(upbeat instrumental music) >> TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (uplifting instrumental music) >> Hey everyone. Welcome to Barcelona, Spain. It's theCUBE Live at MWC '23. I'm Lisa Martin, Dave Vellante, our co-founder, our co-CEO of theCUBE, you know him, you love him. He's here as my co-host. Dave, we have a great couple of guests here to break down day one keynote. Lots of meat. I can't wait to be part of this conversation. Chris Lewis joins us, the founder and MD of Lewis Insight. And Sarbjeet Johal, one of you know him as well. He's a Cube contributor, cloud architect. Guys, welcome to the program. Thank you so much for joining Dave and me today. >> Lovely to be here. >> Thank you. >> Chris, I want to start with you. You have covered all aspects of global telecoms industries over 30 years working as an analyst. Talk about the evolution of the telecom industry that you've witnessed, and what were some of the things you heard in the keynote that excite you about the direction it's going? >> Well, as ever, MWC, there's no lack of glitz and glamour, but it's the underlying issues of the industry that are really at stake here. There's not a lot of new revenue coming into the telecom providers, but there's a lot of adjustment, readjustment of the underlying operational environment. And also, really importantly, what came out of the keynotes is the willingness and the necessity to really engage with the API community, with the developer community, people who traditionally, telecoms would never have even touched. So they're sorting out their own house, they're cleaning their own stables, getting the cost base down, but they're also now realizing they've got to engage with all the other parties. There's a lot of cloud providers here, there's a lot of other people from outside so they're realizing they cannot do it all themselves. It's quite a tough lesson for a very conservative, inward looking industry, right? So should we be spending all this money and all this glitz and glamour of MWC and all be here, or should would be out there really building for the future and making sure the services are right for yours and my needs in a business and personal lives? So a lot of new changes, a lot of realization of what's going on outside, but underlying it, we've just got to get this right this time. >> And it feels like that monetization is front and center. You mentioned developers, we've got to work with developers, but I'm hearing the latest keynote from the Ericsson CEOs, we're going to monetize through those APIs, we're going to charge the developers. I mean, first of all, Chris, am I getting that right? And Sarbjeet, as somebody who's close to the developer community, is that the right way to build bridges? But Chris, are we getting that right? >> Well, let's take the first steps first. So, Ericsson, of course, acquired Vonage, which is a massive API business so they want to make money. They expect to make money by bringing that into the mainstream telecom community. Now, whether it's the developers who pay for it, or let's face it, we are moving into a situation as the telco moves into a techco model where the techco means they're going to be selling bits of the technology to developer guys and to other application developers. So when he says he needs to charge other people for it, it's the way in which people reach in and will take going through those open APIs like the open gateway announced today, but also the way they'll reach in and take things like network slicing. So we're opening up the telecom community, the treasure chest, if you like, where developers' applications and other third parties can come in and take those chunks of technology and build them into their services. This is a complete change from the old telecom industry where everybody used to come and you say, "all right, this is my product, you've got to buy it and you're going to pay me a lot of money for it." So we are looking at a more flexible environment where the other parties can take those chunks. And we know we want collectivity built into our financial applications, into our government applications, everything, into the future of the metaverse, whatever it may be. But it requires that change in attitude of the telcos. And they do need more money 'cause they've said, the baseline of revenue is pretty static, there's not a lot of growth in there so they're looking for new revenues. It's in a B2B2X time model. And it's probably the middle man's going to pay for it rather than the customer. >> But the techco model, Sarbjeet, it looks like the telcos are getting their money on their way in. The techco company model's to get them on their way out like the app store. Go build something of value, build some kind of app or data product, and then when it takes off, we'll take a piece of the action. What are your thoughts from a developer perspective about how the telcos are approaching it? >> Yeah, I think before we came here, like I said, I did some tweets on this, that we talk about all kind of developers, like there's game developers and front end, back end, and they're all talking about like what they're building on top of cloud, but nowhere you will hear the term "telco developer," there's no API from telcos given to the developers to build IoT solutions on top of it because telco as an IoT, I think is a good sort of hand in hand there. And edge computing as well. The glimmer of hope, if you will, for telcos is the edge computing, I believe. And even in edge, I predicted, I said that many times that cloud players will dominate that market with the private 5G. You know that story, right? >> We're going to talk about that. (laughs) >> The key is this, that if you see in general where the population lives, in metros, right? That's where the world population is like flocking to and we have cloud providers covering the local zones with local like heavy duty presence from the big cloud providers and then these telcos are getting sidetracked by that. Even the V2X in cars moving the autonomous cars and all that, even in that space, telcos are getting sidetracked in many ways. What telcos have to do is to join the forces, build some standards, if not standards, some consortium sort of. They're trying to do that with the open gateway here, they have only eight APIs. And it's 2023, eight APIs is nothing, right? (laughs) So they should have started this 10 years back, I think. So, yeah, I think to entice the developers, developers need the employability, we need to train them, we need to show them some light that hey, you can build a lot on top of it. If you tell developers they can develop two things or five things, nobody will come. >> So, Chris, the cloud will dominate the edge. So A, do you buy it? B, the telcos obviously are acting like that might happen. >> Do you know I love people when they've got their heads in the clouds. (all laugh) And you're right in so many ways, but if you flip it around and think about how the customers think about this, business customers and consumers, they don't care about all this background shenanigans going on, do they? >> Lisa: No. >> So I think one of the problems we have is that this is a new territory and whether you call it the edge or whatever you call it, what we need there is we need connectivity, we need security, we need storage, we need compute, we need analytics, and we need applications. And are any of those more important than the others? It's the collective that actually drives the real value there. So we need all those things together. And of course, the people who represented at this show, whether it's the cloud guys, the telcos, the Nokia, the Ericssons of this world, they all own little bits of that. So that's why they're all talking partnerships because they need the combination, they cannot do it on their own. The cloud guys can't do it on their own. >> Well, the cloud guys own all of those things that you just talked about though. (all laugh) >> Well, they don't own the last bit of connectivity, do they? They don't own the access. >> Right, exactly. That's the one thing they don't own. So, okay, we're back to pipes, right? We're back to charging for connectivity- >> Pipes are very valuable things, right? >> Yeah, for sure. >> Never underestimate pipes. I don't know about where you live, plumbers make a lot of money where I live- >> I don't underestimate them but I'm saying can the telcos charge for more than that or are the cloud guys going to mop up the storage, the analytics, the compute, and the apps? >> They may mop it up, but I think what the telcos are doing and we've seen a lot of it here already, is they are working with all those major cloud guys already. So is it an unequal relationship? The cloud guys are global, massive global scale, the telcos are fundamentally national operators. >> Yep. >> Some have a little bit of regional, nobody has global scale. So who stitches it all together? >> Dave: Keep your friends close and your enemies closer. >> Absolutely. >> I know that saying never gets old. It's true. Well, Sarbjeet, one of the things that you tweeted about, I didn't get to see the keynote but I was looking at your tweets. 46% of telcos think they won't make it to the next decade. That's a big number. Did that surprise you? >> No, actually it didn't surprise me because the competition is like closing in on them and the telcos are competing with telcos as well and the telcos are competing with cloud providers on the other side, right? So the smaller ones are getting squeezed. It's the bigger players, they can hook up the newer platforms, I think they will survive. It's like that part is like any other industry, if you will. But the key is here, I think why the pain points were sort of described on the main stage is that they're crying out loud to tell the big tech cloud providers that "hey, you pay your fair share," like we talked, right? You are not paying, you're generating so much content which reverses our networks and you are not paying for it. So they are not able to recoup the cost of laying down their networks. By the way, one thing actually I want to mention is that they said the cloud needs earth. The cloud and earth, it's like there's no physical need to cloud, you know that, right? So like, I think it's the other way around. I think the earth needs the cloud because I'm a cloud guy. (Sarbjeet and Lisa laugh) >> I think you need each other, right? >> I think so too. >> They need each other. When they said cloud needs earth, right? I think they're still in denial that the cloud is a big force. They have to partner. When you can't compete with somebody, what do you do? Partner with them. >> Chris, this is your world. Are they in denial? >> No, I think they're waking up to the pragmatism of the situation. >> Yeah. >> They're building... As we said, most of the telcos, you find have relationships with the cloud guys, I think you're right about the industry. I mean, do you think what's happened since US was '96, the big telecom act when we started breaking up all the big telcos and we had lots of competition came in, we're seeing the signs that we might start to aggregate them back up together again. So it's been an interesting experiment for like 30 years, hasn't it too? >> It made the US less competitive, I would argue, but carry on. >> Yes, I think it's true. And Europe is maybe too competitive and therefore, it's not driven the investment needed. And by the way, it's not just mobile, it's fixed as well. You saw the Orange CEO was talking about the her investment and the massive fiber investments way ahead of many other countries, way ahead of the UK or Germany. We need that fiber in the ground to carry all your cloud traffic to do this. So there is a scale issue, there is a competition issue, but the telcos are very much aware of it. They need the cloud, by the way, to improve their operational environments as well, to change that whole old IT environment to deliver you and I better service. So no, it absolutely is changing. And they're getting scale, but they're fundamentally offering the basic product, you call it pipes, I'll just say they're offering broadband to you and I and the business community. But they're stepping on dangerous ground, I think, when saying they want to charge the over the top guys for all the traffic they use. Those over the top guys now build a lot of the global networks, the backbone submarine network. They're putting a lot of money into it, and by giving us endless data for our individual usage, that cat is out the bag, I think to a large extent. >> Yeah. And Orange CEO basically said that, that they're not paying their fair share. I'm for net neutrality but the governments are going to have to fund this unless you let us charge the OTT. >> Well, I mean, we could of course renationalize. Where would that take us? (Dave laughs) That would make MWC very interesting next year, wouldn't it? To renationalize it. So, no, I think you've got to be careful what we wish for here. Creating the absolute clear product that is required to underpin all of these activities, whether it's IoT or whether it's cloud delivery or whether it's just our own communication stuff, delivering that absolutely ubiquitously high quality for business and for consumer is what we have to do. And telcos have been too conservative in the past. >> I think they need to get together and create standards around... I think they have a big opportunity. We know that the clouds are being built in silos, right? So there's Azure stack, there's AWS and there's Google. And those are three main ones and a few others, right? So that we are fighting... On the cloud side, what we are fighting is the multicloud. How do we consume that multicloud without having standards? So if these people get together and create some standards around IoT and edge computing sort of area, people will flock to them to say, "we will use you guys, your API, we don't care behind the scenes if you use AWS or Google Cloud or Azure, we will come to you." So market, actually is looking for that solution. I think it's an opportunity for these guys, for telcos. But the problem with telcos is they're nationalized, as you said Chris versus the cloud guys are still kind of national in a way, but they're global corporations. And some of the telcos are global corporations as well, BT covers so many countries and TD covers so many... DT is in US as well, so they're all over the place. >> But you know what's interesting is that the TM forum, which is one of the industry associations, they've had an open digital architecture framework for quite some years now. Google had joined that some years ago, Azure in there, AWS just joined it a couple of weeks ago. So when people said this morning, why isn't AWS on the keynote? They don't like sharing the limelight, do they? But they're getting very much in bed with the telco. So I think you'll see the marriage. And in fact, there's a really interesting statement, if you look at the IoT you mentioned, Bosch and Nokia have been working together 'cause they said, the problem we've got, you've got a connectivity network on one hand, you've got the sensor network on the other hand, you're trying to merge them together, it's a nightmare. So we are finally seeing those sort of groups talking to each other. So I think the standards are coming, the cooperation is coming, partnerships are coming, but it means that the telco can't dominate the sector like it used to. It's got to play ball with everybody else. >> I think they have to work with the regulators as well to loosen the regulation. Or you said before we started this segment, you used Chris, the analogy of sports, right? In sports, when you're playing fiercely, you commit the fouls and then ask for ref to blow the whistle. You're now looking at the ref all the time. The telcos are looking at the ref all the time. >> Dave: Yeah, can I do this? Can I do that? Is this a fair move? >> They should be looking for the space in front of the opposition. >> Yeah, they should be just on attack mode and commit these fouls, if you will, and then ask for forgiveness then- >> What do you make of that AWS not you there- >> Well, Chris just made a great point that they don't like to share the limelight 'cause I thought it was very obvious that we had Google Cloud, we had Microsoft there on day one of this 80,000 person event. A lot of people back from COVID and they weren't there. But Chris, you brought up a great point that kind of made me think, maybe you're right. Maybe they're in the afternoon keynote, they want their own time- >> You think GSMA invited them? >> I imagine so. You'd have to ask GSMA. >> I would think so. >> Get Max on here and ask that. >> I'm going to ask them, I will. >> But no, and they don't like it because I think the misconception, by the way, is that everyone says, "oh, it's AWS, it's Google Cloud and it's Azure." They're not all the same business by any stretch of the imagination. AWS has been doing loads of great work, they've been launching private network stuff over the last couple of weeks. Really interesting. Google's been playing catch up. We know that they came in readily late to the market. And Azure, they've all got slightly different angles on it. So perhaps it just wasn't right for AWS and the way they wanted to pitch things so they don't have to be there, do they? >> That's a good point. >> But the industry needs them there, that's the number one cloud. >> Dave, they're there working with the industry. >> Yeah, of course. >> They don't have to be on the keynote stage. And in fact, you think about this show and you mentioned the 80,000 people, the activity going on around in all these massive areas they're in, it's fantastic. That's where the business is done. The business isn't done up on the keynote stage. >> That's why there's the glitz and the glamour, Chris. (all laugh) >> Yeah. It's not glitz, it's espresso. It's not glamour anymore, it's just espresso. >> We need the espresso. >> Yeah. >> I think another thing is that it's interesting how an average European sees the tech market and an average North American, especially you from US, you have to see the market. Here, people are more like process oriented and they want the rules of the road already established before they can take a step- >> Chris: That's because it's your pension in the North American- >> Exactly. So unions are there and the more employee rights and everything, you can't fire people easily here or in Germany or most of the Europe is like that with the exception of UK. >> Well, but it's like I said, that Silicone Valley gets their money on the way out, you know? And that's how they do it, that's how they think it. And they don't... They ask for forgiveness. I think the east coast is more close to Europe, but in the EU, highly regulated, really focused on lifetime employment, things like that. >> But Dave, the issue is the telecom industry is brilliant, right? We keep paying every month whatever we do with it. >> It's a great business, to your point- >> It's a brilliant business model. >> Dave: It's fantastic. >> So it's about then getting the structure right behind it. And you know, we've seen a lot of stratification where people are selling off towers, Orange haven't sold their towers off, they made a big point about that. Others are selling their towers off. Some people are selling off their underlying network, Telecom Italia talking about KKR buying the whole underlying network. It's like what do you want to be in control of? It's a great business. >> But that's why they complain so much is that they're having to sell their assets because of the onerous CapEx requirements, right? >> Yeah, they've had it good, right? And dare I say, perhaps they've not planned well enough for the future. >> They're trying to protect their past from the future. I mean, that's... >> Actually, look at the... Every "n" number of years, there's a new faster network. They have to dig the ground, they have to put the fiber, they have to put this. Now, there are so many booths showing 6G now, we are not even done with 5G yet, now the next 6G you know, like then- >> 10G's coming- >> 10G, that's a different market. (Dave laughs) >> Actually, they're bogged down by the innovation, I think. >> And the generational thing is really important because we're planning for 6G in all sorts of good ways but actually what we use in our daily lives, we've gone through the barrier, we've got enough to do that. So 4G gives us enough, the fiber in the ground or even old copper gives us enough. So the question is, what are we willing to pay for more than that basic connectivity? And the answer to your point, Dave, is not a lot, right? So therefore, that's why the emphasis is on the business market on that B2B and B2B2X. >> But we'll pay for Netflix all day long. >> All day long. (all laugh) >> The one thing Chris, I don't know, I want to know your viewpoints and we have talked in the past as well, there's absence of think tanks in tech, right? So we have think tanks on the foreign policy and economic policy in every country, and we have global think tanks, but tech is becoming a huge part of the economy, global economy as well as national economies, right? But we don't have think tanks on like policy around tech. For example, this 4G is good for a lot of use cases. Then 5G is good for smaller number of use cases. And then 6G will be like, fewer people need 6G for example. Why can't we have sort of those kind of entities dictating those kind of like, okay, is this a wiser way to go about it? >> Lina Khan wants to. She wants to break up big tech- >> You're too young to remember but the IT used to have a show every four years in Geneva, there were standards around there. So I think there are bodies. I think the balance of power obviously has gone from the telecom to the west coast to the IT markets. And it's changing the balance about, it moves more quickly, right? Telecoms has never moved quickly enough. I think there is hope by the way, that telecoms now that we are moving to more softwarized environment, and God forbid, we're moving into CICD in the telecom world, right? Which is a massive change, but I think there's hopes for it to change. The mentality is changing, the culture is changing, but to change those old structured organizations from the British telecom or the France telecom into the modern world, it's a hell of a long journey. It's not an overnight journey at all. >> Well, of course the theme of the event is velocity. >> Yeah, I know that. >> And it's been interesting sitting here with the three of you talking about from a historic perspective, how slow and molasseslike telecom has been. They don't have a choice anymore. As consumers, we have this expectation we're going to get anything we want on our mobile device, 24 by seven. We don't care about how the sausage is made, we just want the end result. So do you really think, and we're only on day one guys... And Chris we'll start with you. Is the theme really velocity? Is it disruption? Are they able to move faster? >> Actually, I think invisibility is the real answer. (Lisa laughs) We want communication to be invisible, right? >> Absolutely. >> We want it to work. When we switch our phones on, we want it to work and we want to... Well, they're not even phones anymore, are they really? I mean that's the... So no, velocity, we've got... There is momentum in the industry, there's no doubt about that. The cloud guys coming in, making telecoms think about the way they run their own business, where they meet, that collision point on the edges you talked about Sarbjeet. We do have velocity, we've got momentum. There's so many interested parties. The way I think of this is that the telecom industry used to be inward looking, just design its own technology and then expect everyone else to dance to our tune. We're now flipping that 180 degrees and we are now having to work with all the different outside forces shaping us. Whether it's devices, whether it's smart cities, governments, the hosting guys, the Equinoxis, all these things. So everyone wants a piece of this telecom world so we've got to make ourselves more open. That's why you get in a more open environment. >> But you did... I just want to bring back a point you made during COVID, which was when everybody switched to work from home, started using their landlines again, telcos had to respond and nothing broke. I mean, it was pretty amazing. >> Chris: It did a good job. >> It was kind of invisible. So, props to the telcos for making that happen. >> They did a great job. >> So it really did. Now, okay, what have you done for me lately? So now they've got to deal with the future and they're talking monetization. But to me, monetization is all about data and not necessarily just the network data. Yeah, they can sell that 'cause they own that but what kind of incremental value are they going to create for the consumers that... >> Yeah, actually that's a problem. I think the problem is that they have been strangled by the regulation for a long time and they cannot look at their data. It's a lot more similar to the FinTech world, right? I used to work at Visa. And then Visa, we did trillion dollars in transactions in '96. Like we moved so much money around, but we couldn't look at these things, right? So yeah, I think regulation is a problem that holds you back, it's the antithesis of velocity, it slows you down. >> But data means everything, doesn't it? I mean, it means everything and nothing. So I think the challenge here is what data do the telcos have that is useful, valuable to me, right? So in the home environment, the fact that my broadband provider says, oh, by the way, you've got 20 gadgets on that network and 20 on that one... That's great, tell me what's on there. I probably don't know what's taking all my valuable bandwidth up. So I think there's security wrapped around that, telling me the way I'm using it if I'm getting the best out of my service. >> You pay for that? >> No, I'm saying they don't do it yet. I think- >> But would you pay for that? >> I think I would, yeah. >> Would you pay a lot for that? I would expect it to be there as part of my dashboard for my monthly fee. They're already charging me enough. >> Well, that's fine, but you pay a lot more in North America than I do in Europe, right? >> Yeah, no, that's true. >> You're really overpaying over there, right? >> Way overpaying. >> So, actually everybody's looking at these devices, right? So this is a radio operated device basically, right? And then why couldn't they benefit from this? This is like we need to like double click on this like 10 times to find out why telcos failed to leverage this device, right? But I think the problem is their reliance on regulations and their being close to the national sort of governments and local bodies and authorities, right? And in some countries, these telcos are totally controlled in very authoritarian ways, right? It's not like open, like in the west, most of the west. Like the world is bigger than five, six countries and we know that, right? But we end up talking about the major economies most of the time. >> Dave: Always. >> Chris: We have a topic we want to hit on. >> We do have a topic. Our last topic, Chris, it's for you. You guys have done an amazing job for the last 25 minutes talking about the industry, where it's going, the evolution. But Chris, you're registered blind throughout your career. You're a leading user of assertive technologies. Talk about diversity, equity, inclusion, accessibility, some of the things you're doing there. >> Well, we should have had 25 minutes on that and five minutes on- (all laugh) >> Lisa: You'll have to come back. >> Really interesting. So I've been looking at it. You're quite right, I've been using accessible technology on my iPhone and on my laptop for 10, 20 years now. It's amazing. And what I'm trying to get across to the industry is to think about inclusive design from day one. When you're designing an app or you're designing a service, make sure you... And telecom's a great example. In fact, there's quite a lot of sign language around here this week. If you look at all the events written, good to see that coming in. Obviously, no use to me whatsoever, but good for the hearing impaired, which by the way is the biggest category of disability in the world. Biggest chunk is hearing impaired, then vision impaired, and then cognitive and then physical. And therefore, whenever you're designing any service, my call to arms to people is think about how that's going to be used and how a blind person might use it or how a deaf person or someone with physical issues or any cognitive issues might use it. And a great example, the GSMA and I have been talking about the app they use for getting into the venue here. I downloaded it. I got the app downloaded and I'm calling my guys going, where's my badge? And he said, "it's top left." And because I work with a screen reader, they hadn't tagged it properly so I couldn't actually open my badge on my own. Now, they changed it overnight so it worked this morning, which is fantastic work by Trevor and the team. But it's those things that if you don't build it in from scratch, you really frustrate a whole group of users. And if you think about it, people with disabilities are excluded from so many services if they can't see the screen or they can't hear it. But it's also the elderly community who don't find it easy to get access to things. Smart speakers have been a real blessing in that respect 'cause you can now talk to that thing and it starts talking back to you. And then there's the people who can't afford it so we need to come down market. This event is about launching these thousand dollars plus devices. Come on, we need below a hundred dollars devices to get to the real mass market and get the next billion people in and then to educate people how to use it. And I think to go back to your previous point, I think governments are starting to realize how important this is about building the community within the countries. You've got some massive projects like NEOM in Saudi Arabia. If you have a look at that, if you get a chance, a fantastic development in the desert where they're building a new city from scratch and they're building it so anyone and everyone can get access to it. So in the past, it was all done very much by individual disability. So I used to use some very expensive, clunky blind tech stuff. I'm now using mostly mainstream. But my call to answer to say is, make sure when you develop an app, it's accessible, anyone can use it, you can talk to it, you can get whatever access you need and it will make all of our lives better. So as we age and hearing starts to go and sight starts to go and dexterity starts to go, then those things become very useful for everybody. >> That's a great point and what a great champion they have in you. Chris, Sarbjeet, Dave, thank you so much for kicking things off, analyzing day one keynote, the ecosystem day, talking about what velocity actually means, where we really are. We're going to have to have you guys back 'cause as you know, we can keep going, but we are out of time. But thank you. >> Pleasure. >> We had a very spirited, lively conversation. >> Thanks, Dave. >> Thank you very much. >> For our guests and for Dave Vellante, I'm Lisa Martin, you're watching theCUBE live in Barcelona, Spain at MWC '23. We'll be back after a short break. See you soon. (uplifting instrumental music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(gentle music) >> Now welcome back to theCUBE, everyone. I'm John Walls, and once again, we're glad to have you here for AWS re:Invent 22. Our coverage continues here on Thursday, day three, of what has been a jam-packed week of tech and AWS, of course, has been the great host for this. It's now a pleasure to welcome in Anurag Gupta, who is the founder and CEO of Shoreline, joining us here as part of the AWS Global Showcase Startup Program, and Anurag, good to see you, sir. Thanks for joining us. >> Thank you so much. >> Tell us about Shoreline, about what you're up to. >> So we're a DevOps company. We're really focused on repairing issues. If you think about it, there are a ton DevOps companies and we all went to the cloud in order to gain faster innovation and by and large check. Then all of the things involved in getting things into production, artifact generation, testing, configuration management, deployment, also by and large, automated. Now pity the poor SRE who's getting the deluge of stuff on them, every week, every two days, sometimes multiple times a day, and it's complicated, right? Kubernetes, VMs, lots of services, multiple clouds, sometimes, and you know, they need to know a little bit about everything. And you know what, there are a ton of companies that actually help you with what we call Day-2 Ops. It's just that most of them help you with observability, telling you what's gone wrong, or incident management, routing something to someone. But you know, back when I was at AWS, I never got really that excited about one more dashboard to look at or one more like better ticket routing. What used to really excite me was having some issue extinguished forever. And if you think about it, like the first five minutes of an incident are detecting and routing. The next hour, two hours, is some human being going in and fixing it, so that feels like the big opportunity to reduce, so hopefully we can talk a little bit about different ways that one can do that. >> What about Day-2 Ops? Just tell me about how you define that. >> So I basically define it as once the software goes into a production, just making sure things stay up and are healthy and you're resilient and you don't get errors and all of those sorts of things because everything breaks sooner or later, you know, to a greater or lesser degree. >> Especially that SRE you're talking about, right? >> Yeah. >> So let's go back to that scenario. Yeah, you pity the poor soul because they do have to be a little expert in everything. >> Exactly. >> And that's really challenging and we all know that, that's really hard. So how do you go about trying to lighten that burden, then? >> So when you look at the numbers, about somewhere between 40% to even 95% of the alarms that fire, the alerts that fire, are false positives and that's crazy. Why is someone waking up just to deal with? >> It's a lot of wasted time, isn't it? >> A lot of wasted time. And you know, you're also training someone into what I call ClickOps, just to go in and click the button and resolve it and you don't actually know if it was the false positive or it's the rare real positive, and so that's a challenge, right? And so the first thing to do is to figure out where the false positives are. Like, let's say Datadog tells you that CPU is high and alarms. Is that a good thing or a bad thing? It's hard for them to tell, right? But you have to then introspect