously would disrupt our data flow and interfere with ongoing operations and satellite control. If GPS went down, I hesitate to say it this way, cause we might elicit some screams from the audience. But if GPS went down a Starbucks, wouldn't be able to handle your mobile order, Uber drivers wouldn't be able to find you. And Domino's certainly wouldn't be able to get there in 30 minutes or less. So with a little bit of tongue in cheek there from a military operations perspective, it's dead serious. We have become accustomed in the commercial world to threats like ransomware and malware. And those things have unfortunately become commonplace in commercial terrestrial networks and computer systems. However, what we're seeing is that our adversaries with the increased competition in space these same techniques are being retooled, if you will, to use against our national security space systems day in and day out. As I said, during my opening remarks on the importance of cyber, the value of these systems is directly tied to their integrity. If commanders in the field, firefighters in California or baristas in Starbucks, can't trust the data they're receiving, then that really harms their decision making capabilities. One of the big trends we've recently seen is the move towards proliferated LEO constellations, obviously Space X's Starlink on the commercial side and on the military side, the work that DARPA and my organization SMC are doing on Blackjack and Casino, as well as some space transport layer constellation work that the space development agency is designing are all really, really important types of mesh network systems that will revolutionaries how we plan and field war fighting systems and commercial communications and internet providing systems. But they're also heavily reliant on cybersecurity. We've got to make sure that they are secured to avoid an accident or international damage. Loss of control of these constellations really could be catastrophic from both a mission perspective or from a satellites tumbling out of low earth orbit perspective. Another trend is introductions in artificial intelligence and machine learning, onboard spacecraft are at the edge. Our satellites are really not so much hardware systems with a little software anymore in the commercial sector and in the defense sector, they're basically flying boxes full of software. And we need to ensure that data that we're getting out of those flying boxes full of software are helping us base our decisions on accurate data and algorithms, governing the right actions and that those systems are impervious to the extent possible to nefarious modifications. So in summation, cybersecurity is a vital element of everything in our national security space goals. And I would argue for our national goals, writ large, including economic and information dimensions, the Space Force leadership at all levels from some of the brand new second lieutenants that general Raymond swore in to the space force this morning, ceremonially from the air force associations, airspace and cyberspace conference to the various highest levels, General Raymond, General DT Thompson, myself, and a number of other senior leaders in this enterprise. We've got to make sure that we're all working together to keep cyber security at the forefront of our space systems cause they absolutely depend on it. >> You mentioned hardware, software threats, opportunities, challenges. I want to ask you because you got me thinking of the minute they're around infrastructure. We've heard critical infrastructure, grids here on earth. You're talking about critical infrastructure, a redefinition of what critical infrastructure is, an extension of what we have. So I'd love to get your thoughts about Space Force's view of that critical infrastructure vis-a-vis the threat vectors, because the term threat vectors has been kicked around in the cyberspace. Oh you have threat vectors. They're always increasing the surface area. If the surface area is from space, it's an unlimited service area. So you got different vectors. So you've got new critical infrastructure developing real time, really fast. And you got an expanded threat vector landscape. Putting that in perspective for the folks that aren't really inside the ropes on these critical issues. How would you explain this and how would you talk about those two things? >> So I tell you, just like, I'm sure people in the security side or the cybersecurity side of the business in the banking industry feel, they feel like it's all possible threat vectors represent a dramatic and protect potentially existential threat to all of the dollars that they have in the banking system, to the financial sector. On the Department of Defense side, we've got to have sort of the same mindset. That threat vector from, to, and through space against critical space systems, ground segments, the launch enterprise, or transportation to orbit and the various different domains within space itself. Like I mentioned before, LEO, MEO and GEO based satellites with different orbits, all of the different mission areas that are accomplished from space that I mentioned earlier, some that I did mention like a weather tactical or wide band communications, various new features of space control. All of those are things that we have to worry about from a cyber security threat perspective. And it's a daunting challenge right now. >> Yeah, that's awesome. And one of the things we've been falling on the hardware side on the ground is the supply chain. We've seen, malware being, really put in a really obscure hardware. Who manufactures it? Is it being outsourced? Obviously government has restrictions, but with the private sector, you mentioned China and the US kind of working together across these peaceful areas. But you got to look at the supply chain. How does the supply chain in the security aspect impact the mission of the US space Force? >> Yeah. Yeah. So how about another, just in terms of an example, another kind of California based historical example. The very first US Satellite, Explorer 1, was built by the jet propulsion laboratory folks, not far from