>>Welcome back everyone to the cubes, live coverage here at VMware Explorer, 22. We're here on the ground on the floor of Mosco. I'm John for David ante. We're at kit Goldberg, CTO of VMware, the star of the show, the headliner@supercloud.world. The event we had just a few weeks ago, kit. Great to see you super excited to, to chat with you. Thanks for coming on. Oh >>Yeah. Happy to be here, man. It's been a wild week. Tons of excitement. We are jazzed. We're jacked, like to look at things >>For both, of course, jacked up and jazzed. Ready to go. So you got UN stage loved your keynote, you know, very CTO oriented, hit the, all your marks cloud native, the vSphere eight intro. Yep. More performance, more power. Yeah, more efficiency. And now the cloud native over the top, you shipped a white paper a few weeks ago, which we discussed at our super cloud event. Yep. You know, really laying out the narrative of cloud native. This is the priority for you. Is that true? Is that your only priority? What are the things going on right now for you that are your top priorities, >>Top priorities. So absolutely at a high level, it's flushing out this vision that, that we're talking about in terms of what we call cross cloud services. Other people call multi-cloud, you guys have super cloud, but the point is, I think what we see is that there's these different sort of vertical silos, the different public clouds they're on-prem data center edge. And what we're looking at is trying to create a new type of cloud something that's more horizontal in architecture. And I think this is something that we realize we've been doing at VMware for a while, and we gave it a name, we call it cross cloud. But what's important is that while we do bring a lot of value there, we can't possibly do everything. This has to be an industrywide movement. And so I think what we're really excited about is figuring out, okay, how do we actually build an architecture and a framework such that there's clear sort of lines of responsibility. Here's what one company does. Here's what another one does make sure that there's clean sort of APIs between that basically an overall architecture and structure. So that's probably one of the, the high level things that we're doing as an organization right now. >>What's been the feedback here at VMware Explorer, obviously the new name, Explorer rag laid that out in the keynote. Yep. It's about moving forward. Not replacing the community. Yep. Extending the world core and exploring new frontiers multicloud. Obviously one of them key. Yeah. Very clever actually names dig into it. It's nuanced. What's been the reaction. Yep. You're right. Yep. You're crazy. I love it. I need it. It's it's too early. It's perfect timing. No, it's a bit of, what's the feedback always a little >>Bit of everything, you know, I think one of us firstno people didn't really understand it. I think people were confused about what it was, but now that we're here in person, I think generally speaking, I'm hearing a lot of positive things about it. We've been gone or been apart for three years now, right? Since the last in person one, and this is an interesting opportunity for recreation sort of rebirth, right? We've certainly lost some traditions during the COVID pandemic, but also gives us the opportunity to build new ones. And to your point, world was always associated with virtualization. And of course, we're still doing that. We're still doing cloud infrastructure, but we're doing so much more. And given this focus on multi-cloud that I just mentioned and how it is the go forward focus for VMware, we wanted to evolve the conference to have that focus. And so I've been actually really pleased to see how many folks for it's their first time here. Right? They haven't been Tom worlds before and you know, this broader sort of conference that we're creating to, to apply to the support, more disciplines, different focus areas, you know, application development, developers, platform teams, you got cloud management things with aria, public cloud management, networking security, and user computing, all in addition to the core infrastructure bits. >>So John all week's been paying homage to, to Andy Grove talking about, let chaos rain and then rain in the chaos. Right. And so when you talk to customers, that chaos message cloud chaos, how is it resonating? Are they aware of that chaos? Are they saying, yes, we have cloud chaos or some saying, eh, yeah. It's okay. Everything's good. And they just maybe have some blind spots. What do >>You think? Yeah. I'm actually surprised at how strongly it's resonating. I mean, I think we knew that we were onto something, but people even love the specific term. They're like cloud chaos. I never thought about it that way, but you're like, you're absolutely right. It was a movie. It's a great, yeah. I know. Sounds like a thriller, but, but what we sort of, the picture we paint there about these silos across clouds, the duplication of technologies, duplication of teams and training, all this stuff. People realize that's where they're at. And it's one of those things where there's this headlong rush to cloud for good reasons. People wanted to be in the agility, but now they're dealing with some of that complexity that, that gets built up there and it absolutely is chaos. And while speed is great, you need to somehow balance that speed with control things like security compliance. These are sort of enterprise requirements that are sort of getting left out. And I think that's the realization, that's the sort of chaos that we're hitting on. >>It's almost like when in bus, in business school, you had the economic lines when break even hits, you know, cloud had a lot of great goodness to it. Yep. A lot of great value. It still does on the CapEx side, but as distributed computing architectures become reality. Yep. Private cloud instantiation of hybrid cloud operations. Now you've got edge and opening up all these new, new net new applications. Yep. What are you seeing there? And it's a question we've been asked some of the folks in the partner network, what are some of those new next gen apps that are gonna be enabled by, by this next wave edge specifically? Yeah. More performance, more application development, more software. Yeah. More faster, cheaper going on here. Kind of a Moore's law vibe there. What's next. >>Yeah. So, you know, when we look at edge, so, okay. Take today. Today. Edge is oftentimes highly customized software and hardware. It's not general purpose or to cloud technologies. And while edge is certainly gonna be limited. You can't just infinitely scale. Like you can in the cloud and the network bandwidth might be a little bit limited. You still wanna imagine it or manage it as if it were another cloud location, right. That like, I wanna be able to address it. Just like I addressed a certain availabilities done within AWS. I wanna be able to say the specific edge location at, you know, wherever somewhere here in San Francisco, let's say right now there's a few different things though. The first of which is that you got to manage at scale. Cause you don't have with cloud, you got a small number of very large locations with edge. >>You got a large number of very small locations. And so it's the scale is inverted there. So what this means is that you probably can't exactly specify which edge you want to go to. What instead you wanna say is more relational. Like I've got an IOT device out there. I want my app to be in data to be near it. And the system needs to figure out, okay, where do I put that thing? And how do I get it near it? And there may be some different constraints. You have cost security, privacy, it may be your edge or maybe telco edge location, you know, one, one of these sorts of things. Right? And so I think where we're going there is to enable the movement of applications and data to the right place. And this again goes back to the whole cross cloud architecture, right? >>You don't wanna be limited in terms of where you put an app, you wanna have that flexibility. This is the whole, you know, we use the term cloud smart. Right. And that's what it means. It's like put the, the app where it needs to be sort of the right tool for the right job. And so I think the innovation though, it's gonna be huge. You're gonna see new application architectures that the app can be placed near a user near a device near like a, an iPhone or near an IOT device, like a video camera. And the way that you manage that is gonna be much kind of infrastructure is code base. Yeah. So I think there's huge possibilities there. And it's really amazing to see just real quick on the telco side, what's happening there as well. The move to 5g, the move to open ran telco is now starting to adopt these data center and cloud technologies kinda standard building blocks that we use now out at the edge. So I think, you know, the amount of innovation that we're gonna see, >>It's really the first time on telco, they actually have a viable, scalable opportunity to, to put real gear data center, liked capabilities yep. At a location for specific purpose. Yeah. The edge function. >>Yeah. And well, and what we, without >>Building a, a monster >>Facility. Exactly. Yeah. It's like the base of a cell tower or something telephone closet. But what we've been able to do is improve these general purpose technologies. Like you look at vSphere in our hypervisor today. We are great at real time workloads, right? Like as a matter of fact, you look at performance on vSphere versus bare metal. Oftentimes an app runs faster on vSphere now because of all the efficiency and scale and so forth we can bring. So it means that these telecom applications that are very latency sensitive can now run fun on there. But Hey, guess what? Once you have a general purpose server that can run some of the telecom apps, well, Hey, you got extra space to run other apps. Maybe you could sell that space to customers or partners. And you know, then you have this new architecture >>Is the dev skill, a, a barrier for the, for the telcos, where are we at >>With that? It, it, it is. I think the barriers are really, how do you provide, I dunno if it's a skill set. I mean, there's probably some skill set aspects. I think in my mind, it's more about giving them the APIs to get access to that. Like, as I said, you're not gonna have developers knowing, okay, here are the specific geographic locations of all the cell towers in San Francisco and set what you're gonna say again, I need to be near this thing. And so you used geolocation and figure out, just put it some, put it in the right place. I don't really care. Right. So again, I think it's an evolution of management evolution of the APIs that developers use to access. Like today, I'm gonna say, okay, I know my app needs to be on the east coast so I can use us east one. I know the specific AZs at a, at a cloud level. That makes sense at an edge level. It doesn't, you're not gonna know. Okay. Like the specific cross streets or whatever, you gotta let the system figure that >>Out kid. I know you gotta go on. Times's tight, real quick. You got a session here on web three. Yeah. The Cube's got the, you know, the cube versus coming soon. We might be heavy. The cube versus coming powered by arm token, we had all kinds of stuff going on. Yep. You saw the preview a couple years ago. We did with the Cuban. Anyway, you did a session on web three and DM. VMware's rolling real quick. What was that about? Yeah, what's the purpose? >>What's the direction. That was a fascinating conversation. So I was talking about web three. It was talking about why enterprises haven't really started even to scratch the surface of the potential of web three. So part of it was like, okay, what is web three? It's a buzz words. We talked through that. We talked through the use of blockchain, how that sits with the core of a lot of web three. We talked about the use of cryptocurrency and how that makes sense. We talked about the consumerization, continuing consumerization of it. We've seen it with end user devices. We may well see it with some of the web three changes around ownership, individual ownership of data, of assets, et cetera. That's gonna have a downstream impact on enterprises, how they go to market their commercial models. So it was a fascinating discussion that unfortunately it's hard to summarize, but gotten to a lot of the nuances of this and some of the, are >>You bullish on >>It? Very bullish, a hundred percent. Like I think blockchain is a hugely enabling technology and not from a cryptocurrency standpoint, put that aside. All the enterprise use cases, we have customers like broad bridge financial today leveraging VMware blockchain, doing a hundred billion in transactions a day with the sort of repo market >>You think defi is booming >>Defi. So I, I think we're just starting to get there. But what you find is oftentimes these trends start on the consumer side and then all of a sudden they surprise enterprises. >>They call it a tri tried tread five traditional fi finance >>Versus okay. >>Any >>Other way around? No, no, no. But I'm saying is that it's, these consumer trends will start to impact enterprises. But what I'm saying is that enterprises need to be ready now or start preparing now for those comings. >>And what's the preparation for that? Just education learning. Yeah. >>Education learning, looking at blockchain, use cases, looking at what will this enable consumers to do that they couldn't do before there is gonna be a democratization of access to data. You're still gonna wanna have gatekeepers. You're still gonna wanna have enterprises or services that add value on top of that, but it's gonna be a bit more of an open ecosystem now, and that's gonna change some of the market dynamics in subtle ways. >>Okay. So we got one minute left. I want to ask you, what's your impression of the super cloud event we had also, you were headlining and you guys were a big part of bringing the, a large C of great people together. Are you happy with the outcome? What do you think's next for? >>Absolutely. No. I was super excited to see how much reception and engagement it got from across the industry. Right? So many different entry participants, so many different customers, partners, et cetera, viewing it online have had a lot of conversations here at explore already. As you know, you know, VMware, we put out a white paper, our point of view on what is a multi-cloud service. What is the taxonomy of those services? Again, as I mentioned before, we need to get as an industry to a place where we have alignment about this overall architecture to enable interoperability. And I think that's really the key thing. If we're gonna make this industry architectural shift, which is what I see coming, this is what we got. >>And you're gonna be jumping all in with this and helping out if we need you >>Hundred percent. All right. >>All in. I really love your transparency on the, on your white paper. Check out the white paper online on vmware.com. It's the cross cloud cloud native. I, I call the, the mission statement. It's not a Jerry McGuire memo. It's more me than that. It's the, it's the direction of cloud native. Yep. And multi-cloud thanks for coming on and, and thanks for doing that too. >>No, of course. And thanks for having me. Thanks. Love the discussion. >>Okay. More live coverage here at world Explorer, VMware Explorer, after the short break.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's day one coverage of HPE discover 22 live from the Venetian in Las Vegas. I got a power panel here, Lisa Martin, with Dave Valante, John furrier, Holger Mueller also joins us. We are gonna wrap this, like you've never seen a rap before guys. Lot of momentum today, lot, lot of excitement, about 8,000 or so customers, partners, HPE leaders here. Holger. Let's go ahead and start with you. What are some of the things that you heard felt saw observed today on day one? >>Yeah, it's great to be back in person. Right? 8,000 people events are rare. Uh, I'm not sure. Have you been to more than 8,000? <laugh> yeah, yeah. Okay. This year, this year. I mean, historically, yes, but, um, >>Snowflake was 10. Yeah. >>So, oh, wow. Okay. So 8,000 was my, >>Cisco was, they said 15, >>But is my, my 8,000, my record, I let us down with 7,000 kind of like, but it's in the Florida swarm. It's not nicely. Like, and there's >>Usually what SFI, there's usually >>20, 20, 30, 40, 50. I remember 50 in the nineties. Right. That was a different time. But yeah. Interesting. Yeah. Interesting what people do and it depends how much time there is to come. Right. And know that it happens. Right. But yeah, no, I think it's interesting. We, we had a good two analyst track today. Um, interesting. Like HPE is kind of like back not being your grandfather's HPE to a certain point. One of the key stats. I know Dave always for the stats, right. Is what I found really interesting that over two third of GreenLake revenue is software and services. Now a love to know how much of that services, how much of that software. But I mean, I, I, I, provocate some, one to ones, the HP executives saying, Hey, you're a hardware company. Right. And they didn't even come back. Right. But Antonio said, no, two thirds is, uh, software and services. Right. That's interesting. They passed the one exabyte, uh, being managed, uh, as a, as a hallmark. Right. I was surprised only 120,000 users if I had to remember the number. Right, right. So that doesn't seem a terrible high amount of number of users. Right. So, but that's, that's, that's promising. >>So what software is in there, cuz it's gotta be mostly services. >>Right? Well it's the 70 plus cloud services, right. That everybody's talking about where the added eight of them shockingly back up and recovery, I thought that was done at launch. Right. >>Still who >>Keep recycling storage and you back. But now it's real. Yeah. >>But the company who knows the enterprise, right. HPE, what I've been doing before with no backup and recovery GreenLake. So that was kind of like, okay, we really want to do this now and nearly, and then say like, oh, by the way, we've been doing this all the time. Yeah. >>Oh, what's your take on the installed base of HP. We had that conversation, the, uh, kickoff or on who's their target, what's the target audience environment look like. It certainly is changing. Right? If it's software and services, GreenLake is resonating. Yeah. Um, ecosystems responding. What's their customers cuz managed services are up too Kubernetes, all the managed services what's what's it like what's their it transformation base look like >>Much of it is of course install base, right? The trusted 20, 30 plus year old HP customer. Who's keeping doing stuff of HP. Right. And call it GreenLake. They've been for so many name changes. It doesn't really matter. And it's kind of like nice that you get the consume pain only what you consume. Right. I get the cloud broad to me then the general markets, of course, people who still need to run stuff on premises. Right. And there's three reasons of doing this performance, right. Because we know the speed of light is relative. If you're in the Southern hemisphere and even your email servers in Northern hemisphere, it takes a moment for your email to arrive. It's a very different user experience. Um, local legislation for data, residency privacy. And then, I mean Charles Phillips who we all know, right. Former president of uh, info nicely always said, Hey, if the CIOs over 50, I don't have to sell qu. Right. So there is not invented. I'm not gonna do cloud here. And now I've kind of like clouded with something like HP GreenLake. That's the customers. And then of course procurement is a big friend, right? Yeah. Because when you do hardware refresh, right. You have to have two or three competitors who are the two or three competitors left. Right. There's Dell. Yeah. And then maybe Lenovo. Right? So, so like a >>Little bit channels, the strength, the procurement physicians of strength, of course install base question. Do you think they have a Microsoft opportunity where, what 365 was Microsoft had office before 365, but they brought in the cloud and then everything changed. Does HP have that same opportunity with kind of the GreenLake, you know, model with their existing stuff. >>It has a GreenLake opportunity, but there's not much software left. It's a very different situation like Microsoft. Right? So, uh, which green, which HP could bring along to say, now run it with us better in the cloud because they've been selling much of it. Most of it, of their software portfolio, which they bought as an HP in the past. Right. So I don't see that happening so much, but GreenLake as a platform itself course interesting because enterprise need a modern container based platform. >>I want, I want to double click on this a little bit because the way I see it is HP is going to its installed base. I think you guys are right on say, this is how we're doing business now. Yeah. You know, come on along. But my sense is, some customers don't want to do the consumption model. There are actually some customers that say, Hey, of course I got, I don't have a cash port problem. I wanna pay for it up front and leave me alone. >>I've been doing this since 50 years. Nice. As I changed it, now <laugh> two know >>Money's wants to do it. And I don't wanna rent because rental's more expensive and blah, blah, blah. So do you see that in the customer base that, that some are pushing back? >>Of course, look, I have a German accent, right? So I go there regularly and uh, the Germans are like worried about doing anything in the cloud. And if you go to a board in Germany and say, Hey, we can pay our usual hardware, refresh, CapEx as usual, or should we bug consumption? And they might know what we are running. <laugh> so not whole, no offense against the Germans out. The German parts are there, but many of them will say, Hey, so this is change with COVID. Right. Which is super interesting. Right? So the, the traditional boards non-technical have been hearing about this cloud variable cost OPEX to CapEx and all of a sudden there's so much CapEx, right. Office buildings, which are not being used truck fleets. So there's a whole new sensitivity by traditional non-technical boards towards CapEx, which now the light bulb went on and say, oh, that's the cloud thing about also. So we have to find a way to get our cost structure, to ramp up and ramp down as our business might be ramping up through COVID through now inflation fears, recession, fears, and so on. >>So, okay. HP's, HP's made the statement that anything you can do in the cloud you can do in GreenLake. Yes. And I've said you can't run on snowflake. You can't run Mongo Atlas, you can't run data bricks, but that's okay. That's fine. Let's be, I think they're talking about, there's >>A short list of things. I think they're talking about the, their >>Stuff, their, >>The operating experience. So we've got single sign on through a URL, right. Uh, you've got, you know, some level of consistency in terms of policy. It's unclear exactly what that is. You've got storage backup. Dr. What, some other services, seven other services. If you had to sort of take your best guess as to where HP is now and peg it toward where Amazon was in which year? >>20 14, 20 14. >>Yeah. Where they had their first conference or the second we invent here with 3000 people and they were thinking, Hey, we're big. Yeah. >>Yeah. And I think GreenLake is the building blocks. So they quite that's the >>Building. Right? I mean similar. >>Okay. Well, I mean they had E C, Q and S3 and SQS, right. That was the core. And then the rest of those services were, I mean, base stock was one of that first came in behind and >>In fairness, the industry has advanced since then, Kubernetes is further along. And so HPE can take advantage of that. But in terms of just the basic platform, I, I would agree. I think it's >>Well, I mean, I think, I mean the software, question's a big one. I wanna bring up because the question is, is that software is getting the world. Hardware is really software scales, everything, data, the edge story. I love their story. I think HP story is wonderful Aruba, you know, hybrid cloud, good story, edge edge. But if you look under the covers, it's weak, right? It's like, it's not software. They don't have enough software juice, but the ecosystem opportunity to me is where you plug and play. So HP knows that game. But if you look historically over the past 25 years, HP now HPE, they understand plug and play interoperability. So the question is, can they thread the needle >>Right. >>Between filling the gaps on the software? Yeah. With partners, >>Can they get the partners? Right. And which have been long, long time. Right. For a long time, HP has been the number one platform under ICP, right? Same thing. You get certified for running this. Right. I know from my own history, uh, I joined Oracle last century and the big thing was, let's get your eBusiness suite certified on HP. Right? Like as if somebody would buy H Oracle work for them, right. This 20 years ago, server >>The original exit data was HP. Oracle. >>Exactly. Exactly. So there's this thinking that's there. But I think the key thing is we know that all modern forget about the hardware form in the platforms, right? All modern software has to move to containers and snowflake runs in containers. You mentioned that, right? Yeah. If customers force snowflake and HPE to the table, right, there will be a way to make it work. Right. And which will help HPE to be the partner open part will bring the software. >>I, I think it's, I think that's an opportunity because that changes the game and agility and speed. If HP plays their differentiation, right. Which we asked on their opening segment, what's their differentiation. They got size scale channel, >>What to the enterprise. And then the big benefit is this workload portability thing. Right? You understand what is run in the public cloud? I need to run it local. For whatever reason, performance, local residency of data. I can move that. There that's the big benefit to the ISVs, the sales vendors as well. >>But they have to have a stronger data platform story in my that's right. Opinion. I mean, you can run Oracle and HPE, but there's no reason they shouldn't be able to do a deal with, with snowflake. I mean, we saw it with Dell. Yep. We saw it with, with, with pure and I, if our HPE I'd be saying, Hey, because the way the snowflake deal worked, you probably know this is your reading data into the cloud. The compute actually occurs in the cloud viral HB going snowflake saying we can separate compute and storage. Right. And we have GreenLake. We have on demand. Why don't we run the compute on-prem and make it a full class, first class citizen, right. For all of our customers data. And that would be really innovative. And I think Mongo would be another, they've got OnPrem. >>And the question is, how many, how many snowflake customers are telling snowflake? Can I run you on premise? And how much defo open years will they hear from that? Right? This is >>Why would they deal Dell? That >>Deal though, with that, they did a deal. >>I think they did that deal because the customer came to them and said, you don't exactly that deal. We're gonna spend the >>Snowflake >>Customers think crazy things happen, right? Even, even put an Oracle database in a Microsoft Azure data center, right. Would off who, what as >>Possible snowflake, >>Oracle. So on, Aw, the >>Snow, the snowflakes in the world have to make a decision. Dave on, is it all snowflake all the time? Because what the reality is, and I think, again, this comes back down to the, the track that HP could go up or down is gonna be about software. Open source is now the software industry. There's no such thing as proprietary software, in my opinion, relatively speaking, cloud scale and integrated, integrated integration software is proprietary. The workflows are proprietary. So if they can get that right with the partners, I would focus on that. I think they can tap open source, look at Amazon with open source. They sucked it up and they integrated it in. No, no. So integration is the deal, not >>Software first, but Snowflake's made the call. You were there, Lisa. They basically saying it's we have, you have to be in snowflake in order to get the governance and the scalability, all that other wonderful stuff. Oh, but we we'll do Apache iceberg. We'll we'll open it up. We'll do Python. Yeah. >>But you can't do it data clean room unless you are in snowflake. Exactly. Snowflake on snowflake. >>Exactly. >>But got it. Isn't that? What you heard from AWS all the time till they came out outposts, right? I mean, snowflake is a market leader for what they're doing. Right. So that they want to change their platform. I mean, kudos to them. They don't need to change the platform. They will be the last to change their platform to a ne to anything on premises. Right. But I think the trend already shows that it's going that way. >>Well, if you look at outpost is an signal, Dave, the success of outpost launched what four years ago, they announced it. >>What >>EKS is beating, what outpost is doing. Outpost is there. There's not a lot of buzz and talk to the insiders and the open source community, uh, EKS and containers. To your point mm-hmm <affirmative> is moving faster on, I won't say commodity hardware, but like could be white box or HP, Dell, whatever it's gonna be that scale differentiation and the edge story is, is a good one. And I think with what we're seeing in the market now it's the industrial edge. The back office was gen one cloud back office data center. Now it's hybrid. The focus will be industrial edge machine learning and AI, and they have it here. And there's some, some early conversations with, uh, I heard it from, uh, this morning, you guys interviewed, uh, uh, John Schultz, right? With the world economic 4k birth Butterfield. She was amazing. And then you had Justin bring up a Hoar, bring up quantum. Yes. That is a differentiator. >>HP. >>Yes. Yeah. You, they have the computing shops. They had the R and D can they bring it to the table >>As, as HPC, right. To what they Schultz for of uh, the frontier system. Right. So very impressed. >>So the ecosystem is the key for them is because that's how they're gonna fill the gaps. They can't, they can't only, >>They could, they could high HPC edge piece. I wouldn't count 'em out of that game yet. If you co-locate a box, I'll use the word box, particularly at a telco tower. That's a data center. Yep. Right. If done properly. Yep. So, you know, what outpost was supposed to do actually is a hybrid opportunity. Aruba >>Gives them a unique, >>But the key thing is right. It's a yin and yang, right? It's the ecosystem it's partners to bring those software workload. Absolutely. Right. But HPE has to keep the platform attractive enough. Right. And the key thing there is that you have this workload capability thing that you can bring things, which you've built yourself. I mean, look at the telcos right. Network function, visualization, thousands of man, years into these projects. Right. So if I can't bring it to your edge box, no, I'm not trying to get to your Xbox. Right. >>Hold I gotta ask you since in the Dave too, since you guys both here and Lisa, you know, I said on the opening, they have serious customers and those customers have serious problems, cyber security, ransomware. So yeah. I teach transformation now. Industrial transformation machine learning, check, check, check. Oh, sounds good. But at the end of the day, their customers have some serious problems. Right? Cyber, this is, this is high