it into something precise like, oh, CPU is high, but response times are standard and the request rate is high. Okay, that's a good thing. I'm going to ignore this. Or CPU is high, but it kind of resolves itself, so I'm going to not wake anybody up. Or CPU is high and oh, it's the darn JVM starting to garbage collect again, so let me go and take a heap dump and give that to my dev team and then bounce the JVM and you know, without waking anybody up, or CPU is high, I have no idea what's going on. Now it's time to wake somebody up. You know, what you want to use humans for is the ability to think about novel stuff, not to do repetitive stuff, so that's the first step. The second step is, about 40% of what remains is repetitive and straightforward. So like a disk is full, I'd better clean up the garbage on the disk or maybe grow the disk. People shouldn't wake up to deal to grow a disk. And so for that, what you want to do is just have those sorts of things get automated away. One of the nice things about Shoreline is, is that we take the experience in what we build for one company, and if they're willing, provide it to everybody else. Our belief is, a central tenant is, if someone somewhere fixes something, everyone everywhere should gain the benefit because we all sit on the same three clouds, we all sit on the same set of database infrastructure, et cetera. We should all get the same benefits. Why do we have to scar our own backs rather than benefiting from somebody else's scar tissue, so that's the second thing. The third thing is, okay, let's say it's not straightforward, not something I've seen before, then in that case, what often happens is on average like eight people get involved. You know, it initially goes to L1 support or L1 ops and, but they don't necessarily know because, as you say, the environment's complex. And so, you know, they go into Slack and they say, "At here, can somebody help me with this?" And those things take a much longer time, so wouldn't it be better that if your best SRE is able to say, "Hey, check these 20 things and then run these actions." We could convert that into like a Jupyter Notebook where you could say the incident got fired I pre-populated all the diagnostics, and then I tell people very precisely, "If you see this, run this, et cetera." Like a wiki, but actually something you could run right in this product. And then, you know, last piece of the puzzle, the smaller piece, is sometimes new things happen and when something new happens, what you want is sort of the central tech of Shoreline, which is parallel distributed, real-time debugging. And so the ability to do, you know, execute a command across your fleet rather than individual boxes so that you can say something like, "I'm hearing that my credit card app is slow. For everything tagged as being part of my credit card app, please run for everything that's running over 90% CPU, please run a top command." And so, you know, then you can run in the same time on one host as you can on 30,000 and that helps a lot. So that's the core of what we do. People use us for all sorts of things, also preventative maintenance, you know, just the proactive regular things. You know, like your car, you do an oil change, well, you know, you need to rotate your certs, certificates. You need to make sure that, you know, there isn't drift in your configurations, there isn't drift in your software. There's also security elements to it, right? You want to make sure that you aren't getting weird inbound/outbound traffic across to ports you don't expect to be open. You don't want to have these processes running, you know, maybe something's bad. And so that's all the kind of weird anomaly detection that's easy to do if you run things in a distributed parallel way across everything. That's super hard to do if you have to go and Whac-A-Mole across one box after the next. >> Well, which leads to a question just in terms of setting priorities then, which is what you're talking about helping companies establish priorities, this hierarchy of level one warning, level two, level three, level four. Sounds like that should be a basic, right? But you're saying that's not, that's not really happening in the enterprise. >> Well, you know, I would say that if you hadn't automated deployments, you should do that first. If you haven't automated your testing pipeline, shame on you, you should do that like a year ago. But now it's time to help people in production because you've done that other work and people are suffering. You know, the crazy thing about the cloud is, is that companies spend about three times more on the human beings to operate their cloud infrastructure as on the cloud infrastructure itself. I've yet to hear anybody say that their cloud bill is too low, you know, so, you know, there's a clearer savings also available. And you know, back when I was at AWS, obviously I had to keep the lights on too, but you know, I had to do that, but it's kind of a tax on my engineers and I'd really spend, prefer to spend the head count on innovation, on doing things that delight my customers. You never delight your customers by keeping the lights on, you just avoid irritating them by turning 'em off, right? >> So why are companies so fixed in on spending so much time on manually repairing things and not looking for these kinds of little, much more elegant solution and cost-efficient, time-saving, so on so forth. >> Yeah, I think there just hasn't been very much in this space as yet because it's a hard, hard problem to solve. You know, automation's a little bit scary and that's the reality of it and the way you make it less scary is by proving it out, by doing the simple things first, like reducing the alert fatigue, you know, that's easy. You know, providing notebooks to people so that they can click things and do things in a straightforward way. That's pretty easy. The full automation, that's kind of the North Star, that's what we aspire to do. But you know, people get there over time and one of our customers had 700 instances of this particular incident solved for them last week. You imagine how many human beings would've been doing it otherwise, you know? >> Right. >> That's just one thing, you know? >> How many did it take the build a pyramid? How many decades did that take, right? You had an announcement this week. I don't think we've talked about that. >> No, yeah, so we just announced Incident Insights, which is a free product that lets people plug into initially PagerDuty and pretty soon the Opsgenie ServiceNow, et cetera. And what you can do is, is you give us an API key read-only and we will suck your PagerDuty data out. We apply some lightweight ML unsupervised learning, and in a couple of minutes, we categorize all of your incidents so that you can understand which are the ones that happen most often and are getting resolved really quickly. That's ClickOps, right? Those alarms shouldn't fire. Which are the ones that involve a lot of people? Those are good candidates to build a notebook. Which are the ones that happen again and again and again? Those are good candidates for automation. And so, I think one of the challenges people have is, is that they don't actually know what their teams are doing and so this is intended to provide them that visibility. One of our very first customers was doing the beta test for us on it. He used to tell us he had about 100 tickets, incidents a week. You know, he brought this tool in and he had 2,100 last week and was all, you know, like these false alarms, so while he's giving us- >> That was eye opening for him to see that, sure. >> And why he's, you know, looking at it, you know, he's just like filing Jiras to say, "Oh, change this threshold, cancel this alarm forever." You know, all of that kind of stuff. Before you get to do the fancy work, you got to clean your room before you get to do anything else, right? >> Right, right, dinner before dessert, basically. >> There you go. >> Hey, thanks for the insights on this and again the name of the new product, by the way, is... >> Incident Insights. >> Incident Insights. >> Totally free. >> Free. >> Yeah, it takes a couple of minutes to set up. Go to the website, Shoreline.io/insight and you can be up and running in a couple of minutes. >> Outstanding, again, the company is Shoreline. This is Anurag Gupta, and thank you for being with us. We appreciate it. >> Appreciate it, thank you. >> Glad to have to here on theCUBE. Back with more from AWA re:Invent 22. You're watching theCUBE, the leader in high-tech coverage. (gentle music)

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase. Topic is MarTech: Emerging Cloud-Scale Experience. This is season two, episode three of the ongoing series covering the exciting startups from the AWS ecosystem. Talk about their value proposition and their company and all the good stuff that's going on. I'm your host, John Furrier. And today we're excited to be joined by Manyam Mallela who's the co-founder and head of AI at Blueshift. Great to have you on here to talk about the Blueshift-Intelligent Customer Engagement, Made Simple. Thanks for joining us today. >> Thank you, John. Thank you for having me. >> So last time we did our intro video. We put it out in the web. Got great feedback. One of the things that we talked about, which is resonating out there in the viral Twitter sphere and in the thought leadership circles is this concept that you mentioned called 10X marketer. That idea that you have a solution that can provide 10X value. Kind of a riff on the 10X engineer in the DevOps cloud world. What does it mean? And how does someone get there? >> Yeah, fantastic. I think that's a great way to start our discussion. I think a lot of organizations, especially as of this current economic environment are looking to say, I have limited resources, limited budgets, how do I actually achieve digital and customer engagement that helps move the needle for my key metrics, whether it's average revenue per user, lifetime value of the user and frequent interactions. Above all, the more frequently a brand is able to interact with their customers, the better they understand them, the better they can actually engage them. And that usually leads to long term good outcomes for both customer and the brand and the organizations. So the way I see 10X marketer is that you need to have tools that give you that speed and agility without hindering your ability to activate any of the campaigns or experience that you want to create. And I see the roadblocks usually for many organizations, is that kind of threefold. One is your data silos. Usually data that is on your sites, does not talk to your app data, does not talk to your social data, does not talk to your CRM data and so forth. So how do I break those silos? The second is channel silos. I actually have customers who are only engaging on email or some are on email and mobile apps. Some are on email and mobile apps and maybe the OTT TV in a Roku or one of the connected TV experiences, or maybe in the future, another Web3 environments. How do I actually break those channel silos so that I get a comprehensive view of the customer and my marketing team can engage with all of them in respect to the channel? So break the channel silos. And the last part, what I call like some of the little talked about is I call the inside silo, which is that, not only do you need to have the data, but you also have to have a common language to share and talk about within your organizations. What are we learning from our customers? What do we translate our learning and insight on this common data platform or fabric into an action? And that requires the shared language of how do I actually know my customers and what do I do with them? Like either the inside silo as well. I think a lot of times organizations do get into this habit like each one speaks their own language, but they don't actually are talking the common language of what did we actually know about the real customer there. >> Yeah, and I think that's a great conversation because there's two, when you hear 10X marketer or 10X conversations, it implies a couple things. One is you're breaking an old way and bringing in something new. And the new is a force multiplier, in this case, 10X marketer. But this is the cloud scale so marketing executives, chiefs, staffs, chiefs of staffs of CMOs and their staffs. They want to get that scale. So marketing at scale is now the table stakes. Now budget constraints are there as well. So you're starting to see, okay, I need to do more with less. Now the big question comes up is ROI. So I want to have AI. I want to have all these force multipliers. What do I got to do with the old? How do I handle that? How do I bring the new in and operationalize it? And if that's the case, I'm making a change. So I have to ask you, what's your view on the ROI of AI marketing, because this is a key component 'cause you've got scale factor here. You've got to force multiplier opportunity. How do you get that ROI on the table? >> I think that as you rightly said, it's table stakes. And I think the ROI of AI marketing starts with one very key simple premise that today some of the tools allow you to do things one at a time. So I can actually say, "can I run this campaign today?" And you can scramble your team, hustle your way, get everybody involved and run that campaign. And then tomorrow I'd say like, Hey, I looked at the results. Can I do this again? And they're like, oh, we just asked for all of us to get that done. How do I do it tomorrow? How do I do it next week? How do I do it for every single week for the rest of the year? That's where I think the AI marketing is essentially taking your insight, taking your creativity, and creating a platform and a tool that allows you to run this every single day. And that's agility at scale. That is not only a scale of the customer base, but scale across time. And that AI-based automation is the key ROI piece for a lot of AI marketing practitioners. So Forrester, for example, did a comprehensive total economic impact study with our customers. And what they found out was actually the 781% ROI that they reported in that particular report is based on three key factors. One is being able to do experiences that are intelligent at scale, day in and day out. So do your targeting, do your recommendations. Not just one day, but do it every single day. And don't hold back yourself on being able to do that. >> I think they got to get the return. They got to get the sales too. This is the numbers. >> That's right. They actually have real dollars, real numbers attached to it. They have a calculator. You can actually go in and plug your own numbers and get what you might expect from your existing customer base. The second is that once you have a unified platform like ours, the 10X marketer that we're talking about is actually able to do more. It's sometimes actually, it's kind of counterintuitive to think that a smaller team does more. But in reality, what we have seen, that is the case. When you actually have the right tools, the smaller teams actually achieve more. And that's the redundant operations, conflicting insights that go away into something more coherent and comprehensive. And that's the second insight that they found. And the third is just having reporting and all of the things in one place means that you can amplify it. You can amplify it across your paid media channels. You can amplify it across your promotions programs and other partnerships that you're running. >> That's the key thing about platforms that people don't understand is that you have a platform and it enables a lot of value. In this case, force multiplier value. It enables more value than you pay for it. But the key is it enables customers to do things without a line of code, meaning it's a platform. They're innovating on top of it. And that's, I think, where the ROI comes in and this leads me where the next question is. I wanted to ask you is, not to throw a wet blanket on the MarTech industry, but I got to think of when I hear marketing automation, I kind of think old. I think old, inadequate antiquated technologies. I think email blasting and just some boring stuff that just gets siloed or it's bespoke from something else. Are marketing automation tools created equal? Does something like, what you guys are doing with SmartHub? Change that, and can you just talk about that 'cause it's not going to go away. It's just another level that's going to be abstracted away under the coverage. >> Yeah, great question. Certainly, email marketing has been practiced for two or three decades now and in some form or another. I think we went from essentially what people call list-based marketing. I have a list, let me keep blasting the same message to everybody and then hopefully something will come out of it. A little bit more of saying, then they can, okay, maybe now I have CRM database and can I do database marketing, which they will call like, "Hey, Hi John. Hi Manyam", which is the first name. And that's all they think will get the customer excited about because you'll call them by name, which is certainly helpful, but not enough. I think now what we call like, the new age that we live in is that we call it graph-based marketing. And the way we materialize that is that every single user is interacting with a brand with their offerings. So that this interaction graph that's happening across millions of customers, across thousands of content articles, videos, shows, products, items, and that graph actually has much richer knowledge of what the customer wants than the first names or list-based ones. So I think the next evolution of marketing automation, even though the industry has been there a while, there is a step change in what can actually be done at scale. And which is taking that interaction graph and making that a part of the experience for the customer, and that's what we enable. That's why we do think of that as a big step change from how people are being practicing list-based marketing. And within that, certainly there is a relation of curve as to how people approach AI marketing and they are in a different spectrum. Some people are still at list-based marketing. Some people are database marketing. And hopefully will move them to this new interaction graph-based marketing. >> Yeah and I think the context is key. I like how you bring up the graph angle on this because the graph databases imply there's a lot of different optionality around what's happened contextually both over time and currently and it adds to it. Makes it smarter. It's not just siloed, just one dimensional. It feels like it's got a lot there. This is clearly I'm a big fan of and I think this is the way to go. As you get more personalization, you get more data. Graphic database makes a lot of sense. So I have to ask you, this is a really cutting edge value proposition, who are the primary buyers and users in an organization that you guys are working with? >> Yeah, great question. So we typically have CMO organizations approaching us with this problem and they usually talk to their CIO organizations, their counterparts, and the chief information officers have been investing in data fabrics, data lakes, data warehouses for the better part of last decade or two, and have some very cutting edge technology that goes into organizing all this data. But that doesn't still solve the problem of how do I take this data and make a meaningful, relevant, authentic experience for the customer. That's the CMO problem. And CMO are now challenge with creating product level experience with every interaction and that's where we coming. So the CMO are the buyers of our SmartHub CDP platform. And we're looking for consolidating hundreds of tools that they had in the past and making that one or two channel marketers. Actually, the 10X marketer that we talk about. And you need the right tool on top of your data lakes and data warehouses to be able to do that. So CMO are also the real drivers of using this technology. >> I think that also place the ROI equation around ROI and having that unified platform. Great call out there. I got to ask you the question here 'cause this comes up a lot and when I hear you talking, I think, okay, all the great stuff you guys have there. But if I'm a company, I want to make my core competencies mine. I don't really want to outsource or buy something that's going to be core to my business. But at the same time as market shifts, the business changes. And sometimes people don't even know what business they're in at the end of the day. And as it gets more complicated too, by the way. So the question comes up with companies and I can see this clearly, do I buy it? Do I build it? When it comes to AI because that's a core competency. Wait a minute, AI. I'm going to maybe buy some chatbot technology. That's not really AI, but it feels like AI, but I'm a company, I want to buy it or build it. That's a choice. What do you see there? 'Cause you guys have a very comprehensive platform. It's hard to replicate, imitates, inimitable. So what's your customers doing with respect buy and build? And where do they get the core competency? What do they get to have as a core competency? >> Fantastic. I think certainly, AI as it applies to at the organization level, I've seen this at my previous organization that I was part of, and there will be product and financial applications that are using AI for the service of that organization. So we do see, depending upon the size of the organization having in-house AI and data science teams. They are focused on these long term problems that they are doing as part of their product itself. Adjacent to that, the CMO organization gets some resources, but not certainly a lot. I think the CMO organization is usually challenged with the task, but not given the hundred people data science and engineering team to be able to go solve that. So what we see among our customer base is that they need agile platform to do most of the things that they need to do on a day to day basis, but augmented with what our in-house data science they have. So we are an extensible platform. What we have seen is that half of our customers use us solely for the AI needs. The other half certainly uses both AI modules that we provide and are actually augmented with things that they've already built. And we do not have a fight in that ring. But we do acknowledge and we do provide the right hooks for getting the data out of our system and bringing their AI back into our system. And we think that at the end of the day, if you want agility for the CMO, there should not be any barriers. >> It's like they're in the data business and that's the focus. So I think with what I hear you saying is that with your technology and platform, you're enabling to get them to be in the data business as fast as possible. >> That's right. >> Versus algorithm business, which they could add to over time. >> Certainly they could add to. But I think the bulk of competencies for the CMO are on the creative side. And certainly wrangling with data pipelines day in and day out and wondering what actually happened to a pipeline in the middle of the night is not probably what they would want to focus on. >> Not their core confidence. Yeah, I got that. >> That's right. >> You can do all the heavy lifting. I love that. I got to ask you on the Blueshift side on customer experience consumption. how can someone experience the product before buying? Is there a trial or POC? What's the scale and scope of operationalizing and getting the Blueshift value proposition in them? >> Yeah, great. So we actually recently released a fantastic way to experience our product. So if you go to our website, there's only one call-to-action saying, explore Blueshift. And if you click on that, without asking, anything other than your business email address, you're shown the full product. You're given a guided tour of all the possibilities. So you can actually experience what your marketing team would be doing in the product. And they call it Project Rover. We launched it very recently and we are seeing fantastic reception to that. I think a lot of times, as you said, there is that question mark of like, I have a marketing team that is already doing X, Y, Z. Now you are asking me to implement Blueshift. How would they actually experience the product? And now they can go in and experience the product. It's a great way to get the gist of the product in 10 clicks. Much more than going through any number of videos or articles. I think people really want to say, let me do those 10 clicks. And I know what impression that I can get from platform. So we do think that's a great way to experience the product and it's easily available from the main website. >> It's in the value proposition. It isn't always a straight line. And you got that technology. And I got to ask from between your experience with the customers that you're talking to, prospects, and customers, where do you see yourself winning deals on Customer Engagement, Made Simple because the word customer engagement's been around for a while, and it's become, I won't say cliche, but there's been different generational evolutions of technology that made that possible. Obviously, we're living in an era of high velocity Omni-Channel, a lot of data, the graph databases you mentioned are in there, big part of it. Where are you winning deals? Where are customers pain points where you are solving that specifically? >> Yeah, great question. So the organizations that come to us usually have one of the dimensions of either they have offering complexity, which is what catalog of content or videos or items do they offer to the customers. And on the data complexity on the other side is to what the scale of customer base that I usually target. And that problem has not gone away. I think the customer engagement, even though has been around for a while, the problem of engaging those customers at scale hasn't gone away and it only is getting harder and harder and organizations that have, especially on what we call the business-to-consumer side where the bulk of what marketing organizations in a B2C segments are doing. I have tens to millions of customers and how do I engage them day in and day out. And I think that all that problem is only getting harder because consumer preferences keeps shifting all the time. >> And where's your sweet spot for your customer? What size? Can you just share the target organization? Is it medium enterprise, large B2C, B2B2C? What's the focus area? >> Yeah, great question. So we have seen like startups that are in Silicon Valley. I have now half a million monthly active users, how do I actually engage them to customers and clients like LendingTree and PayPal and Discovery and BBC who have been in the business for multiple decades, have tens of millions of customers that they're engaging with. So that's kind of our sweet spot. We are certainly not maybe for small shop with maybe a hundred plus customers. But as you reach the scale of tens of thousands of customers, you start seeing this problem. And then you start to look out for solutions that are beyond, especially list-based marketing and email blast. >> So as the scale, you can dial up and down, but you have to have some enough scale to get the data pattern. >> That's right. >> If I can connect the dots there. >> I would probably say, looking at a hundred thousand or more monthly active customer base, and then you're trying to ramp up your own growth based on what you're learning and to engage those customers. >> It's like a bulldozer. You need the heavy equipment. Great conversation. For the last minute we have here Manyam, give you a plug for the company. What's going on? What are you guys doing? What's new? Give some success stories, your latest achievements. Take a minute to give a plug for the company. >> Yeah, great. We have been recognized by Deloitte as the fastest growth startup two years in a row and continuing to be on that streak. We have released currently integrations with AWS partners and Snowflake partners and data lake partners that allow implementing Blueshift a much streamlined experience with bidirectional integrations. We have now hundred plus data connectors and data integrations in our system and that takes care of many of our needs. And now, I think organizations that have been budget constraint and are trying to achieve a lot with a small team are actually going to look at these solutions and say, "Can I get there?" and "Can I become that 10X marketing organization? And as you have said, agility at scale is very, very hard to achieve. Being able to take your marketing team and achieve 10X requires the right platform and the right solution. We are ready for it. >> And every company's in the data business that's the asset. You guys make that sing for them. It's good stuff. Love the 10X. Love the scale. Manyam Mallela, thanks for coming on. Co-founder, Head of AI at Blueshift. This is the AWS Startup Showcase season two, episode three of the ongoing series covering the exciting startups from the AWS ecosystem. I'm John Furrier, your host. Thanks for watching. >> Thank you, John. (upbeat music)

(inspiring music) >> Announcer: theCUBE presents HPE Discover 2022. Brought to you by HPE. >> Welcome back to theCUBE's coverage of HPE Discover 2022, from the Venetian in Las Vegas, the first Discover since 2019. I really think this is my 14th Discover, when you include HP, when you include Europe. And I got to say this Discover, I think has more energy than any one that I've ever seen, about 8,000 people here. Really excited to have one of HPE's longstanding partners, Veeam CTO, Danny Allen is here, joined by David Harvey, Vice President of Strategic Alliances at Veeam. Guys, good to see you again. It was just earlier, let's see, last month, we were together out here. >> Yeah, just a few weeks ago. It's fantastic to be back and what it's telling us, technology industry is coming back. >> And the events business, of course, is coming back, which we love. I think the expectations were cautious. You saw it at VeeamON, a little more than you expected, a lot of great energy. A lot of people, 'cause it was last month, it was their first time out, >> Yes. >> in two years. Here, I think people have started to go out more, but still, an energy that's palpable. >> You can definitely feel it. Last night, I think I went to four consecutive events and everyone's out having those discussions and having conversations, it's good to be back. >> You guys hosted the Storage party last night, which is epic. I left at midnight, I took a picture, it was still packed. I said, okay, time to go, nothing good happens after midnight kids. David, talk about the alliance with HPE, how it's evolved, and where you see it going? >> I appreciate it, and certainly this, as you said, has been a big alliance for us. Over 10 years or so, fantastic integrations across the board. And you touched on 2019 Discover. We launched with GreenLake at that event, we were one of the launch partners, and we've seen fantastic growth. Overall, what we're excited about, is that continuation of the movement of the customer's buying patterns in line with HPE's portfolio and in line with Veeam. We continue to be with all their primary, secondary storage, we continue to be a spearhead position with GreenLake, which we're really excited about. And we're also really excited to hear from HPE, unfortunately under NDA, some of their future stuff they're investing in, which is a really nice invigoration for what they're doing for their portfolio. And we see that being a big deal for us over the next 24 months. >> Your relationship with HPE predates the HP, HPE split. >> Mmm. >> Yes. >> But it was weird, because they had Data Protector, and that was a quasi-competitor, or really not, but it was a competitor, a legacy competitor, of what you guys have, kind of modern data protection I think is the tagline, if I got it right. Post the split, that was an S-curve moment, wasn't it, in terms of the partnership? >> It really was. If you go back 10 years, we did our first integration sending data to StoreOnce and we had some blueprints around that. But now, if you look what we have, we have integrations on the primary side, so, 3PAR, Primera, Nimble, all their top-tier storage, we can manage the snapshots. We have integration on the target side. We integrate with Catalyst in the movement of data and the management of data. And, as David alluded to, we integrate with GreenLake. So, customers who want to take this as a consumption model, we integrate with that. And so it's been, like you said, the strongest relationship that we have on the technology alliance side. >> So, V12, you announced at VeeamON. What does that mean for HPE customers, the relationship? Maybe you guys could both talk about that. >> Technology side, to touch on a few things that we're doing with them, ransomware has been a huge issue. Security's been a big theme, obviously, at the conference, >> Dave: Yeah, you bet. and one of the things we're doing in V12 is adding immutability for both StoreOnce and StoreEver. So, we take the features that our partners have, immutability being big in the security space, and we integrate that fully into the product. So a customer checks a box and says, hey, I want to make sure that the data is secure. >> Yeah, and also, it's another signification about the relationship. Every single release we've done has had HPE at the heart of it, and the same thing is being said with V12. And it shows to our customers, the continual commitment. Relationships come and go. They're hard, and the great news is, 10 years has proven that we get through good times and tricky situations, and we both continue to invest, et cetera. And I think there's a lot of peace of mind and the revenue figures prove that, which is what we're really excited about. >> Yeah I want to come back to that, but just to follow up, Danny, on that immutability, that's a feature that you check? It's service within GreenLake, or within Veeam? How does that all work? >> We have immutability now depending on the target. We introduced the ability to send data, for example, into S3 two years ago, and make it immutable when you send it to an S3 or S3 compatible environment. We added, in Version 11, the ability to take a Linux repository and make it, and harden it, essentially make it immutable. But what we're doing now is taking our partner systems like StoreOnce, like StoreEver, and when we send data there, we take advantage of an API flag or whatever it happens to be, that it makes the data, when it's written to that system, can't be deleted, can't be encrypted. Now, what does that mean for a customer? Well, we do all the hard work in the back end, it's just a check box. They say, I want to make it immutable, and we manage how long it's immutable. Because if you made everything immutable forever, that's