here in El Segundo, up in Pasadena, that satellite, when it was first built in the late 50s weighing a little bit, over 30 pounds. And I'm sure that each and every part was custom made and definitely made by US companies. Fast forward to today. The global supply chain is so tightly coupled, and frankly many industries are so specialized, almost specialized regionally around the planet. We focus every day to guarantee the integrity of every component that we put in our space systems is absolutely critical to the operations of those satellites and we're dependent upon them, but it becomes more difficult and more difficult to understand the heritage, if you will, of some of the parts that are used, the thousands of parts that are used in some of our satellites that are literally school bus sized. The space industry, especially national security space sector is relatively small compared to other commercial industries. And we're moving towards using more and more parts from non US companies. Cybersecurity and cyber awareness have to be baked in from the beginning if we're going to be using parts that maybe we don't necessarily understand 100% like an Explorer one, the lineage of that particular part. The environmental difficulties in space are well known. The radiation environment, the temperature extremes, the vacuum, those require specialized component. And the US military is not the only customer in that space. In fact, we're definitely not the dominant customer in space anymore. All those factors require us along with our other government partners and many different commercial space organizations to keep a very close eye on our supply chains, from a quality perspective, a security perspective and availability. There's open source reporting on supply training intrusions from many different breaches of commercial retailers to the infectious spread of compromised patches, if you will. And our adversaries are aware of these techniques. As I mentioned earlier, with other forms of attack, considering our supply chains and development networks really becomes fair game for our adversaries. So we have to take that threat seriously. Between the government and industry sectors here in the US. We're also working with our industry partners to enact stronger defenses and assess our own vulnerabilities. Last fall, we completed an extensive review of all of our major contracts here at Space and Missile System Center to determine the levels of cyber security requirements we've implemented across our portfolio. And it sounds really kind of businessy geeky, if you will. Hey, we looked at our contracts to make sure that we had the right clauses in our contracts to address cybersecurity as dynamically as we possibly could. And so we found ourselves having to add new language to our contracts, to require system developers, to implement some more advanced protective measures in this evolving cyber security environment. So that data handling and supply chain protections from contract inception to launch and operations were taken into account. Cyber security really is a key performance parameter for us now. Performance of the system, It's as important as cost, it's as important as schedule, because if we deliver the perfect system on time and on cost, it can perform that missile warning or that communications mission perfectly, but it's not cyber secure. If it's doesn't have cyber protections built into it, or the ability to implement mitigations against cyber threats, then we've essentially fielded a shoe box in space that doesn't do the CA the war fighter or the nation any good. Supply chain risk management is a major challenge for us. We're doing a lot to coordinate with our industry partners. We're all facing it head on to try and build secure and trusted components that keep our confidence as leaders, firefighters, and baristas as the case may be. But it is a challenge. And we're trying to rise to that challenge. >> This is so exciting this new area, because it really touches everything. Talk about geeking out on the tech, the hardware, the systems but also you put your kind of MBA hat on you go, what's the ROI of extra development and how things get built. Because the always the exciting thing for space geeks is like, if you're building cool stuff, it's exciting, but you still have to build. And cybersecurity has proven that security has to be baked in from the beginning and be thought as a system architecture. So you're still building things, which means you got to acquire things, you got to acquire parts, you got acquire build software and sustain it. How is security impacting the acquisition and the sustainment of these systems for space? >> Yeah. From initial development, through planning for the acquisition, design, development, our production fielding and sustainment, it impacts all aspects of the life cycle, John. We simply, especially from the concept of baking in cybersecurity, we can't wait until something is built and then try and figure out how to make it cyber secure. So we've moved way further towards working side by side with our system developers to strengthen cybersecurity from the very beginning of a systems development, cyber security, and the resilience associated with it really have to be treated as a key system attribute. As I mentioned earlier, equivalent with data rates or other metrics of performance. We like to talk in the space world about mission assurance and mission assurance has always sort of taken us as we technically geek out. Mission assurance has always taken us to the will this system work in space. Can it work in a vacuum? Can it work in as it transfers through the Van Allen radiation belt or through the Southern hemisphere's electromagnetic anomaly? Will it work out in space? And now from a resiliency perspective, yeah, it has to work in space. It's got to be functional in space, but it's also got to be resistant to these cybersecurity threats. It's not just, I think a General D.T Thompson quoted this term. It's not just widget assurance anymore. It's mission assurance. How does that satellite operator that ground control segment operate while under