stakes poker. Yeah. What do you think HP's position for in the security? You mentioned containers, you got all this stuff, you got open source, supply chain, you have to left supply chain issues. What is their position with security? Cuz that's the big one. >>I, I think they have to have a mature attitude that customers expect from HPE. Right? I don't have to educate HP on security. So they have to have the partner offerings again. We're back at the ecosystem to have what probably you have. So bring your own security apart from what they have to have out of the box to do business with them. This is why the shocker this morning was back up in recovery coming. <laugh> it's kind like important for that. Right? Well >>That's, that's, that's more ransomware and the >>More skeleton skeletons in the closet there, which customers should check of course. But I think the expectations HP understands that and brings it along either from partner or natively. >>I, I think it's, I think it's services. I think point next is the point of integration for their security. That's why two thirds is software and services. A lot of that is services, right? You know, you need security, we'll help you get there. We people trust HP >>Here, but we have nothing against point next or any professional service. They're all hardworking. But if I will have to rely on humans for my cyber security strategy on a daily level, I'm getting gray hair and I little gray hair >>Red. Okay. I that's, >>But >>I think, but I do think that's the camera strategy. I mean, I'm sure there's a lot of that stuff that's beginning to be designed in, but I, my guess is a lot of it is services. >>Well, you got the Aruba. Part of the booth was packed. Aruba's there. You mentioned that earlier. Is that good enough? Because the word zero trust is kicked around a lot. On one hand, on the other hand, other conversations, it's all about trust. So supply chain and software is trusting trust, trust and verified. So you got this whole mentality of perimeter gone mentality. It's zero trust. And if you've got software trust, interesting thoughts there, how do you reconcile zero trust? And then I need trust. What's what's you? What are you seeing older on that? Because I ask people all the time, they're like, uh, I'm zero trust or is it trust? >>Yeah. The middle ground. Right? Trusted. The meantime people are man manipulating what's happening in your runtime containers. Right? So, uh, drift control is a new password there that you check what's in your runtime containers, which supposedly impenetrable, but people finding ways to hack them. So we'll see this cat and mouse game going on all the time. Yeah. Yeah. There's always gonna be the need for being in a secure, good environment from that perspective. Absolutely. But the key is edge has to be more than Aruba, right? If yeah. HV goes away and says, oh yeah, we can manage your edge with our Aruba devices. That's not enough. It's the virtual probability. And you said the important thing before it's about the data, right? Because the dirty secret of containers is yeah, I move the code, but what enterprise code works without data, right? You can't say as enterprise, okay, we're done for the day check tomorrow. We didn't persist your data, auditor customer. We don't have your data anymore. So filling a way to transport the data. And there just one last thought, right? They have a super interesting asset. They want break lands for the venerable map R right. Which wrote their own storage drivers and gives you the chance to potentially do something in that area, which I'm personally excited about. But we'll see what happens. >>I mean, I think the holy grail is can I, can I put my data into a cloud who's ever, you know, call it a super cloud and can I, is it secure? Is it governed? Can I share it and be confident that it's discoverable and that the, the person I give it to has the right to use it. Yeah. And, and it's the correct data. There's not like a zillion copies running. That's the holy grail. And I, I think the answer today is no, you can, you can do that maybe inside of AWS or maybe inside of Azure, look maybe certainly inside of snowflake, can you do that inside a GreenLake? Well, you probably can inside a GreenLake, but then when you put it into the cloud, is it cross cloud? Is it really out to the edge? And that's where it starts to break down, but that's where the work is to be done. That's >>The one Exide is in there already. Right. So men being men. Yeah. >>But okay. But it it's in there. Yeah. Okay. What do you do with it? Can you share that data? What can you actually automate governance? Right? Uh, is that data discoverable? Are there multiple copies of that data? What's the, you know, master copy. Here's >>A question. You guys, here's a question for you guys analyst, what do you think the psychology is of the CIO or CSO when HP comes into town with GreenLake, uh, and they say, what's your relationship with the hyperscalers? Cause I'm a CIO. I got my environment. I might be CapEx centric or Hey, I'm open model. Open-minded to an operating model. Every one of these enterprises has a cloud relationship. Yeah. Yeah. What's the dynamic. What do you think the psychology is of the CIO when they're rationalizing their, their trajectory, their architecture, cloud, native scale integration with HPE GreenLake or >>HP service. I think she or he hears defensiveness from HPE. I think she hears HPE or he hears HPE coming in and saying, you don't need to go to the cloud. You know, you could keep it right here. I, I don't think that's the right posture. I think it should be. We are your cloud. And we can manage whether it's OnPrem hybrid in AWS, Azure, Google, across those clouds. And we have an edge story that should be the vision that they put forth. That's the super cloud vision, but I don't hear it >>From these guys. What do you think psycho, do you agree with that? >>I'm totally to make, sorry to be boring, but I totally agree with, uh, Dave on that. Right? So the, the, the multi-cloud capability from a trusted large company has worked for anybody up and down the stack. Right? You can look historically for, uh, past layers with cloud Foundry, right? It's history vulnerable. You can look for DevOps of Hashi coop. You can look for database with MongoDB right now. So if HPE provides that data access, right, with all the problems of data gravity and egres cost and the workability, they will be doing really, really well, but we need to hear it more, right. We didn't hear much software today in the keynote. Right. >>Do they have a competitive offering vis-a-vis or Azure? >>The question is, will it be an HPE offering or will, or the software platform, one of the offerings and you as customer can plug and play, right. Will software be a differentiator for HP, right. And will be close, proprietary to the point to again, be open enough for it, or will they get that R and D format that, or will they just say, okay, ES MES here on the side, your choice, and you can use OpenShift or whatever, we don't matter. That's >>The, that's the key question. That's the key question. Is it because it is a competitive strategy? Is it highly differentiated? Oracle is a highly differentiated strategy, right? Is Dell highly differentiated? Eh, Dell differentiates based on its breadth. What? >>Right. Well, let's try for the control plane too. Dell wants to be an, >>Their, their vision is differentiated. Okay. But their execution today is not >>High. All right. Let me throw, let me throw this out at you then. I'm I'm, I'm sorry. I'm I'm HPE. I wanna be the glue layer. Is that, does that fly? >>What >>Do you mean? The group glue layer? I'll I wanna be, you can do Amazon, but I wanna be the glue layer between the clouds and our GreenLake will. >>What's the, what's the incremental value that, that glue provides, >>Provides comfort and reliability and control for the single pane of glass for AWS >>And comes back to the data. In my opinion. Yeah. >>There, there there's glue levels on the data level. Yeah. And there's glue levels on API level. Right. And there's different vendors in the different spaces. Right. Um, I think HPE will want to play on the data side. We heard lots of data stuff. We >>Hear that, >>But we have to see it. Exactly. >>Yeah. But it's, it's lacking today. And so, Hey, you know, you guys know better than I APIs can be fragile and they can be, there's a lot of diversity in terms of the quality of APIs and the documentation, how they work, how mature they are, what, how, what kind of performance they can provide and recoverability. And so just saying, oh wow. We are living the API economy. You know, the it's gonna take time to brew, chime in here. Hi. >><laugh> oh, so guys, you've all been covering HPE for a long time. You know, when Antonio stood up on stage three years ago and said by 2022, and here we are, we're gonna be delivering everything as a service. He's saying we've, we've done it, but, and we're a new company. Do you guys agree with that? >>Definitely. >>I, yes. Yes. With the caveat, I think, yes. The COVID pandemic slowed them down a lot because, um, that gave a tailwind to the hyperscalers, um, because of the, the force of massive O under forecasting working at home. I mean, everyone I talked to was like, no one forecasted a hundred percent work at home, the, um, the CapEx investments. So I think that was an opportunity that they'd be much farther along if there's no COVID people >>Thought it wasn't impossible. Yeah. But so we had the old work from home thing right. Where people trying to get people fired at IBM and Yahoo. Right. So I would've this question covering the HR side and my other hat on. Right. And I would ask CHS let's assume, because I didn't know about COVID shame on me. Right. I said, big California, earthquake breaks. Right. Nobody gets hurt, but all the buildings have to be retrofitted and checked for seism logic down. So everybody's working from home, ask CHS, what kind of productivity gap hit would you get by forcing everybody working from home with the office unsafe? So one, one gentleman, I won't know him, his name, he said 20% and the other one's going ha you're smoking. It's 40 50%. We need to be in the office. We need to meet it first night. And now we went for this exercise. Luckily not with the California. Right. Well, through the price of COVID and we've seen what it can do to, to productivity well, >>The productivity, but also the impact. So like with all the, um, stories we've done over two years, the people that want came out ahead were the ones that had good cloud action. They were already in the cloud. So I, I think they're definitely in different company in the sense of they, I give 'em a pass. I think they're definitely a new company and I'm not gonna judge 'em on. I think they're doing great. But I think pandemic definitely slowed 'em down that about >>It. So I have a different take on this. I think. So we've go back a little history. I mean, you' said this, I steal your line. Meg Whitman took one for the Silicon valley team. Right. She came in. I don't think she ever was excited that I, that you said, you said that, and I think you wrote >>Up, get tape on that one. She >>Had to figure out how do I deal with this mess? I have EDS. I got PC. >>She never should have spun off the PC, but >>Okay. But >>Me, >>Yeah, you can, you certainly could listen. Maybe, maybe Gerstner never should have gone all in on services and IBM would dominate something other than mainframes. They had think pads even for a while, but, but, but so she had that mess to deal with. She dealt with it and however, they dealt with it, Antonio came in, he, he, and he said, all right, we're gonna focus the company. And we're gonna focus the mission on not the machine. Remember those yeah. Presentations, but you just make your eyes glaze over. We're going all in on Azure service >>And edge. He was all on. >>We're gonna build our own cloud. We acquired Aruba. He made some acquisitions in HPC to help differentiate. Yep. And they are definitely a much more focused company now. And unfortunately I wish Antonio would CEO in 2015, cuz that's really when this should have started. >>Yeah. And then, and if you remember back then, Dave, we were interviewing Docker with DevOps teams. They had composability, they were on hybrid really early. I think they might have even coined the term hybrid before VMware tri-state credit for it. But they were first on hybrid. They had DevOps, they had infrastructure risk code. >>HPE had an HP had an awesome cloud team. Yeah. But, and then, and then they tried to go public cloud. Yeah. You know, and then, you know, just made them, I mean, it was just a mess. The focus >>Is there. I give them huge props. And I think, I think the GreenLake to me is exciting here because it's much better than it was two years ago. When, when we talked to, when we started, it's >>Starting to get real. >>It's, it's a real thing. And I think the, the tell will be partners. If they make that right, can pull their different >>Ecosystem, >>Their scale and their customers and fill the software gas with partners mm-hmm <affirmative> and then create that integration opportunity. It's gonna be a home run if they don't do that, they're gonna miss the operating, >>But they have to have their own to your point. They have to have their own software innovation. >>They have to good infrastructure ways to build applications. I don't wanna build with somebody else. I don't wanna take a Microsoft stack on open source stack. I'm not sure if it's gonna work with HP. So they have to have an app dev answer. I absolutely agree with that. And the, the big thing for the partners is, which is a good thing, right? Yep. HPE will not move into applications. Right? You don't have to have the fear of where Microsoft is with their vocal large. Right. If AWS kind of like comes up with APIs and manufacturing, right. Google the same thing with their vertical push. Right. So HPE will not have the CapEx, but >>Application, >>As I SV making them, the partner, the bonus of being able to on premise is an attractive >>Part. That's a great point. >>Hold. So that's an inflection point for next 12 months to watch what we see absolutely running on GreenLake. >>Yeah. And I think one of the things that came out of the, the last couple events this past year, and I'll bring this up, we'll table it and we'll watch it. And it's early in this, I think this is like even, not even the first inning, the machine learning AI impact to the industrial piece. I think we're gonna see a, a brand new era of accelerated digital transformation on the industrial physical world, back office, cloud data center, accounting, all the stuff. That's applications, the app, the real world from space to like robotics. I think that HP edge opportunity is gonna be visible and different. >>So guys, Antonio Neri is on tomorrow. This is only day one. If you can imagine this power panel on day one, can you imagine tomorrow? What is your last question for each of you? What is your, what, what question would you want to ask him tomorrow? Hold start with you. >>How is HPE winning in the long run? Because we know their on premise market will shrink, right? And they can out execute Dell. They can out execute Lenovo. They can out Cisco and get a bigger share of the shrinking market. But that's the long term strategy, right? So why should I buy HPE stock now and have a good return put in the, in the safe and forget about it and have a great return 20 years from now? What's the really long term strategy might be unfair because they, they ran in survival mode to a certain point out of the mass post equipment situation. But what is really the long term strategy? Is it more on the hardware side? Is it gonna go on the HPE, the frontier side? It's gonna be a DNA question, which I would ask Antonio. >>John, >>I would ask him what relative to the macro conditions relative to their customer base, I'd say, cuz the customers are the scoreboard. Can they create a value proposition with their, I use the Microsoft 365 example how they kind of went to the cloud. So my question would be Antonio, what is your core value proposition to CIOs out there who want to transform and take a step function, increase for value with HPE? Tell me that story. I wanna hear. And I don't want to hear, oh, we got a portfolio and no, what value are you enabling your customers to do? >>What and what should that value be? >>I think it's gonna be what we were kind of riffing on, which is you have to provide either what their product market fit needs are, which is, are you solving a problem? Is it a pain point is a growth driver. Uh, and what's the, what's that tailwind. And it's obviously we know at cloud we know edge. The story is great, but what's the value proposition. But by going with HPE, you get X, Y, and Z. If they can explain that clearly with real, so qualitative and quantitative data it's home >>Run. He had a great line of the analyst summit today where somebody asking questions, I'm just listening to the customer. So be ready for this Steve jobs photo, listening to the customer. You can't build something great listening to the customer. You'll be good for the next quarter. The next exponential >>Say, what are the customers saying? <laugh> >>So I would make an observation. And my question would, so my observation would be cloud is growing collectively at 35%. It's, you know, it's approaching 200 billion with a big, big four. If you include Alibaba, IBM has actually said, Hey, we're gonna gr they've promised 6% growth. Uh, Cisco I think is at eight or 9% growth. Dow's growing in double digits. Antonio and HPE have promised three to 4% growth. So what do you have to do to actually accelerate growth? Because three to 4%, my view, not enough to answer Holger's question is why should I buy HPE stock? Well, >>If they have product, if they have customer and there's demand and traction to me, that's going to drive the growth numbers. And I think the weak side of the forecast means that they don't have that fit yet. >>Yeah. So what has to happen for them to get above five, 6% growth? >>That's what we're gonna analyze. I mean, I, I mean, I don't have an answer for that. I wish I had a better answer. I'd tell them <laugh> but I feel, it feels, it feels like, you know, HP has an opportunity to say here's the new HPE. Yeah. Okay. And this is what we stand for. And here's the one thing that we're going to do that consistently drives value for you, the customer. And that's gonna have to come into some, either architectural cloud shift or a data thing, or we are your store for blank. >>All of the above. >>I guess the other question is, would, would you know, he won't answer a rude question, would suspending things like dividends and stock buybacks and putting it into R and D. I would definitely, if you have confidence in the market and you know what to do, why wouldn't you just accelerate R and D and put the money there? IBM, since 2007, IBM spent is the last stat. And I'm looking go in 2007, IBM way, outspent, Google, and Amazon and R and D and, and CapEx two, by the way. Yep. Subsequent to that, they've spent, I believe it's the numbers close to 200 billion on stock buyback and dividends. They could have owned cloud. And so look at this business, the technology business by and large is driven by innovation. Yeah. And so how do you innovate if >>You have I'm buying, I'm buying HP because they're reliable high quality and they have the outcomes that I want. Oh, >>Buy their products and services. I'm not sure I'd buy the stock. Yeah. >>Yeah. But she has to answer ultimately, because a public company. Right. So >>Right. It's this job. Yeah. >>Never a dull moment with the three of you around <laugh> guys. Thank you so much for sharing your insights, your, an analysis from day one. I can't imagine what day two is gonna bring tomorrow. Debut and I are gonna be anchoring here. We've got a jam packed day, lots going on, hearing from the ecosystem from leadership. As we mentioned, Antonio is gonna be Tony >>Alma Russo. I'm dying. Dr. >>EDMA as well as on the CTO gonna be another action pack day. I'm excited for it, guys. Thanks so much for sharing your insights and for letting me join this power panel. >>Great. Great to be here. >>Power panel plus me. All right. For Holger, John and Dave, I'm Lisa, you're watching the cube our day one coverage of HPE discover wraps right now. Don't go anywhere, cuz we'll see you tomorrow for day two, live from Vegas, have a good night.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's coverage of HPE. Discover 22 live from the Sans expo center in Las Vegas. Lisa Martin, here with Dave Velante. We've an alumni back joining us to talk about high performance computing and AI, Justin ARD, EVP, and general manager of HPC and AI at HPE. That's a mouthful. Welcome back. >>It is no, it's great to be back and wow, it's great to be back in person as well. >>It's it's life changing to be back in person. The keynote this morning was great. The Dave was saying the energy that he's seen is probably the most out of, of any discover that you've been at and we've been feeling that and it's only day one. >>Yeah, I, I, I agree. And I think it's a Testament to the places in the market that we're leading the innovation we're driving. I mean, obviously the leadership in HPE GreenLake and, and enabling as a service for, for every customer, not just those in the public cloud, providing that, that capability. And then obviously what we're doing at HPC and AI breaking, uh, you know, breaking records and, uh, advancing the industry. So >>I just saw the Q2 numbers, nice revenue growth there for HPC and AI. Talk to us about the lay of the land what's going on, what are customers excited about? >>Yeah. You know, it's, it's a, it's a really fascinating time in this, in this business because we're, you know, we just, we just delivered the first, the world's first exo scale system. Right. And that's, uh, you know, that's a huge milestone for our industry, a breakthrough, you know, 13 years ago, we did the first Petta scale system. Now we're doing the first exo scale system, huge advance forward. But what's exciting too, is these systems are enabling new applications, new workloads, breakthroughs in AI, the beginning of being able to do proper quantum simulations, which will lead us to a much, you know, brighter future with quantum and then actually better and more granular models, which have the ability to really change the world. >>I was telling Lisa that during the pandemic we did, uh, exo scale day, it was like this co yep. You know, produce event. And we weren't quite at exo scale yet, but we could see it coming. And so it was great to see in frontier and, and the keynote you guys broke through that, is that a natural evolution of HPC or is this we entering a new era? >>Yeah, I, I think it's a new era and I think it's a new era for a few reasons because that, that breakthrough really, it starts to enable a different class of use cases. And it's combined with the fact that I think, you know, you look at where the rest of the enterprises data set has gone, right? We've got a lot more data, a lot more visibility to data. Um, but we don't know how to use it. And now with this computing power, we can start to create new insights and new applications. And so I think this is gonna be a path to making HPC more broadly available. And of course it introduces AI models at scale. And that's, that's really critical cause AI is a buzzword. I mean, lots of people say they're doing AI, but when you know, to, to build true intelligence, not, not effectively, you know, a machine that learns data and then can only handle that data, but to build true intelligence where you've got something that can continue to learn and understand and grow and evolve, you need this class of system. And so I think we're at, we're at the forefront of a lot of exciting innovation. H how, >>In terms of innovation, how important is it that you're able to combine as a service and HPC? Uh, what does that mean for, for customers for experimentation and innovation? >>You know, a couple things I've been, I've actually been talking to customers about that over the last day and a half. And, you know, one is, um, you think about these, these systems are, they're very large and, and they're, they're pretty, you know, pretty big bets if you're a customer. So getting early access to them right, is, is really key, making sure that they're, they can migrate their software, their applications, again, in our space, most of our applications are custom built, whether you're a, you know, a government or a private sector company, that's using these systems, you're, you're doing these are pretty specialized. So getting that early access is important. And then actually what we're seeing is, uh, with the growth and explosion of insight that we can enable. And some of the diversity of, you know, new, um, accelerator partners and new processors that are on the market is actually the attraction of diversity. And so making things available where customers can use multimodal systems. And we've seen that in this era, like our customer Lumi and Finland number, the number three fastest system in the world actually has two sides to their system. So there's a compute side, dense compute side and a dense accelerator side. >>So Oak Ridge national labs was on stage with Antonio this morning, the, the talking about frontier, the frontier system, I thought what a great name, very apropo, but it was also just named the number one to the super computing, top 500. That's a pretty big accomplishment. Talk about the impact of what that really means. >>Yeah. I, I think a couple things, first of all, uh, anytime you have this breakthrough of number one, you see a massive acceleration of applications. And if you really, if you look at the applications that were built, because when the us department of energy funded these Exoscale products or platforms, they also funded app a set of applications. And so it's the ability to get more accurate earth models for long term climate science. It's the ability to model the electrical grid and understand better how to build resiliency into that grid. His ability is, um, Dr. Te Rossi talked about a progressing, you know, cancer research and cancer breakthroughs. I mean, there's so many benefits to the world that we can bring with these systems. That's one element. The other big part of this breakthrough is actually a list, a lesser known list from the top 500 called the green 500. >>And that's where we measure performance over power consumption. And what's a huge breakthrough in this system. Is that not only to frontier debut at number one on the top 500, it's actually got the top two spots, uh, because it's got a small test system that also is up there, but it's got the top two spots on the green 500 and that's actually a real huge breakthrough because now we're doing a ton more computation at far lesser power. And that's really important cuz you think about these systems, ultimately you can, you can't, you know, continue to consume power linearly with scaling up performance. There's I mean, there's a huge issue on our impact on our environment, but it's the impact to the power grid. It's the impact to heat dissipation. There's a lot of complexities. So this breakthrough with frontier also enables us no pun intended to really accelerate, you know, the, the capacity and scale of these systems and what we can deliver. >>It feels like we're entering a new Renaissance of HPC. I mean, I'm old enough to remember. I, it was, it wasn't until recently my wife, not recently, maybe five, six years ago, my wife threw out my, my green thinking machines. T-shirt that Danny Hillis gave you guys probably both too young to remember, but you had thinking machines, Ken to square research convex tried to mini build a mini computer HPC. Okay. And there was a lot of innovation going on around that time and then it just became too expensive and, and, and other things X 86 happened. And, and, but it feels like now we're entering a, a new era of, of HPC. Is that valid or is it true? What's that mean for HPC as an industry and for industry? >>Yeah, I think, I think it's a BR I think it's a breadth. Um, it's a market that's opening and getting much more broader the number of applications you can run, you know, and we've traditionally had, you know, scientific applications, obviously there's a ton in energy and, and you know, physics and some of the traditional areas that obviously the department of energy sponsor, but, you know, we saw this with, with even the COVID pandemic, right? Our, our supercomputers were used to identify the spike protein to, to help and validate and test these vaccines and bring them to market and record time. We saw some of the benefits of these breakthroughs. And I think it's this combination of that, that we actually have the data, you know, it's, it's digital, it's captured, um, we're capturing it at, you know, at the edge, we're capturing it and, and storing it obviously more broadly. So we have the access to the data and now we have the compute power to run it. And the other big thing is the techniques around artificial intelligence. I mean, what we're able to do with neural networks, computer vision, large language models, natural language processing. These are breakthroughs that, um, one require these large systems, but two, as you give them a large systems, you can actually really enable acceleration of how sophisticated these, these applications can get. >>Let's talk about the impact of the convergence of HPC and AI. What are some of the things that you're seeing now and what are some of the things that we're gonna see? >>Yeah. So, so I, one thing I like to talk about is it's, it's really, it's not a convergence. I think it's it. Sometimes it gets a little bit oversimplified. It's actually, it's traditional modeling and simulation leveraging machine learning to, to refine the simulation. And this is a, is one of the things we talk about a lot in AI, right? It's using machine learning to actually create code in real time, rather than humans doing it, that ability to refine the model as you're running. So we have an example. We did a, uh, we, we actually launched an open source solution called smart SIM. And the first application of that was climate science. And it's what it's doing is it's actually learning the data from the model as the simulation is running to provide more accurate climate prediction. But you think about that, that could be run for, you know, anything that has a complex model. >>You could run that for financial modeling, you can use AI. And so we're seeing things like that. And I think we'll continue to see that the other side of that is using modeling and simulation to actually represent what you see in AI. So we were talking about the grid. This is one of the Exoscale compute projects you could actually use once you actually get, get the data and you can start modeling the behavior of every electrical endpoint in a city. You know, the, the meter in your house, the substation, the, the transformers, you can start measuring the FX of that. You can then build equations. Well, once you build those equations, you can then take a model, cuz you've learned what actually happens in the real world, build the equation. And then you can provide that to someone who doesn't need a extra scale supercomputer to run it, but that, you know, your local energy company can better understand what's happening and they'll know, oh, there's a problem here. We need to shift the grid or respond more, more dynamically. And hopefully that avoids brownouts or, you know, some of the catastrophic outages