hugely expensive, right? So, it's all about, how long is that immutable before you age it out and make sure the new data coming in is immutable. >> Dave: It's like an insurance policy, you have that overlap. >> Yes. >> Right, okay. And then David, you mentioned the revenue, Lou bears that out. I got the IDC guys comin' on later on today. I'll ask 'em about that, if that's their swim lane. But you guys are basically a statistical tie, with Dell for number one? Am I getting that right? And you're growing at a faster rate, I believe, it's hard to tell 'cause I don't think Dell reports on the pace of its growth within data protection. You guys obviously do, but is that right? It's a statistical tie, is it? >> Yeah, hundred percent. >> Yeah, statistical tie for first place, which we're super excited about. When I joined Veeam, I think we were in fifth place, but we've been in the leader's quadrant of the Gartner Magic- >> Cause and effect there or? (panelists laughing) >> No, I don't think so. >> Dave: Ha, I think maybe. >> We've been on a great trajectory. But statistical tie for first place, greatest growth sequentially, and year-over-year, of all of the data protection vendors. And that's a testament not just to the technology that we're doing, but partnerships with HPE, because you never do this, the value of a technology is not that technology alone, it's the value of that technology within the ecosystem. And so that's why we're here at HPE Discover. It's our joint technology solutions that we're delivering. >> What are your thoughts or what are you seeing in the field on As-a-service? Because of course, the messaging is all about As-a-service, you'd think, oh, a hundred percent of everything is going to be As-a-service. A lot of customers, they don't mind CapEx, they got good, balance sheet, and they're like, hey, we'll take care of this, and, we're going to build our own little internal cloud. But, what are you seeing in the market in terms of As-a-service, versus, just traditional licensing models? >> Certainly, there's a mix between the two. What I'd say, is that sources that are already As-a-service, think Microsoft 365, think AWS, Azure, GCP, the cloud providers. There's a natural tendency for the customer to want the data protection As-a-service, as well for those. But if you talk about what's on premises, customers who have big data centers deployed, they're not yet, the pendulum has not shifted for that to be data protection As-a-service. But we were early to this game ourselves. We have 10,000, what we call, Veeam Cloud Service Providers, that are offering data protection As-a-service, whether it be on premises, so they're remotely managing it, or cloud hosted, doing data protection for that. >> So, you don't care. You're providing the technology, and then your customers are actually choosing the delivery model. Is that correct? >> A hundred percent, and if you think about what GreenLake is doing for example, that started off as being a financial model, but now they're getting into that services delivery. And what we want to do is enable them to deliver it, As-a-service, not just the financial model, but the outcome for the customer. And so our technology, it's not just do backup, it's do backup for a multi-tenant, multi-customer environment that does all of the multi-tenancy and billing and charge back as part of that service. >> Okay, so you guys don't report on this, but I'm going to ask the question anyway. You're number one now, let's call you, let's declare number one, 'cause we're well past that last reporting and you're growin' faster. So go another quarter, you're now number one, so you're the largest. Do you spend more on R&D in data protection than any other company? >> Yes, I'm quite certain that we do. Now, we have an unfair advantage because we have 450,000 customers. I don't think there's any other data protection company out there, the size and scope and scale, that we have. But we've been expanding, our largest R&D operation center's in Prague, it's in Czech Republic, but we've been expanding that. Last year it grew 40% year on year in R&D, so big investment in that space. You can see this just through our product space. Five years ago, we did data protection of VMware only, and now we do all the virtual environments, all the physical environments, all the major cloud environments, Kubernetes, Microsoft 365, we're launching Salesforce. We announced that at VeeamON last month and it will be coming out in Q3. All of that is coming from our R&D investments. >> A lot of people expect that when a company like Insight, a PE company, purchases a company like Veeam, that one of the things they'll dial down is R&D. That did not happen in this case. >> No, they very much treat us as a growth company. We had 22% year-over-year growth in 2020, and 25% year-over-year last year. The growth has been tremendous, they continue to give us the freedom. Now, I expect they'll want returns like that continuously, but we have been delivering, they have been investing. >> One of my favorite conversations of the year was our supercloud conversation, which was awesome, thank you for doing that with me. But that's clearly an area of focus, what we call supercloud, and you don't use that term, I know, you do sometimes, but it's not your marketing, I get that. But that is an R&D intensive effort, is it not? To create that common experience. And you see HPE, attempting to do that as well, across all these different estates. >> A hundred percent. We focus on three things, I always say, our differentiators, simplicity, flexibility, and reliability. Making it simple for the customers is not an easy thing to do. Making that checkbox for immutability? We have to do a lot behind the scenes to make it simple. Same thing on flexibility. We don't care if they're using 3PAR, Primera, Nimble, whatever you want to choose as the primary storage, we will take that out of your hands and make it really easy. You mentioned supercloud. We don't care what the cloud infrastructure, it can be on GreenLake, it can be on AWS, can be on Azure, it can be on GCP, it can be on IBM cloud. It is a lot of effort on our part to abstract the cloud infrastructure, but we do that on behalf of our customers to take away that complexity, it's part of our platform. >> Quick follow-up, and then I want to ask a question of David. I like talking to you guys because you don't care where it is, right? You're truly agnostic to it all. I'm trying to figure out this repatriation thing, cause I hear a lot of hey, Dave, you should look into repatriation that's happened all over the place, and I see pockets of it. What are you seeing in terms of repatriation? Have customers over-rotated to the cloud and now they're pullin' back a little bit? Or is it, as I'm claiming, in pockets? What's your visibility on that? >> Three things I see happening. There's the customers who lifted up their data center, moved it into the cloud and they get the first bill. >> (chuckling) Okay. >> And they will repatriate, there's no question. If I talk to those customers who simply lifted up and moved it over because the CIO told them to, they're moving it back on premises. But a second thing that we see is people moving it over, with tweaks. So they'll take their SQL server database and they'll move it into RDS, they'll change some things. And then you have people who are building cloud-native, they're never coming back on premises, they are building it for the cloud environment. So, we see all three of those. We only really see repatriation on that first scenario, when they get that first bill. >> And when you look at the numbers, I think it gets lost, 'cause you see the cloud is growing so fast. So David, what are the conversations like? You had several events last night, The Veeam party, slash Storage party, from HPE. What are you hearing from your alliance partners and the customers at the event. >> I think Danny touched on that point, it's about philosophy of evolution. And I think at the end of the day, whether we're seeing it with our GSI alliances we've got out there, or with the big enterprise conversations we're having with HPE, it's about understanding which workloads they want to move. In our mind, the customers are getting much smarter in making that decision, rather than experimenting. They're really taking a really solid look. And the work we're doing with the GSIs on workplace modernization, data center transformation, they're really having that investment work up front on the workloads, to be able to say, this works for me, for my personality and my company. And so, to the point about movement, it's more about decisive decision at the start, and not feeling like the remit is, I have to do one thing or another, it's about looking at that workflow position. And that's what we've seen with the revenue part as well. We've seen our movement to GreenLake tremendously grow in the last 18 months to two years. And from our GSI work as well, we're seeing the types of conversations really focus on that workload, compared to, hey, I just need a backup solution, and that's really exciting. >> Are you having specific conversations about security, or is it a data protection conversation still, (David chuckles) that's an adjacency to security? >> That's a great question. And I think it's a complex one, because if you come to a company like Veeam, we are there, and you touched on it before, we provide a solution when something has happened with security. We're not doing intrusion detection, we're not doing that barrier position at the end of it, but it's part of an end-to-end assumption. And I don't think that at this particular point, I started in security with RSA and Check Point, it was about layers of protection. Now it's layers of protection, and the inevitability that at some point something will happen, so about the recovery. So the exciting conversations we're having, especially with the big enterprises, is not about the fear factor, it's about, at some point something's going to occur. Speed of recovery is the conversation. And so for us, and your question is, are they talking to us about security, or more, the continuity position? And that's where the synergy's getting a lot simpler, rather than a hard demark between security and backup. >> Yeah, when you look at the stock market, everything's been hit, but security, with the exception of Okta, 'cause it got that weird benign hack, but security, generally, is an area that CIOs have said, hey, we can't really dial that back. We can maybe, some other discretionary stuff, we'll steal and prioritize. But security seems to be, and I think data protection is now part of that discussion. You're not a security company. We've seen some of your competitors actually pivot to become security companies. You're not doing that, but it's very clearly an adjacency, don't you think? >> It's an adjacency, and it's a new conversation that we're having with the Chief Information Security Officer. I had a meeting an hour ago with a customer who was hit by ransomware, and they got the call at 2:00 AM in the morning, after the ransomware they recovered their entire portfolio within 36 hours, from backups. Didn't even contact Veeam, I found out during this meeting. But that is clearly something that the Chief Information Security Officer wants to know about. It's part of his purview, is the recovery of that data. >> And they didn't pay the ransom? >> And they did not pay the ransom, not a penny. >> Ahh, we love those stories. Guys, thanks so much for coming on theCUBE. Congratulations on all the success. Love when you guys come on, and it was such a fun event at VeeamON. Great event here, and your presence is, was seen. The Veeam green is everywhere, so appreciate your time. >> Thank you. >> Thanks, Dave. >> Okay, and thank you for watching. This is Dave Vellante for John Furrier and Lisa Martin. We'll be back right after this short break. You're watching theCUBE's coverage of HPE Discover 2022, from Las Vegas. (inspiring music)

>>Welcome back to the big apple, everybody. The Cube's continuous coverage here of MongoDB world 2022. We're at the new Javet center. It's it's quite nice. It was built during the pandemic. I believe on top of a former bus terminal. I'm told by our next guest Tony bear, who's the principal at DB insight of data and database expert, longtime analyst, Tony. Good to see you. Thanks for coming >>On. Thanks >>For having us. You face to face >>And welcome to New York. >>Yeah. Right. >>New York is open for business. >>So, yeah. And actually, you know, it's interesting. We've been doing a lot of these events lately and, and especially the ones in Vegas, it's the first time everybody's been out, you know, face to face, not so much here, you know, people have been out and about a lot of masks >>In, >>In New York city, but, but it's good. And, and this new venue is fantastic >>Much nicer than the old Javits. >>Yeah. And I would say maybe 3000 people here. >>Yeah. Probably, but I think like most conferences right now are kind of, they're going through like a slow ramp up. And like for instance, you know, sapphires had maybe about one third, their normal turnout. So I think that you're saying like one third to one half seems to be the norm right now are still figuring out how we're, how and where we're gonna get back together. Yeah. >>I think that's about right. And, and I, but I do think that that in most of the cases that we've seen, it's exceeded people's expectations at tenants, but anyway sure. Let's talk about Mongo, very interesting company. You know, we've been kind of been watching their progression from just sort of document database and all the features and functions they're adding, you just published a piece this morning in venture beat is time for Mongo to get into analytics. Yes. You know? Yes. One of your favorite topics. Well, can they expand analytics? They seem to be doing that. Let's dig into it. Well, >>They're taking, they've been taking slow. They've been taking baby steps and there's good reason for that because first thing is an operational database. The last thing you wanna do is slow it down with very complex analytics. On the other hand, there's huge value to be had if you would, if you could, you know, turn, let's say a smart, if you can turn, let's say an operational database or a transaction database into a smart transaction database. In other words, for instance, you know, let's say if you're, you're, you're doing, you know, an eCommerce site and a customer has made an order, that's basically been out of the norm. Whether it be like, you know, good or bad, it would be nice. Basically, if at that point you could then have a next best action, which is where analytics comes in. But it's a very lightweight form of analytics. It's not gonna, it's actually, I think probably the best metaphor for this is real time credit scoring. It's not that they're doing your scoring you in real time. It's that the model has been computed offline so that when you come on in real time, it can make a smart decision. >>Got it. Okay. So, and I think it was your article where I, I wrote down some examples. Sure. Operational, you know, use cases, patient data. There's certainly retail. We had Forbes on earlier, right? Obviously, so very wide range of, of use cases for operational will, will Mongo, essentially, in your view, is it positioned to replace traditional R D BMS? >>Well, okay. That's a long that's, that's much, it's >>Sort of a loaded question, but >>That's, that's a very loaded question. I think that for certain cases, I think it will replace R D BMS, but I still, I mean, where I, where I depart from Mongo is I do not believe that they're going to replace all R D D BMSs. I think, for instance, like when you're doing financial transactions, you know, the world has been used to table, you know, you know, columns and rows and tables. That's, it's a natural form for something that's very structured like that. On the other hand, when you take a look, let's say OT data, or you're taking a look at home listings that tends to more naturally represent itself as documents. And so there's a, so it's kind of like documents are the way that let's say you normally see the world. Relational is the way that you would structure the world. >>Okay. Well, I like that. So, but I mean, in the early days, obviously, and even to this day, it's like the target for Mongo has been Oracle. Yeah. Right, right. And so, and then, you know, you talk to a lot of Oracle customers as do I sure. And they are running the most mission, critical applications in the world, and it's like banking and financial and so many. And, and, and, you know, they've kind of carved out that space, but are we, should we be rethinking the definition of, of mission critical? Is that changing? >>Well, number one, I think what we've traditionally associated mission critical systems with is our financial transaction systems and to a less, and also let's say systems that schedule operations. But the fact is there are many forms of operations where for instance, let's say you're in a social network, do you need to have that very latest update? Or, you know, basically, can you go off, let's say like, you know, a server that's eventually consistent. In other words, the, do you absolutely have, you know, it's just like when you go on Twitter, do you naturally see all the latest tweets? It's not the system's not gonna crash for that reason. Whereas let's say if you're doing it, you know, let's say an ATM banking ATM system, that system better be current. So I think there's a delineation. The fact is, is that in a social network, arguably that operational system is mission critical, but it's mission critical in a different way from a, you know, from, let's say a banking system. >>So coming back to this idea of, of this hybrid, I think, you know, I think Gartner calls it H tab hybrid, transactional analytics >>Is changed by >>The minute, right. I mean, you mentioned that in, in your article, but basically it's bringing analytics to transactions bringing those, those roles together. Right. Right. And you're saying with Mongo, it's, it's lightweight now take, you use two other examples in your article, my SQL heat wave. Right. I think you had a Google example as well, DB, those are, you're saying much, much heavier analytics, is that correct? Or >>I we'll put it this way. I think they're because they're coming from a relational background. And because they also are coming from companies that already have, you know, analytic database or data warehouses, if you will, that their analytic, you know, capabilities are gonna be much more fully rounded than what Mongo has at this point. It's not a criticism of a Mongo MongoDB per >>Per, is that by design though? Or ne not necessarily. Is that a function of maturity? >>I think it's function of maturity. Oh, okay. I mean, look, to a certain extent, it's also a function of design in terms of that the document model is a little, it's not impossible to basically model it for analytics, but it takes more, you know, transformation to, to decide which, you know, let's say field in that document is gonna be a column. >>Now, the big thing about some of these other, these hybrid systems is, is eliminating the need for two databases, right? Eliminating the need for, you know, complex ETL. Is, is that a value proposition that will emerge with, with Mongo in your view? >>You know, I, I mean, put it this way. I think that if you take a look at how they've, how Mongo is basically has added more function to its operations, someone talking about analytics here, for instance, adding streaming, you know, adding, adding, search, adding time series, that's a matter of like where they've eliminated the need to do, you know, transformation ETL, but that's not for analytics per se for analytics. I think through, you know, I mean through replication, there's still gonna be some transformation in terms of turning, let's say data, that's, that's formed in a document into something that's represented by columns. There is a form of transformation, you know, so that said, and Mongo is already, you know, it has some NA you know, nascent capability there, but it's all, but this is still like at a rev 1.0 level, you know, I expect a lot more >>Of so refin you, how Amazon says in the fullness of time, all workloads will be in the cloud. And we could certainly debate that. What do we mean by cloud? So, but there's a sort of analog for Mongo that I'll ask you in the fullness of time, will Mongo be in a position to replace data warehouses or data lakes? No. Or, or, or, and we know the answer is no. So that's of course, yeah. But are these two worlds on a quasi collision course? I think they >>More on a convergence course or the collision course, because number one is I said, the first principle and operational database is the last thing you wanna do is slow it down. And to do all this complex modeling that let's say that you would do in a data bricks, or very complex analytics that you would do in a snowflake that is going to get, you know, you know, no matter how much you partition the load, you know, in Atlas, and yes, you can have separate nodes. The fact is you really do not wanna burden the operational database with that. And that's not what it's meant for, but what it is meant for is, you know, can I make a smart decision on the spot? In other words, kinda like close the loop on that. And so therefore there's a, a form of lightweight analytic that you can perform in there. And actually that's also the same principle, you know, on which let's say for instance, you know, my SQL heat wave and Allo DBR based on, they're not, they're predicated on, they're not meant to replace, you know, whether it be exit data or big query, the idea there is to do more of the lightweight stuff, you know, and keep the database, you know, keep the operations, you know, >>Operating. And, but from a practitioner's standpoint, I, I, I can and should isolate you're saying that node, right. That's what they'll do. Sure. How does that affect cuz my understanding is that that the Mon Mongo specifically, but I think document databases generally will have a primary node. Right? And then you can set up secondary nodes, which then you have to think about availability, but, but would that analytic node be sort of fenced off? Is that part of the >>Well, that's actually what they're, they've already, I mean, they already laid the groundwork for it last year, by saying that you can set up separate nodes and dedicate them to analytics and what they've >>As, as a primary, >>Right? Yes, yes. For analytics and what they've added, what they're a, what they are adding this year is the fact to say like that separate node does not have to be the same instance class, you know, as, as, as, as the, >>What, what does that mean? Explain >>That in other words, it's a, you know, you could have BA you know, for instance, you could have a node for operations, that's basically very eye ops intensive, whereas you could have a node let's say for analytics that might be more compute intensive or, or more he, or, or more heavily, you know, configured with, with memory per se. And so the idea here is you can tailor in a node to the workload. So that's, you know what they're saying with, you know, and I forget what they're calling it, but the idea that you can have a different type, you can specify a different type of node, a different type of instance for the analytic node, I think is, you know, is a major step forward >>And that, and that that's enabled by the cloud and architecture. >>Of course. Yes. I mean, we're separating, compute from data is, is, is the starter. And so yeah. Then at that point you can then start to, you know, you know, to go less vanilla. I think, you know, the re you know, the, you know, the, I guess the fruition of this is going to be when they say, okay, you can run your, let's say your operational nodes, you know, dedicated, but we'll let you run your analytic nodes serverless. Can't do it yet, but I've gotta believe that's on the roadmap. >>Yeah. So seq brings a lot of overhead. So you get MQL, but now square this circle for me, cuz now you got Mago talking sequel. >>They had to start doing that some time. I mean, and I it's been a court take I've had from them from the, from the get go, which I said, I understand that you're looking at this as an alternative to SQL and that's perfectly valid, but don't deny the validity of SQL or the reason why we, you know, we need it. The fact is that you have, okay, the number, you know, according to Ty index, JavaScript is the seventh, most popular language. Most SQL follows closely behind at the ninth, most popular language you don't want to cl. And the fact is those people exist in the enterprise and they're, and they're disproportionately concentrated in analytics. I mean, you know, it's getting a little less, so now we're seeing like, you know, basically, you know, Python, the programmatic, but still, you know, a lot of sequel expertise there. It does not make, it makes no sense for Mongo to, to, to ignore or to overlook that audience. I think now they're, you know, you know, they're taking baby steps to start, you know, reaching out to them. >>It's interesting. You see it going both ways. See Oracle announces a Mongo, DB, Mongo. I mean, it's just convergence. You called it not, I love collisions, you know, >>I know it's like, because you thrive on drama and I thrive on can't. We all love each other, but you know, act. But the thing is actually, I've been, I wrote about this. I forget when I think it was like 2014 or 2016. It's when we, I was noticed I was noting basically the, you know, the rise of all these specialized databases and probably Amazon, you know, AWS is probably the best exemplar of that. I've got 15 or 16 or however, number of databases and they're all dedicated purpose. Right. But I also was, you know, basically saw that inevitably there was gonna be some overlap. It's not that all databases were gonna become one and the same we're gonna be, we're gonna become back into like the, you know, into a pan G continent or something like that. But that you're gonna have a relational database that can do JSON and, and a, and a document database that can do relational. I mean, you know, it's, to me, that's a no brainer. >>So I asked Andy Ja one time, I'd love to get your take on this, about those, you know, multiple data stores at the time. They probably had a thousand. I think they're probably up to 15 now, right? Different APIs, different S et cetera. And his response. I said, why don't you make it easier for, for customers and maybe build an abstraction or converge these? And he said, well, it's by design. What if you buy this? And, and what your thoughts are, cuz I, you know, he's a pretty straight shooter. Yeah. It's by design because it allows us as the market moves, we can move with it. And if we, if we give developers access to those low level primitives and APIs, then they can move with, with at market speed. Right. And so that again, by design, now we heard certainly Mongo poo pooing that today they didn't mention, they didn't call out Amazon. Yeah. Oracle has no compunction about specifically calling out Amazon. They do it all the time. What do you make of that? Can't Amazon have its cake and eat it too. In other words, extend some of the functionality of those specific databases without going to the Swiss army. >>I I'll put it this way. You, you kind of tapped in you're, you're sort of like, you know, killing me softly with your song there, which is that, you know, I was actually kind of went on a rant about this, actually know in, you know, come, you know, you know, my year ahead sort of out predictions. And I said, look, cloud folks, it's great that you're making individual SAS, you know, products easy to use. But now that I have to mix and match SAS products, you know, the burden of integration is on my shoulders. Start making my life easier. I think a good, you know, a good example of this would be, you know, for instance, you could take something like, you know, let's say like a Google big query. There's no reason why I can't have a piece of that that might, you know, might be paired, say, you know, say with span or something like that. >>The idea being is that if we're all working off a common, you know, common storage, we, you know, it's in cloud native, we can separate the computer engines. It means that we can use the right engine for the right part of the task. And the thing is that maybe, you know, myself as a consumer, I should not have to be choosing between big query and span. But the thing is, I should be able to say, look, I want to, you know, globally distribute database, but I also wanna do some analytics and therefore behind the scenes, you know, new microservices, it could connect the two wouldn't >>Microsoft synapse be an example of doing that. >>It should be an example. I wish I, I would love to hear more from Microsoft about this. They've been radio silent for about the past two or three years in data. You hardly hear about it, but synapse is actually those actually one of the ideas I had in mind now keep in mind that with synapse, you're not talking about, let's say, you know, I mean, it's, it's obviously a sequel data warehouse. It's not pure spark. It's basically their, it was their curated version of spark, but that's fine. But again, I would love to hear Microsoft talk more about that. They've been very quiet. >>Yeah. You, you, the intent is there to >>Simplify >>It exactly. And create an abstraction. Exactly. Yeah. They have been quiet about it. Yeah. Yeah. You would expect that, that maybe they're still trying to figure it out. So what's your prognosis from Mongo? I mean, since this company IP, you know, usually I, I tell and I tell everybody this, especially my kids, like don't buy a stock at IPO. You'll always get a better chance at a cheaper price to buy it. Yeah. And even though that was true with Mongo, you didn't have a big window. No. Like you did, for instance, with, with Facebook, certainly that's been the case with snowflake and sure. Alibaba, I mean, I name a zillion style was almost universal. Yeah. But, but since that, that, that first, you know, few months, period, this, this company has been on a roll. Right. And it, it obviously has been some volatility, but the execution has been outstanding. >>No question about that. I mean, the thing is, look what I, what I, and I'm just gonna talk on the product side on the sales side. Yeah. But on the product side, from the get go, they made a product that was easy for developers. Whereas let's say someone's giving an example, for instance, Cosmo CB, where to do certain operations. They had to go through multiple services in, you know, including Azure portal with Atlas, it's all within Atlas. So they've really, it's been kinda like design thinking from the start initially with, with the core Mongo DB, you know, you, the on premise, both this predates Atlas, I mean, part of it was that they were coming with a language that developers knew was just Javas script. The construct that they knew, which was JS on. So they started with that home core advantage, but they weren't the only ones doing that. But they did it with tooling that was very intuitive to developers that met developers, where they lived and what I give them, you know, then additional credit for is that when they went to the cloud and it wasn't an immediate thing, Atlas was not an overnight success, but they employed that same design thinking to Atlas, they made Atlas a good cloud experience. They didn't just do a lift and shift the cloud. And so that's why today basically like five or six years later, Atlas's most of their business. >>Yeah. It's what, 60% of the business now. Yeah. And then Dave, on the, on the earning scholar, maybe it wasn't Dave and somebody else in response to question said, yeah, ultimately this is the future will be be 90% of the business. I'm not gonna predict when. So my, my question is, okay, so let's call that the midterm midterm ATLA is gonna be 90% of the business with some exceptions that people just won't move to the cloud. What's next is the edge. A new opportunity is Mongo architecturally suited for the, I mean, it's certainly suited for the right, the home Depot store. Sure. You know, at the edge. Yeah. If you, if you consider that edge, which I guess it is form of edge, but how about the far edge EVs cell towers, you know, far side, real time, AI inferencing, what's the requirement there, can Mongo fit there? Any thoughts >>On that? I think the AI and the inferencing stuff is interesting. It's something which really Mongo has not tackled yet. I think we take the same principle, which is the lightweight stuff. In other words, you'll say, do let's say a classification or a prediction or some sort of prescriptive action in other words, where you're not doing some convolution, neural networking and trying to do like, you know, text, text to voice or, or, or vice versa. Well, you're not trying to do all that really fancy stuff. I think that's, you know, if you're keeping it SIM you know, kinda like the kiss principle, I think that's very much within Mongo's future. I think with the realm they have, they basically have the infrastructure to go out to the edge. I think with the fact that they've embraced GraphQL has also made them a lot more extensible. So I think they certainly do have, you know, I, I do see the edge as being, you know, you know, in, in, you know, in their, in their pathway. I do see basically lightweight analytics and lightweight, let's say machine learning definitely in their >>Future. And, but, and they would, would you agree that they're in a better position to tap that opportunity than say a snowflake or an Oracle now maybe M and a can change that. R D can maybe change that, but fundamentally from an architectural standpoint yeah. Are they in a better position? >>Good question. I think that that Mongo snowflake by virtual fact, I mean that they've been all, you know, all cloud start off with, I think makes it more difficult, not impossible to move out to the edge, but it means that, and I, and know, and I, and I said, they're really starting to making some tentative moves in that direction. I'm looking forward to next week to, you know, seeing what, you know, hearing what we're gonna, what they're gonna be saying about that. But I do think, right. You know, you know, to answer your question directly, I'd say like right now, I'd say Mongo probably has a, you know, has a head start there. >>I'm losing track of time. I could go forever with you. Tony bear DB insight with tons of insights. Thanks so much for coming back with. >>It's only one insight insight, Dave. Good to see you again. All >>Right. Good to see you. Thank you. Okay. Keep it right there. Right back at the Java center, Mongo DB world 2022, you're watching the cube.