attack? So let me break your question a little bit, just for purposes of discussion into really two parts, cybersecurity, for systems that are new and cybersecurity for systems that are in sustainment are kind of old and legacy. Obviously there's cyber vulnerabilities that threatened both, and we really have to employ different strategies for defensive of each one. For new systems. We're desperately trying to implement across the Department of Defense and particularly in the space world, a kind of a dev sec ops methodology and practice to delivering software faster and with greater security for our space systems. Here at SMC, we have a program called enterprise ground services, which is a toolkit, basically a collection of tools for common command and control of different satellite systems, EGS as we call it has an integrated suite for defensive cyber capabilities. Network operators can use these tools to gain unprecedented insight to data flows and to monitor space network traffic for anomalies or other potential indicators of a bad behavior, malicious behavior, if you will, it's rudimentary at this point, but because we're using DevSecOps and that incremental development approach, as we scale it, it just becomes more and more capable. Every product increment that we feel. Here at LA Air Force Base, we have the United Space Force's West Coast Software Factory, which we've dubbed the Kobayashi Maru. They're using those agile DevOps software development practices to deliver a space awareness software to the combined space operations center. Affectionately called the CSpock that CSpock is just on the road from Cal Poly there in San Luis Obispo at Vandenberg Air Force Base. They've so securely linked the sea Spock with other space operation centers around the planet, our allies, Australia, Canada, and the UK. We're partnering with all of them to enable secure and enhanced combined space operations. So lots of new stuff going on as we bake in new development capabilities for our space systems. But as I mentioned earlier, we've got large constellations of satellites on orbit right now. Some of them are well in excess of a decade or more or old on orbit. And so the design aspects of those satellites are several decades old. But we still have to worry about them cause they're critical to our space capabilities. We've been working with an air force material command organization called CROWS, which stands for the Cyber Resiliency Office for Weapon Systems to assess all of those legacy platforms from a cyber security perspective and develop defensive strategies and potential hardware and software upgrades to those systems to better enable them to live through this increasingly cybersecurity concerned era that we currently live in. Our industry partners have been critical to both of those different avenues. Both new systems and legacy systems. We're working closely with them to defend and upgrade national assets and develop the capabilities to do similar with new national assets coming online. The vulnerabilities of our space systems really kind of threatened the way we've done business in the past, both militarily and in the case of GPS economically. The impacts of that cybersecurity risk are clear in our acquisition and sustainment processes, but I've got to tell you, as the threat vectors change, as the vulnerabilities change, we've got to be nimble enough, agile enough, to be able to bounce back and forth. We can't just say, many people in the audience are probably familiar with the RMF or the Risk Management Framework approach to reviewing the cyber security of a system. We can't have program managers and engineers just accomplish an RMF on a system. And then, hey, high five, we're all good. It's a journey, not a destination, that's cybersecurity. And it's a constant battle rhythm through our weapon systems lifecycle, not just a single event. >> I want to get to this commercial business needs and your needs on the next question. But before I go there, you mentioned agile. And I see that clearly because when you have accelerated innovation cycles, you've got to be faster. And we saw this in the computer industry, mainframes, mini computers, and then we started getting beyond maybe when the internet hit and PCs came out, you saw the big enterprises, the banks and government start to work with startups. And it used to be a joke in the entrepreneurial circles is that, there's no way if you are a startup you're ever going to get a contract with a big business enterprise. Now that used to be for public sector and certainly for you guys. So as you see startups out there and there's acquisition involved, I'm sure would love to have a contract with Space Force. There's an ROI calculation where if it's in space and you have a sustainment view and it's software, you might have a new kind of business model that could be attractive to startups. Could you share your thoughts on the folks who want to be a supplier to you, whether they're a startup or an existing business that wants to be agile, but they might not be that big company. >> John, that's a fantastic question. We're desperately trying to reach out to those new space advocates, to those startups, to those what we sometimes refer to, within the Department of Defense, those non traditional defense contractors. A couple of things just for thinking purposes on some of the things that we're trying to highlight. Three years ago, we created here at Space and Missile System Center, the Space Enterprise Consortium to provide a platform, a contractual vehicle, really to enable us to rapidly prototype, development of space systems and to collaborate between the US Space Force, traditional defense contractors, non traditional vendors like startups, and even some academic institutions. SPEC, as we call it, Space Enterprise Consortium uses a specialized contracting tool to get contracts awarded quickly. Many in the audience may be familiar with other transaction agreements. And that's what SPEC is based on. And so far in just three years, SPEC has awarded 75 different prototyping contracts worth over $800 million with a 36% reduction in time to award. And