we've >>Seen so they can deploy that model, which, which inherently has that intelligence on sort of more cost effective systems and then apply it to a much broader range. Do any of those, um, smart simulations on, on climate suggest that it's, it's all a hoax. You don't have to answer that question. <laugh> um, what, uh, >>The temperature outside Dave might, might give you a little bit of an argument to that. >>Tell us about quantum, what's your point of view there? Is it becoming more stable? What's H HPE doing there? >>Yeah. So, so look, I think there's, there's two things to understand with quantum there's quantum hardware, right? Fundamentally, um, how, um, how that runs very differently than, than how we run traditional computers. And then there's the applications. And ultimately a quantum application on quantum hardware will be far more efficient in the future than, than anything else. We, we see the opportunity for, uh, much like we see with, you know, with HPC and AI, we just talked about for quantum to be complimentary. It runs really well with certain applications that fabricate themselves as quantum problems and some great examples are, you know, the, the life sciences, obviously quantum chemistry, uh, you see some, actually some opportunities in, in, uh, in AI and in other areas where, uh, quantum has a very, very, it, it just lends itself more naturally to the behavior of the problem. And what we believe is that in the short term, we can actually model quantum effectively on these, on these super computers, because there's not a perfect quantum hardware replacement over time. You know, we would anticipate that will evolve and we'll see quantum accelerators much. Like we see, you know, AI accelerators today in this space. So we think it's gonna be a natural evolution in progression, but there's certain applications that are just gonna be solved better by quantum. And that's the, that's the future we'll we'll run into. And >>You're suggesting if I understood it correctly, you can start building those applications and, and at least modeling what those applications look like today with today's technology. That's interesting because I mean, I, I think it's something rudimentary compared to quantum as flash storage, right? When you got rid of the spinning disc, it changed the way in which people thought about writing applications. So if I understand it, new applications that can take advantage of quantum are gonna change the way in which developers write, not one or a zero it's one and virtually infinite <laugh> combinations. >>Yeah. And I actually, I think that's, what's compelling about the opportunity is that you can, if you think about a lot of traditional the traditional computing industry, you always had to kind of wait for the hardware to be there, to really write, write, and test the application. And we, you know, we even see that with our customers and HPC and, and AI, right? They, they build a model and then they, they actually have to optimize it across the hardware once they deploy it at scale. And with quantum what's interesting is you can actually, uh, you can actually model and, and, and make progress on the software. And then, and then as the hardware becomes available, optimize it. And that's, you know, that's why we see this. We talk about this concept of quantum accelerators as, as really interesting, >>What are the customer conversations these days as there's been so much evolution in HPC and AI and the technology so much change in the world in the last couple of years, is it elevating up the CS stack in terms of your conversations with customers wanting to become familiar with Exoscale computing? For example? >>Yeah. I, I think two things, uh, one, one is we see a real rise in digital sovereignty and Exoscale and HPC as a core fund, you know, fundamental foundation. So you see what, um, you know, what Europe is doing with the, the, the Euro HPC initiative, as one example, you know, we see the same kind of leadership coming out of the UK with the system. We deployed with them in Archer two, you know, we've got many customers across the globe deploying next generation weather forecasting systems, but everybody feels, they, they understand the foundation of having a strong supercomputing and HPC capability and competence and not just the hardware, the software development, the scientific research, the, the computational scientists to enable them to remain competitive economically. It's important for defense purposes. It's important for, you know, for helping their citizens, right. And providing, you know, providing services and, and betterment. >>So that's one, I'd say that's one big theme. The other one is something Dave touched on before around, you know, as a service and why we think HP GreenLake will be, uh, a beautiful marriage with our, with our HPC and AI systems over time, which is customers also, um, are going to scale up and build really complex models. And then they'll simplify them and deploy them in other places. And so there's a number of examples. We see them, you know, we see them in places like oil and gas. We see them in manufacturing where I've gotta build a really complex model, figure out what it looks like. Then I can reduce it to a, you know, to a, uh, certain equation or application that I can then deploy. So I understand what's happening and running because you, of course, as much as I would love it, you're not gonna have, uh, every enterprise around the world or every endpoint have an exit scale system. Right. So, so that ability to, to, to really provide an as a service element with HP GreenLake, we think is really compelling. >>HP's move into HPC, the acquisitions you've made it really have become a differentiator for the company. Hasn't it? >>Yeah. And I, and I think what's unique about us today. If you look at the landscape is we're, we're really the only system provider globally. Yeah. You know, there are, there are local players that we compete with. Um, but we are the one true global system provider. And we're also the only, I would say the only holistic innovator at the system level to, to, you know, to credit my team on the work they're doing. But, you know, we're, we're also very committed to open standards. We're investing in, um, you know, in a number of places where we contribute the dev the software assets to open source, we're doing work with standards bodies to progress and accelerate the industry and enable the ecosystem. And, uh, and I think that, you know, ultimately the, the, the last thing I'd say is we, we are so connected in, um, with, through our, through the legacy or the, the legend of H Hewlett Packard labs, which now also reports into me that we have these really tight ties into advanced research and that some of that advanced research, which isn't just, um, around kind of core processing Silicon is really critical to enabling better applications, better use cases and accelerating the outcomes we see in these systems going forward. >>Can >>You double click on that? I, I, I wasn't aware that kind of reported into your group. Yeah. So, you know, the roots of HP are invent, right? Yeah. HP labs are, are renowned. It kinda lost that formula for a while. And now it's sounds like it's coming back. What, what, what are some of the cool things that you guys are working on? Well, >>You know, let me, let me start with a little bit of recent history. So we just talked about the exo scale program. I mean, that was a, that's a great example of where we had a public private partnership with the department of energy and it, and it wasn't just that we, um, you know, we built a system and delivered it, but if you go back a decade ago, or five years ago, there were, there were innovations that were built, you know, to accelerate that system. One is our Slingshot fabric as an example, which is a core enable of, of acceler, you know, of, of this accelerated computing environment, but others in software applications and services that allowed us to, you know, to really deliver a, a complete solution into the market. Um, today we're looking at things around trustworthy and ethical AI, so trustworthy AI in the sense that, you know, the models are accurate, you know, and that's, that's a challenge on two dimensions, cuz one is the, model's only as good as the data it's studying. >>So you need to validate that the data's accurate and then you need to really study how, you know, how do I make sure that even if the data is accurate, I've got a model that then, you know, is gonna predict the right things and not call a, a dog, a cat, or a, you know, a, a cat, a mouse or whatever that is. But so that's important. And, uh, so that's one area. The other is future system architectures because, um, as we've talked about before, Dave, you have this constant tension between the fabric, uh, you know, the interconnect, the compute and the, and the storage and, you know, constant, constantly balancing it. And so we're really looking at that, how do we do more, you know, shared memory access? How do we, you know, how do we do more direct rights? Like, you know, looking at some future system architectures and thinking about that. And we, you know, we think that's really, really critical in this part of the business because these heterogeneous systems, and not saying I'm gonna have one monolithic application, but I'm gonna have applications that need to take advantage of different code, different technologies at different times. And being able to move that seamlessly across the architecture, uh, we think is gonna be the, you know, a part of the, the hallmark of the Exoscale era, including >>Edge, which is a completely different animal. I think that's where some disruption is gonna gonna bubble up here in the next decade. >>So, yeah know, and, and that's, you know, that's the last thing I'd say is, is we look at AI at scale, which is another core part of the business that can run on these large clusters. That means getting all the way down to the edge and doing inference at scale, right. And, and inference at scale is, you know, I, I was, um, about a month ago, I was at the world economic forum. We were talking about the space economy and it's a great, you know, to me, it's the perfect example of inference, because if you get a set of data that you know, is, is out at Mars, it doesn't matter whether, you know, whether you wanna push all that data back to, uh, to earth for processing or not. You don't really have a choice, cuz it's just gonna take too long. >>Don't have that time. Justin, thank you so much for spending some of your time with Dave and me talking about what's going on with HBC and AI. The frontier just seems endless and very exciting. We appreciate your time on your insights. >>Great. Thanks so much. Thanks. >>Yes. And don't call a dog, a cat that I thought I learned from you. A dog at no, Nope. <laugh> Nope. <laugh> for Justin and Dave ante. I'm Lisa Martin. You're watching the Cube's coverage of day one from HPE. Discover 22. The cube is, guess what? The leader, the leader in live tech coverage will be right back with our next guest.

>>Welcome to the cubes coverage of Dell tech world 2022. My name is Dave Volante and I'm currently in our studios outside of Boston. As we prepare to gather for the first in person Dell technologies world since 20 19, 1 of the major structural change and the technology business during the pandemic was IBM's spin out of Kendra. A world class technology services provider that lived inside of IBM. Kendra is a large business with trailing 12 month revenues north of 18 billion. It's got 90,000 employees worldwide. Kendra has long term predictable cash flows. And in my view is one of the most undervalued companies in the technology sector. As a separate company, Kendra is able to turn many of its former internal IBM roadblocks into tailwinds and ecosystem. Partnerships are one of the best examples of new opportunities that are opening up for the newly separate at company. In this next segment, we're gonna dig into a new partnership between Kendra and Dell technologies and what is the most critical priority for organizations today? Cyber resiliency and with me are two really impressive and talented guests. Chris Lovejoy is global security and resiliency practice leader at Kendra. Michelle Weston is vice president of, of global offerings for security and resiliency also at kindred ladies. Welcome to the cube. Thanks for coming on and spending some time with us. >>Thank thank you. >>Okay. Let's zoom out a little bit and start with a big picture. What would you say are, are the one or two major trends or changes even in cyber that you've seen since the pandemic, maybe Chris, you could start us off and Michelle, you can chime in. >>Sure. Happy to. And, um, you know, I think part of this actually preceded the pandemic and, um, you know, the fact is, you know, a lot of organizations have been engaging in the adoption of new technologies, you know, be it cloud AI IOT, what, what, whatever that may be. Um, and they've been introducing that technology without, um, adequate security control and during the COVID pandemic, um, when, you know, technology transformation happened for existential reasons, what we were seeing is organizations throwing at even more technology at cyclic, right, with absolutely no security control whatsoever. And in the meantime, the regulators who are, you know, watching this in, you know, horror are introducing new requirements in and around, um, what we're calling cyber resilience today. And it's all based on this concept that, you know, conventional cybersecurity assume that the adversaries could be kept out of organizations. >>Um, you could protect the organization and sort of block it, um, as rising numbers of disruptive attacks, like, you know, ransomware attacks have shown those approaches don't work. And so, um, what we're seeing is that the market is really moving toward this concept of cyber resiliency, which goes beyond cybersecurity. It assumes that the advanced a adversaries are frankly, many adversaries can overcome, um, conventional protections and that, um, they, that organization need to prepare to recover. Um, so our approach, the approach that we're taking to the market is really to help organizations in binding security plus continuity plus disaster recovery, then giving them the ability to anticipate, protect, um, with stand and recovery from any adverse condition associated with their cyber real estate. Um, and this is why we're so excited to work with Dell, uh, because they're really, uh, paving the roads for us to actually, you know, work together in solving these needs for our clients. >>Got it. That makes sense. And now Michelle, as Chris was saying, these worlds are coming together. What used to be adjacencies, oftentimes they, after thoughts, bolted on, and now you've got the work from home and, and hybrid work, not to mention, as Chris was saying, you're injecting AI and all this data, you know, this is a complicated situation for a lot of people, isn't it? >>Yeah. And it was only even more complicated during, during the pandemic as well. I think, uh, another trend that we saw was the end enterprise was outside the enterprise, right? Uh, everyone was working from home. They weren't in the data centers, their own resiliency and security protocols were already at risk because they were so manual and people intensive. And yet we know, you know, the bad actors actually took advantage of, of that right. Uh, data centers were, uh, less monitored. Um, we had all of the employees working from home. Now, the enterprise is outside of the enterprise, but you still need security and resiliency for all of those endpoints. Right. And I think that's driving a higher need, um, coming out of the PA the pandemic and even with this hybrid model, okay. We'll return to work, but not, not in the same fashion that we did prior to the pandemic. >>That's the new reality. The other thing that I would say is that those customers that had adopted cloud already and cloud enabled their business, they were able to fare, um, the best during the pandemic. They were able to sustain their businesses. Um, alternatively, and it's kind of a different lens to it. I think the pandemic actually drove new ways of working and some really creative solutions. I mean, if you look at, um, you know, food delivery services that, uh, proliferated during the pandemic, or, uh, that are now offering fitness online, um, fitness classes online, people had to think, um, intelligently and, and creatively on how they sustain their businesses. So I think all of that's coming together, but certainly this need of, as you said, not thinking of security and resiliency as an afterthought, but as a forethought planning for those things efficiently and effectively, that we find customers that do that, uh, do it the best. And, uh, I think that Kendra offers a unique value pro in here because bringing both together is a journey that we started a couple of years ago that we've only accelerated with the, uh, spin of the Kendra company. >>Yeah. Interesting. So I wanted to talk about that partnership because mm-hmm, <affirmative>, you know, Dell's got this massive channel, it's got infrastructure technology expertise, uh, but Dell, you know, Dell's a product company, Kendra is a services company, so it's a really good match in that sense. Right. Uh, maybe you could talk about how the partnership came together and, you know, what are the critical aspects that folks need to be aware of? >>Yeah. I would say Dell's an excellent partner for us and they have been for a number of years. So in a lot of ways that's not new. Okay. Uh, we've been partnering in market together for quite quite some time. In fact, the solution that we'll talk about today was first put into market in 2018. And you're absolutely right. We, we come together in the best ways. They're leveraging our strengths with regard to manage services, professional services. And we are certainly looking at them as a key technology provider, um, for our portfolio, we've worked together for years. Uh, we manage backup environments based on their data protection solutions, including data domain, but what was unique. And I think we were both ahead of the market at the time, um, was the 2018 solution that we put in to market and have only enhanced and augmented it ever since it's, it's called, um, cyber volt is, is the solution from Dell technologies. >>We certainly manage that solution in market for them today. And then we have unique differentiation in our Kindra portfolio that we've integrated with that and add to, um, their cyber incident recovery features, um, Dell initially put the solution in market coming out of, um, some of the ransomware attacks that they had cyber attacks that they had. They realized there was a need to protect the large data domain install base around the world. Um, they developed some proprietary solar solution, uh, software on top of their large data domain boxes and, and any cyber incident recovery solution. You need a, a few things you need the ability to assure imutable storage, a, a copy that you can assure has not been altered so that when you initiate the recovery, you know that you've got a clean copy and you're not propagating whatever is there. Um, so the solution has that, um, it has the other component that you need, which is the ability to scan the data for anomalies, right? >>So they're scanning the backup files continuously to look for anomalies. And then lastly, you need some form of data mover, which the data domain, um, solution offers. So they came to us in 2018 and said, look, we've got this solution. We think we're ahead of the market. Uh, we were also investing in cyber incident recovery with a key asset that we acquired in market in 2015, um, that we've continued to bake cyber incident recovery features and functions into, and they said, let's marry the two. And let's have you provide all of the managed services capabilities around this for clients. Um, that is a key piece because when it comes to cyber, uh, there's always a level of confidence that customers have, right? Yes. I can recover from any adverse condition. If you ask them, can you recover from a cyber attack with a hundred percent assurance? I don't think there's a customer today that could say given how sophisticated and how much these, these attack vectors are changing, that, that they, they have that a hundred percent confidence level. So a managed service provider, a phone, a friend in the event of is a, is a unique value proposition. Um, and that's what the two companies are bringing together, uh, for customers today. >>Got it. Thank you. So, so Chris, maybe as a services company, you, you, you have to be ignite, you know, to technology, you know, the best fit, et cetera. But, but prior to the spin, we never would've heard it, something like this. And so what, maybe you could talk about the partnership from your perspective. >>Yeah, no, absolutely. And I, I do wanna, um, you know, sort of double click on this a little bit, you, and you mentioned it in your opening, you know, headwinds being wins now. And I think this is important, incredibly important. You know, what people don't realize about Kendra is that, you know, we were never able to, as the services organization, um, that was really focused on strategic outsourcing and providing other kinds of services to, uh, clients while under the IBM banner are really never able to talk about the technical depth that we had across any number of platforms, including, um, the hyperscalers. And we have thousands upon thousands of people with hyperscaler certifications. Um, we have experience with pretty much every security and resilience technology out there. Um, we have broad and early with organizations like yours, that we were never able to speak about now, you know, when it comes to a client, you know, let's be realistic. >>Everybody is engaged in some sort of it modernization program. And while, and we have to realize also that those it modernization programs, you know, oftentimes they have no destination per se. You know, we talk about them as a journey, but we, if no destination, they just keep going and going and going. And the directions change every day, depending on, you know, what the strategic, uh, requirements are from whatever C-suite, you have, you know, sitting at the table, uh, what the competitive trends are, what the market is telling you, et cetera. And so what clients are saying to us is that the value we offer is that we can untangle the mess. That is their environment. We can meet them where they are, we can get them where they wanna go. And so, you know, when it comes to a relationship with Dell, you know, we believe that, you know, particularly in the area of security, in resilience, that there is a unique proposition to be had around the services and the cross platform experience and certifications and skills that our, um, our teams have married with the technology advances that Dell has made in the, in, in the world, as well as our experience in, you know, sort of the two that has have been frankly, hidden over the past few years. >>I think we have some, uh, something unique that we can offer to the market. Particularly, as I said, in this space of security and resilience, where all of our clients are, you know, looking for some sort of solution to this, you know, gee, I can't spend enough money to protect myself. I need to make sure that if the worst happens that I can bring myself back again, that's what we can do for our clients. >>Great. Thank you, Michelle. I wanna go back to the solution for a moment. You mentioned a number of things, integrations. I got like a zillion questions here. I'm interested in what kind of integrations you talked about imutability where does, where does that occur? Is that in the cloud? Is that the, you know, Dell technology is scan for anomalies again, what is that? Is that some kind of, you know, AI magic, you got a high speed data mover. Is there an air gap involved, maybe help me fill in some of those gaps. >>Yeah. And I think you, I think you've netted out the solution. Any cyber incident recovery solution in my mind would have those three things. They have some form of imutable storage. Uh, this could be cloud object storage in the case of the Dell solution, they're actually using their retention lock feature on the large data domain devices. Right? So think of this solution as having two data domains, they both have this retention lock feature. That's the imutable storage. They're able to move data and forth between the two, uh, that's another key piece. And then finally, for any incident recovery solution, you need the ability to scan and make sure that there aren't anomalies, um, in this case, in the backup files. So they're using a, a third party to scan thatno scan those files for anomalies. And when when's detected, that kind of gives the indication that something may be there and then they can go in and triage it and, and, and clean the environment if needed. >>Um, so we certainly manage that end to end, and that is one approach. It is an on-premise approach. It uses the data domain, uh, technologies. We know that clients have a lot more than that, right? So where Kendra comes in with its cyber incident recovery solution that also integrates with Dell's cyber incident recovery solution is we support cloud, um, multiple infrastructure. We have also imutable storage that we leverage. Um, and then in terms of our anomaly scanning capabilities, in this case, we're using technology that we had originally developed in IBM research that we integrated into the software product. Um, again, this is on an acquisition we did in market five years ago, called son Nobi. It's a software product. Um, it ingests and automates all of your workflows in the, in, in the event of any failover failback, any, uh, outage, including cyber and that technology underpin a lot of what we do on the incident recovery perspective, Dells use data domain. >>We've used the software, all both solutions have all three components of the cyber incident recovery, uh, solution when they're integrated, there's real power there, right? Because now you're looking at protection, not just of the backup environ, um, but all environments, including production, you're looking at being able to scale beyond OnPrem. Um, and more importantly, you're looking at the speed to recover, right? The not needing to rehydrate the data, but to be able to recover with the RTOs and RPOs that are expected, um, of our customers on the resiliency orchestration side, the Kendra solution. Um, this is, this is push of a button fail over, fail back in the event of an outage. Um, you can recover the entire hybrid estate in the matter of minutes and what we know with respect to any outage it's costly. We know know that downtime is costly, but with respect to cyber, we know that that's more costly than a typical outage, sometimes four X, um, you don't always recover from the brand damage from the loss of customers. So being down and, and coming up as quickly as you can, with the additional data verification, data validation and assurance that you're not propagating, whatever is there is the value prop, um, that both CU, both companies are really serving. >>And where does an air gap fit in into this equation? Is that yet another layer of protection what's best practice there? >>Um, so think of the air gap is just between the data movement and the immune storage, right? You need to be able to cut connection in a way, right. That is an air gap solution. And it's based on the imutable storage that both have. >>Okay. And that would be, it could be local, I guess, but it also could be, it should be maybe remote. Yes. Mm-hmm >><affirmative> okay. Exactly. And, and the ability to manage and orchestrate that air gap is a key value prop again, of the Kendra solution. >>Okay. And so I've mentioned local or remote. I mean, obviously the trade off is recovery time, you know, uh, I guess RTO, um, but, but <laugh> and RPO. So a lot of layers is, is what I'm hearing is that's always security pros in this framework. >>Let me give you another example, the reason why this is so important. Um, most of our Dr. Processes today, they all rely on people, right? We had a large client that was impacted when we were IBM. They were impacted with pet. They had a great Dr plan. They were a customer of ours. Um, we managed that service for them. Their Dr. Plan was still people intensive. And when that attack happened, it took out the badge readers to the people that you've invested in. Can't get on site to manage the incident, can't bring up the environment. And then if you look at going back to the very beginning of our conversation, COVID being sort of, uh, another way that that happened with access and the ability to continuously monitor and have the people on site that ability was impacted. So this is where you need to invest in technology, uh, P and processes to make sure that you are as robust as you can be. And as Chris said, your ability to anticipate with stand and recover from any adverse condition, that's, that's the value prop that our global practice brings. Yeah. >>To your, to your point, the adversary is well funded and motivated. Chris, we'll give you the last word, where do, where do you wanna see this partnership go? You know, kinda what what's next? What should we look for in the coming months and in, in years? >>Yeah. I'm, you know, I think, you know, very simply, and I'm going put my CISO hat on right. For a minute, because I think it's important to speak, you know, for the customer as a customer, you know, at the end of the day, I, I think most C-suite executives do don't realize the extent to which security, continuity and disaster recovery have been separate silos. And what is shocking to our clients when they get into a ransomware event in particular is the fact that they have their, um, systems, their services, their data is locked up, their backups have been sort of implemented or have, have been, you know, sort of subverted. They call in the pros, they call in the folks that help them with the incident response. The incident responders are able to identify the ransomware strain. They're able to contain the ransomware strain, but the damage is done. >>Now, what, how do you bring the environment back? How do you that the data is good? How do you, how do you find the system configurations and load them again? In what order do you load them? What they don't realize is that security and recovery, they have to be merged together. And so what I think that we can do it, it's not just, you know, build customer demand is not just sell a solution. We can really help clients. And so my hope is that we are able to bring cyber resilience into every organization, every large enterprise out there that needs to, you know, continually service their clients and their employees. They need to stay in business that we're able to bring the solution to them in such a way that they're able to, you know, bring back their environments to serve their clients when the worst does happen. >>Great. Yes. Thank you. We're definitely seeing that data protection world and the cybersecurity world. They, they adjacencies, but they really are coming together and part of a comprehensive plan. Okay. We have to leave it there. Thanks so much folks for coming on the cube really appreciate your time and your insights. >>Thanks for having us. And >>Thank you. Thank you for watching the Cube's coverage of Dell technologies world 2022. Keep it right there. We're running all week with live coverage from the show floor. We're pumping in deep dives like this one throughout the week. So don't go away.