(upbeat music) >> Welcome to this CUBE Power Panel where we're going to talk about application modernization, also success templates, and take a look at some new survey data to see how CIOs are thinking about digital transformation, as we get deeper into the post isolation economy. And with me are three familiar VIP guests to CUBE audiences. Tony Bear, the principal at DB InSight, Doug Henschen, VP and principal analyst at Constellation Research and Sanjeev Mohan principal at SanjMo. Guys, good to see you again, welcome back. >> Thank you. >> Glad to be here. >> Thanks for having us. >> Glad to be here. >> All right, Doug. Let's get started with you. You know, this recent survey, which was commissioned by Couchbase, 650 CIOs and CTOs, and IT practitioners. So obviously very IT heavy. They responded to the following question, "In response to the pandemic, my organization accelerated our application modernization strategy and of course, an overwhelming majority, 94% agreed or strongly agreed." So I'm sure, Doug, that you're not shocked by that, but in the same survey, modernizing existing technologies was second only behind cyber security is the top investment priority this year. Doug, bring us into your world and tell us the trends that you're seeing with the clients and customers you work with in their modernization initiatives. >> Well, the survey, of course, is spot on. You know, any Constellation Research analyst, any systems integrator will tell you that we saw more transformation work in the last two years than in the prior six to eight years. A lot of it was forced, you know, a lot of movement to the cloud, a lot of process improvement, a lot of automation work, but transformational is aspirational and not every company can be a leader. You know, at Constellation, we focus our research on those market leaders and that's only, you know, the top 5% of companies that are really innovating, that are really disrupting their markets and we try to share that with companies that want to be fast followers, that these are the next 20 to 25% of companies that don't want to get left behind, but don't want to hit some of the same roadblocks and you know, pioneering pitfalls that the real leaders are encountering when they're harnessing new technologies. So the rest of the companies, you know, the cautious adopters, the laggards, many of them fall by the wayside, that's certainly what we saw during the pandemic. Who are these leaders? You know, the old saw examples that people saw at the Amazons, the Teslas, the Airbnbs, the Ubers and Lyfts, but new examples are emerging every year. And as a consumer, you immediately recognize these transformed experiences. One of my favorite examples from the pandemic is Rocket Mortgage. No disclaimer required, I don't own stock and you're not client, but when I wanted to take advantage of those record low mortgage interest rates, I called my current bank and some, you know, stall word, very established conventional banks, I'm talking to you Bank of America, City Bank, and they were taking days and weeks to get back to me. Rocket Mortgage had the locked in commitment that day, a very proactive, consistent communications across web, mobile, email, all customer touchpoints. I closed in a matter of weeks an entirely digital seamless process. This is back in the gloves and masks days and the loan officer came parked in our driveway, wiped down an iPad, handed us that iPad, we signed all those documents digitally, completely electronic workflow. The only wet signatures required were those demanded by the state. So it's easy to spot these transformed experiences. You know, Rocket had most of that in place before the pandemic, and that's why they captured 8% of the national mortgage market by 2020 and they're on track to hit 10% here in 2022. >> Yeah, those are great examples. I mean, I'm not a shareholder either, but I am a customer. I even went through the same thing in the pandemic. It was all done in digital it was a piece of cake and I happened to have to do another one with a different firm and stuck with that firm for a variety of reasons and it was night and day. So to your point, it was a forced merge to digital. If you were there beforehand, you had real advantage, it could accelerate your lead during the pandemic. Okay, now Tony bear. Mr. Bear, I understand you're skeptical about all this buzz around digital transformation. So in that same survey, the data shows that the majority of respondents said that their digital initiatives were largely reactive to outside forces, the pandemic compliance changes, et cetera. But at the same time, they indicated that the results while somewhat mixed were generally positive. So why are you skeptical? >> The reason being, and by the way, I have nothing against application modernization. The problem... I think the problem I ever said, it often gets conflated with digital transformation and digital transformation itself has become such a buzzword and so overused that it's really hard, if not impossible to pin down (coughs) what digital transformation actually means. And very often what you'll hear from, let's say a C level, you know, (mumbles) we want to run like Google regardless of whether or not that goal is realistic you know, for that organization (coughs). The thing is that we've been using, you know, businesses have been using digital data since the days of the mainframe, since the... Sorry that data has been digital. What really has changed though, is just the degree of how businesses interact with their customers, their partners, with the whole rest of the ecosystem and how their business... And how in many cases you take look at the auto industry that the nature of the business, you know, is changing. So there is real change of foot, the question is I think we need to get more specific in our goals. And when you look at it, if we can boil it down to a couple, maybe, you know, boil it down like really over simplistically, it's really all about connectedness. No, I'm not saying connectivity 'cause that's more of a physical thing, but connectedness. Being connected to your customer, being connected to your supplier, being connected to the, you know, to the whole landscape, that you operate in. And of course today we have many more channels with which we operate, you know, with customers. And in fact also if you take a look at what's happening in the automotive industry, for instance, I was just reading an interview with Bill Ford, you know, their... Ford is now rapidly ramping up their electric, you know, their electric vehicle strategy. And what they realize is it's not just a change of technology, you know, it is a change in their business, it's a change in terms of the relationship they have with their customer. Their customers have traditionally been automotive dealers who... And the automotive dealers have, you know, traditionally and in many cases by state law now have been the ones who own the relationship with the end customer. But when you go to an electric vehicle, the product becomes a lot more of a software product. And in turn, that means that Ford would have much more direct interaction with its end customers. So that's really what it's all about. It's about, you know, connectedness, it's also about the ability to act, you know, we can say agility, it's about ability not just to react, but to anticipate and act. And so... And of course with all the proliferation, you know, the explosion of data sources and connectivity out there and the cloud, which allows much more, you know, access to compute, it changes the whole nature of the ball game. The fact is that we have to avoid being overwhelmed by this and make our goals more, I guess, tangible, more strictly defined. >> Yeah, now... You know, great points there. And I want to just bring in some survey data, again, two thirds of the respondents said their digital strategies were set by IT and only 26% by the C-suite, 8% by the line of business. Now, this was largely a survey of CIOs and CTOs, but, wow, doesn't seem like the right mix. It's a Doug's point about, you know, leaders in lagers. My guess is that Rocket Mortgage, their digital strategy was led by the chief digital officer potentially. But at the same time, you would think, Tony, that application modernization is a prerequisite for digital transformation. But I want to go to Sanjeev in this war in the survey. And respondents said that on average, they want 58% of their IT spend to be in the public cloud three years down the road. Now, again, this is CIOs and CTOs, but (mumbles), but that's a big number. And there was no ambiguity because the question wasn't worded as cloud, it was worded as public cloud. So Sanjeev, what do you make of that? What's your feeling on cloud as flexible architecture? What does this all mean to you? >> Dave, 58% of IT spend in the cloud is a huge change from today. Today, most estimates, peg cloud IT spend to be somewhere around five to 15%. So what this number tells us is that the cloud journey is still in its early days, so we should buckle up. We ain't seen nothing yet, but let me add some color to this. CIOs and CTOs maybe ramping up their cloud deployment, but they still have a lot of problems to solve. I can tell you from my previous experience, for example, when I was in Gartner, I used to talk to a lot of customers who were in a rush to move into the cloud. So if we were to plot, let's say a maturity model, typically a maturity model in any discipline in IT would have something like crawl, walk, run. So what I was noticing was that these organizations were jumping straight to run because in the pandemic, they were under the gun to quickly deploy into the cloud. So now they're kind of coming back down to, you know, to crawl, walk, run. So basically they did what they had to do under the circumstances, but now they're starting to resolve some of the very, very important issues. For example, security, data privacy, governance, observability, these are all very big ticket items. Another huge problem that nav we are noticing more than we've ever seen, other rising costs. Cloud makes it so easy to onboard new use cases, but it leads to all kinds of unexpected increase in spikes in your operating expenses. So what we are seeing is that organizations are now getting smarter about where the workloads should be deployed. And sometimes it may be in more than one cloud. Multi-cloud is no longer an aspirational thing. So that is a huge trend that we are seeing and that's why you see there's so much increased planning to spend money in public cloud. We do have some issues that we still need to resolve. For example, multi-cloud sounds great, but we still need some sort of single pane of glass, control plane so we can have some fungibility and move workloads around. And some of this may also not be in public cloud, some workloads may actually be done in a more hybrid environment. >> Yeah, definitely. I call it Supercloud. People win sometimes-- >> Supercloud. >> At that term, but it's above multi-cloud, it floats, you know, on topic. But so you clearly identified some potholes. So I want to talk about the evolution of the application experience 'cause there's some potholes there too. 81% of their respondents in that survey said, "Our development teams are embracing the cloud and other technologies faster than the rest of the organization can adopt and manage them." And that was an interesting finding to me because you'd think that infrastructure is code and designing insecurity and containers and Kubernetes would be a great thing for organizations, and it is I'm sure in terms of developer productivity, but what do you make of this? Does the modernization path also have some potholes, Sanjeev? What are those? >> So, first of all, Dave, you mentioned in your previous question, there's no ambiguity, it's a public cloud. This one, I feel it has quite a bit of ambiguity because it talks about cloud and other technologies, that sort of opens up the kimono, it's like that's everything. Also, it says that the rest of the organization is not able to adopt and manage. Adoption is a business function, management is an IT function. So I feed this question is a bit loaded. We know that app modernization is here to stay, developing in the cloud removes a lot of traditional barriers or procuring instantiating infrastructure. In addition, developers today have so many more advanced tools. So they're able to develop the application faster because they have like low-code/no-code options, they have notebooks to write the machine learning code, they have the entire DevOps CI/CD tool chain that makes it easy to version control and push changes. But there are potholes. For example, are developers really interested in fixing data quality problems, all data, privacy, data, access, data governance? How about monitoring? I doubt developers want to get encumbered with all of these operationalization management pieces. Developers are very keen to deliver new functionality. So what we are now seeing is that it is left to the data team to figure out all of these operationalization productionization things that the developers have... You know, are not truly interested in that. So which actually takes me to this topic that, Dave, you've been quite actively covering and we've been talking about, see, the whole data mesh. >> Yeah, I was going to say, it's going to solve all those data quality problems, Sanjeev. You know, I'm a sucker for data mesh. (laughing) >> Yeah, I know, but see, what's going to happen with data mesh is that developers are now going to have more domain resident power to develop these applications. What happens to all of the data curation governance quality that, you know, a central team used to do. So there's a lot of open ended questions that still need to be answered. >> Yeah, That gets automated, Tony, right? With computational governance. So-- >> Of course. >> It's not trivial, it's not trivial, but I'm still an optimist by the end of the decade we'll start to get there. Doug, I want to go to you again and talk about the business case. We all remember, you know, the business case for modernization that is... We remember the Y2K, there was a big it spending binge and this was before the (mumbles) of the enterprise, right? CIOs, they'd be asked to develop new applications and the business maybe helps pay for it or offset the cost with the initial work and deployment then IT got stuck managing the sprawling portfolio for years. And a lot of the apps had limited adoption or only served a few users, so there were big pushes toward rationalizing the portfolio at that time, you know? So do I modernize, they had to make a decision, consolidate, do I sunset? You know, it was all based on value. So what's happening today and how are businesses making the case to modernize, are they going through a similar rationalization exercise, Doug? >> Well, the Y2K era experience that you talked about was back in the days of, you know, throw the requirements over the wall and then we had waterfall development that lasted months in some cases years. We see today's most successful companies building cross functional teams. You know, the C-suite the line of business, the operations, the data and analytics teams, the IT, everybody has a seat at the table to lead innovation and modernization initiatives and they don't start, the most successful companies don't start by talking about technology, they start by envisioning a business outcome by envisioning a transformed customer experience. You hear the example of Amazon writing the press release for the product or service it wants to deliver and then it works backwards to create it. You got to work backwards to determine the tech that will get you there. What's very clear though, is that you can't transform or modernize by lifting and shifting the legacy mess into the cloud. That doesn't give you the seamless processes, that doesn't give you data driven personalization, it doesn't give you a connected and consistent customer experience, whether it's online or mobile, you know, bots, chat, phone, everything that we have today that requires a modern, scalable cloud negative approach and agile deliver iterative experience where you're collaborating with this cross-functional team and course correct, again, making sure you're on track to what's needed. >> Yeah. Now, Tony, both Doug and Sanjeev have been, you know, talking about what I'm going to call this IT and business schism, and we've all done surveys. One of the things I'd love to see Couchbase do in future surveys is not only survey the it heavy, but also survey the business heavy and see what they say about who's leading the digital transformation and who's in charge of the customer experience. Do you have any thoughts on that, Tony? >> Well, there's no question... I mean, it's kind like, you know, the more things change. I mean, we've been talking about that IT and the business has to get together, we talked about this back during, and Doug, you probably remember this, back during the Y2K ERP days, is that you need these cross functional teams, we've been seeing this. I think what's happening today though, is that, you know, back in the Y2K era, we were basically going into like our bedrock systems and having to totally re-engineer them. And today what we're looking at is that, okay, those bedrock systems, the ones that basically are keeping the lights on, okay, those are there, we're not going to mess with that, but on top of that, that's where we're going to innovate. And that gives us a chance to be more, you know, more directed and therefore we can bring these related domains together. I mean, that's why just kind of, you know, talk... Where Sanjeev brought up the term of data mesh, I've been a bit of a cynic about data mesh, but I do think that work and work is where we bring a bunch of these connected teams together, teams that have some sort of shared context, though it's everybody that's... Every team that's working, let's say around the customer, for instance, which could be, you know, in marketing, it could be in sales, order processing in some cases, you know, in logistics and delivery. So I think that's where I think we... You know, there's some hope and the fact is that with all the advanced, you know, basically the low-code/no-code tools, they are ways to bring some of these other players, you know, into the process who previously had to... Were sort of, you know, more at the end of like a, you know, kind of a... Sort of like they throw it over the wall type process. So I do believe, but despite all my cynicism, I do believe there's some hope. >> Thank you. Okay, last question. And maybe all of you could answer this. Maybe, Sanjeev, you can start it off and then Doug and Tony can chime in. In the survey, about a half, nearly half of the 650 respondents said they could tangibly show their organizations improve customer experiences that were realized from digital projects in the last 12 months. Now, again, not surprising, but we've been talking about digital experiences, but there's a long way to go judging from our pandemic customer experiences. And we, again, you know, some were great, some were terrible. And so, you know, and some actually got worse, right? Will that improve? When and how will it improve? Where's 5G and things like that fit in in terms of improving customer outcomes? Maybe, Sanjeev, you could start us off here. And by the way, plug any research that you're working on in this sort of area, please do. >> Thank you, Dave. As a resident optimist on this call, I'll get us started and then I'm sure Doug and Tony will have interesting counterpoints. So I'm a technology fan boy, I have to admit, I am in all of all these new companies and how they have been able to rise up and handle extreme scale. In this time that we are speaking on this show, these food delivery companies would have probably handled tens of thousands of orders in minutes. So these concurrent orders, delivery, customer support, geospatial location intelligence, all of this has really become commonplace now. It used to be that, you know, large companies like Apple would be able to handle all of these supply chain issues, disruptions that we've been facing. But now in my opinion, I think we are seeing this in, Doug mentioned Rocket Mortgage. So we've seen it in FinTech and shopping apps. So we've seen the same scale and it's more than 5G. It includes things like... Even in the public cloud, we have much more efficient, better hardware, which can do like deep learning networks much more efficiently. So machine learning, a lot of natural language programming, being able to handle unstructured data. So in my opinion, it's quite phenomenal to see how technology has actually come to rescue and as, you know, billions of us have gone online over the last two years. >> Yeah, so, Doug, so Sanjeev's point, he's saying, basically, you ain't seen nothing yet. What are your thoughts here, your final thoughts. >> Well, yeah, I mean, there's some incredible technologies coming including 5G, but you know, it's only going to pave the cow path if the underlying app, if the underlying process is clunky. You have to modernize, take advantage of, you know, serverless scalability, autonomous optimization, advanced data science. There's lots of cutting edge capabilities out there today, but you know, lifting and shifting you got to get your hands dirty and actually modernize on that data front. I mentioned my research this year, I'm doing a lot of in depth looks at some of the analytical data platforms. You know, these lake houses we've had some conversations about that and helping companies to harness their data, to have a more personalized and predictive and proactive experience. So, you know, we're talking about the Snowflakes and Databricks and Googles and Teradata and Vertica and Yellowbrick and that's the research I'm focusing on this year. >> Yeah, your point about paving the cow path is right on, especially over the pandemic, a lot of the processes were unknown. But you saw this with RPA, paving the cow path only got you so far. And so, you know, great points there. Tony, you get the last word, bring us home. >> Well, I'll put it this way. I think there's a lot of hope in terms of that the new generation of developers that are coming in are a lot more savvy about things like data. And I think also the new generation of people in the business are realizing that we need to have data as a core competence. So I do have optimism there that the fact is, I think there is a much greater consciousness within both the business side and the technical. In the technology side, the organization of the importance of data and how to approach that. And so I'd like to just end on that note. >> Yeah, excellent. And I think you're right. Putting data at the core is critical data mesh I think very well describes the problem and (mumbles) credit lays out a solution, just the technology's not there yet, nor are the standards. Anyway, I want to thank the panelists here. Amazing. You guys are always so much fun to work with and love to have you back in the future. And thank you for joining today's broadcast brought to you by Couchbase. By the way, check out Couchbase on the road this summer at their application modernization summits, they're making up for two years of shut in and coming to you. So you got to go to couchbase.com/roadshow to find a city near you where you can meet face to face. In a moment. Ravi Mayuram, the chief technology officer of Couchbase will join me. You're watching theCUBE, the leader in high tech enterprise coverage. (bright music)

(upbeat music) >> We're back at the ARIA in Las Vegas you're watching theCUBE's coverage of Veeamon 2022 live in person, but there's a big hybrid event going on. Close to 40,000 people watching online. This is the CEO segment. The newly minted CEO Anand Eswaran is here. And it's great to see you. Thanks for coming on. First time on theCUBE. >> Yeah, first time on theCUBE, excited to be here. Newly minted, those words, I haven't heard those for a long time. But thank you for the warm introduction, Dave. >> So why Veeam? What did you see that attracted you to Veeam? You have a great career, awesome resume. Why Veeam? >> A lot of different things. You know, it started with when I spent a good bit of time... I spent months with, you know, Insight, with Bill Largent who is now the chair of the board. And for me a few things, one, it started with the company culture. I absolutely loved... I spent time with our engineering team that it's an innovation-focused culture. It's an engineering-focused culture, which is so critical to any software company. And so that was the first place. And I spent a good bit of time with customers, reading, research, you know, brilliant products, always innovating. You know, even though it's a category you would think is fairly mature. I mean, when Veeam for example, did instant recovery. I mean, that was extreme innovation. And so that was the first thing which was appealing. The second thing was, you know, yes, we've reached a billion, but it still has that, you know, feel of a start up. It still has that feel of a... Jeff Bezos says this best, "A day one culture." Which is super critical, you know, we scale but you don't want to lose your soul and what made you special in the first place. And then you come down to the rest of the stuff, it's an execution machine. You know, it's an absolutely interesting category, especially in the day and the world we live in right now. You know, the proliferation of bad actors, security, backup, recovery, you know, everything you... Ransomware is starting to become you know, a meaningful threat to every company. So many different things coming together. This category is an interesting place. But that's not all, I feel that we are going to see this category evolve and shape very differently. You're going to see adjacencies coming and you're going to see in a couple of years or three years you're not going to just look at this and say, "Hey, it's backup and recovery." And so it's an opportunity to shape what is going to be a very important inflection point in this whole space. So a whole bunch of things. Excited about it. >> So flip question, why Anand? What did Insight see and the board see? What do you see as your key skills that they wanted here? Go after that opportunity- >> You should also get insight on this man. And you should ask them this question. I know Peter and Sokolov (laughs) >> So, you know, I don't know... I'll tell you where I think there's relevant experience is if I look at the future of Veeam. I think the first thing is we've got to think through what the next evolution of Veeam is. You know, there's a ton of work to do even in the path we are on, on data protection. And the team is absolutely brilliant at that. But how do you start to think ahead? How do you think about data management? How do you think about, you know, where are the adjacencies and how does it... How do you shape and reshape the category? You know, and I have some experiences in that. As I look at growth, Veeam has done a phenomenal job you know, 35,000 partners, an execution machine. I mean, just last year we grew ARR, you know, 27% we are sustaining that growth. But as I look ahead, you know there's huge opportunities to further accelerate our share in the enterprise to actually go work with creating multiple layers of partnerships beyond the very successful partnerships we already have. You know, how do you start to get GSIs in the mix? How do you start to get MSPs in the mix? How do you start to actually get to being a core part of the portfolio and platform of our primary storage partners, HPE, Pure Storage and so on. So reinvigorating and creating a multidimensional partnership strategy is key as well. And then just my experience in, you know I ran the enterprise for Microsoft and so those sort of experiences sort of are very relevant to our next step of the journey as well. And finally, you know I think the one thing which matters most for me and yeah, you realize... Again, I think we've forgotten what it means to have a microphone on. But culture, you know, I spent a lot of time in every company I've worked in, in contributing to the culture of what shapes and you know how do you create a purpose-led company and how do you get on that path? Which is a very, very important conversation inside Veeam. you know, and we already do that... You know, there's a huge focus on purpose. There's a huge focus on diversity. There's a huge focus on inclusion, but you know, the cultural aspect of Veeam attracted me to it. And I think my work and my passion for it attracted me to Veeam as well. So just a few of those things. >> Yeah, you speak from the heart, you can sense that. Dave and I were talking with Zias about platform versus product. Now you've got some experience with platforms, obviously, Microsoft, you know the amazing platform. RingCentral Zias brought up. And then I brought up HP, which actually never could figure out its software platform. So you've seen some successes. You've seen some, you know, couldn't ever get there. Do you see Veeam as a platform company? >> You know, the way I look at it is this. I mean, I may actually not answer your question directly but I'll answer the question. >> Dave: Okay. >> Which is, if you look at the biggest successes in the industry, call it Microsoft, Adobe now- >> Dave: Sure. >> Salesforce, eventually the path from a high growth startup to scale is platform and partners. That is the key. >> Dave: Ecosystem- >> So yeah. Platform and the ecosystem. So it all comes together. And so, yes, I mean, I think we already do that. I mean, we have a singular platform today for the multiple workloads we protect from, you know physical to cloud, to Kubernetes to the hybrid architectures the ability to actually, you know restore your data into any cloud, you know, back up from AWS restore into Azure or a physical data center. So we already have a robust platform in place but the scale or the growth from where we are a billion to the next set of milestones 2, 3, 5, 10 is going to be an absolute maturity and amp of platform partnerships ecosystem. >> That's a high wire act. When you talk about platform and scaling, you know, think about moving forward, when you have pressure to grow, often the easiest thing to grow is to acquire and add adjacencies that might not be as core to your core value proposition as they could be. How do you navigate that as you move forward in a world where... Look, Veeam was founded in an age when it was all about meantime between failure, recovery point, recovery time objectives. Now the big concern is malicious actors. So Veeam has been able to navigate that transition very well so far, but how do you do that? How do you balance that moving forward? This idea of platform is a desirous state to be in but you don't want to be a fake platform where you just glue a bunch of things on. >> It all comes down to thinking through where we see the world going from this point in time. How do you see technology evolving? How do you see the outside's, you know influences evolving. And when I say influences, it's, you know, just a euphemism for all the bad actors we expect to see getting even more active. So, you know, the way I think about it is either platform or acquisitions are not things you do piecemeal or point in time. It all needs to accrue to a larger strategy of how you create the ability for all of your customers to own protect, secure, you know their data and eventually create intelligence from it so that they can actually be proactive about it. So that, you know, if that's the thing, you know, our ambition is starting to become how do we sort of secure the world's data and help companies create intelligence from it so they can be proactive about it? You know, everything else sort of accrues from there the platform we evolve from the platform we already have, you know, stems from it. The acquisitions we may do, will do evolves from it. It all are... You know, its pieces coming together to the overall puzzle framework we've already created. >> Yeah. I have so many questions for you. And I want to get into a little bit of your philosophy, but before we do it, I want touch on the TAM a little bit more. You mentioned in the analyst discussion this morning that the market's fragmented. A lot of people think, "Oh, backup, storage, we'll just put it together. You know, Dell now or EMC brought it all together." But they're just dramatically different markets. You're seeing some of your competitors. One in particular is now kind of pivoting to security. It's an adjacency, but it's, yeah, I'm not sure you want to walk into that mess but it's clearly part of a data protection strategy. And you said you want your... My words, legacy to be a significant increase in market share, dominant position in the market. Even if it's number two, whatever, number one's nice, great. But much larger share than what is your 10, 12% today. How do you think about the TAM? It's so undercounted, I think. You know, we used to look at purpose built backup appliances, "Oh, it's a couple billion dollar market and it's a ceiling there." It reminds me of service management with ServiceNow. It's virtually unlimited TAM because it's data. How do you look at the TAM? >> How much time do you have? >> I know, I got so many questions- >> But I'll tell you this, right? You got to piece this question very carefully because I'll look at it in a variety of different ways. Number one, if you do nothing, if you just do nothing. I mean, today, as I shared in IDCs latest report last week we were joined number one, you know, for the first time we actually got- >> Dave: Yeah. Congratulations. That's a big milestone. >> That's huge, that's exciting. >> Dave: And that's revenue by the way. That's not licenses- >> Yeah. That's in share. But the thing is this, right? If, if you look at share, we are at 12%, you know as is the... You know, so 12% is not representative of how I think about number one. When you look at a market with a clear winner you expect to see 40 to 60% market share. So doing nothing is an opportunity to actually continue the path we are on which is taking share from every one of the top five significantly and growing as fast as we are. I mean, we are going to be on a path to, you know doubling our market share in the next two to three years. So there's share to gain doing nothing. And this is... You know what? This is the first and the most simplest aspect of TAM. Now layer in other aspects of TAM but just still stay in data protection. You know, talk about every single SaaS workload coming on. I mean, I shared 270 million Teams' users right now monthly actives. The TAM, if you were able to secure every one of those Teams' users and protect the data, I mean that's close to 6 or $7 billion. It's not factored in into any of the TAM numbers you see right now. Gartner talked about 13. You know, others talked about TAM being 40. I mean, but SaaS workloads, you know each of them are not factored in as much as it could be right now. So, you know, we are bringing in Salesforce, Microsoft 365. We secure 11 million paid users with Microsoft 365 backup. And so add all of them on, execute. We see a path to taking share and getting from here 12 to 25 to 40 and being an outsize number one. And then you'd come down to what you said, which is how do you think about adjacencies? Now, at Veeam, yeah, messaging is important, but unlike some of the competitors, we don't use words frivolously. If we say something, data protection, modern data protection, ransomware attacks, we mean it. And there's product truth behind it. We do not use frivolous security words to create a message and get attention and have no product truth behind it. That's where we are. We expect to see adjacencies come up. We expect Veeam to beyond execution and bringing in more SaaS workloads to look at the next layer of data management. We expect us to create partnerships which allow us to go do that meaningfully. And as time goes, you should expect us to be the prime influencer in reshaping this category with other adjacencies coming in. But we talk about it and there's product truth behind it. >> I wanted to get into your philosophy of management a little bit. I went to your LinkedIn recently and I loved the little graphic that you had. But I know a lot of people put up a picture of a pretty lake or mountains. I got theCUBE up there. You had a number of items. I wonder if I could read. You had a rocket ship, which was very cool. You had teamwork, you had innovation. I wrote down ABC, always be closing, Alec Baldwin. But everybody sells, I think is what it was and then keep it simple. >> Anand: Yep. >> I really like that. I mean, people going to... If they're going to evaluate Veeam they're going to go to your LinkedIn page. So tell us where that came from what your philosophy is as a manager. >> Yeah, no. So there's a few things and this is the philosophies which I put on is a meld of what I believe in and what Veeam believes in and has believed in for a long time which is life starts with a customer. For us, everything starts with a customer. You know, even the product creation philosophy 15 years back was, "Hey let's not just create some check marks and create a feature because someone, you know thought it's an important check mark to have." What is the value it creates for the customer? And is it different enough, unique enough, where, you know, it actually creates a moment where the customer sees the value impact their core business. That's where it all starts for us at Veeam. And then everything we do relates back to, "Is this moving the ball enough for our customers and for our partners and for our developers and users?" Everything comes back to there. Are we easy enough to do business with? You know, are we keeping it simple? Simple to use. A product should be really simple. It should be brain dead simple, you know are our processes such that, you know it's easy for us to connect with our partners, connect with our customers, connect with our users, you know it all comes back to keeping it really simple. And then, you know, I come down to a set of personal philosophies, which matters as well, which is, you know, how do we make sure that, you know, we used to say everyone is in sales, but we got to evolve it. Everyone is in customer success because we all know that it's not just the first sale which matters which was true 15 years back, what matters today is, yeah, the sale matters, everybody is there to sell. But what matters even more is the whole company rallies behind the customer's success at every step along the way. Because when you do that, you don't need to sell. You know, you get in through BBR and then we have a world of workloads to actually create value for the customer with, from, you know Microsoft 365 backup or, you know soon to come Salesforce backup cost. And we see that on net retention or, you know... And it's manifested in numbers, right? It's manifested in growth. It's manifested in net retention and it's manifested in NPS. I mean, Dave, I'm hugely excited about that, man. NPS of 80 where we are. I mean, you guys have been around for quite a bit. I mean, that's huge numbers. I mean, that's- >> Apple's- >> Apple was 76 or 77. And so eventually that is what matters more for me because it's... Share is important. And I'm excited about, you know, IDC saying, "You're joint number one." Hugely important, but that is a consequence. Growth is important. 