because it's a consortium based competition for these kinds of prototyping efforts, the barrier to entry for small and nontraditional, for startups, even for academic institutions to be able to compete for these kinds of prototyping has really lowered. These types of partnerships that we've been working through on spec have really helped us work with smaller companies who might not have the background or expertise in dealing with the government or in working with cyber security for their systems, both our developmental systems and the systems that they're designing and trying to build. We want to provide ways for companies large and small to partner together in support kind of mutually beneficial relationships between all. Recently at the Annual Air Force Association conference that I mentioned earlier, I moderated a panel with several space industry leaders, all from big traditional defense contractors, by the way. And they all stressed the importance of building bridges and partnerships between major contractors in the defense industry and new entrance. And that helps us capture the benefits of speed and agility that come with small companies and startups, as well as the expertise and specialized skill sets of some of those larger contractors that we rely on day in and day out. Advanced cyber security protections and utilization of secure facilities are just a couple of things that I think we could be prioritizing more so in those collaborations. As I mentioned earlier, the SPEC has been very successful in awarding a number of different prototyping contracts and large dollar values. And it's just going to get better. There's over 400 members of the space enterprise consortium, 80% of them are non traditional kinds of vendors. And we just love working with them. Another thing that many people in the audience may be familiar with in terms of our outreach to innovators, if you will, and innovators that include cyber security experts is our space pitch day events. So we held our first event last November in San Francisco, where we awarded over a two day period about $46 million to 30 different companies that had potentially game changing ideas. These were phase two small business innovative research efforts that we awarded with cash on the spot. We're planning on holding our second space pitch day in the spring of 2021. We're planning on doing it right here in Los Angeles, COVID-19 environment permitting. And we think that these are fantastic venues for identifying and working with high-speed startups, and small businesses who are interested in really, truly partnering with the US Air Force. It's, as I said before, it's a really exciting time to be a part of this business. And working with the innovation economy is something that the Department of Defense really needs to do in that the innovation that we used to think was ours. That 80% of the industrial base innovation that came from the Department of Defense, the script has been flipped there. And so now more than 70%, particularly in space innovation comes from the commercial sector, not from the defense business itself. And so that's a tsunami of investment and a tsunami of a capability. And I need to figure out how to get my surfboard out and ride it, you know what I mean? >> Yeah, It's one of those things where the script has been flipped, but it's exciting because it's impacting everything. When you're talking about systems architecture? You're talking about software, you're talking about a business model. You're talking about dev sec opsx from a technical perspective, but now you have a business model innovation. All the theaters are exploding in innovation, technical, business, personnel. This brings up the workforce challenge. You've got the cyber needs for the US Space Force, It's probably great ROI model for new kinds of software development that could be priced into contracts. That's a entrepreneurial innovation, you've got the business model theater, you've got the personnel. How does the industry adopt and change? You guys are clearly driving this. How does the industry adjust to you? >> Yeah. So I think a great way to answer that question is to just talk about the kind of people that we're trying to prioritize in the US Space Force from an acquisition perspective, and in this particular case from a cybersecurity perspective. As I mentioned earlier, it's the most exciting time to be in space programs, really since the days of Apollo. Just to put it in terms that maybe have an impact with the audience. From 1957 until today, approximately 9,000 satellites have been launched from the various space varying countries around the planet. Less than 2000 of those 9,000 are still up on orbit and operational. And yet in the new space regime players like Space X have plans to launch, 12,000 satellites for some of their constellations alone. It really is a remarkable time in terms of innovation and fielding of space capabilities and all of those space capabilities, whether they're commercial, civil, or defense are going to require appropriate cybersecurity protections. It's just a really exciting time to be working in stuff like this. And so folks like the folks in this audience who have a passion about space and a passion about cybersecurity are just the kind of people that we want to work with. Cause we need to make sure our systems are secure and resilient. We need folks that have technical and computing expertise, engineering skills to be able to design cyber secure systems that can detect and mitigate attacks. But we also, as you alluded to, we need people that have that business and business acumen, human networking background, so that we can launch the startups and work with the non traditional businesses. Help to bring them on board help, to secure both their data and our data and make sure our processes and systems are free as much as possible from attack. For preparation, for audience members who are young and maybe thinking about getting into this trade space, you got to be smart on digital networking. You got to understand basic internet protocols, concepts, programming languages, database design. Learn