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

>>Welcome back to the cubes coverage of ADA reinvent 2021 here, the cube coverage. I'm Judd for a, your host we're on the ground with two sets on the floor, real event. Of course, it's hybrid. It's online as well. You can check it out there. All the on-demand replays are there. We're here with Sandy Carter, worldwide vice president, public sector partners and programs. And we've got Fred Swanick, her founder, and chief curator of the room. We're talking about getting the best talent programming and in the cloud, doing great things, innovation all happening, Sandy. Great to see you. Thanks for coming on the cube, but appreciate it. Thanks for halfway to see. Okay. So tell us about the room. What is the room what's going on? >>Um, well, I mentioned in the room is to help the world's most extraordinary do us to fulfill their potential. So, um, it's a community of exceptional talent that we are building throughout the world, um, and connecting this talent to each other and connecting them to the organizations that are looking for people who can really move the needle for those organizations. >>So what kind of results are you guys seeing right now? Give us some stats. >>Well, it's a, it's a relatively new concept. So we're about 5,000 members so far, um, from 77 different countries. Um, and this is, you know, we're talking about sort of the top two to 3% of talent in different fields. Um, and, um, as we go forward, you know, we're really looking, seeing this as an opportunity to curate, um, exceptional talent. Um, and it feels like software engineering, data science, UX, UI design, cloud computing, um, and, uh, it really helped to, um, identify diverse talent as well from pockets that have typically been untapped for technology. Okay. >>I want to ask you kind of, what's the, how you read the tea leaves. How do I spot the talent, but first talk about the relationship with Amazon. What's the program together? How you guys working together? It's a great mission. I mean, we need more people anyway, coding everywhere, globally. What's the AWS connection. >>So Fred and I met and, uh, he had this, I mean the brilliant concept of the room. And so, uh, obviously you need to run that on the cloud. And so he's got organizations he's working at connecting them through the room and kind of that piece that he was needing was the technology. So we stepped in to help him with the technology piece because he's got all the subject matter expertise to train 3 million Africans, um, coming up on tech, we also were able to provide him some of the classwork as well for the cloud computing models. So some of those certs and things that we want to get out into the marketplace as well, we're also helping Fred with that as well. So >>I mean, want to, just to add onto that, you know, one of the things that's unique about the room is that we're trying to really build a long-term relationship with talent. So imagine joining the room as a 20 year old and being part of it until you're 60. So you're going to have a lot of that. You collect on someone as they progress through different stages of their career and the ability for us to leverage that data, um, and continuously learn about someone's, you know, skills and values and use, um, predictive algorithms to be able to match them to the right opportunities at the right time of their lives. And this is where the machine learning comes in and the, you know, the data lake that we're building to build to really store this massive data that we're going to be building on the top talent to the world. >>You know, that's a really good point. It's a list that's like big trend in tech where it's, it's still it's over the life's life of the horizon of the person. And it's also blends community, exactly nurturing, identifying, and assisting. But at the same day, not just giving people the answer, they got to grow on their own, but some people grow differently. So again, progressions are nonlinear sometimes and creativity can come out of nowhere. Got it. Uh, which brings me up to my number one question, because this always was on my mind is how do you spot talent? What's the secret sauce? >>Well, there is no real secret source because every person is unique. So what we look for are people who have an extra dose of five things, courage, passion, resilience, imagination, and good values, right? And this is what we're looking for. And you will someone who is unusually driven to achieve great things. Um, so of course, you know, you look at it from a combination of their, their training, you know, what they, what they've learned, but also what they've actually done in the workplace and feedback that you get from previous employers and data that we collect through our own interactions with this person. Um, and so we screened them through, you know, with the town that we had, didn't fly, we take them through really rigorous selection process. So, um, it takes, uh, for example, people go through an online assessments and then they go through an in-person interview and then we'll take them through a one to three month bootcamp to really identify, you know, people who are exceptional and of course get data from different sources about the person as well. >>Sandy, how do you see this collaboration helping, uh, your other clients? I mean, obviously talent, cross pollinates, um, learnings, what's your, you see this level of >>It has, uh, you know, AWS grows, obviously we're going to need more talent, especially in Africa because we're growing so rapidly there and there's going to be so much talent available in Africa here in just a few short years. Most of the tech talent will be in Africa. I think that that's really essential, but also as looking after my partners, I had Fred today on the keynote explaining to all my partners around the world, 55,000 streaming folks, how they can also leverage the room to fill some of their roles as well. Because if you think about it, you know, we heard from Presidio there's 3 million open cyber security roles. Um, you know, we're training 20 of mine million cloud folks because we have a gap. We see a gap around the world. And part of my responsibility with partners is making sure that they can get access to the right skills. And we're counting on the room and what Fred has produced to produce some of those great skills. You have AI, AML and dev ops. Tell us some of the areas you haven't. >>You know, we're looking at, uh, business intelligence, data science, um, full-stack software engineering, cybersecurity, um, you know, IOT talent. So fields that, um, the world needs a lot more talented. And I think today, a lot of technology, um, talent is moving from one place to another and what we need is new supply. And so what the room is doing is not only a community of top 10, but we're actually producing and training a lot more new talent. And that was going to hopefully, uh, remove a key bottleneck that a lot of companies are facing today as they try to undergo the digital trends. >>Well, maybe you can add some hosts on there. We need some cube hosts, come on, always looking for more talent on the set. You could be there. >>Yeah. The other interesting thing, John, Fred and I on stage today, he was talking about how easy to the first narrative written for easy to was written by a gentleman out of South Africa. So think about that right. ECE to talent. And he was talking about Ian Musk is based, you know, south African, right? So think about all the great talent that exists. There. There you go. There you go. So how do you get access to that talent? And that's why we're so excited to partner with Fred. Not only is he wicked impressive when a time's most influential people, but his mission, his life purpose has really been to develop this great talent. And for us, that gets us really excited because we, yeah, >>I think there's plenty of opportunities to around new business models in the U S for instance, um, my friends started upstart, which they were betting on people almost like a stock market. You know, almost like currency will fund you and you pay us back. And there's all kinds of gamification techniques that you can start to weave into the system. Exactly. As you get the flywheel going, exactly, you can look at it holistically and say, Hey, how do we get more people in and harvest the value of knowledge? >>That's exactly. I mean, one of the elements of the technology platform that we developed to the Amazon with AWS is the room intelligence platform. And in there is something called legacy points. So every time you, as a member of the room, give someone else an opportunity. You invest in their venture, you hire them, you mentor them, you get points and you can leverage those points for some really cool experiences, right? So you want to game-ify um, this community that is, uh, you know, essentially crowdsourcing opportunities. And you're not only getting things from the room, but you're also giving to others to enable everyone to grow. >>Yeah, what's the coolest thing you've seen. And this is a great initiative. First of all, it's a great model. I think it's, this is the future. Cause I'm a big believer that communities groups, as we get into this hybrid world is going to open up the virtualization. What the virtual world has shown us is virtualization, which is a cloud technology when Amazon started with Zen, which is virtualization technology, but virtualization, conceptually is replicating things. So if you think hybrid world, you can blend the connect people together. So now you have this social construct, this connective tissue between relationships, and it's always evolving, you know, this and you've been involved in community from, from, from the early days when you have that social evolution, it's not software as a mechanism. It's a human thing. Exactly. It's organism, it evolves. And so if you can get the software to think like that and the group to drive the behavior, it's not community software. >>Exactly. I mean, we say that the room is not an online community. It's really an offline community powered by technology. So our vision is to actually have physical rooms in different cities around the world, whether it's talent gathers, but imagine showing up at a, at a room space and we've got the technology to know what your interests are. We know that you're working on a new venture and there's this, there's a venture capitalists in that area, investing that venture, we can connect you right then that space powered by the, >>And then you can have watch parties. For instance, there's an event going on in us. You can do some watch parties and time shifted and then re replicated online and create a localization, but yet have that connection in >>Present. Exactly, exactly. Exactly. So what are the >>Learnings, what's your big learning share with the audience? What you've learned, because this is really kind of on the front edge of the new kind of innovation we're seeing, being enabled with software. >>I mean, one thing we're learning is that, uh, talent is truly, uh, evenly distribute around the world, but what is not as opportunity. And so, um, there's some truly exceptional talent that is hidden and on tap today. And if we can, you know, and, and today with the COVID pandemic companies or around the world, a lot more open to hiring more talent. So there's a huge opportunity to access new talent from, from sources that haven't been tapped before. Well, but also learnings the power of blending, the online and offline world. So, um, you know, the room is, as I mentioned, brings people together, normally in line, but also offline. And so when you're able to meet talent and actually see someone's personality and get a sense of the culture fit the 360 degree for your foot, some of that, you can't just get on a LinkedIn. Yes. That I built it to make a decision, to hire someone who is much better. And finally, we're also learning about the importance of long-term relationships. One of my motives in the room is relationships not transactions where, um, you actually get to meet someone in an environment where they're not pretending in an interview and you get to really see who they are and build relationships with them before you need to hide them. And these are some really unique ways that we think we can redefine how talent finds opportunity in the 21st. So >>You can put a cube in every room, we pick >>You up because, >>And the cube, what we do here is that when people collaborate, whether they're doing an interview together, riffing and sharing content is creating knowledge, but that shared experience creates a bonding. So when you have that kind of mindset and this room concept where it's not just resume, get a job, see you later, it's learning, having peers and colleagues and people around you, and then seeing them in a journey, multiple laps around the track of humans >>And going through a career, not just a job. >>Yes, exactly. And then, and then celebrating the ups and downs in learning. It's not always roses, as you know, it's always pain before you accelerate. >>Exactly. And you never quite arrive at your destination. You're always growing, and this is where technology can really play. >>Okay. So super exciting. Where's this go next, Sandy. And next couple of minutes left in. >>So, um, one of the things that we've envisioned, so this is not done yet, but, um, Fred and I imagined like, what if you could have an Alexa set up and you could say, Hey, you know, Alexa, what should be my next job? Or how should I go train? Or I'm really interested in being on a Ted talk. What could I do having an Alexa skill might be a really cool thing to do. And with the great funding that Fred Scott and you should talk about the $400 million to that, he's already raised $400 million. I mean, there, I think the sky's the limit on platforms. Like >>That's a nice chunk of change. There it is. We've got some fat financing as they say, >>But, well, it's a big mission. So to request significant resources, >>Who's backing you guys. What's the, who's the, where's the money coming from? >>It's coming from, um, the MasterCard foundation. They, our biggest funder, um, as well as, um, some philanthropists, um, and essentially these are people who truly see the potential, uh, to unlock, um, opportunity for millions of people global >>For Glen, a global scale. The vision has global >>Executive starting in Africa, but truly global. Our vision is eventually to have a community of about 10 to 20 million of the most extraordinary doers in the world, in this community, and to connect them to opportunity >>Angela and diverse John. I mean, this is the other thing that gets me excited because innovation comes from diversity of thought and given the community, we'll have so many diverse individuals in it that are going to get trained and mentored to create something that is amazing for their career as well. That really gets me excited too, as well as Amazon website, >>Smart people, and yet identifying the fresh voices and the fresh minds that come with it, all that that comes together, >>The social capital that they need to really accelerate their impact. >>Then you read the room and then you get wherever you need. Thanks so much. Congratulations on your great mission. Love the room. Um, you need to be the in Cuban, every room, you gotta get those fresh voices out there. See any graduates on a great project, super exciting. And SageMaker, AI's all part of, it's all kind of, it's a cool wave. It's fun. Can I join? Can I play? I tell you I need a room. >>I think he's top talent. >>Thanks so much for coming. I really appreciate your insight. Great stuff here, bringing you all the action and knowledge and insight here at re-invent with the cube two sets on the floor. It's a hybrid event. We're in person in Las Vegas for a real event. I'm John ferry with the cube, the leader in global tech coverage. Thanks for watching.

(melodic music) >> Welcome to this CUBE conversation, I'm Lisa Martin, Richard Hammel joins me next, manager of threat intelligence at NetScout. Richard, welcome back to theCUBE. >> Thanks Lisa it's nice to be back. Thank you for having me. >> We have a lot to talk about in the next 15 to 20 minutes. We're going to be talking about the NetScout threat intelligence report. The report covers the first half of 2021, January one to June 30th. Unprecedented events of 2020 Richard, spilling into 2021. How have the events of 2020 impacted the threat landscape? What are you seeing? >> I would say that it's significantly impacted it. The COVID pandemic and all that happened with remote work and education moving to remote, all of that had a hand in exponentially increasing the threat landscape that adversaries have at their disposal to compromise unknowing victims, to launch attacks. There's so much more that adversaries are able to really hook into. Just in the first half of 2021, we saw almost 5.4 million DDoS attacks. And if you go back to last year, we broke a record at 10 million, just over 10 million, and we're well on track to hit 11 million at the end of this year. So you can see how it's impacted. And even as much as some things are starting to tail off or taper off a little bit, as things start to get back to normal, we start to resume travel, we resume going to the office. There's still that tail end, we're still seeing this kind of heightened attack landscape, and there's lots of different phenomenon that's happening as a result, which we'll talk about throughout this interview. >> Yeah, we'll dissect that you said on pace for a record breaking 11 million DDoS attacks it by the end of 2021. One of the things I want to talk about is speed. I noticed in the report that seven attack vectors in seven months, which means that threat actors exploited, or weaponized seven, at least seven of the new DDoS specters in just seven months time. Why is that significant? >> You know, I'll even raise the ante a little bit just after the throw report. There's an eight factor. And so this is the nature that we're in. This is, the, really the age of innovation. And we've been in kind of an innovative space in the crime world for a couple of years now, where we continue to see this domino effect for lack of a better way of describing it, where it's just one after the next step to the next. And then you add in this compounding thing where you have more devices than ever before connected to the internet. And I have all that much more exposure for these things to take advantage of you. And so we see adversaries innovating. And one of the ways in which we see that is, they operate like a business enterprise. They have functional components for different things. And as you kind of fragments that business structure in the crime world, you get specialized areas for certain things. And so you have adversaries that are niche in a certain area, whether it's distribution of malware or it's launching a DDoS attack, or maybe it's just finding a reflectors amplifiers to launch those DDoS attacks, you have all of these kind of niche areas and the more you can consolidate or collapsed those different skillsets into different components, you're going to find it, it iterates a much more rapidly. It's the same thing that happens as entrepreneurs in the business enterprise. Do you outsource what you're not the expert at? And you outsource it to somebody who is an expert and we see the same phenomenon happening in the cyber-crime world. >> So the rate of discovery to weaponization is getting shorter. >> Super fast. And we've seen things weaponized, a short as one to two days from the time of proof of concept comes online to when an adversary adopts this into their tools or their toolkits. And so on most often, the way we see this adopted is maybe a bot picks it up. So you have like your Mariah's, your satory's, your dash, all these different IOT related bots out there that have capabilities, but then you also have these platforms called booter stressors. And adversaries, just continue to add vectors there. There's no reason to remove them because they're still effective. And so we see this continual add of new ways to compromise and new ways to attack somebody that just always goes up into the right. >> Up into the right, in some cases can be good, in this case, it's obviously it's a sign of distress. One of the things the report showed Richard, was the development of adaptive DDoS. Just the name adaptive leads me to think of evasive tactics, you know, that threat actors are employing, talk to us about adaptive DDoS and what the report showed for the first half of 2021. >> Sure. So the biggest thing we saw with adaptive DDoS and I have to preface this by one of the changes that we saw over the first half of 2021. Going into the first half of the year, DNS reflection amplification was kind of the predominant preferred method by adversaries. There's so many DNS servers out there. So it's something they're able to do. Well, we saw a different type of attack called TCP act floods actually surpassed that. And TCP act floods are a little bit different because it uses a different internet protocol. Now what's significant about TCP based connections is it's connection oriented. So requires what we would call a three-way handshake. So there's packets going to the target, they're coming back to the adversary, they're going to the target. And in most cases they're spoofing of IP addresses. So it never really goes to the actual adversary, but somebody else, right? And so it's much more process intensive or network intensive. And so you can basically launch these TCP floods, these scent attacks, these act floods, whatever they might be. And you're creating a bunch of different connections on that targeted entity and you're spoofing the source. So in other words, let's just say, I am victim one and there's an adversary out there that wants to target me. So they're going to actually spoof my IP address and they're going to send a bunch of these syn flood or a sin, you know, acts or TCPI floods or whatever they might be, to all these DNS servers around the world. And so they're all going to reply to their suppose source of those packets, which in fact, a spoofed, right? And so now you're getting all this flood attacks. And so what we're seeing here is a switch. We're moving from kind of the just connection list, the UDP based stuff the DNS reflection amplification to a more niche things such as TCP act floods. And it's the first time we've ever seen TCP act floods take first place. And what's notable about that is that there are certain types of DDoS mitigation that is susceptible to this kind of attack. And so what we see adversaries do is they'll watch that attack and the monitor did the, did my victim go down? If they didn't go down, they'll pivot, they'll try something else. Maybe they'll try typical volumetric attack. If that succeeds what, okay. We took one layer of the defense down. So is there anything else preventing us from taking our target offline? Well, maybe there's a second layer of defense. So now let's try this other thing and see if that works. And so we actually saw this successful against a commercial banks and payment card processors, where they used TCP act floods to bypass one layer. Then they use volumetric bypass the second, and then on a completely different target, we saw it in reverse. And so we see adversaries adapting to how we're putting our security posture is in place. What we're doing to defend our organizations and networks and adversaries are very quickly iterating and pivoting to follow what we're doing and overcome that. >> And when you say quickly, how quickly are we talking? Is this a matter of days? >> Well, in the case of the attacks that we're talking about, we're talking about seconds or minutes because they're actually launching the attack and they're sitting there watching to see if that goes down and if it doesn't go down, they can pivot really, really quickly and launch a secondary attack. And so in these cases it's really, really rapid and really fast. >> Wow. Another thing that I read in the report and that you sort of intimated a minute ago was the amount of collateral damage seems to also be expanding with what you're seeing in the threat landscape. Talk to us about the risks there and the collateral damage and get us some examples of that actually happening. >> So I think that the biggest example of this and this isn't actually DDoS related, but if you look at like the colonial pipeline incident that happened, right? So they didn't actually go after colonial pipeline. They went after a vendor that provides some sort of service to them. And that resulted in Colonial saying, "we got to shut down our pipeline "because now we can't build our customers." So that's like one aspect of collateral damage. Well, let's translate that to the DDoS world. What happens when a DNS server goes offline, that services 1000 different websites. Now you have all of these other websites that can't be accessed. Well, what happens if an adversary goes after a VPN for a prominent enterprise, they successfully take down that VPN concentrator, and now all of their remote workforce can no longer access those sources. In fact, there's something we're calling connectivity supply chain, which is what adversaries are moving to both in the corporate world, as well as commercial. VPNs increasingly used by gamers, for instance, to mask their IPS because DDoS attacks predominantly target gamers, 80, 85% of all attacks are against gamers. And so they're using VPNs to mask their source. Well, an adversary says, well, hey, I can't go after the individual because I don't know their IP, but I know what your VPN are using. So maybe if I target all the VPN nodes that are publicly available for that VPN concentrator or VPN service provider, now I can take them offline. But it as a consequence, you're not just taking off your individual target. You're taking off every single person that's using that VPN. >> Right. >> This is the collateral damage impact we're talking about. It can be very, very far reaching. >> You mentioned the conductivity supply chain. Let's go ahead and dissect that. Cause that was something else that the report showed was that there was vital components of what NetScout calls the conductivity supply chain, which you'll helped define, are under increasing attack, define the connectivity supply chain and tell us what the report is showing. >> So supply chain comes in many forms and fashion. You have your physical supply chain, you have your vendors that provide software. You have actual movers like such as semis and trains, and you have pipelines to get crude oil to places. All of these things are supply chain, but what's the underlying foundation behind these? How do all of these operate? And more and more in today's day and age, you rely on internet connectivity. You rely on that backbone to be able to operate your systems across a remote space, whether that's internationally, or if it's different countries, if it's just different states, you have to have some way of connecting all those things. And we're not often doing things physically in person there, right? We do this by remote access. We do this by having certain websites or controllers. And all of these things rely on a few critical things that if you were to take them offline, it would prevent you from doing this kind of management. So DNS servers, VPNs, I already talked about whether it's commercial or corporate to access your company's assets. And then you have internet exchanges. If any, one of these things went down from a DDoS attack, you're talking about massive collateral damage. And so what we're calling the conductivity supply chain is really just that, what connects all of us together? That's that's the internet and what makes the internet tick? And here at NetScout, we call ourselves guardians of the connected world. And though that might seem a little bit weird to say it that way. It's absolutely true because our primary goal, here at NetScout, is to make sure that organizations maintain that connection that allows them to really just live, breathe, survive, do their business, without that, you can't conduct business. >> Right? And we saw that the rapid pivot last year, and so many businesses and any, every industry had to rapidly pivot and shift to digital, but the risks as the innovation of technology, for use for good, continues do does it's innovation and use for adversarial things. Another thing that report showed, triple extortion. Talk about that. What you saw, what does that mean for businesses? >> So the triple extortion is three pronged attack. And, everybody here is going to know exactly what I'm talking about when I say ransomware, because ransomware is the biggest threat to the cyber world, really not even just the cyber world, just anybody that has a computer or device or anything, right? Whether it's a business, it's a user, it's a school, hospitals. Everybody is at risk for this and adversaries see the success that ransomware is having and more and more operators get involved in this. Well, what we're seeing here is that they are not satisfied with just encrypting your files and getting a one-time payment. No, they've got to take it a step further. And in fact, the double extortion has been ongoing since, as far back as 2013. When a popular, "Gameover Zeus" variant was distributing CryptoLocker ransomware. And so you have like your initial compromise and data theft and wire transfers of bank stuff followed by ransomware. I already stole your money from your bank. And now you're going to pay me a ransomware to decrypt your files. Well, let's move forward to today's day and age. And over the past year, one of the things we've seen is that adversaries are now adding a third tactics to this the DDoS. And so they will encrypt your files. They'll demand. Hey, you're going to pay us this amount of Bitcoin in order to decrypt your files. But you know, we're already in your system. So, you know, let's just steal your data. And then after you pay us for the decryption, we're going to hold your data hostage until you pay us again. Or maybe we're going to use that data as a lever to get you to pay that initial ransomware. Well, that's still not enough because more and more security researchers, like myself say don't pay. And I'm saying that right here, in plain English, do not pay the ransomware because it has detrimental effects. They, you don't even know if they're going to decrypt your files and you don't know if they're going to come back. Maybe you pay them. They never send you a decryption key. You pay them. And lo and behold, they're part of some terrorist organization. So now you're actually complicit in funding these guys, and the more success that these ransom operators have, the more they're going to do it. And so it has a lot of really negative consequences. Well, let's add another lever. Let's add DDoS to this. So it's not enough. We encrypted your files. It's not enough. We stole your data. Let's knock your network offline. So now you have no recourse whatsoever, except to pay us in order to resume services. And we're seeing at least four or five different ransomware groups of gangs actually use this triple extortion to go after their victims. And so it's something that we expect to see down the road and more and more operators continue to kind of adopt this. >> Lisa: Yeah. The report showed that there was a ransomware group that in the first half of 2021 alone, that vetted a hundred million dollars. So ransomware as a service, this is a big business. You say, don't pay, what can organizations do to defend themselves against triple extortion, even single or double? >> Yeah. So I mean, the thing is, preparation is key for a lot of this and not just for the ransomware piece and triple extortion, but DDoS in general preparation goes a long way to mitigating this potential threat. And one of the things we'd like to say here is that 80% of the things you can do to defend against ransomware also works for defending against DDoS. And the key word here is preparation. Making sure that you've done your, initial observations of your network. You understand what is in your network, every device, not just like the core critical systems, because there could be that IOT device sitting there on their fringe somewhere that has, for whatever reason, access to a system that if encrypted would cause detrimental harm to your company. So not only do you want to inventory your system, you also want to figure out, are they pastorally up to date? Do we allow on an authenticated logins? Are there using default usernames and passwords? In fact, the vast majority of ransomware today, the initial infection vector is either going to be some sort of spam messaging or brute forcing RDP, SSH, and Telnet, the tried and true methods that they've been using for five, six, seven years. They are still successful using to get into organizations. And so making sure that you're sufficiently locking those down. Specifically on the ransomware side, if you want to prevent those, not only are you going to do this preparation, but you're going to make sure that you isolate your critical systems. You shouldn't have everything connected to one spot. If somebody compromises one device, they should not be able to encrypt your entire network. They absolutely should never be able to encrypt your backup files and have backup files, right? So there's a lot of different things you can do here. And by practicing a lot of this preparation, this isolation, the segmenting of your networks, you're also helping in the DDoS space because if they go after one network asset, you'll have all this to fall back on. There was one significant difference between ransomware and DDoS. Ransomware, after you've been infected, unless you have backups or you pay the ransomware, your files are pretty much gone. Unless there's some decrypted that can be had, or the government has some sort of campaign that gets you the caption keys and they helped you with the decryption. So in those cases, if you get encrypted, there's often not a whole lot of recourse, unless you have prepared ahead of time. With DDoS, however, the vast majority, 99% of all DDoS attacks can be prevented if you have a mitigation and protection solution in place. And even if you get DDoS, oftentimes they're, short-lived in fact, the vast majority of DDoS attacks last less than 15 minutes. And so it's not like your stuff is going to be encrypted for days on end or weeks on end. You're going to get hits, you might go down for a period of time, but you can recover services. And during that recovery period, you can go and you can seek mitigation protection services. And so there's a big difference between DDoS and ransomware in that regard. >> That's a great way of describing that. And we've talked a lot about ransomware is it's been on the increase the last year and a half. We've talked about how it's not a matter of if we get attacked, it's a matter of when. But your distinction between ransomware and DDoS attacks show that both with preparation and the right tools, are preventable and recoverable provided organizations have put the proper tools and mechanisms in place to do that. And given how quickly we're seeing the adaptation of the threat actors, organizations, if they're not already on that preparation train, need to catch up. >> Absolutely. They need to get busy right away. There's there's really no delay. Like I said, like you said, it's not if, it's when. And so every single person, every organization, I would take a step further, not even organizations, every single individual that has a computer or some sort of internet connection at home needs to realize that they absolutely can be and are the target of these attacks. We've said it now for the past year and a half, that within five minutes of an IOT device going online, you're getting brute force attempts and that's any IOT device. That's something you connect that maybe you never even realize you can log into and change your password. Well, if it's online, then chances are somebody is trying to brute force that to access it and use it in the varies ways. >> And, and as we all sort of anticipate, we're going to be in this hybrid work environment, work from anywhere environment for quite a while longer. One last question want to ask you, when you talk about all the proliferation of IOT devices, and we're still on this work from anywhere situation, botnets? What are some of the things that the report showed and how can organizations protect all in a, you know, growing number of vulnerable IOT devices from botnets? >> So I think the biggest thing to protect against a IOT compromise is just simply patching up that your passwords Mariah has been out there for a long time, 2016. You know, we saw the dine attacks, but it's still using the same usernames and passwords. Sure, they add more to the list, but the predominant ones that are successful in compromised devices have been around for many years, but they're still successful at compromising these IOT devices. In fact, in the report, one of the things we wanted to show is actually, where are these botnets? How are they being used and specifically in a DDoS nature? And so we actually took all of the IP addresses that we're seeing from bots that are either coming back into our honeypot or things that we scan for. You know, and what we've determined. And that is that roughly 200 to 208,000 of the IP addresses. IP addresses that both we collected as well as a new partner of ours called Gray Noise. They've agreed to partner with us on this short report and you'll see that in the, in the report, if you actually read it. We took these lists of nodes and we compare that to what we're seeing in the DDoS attack landscape. And it turns out that approximately 200,000 of these contributed to more than 2.8 million DDoS attacks in the first half of 2021. Now there was 5.4 million tax total. So more than half of those had some form of DDoS botnet IOT representation. And so that should tell you that these botnets are huge and they're everywhere and they're active. And so the report actually walks you through where these are at, where the density zones are in clusters of these botnets, as well as what botnets in those high density zones are using to compromise other IOT devices. And so it's definitely a very informative read. And I think that you'll, you'll figure out that this isn't, something we talk about in the abstract, right? This is a botnet in my backyard, and I should absolutely be concerned of any IOT device in my home. >> Right. And the, the NetScout threat intelligence report, which Richard has just walked us through is not only available online. It's interactive. It's a great report. I've looked at the PDF, but Richard work in folks go to actually interact with the document and actually glean even more information about how they can prepare and defend. >> Yeah. So netscout.com/starreport. And as Lisa said, it is interactive. So you will need to sign up for the site and you can do both. You can either view the interactive webpage, or you can download the PDF, whatever your reading preference is. But I do encourage the interactive portion because for instance, like this botnet density map that I show, or that I that talked about, you can actually page through month over month to see where those density clusters are. And it is very souther animations. There's other maps in there so there's definitely a lot more value to perusing the interactive nature. >> A lot of granularity. Richard, thank you so much for joining me today, talking about what the first half of 2021 showed. And I can't wait to talk to you next year when we're going to be looking at the second half of the year where we are, with respect to that record, breaking 11 million DDoS attacks. Thank you for taking your time to explain the top trends in the report and for showing folks where they can go to interact with it. >> Well, thank you, Lisa. And thank you to theCUBE for hosting the interview. Definitely appreciate it. >> Our pleasure. For Richard Hammel, I am Lisa Martin, you're watching a CUBE conversation. (melodic music)