25% ARR growth in Q1, super important but that is a consequence. What really matters is value for your customers. And the number one metric I look at is NPS, you know and NPS at 80, all the other things start to happen pair it with the engineering culture the innovation culture we have, long roadmap ahead. >> Veeam has made some... What appear to be, from the outside anyway, pretty successful acquisitions. Kasten is an obvious one. I remember it wasn't the first time I met Ratmir. It was maybe the third or fourth time we were at like a late night, Peter Bell party this Highland Capital at VMware. And we were walking down Howard Street. I see Ratmir and some of his colleagues, we start chatting. We, you know, got into a good conversation. I'm like, "What about an IPO?" He goes, "We're not doing an IPO. We don't need to do an IPO." And then several years later on theCUBE, he's like, "No, I'd be open to an IPO." And then of course the big acquisition happened. So you've got an opportunity here M and A obviously is a possibility. But what about the IPO in your future? Presumably, that's something Insight wants to do. What can you tell us about that? >> No, it's a great question. I was waiting when you were going to ask me that question. But this is what I would say which is, by the way, Veeam, at today's numbers, I mean, we shared numbers at the end of last year. 1.1 billion in ARR, 1.2 in revenues, 99.99% organic, right? You know, Kasten was the only acquisition we shared how Kasten is a blip at this point in time. And so the philosophy has always been organic. And as I look ahead, this is how I think about it. I think the pace of market change is going to be extreme. And so we will be a lot more open-minded, thinking about acquisitions for complimentary technologies which allow us to expand TAM and think about adjacencies, more to come there. IPO, see the good thing is this, a lot of companies want to enter the public markets to raise money, create liquidity. That's not the primary lens for us. And so the good news is that, you know we are incredibly profitable. We shared, you know, 30% EBITDA, you know, for 2021. So money is not the issue, but we do think that we entering the public markets is a good thing for a variety of other reasons, because when you are public and it comes with the, you know, transparency, which we believe we're already transparent. But it puts the focus on you. And that creates even better growth impetus. Especially as you go work with large enterprise customers they are a lot more amenable and you know and so we feel that it's a strategy of growth not a strategy of liquidity for us, but stay tuned. You know, I fully expect for something like that to happen sometime towards the middle of next year, to the end of next year. >> Yeah, we had a similar conversation with Frank Slootman they obviously were able to raise money. But wow, what a change since the snowflake IPO in terms of just the brand value. And again, so many questions. I thought your keynote was great, by the way. >> Anand: Thank you. I love the focus on, you know, ransomware, of course. I thought the bot jokes were great. Keep 'em coming. I mean, I really did enjoy- >> (laughs) Absolutely. >> It lightens things up. So thanks so much for coming on theCUBE, really appreciate your time. >> Absolutely appreciate it, Dave and Dave. By the way, I mean, it's funny, I mean about, you know, Dave and Dave, Dave and David reminded me of Thompson and Thompson, guess which comic book they're from? >> Thompson and Thompson- >> Thompson and Thompson. I don't know. >> Don't know. >> Tin Tin. >> Oh (laughs). >> (laughs) So you got to go read up. You guys don't look anything like that, but Dave and Dave, was an absolute pleasure. My first theCUBE and look forward to many more to come. >> Love to have you back- >> Absolutely. >> All right. Thank you for watching. >> Thank you. >> Keep it right there. TheCUBE's coverage of Veeamon 2022, 2 days of wall to wall coverage here at the ARIA in Las Vegas, we'll be right back. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Mobile World Congress is alive, theCUBE will be there and we'll certainly let you know if it's alive and well when we get on the ground. Now, as we approach a delayed mobile world congress, it's really appropriate to reflect in the state of the telecoms industry. Let's face it. Telcos have done of really good job of keeping us all connected during the pandemic, supporting work from home and that whole pivot, accommodating the rapid shift to landline traffic, securing the network and keeping it up and running but it doesn't change the underlying fundamental dilemma that Telco has faced. Telco is a slow growth, no growth industry, with revenue expectations in the low single digits. And at the same time network traffic continues to grow at 20% annually. And last year it grew at 40% to 50%. Despite these challenges, Telcos are still investing in the future. For example, the Telco industry collectively is selling out more than a trillion dollars in the first half of this decade on 5G and fiber infrastructure. And it's estimated that there are now more than 200 5G networks worldwide. But a lot of questions remain, not the least of which is, can and should Telcos go beyond connectivity and fiber. Can the Telcos actually monetize 5G or whatever's next beyond 5G? Or is that going to be left to the ecosystem? Now what about the ecosystem? How is that evolving? And very importantly, what role will the Cloud Hyperscalers play in Telco? Are they infrastructure on which the Telcos can build or are they going to suck the value out of the market as they have done in the enterprise? Hello everyone, and welcome to this week's Wiki Bond Cube Insights powered by ETR. In this breaking analysis, it's my pleasure to welcome a long time telecoms industry analyst and colleague, and the founding director of Lewis Insight, Mr. Chris Lewis. Chris, welcome to the program. Thanks for coming on >> Dave, it's a pleasure to be here. Thank you for having me. >> It is really our pleasure. So, we're going to cover a lot of ground today. And first thing, we're going to talk about Mobile World Congress. I've never been, you're an expert at that and what we can expect. And then we're going to review the current state of telecoms infrastructure, where it should go. We're going to dig into transformation. Is it a mandate? Is it aspirational? Can Telcos enter adjacent markets in ways they haven't been able to in the past? And then how about the ecosystem? We're going to talk about that, and then obviously we're going to talk about Cloud as I said, and we'll riff a little bit on the tech landscape. So Chris, let's get into it, Mobile World Congress, it's back on, what's Mobile World Congress typically like? What's your expectation this year for the vibe compared to previous events? >> Well Dave, the issue of Mobile World Congress is always that we go down there for a week into Barcelona. We stress ourselves building a matrix of meetings in 30 minutes slots and we return at the end of it trying to remember what we'd been told all the way through. The great thing is that with the last time we had a live, with around 110,000 people there, you could see anyone and everyone you needed to within the mobile, and increasingly the adjacent industry and ecosystem. So, he gave you that once a year, big download of everything new, obviously because it's the Mobile World Congress, a lot of it around devices, but increasingly over the last few years, we saw many, many stands with cars on them because the connected car became an issue, a lot more software oriented players there, but always the Telcos, always the people providing the network infrastructure. Increasingly in the last few years people provided the software and IT infrastructure, but all of these people contributing to what the network should be in the future, what needs to be connected. But of course the reach of the network has been growing. You mentioned during lockdown about connecting people in their homes, well, of course we've also been extending that connection to connect things whether it's in the home or the different devices, monitoring of doorbells and lights and all that sort of stuff. And in the industry environment, connecting all of the robots and sensors. So, actually the perimeter, the remit of the industry to connect has been expanding, and so is the sort of remit of Mobile World Congress. So, we set an awful lot of different suppliers coming in, trying to attach to this enormous market of roughly $1.5 trillion globally. >> Chris, what's the buzz in the industry in terms of who's going to show up. I know a lot of people have pulled out, I've got the Mobile World Congress app and I can see who's attending. And it looks like quite a few people are going to go but what's your expectation? >> Well, from an analyst point of view, obviously I'm mainly keeping up with my clients and trying to get new clients. I'm looking at it and going most of my clients are not attending in person. Now, of course, we need the DSMA, we need Mobile World Congress for future for the industry interaction. But of course, like many people having adopted and adapted to be online, then they're putting a lot of the keynotes online, a lot of the activities will be online. But of course many of the vendors have also produced their independent content and content to actually deliver to us as analysts. So, I'm not sure who will be there. I like you, but you'll be on the ground. You'll be able to report back and let us know exactly who turned up. But from my point of view, I've had so many pre-briefs already, the difference between this year and previous years, I used to get loads of pre-briefs and then have to go do the briefs as well. So this year I've got the pre-brief so I can sit back, put my feet up and wait for your report to come back as to what's happening on the ground. >> You got it. Okay, let's get into a little bit and talk about Telco infrastructure and the state, where it is today, where it's going, Chris, how would you describe the current state of Telco infrastructure? Where does it need to go? Like, what is the ideal future state look like for Telcos in your view? >> So there's always a bit of an identity crisis when it comes to Telco. I think going forward, the connectivity piece was seen as being table stakes, and then people thought where can we go beyond connectivity? And we'll come back to that later. But actually to the connectivity under the scenario I just described of people, buildings, things, and society, we've got to do a lot more work to make that connectivity extend, to be more reliable, to be more secure. So, the state of the network is that we have been building out infrastructure, which includes fiber to connect households and businesses. It includes that next move to cellular from 4G to 5G. It obviously includes Wi-Fi, wherever we've got that as well. And actually it's been a pretty good state, as you said in your opening comments they've done a pretty good job keeping us all connected during the pandemic, whether we're a fixed centric market like the UK with a lot of mobile on top and like the US, or in many markets in Africa and Asia, where we're very mobile centric. So, the fact is that every country market is different, so we should never make too many assumptions at a very top level, but building out that network, building out the services, focusing on that connectivity and making sure we get that cost of delivery right, because competition is pushing us towards having and not ever increasing prices, because we don't want to pay a lot extra every time. But the big issue for me is how do we bring together the IT and the network parts of this story to make sure that we build that efficiency in, and that brings in many questions that we going to touch upon now around Cloud and Hyperscalers around who plays in the ecosystem. >> Well, as you know, Telco is not my wheelhouse, but hanging around with you, I've learned, you've talked a lot about the infrastructure being fit for purpose. It's easy from an IT perspective. Oh yeah, it's fossilized, it's hardened, and it's not really flexible, but the flip side of that coin is as you're pointing out, it's super reliable. So, the big talk today is, "Okay, we're going to open up the network, open systems, and Open RAN, and open everything and microservices and containers. And so, the question is this, can you mimic that historical reliability in that open platform? >> Well, for me, this is the big trade-off and in my great Telco debate every year, I always try and put people against each other to try and to literally debate the future. And one of the things we looked at was is a more open network against this desire of the Telcos to actually have a smaller supplier roster. And of course, as a major corporation, these are on a national basis, very large companies, not large compared to the Hyperscalers for example, but they're large organizations, and they're trying to slim down their organization, slim down the supplier ecosystem. So actually in some ways, the more open it becomes, the more someone's got to manage and integrate all those pieces together. And that isn't something we want to do necessarily. So, I see a real tension there between giving more and more to the traditional suppliers. The Nokia's, Ericsson's, Huawei's, Amdocs and so on, the Ciscos. And then the people coming in breaking new ground like Mavenir and come in, and the sort of approach that Rakuten and Curve taken in bringing in more open and more malleable pieces of smaller software. So yeah, it's a real challenge. And I think as an industry which is notorious for being slow moving, actually we've begun to move relatively quickly, but not necessarily all the way through the organization. We've got plenty of stuff sitting on major or mainframes still in the back of the organization. But of course, as mobile has come in, we've started to deal much more closely, uninteractively in real time, God forbid, with the customers. So actually, at that front end, we've had to do things a lot more quickly. And that's where we're seeing the quickest adaptation to what you might see in your IT environment as being much more, continuous development, continuous improvement, and that sort of on demand delivery. >> Yeah, and we're going to get to that sort of in the Cloud space, but I want to now touch on Telco transformation which is sort of the main theme of this episode. And there's a lot of discussion on this topic, can Telcos move beyond connectivity and managing fiber? Is this a mandate? Is it a pipe dream that's just aspirational? Can they attack adjacencies to grow beyond the 1% a year? I mean, they haven't been successful historically. What are those adjacencies that might be, an opportunity and how will that ecosystem develop? >> Sure. >> So Chris, can and should Telcos try to move beyond core connectivity? Let's start there. >> I like what you did there by saying pipe dreams. Normally, pipe is a is a negative comment in the telecom world. But pipe dream gives it a real positive feel. So can they move beyond connectivity? Well, first of all, connectivity is growing in terms of the number of things being connected. So, in that sense, the market is growing. What we pay for that connectivity is not necessarily growing. So, therefore the mandate is absolutely to transform the inner workings and reduce the cost of delivery. So, that's the internal perspective. The external perspective is that we've tried in many Telcos around the world to break into those adjacent markets, being around media, being enterprise, being around IOT, and actually for the most part they've failed. And we've seen some very significant recent announcements from AT&T, Verizon, BT, beginning to move away from, owning content and not delivering content, but owning content. And the same as they've struggled often in the enterprise market to really get into that, because it's a well-established channel of delivery bringing all those ecosystem players in. So, actually rather than the old Telco view of we going to move into adjacent markets and control those markets, actually moving into them and enabling fellow ecosystem players to deliver the service is what I think we're beginning to see a lot more of now. And that's the big change, it's actually learning to play with the other people in the ecosystem. I always use a phrase that there's no room for egos in the ecosystem. And I think Telcos went in initially with an ego thinking we're really important, we are on connectivity. But actually now they're beginning to approach the ecosystem things saying, "How can we support partners? How can we support everyone in this ecosystem to deliver the services to consumers, businesses and whomever in this evolving ecosystem?" So, there are opportunities out there, plenty of them, but of course, like any opportunity, you've got to approach it in the right way. You've got to get the right investment in place. You've got to approach it with the right open API so everyone can integrate with your approach, and approach it, do I say with a little bit of humility to say, "Hey, we can bring this to the table, how do we work together? >> Well, it's an enormous market. I think you've shared with me, it's like 1.4 trillion. And I want to stay on these adjacencies for a minute, because one of the obvious things that Telcos will talk about is managed services. And I know we have to be careful of that term in an IT context, that it's different in a, you're talking about managing connectivity, but there's professional services. That's a logical sort of extension of their business and probably a safe adjacency, maybe not even adjacency, but they're not going to get into devices. I mean, they'll resell devices, but they're not going to be, I would presume not go back to trying to make devices, but there's certainly the edge and that's so, it'll define in opaque, but it's huge. If there's 5G, there's the IT component and that's probably a partnership opportunity. And as you pointed out, there's the ecosystem, but I wonder, how do you think about 5G as an adjacency or indoor opportunity? Is it a revenue opportunity for Telcos or is that just something that is really aspirational? >> Oh, absolutely it's a revenue opportunity, but I prefer to think of 5G as being a sort of a metaphor for the whole future of telecom. So, we usually talk, and MWC would normally talk about 5G just as a mobile solution. Of course, what you can get with, you can use this fixed wireless access approach, where the roots that sits in your house or your building. So, it's a potential replacement for some fixed lines. And of course, it's also, gives you the ability to build out, let's say in a manufacturing or a campus environment, a private 5G network. So, many of the early opportunities we're seeing with 5G are actually in that more private network environment addressing those very low latency, and high bandwidth requirements. So yeah, there are plenty of opportunities. Of course, the question here is, is connectivity enough, or especially with your comment around the edge, at the edge we need to manage connectivity, storage, compute, analytics, and of course the applications. So, that's a blend of players. It's not going to be in the hands of one player. So yes, plenty of opportunities but understanding what comes the other way from the customer base, where that's, you and I in our homes or outward as an about, or from a business point of view, an office or a campus environment, that's what should be driving, and not the technology itself. And I think this is the trap that the industry has fallen into many times, is we've got a great new wave of technology coming, how can we possibly deliver it to everybody rather than listening to what the customers really require and delivering it in a way consumable by all those different markets. >> Yeah now, of course all of these topics blend together. We try to keep them separately, but we're going to talk about Cloud, we're going to talk about competition, But one of the areas that we don't have a specific agenda item on is, is data and AI. And of course there's all this data flowing through the network, so presumably it's an opportunity for the Telcos. At the same time, they're not considered AI experts. They do when you talk about Edge, they would appear to have the latency advantage because of the last mile and their proximity, to various end points. But the Cloud is sort of building out as well. How do you think about data and AI as an opportunity for Telco? >> I think the whole data and AI piece for me sits on top of the cake or pie, whatever you want to call it. What we're doing with all this connectivity, what we're doing with all these moving parts and gathering information around it, and building automation into the delivery of the service, and using the analytics, whether you call it ML or AI, it doesn't really matter. But actually using that information to deliver a better service, a better outcome. Now, of course, Telcos have had much of this data for years and years, for decades, but they've never used it. So, I think what's happening is, the Cloud players are beginning to educate many of the Telcos around how valuable this stuff is. And that then brings in that question of how do we partner with people using open APIs to leverage that data. Now, do the Telcos keep hold of all that data? Do they let the Cloud players do all of it? No, it's going to be a combination depending on particular environments, and of course the people owning their devices also have a vested interest in this as well. So, you've always got to look at it end to end and where the data flows are, and where we can analyze it. But I agree that analysis on the device at the Edge, and perhaps less and less going back to the core, which is of course the original sort of mandate of the Cloud. >> Well, we certainly think that most of the Edge is going to be about AI inferencing, and then most of the data is going to stay at the edge. Some will come back for sure. And that is big opportunity for whether you're selling compute or conductivity, or maybe storage as well, but certainly insights at the Edge. >> Everything. >> Yeah. >> Everything, yeah. >> Let's get into the Cloud discussion and talk about the Hyperscalers, the big Hyperscaler elephant in the room. We're going to try to dig into what role the Cloud will play in the transformation of telecoms on Telecom TV at the great Telco debate. You likened the Hyperscalers, Chris, to Dementors from Harry Potter hovering over the industry. So, the question is, are the Cloud players going to suck the value out of the Telcos? Or are they more like Dobby the elf? They're powerful, there's sometimes friendly but they're unpredictable. >> Thank you for extending that analogy. Yes, it got a lot of reaction when I use that, but I think it indicates some of the direction of power shift where, we've got to remember here that Telcos are fundamentally national, and they're restricted by regulation, and the Cloud players are global, perhaps not as global as they'd like be, but some regional restrictions, but the global players, the Hyperscalers, they will use that power and they they will extend their reach, and they are extending their reach. If you think they now command some fantastic global networks, in some ways they've replaced some of the Telco international networks, all the submarine investments that tend to be done primarily for the Hyperscalers. So, they're building that out. So, as soon as you get onto their network, then you suddenly become part of that environment. And that is reducing some of the spend on the longer distances we might have got in the past approaches from the Telcos. Now, does that mean they're going to go all the way down and take over the Telcos? I don't believe so, because it's a fundamentally different business digging fiber in people's streets and delivering to the buildings, and putting antennas up. So, they will be a coexistence. And in fact, what we've already seen with Cloud and the Hyperscalers is that they're working much more close together than people might imagine. Now, you mentioned about data in the previous question, Google probably the best known of the of the AI and ML delivers from the Cloud side, working with many of the Telcos, even in some cases to actually have all the data outsourced into the Google Cloud for analytics purposes. They've got the power, the heavy lifting to do that. And so, we begin to see that, and obviously with shifting of workloads as appropriate within the Telco networking environment, we're seeing that with AWS, and of course with Azure as well. And Azure of course acquired a couple of companies in affirmed and Metro switch, which actually do some of the formal 5G core and the likes there within the connectivity environment. So, it's not clean cuts. And to go back to the analogy, those Dementors are swooping around and looking for opportunities, and we know that they will pick up opportunities, and they will extend their reach as far as they can down to that edge. But of course, the edge is where, as you rightly say, the Telcos have the control, they don't necessarily own the customer. I don't believe anyone owns the customer in this digital environment, because digital allows you to move your allegiance and your custom elsewhere anyway. So, but they do own that access piece, and that's what's important from a national point of view, from an economic point of view. And that's why we've seen some of the geopolitical activity banning Huawei from certain markets, encouraging more innovation through open ecosystem plays. And so, there is a tension there between the local Telco, the local market and the Hyperscaler market, but fundamentally they've got an absolute brilliant way of working together using the best of both worlds to deliver the services that we need as an economy. >> Well, and we've talked about this you and I in the past where the Telcos, portions of the Telco network could move into the Cloud. And there of course the Telcos all run the big data centers, and portions of that IT infrastructure could move into the Cloud. But it's very clear, they're not going to give up the entire family jewels to the Cloud players. Why would they? But there are portions of their IT that they could move into. Particularly, in the front end, they want to build like everybody. They want to build an abstraction layer. They're not going to move their core systems and their backend Oracle databases, they're going to put a brick wall around those, but they wanted abstraction layer, and they want to take advantage of microservices and use that data from those transaction systems. But the web front end stuff makes sense to put into Cloud. So, how do you think about that? >> I think you've hit the nail on the head. So you can't move those big backend systems straight away, gradually over time, you will, but you've got to go for those easy wins. And certainly in the research I've been doing with many of my clients, they're suggested that front end piece, making sure that you can onboard customers more easily, you can get the right mix of services. You can provide the omnichannel interaction from that customer experience that everybody talks about, for which the industry is not very well known at all by the way. So, any improvement on that is going to be good from an MPS point of view. So yeah, leveraging what we might, what we call BSS OSS in the telecom world, and actually putting that into the Cloud, leveraging both the Hyperscalers, but also by the way, many of the traditional players who people think haven't moved Cloud wards, but they are moving Cloud wards and they're embracing microservices and Cloud native. So, what you would have seen if we'd been in person down in Barcelona next week, would be a lot of the vendors who perhaps traditionally seems a bit slow moving, actually have done a lot of work to move their portfolio into the Cloud and into Cloud native environments. And yes, as you say, we can use that front end, we can use the API openness that's developed by people at the TM forum, to actually make sure we don't have to do the backend straight away, do it over time. Because of course the thing that we're not touching upon here, is the revenue stream is a consistent revenue stream. So, just because you don't need to change the backend to keep your revenue stream going, this is on a new, it keeps delivering every month, we keep paying our 50, 40, whatever bucks a month into the Telco pot. That's why it's such a big market, and people aren't going to stop doing that. So, I think the dynamics of the industry, we often spend a lot of time thinking about the inner workings of it and the potential of adjacent markets, whereas actually, we keep paying for this stuff, we keep pushing revenue into the pockets of all the Telcos. So, it's not a bad industry to be in, even if they were just pushed back to be in the access market, it's a great business. We need it more and more. The elasticity of demand is very inelastic, we need it. >> Yeah, it's the mother of old golden geese. We don't have a separate topic on security, and I want to touch on security here, is such an important topic. And it's top of mind obviously for everybody, Telcos, Hyperscalers, the Hyperscalers have this shared responsibility model, you know it well. A lot of times it's really confusing for customers. They don't realize it until there has been a problem. The Telcos are going to be very much tuned into this. How will all this openness, and we're going to talk about technology in a moment, but how will this transformation in your view, in the Cloud, with the shared responsibility model, how will that affect the whole security posture? >> Security is a great subject, and I do not specialize in it. I don't claim to be an expert by any stretch of the imagination, but I would say security for me is a bit like AI and analytics. It's everywhere. It's part of everything. And therefore you cannot think of it as a separate add on issue. So, every aspect, every element, every service you build into your micro services environment has to think about how do you secure that connection, that transaction, how do you secure the customer's data? Obviously, sovereignty plays a role in that as well in terms of where it sits, but at every level of every connection, every hop that we look through, every route to jump, we've got to see that security is built in. And in some ways, it's seen as being a separate part of the industry, but actually, as we collapse parts of the network down, we're talking about bringing optical and rooting together in many environments, security should be talked about in the same breath. So when I talked about Edge, when I talked about connectivity, storage, compute, analytics, I should've said security as well, because I absolutely believe that is fundamental to