what you can for penetration or vulnerability testing and a risk assessment. I will tell you this, and I don't think he will, I know he will not mind me telling you this, but you got to be a lifelong learner and so two years ago, I'm at home evening and I get a phone call on my cell phone and it's my boss, the commander of Air Force Space command, General, J. Raymond, who is now currently the Chief of Space Operations. And he is on temporary duty, flying overseas. He lands where he's going and first thing he does when he lands is he calls me and he goes JT, while I was traveling, I noticed that there were eBooks available on the commercial airliner I was traveling on and there was an ebook on something called scrumming and agile DevSecOps. And I read it, have you read it? And I said, no, sir. But if you tell me what the title of the book is, I will read it. And so I got to go to my staff meeting, the very next week, the next time we had a staff meeting and tell everybody in the staff meeting, hey, if the four star and the three star can read the book about scrumming, then I'm pretty sure all of you around this table and all our lieutenants and our captains our GS13s, All of our government employees can get smart on the scrumming development process. And interestingly as another side, I had a telephone call with him last year during the holidays, where he was trying to take some leave. And I said, sir, what are you up to today? Are you making eggnog for the event tonight or whatever. And the Chief of Space Operations told me no, I'm trying to teach myself Python. I'm at lesson two, and it's not going so well, but I'm going to figure this out. And so that kind of thing, if the chief of staff or the Chief of Space Operations can prioritize scrumming and Python language and innovation in his daily schedule, then we're definitely looking for other people who can do that. And we'll just say, lower levels of rank throughout our entire space force enterprise. Look, we don't need people that can code a satellite from scratch, but we need to know, we need to have people that have a basic grasp of the programming basics and cybersecurity requirements. And that can turn those things into meaningful actions, obviously in the space domain, things like basic physics and orbital mechanics are also important spaces, not an intuitive domain. So under understanding how things survive on orbit is really critical to making the right design and operational decisions. And I know there's probably a lot, because of this conference. I know there's probably a whole lot of high speed cybersecurity experts out in the audience. And I need those people in the US Space Force. The country is counting on it, but I wouldn't discount having people that are just cyber aware or cyber savvy. I have contracting officers and logisticians and program managers, and they don't have to be high end cybersecurity experts, but they have to be aware enough about it to be able to implement cyber security protections into our space systems. So the skill set is really, really broad. Our adversaries are pouring billions of dollars into designing and fielding offensive and destructive space, cybersecurity weapons. They repeatedly shown really a blatant disregard of safety and international norms for good behavior on orbit. And the cyber security aspects of our space systems is really a key battleground going forward so that we can maintain that. As I mentioned before, peaceful global comments of space, we really need all hands on deck. If you're interested in helping in uniform, if you're interested in helping, not in uniform, but as a government employee, a commercial or civil employee to help us make cyber security more important or more able to be developed for our space systems. And we'd really love to work with you or have you on the team to build that safe and secure future for our space systems. >> Lieutenant General John Thompson, great insight. Thank you for sharing all that awesome stories too, and motivation for the young next generation. The United States Space Force approach to cybersecurity. Really amazing talk, thank you for your time. Final parting question is, as you look out and you have your magic wand, what's your view for the next few years in terms of things that we could accomplish? It's a super exciting time. What do you hope for? >> So first of all, John, thanks to you and thanks to Cal Poly for the invitation and thanks to everybody for their interest in cybersecurity, especially as it relates to space systems, that's here at the conference. There's a quote, and I'll read it here from Bernard Schriever, who was the founder, if you will, a legend in a DoD space, the founder of the Western development division, which was a predecessor organization to Space and Missile System Center, General Schriever, I think captures the essence of how we see the next couple of years. "The world has an ample supply of people "who can always come up with a dozen good reasons "why new ideas will not work and should not be tried, "but the people who produce progress are breed apart. "They have the imagination, "the courage and the persistence to find solutions." And so I think if you're hoping that the next few years of space innovation and cybersecurity innovation are going to be upon a pony ride at the County fair, then perhaps you should look for another line of work, because I think the next few years in space and cybersecurity innovation are going to be more like a rodeo and a very dynamic rodeo as it goes. It is an awesome privilege to be part of this ecosystem. It's really an honor for me to be able to play some small role in the space ecosystem and trying to improve it while I'm trying to improve the chances of the United States of America in a space war fighting environment. And so I thank all of you for participating today and for this little bit of time that you've allowed me to share with you. Thank you. >> Sir, thank you for your leadership and thank you for the time for this awesome event, Space and Cyber Cybersecurity Symposium 2020, I'm John Furrier on behalf of Cal Poly, thanks for watching. (mellow music)