(soft electronic music) >> Welcome to this CUBE Conversation. I'm Lisa Martin. This conversation is part of our third AWS Startup Showcase for this year. I'm pleased to welcome Knox Anderson, the VP of Product Management at Sysdig. Knox, welcome to the program. >> Thanks for having me, Lisa. >> Talk to me a little bit about Sysdig, secure DevOps for containers, Kubernetes, and cloud. Give the audience an overview of what you guys do. >> So Sysdig is this secure DevOps platform that provides observability, security, and compliance functions for anyone that's adopting Kubernetes and Cloud. We really secure the entire lifecycle from source to production, so do things like scan your ISE for misconfiguration, monitor your runtime environments for threats and operational best practices. We provide a lot of capabilities around Prometheus Monitoring, as well, and then also let organizations perform incident response and compliance audits against these environments. >> So founded in 2013, talk to me about the gap in the market that you guys saw then and what some of the key challenges are that you saw for your customers. >> Yeah so we came to market around the same time as containers and Kubernetes and I'd say 2015 to 2018 we kept on saying it's the year of Kubernetes, it's the year of Kubernetes, it's the year of Kubernetes. And then really during the last year and a half in the COVID pandemic, Kubernetes has gone gangbusters. Every major cloud is seeing a huge adoption in their Kubernetes services so that's really our wedge into a lot of organizations. They're changing their platform to take advantages of containers and Kubernetes and you really have to rethink all of your security tooling, and that's when a company like Sysdig comes in. >> Talk to me about customers in terms of, especially in the last year and a half when things have been so dynamic, we've seen so much too, on the threat landscape front changing. Give me an example of a customer or two that you're really helped with solving some of their major challenges, here. >> Yeah, a great customer that we work with is SAP Concur and they kind of encompass a lot of the things that are nice about modern DevOps. So it's a DevOps team that's running a Kubernetes platform that thousands of developers are building their apps and deploying those onto. And they chose Sysdig because really it's not scalable to have every single data team ping that DevOps team and say what's the performance of my service, how is it responding, how can I get scanning integrated with that and so they use Sysdig as a platform that allows developers to easily onboard onto their Kubernetes clusters and then ensure that they're meeting compliance needs and FedRAMP needs for that platform that they deliver their core business apps on. >> Let's talk about the Sysdig's commitment to opensource on the Falco project. >> So Falco is a opensource project that we started at Sysdig, it's built on top of our core system core instrumentation. And so Falco meets a lot of your IDS or your file integrity monitoring requirements that you might have as you move to Kubernetes. And really, it's something we started at about 2016. In 2019, we donated that project to the CMCS which is the same governance body behind Kubernetes, Prometheus, and other kind of core building blocks of the climate of ecosystem. Since then, it's grown immensely. Companies like Shopify are using it to make sure that their PCI apps that they run Kubernetes are fully compliant. And so it's something that we are constantly contributing to the community also from even companies like AWS is a core contributor to the Falco project. And I'm really excited to see where it goes over the next year as Falco extends to also cover some cloud security use cases. >> What can you tell me about the relationship that Sysdig and AWS have? >> They've been a great partner. We internally run our SaaS on AWS so we're using AWS services to deliver our product to our customers. And then we've also really worked closely around how you can provide better security for services like Fargate. So we did working sessions with their engineering teams, learned what we could do to get the visibility that we need for tools like Falco and Sysdig to work seamlessly in Fargate environments. And last April we were able to kind of, AWS released that new functionality, Sysdig built on top of that, and we've already seen great adoption of customers using the Sysdig product on top of Fargate. >> Excellent. Well thank you very much, Knox, for stopping by theCUBE telling us about Sysdig, what you guys are doing ahead of the AWS Startup Showcase. We appreciate your time and your information. >> Thanks for having me. >> For Knox Anderson, I'm Lisa Martin. You're watching this CUBE Conversation. (soft electronic music)

(upbeat music) >> Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, Fast Data, a retail industry business imperative. My name is Brent Biddulph, Global Managing Director of Retail and Super Bids here at Cloudera and today's hosts. Joining me today is our feature speaker Brian Kilcourse, Managing Partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in empirical segments. At the end of today's session I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data ingest, analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Cloudera is supporting retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases, primarily across four key business pillars and these will be very familiar to those in the industry. Personalize interactions of course plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the omni-channel journey, moving into the merchandising line of business, focused on localizing sorbet, promotional planning, forecasting, demand forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization, right now as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, which is pretty exciting to me as a former store operator, what's happening with physical brick and mortar right now, especially for traditional retailers. The whole re-imagining of stores right now is on fire in a lot of focus because frankly this is where fulfillment is happening, this is where customers steal 80% of revenue is driven through retail through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had in decades and a lot of it has to do of course with IoT data and analytics in the new technologies that really help drive benefits for retailers from a brick and mortars standpoint. And then finally, to wrap up before handing off to Brian, as you'll see, all of these lines of businesses are rogue, really experiencing the need for speed, fast data. So we're moving beyond just discovery analytics, things that happened five, six years ago with big data, et cetera and we're really moving into real time capabilities because that's really where the difference makers are, that's where the competitive differentiation is across all of these lines of business and these four key pillars within retail. The dependency on fast data is evident, it's something that we all read in terms of those that are students of the industry if you will, that we're all focused on in terms of bringing value to the individual lines of business but more importantly to the overall enterprise. So without further ado, I really want to have Brian speak here as a third party analyst. He's close in touch with what's going on retail talking to all the solution providers, all the key retailers about what's important, what's on their plate, what are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward. So Brian off to you. >> Well, thanks, Brent. I appreciate the introduction. And I was thinking as you were talking, what is fast data? Well, fast data is fast data, it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday. Fast data is data that's coming at you in something approaching real time and we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years and what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry. I was a retail technologist my entire working life and so we started this company. So I have a built-in bias of course, and that is that the difference between the winners in the retail world and in fact in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, one other thing about RSR Research, our research is free to the entire world. We don't have a paywall that you have to get behind, all you have to do is sign into our website, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you and we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and is being driven by the real world. We saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one, do I redirect my replenishments to store B because store A is impacted by the pandemic, those kinds of things. These two drawings are actually from a book that came out in 1997 and it was a really important book for me personally is by a guy named Steven Hegel and the name of the book was "The Adaptive Enterprise." When you think about your business model and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all, it changes once in a generation or maybe once in a lifetime, but it's established quite early. And then from that point on, it's basically a wash, rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out, season in and season out and the most important pieces of information that you have is the transaction data from the last cycle. So Brent knows this from his experience as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature, it's typically pulled from your ERP or from your best of breed solution set. On the right is where the world is really going, and before we get into the details of this, I'll just use a real example. I'm sure like me, you've watched the path of hurricanes as they go up to the Florida Coast. And one of the things you might've noticed is that there are several different possible paths. These are models and you'll hear a lot about models when you talk to people in the AI world. These are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane. Based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the real hurricane progresses, they will see if it's following that path or if it's varying, it's going down a different path and based on that they will adapt to a new model. And that is what I'm talking about here. Not everything is of course is life and death as a hurricane but it's basically the same concept. What's happening is you have your internal data that you've had since this command and control model that we've mentioned on the left and you're taking an external data from the world around you and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside. Back to my COVID example, when people were tracking the path of the pandemic through communities, they learned that customers or consumers would favor certain stores to pick up what they needed to get. So they would avoid some stores and they would favor other stores and that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees, they wanted to know where they could get their employees to service these customers, how far away were they, were they in a community that was impacted or were they relatively safe. These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So first of all, there's a context for these decisions, there's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time and that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view and based on those two inputs what's happening internally, where your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary and this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. If you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see and then because it's an intrinsically more complicated model to automate decision-making where it makes sense to do so. That's pretty complicated and I talk about new data and as I said earlier, the old data is all transactional in nature, mostly about sales. Retail has been a wash in sales data for as long as I can remember, they throw most of it away but they do keep enough to create the forecast for the next business cycle. But there's all kinds of new information that they need to be thinking about and a lot of this is from the outside world and a lot of this is non-transactional in nature. So let's just take a look at some of them. Competitive information. Retailers are always interested in what the competitor is up to, what are they promoting? How well are they doing? Where are they? What kind of traffic are they generating? Sudden and significant changes in customer behaviors and sentiment, COVID is a perfect example of something that would cause this, consumers changing their behaviors very quickly. And we have the ability to observe this because in a great majority of cases nowadays, retailers have observed that customers start their shopping journey in the digital space. As a matter of fact, Google recently came out and said that 63% of all sales transactions begin in the digital domain, even if many of them end up in the store. So we have the ability to observe changes in consumer behavior, what are they looking at? When are they looking at it? How long do they spend looking at it? What else are they looking at while they're doing that? What is the outcome of them looking? Market metrics certainly, what's going on in the marketplace around you? A good example of this might be something related to a sporting event. If you've planned based on normal demand and for your store and there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand, so understanding what's going on in the market is really important. Location, demographics and psychographics. Demographics have always been important to retailers, but now we're talking about dynamic demographics. What customers or what consumers are in your market in something approaching real time. Psychographics has more to do with their attitudes, what kind of folks are in a particular marketplace, what do they think about, what do they favor, and all those kinds of interesting details. Real time environmental and social incidents, of course, I mentioned hurricanes and so that's fairly self-evident. Disruptive events, sporting events, et cetera, these are all real. And then we get the real time Internet-of-Things, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where it really gets interesting, this is where the supply chain people will start talking about the digital twin to their physical world. If you can't say something you can't manage it and retailers want to be able to manage things in real time. So IoT along with AI analytics and the data that's generated is really, really important for them going forward. Community health, we've been talking a lot about that, the progression of the flu, et cetera, et cetera. Business schedules, commute patterns, school schedules, and weather, these are all external data that are interesting to retailers and can help them to make better operational decisions in something approaching real time. I mentioned the automation of decision-making, this is a chart from Gardner and I'd love to share with you. It's a really good one because it describes very simply what we're talking about and it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data, we're getting more data all the time. Retailers for a long time now since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Sometime in the not-too-distant past this data was started to be used to make diagnostic decisions, not only what happened but why did it happen? And we might think of this as, for example, if sales were depressed and for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics and this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or the cannibalization effect of your category plans if you happen to be a grocer. And based on that, the human will make a decision as to what they need to do next. Then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data. AI if I could simplify it to the next maximum extent, it essentially is a data tool that allows you to see patterns in data which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that of course, because it uses math instead of rules. So instead of an if then or else statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data and based on those we can make some models. For example, my guy in my (chuckles) just turned 70. My 70 year old man, I'm a white guy, I live in California, I have a certain income and a certain educational level. I'm likely to behave in this way based on a model, that's pretty simplistic but based on that, you can see that when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, they might be expected to make a certain action. And so this is where prescriptive really comes into play. AI makes that possible. And then finally, when you start to think about moving closer to the customer or something approaching a personalized level, a one-to-one level, you suddenly find yourself in the situation of having to make not thousands of decisions but tens of millions of decisions and that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting and it's new, it's just the latest turn of the technology screw and it allows us to use this new data to basically automate decision-making in the business in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. This happens to be from a location analytics study we just published last week, and we asked retailers, what are the big challenges? What's been going on in the last 12 months for them, and what's likely to be happening for them in the next few years and it's just fascinating because it speaks to the need for faster decision-making. The challenges in the last 12 months are all related to COVID. First of all, fulfilling growing online demand, this is a very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand and this is one of those areas where retailers are now finding themselves needing to look at that exogenous or that external data that I mentioned to you. Last year sales were not a good predictor of next year sales, they needed to look at sentiment, they needed to look at the path of the disease, they needed to look at the availability of products, alternate sourcing, global political issues, all of these things get to be pretty important and they affect the forecast. And then finally, managing the movement of the supply through the supply chain so that they could identify bottlenecks. Now, point to one of them which we can all laugh at now because it's kind of funny, it wasn't funny at the time. We ran out of toilet paper (laughs) toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population and yet we ran out. And the thing we didn't expect when the COVID pandemic hit was that people would panic and when people panic they do funny things. One of the things I do is buy up all the available toilet paper, I'm not quite sure why that happen but it did happen and it drained the supply chain. So retailers needed to be able to see that, they needed to be able to find alternative sources, they needed to be able to do those kinds of things. This gets to the issue of visibility, real-time data, fast data. Tomorrow's challenge is kind of interesting because one of the things that retailers put at the top of their list is improve inventory productivity. The reason that they are interested in this is because they will never spend as much money on anything as they will on inventory and they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big. But in this complex, fast moving world that we live in today is this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCs and the warehouses and their picking capacity. We're talking about Echo's, we're talking about Echo's level of decision-making about what's flowing through the supply chain all the way from the manufacturing door to the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it. You'll hear retailers and people like me talk about the digital twin, this is where this really becomes important. And again, the digital twin is enabled by IoT and AI analytics. And finally, they need to increase their profitability for online fulfillment. This is a huge issue, for some grocers the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020 what they needed to do to fulfill those customer orders in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those features to be available to them all the time and many people really like them. Now retailers need to find out how to do it profitably and one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. Now when we think about the hard one wisdom that retailers have come up with we think about these things, better visibility has led to better understanding which increases their reaction time which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common and in our research, we separate over-performers, who we call retail winners from everybody else, average and under-performers. And we've noticed throughout the life of our company that retail winners don't just do all the same things that others do, they tend to do other things and this shows up in this particular graph. This again is from the same study. So what are the opportunities to address these challenges I mentioned to you in the last slide? First of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand on the consumer side. A better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase, observing things that are happening in the marketplace around the retailer so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase as they engage with us. One of the things we all know about consumers now is that they are in control and literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs. Optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in-store, buy online pick up at a locker, a direct to consumer, all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important and never goes away is the reduction of waste, shrink within the supply chain. I'm embarrassed to say that when I was a retail executive in the nineties, we were no more certain of consumer demand than anybody else was but we wanted to commit to very high service levels for some of our key categories somewhere approaching 95% and we found the best way to do that was to flood the supply chain with inventory. It sounds irresponsible now, but in those days that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world, money is too tight and we can't have that inventory sitting around and move to the right places once we discover what the right places. We have to be able to predict, observe, and respond in something much closer to real time. Onto the next slide, the simple message here, again a difference between winners and everybody else. The messages, if you can't see it you can't manage it. And so we asked retailers to identify to what extent an AI enabled supply chain can help their company address some issues. Look at the differences here, they're shocking. Identifying network bottlenecks, this is the toilet paper story I told you about. Over half of retail winners feel that that's very important, only 19% of average and under-performers, no surprise that they're average and under-performers. Visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list but you get the just, retail winners understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today and in order to do that you need to be able to number one, see it and number two, you need to be able to analyze it, and number three, you have to be able to make decisions based on what you saw. Just some closing observations and I hope this was interesting for you. I love talking about this stuff, you can probably tell I'm very passionate about it. But the rapid pace of change in the world today is really underscoring the importance, for example, of location intelligence as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes and how products are brought to the market. And in order to do that they need to be able to see people, they need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it and based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in, it's a real-time world, it's a sense and respond world and it's the way forward. So Brent, I hope that was interesting for you. I really enjoyed talking about this as I said, we'd love to hear a little bit more. >> Hey, Brian, that was excellent. I always love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. One of the higher level research articles around fast data frankly, is the whole notion of IoT, right? Now many does a lot of work in this space. What I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year between now and 2025. Now, how's that possible? Well, part of it is because of the Kinsey captures not only traditional retail but also QSRs and entertainment venues, et cetera, that's considered all of retail. But it's a staggering number and it really plays to the effect that real time can have on individual enterprises, in this case we're talking of course about retail. So a staggering number and if you think about it, from streaming video to sensors, to beacons, RFID, robotics, autonomous vehicles retailers are asking today, even pizza delivery and autonomous vehicles. If you think about it, it shouldn't be that shocking, but when they were looking at 12 different industries, retail became like the number three out of 12 and there's a lot of other big industries that will be leveraging IoT in the next four years. So retailers in the past have been traditionally a little stodgy about their spend in data and analytics. I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy and IoT really is the next frontier, which is kind of the definition of fast data. So I just wanted to share just a few examples or exemplars of retailers that are leveraging the Cloudera technology today. So now they pay for advertisement at the end of this, right? So what is Cloudera bringing to market here? So across all retail verticals, if we look at, for example, a well-known global mass virtual retailer, they're leveraging Cloudera data flow which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So it is best to class movement of data from an ingest standpoint, but we're also able to help the round trip. So we'll pull up sensor data off all the refrigeration units for this particular retailer, they'll hit it up against the product lifecycle table, they'll understand temperature fluctuations of 10, 20 degrees based on fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration, they'll know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. So this particular customer leverages further data flow to understand temperature fluctuations, the impact on the product life cycle and the roundtrip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager. Hey, you had a 20 degree drop in temperature, we suggest you lower the price on these products that we know are in that cooler for the next couple of days by 20%. So you don't have to worry about freshness issues and or potential shrink. The grocery with fresh product, if you don't sell it, you smell it, you throw it away, it's cost to the bottom line. So critically important and tremendous ROI opportunity that we're helping to enable there. From a leading global drugstore retailer, so this is more about data processing and we're excited of the recent partnership with the Nvidia. So fast data isn't always at the edge with IoT, it's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will never achieve personalization, you will never achieve one-on-one communications with retailers or with customers, and why is that? Because customers in many cases are touching your brand several times a week. So if taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization, in fact, you may offend customers by offers you might push out based on what they just bought yesterday you had no idea of it. So that's what we're really excited about, again with the computation speed that Nvidia brings to Cloudera. We're already doing this today, we've already been providing levels of exponential speed and processing data, but when Nvidia brings to the party is course GPUs right, which is another exponential improvement to processing workloads like demand forecast, customer profiles. These things need to happen behind the scenes in the back office much faster than retailers have been doing in the past. That's just the world we all live in today. And then finally, from a proximity marketing standpoint or just from an in-store operations standpoint, retailers are leveraging Cloudera today, not only data flow but also of course our compute and storage platform and ML, et cetera, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line, you can now start to understand how to better merchandise the store, where the hotspots are, how to in real time improve your customer service. And from a proximity marketing standpoint, understand how to engage with the customer for right at the moment of truth, right, when they're right there in front of the particular department or category, upward leveraging mobile device. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. From an overall platform standpoint of course, Cloudera is an enterprise data platform, right? So we're helping to enable the entire data life cycle, so we're not a data warehouse, we're much more than that. So we have solutions to ingest data from the Edge, from IoT, leading practice solutions to bring it in. We also have experiences to help leverage the analytic capabilities of data engineering, data science, analytics and reporting. We're not encroaching upon the legacy solutions that many retailers have today, we're providing a platform that's open source that helps weave all this mess together that existed retail today from legacy systems because no retailer frankly is going to rip and replace a lot of stuff that they have today. Right. And the other thing the Cloudera brings to market is this whole notion of on-prem hybrid cloud and multicloud, right. So our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. We're kind of religious about open source and lack of vendor lock-in, maybe to our fault, but as a company we pull that together from a data platform standpoint so it's not a rip or replace situation. It's like helping to connect legacy systems, data and analytics, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. And then finally, I want to thank everyone for joining today's session, I hope you found it informative. I can't thank Brian Kilcourse enough, like he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course in talking to a lot of our partners in other technology companies out there as well. But I really appreciate everyone joining the session, and Brian, I'm going to kind of leave it open to you to any closing comments that you might have based on what we're talking about today in terms of fast data and retail. >> First of all, thank you, Brent. And this is an exciting time to be in this industry. And I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that and it frankly wasn't even usable. But what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that make us a trusted provider of their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community and I'm glad to be a part of it and I'm glad to be working with you. So thank you, Brent. >> Yeah, of course, Brian. And one of the exciting things for me too, I've being in the industry as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and IT for a change, right? (Brian laughs) They've all kind of come to this final pinnacle of this is what it's going to take to compete. You and I talked to a lot of colleagues, even salespeople within Cloudera, like, oh, retail, very stodgy, slow to move. That's not the case anymore. >> No. >> Everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry that we couldn't see years ago, Brian. I mean, retailers are like pulling people from some of the greatest tech companies out there, right? From a data science, data engineering standpoint, application developers. Retail is really getting its legs right now in terms of go to market and the leverage of data and analytics, which to me is very exciting. >> Well, you're right. I mean, I became a CIO around the time that point of sale and data warehouses were starting to happen, data cubes and all those kinds of things. And I never thought I would see a change that dramatic as the industry experience back in those days, 1989, 1990, this changed doors that, but the good news is again, as the technology is capable, we're talking about making technology and information available to retail decision-makers that consumers carry around in their purses and pockets as they're right now today. So the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. >> Yeah, for sure. Hey, thanks everyone. We'll wrap up, I know we ran a little bit long, but appreciate everyone hanging in here with us. We hope you enjoyed the session. Our contact information is right there on the screen, feel free to reach out to either Brian and I. You can go to cloudera.com, we even have joint sponsored papers with RSR, you can download there as well as other eBooks, other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time today.