every chain in the link and let's face it, we've got a lot of links in the chain. >> Yeah, 100%. Okay, let's hit on technologies and competition, we kind of blend those together. What technology should we be paying attention to that are going to accelerate this transformation. We hear a lot about 5G, Open RAN. There's a lot of new tech coming in. What are you watching? Who are the players that we maybe should be paying attention to, some that you really like, that are well positioned? >> We've touched upon it in various of the questions that have proceeded this. So, the sort of Cloudification of the networking environment is obviously really important. The automation of the process we've got to move away from bureaucratic manual processes within these large organizations, because we've got to be more efficient, we've got to be more reliable. So, anything which is related to automation. And then the Open RAN question is really interesting. Once again, you raised this topic of when you go down an Open RAN routes or any open route, it ultimately requires more integration. You've got more moving parts from more suppliers. So, therefore there are potential security issues there, depending on how it's defined, but everybody is entering the Open RAN market. There are some names that you will see regularly next week, being pushed, I'm not going to push them anymore, because some of them just attract the oxygen of attention. But there are plenty out there. The good news is, the key vendors who come from the more traditional side are also absolutely embracing that and accept the openness. But I think the piece which probably excites me more, apart from the whole shift towards Cloud and microservices, is the coming together, the openness between the IT environment and the networking environment. And you see it, for example, in the Open RAN, this thing called the RIC, the RAN Interconnection Controller. We're actually, we're beginning to find people come from the IT side able to control elements within the wireless controller piece. Now that that starts to say to me, we're getting a real handle on it, anybody can manage it. So, more specialization is required, but understanding how the end to end flow works. What we will see of course is announcements about new devices, the big guys like Apple and Samsung do their own thing during the year, and don't interrupt their beat with it with MWC, but you'll see a lot of devices being pushed by many other providers, and you'll see many players trying to break into the different elements of the market. But I think mostly, you'll see the people approaching it from more and more Cloudified angle where things are much more leveraging, that Cloud capability and not relying on the sort of rigid and stodgy infrastructure that we've seen in the past >> Which is kind of interesting because Cloud, a lot of the Clouds are Walled Gardens, at the same time they host a lot of open technologies, and I think as these two worlds collide, IT and the Telco industry, it's going to be interesting to see how the Telco developer ecosystem evolves. And so, that's something that we definitely want to watch. You've got a comment there? >> Yeah, I think the Telco developer they've not traditionally been very big in that area at all, have they? They've had their traditional, if you go back to when you and I were kids, the plain old telephone service was a, they were a one trick pony, and they've moved onto that. In some ways, I'd like them to move on and to have the one trick of plain old broadband that we just get broadband delivered everywhere. So, there are some issues about delivering service to all parts of every country, and obviously the globe, whether we do that through satellite, we might see some interesting satellite stuff coming out during NWC. There's an awful lot of birds flying up there trying to deliver signal back to the ground. Traditionally, that's not been very well received, with the change in generation of satellite might help do that. But we've known traditionally that a lot of developer activity in there, what it does bring to the four though, Dave, is this issue of players like the Ciscos and Junipers, and all these guys of the world who bring a developer community to the table as well. This is where the ecosystem play comes in, because that's where you get the innovation in the application world, working with channels, working with individual applications. And so it's opening up, it's basically building a massive fabric that anybody can tap into, and that's what becomes so exciting. So, the barriers to entry come down, but I think it will see us settling down, a stabilization of relationship between the Telcos and the Hyperscalers, because they need each other as we talked about previously, then the major providers, the Ciscos, Nokias, Ericssons, Huawei's, the way they interact with the Telcos. And then allowing that level of innovation coming in from the smaller players, whether it's on a national or a global basis. So, it's actually a really exciting environment. >> So I want to continue that theme and just talk about Telco in the enterprise. And Chris, on this topic, I want to just touch on some things and bring in some survey data from ETR, Enterprise Technology Research, our partner. And of course the Telcos, they've got lots of data centers. And as we talked about, they're going to be moving certain portions into the Cloud, lots of the front end pieces in particular, but let's look at the momentum of some of the IT players within the ETR dataset, and look at how they compare to some of the Telcos that ETR captures specifically within the Telco industry. So, we filtered this data on the Telco industry. So, this is our X, Y graph that we show you oftentimes on the vertical axis, is net score which measures spending momentum, and in the horizontal axis is market share, which is a measure of pervasiveness in the dataset. Now, this data is for shared accounts just in the Telco sector. So we filtered on certain sectors, like within the technology sectors, Cloud, networking, and so it's narrow, it's a narrow slice of the 1500. It respondents, it represents about 133 shared accounts. And a couple of things to jump right out. Within the Telco industry, it's no surprise, but Azure and AWS have massive presence on the horizontal axis, but what's notable as they score very highly in the vertical axis, with elevated spending velocity on their platforms within Telco. Google Cloud doesn't have as much of a presence, but it's elevated as well. Chris was talking about their data posture before, Arista and Verizon, along with VMware are also elevated, as is Aruba, which is HPEs networking division, but they don't have the presence on the horizontal axis. And you got Red Hat OpenStack is actually quite prominent in Telco as we've reported in previous segments. Is no surprise You see Akamai there. Now remember, this survey is weighted toward enterprise IT, so you have to take that into consideration, but look at Cisco, very strong presence, nicely elevated as is Equinox, both higher than many of the others including Dell, but you could see Dell actually has pretty respectable spending in Telco. It's an area that they're starting to focus on more. And then you got that cluster below, your Juniper, AT&T, Oracle, the rest of HPE TELUM and Lumen which is formerly, century link via IBM. Now again, I'm going to caution you. This is an enterprise IT heavy survey, but the big takeaway is the Cloud players have a major presence inside of firms that say they're in the telecommunications industry. And certain IT players like Cisco, VMware and Red Hat appear to be well positioned inside these accounts. So Chris, I'm not sure if any of this commentary resonates with you, but it seems that the Telcos would love to partner up with traditional IT vendors and Cloud players, and maybe find ways to grow their respective businesses. >> I think some of the data points you brought out there are very important. So yes, we've seen a Microsoft Azure and AWS very strong working with Telcos. We've seen Google Cloud platform actually really aggressively pushed into the market certainly the last 12, 24 months. So yeah, they're well positioned, and they all come from a slightly different background. As I said, the Google with this, perhaps more data centric approach in its analytics, tools very useful, AWS with this outpost reaching out, connecting out, and as you'll, with its knowledge of the the Microsoft business market certainly pushing into private networks as well, by the way. So yeah, and Cisco, of course in there does have, and it's a mass scale division, a lot of activity there, some of the people collapsing, some of that rooting an obstacle together, their big push on Silicon. So, what you've got here is a sort of cross representation of many of the different sorts of suppliers who are active in this market. Now Telcos is a big spenders, the telecom market, as we said, a $1.4 trillion market, they spend a lot, they probably have to double bubble spend at the moment to get over the hump of 5G investment, to build out fiber where they need to build out. So, any anything that relates to that is of course a major spending opportunity, a major market opportunity for players. And we know when you need the infrastructure behind it, whether it's in data centers or in their own data centers or in the Cloud to deliver against it. So, what I do like about this as an analyst, a lot of people would focus on one particular piece of the market. So you specialize on handsets, people specialize on home markets and home gateways. So, I tend to sit back and try and look at the big picture, the whole picture. And I think we're beginning to see some very good momentum where people are, where companies are building upon, of course their core business within the telecom industry, extending it out. But the lines of demarcation are blurring between enterprise, Telco, and indeed moving down into small business. And you think about the SD-WAN Market, which came from nowhere to build a much more flexible solution for connecting people over the wide area network, which has been brilliant during the pandemic, because it's allowed us to extend that to home, but be of course, build a campus ready for the future as well. So there are plenty of opportunities out there. I think the big question in my mind is always about from going into the Telco, as I said, whether they wannna reduce the number of suppliers on the roster. So that puts a question mark against some of the open approaches, and then from the Telco to the end customer, because it goes to the Telcos, 30% of their revenue comes from the enterprise market, 60% from the consumer market. How do they leverage the channel? Which includes all the channels, we talked about security, all of the IT stuff that you've already touched upon and the Cloud. It's going to be a very interesting mix and balancing act between different channels to get the services that the customers want. And I think increasingly, customers are more aware of the opportunities open to them to reach back into this ecosystem and say, "Yeah, I want a piece of humans to Telco, but I want it to come to me through my local integrated channel, because I need a bit of their expertise on security." So, fascinating market, and I think not telecom's no longer considered in isolation, but very much as part of that broader digital ecosystem. >> Chris, it's very hard to compress an analysis of a $1.4 trillion business into 30 or 35 minutes, but you're just the guy to help me do it. So, I got to really thank you for participating today and bringing your knowledge. Awesome. >> Do you know, it's my pleasure. I love looking at this market. Obviously I love analogies like Harry Potter, which makes it bring things to life. But at the end of the day, we as people, we want to be connected, we as business, we want to be connected, in society we want to be connected. So, the fundamental of this industry are unbelievably strong. Let's hope that governments don't mess with it too much. And let's hope that we get the right technology comes through, and help support that world of connectivity going forward. >> All right, Chris, well, I'll be texting you from Mobile World Congress in Barcelona, and many thanks to my colleague, Chris Lewis, he brought some serious knowledge today and thank you. And remember, I publish each week on wikibond.com and siliconangle.com. And these episodes are all available as podcasts. You just got to search for Breaking Analysis podcasts. You can always connect with me on twitter @dvellante or email me at dave.vellante@siliconangle.com. And you can comment on my LinkedIn post, and don't forget to check out etr.plus for all the survey data. This is Dave Vellante, for theCUBE Insights powered by ETR. Be well, and we'll see you next time. (upbeat music)

>>Please >>welcome back to HPD discovered 2021. The cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover and you talked about thriving in the age of insights and how to craft a data centric strategy. And you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, Right? Yeah, data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight, right? Uh and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new asia. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave You know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is no barriers to insight. You know when we are awash with data. So that's a statement. Right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier. We spoke about barriers to incite when we are washed with data. The second barrier is uh that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of minutes. You know, we we all know those who are financially attuned with know about this uh incident, But this is not the only incident. There are many of them out there and for for that particular May six event, uh you know, it took a long time to get insight months. Yeah, before we for months we had no insight as to what happened, why it happened, right. Um, and and there were many other incidences like this and the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um, one of our customers decided to take the hard road to go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information, took the took the tough took the tough road and analyze that data took a long time to assemble. And they discovered that there was quote stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well, the reason is the process reports that everybody sees the rule in there that says all trades, less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones to get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? By not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with a corresponding other hundreds of millions of them. They say, well I'm going to get all these word pairs. Someone creative thought of a willing source. And you thought it was the United Nations, you see. So sometimes you think you don't have the right data with you, but there might be another source. And the willing one that could give you that data Right? The 2nd 1 has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right? Massive amounts of data, loss of sensors, collect loss of data. And uh, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So, these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data silo the Federated it various associated with data. That's tough to get that. They just took the hard road, right? And, and sometimes, thirdly, you just have to be creative to get the right data. You need, >>wow, I I'll tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months. Nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends, right? Like, uh, you know, we if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees. And in this case uh before the change in the rules. Right? But by the way, after the discovery, uh authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such there are times you just need to go back to the raw data. >>I want to ask, >>it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data but but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing will a two part question will the consumer market as has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge you can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now. What you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model that you have trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swamp they made >>is that learning from the edge? You're learning at the edge? In other words? >>Yes. >>Yeah, I understand the question. Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different Edge devices, assemble it back to the cloud side to to do the learning right. With someone you can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front. And then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again, you know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of insight? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and where will your data be, you know, going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um Let me give you the, you use the autonomous car example, let me use that. And We have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars. Right? Um, so this data collection cars they collect as a fleet of them collect 10 petabytes a day and when it came to us uh building a storage system yeah, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? Should what should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 16 seventies we call a large part of our D. N. A junk DNA. Today we realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know? So, so what's jumped in the yesterday could be valuable today or what's junk today could be valuable tomorrow. Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here. Right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore they did not just need to collect data that is uh that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for. Right? So they want it all. So I tell us them. So what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find out. Of course they also come back to us rightfully that, you know, we have to then work out a way to help them build that system, you know, so that health care, right? And and if you go to other industries like banking, they say they can't afford to keep them on, but they are regulated. Seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can stall right on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future right? Don't worry. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know. Well notwithstanding supply constraints on things like nand, we know the prices of storage is gonna continue to decline. We also know and not a lot of people are really talking about this but the processing power but he says moore's law is dead. Okay, it's waning. But the processing power when you combine the Cpus and N. P. U. S. And Gpus and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're going to have cheaper storage and it's going to be less expensive processing. And so as an ai practitioner, what can you do with that? >>So the amount of data that's gonna come in, it's gonna we exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right? So so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices data back to a central as a bunch of central. Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there, Were you given storage costs dropping? You'll still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data, you want rich data to train with. Right? Some sometimes you wanna mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge today, we don't have enough power. Right? The edge typically is characterized by a lower uh energy capability and therefore lower compute power. But soon, you know, even with lower energy they can do more with compute power, improving in energy efficiency, Right? Uh So learning at the edge today we do influence at the edge. So we data model deploy and you do in France at the age, that's what we do today. But more and more I believe given a massive amount of data at the edge, you, you have to have to start doing machine learning at the edge and, and if when you don't have enough power then you aggregate multiple devices, compute power into a swamp and learn as a swan. >>Oh, interesting. So now of course, if, if I were sitting and fly, fly on the wall in hp board meeting, I said okay. HB is as a leading provider of compute how do you take advantage of that? I mean we're going, we're, I know its future, but you must be thinking about that and participating in those markets. I know today you are, you have, you know, edge line and other products. But there's, it seems to me that it's, it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for the customers? The world will have to have a balance right? Where today the default? Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right? And it has to be hybrid and then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different. They are on a lot of times. On the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery power. Right? Um, so you have to build systems that adapt to it. But at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018 Uh the next four years from 2018, right $4 billion dollars invested to strengthen our edge portfolio. Edge product lines, Right. Edge solutions. >>I can doctor go, I could go on for hours with you. You're you're just such a great guest. Let's close. What are you most excited about in the future? Of of of it. Certainly H. P. E. But the industry in general. >>Yeah. I think the excitement is uh the customers, right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy, right? Just like you know uh you know, the the statement made was was so was profound, right? Um And Antonio said we are in the age of insight powered by data. That's the first line, right. Uh The line that comes after that is as such were becoming more and more data centric with data, the currency. Now the next step is even more profound. That is um You know, we are going as far as saying that you know um data should not be treated as cost anymore. No. Right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right? In thinking that is going to change the way we look at data, the way we value it. So that's a statement that this is the exciting thing because because for for me, a city of Ai right uh machine is only as intelligent as the data you feed it with data is a source of the machine learning to be intelligent. So, so that's that's why when when people start to value data, right? And and and say that it is an investment when we collect it, it is very positive for AI because an AI system gets intelligent, get more intelligence because it has a huge amounts of data and the diversity of data. So it would be great if the community values values data. Well, >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know, data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know, Doug Laney, when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr >>yeah. Question is is the process and methods evaluation right. But I believe we'll get there, we need to get started and then we'll get there. Believe >>doctor goes on >>pleasure. And yeah. And then the Yeah, I will well benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there, thank you for spending some time with us and keep it right there for more great interviews from HP discover 21 this is Dave Volonte for the cube. The leader in enterprise tech coverage right back

>>Welcome back to HPD discovered 2021 the cubes virtual coverage, continuous coverage of H P. S H. P. S. Annual customer event. My name is Dave Volonte and we're going to dive into the intersection of high performance computing data and AI with DR Eng limb go who is the senior vice president and CTO for AI at Hewlett Packard enterprise Doctor go great to see you again. Welcome back to the cube. >>Hello Dave, Great to talk to you again. >>You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the day two keynotes here at discover you talked about thriving in the age of insights and how to craft a data centric strategy and you addressed you know some of the biggest problems I think organizations face with data that's You got a data is plentiful but insights they're harder to come by. And you really dug into some great examples in retail banking and medicine and health care and media. But stepping back a little bit with zoom out on discovered 21, what do you make of the events so far? And some of your big takeaways? >>Mm Well you started with the insightful question, right? Yeah. Data is everywhere then. But we like the insight. Right? That's also part of the reason why that's the main reason why you know Antonio on day one focused and talked about that. The fact that we are now in the age of insight. Right? Uh and and uh and and how to thrive thrive in that in this new age. What I then did on the day to kino following Antonio is to talk about the challenges that we need to overcome in order in order to thrive in this new age. >>So maybe we could talk a little bit about some of the things that you took away in terms I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What we take away from some of the ones you talked about today? >>Oh, very pertinent question. Dave you know the two challenges I spoke about right now that we need to overcome in order to thrive in this new age. The first one is is the current challenge and that current challenge is uh you know stated is you know, barriers to insight, you know when we are awash with data. So that's a statement right? How to overcome those barriers. What are the barriers of these two insight when we are awash in data? Um I in the data keynote I spoke about three main things. Three main areas that received from customers. The first one, the first barrier is in many with many of our customers. A data is siloed. All right. You know, like in a big corporation you've got data siloed by sales, finance, engineering, manufacturing, and so on, uh supply chain and so on. And uh, there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the that was the first barrier we spoke about barriers to incite when we are washed with data. The second barrier is uh, that we see amongst our customers is that uh data is raw and dispersed when they are stored and and uh and you know, it's tough to get tough to to get value out of them. Right? And I in that case I I used the example of uh you know the May 6 2010 event where the stock market dropped a trillion dollars in in tens of ministerial. We we all know those who are financially attuned with know about this uh incident But this is not the only incident. There are many of them out there and for for that particular May six event uh you know, it took a long time to get insight months. Yeah before we for months we had no insight as to what happened, why it happened, right. Um and and there were many other incidences like this. And the regulators were looking for that one rule that could, that could mitigate many of these incidences. Um one of our customers decided to take the hard road go with the tough data right? Because data is rolling dispersed. So they went into all the different feeds of financial transaction information. Uh took the took the tough uh took the tough road and analyze that data took a long time to assemble and they discovered that there was court stuffing right? That uh people were sending a lot of traits in and then cancelling them almost immediately. You have to manipulate the market. Um And why why why didn't we see it immediately? Well the reason is the process reports that everybody sees uh rule in there that says all trades. Less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 103 100 100 shares trades uh to fly under the radar to do this manipulation. So here is here the second barrier right? Data could be raw and dispersed. Um Sometimes you just have to take the hard road and um and to get insight And this is 1 1 great example. And then the last barrier is uh is has to do with sometimes when you start a project to to get insight to get uh to get answers and insight. You you realize that all the datas around you but you don't you don't seem to find the right ones To get what you need. You don't you don't seem to get the right ones. Yeah. Um here we have three quick examples of customers. 111 was it was a great example right? Where uh they were trying to build a language translator, a machine language translator between two languages. Right? But not do that. They need to get hundreds of millions of word pairs, you know, of one language compared uh with the corresponding other hundreds of millions of them. They say we are going to get all these word pairs. Someone creative thought of a willing source and a huge, so it was a United Nations you see. So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data right. The second one has to do with uh there was uh the uh sometimes you you may just have to generate that data, interesting one. We had an autonomous car customer that collects all these data from their cars, right, massive amounts of data, loss of senses, collect loss of data. And uh you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car uh in in um in fine weather and collected the car driving on this highway in rain and also in stone, but never had the opportunity to collect the car in hale because that's a rare occurrence. So instead of waiting for a time where the car can dr inhale, they build a simulation you by having the car collector in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated the fact that data is silo Federated, it various associated with data. That's tough to get that. They just took the hard road, right? And sometimes, thirdly, you just have to be creative to get the right data you need, >>wow, I tell you, I have about 100 questions based on what you just said. Uh, there's a great example, the flash crash. In fact, Michael Lewis wrote about this in his book, The Flash Boys and essentially right. It was high frequency traders trying to front run the market and sending in small block trades trying to get on the front end it. So that's and they, and they chalked it up to a glitch like you said, for months, nobody really knew what it was. So technology got us into this problem. I guess my question is, can technology help us get out of the problem? And that maybe is where AI fits in. >>Yes, yes. Uh, in fact, a lot of analytics, we went in, uh, to go back to the raw data that is highly dispersed from different sources, right, assemble them to see if you can find a material trend, right? You can see lots of trends right? Like, uh, you know, we, if if humans look at things right, we tend to see patterns in clouds, right? So sometimes you need to apply statistical analysis, um math to be sure that what the model is seeing is is real. Right? And and that required work. That's one area. The second area is uh you know, when um uh there are times when you you just need to to go through that uh that tough approach to to find the answer. Now, the issue comes to mind now is is that humans put in the rules to decide what goes into a report that everybody sees in this case uh before the change in the rules. Right? But by the way, after the discovery, the authorities change the rules and all all shares, all traits of different any sizes. It has to be reported. No. Yeah. Right. But the rule was applied uh you know, to say earlier that shares under 100 trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and and under for understandable reasons. I mean they probably didn't want that for various reasons not to put everything in there so that people could still read it uh in a reasonable amount of time. But uh we need to understand that rules were being put in by humans for the reports we read. And as such, there are times you just need to go back to the raw data. >>I want to ask, >>albeit that it's gonna be tough. >>Yeah. So I want to ask a question about AI is obviously it's in your title and it's something you know a lot about but and I want to make a statement, you tell me if it's on point or off point. So it seems that most of the Ai going on in the enterprise is modeling data science applied to troves of data >>but >>but there's also a lot of ai going on in consumer whether it's you know, fingerprint technology or facial recognition or natural language processing. Will a two part question will the consumer market has so often in the enterprise sort of inform us uh the first part and then will there be a shift from sort of modeling if you will to more you mentioned autonomous vehicles more ai influencing in real time. Especially with the edge. She can help us understand that better. >>Yeah, it's a great question. Right. Uh there are three stages to just simplify, I mean, you know, it's probably more sophisticated than that but let's simplify three stages. All right. To to building an Ai system that ultimately can predict, make a prediction right or to to assist you in decision making, have an outcome. So you start with the data massive amounts data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data and the machine uh starts to evolve a model based on all the data is seeing. It starts to evolve right to the point that using a test set of data that you have separately campus site that you know the answer for. Then you test the model uh you know after you trained it with all that data to see whether it's prediction accuracy is high enough and once you are satisfied with it, you you then deploy the model to make the decision and that's the influence. Right? So a lot of times depend on what what we are focusing on. We we um in data science are we working hard on assembling the right data to feed the machine with, That's the data preparation organization work. And then after which you build your models, you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you you pick one model and the prediction isn't that robust, it is good but then it is not consistent right now what you do is uh you try another model so sometimes it's just keep trying different models until you get the right kind. Yeah, that gives you a good robust decision making and prediction after which It is tested well Q eight. You would then take that model and deploy it at the edge. Yeah. And then at the edges is essentially just looking at new data, applying it to the model, you're you're trained and then that model will give you a prediction decision. Right? So uh it is these three stages. Yeah, but more and more uh you know, your question reminds me that more and more people are thinking as the edge become more and more powerful. Can you also do learning at the edge? Right. That's the reason why we spoke about swarm learning the last time, learning at the edge as a swamp, right? Because maybe individually they may not have enough power to do so. But as a swampy me, >>is that learning from the edge or learning at the edge? In other words? Yes. Yeah. Question Yeah. >>That's a great question. That's a great question. Right? So uh the quick answer is learning at the edge, right? Uh and also from the edge, but the main goal, right? The goal is to learn at the edge so that you don't have to move the data that the Edge sees first back to the cloud or the core to do the learning because that would be the reason. One of the main reasons why you want to learn at the edge, right? Uh So so that you don't need to have to send all that data back and assemble it back from all the different edge devices, assemble it back to the cloud side to to do the learning right? With swampland. You can learn it and keep the data at the edge and learn at that point. >>And then maybe only selectively send the autonomous vehicle example you gave us. Great because maybe there, you know, there may be only persisting, they're not persisting data that is inclement weather or when a deer runs across the front and then maybe they they do that and then they send that smaller data set back and maybe that's where it's modelling done. But the rest can be done at the edges. It's a new world that's coming down. Let me ask you a question, is there a limit to what data should be collected and how it should be collected? >>That's a great question again. You know uh wow today, full of these uh insightful questions that actually touches on the second challenge. Right? How do we uh in order to thrive in this new age of inside? The second challenge is are you know the is our future challenge, right? What do we do for our future? And and in there is uh the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that I talk about what to collect right? When to organize it when you collect and then where will your data be, you know going forward that you are collecting from? So what, when and where for the what data for the what data to collect? That? That was the question you ask. Um it's it's a question that different industries have to ask themselves because it will vary, right? Um let me give you the you use the autonomous car example, let me use that. And you have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from the fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars collecting data so they can train and eventually deploy commercial cars, right? Um so this data collection cars they collect as a fleet of them collect temporal bikes a day. And when it came to us building a storage system to store all of that data, they realized they don't want to afford to store all of it. Now, here comes the dilemma, right? What should I after I spent so much effort building all these cars and sensors and collecting data, I've now decide what to delete. That's a dilemma right now in working with them on this process of trimming down what they collected. You know, I'm constantly reminded of the sixties and seventies, right? To remind myself 60 and seventies, we call a large part of our D. N. A junk DNA. Today. We realize that a large part of that what we call john has function as valuable function. They are not jeans, but they regulate the function of jeans, you know, So, so what's jump in the yesterday could be valuable today or what's junk today could be valuable tomorrow, Right? So, so there's this tension going on right between you decided not wanting to afford to store everything that you can get your hands on. But on the other hand, you you know, you worry you you you ignore the wrong ones, right? You can see this tension in our customers, right? And it depends on industry here, right? In health care, they say I have no choice. I I want it. All right. One very insightful point brought up by one health care provider that really touched me was, you know, we are not we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But you also care for the people were not caring for. How do we find them? Mhm. Right. And that therefore, they did not just need to collect data. That is that they have with from their patients. They also need to reach out right to outside data so that they can figure out who they are not caring for, right? So they want it all. So I tell us them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and find that. Of course they also come back to us rightfully that you know, we have to then work out a way to help them build that system, you know? So that's health care, right? And and if you go to other industries like banking, they say they can't afford to keep them off, but they are regulated, seems like healthcare, they are regulated as to uh privacy and such. Like so many examples different industries having different needs, but different approaches to how what they collect. But there is this constant tension between um you perhaps deciding not wanting to fund all of that uh all that you can store, right? But on the other hand, you know, if you if you kind of don't want to afford it and decide not to store some uh if he does some become highly valuable in the future, right? Yeah. >>We can make some assumptions about the future, can't we? I mean, we know there's gonna be a lot more data than than we've ever seen before. We know that we know well notwithstanding supply constraints on things like nand. We know the prices of storage is going to continue to decline. We also know, and not a lot of people are really talking about this but the processing power but he says moore's law is dead okay. It's waning. But the processing power when you combine the Cpus and NP US and GPUS and accelerators and and so forth actually is is increasing. And so when you think about these use cases at the edge, you're going to have much more processing power, you're gonna have cheaper storage and it's going to be less expensive processing And so as an ai practitioner, what can you do with that? >>Yeah, it's highly again, another insightful questions that we touched on our keynote and that that goes up to the why I do the where? Right, When will your data be? Right. We have one estimate that says that by next year there will be 55 billion connected devices out there. Right. 55 billion. Right. What's the population of the world? Of the other? Of 10 billion? But this thing is 55 billion. Right? Uh and many of them, most of them can collect data. So what do you what do you do? Right. Um So the amount of data that's gonna come in, it's gonna weigh exceed right? Our drop in storage costs are increasing computer power. Right? So what's the answer? Right. So, so the the answer must be knowing that we don't and and even the drop in price and increase in bandwidth, it will overwhelm the increased five G will overwhelm five G. Right? Given amount 55 billion of them collecting. Right? So, the answer must be that there might need to be a balance between you needing to bring all that data from the 55 billion devices of data back to a central as a bunch of central Cause because you may not be able to afford to do that firstly band with even with five G. M and and SD when you'll still be too expensive given the number of devices out there. Were you given storage cause dropping will still be too expensive to try and store them all. So the answer must be to start at least to mitigate the problem to some leave both a lot of the data out there. Right? And only send back the pertinent ones as you said before. But then if you did that, then how are we gonna do machine learning at the core and the cloud side? If you don't have all the data you want rich data to train with. Right? Some sometimes you want a mix of the uh positive type data and the negative type data so you can train the machine in a more balanced way. So the answer must be eventually right. As we move forward with these huge number of devices out of the edge to do machine learning at the edge. Today, we don't have enough power. Right? The edge typically is characterized by a lower uh, energy capability and therefore lower compute power. But soon, you know, even with lower energy, they can do more with compute power improving in energy efficiency, Right? Uh, so learning at the edge today, we do influence at the edge. So we data model deploy and you do influence at the age, that's what we do today. But more and more, I believe, given a massive amount of data at the edge, you you have to have to start doing machine learning at the edge. And and if when you don't have enough power, then you aggregate multiple devices, compute power into a swamp and learn as a swan, >>interesting. So now, of course, if I were sitting and fly on the wall in HP board meeting, I said, okay, HP is as a leading provider of compute, how do you take advantage of that? I mean, we're going, I know it's future, but you must be thinking about that and participating in those markets. I know today you are you have, you know, edge line and other products. But there's it seems to me that it's it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that >>opportunity for your customers? Uh the world will have to have a balance right? Where today the default, Well, the more common mode is to collect the data from the edge and train at uh at some centralized location or a number of centralized location um going forward. Given the proliferation of the edge devices, we'll need a balance. We need both. We need capability at the cloud side. Right. And it has to be hybrid. And then we need capability on the edge side. Yeah. That they want to build systems that that on one hand, uh is uh edge adapted, right? Meaning the environmentally adapted because the edge different they are on a lot of times on the outside. Uh They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? Um so you have to build systems that adapt to it, but at the same time they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run rich a set of applications. So yes. Um that's that's also the insightful for that Antonio announced in 2018, Uh the next four years from 2018, right, $4 billion dollars invested to strengthen our edge portfolio, edge product lines, right Edge solutions. >>I get a doctor go. I could go on for hours with you. You're you're just such a great guest. Let's close what are you most excited about in the future of of of it? Certainly H. P. E. But the industry in general. >>Yeah I think the excitement is uh the customers right? The diversity of customers and and the diversity in a way they have approached their different problems with data strategy. So the excitement is around data strategy right? Just like you know uh you know the the statement made was was so was profound. Right? Um And Antonio said we are in the age of insight powered by data. That's the first line right? The line that comes after that is as such were becoming more and more data centric with data the currency. Now the next step is even more profound. That is um you know we are going as far as saying that you know um data should not be treated as cost anymore. No right. But instead as an investment in a new asset class called data with value on our balance sheet, this is a this is a step change right in thinking that is going to change the way we look at data the way we value it. So that's a statement that this is the exciting thing because because for for me a city of AI right uh machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So so that's that's why when when people start to value data right? And and and say that it is an investment when we collect it. It is very positive for ai because an Ai system gets intelligent, more intelligence because it has a huge amounts of data and the diversity of data. So it'd be great if the community values values data. Well >>you certainly see it in the valuations of many companies these days. Um and I think increasingly you see it on the income statement, you know data products and people monetizing data services and maybe eventually you'll see it in the in the balance. You know Doug Laney when he was a gardener group wrote a book about this and a lot of people are thinking about it. That's a big change isn't it? Dr >>yeah. Question is is the process and methods evaluation. Right. But uh I believe we'll get there, we need to get started then we'll get their belief >>doctor goes on and >>pleasure. And yeah and then the yeah I will will will will benefit greatly from it. >>Oh yeah, no doubt people will better understand how to align you know, some of these technology investments, Doctor goes great to see you again. Thanks so much for coming back in the cube. It's been a real pleasure. >>Yes. A system. It's only as smart as the data you feed it with. >>Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HP discover 21. This is dave a lot for the cube. The leader in enterprise tech coverage right back.

(upbeat music) >> Hello, welcome back to "theCUBE"'s coverage of DockerCon 2021. I'm John Furrier, host of "theCUBE". Great lineup in this event. Got some great guests. Matt Falk, VP of engineering at Orbital Insight. Matt, great to see you. Great keynote, thanks for coming on this cube. Appreciate it. >> Great, thanks for having me, John. >> So you at Orbital Insight, you guys doing some cutting edge work. Geospatial, big data, real-world problems. I mean, it's almost sci-fi for me. I mean, I just love how space, cybersecurity, data, all kind of rolling into like this whole very cool vibe. You know, drones, satellites, all this kind of you know, stuff going on in the cloud. But there's like real action happening, right? (chuckles) We all live on GPSes. Like, this is like very cool and relevant technology happening right now. Give us your take. What are you guys seeing, how's business? Give us a quick overview of what your journey is and how you guys are executing. >> Sure. And I think you're right there, it is a little bit like sci-fi actually, even to myself. Even having been in the industry for a few years at this point. You know, we all think about big data, it's become much more a thing especially over the past decade or two. Everything that we try and solve as big data, artificial intelligence, machine learning, they all thrive, they all need this big data. An untapped area about big data though, is geospatial data. And really data that comes from overhead sensors, coming from space. So to me, that feels a little bit like sci-fi like you're saying because that time is now. That time for us to be able to use and harness that data and provide actual or meaningful insights is here. You know, as a company for Orbital Insight, we got into it about seven, eight years ago and the title wave of this data was just forming. There weren't as many satellite provider companies, there weren't as many different types of disparate geospatial data. And by geospatial data you know, anything with a latitude and longitude associated with it, right? This data was, it was there but it wasn't as abundant there. It wasn't clear about how we could use that data. And over the past few years so many new use cases had really popped out and so many new disparate types of data and it's really about the fusion of all of them and getting more and more of that data. So right now the most exciting thing is really just how much of that data exists and how much is going to exist in the next few years. And honestly, we want to ride that tidal wave along with our customers. We can deal with many different types of data here. It's overhead satellite imagery, it's cell phone pings, it's identification system from ships, it's everything that you can get your hands on and incorporate into this platform. And then using this to feed the artificial intelligence and machine learning algorithms to derive new insights so it's sci-fi but it is here. >> Yeah and it's real computer science problems too. A lot of networking as well. You got this and it's transitioning too. You were out early doing these new use cases. But what's interesting about your journey and I want to get your thoughts on this, is that you guys really evolve from tackling these first kind of time problems, making solutions out of them to sequencing it to a fully on, fully-built scalable insight platform. Okay and this the pattern that we see in cloud native. Companies go from going in and doing things, that one-off, one-offs projects, POCs and then sequencing to a either cloud native or full blown platform. You guys have had that journey, take us through that effort and what's the result today? >> No, that's exactly right. The way we started, just like you mentioned, with many other companies was really around this proof of concept idea. It was going out, talking to customers, finding what their pain points were and figuring out what can we do to solve those pain points. So it was all about, "Okay, for this particular customer "take this data set, take satellite imagery "of this location, take cell phone things for this location, "take a digital elevation model from this area "of the planet, fuse them together in some "very specific custom way to try and solve that problem." And that's how we started. Over the first few years of that, this doesn't really scale as well. We had to keep building new solutions to solve new problems. What we started to identify was that there's so much commonality, there's so much overlap between a lot of these problems. So the solutions for them you know, if we're taking truck counts, if we're able to look at parking lots and detect cars from satellite imagery and use that to determine level of trends or sales at you know, a retail store. Well, we can also use that same overhead imagery to detect cars and look for their movement patterns, look for how they're going from a port to a particular warehouse or how they're driving on the road. Same thing with trucks. When we started identifying that a lot of the value from these different analytics and data sources can be used to solve many different problems in different ways but the underlying core technology is very similar. So for us about a few years ago, about two or three years ago at this point, we started really developing this into a platform that can solve generally any question about geospatial data analytics. So instead of, "I have a very specific problem, I wanted "to count the number of trees in this particular area." It's, "No, no, you want to do something similar to land use. "You want to try and classify different areas of the planet "that are covered in this particular type of land use "and then use that as an estimate for how many trees "there are." So we started to find these commonalities and that allowed us to really build this generic platform we call GO and use that to start solving many, many more questions. >> That's good. So real world problems are emerging. I'd love to get your thoughts on what those geospatial problems are because you can almost think about how the traditional distributed computing world looks at the edge for instance. Like you know, the intelligent edge or industrial edge, edge of the network as they call it in the hybrid world. You're bringing not just moving packets, you're talking about other data media. So pictures, images and (chuckles) different data sources. This is a huge aperture that changes the game on the analytics. Could you share some of the problems you saw that opened up with the new types of sources? Because it's not just packet to a device or an edge point in the traditional sense. >> Sure, sure. No, that's a great question for us. And I think a lot of people that don't really understand or haven't dealt with geospatial data before, don't understand necessarily a lot of the nuances that come with this data, there are quite a few of them. So I'll focus on satellite imagery in particular for the first part here. But you know, when people think about, even today what we can do with artificial intelligence. Computer vision on an image, you know, most people go to the images they can see from the cell phones. Most people start to think about self-driving cars and seeing the resolution of those images. Well, when you're dealing with overhead imagery, satellite imagery in particular, the game changes, the game is completely different. Yes, you're still working with an image but so many of the pieces of that image are just different and more complex. So for instance, when you're taking an image miles away from the planet, the first thing you have to realize is your resolution is not quite what people think. In particularly now, if we go outside and we stand in a satellite image, the best commercial sensors today, we're at most one pixel. And with one pixel you really can't identify people. You know, even a car or a truck at most, you're looking at 15 to 20 pixels and it becomes extremely difficult to classify objects at that resolution or at that scale compared to if you're using a cell phone picture, for instance. So that's really for us the first you know, major difficulty or change that comes into play. The second one would be the temporal aspect. So not only the spatial resolution but temporal resolution. You don't get an image every day. You don't even get an image, sometimes every week. So how do you, do you impute different data, impute different points to make your overall analysis worthwhile? Again, a slew of additional challenges that come with that. Other things like for instance georegistration or orthorectification. So the idea, when you take a satellite image of somewhere on the planet, you actually don't get very precisely where that image is. It could be off by five, 10, 15 or 20 meters even and you have to do work to actually relocate that image in the right particular position. Orthorectification refers to the angle at which the image was taken. If you're taking an image from a bird's eye view, yeah you're going to be able to see straight down and you're going to see buildings and they look like you know, squares of certain right edged polygons like that. But you can also take an angle 30 centimeters or excuse me, 30 degrees off, you can look at the side when you're taking this image and then the satellite image looks completely different. So that's another technique that we have to combat and another difficulty we have to work with in order to make this data usable. So for satellite imagery for other data sources too, there's a slew of additional challenges that we have to compete, that we have to fix and work with to make this data usable. And that's what our platform does. It takes this data, it fixes all those issues and allows you to compute the analytics on top of them. >> I love it. I mean, as a consumer, I can relate to like, say Google. I know there's trees there but they look like they just put trees there 'cause they can. Like there was a tree there and they fill it out, right? I mean kind of similar things going on there. All great stuff. I love the tech and I think it's going to be one of these eras that's going to be super valuable as more of these use cases can get this tap the data for not only insights but also maybe for features in software. Which brings me to the next question. How do people use you guys? I mean, as you have these use cases that are emerging, a lot of disparate use cases, different data sources now analytics as a platform, are you guys selling software is it a service, as a fee? Can you explain you know, how I might want to geek out and integrate you into my product or feature? Or do you do it that way? How does it work? >> Oh sure, thank you. So, there's two different aspects here really. And one of them is how people actually have access to this data. So the first is that we actually make this data available. And the second is the analytics that we put on top of this data. So for the first piece, a lot of these data sets are extremely expensive. So the average even consumer or a lot of businesses, it's much too expensive to go and actually buy all this satellite imagery or all this geolocation ping data or shipping information. It's just too expensive to buy these disparate data sources if you only have particular, single need for them. So the first thing our platform does, is it integrates with many different data providers. It integrates with, like I said, anything that has a latitude and longitude with it, we try and get that into our platform. And we become the broker almost, the provider of all that disparate data into a single unified source. So that's that first aspect, that's how they use us to get access to that data that otherwise they wouldn't be able to get access to. The second piece is the analytics. So for this, our platform, it does really you put three things into our platform. You ask where you want to look on the planet, what you want to look for and when do you want to look for it? And our platform takes care of going and getting all that information that it needs to compute that answer. Then using our custom analytics to derive what you're looking for, the question you're actually asking and produce similar to a data feed but it's much more custom than that, particular insights for the customer. So what comes out of the platform is effectively a time series that you're able to go explore and drill down into further. So a particular example of this is for supply chain right now and this is a problem that we're very passionate about right now. Especially with last year, how COVID impacted things. But from a supply chain perspective, we're actually able to identify locations on the planet that you're interested in, typically operating facilities and start looking at trends for where people are going to and from that particular facility. So we can see, "Oh, there were a hundred people that visited "this facility on a you know, the last seven days." And maybe produce a time series of how many people were there each day. But we can also then say, "Of those hundred people, "16 of them came from this location, 52 of them came "from this location, 53 of them visited this location "two days after visiting that location." And we can start to build this entire traceability map of that particular location and that our customers can use to identify patterns and then anomalies really, in their own supply chains. Or different things about their operating facilities. >> So pinging, like graph data, for instance. We got some insights into how to restructure their value chains or reconfigure their economics. Something like that would be like a use case. >> Exactly, exactly. Finding further efficiencies, ways they can optimize their supply chains or anticipating disruptions in it. If they know that part of their supply chain is dependent on you know, a particular facility, a particular location or a particular region. And they know from other news that something is about to happen to that region, they can know practically how to change their supply chain in order to you know, alleviate that pain before it even happens. >> Well, real time in the news just recently, just this month earlier in the month, we saw that gas shortage or stoppage or shortage/supply chain disruption, happen in the East coast, right? From the pipeline hack, the ransomware attack. That's a good example. I mean, some people don't even know the difference between a supply chain disruption and a shortage, there are two different things so I saw that big debate happening. This is kind of real world example where you can say, "Okay, we have a supply chain, "potential predictive disruption." Then maybe look at ways to do that. Am I thinking in the right way here? >> No, that's exactly the right way to think about it. You can start to see... So from that event, if you're operating a facility or a facility warehouse, you can look at that event, ask the question of, "How is this going to impact "my supply chain?" And the first thing you need to know is are you dependent on that, is that something that actually impacts or it plays a part in your supply chain? So you'd use our software, plug into your own operating facility, start to trace where people are coming from or going to. First thing you can do then, is identify, "Is that location "part of my supply chain?" If not, you know potentially you're in the clear. If so, then you can start to identify different locations that might be a suitable replacement for your supply chain. So can you practically avoid that going forward and make that move sooner than you would've been able to otherwise. >> I love the complexity challenge here. You guys doing the heavy lifting here in offering as a service makes total sense. You can almost democratizing the whole complexity of the data acquisition and then you know, providing value on top of it. The question I have for you is, what other learnings have you had? I mean, what was some of the difficulties? You mentioned you know, the artifacts, atmosphere, haze, noise, spatial temporal frequencies before. What are some of the other things that you're seeing and learnings that people might not know about that you guys have solved in this data capturing from the satellites? >> That's a great question. And there's plenty of them. A lot of things I think it would come down to is how to use this data or how best to combat some of the challenges like we talked about earlier, come with this data set. In particular, if we look at the foot traffic data that we look at. So pingings coming from different cell phones or what we call geolocation pings. Largely, you can think of that as any IOT device that's pinging their location you know, we can aggregate that data in and start using it within the platform. And what we've learned for that data is, it's very dependent on how you can actually get that to be normalized. And what I mean by that is none of this data is providing a complete picture of what's actually there. So again, if we look back at you know, even from image perspective when you're getting a satellite image when you're getting cell phone pings, you're only getting at best, 15 to 20% of the actual picture. And the challenges are really about going from that 20% view to the full contextual 100% view. So tactically, what that looks like for geolocation pings, where you're not getting geolocation pings from every person on the planet, we're not getting pings from every IOT device or every cell phone. We're getting a particular, almost randomized subset of those pings and they're all anonymized. So how do you go from that to an actual insight? How do you go from that to a full complete view of what's happening? And that's where our normalization algorithms come into play and other capabilities that we have that take that data and try and extrapolate what's truly happening. So if you're looking at you know, for instance if you look at a gas station. And it's a gas station in a you know, an area that's not very highly populated. And you're only getting two or three pings a day or some days you're getting none. Is that truly a signal of no one's going to that gas station or are you just missing the data? And you don't always know so part of what we've learned is how to take that data and actually translate it into the complete picture. We have very complex algorithms and they're constantly being improved on to account for differences like people turning their cell phone off or more than one person being in a car or things like that. So that's what we've really learned in it. It's all about taking that incomplete picture and trying to produce the most complete picture with as most context as possible to solve problems. >> So what's the secret sauce on all of this? Is it algorithms, is it data usage, all the above? I mean, take me through some of the secret sauce that's going on that you guys are building to make all this work. >> Sure, sure. And I'll go into it as much as I can. (John and Matt laugh) >> (speaking faintly) >> But there's a few different- Exactly. But a few different pieces really. The first one is the data itself, right? At the end of the day, no matter how good your machine learning capabilities are, if you don't have the data, you can't do anything. And this is true for all types of artificial intelligence or machine learning algorithm. If you don't have something to allow the system to learn from, you're at a loss. So the first piece of it really is getting the right data and making sure we have enough different or disparate data sources to really complete that overall picture. The second piece of it is allowing our platform to do this at a high scale. So it'd be one thing if you can produce a particular algorithm and get it to run in a single location one time. But it's all about for us, asking that aggregate question. So we're not you know, if somebody is asking about a particular gas station or a retail store, more often than not, they're not caring about just one location. They're caring about the aggregate, they're trying to look at this country as a whole and seeing what the trends or patterns are. So the second piece of secret sauce really is our platform and the ability to scale that up dynamically and allow you to ask any size question. So not just one AOI at a particular location but thousands of different locations and how that answer really compiles together. Third one is definitely the artificial intelligence and machine learning. For us, that is a extremely core competency. Something that allows us to really take that data and produce the insights. And that's a key factor of it. Like I mentioned, with the different challenges, part of our secret sauce there is not just the algorithms themselves but additional techniques or different R&D that we can do to solve or combat some of the additional issues that we have with overhead sensors. In particular, I'll point out two here but one is the rare object issue. So a lot of times if you're doing with satellite imagery and you're trying to find an object, it's very difficult to find a satellite image of that object. If you're looking for a particular type of ship you might only find one or two of them in thousands of images. So how do you build a machine learning algorithm that really uses that really small amount of data to produce an algorithm? And this is where our R&D capabilities come into play. And one to highlight is synthetic data. So the ability to produce almost fake or generated satellite images that actually produce these objects you're looking for so that we can train or learn off of that. So things like that really build our, I'll say, our secret sauce, are our R&D core competencies. The ability to produce newer novel techniques to generate data where satellite images or other geospatial data have deficiencies that we can combat. >> Yeah, I like that feature. Because you're almost saying, "The ship might look like this "depending upon where they're looking and muting that in there, good call. I guess the question I have for you is first of all, great tech loved the story. You guys are onto some really cool stuff and very relevant. The question, is in minds of peoples right now who are watching is why now for the critical time, why is now a critical time for geospatial analytics? What's your answer to that? >> Sure. That's a great, actually the answer is great for us too as a company. As I was kind of alluding to in the beginning, there's this tidal wave of geospatial data. And you know, if we were to look at five, 10, 15 years ago, the data itself and the technology was not really there to allow us to do what we're trying to do now. If you look back, I think it was in 2013, there was a particular computer vision paper that came out that really was the birth of the CNN world. And for that, that is a core compute capability that allows us to do the computer vision we need to be able to do. So