>>Thank you and good morning or afternoon, everyone, depending on where you're coming to us from and welcome to today's breakout session, fast data, a retail industry business imperative. My name is Brent Bedell, global managing director of retail, consumer bids here at Cloudera and today's hosts joining today. Joining me today is our feature speaker Brian Hill course managing partner from RSR. We'll be sharing insights and implications from recently completed research across retailers of all sizes in vertical segments. At the end of today's session, I'll share a brief overview on what I personally learned from retailers and how Cloudera continues to support retail data analytic requirements, and specifically around streaming data, ingest analytics, automation for customers around the world. There really is the next step up in terms of what's happening with data analytics today. So let's get started. So I thought it'd be helpful to provide some background first on how Clare to Cloudera is supporting and retail industry leaders specifically how they're leveraging Cloudera for leading practice data analytics use cases primarily across four key business pillars. >>And these will be very familiar to, to those in the industry. Personalize interactions of course, plays heavily into e-commerce and marketing, whether that's developing customer profiles, understanding the OB omni-channel journey, moving into the merchandising line of business focused on localized promotional planning, forecasting demand, forecast accuracy, then into supply chain where inventory visibility is becoming more and more critical today, whether it's around fulfillment or just understanding where your stuff is from a customer perspective. And obviously in and outbound route optimization right now, as retailers are taking control of actual delivery, whether it's to a physical store location or to the consumer. And then finally, uh, which is pretty exciting to me as a former store operator, you know, what's happening with physical brick and mortar right now, especially for traditional retailers. Uh, the whole re-imagining of stores right now is on fire in a lot of focus because, you know, frankly, this is where fulfillment is happening. >>Um, this is where customers, you know, still 80% of revenue is driven through retail, through physical brick and mortar. So right now store operations is getting more focused and I would say it probably is had and decades. Uh, and a lot of has to do for us with IOT data and analytics in the new technologies that really help, uh, drive, uh, benefits for retailers from a brick and mortar standpoint. And then, and then finally, um, you know, to wrap up before handing off to Brian, um, as you'll see, you know, all of these, these lines of businesses are raw, really experiencing the need for speed, uh, you know, fast data. So we're, we're moving beyond just discovery analytics. You don't things that happened five, six years ago with big data, et cetera. And we're really moving into real time capabilities because that's really where the difference makers are. >>That's where the competitive differentiation as across all of these, uh, you know, lines of business and these four key pillars within retail, um, the dependency on fast data is, is evident. Um, and it's something that we all read, you know, you know, in terms of those that are students of the industry, if you will, um, you know, that we're all focused on in terms of bringing value to the individual, uh, lines of business, but more importantly to the overall enterprise. So without further ado, I, I really want to, uh, have Brian speak here as a, as a third party analyst. You know, he, he's close in touch with what's going on, retail talking to all the solution providers, all the key retailers about what's important, what's on their plate. What are they focusing on right now in terms of fast data and how that could potentially make a difference for them going forward? So, Brian, uh, off to you, >>Well, thanks, Brent. I appreciate the introduction. And I was thinking, as you were talking, what is fast data? Well, data is fast. It is fast data it's stuff that comes at you very quickly. When I think about the decision cycles in retail, they were, they were, they were time phased and there was a time when we could only make a decision perhaps once a month and then met once a week and then once a day, and then intraday fast data is data that's coming at you and something approaching real time. And we'll explain why that's important in just a second. But first I want to share with you just a little bit about RSR. We've been in business now for 14 years. And what we do is we studied the business use cases that drive the adoption of technology in retail. We come from the retail industry, I was a retail technologist, my entire working life. >>And so we started this company. So I'm, I have a built in bias, of course, and that is that the difference between the winners in the retail world and in fact, in the entire business world and everybody else is how they value the strategic importance of information, and really that's where the battle is being fought today. We'll talk a little bit about that. So anyway, uh, one other thing about RSR research, our research is free to the entire world. Um, we don't, we don't have a paywall. You have to get behind. All you have to do is sign into our website, uh, identify yourself and all of our research, including these two reports that we're showing on the screen now are available to you. And we'd love to hear your comments. So when we talk about data, there's a lot of business implications to what we're trying to do with fast data and as being driven by the real world. >>Uh, we saw a lot of evidence of that during the COVID pandemic in 2020, when people had to make many decisions very, very quickly, for example, a simple one. Uh, do I redirect my replenishments to store B because store a is impacted by the pandemic, those kinds of things. Uh, these two drawings are actually from a book that came out in 1997. It was a really important book for me personally is by a guy named Steven Hegel. And it was the name of the book was the adaptive enterprise. When you think about your business model, um, and you think about the retail business model, most of those businesses are what you see on the left. First of all, the mission of the business doesn't change much at all. It changes once in a generation or maybe once in a lifetime, um, but it it's established quite early. >>And then from that point on it's, uh, basically a wash rinse and repeat cycle. You do the things that you do over and over and over again, year in and year out season in and season out. And the most important piece of information that you have is the transaction data from the last cycle. So a Brent knows this from his experience as a, as a retailer, the baseline for next year's forecast is last year's performance. And this is transactional in nature. It's typically pulled from your ERP or from your best of breed solution set on the right is where the world is really going. And before we get into the details of this, I'll just use a real example. I'm I'm sure like, like me, you've watched the path of hurricanes as they go up to the Florida coast. And one of the things you might've noticed is that there's several different possible paths. >>These are models, and you'll hear a lot about models. When you talk to people in the AI world, these are models based on lots and lots of information that they're getting from Noah and from the oceanographic people and all those kinds of folks to understand the likely path of the hurricane, based on their analysis, the people who watch these things will choose the most likely paths and they will warn communities to lock down and do whatever they need to do. And then they see as the, as the real hurricane progresses, they will see if it's following that path, or if it's varying, it's going down a different path and based on that, they will adapt to a new model. And that is what I'm talking about here now that not everything is of course is life and death as, as a hurricane. But it's basically the same concept what's happening is you have your internal data that you've had since this, a command and control model that we've mentioned on the left, and you're taking an external data from the world around you, and you're using that to make snap decisions or quick decisions based on what you see, what's observable on the outside, back to my COVID example, um, when people were tracking the path of the pandemic through communities, they learn that customers or consumers would favor certain stores to pick up their, what they needed to get. >>So they would avoid some stores and they would favor other stores. And that would cause smart retailers to redirect the replenishments on very fast cycles to those stores where the consumers are most likely to be. They also did the same thing for employees. Uh, they wanted to know where they could get their employees to service these customers. How far away were they, were they in a community that was impacted or were they relatively safe? These are the decisions that were being made in real time based on the information that they were getting from the marketplace around them. So, first of all, there's a context for these decisions. There's a purpose and the bounds of the adaptive structure, and then there's a coordination of capabilities in real time. And that creates an internal feedback loop, but there's also an external feedback loop. This is more of an ecosystem view. >>And based on those two, those two inputs what's happening internally, what your performance is internally and how your community around you is reacting to what you're providing. You make adjustments as necessary. And this is the essence of the adaptive enterprise. Engineers might call this a sense and respond model. Um, and that's where retail is going. But what's essential to that is information and information, not just about the products that you sell or the stores that you sell it in, or the employees that you have on the sales floor or the number of market baskets you've completed in the day, but something much, much more. Um, if you will, a twin, a digital twin of the physical assets of your business, all of your physical assets, the people, the products, the customers, the buildings, the rolling stock, everything, everything. And if you can create a digital equivalent of a physical thing, you can then analyze it. >>And if you can analyze it, you can make decisions much, much more quickly. So this is what's happening with the predict pivot based on what you see, and then, because it's an intrinsically more complicated model to automate, decision-making where it makes sense to do so. That's pretty complicated. And I talk about new data. And as I said earlier, the old data is all transactional in nature. Mostly about sales. Retail has been a wash in sales data for as long as I can remember throw, they throw most of it away, but they do keep enough to create the forecast the next for the next business cycle. But there's all kinds of new information that they need to be thinking about. And a lot of this is from the outside world. And a lot of this is non-transactional nature. So let's just take a look at some of them, competitive information. >>Those are always interested in what the competitor is up to. What are they promoting? How well are they they doing, where are they? What kind of traffic are they generating sudden and stuff, significant changes in customer behaviors and sentiment COVID is a perfect example of something that would cause this consumers changing their behaviors very quickly. And we have the ability to, to observe this because in a great majority of cases, nowadays retailers have observed that customers start their, uh, shopping journey in the digital space. As a matter of fact, Google recently came out and said, 60%, 63% of all, all sales transactions begin in the digital domain. Even if many of them end up in the store. So we have the ability to observe changes in consumer behavior. What are they looking at? When are they looking at it? How long do they spend looking at it? >>What else are they looking at while they're, while they're doing that? What are the, what is the outcome of that market metrics? Certainly what's going on in the marketplace around you? A good idea. Example of this might be something related to a sporting event. If you've planned based on normal demand and for, for your store. And there's a big sporting event, like a football match or a baseball game, suddenly you're going to see a spike in demand. So understanding what's going on in the market is really important. Location, demographics and psychographics, demographics have always been important to retailers, but now we're talking about dynamic demographics, what customers, or what consumers are, are in your market, in something approaching real time, psychographics has more to do with their attitudes. What kind of folks are, are, are in them in a particular marketplace? What do they think about what do they favor? >>And all those kinds of interesting deep tales, real-time environmental and social incidents. Of course, I mentioned hurricanes. And so that's fairly, self-evident disruptive events, sporting events, et cetera. These are all real. And then we get the real time internet of things. These are, these are RFID sensors, beacons, video, et cetera. There's all kinds of stuff. And this is where, yeah, it's interesting. This is where the supply chain people will start talking about the difference, little twin to their physical world. If you can't say something, you can manage it. And retailers want to be able to manage things in real time. So IOT, along with it, the analytics and the data that's generated is really, really important for them going forward, community health. We've been talking a lot about that, the progression of the flu, et cetera, et cetera, uh, business schedules, commute patterns, school schedules, and whether these are all external data that are interesting to retailers and can help them to make better operational in something approaching real time. >>I mentioned the automation of decision making. This is a chart from Gardner, and I'd love to share with you. It's a really good one because it describes very simply what we're talking about. And it also describes where the inflection of new technology happens. If you look on the left there's data, we have lots and lots of data. We're getting more data all the time, retailers for a long time. Now, since certainly since the seventies or eighties have been using data to describe what happened, this is the retrospective analysis that we're all very familiar with, uh, data cubes and those kinds of things. And based on that, the human makes some decisions about what they're going to do going forward. Um, sometime in the not too distant past, this data was started to be used to make diagnostic decisions, not only what happened, but why did it happen? >>And me might think of this as, for example, if sales were depressed for a certain product, was it because we had another product on sale that day, that's a good example of fairly straightforward diagnostics. We then move forward to what we might think of as predictive analytics. And this was based on what happened in the past and why it happened in the past. This is what's likely to happen in the future. You might think of this as, for example, halo effect or, or the cannibalization effect of your category plans. If you're, if you happen to be a grocer and based on that, the human will make a decision as to what they need to do next then came along AI, and I don't want to oversell AI here. AI is a new way for us to examine lots and lots of data, particularly unstructured data AI. >>If I could simplify it to its maximum extent, it essentially is a data tool that allows you to see patterns in data, which might be interesting. It's very good at sifting through huge data sets of unstructured data and detecting statistically significant patterns. It gets deeper than that, of course, because it uses math instead of rules. So instead of an if then, or else a statement that we might've used with our structured data, we use the math to detect these patterns in unstructured data. And based on those, we can make some models. For example, uh, my guy in my, in my, uh, just turned 70 on my 70 year old man, I'm a white guy. I live in California. I have a certain income and a certain educational level. I'm likely to behave in this way based on a model that's pretty simplistic. But based on that, you can see that. >>And when another person who meets my psychographics, my demographics, my age group, my income level and all the rest, um, you, they might, they might be expected to make a certain action. And so this is where prescriptive really comes into play. Um, AI makes that possible. And then finally, when you start to think about moving closer to the customer on something, approaching a personalized level, a one-to-one level, you, you suddenly find yourself in this situation of having to make not thousands of decisions, but tens of millions of decisions. And that's when the automation of decision-making really gets to be pretty important. So this is all interesting stuff, and I don't want to oversell it. It's exciting. And it's new. It's just the latest turn of the technology screw. And it allows us to use this new data to basically automate decision-making in the business, in something approaching real time so that we can be much, much more responsive to real-time conditions in the marketplace. >>Very exciting. So I hope this is interesting. This is a piece of data from one of our recent pieces of research. Uh, this happens to be from a location analytics study. We just published last week and we asked retailers, what are the big challenges what's been going on in the last 12 months for them? And what's likely to be happening for them in the next few years. And it's just fascinating because it speaks to the need for faster decision-making there. The challenges in the last 12 months were all related to COVID. First of all, fulfilling growing online demand. This is a very, very real time issue that we all had to deal with. But the next one was keeping forecasts in sync with changing demand. And this is one of those areas where retailers are now finding themselves, needing to look at that exoticness for that external data that I mentioned to you last year, sales were not a good predictor of next year of sales. >>They needed to look at sentiment. They needed to look at the path of the disease. They needed to look at the availability of products, alternate sourcing, global political issues. All of these things get to be pretty important and they affect the forecast. And then finally managing a supply them the movement of the supply through the supply chain so that they could identify bottlenecks now, point to one of them, which we can all laugh at now because it's kind of funny. It wasn't funny at the time we ran out of toilet paper, toilet paper was a big problem. Now there is nothing quite as predictable as toilet paper, it's tied directly to the size of the population. And yet we ran out and the thing we didn't expect when the COVID pandemic hit was that people would panic. And when people panic, they do funny things. >>One of the things I do is buy up all the available toilet paper. I'm not quite sure why that happened. Um, but it did happen and it drained the supply chain. So retailers needed to be able to see that they needed to be able to find alternative sources. They needed to be able to do those kinds of things. This gets to the issue of visibility, real time data, fast data tomorrow's challenge. It's kind of interesting because one of the things that they've retailers put at the top of their list is improved inventory productivity. Uh, the reason that they are interested in this is because then we'll never spend as much money, anything as they will on inventory. And they want the inventory to be targeted to those places where it is most likely to be consumed and not to places where it's least likely to be consumed. >>So this is trying to solve the issue of getting the right product at the right place at the right time to the right consumer and retailers want to improve this because the dollars are just so big, but in this complex, fast moving world that we live in today, it's this requires something approaching real-time visibility. They want to be able to monitor the supply chain, the DCS and the warehouses. And they're picking capacity. We're talking about each of us, we're talking about each his level. Decision-making about what's flowing through the supply chain all the way from the, from the manufacturing doctor, the manufacturer through to consumption. There's two sides of the supply chain and retailers want to look at it, you'll hear retailers and, and people like me talk about the digital twin. This is where this really becomes important. And again, the digital twin is, is enabled by IOT and AI analytics. >>And finally they need to re to increase their profitability for online fulfillment. Uh, this is a huge issue, uh, for some grocers, the volume of online orders went from less than 10% to somewhere north of 40%. And retailers did in 2020, what they needed to do to fulfill those customer orders in the, in the year of the pandemic, that now the expectation that consumers have have been raised significantly. They now expect those, those features to be available to them all the time. And many people really liked them. Now retailers need to find out how to do it profitably. And one of the first things they need to do is they need to be able to observe the process so that they can find places to optimize. This is out of our recent research and I encourage you to read it. >>And when we think about the hard one wisdoms that retailers have come up with, we think about these things better visibility has led to better understanding, which increases their reaction time, which increases their profitability. So what are the opportunities? This is the first place that you'll see something that's very common. And in our research, we separate over performers, who we call retail winners from everybody else, average and under-performers, and we've noticed throughout the life of our company, that retail winners, don't just do all the same things that others do. They tend to do other things. And this shows up in this particular graph, this again is from the same study. So what are the opportunities to, to address these challenges? I mentioned to you in the last slide, first of all, strategic placement of inventory throughout the supply chain to better fulfill customer needs. This is all about being able to observe the supply chain, get the inventory into a position where it can be moved quickly to fast changing demand. >>And on the consumer side, a better understanding and reacting to unplanned events that can drive a dramatic change in customer behavior. Again, this is about studying the data, analyzing the data and reacting to the data that comes before the sales transaction. So this is observing the path to purchase observing things that are happening in the marketplace around the retailer, so that they can respond very quickly, a better understanding of the dramatic changes in customer preference and path to purchase. As they engage with us. One of the things we, all we all know about consumers now is that they are in control and the literally the entire planet is the assortment that's available to them. If they don't like the way they're interacting with you, they will drop you like a hot potato and go to somebody else. And what retailers fear justifiably is the default response to that is to just see if they can find it on Amazon. >>You don't want this to happen if you're a retailer. So we want to observe how we are interacting with consumers and how well we are meeting their needs, optimizing omni-channel order fulfillment to improve profitability. We've already mentioned this, uh, retailers did what they needed to do to offer new fulfillment options to consumers. Things like buy online pickup curbside, buy online pickup in store, buy online, pick up at a locker, a direct to consumer all of those things. Retailers offer those in 2020 because the consumers demand it and needed it. So when retailers are trying to do now is to understand how to do that profitably. And finally, this is important. It never goes away. Is the reduction of waste shrink within the supply chain? Um, I'm embarrassed to say that when I was a retail executive in the nineties, uh, we were no more certain of consumer demand than anybody else was, but we, we wanted to commit to very high service levels for some of our key county categories somewhere approaching 95%. >>And we found the best way to do that was to flood the supply chain with inventory. Uh, it sounds irresponsible now, but in those days, that was a sure-fire way to make sure that the customer had what she was looking for when she looked for it. You can't do that in today's world. Money is too tight and we can't have that, uh, inventory sitting around and move to the right places. Once we discovered what the right place is, we have to be able to predict, observe and respond in something much closer to your time. One of the next slide, um, the simple message here, again, a difference between winners and everybody else, the messages, if you can't see it, you can't manage it. And so we asked retailers to identify, to what extent an AI enabled supply chain can help their company address some issues. >>Look at the differences here. They're shocking identifying network bottlenecks. This is the toilet paper story I told you about over half of retail winners, uh, feel that that's very important. Only 19% of average and under performers, no surprise that their average and under-performers visibility into available to sell inventory anywhere within the enterprise, 58% of winners and only 32% of everybody else. And you can go on down the list, but you get the just retail winners, understand that they need to be able to see their assets and something approaching real time so that they can make the best decisions possible going forward in something approaching real time. This is the world that we live in today. And in order to do that, you need to be able to number one, see it. And number two, you need to be able to analyze it. And number three, you have to be able to make decisions based on what you saw, just some closing observations on. >>And I hope this was interesting for you. I love talking about this stuff. You can probably tell I'm very passionate about it, but the rapid pace of change in the world today is really underscoring the importance. For example, of location intelligence, as a key component of helping businesses to achieve sustainable growth, greater operational effectiveness and resilience, and ultimately your success. So this is really, really critical for retailers to understand and successfully evolving businesses need to accommodate these new consumer shopping behaviors and changes in how products are brought to the market. So that, and in order to do that, they need to be able to see people. They need to be able to see their assets, and they need to be able to see their processes in something approaching real time, and then they need to analyze it. And based on what they've uncovered, they need to be able to make strategic and operational decision making very quickly. This is the new world we live in. It's a real-time world. It's a, it's a sense and respond world and it's the way forward. So, Brent, I hope that was interesting for you. I really enjoyed talking about this, as I said, we'd love to hear a little bit more. >>Hey, Brian, that was excellent. You know, I always love me love hearing from RSR because you're so close to what retailers are talking about and the research that your company pulls together. Um, you know, one of the higher level research articles around, uh, fast data frankly, is the whole notion of IOT, right? And he does a lot of work in this space. Um, what I find fascinating based off the recent research is believe it or not, there's $1.2 trillion at stake in retail per year, between now and 2025. Now, how is that possible? Well, part of it is because the Kinsey captures not only traditional retail, but also QSRs and entertainment then use et cetera. That's considered all of retail, but it's a staggering number. And it really plays to the effect that real-time can have on individual enterprises. In this case, we're talking of course, about retail. >>So a staggering number. And if you think about it from streaming video to sensors, to beacons, RFID robotics, autonomous vehicles, retailers are testing today, even pizza delivery, you know, autonomous vehicle. Well, if you think about it, it shouldn't be that shocking. Um, but when they were looking at 12 different industries, retail became like the number three out of 12, and there's a lot of other big industries that will be leveraging IOT in the next four years. So, um, so retailers in the past have been traditionally a little stodgy about their spend in data and analytics. Um, I think retailers in general have got the religion that this is what it's going to take to compete in today's world, especially in a global economy. And in IOT really is the next frontier, which is kind of the definition of fast data. Um, so I, I just wanted to share just a few examples or exemplars of, of retailers that are leveraging Cloudera technology today. >>So now, so now the paid for advertisement at the end of this, right? So, so, you know, so what bringing to market here. So, you know, across all retail, uh, verticals, you know, if we look at, you know, for example, a well-known global mass virtual retailer, you know, they're leveraging Cloudera data flow, which is our solution to move data from point to point in wicked fast space. So it's open source technology that was originally developed by the NSA. So, um, it is best to class movement of data from an ingest standpoint, but we're also able to help the roundtrip. So we'll pull the sensor data off all the refrigeration units for this particular retailer. They'll hit it up against the product lifecycle table. They'll understand, you know, temperature fluctuations of 10, 20 degrees based on, you know, fresh food products that are in the store, what adjustments might need to be made because frankly store operators, they'll never know refrigeration don't know if a cooler goes down and they'll have to react quickly, but they won't know that 10, 20 degree temperature changes have happened overnight. >>So this particular customer leverages father a data flow understand temperature, fluctuations the impact on the product life cycle and the round trip communication back to the individual department manager, let's say a produce department manager, deli manager, meat manager, Hey, you had, you know, a 20 degree drop in temperature. We suggest you lower the price on these products that we know are in that cooler, um, for the next couple of days by 20%. So you don't have to worry, tell me about freshness issues and or potential shrink. So, you know, the grocery with fresh product, if you don't sell it, you smell it, you throw it away. It's lost to the bottom line. So, you know, critically important and, you know, tremendous ROI opportunity that we're helping to enable there, uh, from a, a leading global drugstore retailer. So this is more about data processing and, you know, we're excited to, you know, the recent partnership with the Vidia. >>So fast data, isn't always at the edge of IOT. It's also about workloads. And in retail, if you are processing your customer profiles or segmentation like intra day, you will ever achieve personalization. You will never achieve one-on-one communications with readers killers or with customers. And why is that? Because customers in many cases are touching your brand several times a week. So taking you a week or longer to process your segmentation schemes, you've already lost and you'll never achieve personalization in frack. In fact, you may offend customers by offering. You might push out based on what they just bought yesterday. You had no idea of it. So, you know, that's what we're really excited about. Uh, again, with, with the computation speed, then the video brings to, to Cloudera, we're already doing this today already, you know, been providing levels, exponential speed and processing data. But when the video brings to the party is course GPU's right, which is another exponential improvement, uh, to processing workloads like demand forecast, customer profiles. >>These things need to happen behind the scenes in the back office, much faster than retailers have been doing in the past. Um, that's just the world we all live in today. And then finally, um, you know, proximity marketing standpoint, or just from an in-store operation standpoint, you know, retailers are leveraging Cloudera today, not only data flow, but also of course our compute and storage platform and ML, et cetera, uh, to understand what's happening in store. It's almost like the metrics that we used to look at in the past in terms of conversion and traffic, all those metrics are now moving into the physical world. If you can leverage computer vision in streaming video, to understand how customers are traversing your store, how much time they're standing in front of the display, how much time they're standing in checkout line. Um, you can now start to understand how to better merchandise the store, um, where the hotspots are, how to in real time improve your customer service. >>And from a proximity marketing standpoint, understand how to engage with the customer right at the moment of truth, right? When they're right there, um, in front of a particular department or category, you know, of course leveraging mobile devices. So that's the world of fast data in retail and just kind of a summary in just a few examples of how folks are leveraging Cloudera today. Um, you know, from an overall platform standpoint, of course, father as an enterprise data platform, right? So, you know, we're, we're helping to the entire data life cycle. So we're not a data warehouse. Um, we're much more than that. So we have solutions to ingest data from the edge from IOT leading practice solutions to bring it in. We also have experiences to help, you know, leverage the analytic capabilities of, uh, data engineering, data science, um, analytics and reporting. Uh, we're not, uh, you know, we're not, we're not encroaching upon the legacy solutions that many retailers have today. >>We're providing a platform, this open source that helps weave all of this mess together that existed retail today from legacy systems because no retailer, frankly, is going to rip and replace a lot of stuff that they have today. Right. And the other thing that Cloudera brings to market is this whole notion of on-prem hybrid cloud and multi-cloud right. So our whole, our whole culture has been built around open source technology as the company that provides most of the source code to the Apache network around all these open source technologies. Um, we're kind of religious about open source and lack of vendor lock-in, uh, maybe to our fault. Uh, but as a company, we pull that together from a data platform standpoint. So it's not a rip and replace situation. It's like helping to connect legacy systems, data and analytics, um, you know, weaving that whole story together to be able to solve this whole data life cycle from beginning to end. >>And then finally, you know, just, you know, I want to thank everyone for joining today's session. I hope you found it informative. I can't say Brian killed course enough. Um, you know, he's my trusted friend in terms of what's going on in the industry. He has much broader reach of course, uh, in talking to a lot of our partners in, in, in, in other, uh, technology companies out there as well. But I really appreciate everyone joining the session and Brian, I'm going to kind of leave it open to you to, you know, any closing comments that you might have based on, you know, what we're talking about today in terms of fast data and retail. >>First of all, thank you, Brent. Um, and this is an exciting time to be in this industry. Um, and I'll just leave it with this. The reason that we are talking about these things is because we can, the technology has advanced remarkably in the last five years. Some of this data has been out there for a lot longer than that in it, frankly wasn't even usable. Um, but what we're really talking about is increasing the cycle time for decisions, making them go faster and faster so that we can respond to consumer expectations and delight them in ways that that make us a trusted provider of their life, their lifestyle needs. So this is really a good time to be a retailer, a real great time to be servicing the retail technology community. And I'm glad to be a part of it. And I was glad to be working with you. So thank you, Brian. >>Yeah, of course, Brian, and one of the exciting things for me to not being in the industry, as long as I have and being a former retailer is it's really exciting for me to see retailers actually spending money on data and it for a change, right? They've all kind of come to this final pinnacle of this is what it's going to take to compete. Um, you know, you know, and I talked to, you know, a lot of colleagues, even, even salespeople within Cloudera, I like, oh, retail, very stodgy, you know, slow to move. That's not the case anymore. Um, you know, religion is everyone's, everyone gets the religion of data and analytics and the value of that. And what's exciting for me to see as all this infusion of immense talent within the industry years ago, Brian, I mean, you know, retailers are like, you know, pulling people from some of the, you know, the greatest, uh, tech companies out there, right? From a data science data engineering standpoint, application developers, um, retail is really getting this legs right now in terms of, you know, go to market and in the leverage of data and analytics, which to me is very exciting. Well, >>You're right. I mean, I, I became a CIO around the time that, uh, point of sale and data warehouses were starting to happen data cubes and all those kinds of things. And I never thought I would see a change that dramatic, uh, as the industry experience back in those days, 19 89, 19 90, this changed doors that, but the good news is again, as the technology is capable, uh, it's, it's, we're talking about making technology and information available to, to retail decision-makers that consumers carry around in their pocket purses and pockets is there right now today. Um, so the, the, the question is, are you going to utilize it to win or are you going to get beaten? That's really what it boils down to. Yeah, >>For sure. Uh, Hey, thanks everyone. We'll wrap up. I know we ran a little bit long, but, uh, appreciate, uh, everyone, uh, hanging in there with us. We hope you enjoyed the session. The archive contact information is right there on the screen. Feel free to reach out to either Brian and I. You can go to cloudera.com. Uh, we even have, you know, joint sponsored papers with RSR. You can download there as well as eBooks and other assets that are available if you're interested. So thanks again, everyone for joining and really appreciate you taking the time. >>Hello everyone. And thanks for joining us today. My name is Brent Bedell, managing director retail, consumer goods here at Cloudera. Cloudera is very proud to be partnering with companies like three soft to provide data and analytic capabilities for over 200 retailers across the world and understanding why demand forecasting could be considered the heartbeat of retail. And what's at stake is really no mystery to most, to most retailers. And really just a quick level set before handing this over to my good friend, uh, Camille three soft, um, you know, IDC Gartner. Um, many other analysts have kind of summed up an average, uh, here that I thought would be important to share just to level set the importance of demand forecasting or retail. And what's at stake. I mean the combined business value for retailers leveraging AI and IOT. So this is above and beyond. What demand forecasting has been in the past is a $371 billion opportunity. >>And what's critically important to understand about demand forecasting. Is it directly impacts both the top line and the bottom line of retail. So how does it affect the top line retailers that leverage AI and IOT for demand forecasting are seeing average revenue increases of 2% and think of that as addressing the in stock or out of stock issue in retail and retail is become much more complex now, and that is no longer just brick and mortar, of course, but it's fulfillment centers driven by e-commerce. So inventory is now having to be spread over multiple channels. Being able to leverage AI and IOT is driving 2% average revenue increases. Now, if you think about the size of most retailers or the average retailer that on its face is worth millions of dollars of improvement for any individual retailer on top of that is balancing your inventory, getting the right product in the right place and having productive inventory. >>And that is the bottom line. So the average inventory reduction, leveraging AI and IOT as the analyst have found, and frankly, having spent time in this space myself in the past a 15% average inventory reduction is significant for retailers not being overstocked on product in the wrong place at the wrong time. And it touches everything from replenishment to out-of-stocks labor planning and customer engagement for purposes of today's conversation. We're going to focus on inventory and inventory optimization and reducing out-of-stocks. And of course, even small incremental improvements. I mentioned before in demand forecast accuracy have millions of dollars of direct business impact, especially when it comes to inventory optimization. Okay. So without further ado, I would like to now introduce Dr. Camille Volker to share with you what his team has been up to. And some of the amazing things that are driving at top retailers today. So over to you, Camille, >>Uh, I'm happy to be here and I'm happy to speak to you, uh, about, uh, what we, uh, deliver to our customers. But let me first, uh, introduce three soft. We are a 100 person company based in Europe, in Southern Poland. Uh, and we, uh, with 18 years of experience specialized in providing what we call a data driven business approach, uh, to our customers, our roots are in the solutions in the services. We originally started as a software house. And on top of that, we build our solutions. We've been automation that you get the software for biggest enterprises in Poland, further, we understood the meaning of data and, and data management and how it can be translated into business profits. Adding artificial intelligence on top of that, um, makes our solutions portfolio holistic, which enables us to realize very complex projects, which, uh, leverage all of those three pillars of our business. However, in the recent time, we also understood that services is something which only the best and biggest companies can afford at scale. And we believe that the future of retail, uh, demon forecasting is in the product solutions. So that's why we created occupy our AI platform for data driven retail. That also covers this area that we talked about today. >>I'm personally proud to be responsible for our technology partnerships with other on Microsoft. Uh, it's a great pleasure to work with such great companies and to be able to, uh, delivered a solution store customers together based on the common trust and understanding of the business, uh, which cumulates at customer success at the end. So why, why should you analyze data at retail? Why is it so important? Um, it's kind of obvious that there is a lot of potential in the data per se, but also understanding the different areas where it can be used in retail is very important. We believe that thanks to using data, it's basically easier to the right, uh, the good decisions for the business based on the facts and not intuition anymore. Those four areas that we observe in retail, uh, our online data analysis, that's the fastest growing sector, let's say for those, for those data analytics services, um, which is of course based on the econ and, uh, online channels, uh, availability to the customer. >>Pandemic only speeds up this process of engagement of the customers in that channel, of course, but traditional offline, um, let's say brick and mortar shops. Uh, they still play the biggest role for most of the retailers, especially from the FMCG sector. However, it's also very important to remember that there is plenty of business, uh, related questions that meet that need to be answered from the headquarter perspective. So is it actually, um, good idea to open a store in a certain place? Is it a good idea to optimize a stock with Saturday in producer? Is it a good idea to allocate the goods to online channel in specific way, those kinds of questions they are, they need to be answered in retail every day. And with that massive amount of factors coming into that question, it's really not, not that easy to base, only on the intuition and expert knowledge, of course, uh, as Brent mentioned at the beginning, the supply chain and everything who's relates to that is also super important. We observe our customers to seek for the huge improvements in the revenue, just from that one single area as well. Okay. >>So let me present you a case study of one of our solutions, and that was the lever to a leading global grocery retailer. Uh, the project started with the challenge set of challenges that we had to conquer. And of course the most important was how to limit overstocks and out of stocks. Uh, that's like the holy grail in of course, uh, how to do it without flooding the stores with the goods and in the same time, how to avoid empty shelves, um, from the perspective of the customer, it was obvious that we need to provide a very well, um, a very high quality of sales forecast to be able to ask for, uh, what will be the actual sales of the individual product in each store, uh, every day, um, considering huge role of the perishable goods in the specific grocery retailer, it was a huge challenge, uh, to provide a solution that was able to analyze and provide meaningful information about what's there in the sales data and the other factors we analyzed on daily basis at scale, however, uh, our holistic approach implementing AI with data management, uh, background, and these automation solutions all together created a platform that was able to significantly increase, uh, the sales for our customer just by minimizing out of stocks. >>In the same time we managed to not overflow the stock, the shops with the goods, which actually decreased losses significantly, especially on the fresh fruit. >>Having said that this results of course translate into the increase in revenue, which can be calculated in hundreds of millions of dollars per year. So how the solution actually works well in its principle, it's quite simple. We just collect the data. We do it online. We put that in our data lake, based on the cloud, there are technology, we implement our artificial intelligence models on top of it. And then based on the aggregated information, we create the forecast and we do it every day or every night for every single product in every single store. This information is sent to the warehouses and then the automated replenishment based on the forecast is on the way the huge and most important aspect of that is the use of the good tools to do the right job. Uh, having said that you can be sure that there is too many information in this data, and there is actually two-minute forecast created every night that any expert could ever check. >>This means our solution needs to be, uh, very robust. It needs to provide information with high quality and high porosity. There is plenty of different business process, which is on our forecast, which need to be delivered on time for every product in each individual shop observing the success of this project and having the huge market potential in mind, we decided to create our QB, which can be used by many retailers who don't want to create a dedicated software for that. We'll be solving this kind of problem. Occupy is, uh, our software service offering, which is enabling retailers to go data driven path management. >>We create occupant with retailers, for retailers, uh, implementing artificial intelligence, uh, on top of data science models created by our experts, uh, having data, data analysis in place based on data management tools that we use we've written first, um, attitude. The uncertain times of pandemic clearly shows that it's very important to apply correction factors, which are sometimes required because we need to respond quickly to the changes in the sales characteristics. That's why occupy B is open box solution, which means that you basically can implement that in your organization. We have without changing the process internally, it's all about mapping your process into this into the system, not the other way around the fast trends and products, collection possibilities allow the retailers to react to any changes, which are pure in the sales every day. >>Also, it's worth to mention that really it's not only FMCG. And we believe that different use cases, which we observed in fashion health and beauty, common garden pharmacies and electronics, flavors of retail are also very meaningful. They also have one common thread. That's the growing importance of e-commerce. That's why we didn't want to leave that aside of occupant. And we made everything we can to implement a solution, which covers all of the needs. When you think about the factors that affect sales, there is actually huge variety of data and that we can analyze, of course, the transactional data that every dealer possesses like sales data from sale from, from e-commerce channel also, uh, averaging numbers from weeks, months, and years makes sense, but it's also worth to mention that using the right tool that allows you to collect that data from also internal and external sources makes perfect sense for retail. Uh, it's very hard to imagine a competitive retailer that is not analyzing the competitor's activity, uh, changes in weather or information about some seasonal stores, which can be very important during the summer during the holidays, for example. Uh, but on the other hand, um, having that information in one place makes the actual benefit and environment for the customer. >>Okay. Demon forecasting seems to be like the most important and promising use case. We can talk about when I think about retail, but it's also their whole process of replenishment that can cover with different sets of machine learning models. And they done management tools. We believe that analyzing data from different parts of the retail, uh, replenishment process, uh, can be achieved with implementing a data management solution based on caldera products and with adding some AI on top of it, it makes perfect sense to focus on not only demand forecasting, but also further use cases down the line when it comes to the actual benefits from implementing solutions for demand management, we believe it's really important to analyze them holistically. First is of course, out of stocks, memorization, which can be provided by simply better sales focus, but also reducing overstocks by better inventory management can be achieved in, in the same time. Having said that we believe that analyzing data without any specific new equipment required in point of sales is the low hanging fruit that can be easily achieved in almost every industry in almost every regular customer. >>Hey, thanks, Camille, having worked with retailers in this space for a couple of decades, myself, I was really impressed by a couple of things and they might've been understated, frankly. Um, the results of course, I mean, you, you know, as I kind of set up this session, you doubled the numbers on the statistics that the analysts found. So obviously in customers you're working with, um, you know, you're, you're doubling average numbers that the industry is having and, and most notably how the use of AI or occupy has automated so many manual tasks of the past, like tour tuning, item profiles, adding new items, et cetera. Uh, and also how quickly it felt like, and this is my, my core question. Your team can cover, um, or, or provide the solution to, to not only core center store, for example, in grocery, but you're covering fresh products. >>And frankly, there are, there are solutions out on the market today that only focus on center store non-perishable department. So I was really impressed by the coverage that you're able to provide as well. So can you articulate kind of what it takes to get up and running and your overall process to roll out the solution? I feel like based on what you talked about, um, and how you were approaching this in leveraging AI, um, that you're, you're streamlining processes of legacy demand, forecasting solutions that required more manual intervention, um, how quickly can you get people set up and what is the overall process like to get started with soft? >>Yeah, it's usually it takes three to six months, uh, to onboard a new customer to that kind of solution. And frankly it depends on the data that the customer, uh, has. Uh, usually it's different, uh, for smaller, bigger companies, of course. Uh, but we believe that it's very important to start with a good foundation. The platform needs to be there, the platform that is able to, uh, basically analyze or process different types of data, structured, unstructured, internal, external, and so on. But when you have this platform set, it's all about starting ingesting data there. And usually for a smaller companies, it's easier to start with those, let's say, low hanging fruits. So the internal data, which is there, this data has the highest veracity is already easy to start with, to work with them because everyone in the organization understands this data for the bigger companies. It might be important to ingest also kind of more unstructured data, some kind of external data that need to be acquired. So that may, that may influence the length of the process. But we usually start with the customers. We have, uh, workshops. That's very important to understand their business because not every deal is the same. Of course, we believe that the success of our customers comes also due to the fact that we train those models, those AI models individually to the needs of our >>Totally understand and POS data, every retailer has right in, in one way shape or form. And it is the fundamental, uh, data point, whether it's e-comm or the brick and mortar data, uh, every retailer has that data. So that, that totally makes sense. But what you just described was bunts. Um, there are, there are legacy and other solutions out there that this could be a, a year or longer process to roll out to the number of stores, for example, that you're scaling to. So that's highly impressive. And my guess is a lot of the barriers that have been knocked down with your solution are the fact that you're running this in the cloud, um, you know, on, from a compute standpoint on Cloudera from a public cloud stamp point on Microsoft. So there's, there's no, it intervention, if you will, or hurdles in preparation to get the database set up and in all of the work, I would imagine that part of the time-savings to getting started, would that be an accurate description? >>Yeah, absolutely. Uh, in the same time, this actually lowering the business risks, because we simply take data and put that into the data lake, which is in the cloud. We do not interfere with the existing processes, which are processing this data in the combined. So we just use the same data. We just already in the company, we ask some external data if needed, but it's all aside of the current customers infrastructure. So this is also a huge gain, as you said, right? >>And you're meeting customers where they are. Right. So, as I said, foundationally, every retailer POS data, if they want to add weather data or calendar event data or, you know, want incorporate a course online data with offline data. Um, you have a roadmap and the ability to do that. So it is a building block process. So getting started with, for data, uh, as, as with POS online or offline is the foundational component, which obviously you're very good at. Um, and then having that ability to then incorporate other data sets is critically important because that just improves demand, forecast accuracy, right. By being able to pull in those, those other data sources, if you will. So Camille, I just have one final question for you. Um, you know, there, there are plenty of not plenty, but I mean, there's enough demand forecasting solutions out on the market today for retailers. One of the things that really caught my eye, especially being a former retailer and talking with retailers was the fact that you're, you're promoting an open box solution. And that is a key challenge for a lot of retailers that have, have seen black box solutions come and go. Um, and especially in this space where you really need direct input from the, to continue to fine tune and improve forecast accuracy. Could you give just a little bit more of a description or response to your approach to open box versus black box? >>Yeah, of course. So, you know, we've seen in the past the failures of the projects, um, based on the black box approach, uh, and we believe that this is not the way to go, especially with this kind of, uh, let's say, uh, specialized services that we provide in meaning of understanding the customer's business first and then applying the solution, because what stands behind our concept in occupy is the, basically your process in the organization as a retailer, they have been optimized for years already. That's where retailers put their, uh, focus for many years. We don't want to change that. We are not able to optimize it properly. For sure as it combined, we are able to provide you a tool which can then be used for mapping those very well optimized process and not to change them. That's our idea. And the open box means that in every process that you will map in the solution, you can then in real time monitor the execution of those processes and see what is the result of every step. That way we create truly explainable experience for our customers, then okay, then can easily go for the whole process and see how the forecast, uh, was calculated. And what is the reason for a specific number to be there at the end of the day? >>I think that is, um, invaluable. Um, can be, I really think that is a differentiator and what three soft is bringing to market with that. Thanks. Thanks everyone for joining us today, let's stay in touch. I want to make sure to leave, uh, uh, Camille's information here. Uh, so reach out to him directly or feel free at any, any point in time, obviously to reach out to me, um, again, so glad everyone was able to join today, look forward to talking to you soon.

>> Narrator: From theCUBES studios in Palo Alto in Boston connecting with thought leaders all around the world. This is theCUBE conversation. >> Hello and welcome to the 2021 AWS global public sector partner awards. I'm your host Natalie Erlich. Today we're going to highlight the most valuable valuable Amazon connect appointment. And we are now joined by Steve Careful, adult social care expert PA consulting group and Graham Allen, the director of adults health and care at Hampshire county council. Welcome gentlemen to today's session. >> Thank you Natalie >> I love you Natalie. >> Well by now we are really familiar the call to shelter in place and how it especially affected the most vulnerable of people. Give us some experience or some insight on your experience with that, especially in light of some of the technology that was deployed. Let's start with you, Graham. >> Yeah, Thank you. So just by way of context, Hampshire county council is one of the largest areas of local government in England. So we have a population of 1.4 million people. And when a lockdown was imposed by the national government of England in the 23rd of March 2020. Shortly thereafter the evidence in terms of vulnerabilities around COVID-19 strongly identified that people with a range of clinical conditions were most vulnerable and needed to shield and self issolate. And for the size of our population, we quickly were advised that roughly some 30,000 people in the initial carts because of political vulnerabilities needed to sheild and receive a variety of support shortly after that through the summer of 2020 that number increased some 50,000. And then by January of this year that number further increased based on the scientific and medical evidence to 83,000 people in total. So that represented a huge challenge for us in terms of offering support, being able to make sure that not only practical tasks related to obtaining shopping food and so on and so forth, but also medications but also the real risks of self isolation. Many of the people that we were needing to support when here the two known to us as a social care provider. They were being advised through clinical medical evidence needs and many of those people lived alone. So the real risk of self isolation not seeing anyone potentially for an extended period of time and the risks of their wellbeing was something very significant to us. So we needed very rapidly to develop a solution in terms of making contact, being able to offer that support. >> Yeah and I'd love it now to get your take Steve on how PA consulting group helped deliver on that call on that need. >> True so we have an existing relationship with Graham and the council, we've been working together for number of years, delivering care technology solutions to service users around the county. We were obviously aware there was a major issue as COVID and lockdown began. So we sat down with Graham and his colleagues to ask what we could do to help. We used our relationship with AWS and our knowledge of the connect platform to suggest a mechanism for making outbound calls really at scale. And that was the beginning of the process. We were very quickly in a position where we were able to actually get that service running live. In fact, we had a working prototype within four days and a live service in seven days. And from that point on of those many thousands of people that Graham's alluded to, we were calling up to two and a half thousand a day to ask them did they need any help? Were they okay? If they did need help, If they responded yes, to those, to that question we were then able to put them through to a conventional call handler in our call center where a conversation could take place about what their needs were. And as Graham said, in many cases that was people who couldn't get out to get food shopping, people who were running short of clinical medical supplies, people who needed actually some interesting things pet care came up quite often people who couldn't leave the house home and look after their dog, they just needed some help locally. So we had to integrate with local voluntary services to get those those kinds of results and support delivered to them across the whole of Hampshire and ultimately throughout the whole of the COVID experience. So coming right up until March of this year. >> Right well, as the COVID pandemic progressed and, you know evolved in different stages, you know, with variants and a variety of different issues that came up over the last year or so, you know how did the technology develop how did the relationship develop and, you know tell us about that process that you had with each other. >> So the base service remained very consistent that different points in the year, when there were different issues that may be needed to be communicated to to the service users we were calling we would change and update the script. We would improve the logistics of the service make it simpler for colleagues in the council to get the data into the system, to make the calls. And basically we did that through a constant series of meetings checkpoint, staying in touch and really treating this as a very collaborative exercise. So I don't think for all of us COVID was a constant stream of surprises. Nobody could really predict what was going to happen in a week or a month. So we just have to all stay on our toes keep in touch and be flexible. And I think that's where our preferred way of working and that of AWS and the Hampshire team we were working with we really were able to do something that was special and I'm very fleet of foot and responsive to needs. >> Right and I'd also love to get Graham's insight on this as well. What of results have you seen, you know do you have any statistics on the impact that it made on people? Did you receive any qualitative feedback from the people that use the service? >> Yeah, no, absolutely. We did. And one of the things we were very conscious of from day one was using a system which may have been unfamiliar to people when the first instance in terms of receiving calls, the fact that we were able to use human voice within the call technology, I think really, really assisted. We also did a huge amount of work within a Hampshire county council. Clearly in terms of the work we do day in, day out we're well-known to our local population. We have a huge range of different responsibilities ranging from maintenance of the roads through to the provision of local services, like libraries and so on and so forth, and also social care support. So we were able to use all of that to cover last. And Steve has said through working very collaboratively together with a trusted brand Hampshire county council working with new technology. And the feedback that we received was both very much data-driven in real time, in terms of successful calls and also those going through to call handlers and then the outcomes being delivered through those call handlers to live services out and about around the county but also that qualitative impact that we had. So across Hampshire county council we have some 76 elected members believe me they were very active. They were very interested in the work that we were doing in supporting our most vulnerable residents. And they were receiving literally dozens of phone calls as a thank you by way of congratulating. But as I say, thanking us and our partners PA at district council partners and also the voluntary community sector in terms of the very real support that was being offered to residents. So we had a very fully resolved picture of precisely what was happening literally minute by minute on a live dashboard. In terms of outgoing calls calls going through the call handlers and then successful call completion in terms of the outcomes that were being delivered on the ground around the County of Hampshire. So a phenomenally successful approach well appreciated and well, I think applauded by all those receiving calls. >> Terrific insight. Well, Steve, I'd love to hear from you more about the technology and how you put the focus on the patient on the person really made it more people focused and you know, obviously that's so critical in such a time of need. >> Yeah, you're absolutely right, Natalie. We, I think what we were able to do because I myself and my immediate team have worked with Hampshire and other local authorities on the social care side for so long. We understood the need to be very person focused. I think sometimes with technology, it comes in with it with a particular way of operating that isn't necessarily sensitive to the audience. And we knew we had to get this right from day one. So Graham's already mentioned the use of human voice invoicing the bulk call. that was very, very important. We selected a voice actress who had a very reassuring clear tone recognizing that many of the individuals we were calling would have been would have been older people maybe a little hard of hearing. We needed to have the volume in the call simple things like this were very important. One of the of the debates I remember having very early on was the choice as to whether the response that somebody would give to the question, do you need this? Or that could be by pressing a digital on the phone. We understood that again, because potentially of frailty maybe a little lack of dexterity amongst some of the people we'd be calling that might be a bit awkward for them to take the phone away from their face and find the button and press the button in time. So we pursued the idea of an oral response. So if you want this say, yes if you don't want it to say no and those kinds of small choices around how the technology was deployed I think made a really big difference in terms of of acceptance and adoption and success in the way the service run. >> Terrific. Well Graham I'd like to shift it to you. Could you give us some insight on the lessons that you learned as a result of this pandemic and also trying to move quickly to help people in your community? >> Yeah, I think the lessons in some of the lessons that we've, again learned through our response to the pandemic, are lessons that to a degree have traveled with us over a number of years in terms of the way that we've used technology over a period, working with PA, which is be outcome focused. It's sometimes very easy to get caught up in a brilliant new piece of technology. But as Steve has just said, if it's not meeting the need if we're not thinking about that human perspective and thinking about the humanity and the outcomes that we're seeking to deliver then to some degree it's going to fail And this might certainly did not fail in any way shape or form because of the thoughtfulness that was brought forward. I think what we learned from it is how we can apply that as we go forward to the kinds of work that we do. So, as I've already said we've got a large population, 1.4 million people. We are moving from some really quite traditional ways of responding to that population, accelerated through our response to COVID through using AI technologies. Thinking about how we embed that more generally would a service offer not only in terms of supporting people with social care needs but that interface between ourselves and colleagues within the health sector, the NHS to make sure that we're thinking about outcomes and becoming much more intuitive in terms of how we can engage with our population. It's also, I think about thinking across wider sectors in terms of meeting people's needs. One of the, I think probably unrealized things pre COVID was the using virtual platforms of various kinds of actually increased engagement with people. We always thought in very traditional ways in order to properly support our population we must go out and meet them face to face. What COVID has taught us is actually for many people the virtual world connecting online, having a variety of different technologies made available to support them in their daily living is something that they've absolutely welcomed and actually feel much safer through being able to do the access is much more instant. You're not waiting for somebody to call. You're able to engage with a trusted partner, you know face-to-face over a virtual platform and get an answer more or less then and there. So I think there's a whole range of opportunities that we've learned, some of which we're already embedding into our usual practice. If I can describe anything over the last 15 months as usual but we're taking it forward and we hope to expand upon that at scale and at pace. >> Yeah, that's a really excellent point about the rise of hybrid care, both in the virtual and physical world. What can we expect to see now, moving forward like to shift over to our other guests, you know, what do you see next for technology as a result of the pandemic? >> Well, there's certainly been an uptake in the extent to which people are comfortable using these technologies. And again, if you think about the kind of target group that Graham and his colleagues in the social care world are dealing with these are often older people people with perhaps mobility issues, people with access issues when it comes to getting into their GP or getting into hospital services. The ability for those services to go out to them and interact with them in a much more immediate way in a way that isn't as intrusive. It isn't as time consuming. It doesn't involve leaving the house and finding a ways on public transport to get to see a person who you're going to see for five minutes in a unfamiliar building. I think that that in a sense COVID has accelerated the acceptance that that's actually pretty good for some people. It won't suit everybody and it doesn't work in every context, but I think where it's really worked well and works is a great example of that. Is in triaging and prioritizing. Ultimately the kinds of resources Graham's talked about the people need to access the GPs and the nurses and the care professionals are in short supply. Demand will outstrip will outstrip supply. therefore being able to triage and prioritize in that first interaction, using a technology ruse enables you to ensure you're focusing your efforts on those who've got the most urgent or the greatest need. So it's a kind of win all around. I think there's definitely been a sea change and it's hard to see hard to see people going back just as the debate about, will everybody eventually go back to offices, having spent a working at home? You know, I think the answer is invariably going to be no, some will but many won't. And it's the same with technology. Some will continue to interact through a technology channel. They won't go back to the face-to-face option that they had previously. >> Terrific. Well, thank you both very much. Steve Careful PA consulting group and Graham Allen Hampshire county council really appreciate your, your insights on how this important technology helped people who were suffering in the midst of the pandemic. Thank you. >> Steve: You're welcome. >> Graham: Thank you. >> Well, that's all for this session. Thank you so much for watching. (upbeat music)

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

>>Mhm Welcome to the cubes coverage of HP discover 2021. I'm your host lisa martin. I've got a couple of guests with me here from emphasis. Alumni Yuma lacks empathy. Is back. Senior vice president and regional head of EMEA emphasis Yuma. It's great to see you welcome back to the program. >>Yeah. Hi Liza. It's great to be back for discover 2021. It's been a great opportunity to meet with health, a lot of our stakeholders and HP. >>Excellent. We're gonna dig into that. And so do Cutie is here as well. The Cto Cloud Advisory, VP hybrid cloud engineering platforms and automation at emphasis. Sergey Welcome to the program. >>Thank you lisa. It's a pleasure to be in the program is my first time but I really enjoy it. Well, >>Welcome. Welcome. So the next 15 minutes or so we're gonna unpack a survey that was just done as we know cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is a hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >>Uh lisa if you uh if you look at the pre covid scenario and what there are many, many clients which actually made a significant move into cloud, but there were many few, a few of the companies who didn't really take a mature uh cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the when the covid hit them because they were able to be very resilient. But at the same time they were able to the cloud adoption really help them to improve their business profits. Uh When we did this cloud radar survey across all the geography is we didn't get across the U. S. The latin, the issue pacific the EMEA markets. And when we looked at uh what our clients and enterprises were able to recover and get all of this whole cloud adoption. We've we've got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >>Yeah, that's huge enterprises. The survey found can add up to you said 414 billion and that new profits annually through effective cloud adoption and sticking with you for a second. What does emphasis described as effective cloud adoption? >>When we look at cloud adoption, we have enterprises who started shifting workloads which are very comfortable for them. And then uh then they started to take the more mature understanding of moving workloads which were very critical to the business. So when we look at effective, it is a combination of both the ones that were very easy to go to the cloud, the ones that made business is able to bring in new applications and new go to markets uh to their segments to their clients. But then it is also about taking some of those legacy world clothes and making a choice the right choice to take it by transforming those applications and environments uh, into the cloud direction. And that's what we call us effective. It's just not the easy ones but also those complex and legacy rebuild ones that that effectively goes on to transform itself into a new way for the for their clients and for the experience of the users. >>It's a big changes coming, big opportunities. So as we see, we've talked about this for many times, more and more companies moving to multi cloud arrangements for a variety of reasons, what have been some of the things that emphasis has experienced and what are some of your viewpoints on a multi cloud? >>Thank you, lisa. So, um, if you look around >>right, you know, hybrid >>cloud has been the new normal. Right? And um, and if you look at it, private cloud is becoming an essential component for hosting applications. You know, uh you know, when you look at it, it's more about applications which have low latency requirements, it has regulatory requirements or it has a static demand of infrastructure. Now, what emphasis has done in this space is is that, you know, we have um we have developed a framework which we call it as a right cloud solution framework >>and this is >>focused on implementing a hybrid, multi cloud leveraging and in house developed tools and frameworks as well as platforms along with our strategic partner ecosystem, >>that is our biggest contribution >>onto the hybrid multi cloud world. Now, the foundation of our framework is emphasis public cloud platform. It's a unified multi cloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environment. It can be a private, it can be public or it can be on the edge. >>Now, apart from all of these >>things, it also offers features and functionalities very similar to the hyper scholars and either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems. It brings all of these functionalities >>seamlessly >>across the >>multiple hybrid >>ecosystem protect. That's the biggest contribution from emphasis in this space. >>Got it. Okay. As we see the just clear growth of multi cloud in every industry. Talk to us about what the cloud radar survey uncovered with respective you've mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >>So I did mention about it uh Liza in in the previous question as well. Then we looked at when we look at enterprises trying to take the cloud adoption. The big benefits for the enterprises do happen when they crossed that uh layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for their customers, for their end users and internally as well for their various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of uh boosting profits for them at the same time, cutting costs within the, within the internal stakeholders. So at one stage you're optimizing your cost at another stage, you're bringing in a easiness for your clients to operate on, which is actually creating that enlarged profit boost. >>We're sticking with you for a second. If we unpack that growth, that business profit growth opportunity that the survey uncovered, Are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >>So, if you if you look at uh traditionally cloud was considered uh the enabler for quick, faster time to market. But now cloud has become the central theme for resilience. If you look at the covid pandemic, uh, those, those enterprises which were already cloud enabled, we're able to resiliently and sustain their business and grow their businesses. So as economy started opening up, if I can talk about an automotive client who is today enriching businesses out of china because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at Today enterprises are in somewhere around 17-18% of of cloud adopt mint and if they can take that to the 40%, that's when they will see that kind of boosted profits. And we can clearly see about $400 plus billion dollars of profits that enterprises can make. >>All right, so let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud. Talk to me about how the experience of working with HP in creating joint solution suites is going to help the customers facilitate and drive that transformation. >>Thank you lisa. So if you look at H P E, H P E comes with a fine set of technology and commercial constructs, you know, that complements our right cloud framework >>and they offer >>the solutions. The whole sort of a lot of solutions offer private cloud as a service which is a major component of our right club framework. >>Either it is a >>continuous service with HP is as ephemeral data platform on HP hardware, or >>Vida as a >>service based on a compose Herbal and Converse infrastructure or H P. S cloud built on >>HPC cloud, build on Cray systems >>and all of them commercially supported with an H. P. S. Green leg offering makes it very attractive for our customers. Now, these integrations have helped us in providing a >>very similar >>metering and billing along with the chargeback solutions, very much in line with what is being provided by Hyper scholars. Apart from this, we >>also work very closely with >>H P E >>to create a >>very compelling sourcing strategy for driving hybrid, cloud driven digital transformation while taking cost out and protecting the existing investments through various financial models for our customers, helping them in terms of transforming their digital estate in the, in the new cloud world. >>And um, I want to get your perspective as well, the HP emphasis partnership talk to me about that being a win win for your clients in every industry. >>So actually uh Liza is a great question and this probably is my third uh cube interview and I've told this previously as well in my previous interviews as well. The relationship between emphasis and hedge P. Is very very strategy and it's it's very very top down driven. And today we've seen very high transformative opportunities that two organizations have come together and we won't call it win win but we call it a win win win which is essentially win for HP win for emphasis but even for the clients as well. So if you look at some of the engagements that we have jointly done, everything has been transformative. I can talk about uh energy client where we've done a huge which will V. D. I. Uh engagement with them where we have been able to take them very uh seamlessly when the covid pandemic hit them so that there are significant part of their right to users but be able to operate from their residences. Uh I can talk about a great story about how we had enabled Green Lake for a wind energy company. Uh and how that Green Lake capability help the customer to migrate the application seamlessly uh to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector where we've really done work very closely with PHP across all regions and all geography is uh to make this what I would call when when very partnership. >>I like that when when when who wouldn't want that one more question for you. Talk to me about the next, as we talked about some of those survey results and I think folks can find that survey, the cloud radar survey on the emphasis dot com website. I found it on the homepage there. But looking at how much Transformation is expected in the next 12 months or so, what are some of the things that we can expect from emphasis on H. P. E. to help drive and catalyze that growth that you expect to see in the next 12 months? >>Yeah. And I was talking to you before this interview and you said that yes, we gotta look at this. And I was feeling very happy that you have the opportunity to look at the side. And you said that look there's an opportunity to also make to continuously provide feedback. And we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We have a big learning company Uh and when it comes to uh the next 12 months of agenda, I think the pipeline is very robust for both us and the hp. In terms of the way we want to take proactive transformational opportunities to the to our clients create a value differentiation on the hybrid cloud for them. And uh clearly uh this this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth uh in the cloud space for both emphasis and H. B. >>Excellent. Well gentlemen, thank you for joining me today, talking to me about what emphasis and HP are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >>Thank you lisa. Thank you. Thank you for giving us this >>opportunity. Absolutely. For election. Saw ju I'm lisa martin. You're watching the cubes coverage of HP discover 2021. Yeah. Mhm. Yeah.

>>Welcome to the cubes coverage of HP discover 2021. I'm your host lisa martin. I've got a couple of guests with me here from emphasis. Alumni Yuma lacks empathy. Is back. Senior vice president and regional head of EMEA emphasis Yuma. It's great to see you welcome back to the program. >>Yeah. Hi Liza. It's great to be back for discover 2021. It's been a great opportunity to meet with a lot of our stakeholders and hp. >>Excellent. We're gonna dig into that. And so do Cutie is here as well. The CTO Cloud Advisory, VP hybrid cloud engineering platforms and automation at emphasis Sergey Welcome to the program. >>Thank you lisa. It's a pleasure to be in the program is my first time but I really enjoy it. Well >>Welcome. Welcome. So the next 15 minutes or so we're gonna unpack a survey that was just done as we know cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is a hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >>Uh lisa if you uh if you look at the pre covid scenario and what there are many, many clients which actually made a significant move into cloud, but there were many few, a few of the companies who didn't really take a mature uh cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the when the covid hit them because they were able to be very resilient, but at the same time they were able to the cloud adoption really help them to improve their business profits. Uh When we did this cloud reader survey across all the geography is we didn't get across the U. S. The latin, the issue pacific the email markets. And when we looked at uh what our clients and enterprises were able to recover and get all of this whole cloud adoption. We've got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >>Yeah, that's huge. Enterprises the survey found can add up to you said 414 billion and that new profits annually through effective cloud adoption and sticking with you for a second. What does emphasis described as effective cloud adoption? >>When we look at cloud adoption, we have enterprises who started shifting workloads which are very comfortable for them. And then uh then they started to take the more mature understanding of moving workloads which were very critical to the business. So when we look at effective, it is a combination of both the ones that were very easy to go to the cloud, the ones that made business is able to bring in new applications and new, go to markets uh, to their segments to their clients. But then it is also about taking some of those legacy world clothes and making a choice the right choice to take it by transforming those applications and environments uh, into the cloud direction. And that's what we call as effective. It's just not the easy ones, but also those complex and legacy rebuild ones that that effectively goes on to transform itself into a new way for the for their clients and for the experience of the users. >>It's a big changes coming, big opportunities. We see, we've talked about this for many times more and more companies moving to multi cloud arrangements for a variety of reasons. What have been some of the things that emphasis has experienced and what are some of your viewpoints on a multi cloud? >>Thank you, lisa. So, um, if you look around right, you know, hybrid cloud has been the new normal. Right? And um and if you look at it, private cloud is becoming an essential component for hosting applications. You know, uh you know, when you look at it, it's more about applications which have low latency requirements, you know, it has regulatory requirements or it has a static demand of infrastructure. Now, what emphasis has done in this space is is that, you know, we have um we have developed a framework which we call it as a right loud solution framework and this is focused on implementing a hybrid multi cloud leveraging an in house developed tools and frameworks as well as platforms along with our strategic Puerto rico system, that is our biggest contribution onto the hybrid multi cloud world. Now, the foundation of our framework is emphasis Polly cloud platform. It's a unified multi cloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environment. It can be a private, it can be public or it can be on the edge. Now, apart from all of these things, it also offers features and functionality is very similar to the hyper scholars and either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems. It brings all of these functionalities seamlessly across the multiple hybrid ecosystem. That's the biggest contribution from emphasis in this space. >>Got it. Okay. As we see the just clear growth of multi cloud in every industry. Talk to us about what the cloud radar survey uncovered with respective you mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >>So I did mention about it uh lisa in in the previous question as well. When we looked at when we look at enterprises trying to take the cloud adoption, the big benefits for the enterprises do happen when they crossed that uh layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for their customers for their end users and internally as well for their various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of uh boosting profits for them at the same time, cutting costs within the, within the internal stakeholders. So at one stage you're optimizing your cost at another stage, you're bringing in the easiness for your clients to operate on, which is actually creating that enlarged profit boost. >>I'm sticking with you for a second. If we unpack that growth, that business profit growth opportunity that you the survey uncovered, Are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >>So, if you if you look at uh traditionally cloud was considered uh the enabler for quick, faster time to market. But now cloud has become the central theme for resilience. If you look at the covid pandemic, uh, those, those enterprises which were already cloud enabled, we're able to resiliently and sustain their business and grow their businesses. So as economy started opening up, if I can talk about an automotive client who is today enriching businesses out of china because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at Today, enterprises are in somewhere around 17-18% of of cloud adopt mint and if they can take that to the 40%, that's when they will see that kind of boosted profits. And we can clearly see about $400 plus billion dollars of profits that enterprises can make. >>All right, so let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud. Talk to me about how the experience of working with HP in creating joint solution suites is going to help the customers facilitate and drive that transformation. >>Thank you lisa. So if you look at H P E, H P E comes with a fine set of technology and commercial constructs, you know, that complements our right cloud framework and they offer the solutions. The whole sort of a lot of solutions offer private cloud as a service which is a major component of our right club framework. Either it is a continuous service with HP is is immoral data platform on HP hardware or video as a service based on a compose Herbal and Converse infrastructure or H. P. S cloud built on HPC cloud, build on Cray systems and all of them commercially supported with an H. P. S. Green leg offering makes it very attractive for our customers. Now, these integrations have helped us in providing a very similar metering and billing along with the chargeback solutions, very much in line with what is being provided by Hyper scholars. Apart from this, we also work very closely with H. P. E to create a very compelling sourcing strategy for driving hybrid cloud driven digital transformation while taking cost out and protecting the existing investments through various financial models for our customers, helping them in terms of transforming their digital estate in the, in the new cloud world. >>And um, I want to get your perspective as well. The HP emphasis partnership talk to me about that being a win win for your clients in every industry. >>So actually uh Visa is a great question and this probably is my third uh cube interview and I've told this previously as well in my previous interviews as well, the relationship between emphasis and hedge P is very very strategy and it's it's very very top down driven. And today we've seen very high transformative opportunities that two organizations have come together and we won't call it win win, but we call it a win win win, which is essentially win for HPV win for emphasis, but even for the clients as well. So if you look at some of the engagements that we have jointly done, everything has been transformative. I can talk about uh energy client where we've done a huge which will be D I uh engagement with them, where we have been able to take them very uh seamlessly when the covid pandemic hit them so that there are significant part of their right to users but be able to operate from their residences. I can talk about a great story about how we had enabled Green Lake for a wind energy company. Uh and how that Green Lake capability help the customer to migrate the application seamlessly uh to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector, where we've really done work very closely with HP across all regions and all geography is uh to make this what I would call a win win win partnership. >>I like that when when when who wouldn't want that. One more question for you talk to me about the next, as we talked about some of those survey results and I think folks can find that survey the cloud radar survey on the emphasis dot com website. I found it on the homepage there. But looking at how much Transformation is expected in the next 12 months or so, what are some of the things that we can expect from emphasis on H. P. E. to help drive and catalyze that growth that you expect to see in the next 12 months? >>Yeah. And I was talking to you before this interview and you said that yes, we gotta look at this. And I was feeling very happy that you have the opportunity to look at the side. And you said that look there's an opportunity to also make to continuously provide feedback. And we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We have a big learning company Uh and when it comes to uh the next 12 months of agenda, I think the pipeline is very robust for both us and the hp. In terms of the way we want to take proactive transformational opportunities to the to our clients create a value differentiation on the hybrid cloud for them. And uh clearly uh this this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth uh in the cloud space uh for both emphasis and H. B. >>Excellent. Well gentlemen, thank you for joining me today, talking to me about what emphasis and HP are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >>Thank you lisa. Thank you. Thank you for giving us this opportunity. >>Absolutely. For election Soju. I'm lisa martin. You're watching the cubes coverage of HP discover 2021. Yeah, yeah.