that was a extreme catalyst for companies like ours, are being able to do this type of data fusion analytics. And the second one is the birth of the new sensors coming up right now. If you look back five years ago and where we're going five years from now, it's almost like Moore's Law. Where every other year, things are just starting to double. There's more and more satellites being thrown up, there's more and more data being thrown out and frankly, it's almost too much data at this point. There's just more and more data coming up. We already have petabytes of satellite imagery in our system, hundreds of terabytes of IOT device data and it's everyday just more and more of this data is coming up and being produced. So now is that perfect time because the data is finally there and it's only getting better over time. >> Yeah, I know we have a little bit of time left. I do want to ask just kind of, I'm curious. I'm sure people are too. As leader in that company, as engineering leader, you got a team that's working on some pretty cool stuff. A lot of computer science, a lot of new technology opportunities, kind of new problems that are emerging that are exciting. So everyone likes to solve hard problems, right? You got one, right? You got synthetic data, massive ingestion pipelines, normalizing algorithms, spatial imputation, et cetera, all this good stuff. How do you organize, how do you attract people? How are you looking at this? Because you have to lean into this. It's not like you're waiting for the market to come to you. You guys are going out there, making the market technically as well. So how do you organize, how do you recruit? (chuckles) Take us through some of the inside the ropes there. >> Sure, sure. So I'll start with kind of just how our engineering team is organized right now and where are we try and do find people and pull additional folks into our team. Right now we are split into four or five different areas. So like most cloud based platforms, we do have an infrastructure team. So you know, DevOps, site reliability, IT, everything that goes into that core cloud layer. So we do have an infrastructure team that builds that. On top of that, we do have our platform engineering team. So that team largely builds our microservices that play together to produce our external API. On top of that, we have a product engineering team that builds really with developing our UI, adding in our UX, making sure everything on top of the API plays nicely together. And also building a few additional dockerized computer vision and machine learning models that can plug into the platform. Separately, we have our R&D team. This is like as you know, where we talked about our synthetic data and all the other research areas that we get into, they focus there. Then we have our senior data engineering team. This team is largely focused on pulling disparate data sources, massaging them, cleaning them up into the right format so that it can be plugged into our platform. So from this, this is kind of how our team is structured. You're right, it's a ton of technical challenge. A lot of fun challenges. We're about 50 engineers right now. We're actually, we're looking to grow almost doubling in size by the end of the year. We're going to be bringing on an additional 30 people over the next few months. And what we're looking for is people from a wide area of expertise. So people that have you know, microservice core platform backgrounds, able to build on the backend system, deal with tons of tons of you know, transactions per second and really allow us to scale our platform. That's one set of expertise we like. Another one is people that really just have geospatial data backgrounds. And which to be honest at this point, it's somewhat of a rare niche finding people that have worked on a platform but also worked in geospatial data. But that's something that we love to bring into our system so we can add additional expertise and eventually get new data sources in. And then lastly, it's really around that core competency of machine learning and artificial intelligence. So we will look for anybody that has machine learning you know deep math, deep computer science background to come in and be a part of that team. If they're capable from a research perspective, we are actually you know, it's possible to teach them some of the computer vision aspects as well. So, if they have a computer vision background, great. If they have a data science and machine learning background, great. We want that diverse set of interests and diverse set of thinking to come in and really build our R&D team as well. >> Yeah. And I obviously DockerCon is here. You're talking about containers and that leads into Kubernetes, microservices, all kinds of cloud native technologies. Because what you guys are doing is you're taking an old construct. I mean, fairly old, I mean it's you know, it's data. But you're leveraging it in new ways. In a way that's kind of what cloud native's about. How are you seeing that world evolve? Obviously we're here at DockerCon, containers helps big time thoughts on the containerization wave that continues. And you got Kubernetes and more and more cloud natives, more SRE's are going to be hired. Again, people are scaling up. What's your take on what's going on around DockerCon? >> No, this is actually for us. It's really powerful and it's a really powerful tool. Whether it's Dock or (speaking faintly), the idea of containerization as a whole, it really allows our platform to get to that next level of scale. One piece I you know, originally we were not a microservice platform. Like I said, we were starting to do some more POCs. As we got into this platform play, one of the things we knew we needed to be able to do was scale different parts of our system. So whether it was scaling to ingest more data, scaling to involve new algorithms or scaling really to involve or be able to compute massive computation requests that come from our customer. This requires different pieces of our system to scale. If we were a monolithic application, if we were running on premise, that type of scale just wouldn't really be possible at the level that we need it to. So for us, the solution is all around being able to dockerize different parts of our system, keep them isolated, keep them talking to each other via different interfaces. And then as need be horizontally scale different pieces of our system to compat with that. So really the you know, Kubernetes Docker together, the ability for us it's allowing our developers to focus on the code that they need to be writing and not focusing on the SRE or the DevOps perspective of it. And then letting our DevOps team use these additional tools to make themselves more efficient. You know, we can do that with a smaller team now we don't need a team of 50 people on DevOps or infrastructure. You can do it with five or six solid engineers that can really you know, manage your entire environment. >> Yeah, I think having that horizontal scalability is critical and the containerizing it, so many benefits there allowing things to be completely portable and integrating really well. Great stuff. Unbelievable gems dropped here. My final question for you while I got you here is you know, as you look at other peers and people in the marketplace, the people who were on the right side of history are experiencing, certainly entrepreneurs and people who are in businesses and enterprises are waking up and going, "Hey, that can really change the game and flip "the script with cloud native." So people are experiencing similar journeys where they got product engineering saying, "We are more of a platform. "I could sequence and build out that platform and then build "my infrastructure on the cloud." So you're starting to see these point applications turn into platforms. What's your advice to people out there that are going to move from product engineering departments or groups to bring on that platform construction or that work and then build that infrastructure like you guys are? What's your advice to folks that are going to make that journey? >> No, that's a great question, John. I think the quick advice I would give to anybody you know, that has a product engineering team considering moving to the platform right now is do it now. There's no time better than right now and what I mean by that is, the longer you delay the harder it gets. You're going to be missing out on a lot of the new technologies that are really being solidified as part of the cloud computing world. Yes you know, there are trade-offs. Especially you know, you might have to go to your exec team or your product team and make these trade-offs and you won't be able to develop teachers as quickly as you're spending time porting to a platform play. But the benefits are amazing. And once you actually get there, you'll really be thankful that you took the time to do it. Yes it's, you know again, it's going to be challenging because it's one of those things where it's an engineering benefit at first. It's not going to be something you're going to say, "Yes product, "in two months, you're going to get this benefit from it." Or, "In you know, three months, you're going to get "this benefit for it." It's, "One year from now, this is how our platforms "are written, our new product is really going to be able "to expand and grow." And the best way to get there is to just do it now. Really starting encapsulating your system, break it out into different pieces, put it in a cloud, allow it to scale. And so yeah, my advice is to just bite the bullet and do it now. >> So people who buy into that notion of moving from monolithic to microservice based applications want that horizontal scalability, as you mentioned. What are some of the first principles in that platform? What's on the mind of the architect or the leader as they start thinking about those first principles for the modern platform? >> Sure. I'd say the first one is don't over design. So some people have a tendency when they start thinking about microservices is they really go to microservice almost nanoservices. They really start breaking off you know, as many different pieces of the code, making them as small as possible. And to some extent, that's what you want to do with microservices but you don't want to go too far. I mean, it's easy to go down that rabbit hole. So in particular, there are certain services or microservices that you find out, they're tightly coupled. They're constantly passing data back and forth and that's when you realize, passing data back and forth between two different logical separation of code, it takes time. So it might make sense for them to be one unified microservice as opposed to two. So the most important thing to think about is you know, what pieces really make sense to logically separate and how does that actually impact the flow of data or flow of information through your system? If you're adding too many hops between you know, a certain end point and the call to the backend system, it might be time to rethink the way you're breaking system the system down. But you really want to start out with what can be broken down into the logically encapsulated pieces? And that's where we want to pull our microservices. >> Highly cohesive decoupling, that's a concept. An operating system as we say, it's the platform, that's the cloud. >> It's not new, that's right. >> It's been around. Matt, great interview. Thanks for dropping the gems and sharing your knowledge. And congratulations for the work you're doing at Orbital Insight. Great focus, love the company, love the excitement. Thanks for coming on. >> Perfect. Pleasure chatting with you too, John. And thanks for having me. And thanks for having me be a part of DockerCon. >> All right. DockerCon 2021, CUBE coverage. I'm John Furrier, host of "theCUBE". Thanks for watching. (lighthearted music)

>> From The Cube Studios in Palo Alto in Boston, bringing you data-driven insights from The Cube in ETR. This is "Breaking Analysis" with Dave Vellante >> The pandemic not only accelerated the shift to digital but it also highlighted a rush of cyber criminal sophistication, collaboration, and chaotic responses by virtually every major company in the planet. The SolarWinds hack exposed supply chain weaknesses and so-called island hopping techniques that are exceedingly difficult to detect. Moreover, the will and aggressiveness of well-organized cybercriminals has elevated to the point where incident responses are now met with counter attacks, designed to both punish and extract money from victims via ransomware and other criminal activities. The only upshot is the cybersecurity market remains one of the most enduring and attractive investment sectors for those that can figure out where the market is headed and which firms are best positioned to capitalize. Hello, everyone. And welcome to this week's Wikibon Cube Insights powered by ETR. In this "Breaking Analysis" we'll provide our quarterly update of the security industry, and share new survey data from ETR and the Cube community that will help you navigate through the maze of corporate cyber warfare. We'll also share our thoughts on the game of 3D chess that Okta CEO, Todd McKinnon, is playing against the market. Now, we all know this market is complicated, fragmented and fast moving. And this next chart says it all. It's an interactive graphic from Optiv, a Denver, Colorado-based SI that's focused on cybersecurity. They've done some really excellent research and put together this awesome taxonomy, and it mapped vendor names therein. And this helps users navigate the complex security landscape. And there are over a dozen major sectors, high-level sectors within the security taxonomy and nearly 60 subsectors. From monitoring, vulnerability assessment, identity, asset management, firewalls, automation, cloud, data center, sim, threat detection and intelligent endpoint network, and so on and so on and so on. But this is a terrific resource, and going to help you understand where players fit and help you connect the dots in the space. Now let's talk about what's going on in the market. The dynamics in this crazy mess of a landscape are really confusing sometimes. Now, since the beginning of cyber time, we've talked about the increasing sophistication of the adversary, and the back and forth escalation between good and evil. And unfortunately, this trend is unlikely to stop. Here's some data from Carbon Black's annual modern bank heist report. This is the fourth, and of course now, VMware's brand, highlights the Carbon Black study since the acquisition, and to catalyze the creation of VMware's cloud security division. Destructive malware attacks, according to the recent study are up 118% from last year. Now, one major takeaway from the report is that hackers aren't just conducting wire fraud, they are. 57% of the banks surveyed, saw an increase in wire fraud, but the cybercriminals are also targeting non-public information such as future trading strategies. This allows the bad guys to front-run large block trades and profit. It's become a very lucrative practice. Now the prevalence of so-called island hopping is up 38% from already elevated levels. This is where a virus enters a company supply chain via a partner, and then often connects with other stealthy malware downstream. These techniques are more common where the malware will actually self-form with other infected parts of the supply chain and create actions with different signatures, designed to identify and exfiltrate valuable information. It's a really complex problem. Of major concern is that 63% of banking respondents in the study reported that responses to incidents were then met with retaliation designed to intimidate, or initiate ransomware tax to extract a final pound of flesh from the victim. Notably, the study found that 75% of CISOs reported to the CIO, which many feel is not the right regime. The study called for a rethinking of the right cyber regime where the CISO has increased responsibility and a direct reporting line to the CEO, or perhaps the COO, with greater exposure to boards of directors. So, many thanks to VMware and Tom Kellerman specifically for sharing this information with us this past week. Great work by your team. Now, some of the themes that we've been talking about for several quarters are shown in the lower half of the chart. Cloud, of course is the big driver thanks to work-from-home and to the pandemic. And the interesting corollary of course, is we see a rapid rethinking of end point and identity access management, and the concept of zero trust. In a recent ESG survey, two thirds of respondents said that their use of cloud computing necessitated a change in how they approach identity access management. Now, as shown in the chart from Optiv, the market remains highly fragmented, and M&A is of course, way up. Now, based on our research, it looks like transaction volume has increased more than 40% just in the last five months. So let's dig into the M&A, the merger and acquisition trends for just a moment. We took a five-month snapshot and we were able to count about 80 deals that were completed in that timeframe. Those transactions represented more than $20 billion in value. Some of the larger ones are highlighted here. The biggest of course, being the Thoma Bravo, taking Proofpoint private for a $12 plus billion price tag. The stock went from the low 130s and is trading in the low 170s based on the $176 per share offer. So there's your arbitrage, folks. Go for it. Perhaps the more interesting acquisition was Auth0 by Optiv for 6.5 billion, which we're going to talk about more in a moment. There was more private equity action we saw as Insight bought Armis, an IOT security play, and Cisco shelled out $730 million for IMImobile, which is more of an adjacency to cyber, but it's going to go under Cisco security and applications business run by Jeetu Patel. But these are just the tip of the iceberg. Some of the themes that we see connecting the dots of these acquisitions are first, SIs like Accenture, Atos and Wipro are making moves in cyber to go local. They're buying SecOps expertise, as I say, locally in places like France, Germany, Netherlands, Canada, and Australia, that last mile, that belly to belly intimate service. Israeli-based startups chocked up five acquired companies in the space over the last five months. Also financial services firms are getting into the act with Goldman and MasterCard making moves to own its own part of the stack themselves to combat things like fraud and identity theft. And then finally, numerous moves to expand markets. Okta with Auth0, CrowdStrike buying a log management company, Palo Alto, picking up dev ops expertise, Rapid7 shoring up it's Coobernetti's chops, Tenable expanding beyond Insights and going after identity, interesting. Fortinet filling gaps in a multi-cloud offering. SailPoint extending to governance risk and compliance, GRC. Zscaler picked up an Israeli firm to fill gaps in access control. And then VMware buying Mesh7 to secure modern app development and distribution service. So tons and tons of activity here. Okay, so let's look at some of the ETR data to put the cyber market in context. ETR uses the concept of market share, it's one of the key metrics which is a measure of pervasiveness in the dataset. So for each sector, it calculates the number of respondents for that sector divided by the total to get a sense for how prominent the sector is within the CIO and IT buyer communities. Okay, this chart shows the full ETR sector taxonomy with security highlighted across three survey periods; April last year, January this year, and April this year. Now you wouldn't expect big moves in market share over time. So it's relatively stable by sector, but the big takeaway comes from observing which sectors are most prominent. So you see that red line, that dotted line imposed at the 60% level? You can see there are only six sectors above that line and cyber security is one of them. Okay, so we know that security is important in a large market. But this puts it in the context of the other sectors. However, we know from previous breaking analysis episodes that despite the importance of cyber, and the urgency catalyzed by the pandemic, budgets unfortunately are not unlimited, and spending is bounded. It's not an open checkbook for CSOs as shown in this chart. This is a two-dimensional graphic showing market share in the horizontal axis, or pervasiveness in net score in the vertical axis. Net score is ETR's measurement of spending velocity. And we've superimposed a red line at 40% because anything over 40%, we consider extremely elevated. We've filtered and limited the number of sectors to simplify the graphic. And you can see, in the sectors that we've highlighted, only the big four are above that 40% line; AI, containers, RPA, and cloud. They exceed that sort of 40% magic waterline. Information security, you can see that as highlighted and it's respectable, but it competes for budget with other important sectors. So this is of course creates challenges for organization, because not only are they strapped for talent as we've reported, they like everyone else in IT face ongoing budget pressures. Research firm, Cybersecurity Ventures estimates that in 2021, $6 trillion worldwide will be lost on cyber crime. Conversely, research firm, Cannolis peg security spending somewhere around $60 billion annually. IDC has at higher, around $100 billion. So either way, we're talking about spending between 1 to 1.6% annually of how much the bad guys are taking out. That's peanuts really when you consider the consequences. So let's double-click into the cyber landscape a bit and further look at some of the companies. Here's that same X/Y graphic with the companies ETR captures from respondents in the cybersecurity sector. That's what's shown on the chart here. Now, the usefulness of the red lines is 20% on the horizontal indicates the largest presence in the survey, and the magic 40% line that we talked about earlier shows those firms with the most elevated momentum. Only Microsoft and Palo Alto exceed both high watermarks. Of course, Splunk and Cisco are prominent horizontally. And there are numerous companies to the left of the 20% line and many above that 40% high watermark on the vertical axis. Now in the bottom left quadrant, that includes many of the legacy names that have been around for a long time. And there are dozens of companies that show spending momentum on their platforms, i.e above single digits. So that picture is like the first one we showed you, very, very crowded space. But so let's filter it a bit and only include companies in the ETR survey that had at least 100 responses. So an N of 100 or greater. So it was a little easier to read but still it's kind of crowded when you think about it. Okay, so same graphic, and we've superimposed the data that determined the plot position over in the bottom right there. So there's net score and shared in, including only companies with more than 100 N. So what does this data tell us about the market? Well, Microsoft is dominant as always, it seems in all dimensions but let's focus on that red line for a moment. Some of the names that we've highlighted over the past two years show very well here. First, I want to talk about Palo Alto Networks. Pre-COVID as you might recall, we highlighted the valuation divergence between Palo Alto and Fortinet. And we said Fortinet was executing better on its cloud strategy, and Palo Alto was at the time struggling with the transition especially with its go-to-market and its Salesforce compensation, and really refreshing its portfolio. But we told you that we were bullish on Palo Alto Networks at the time because of its track record, and the fact that CIOs consistently told us that they saw Palo Alto as a thought leader in the space that they wanted to work with. They said that Palo Alto was the gold standard, the best, especially larger company CISOs. So that gave us confidence that Palo Alto, a very well-run company was going to get its act together and perform better. And Palo Alto has just done just that. As we expected, they've done very well and rapidly moving customers to the next generation of platforms. And we're very impressed by the company's execution. And the stock has generally reflected that. Now, some other names that hit our radar in the ETR data a couple of years ago, continue to perform well. CrowdStrike, Zscaler, SailPoint, and CloudFlare. Now, CloudFlare just reported and beat earnings but was off, the stock fell on headwinds for tech overall, the big rotation. But the company is doing very well and they're growing rapidly and they have momentum as you can see from the ETR data. Now, we put that double star around Proofpoint to highlight that it was worthy of fetching $12.5 billion from private equity firm. So nice exit there, supporting the continued consolidation trend that we've predicted in cybersecurity. Now let's turn our attention to Okta and Auth0. This is where it gets interesting, and is a clever play for Okta we think, and we want to drill into it a bit. Okta is acquiring Auth0 for big money. Why? Well, we think Todd McKinnon, Okta CEO, wants to run the table on identity and then continue to expand as TAM has to do that, to justify his lofty valuation. So Okta's ascendancy around identity and single sign-on is notable. The fragmented pictures that we've shown you, they scream out for simplification and trust, and that's what Okta brings. But it competes with some major players, most notably Microsoft with active directory. So look, of course, Microsoft is going to dominate in its massive customer base, but the rest of the market, that's like (indistinct) wide open. And we think McKinnon saw the opportunity to go dominate that sector. Now Okta comes at this from an enterprise perspective bringing top-down trust to the equation, and throwing a big blanket over all the discreet SaaS platforms and unifying employee access. Okta's timing was perfect. It was founded in 2009, just as the massive SaaSifiation trend was happening around CRM and HR, and service management and cloud, et cetera. But the one thing that Okta didn't have that Auth0 does is serious developer chops. While Okta was crushing it with its enterprise sales strategy, Auth0 was laser-focused on developers and building a bottoms up approach to identity. By acquiring Auth0, Okta can dominate both sides of the barbell and then capture the fat middle. So yes, it's a pricey acquisition, but in our view, it's a great move by McKinnon. Now, I don't know McKinnon personally, but last week I spoke to Arun Shrestha, who's the CEO of security specialist, BeyondID, they're a platinum services partner of Okta. And they're a zero trust expert. He worked for Okta for a number of years and shared with me a bit about McKinnon's style, and think big approach. Arun said something that caught my attention. He said, firewalls used to be the perimeter, now people are. And while that's self-serving to Okta and probably BeyondID, it's true. People, apps and data are the new perimeter, and they're not in one location. And that's the point. Now, unfortunately, I had lined up an interview with Diya Jolly, who was the chief product officer at Okta and a Cube alum for this past week, knowing that we were running this segment in this episode but she unfortunately fell ill the day of our interview and had to cancel. But I want to follow up with her, and understand how she's thinking about connecting the dots with Auth0 with devs and enterprises and really test our thesis there. This is a really interesting chess match that's going on. Let's look a little deeper into that identity space. This chart here shows some of the major identity players. It has some of the leaders in the identity market, and is a breakdown at ETR's net score. Now net score comprises five elements. The lime green is, we're adding the platform new. The forest green is we're spending 6% or more relative to last year. The gray is flat send plus or minus flat spend, plus or minus 5%. The pinkish is spending less. And the bright red is we're exiting the platform, retiring. Now you subtract the red from the green, and that gets you the result for net score which you can see super-imposed on the right hand chart at the bottom, that first column there. The far column is shared in which informs and indicates the number of responses and is a proxy for presence in the market. Oh, look at the top two players in terms of spending momentum. Now SailPoint is right there, but Auth0 combined with Okta's distribution channel will extend Okta's lead significantly in our view. And then there's Microsoft. Now just a caveat, this includes all of Microsoft's security offerings, not just identity, but it's there for context. And CyberArk as well includes this acquisition of adaptive, but also other parts of CyberArk's portfolio. So you can see some of the other names that are there, many of which you'll find in the Gartner magic quadrant for identity. And as we said, we really like this move by Okta. It combines positive market forces with lead offerings from very well-run companies that have winning DNA and passionate people. Now, to further emphasize what's happening here, take a look at this. This chart shows ETR data for Okta within SailPoint and CyberArk accounts. Out of the 230 CyberArk and SailPoint customers in the dataset, there are 81 Okta accounts. That's a 35% overlap. And the good news for Okta is that within that base of SailPoint and CyberArk accounts, Okta is shown by the net score line, that green line has a very elevated spending in momentum. And the kicker is, if you read the fine print in the right hand column, ETR correctly points out that while SailPoint and CyberArk have long been partners with Okta, at the recent Octane21 event, Okta's big customer event, The company announced that it was expanding into privileged access management, PAM, and identity governance. Hello, and welcome to co-opetition in the 2020s. Now, our current thinking is that this bodes very well for Okta and CyberArk and SailPoint. Well, they're going to have to make some counter moves to fend off the onslaught that is coming. Now, let's wrap up with what has become a tradition in our quarterly security updates. Looking at those two dimensions of net score and market share, we're going to see which companies crack the top 10 for both measures within the ETR dataset. We do this every quarter. So here in the left, we have the top 20, sorted by net score spending momentum and on the right, we sort by shared N. So it's again, top 20, which informs, shared N informs the market share metric or presence in the dataset. That red horizontal lines, those two lines on each separate the top 10 from the remaining 10 within those top 20. And our method, what we do is we assign four stars to those companies that crack the top 10 for both metrics. So again, you see Microsoft, Palo Alto Networks, Okta, CrowdStrike, and Fortinet. Fortinet by the way, didn't make it last quarter. They've kind of been in and out and on the bubble, but company is very strong, and doing quite well. Only the other four did last quarter. They were the same for last quarter. And we give two stars to those companies that make it in both categories within the top 20 but didn't make the top 10. So Cisco, Splunk, which has been steadily decelerating from a spending momentum standpoint, and Zscaler, which is just on the cusp. We really like Zscaler and the company has great momentum, but that's the methodology. That is what it is. Now you can see, we kept Carbon Black on the right most chart, it's like kind of cut off, it's number 21. Only because they're just outside looking in on net score. You see them there, they're just below on net score, number 11. And VMware's presence in the market we think, that Carbon Black is right really worth paying attention to. Okay, so we're going to close with some summary and final thoughts. Last quarter, we did a deeper dive on the SolarWinds hack, and we think the ramifications are significant. It has set the stage for a new era of escalation and adversary sophistication. Now, major change we see is a heightened awareness that when you find intruders, you'd better think very carefully about your next moves. When someone breaks into your house, if the dog barks, or if you come down with a baseball bat or other weapon, you might think the intruder is going to flee. But if the criminal badly wants what you have in your house and it's valuable enough, you might find yourself in a bloody knife fight or worse. Well, what's happening is intruders come to your company via island hopping or insider subterfuge or whatever method. And they'll live off the land stealthily using your own tools against you so that you can't find them so easily. So instead of injecting new tools in that send off an alert, they just use what you already have there. That's what's called living off the land. They'll steal sensitive data, for example, positive COVID test results when that was really, really sensitive, obviously still is, or other medical data. And when you retaliate, they will double-extort you. They'll encrypt your data and hold it for ransom, and at the same time threaten to release the sensitive information, crushing your brand in the process. So your response must be as stealthy as their intrusion, as you marshal your resources and devise an attack plan. And you face serious headwinds. Not only is this a complicated situation, there's your ongoing and acute talent shortage that you tell us about all the time. Many companies are mired in technical debt, that's an additional challenge. And then you've got to balance the running of the business while actually effecting a digital transformation. That's very, very difficult, and it's risky because the more digital you become, the more exposed you are. So this idea of zero trust, people used to call it a buzzword, it's now a mandate along with automation. Because you just can't throw labor at the problem. This is all good news for investors as cyber remains a market that's ripe for valuation increases and M&A activity, especially if you know where to look. Hopefully we've helped you squint through the maze a little bit. Okay, that's it for now. Thanks to the community for your comments and insights. Remember I publish each week on wikibon.com and siliconangle.com. These episodes, they're all available as podcasts. All you got to do is search breaking analysis podcasts, put in the headphones, listen when you're in your car, or out for your walk or run, and you can always connect on Twitter @DVellante, or email me at david.vellante@siliconangle.com. I appreciate the comments on LinkedIn and in Clubhouse, please follow me, so you're notified when we start a room and riff on these topics and others. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for The Cube Insights powered by ETR. Be well, and we'll see you next time. (light instrumental music)

(digital music) >> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> The storage business as we know it has changed forever. On-prem storage was once a virtually unlimited and untapped bastion of innovation, VC funding and lucrative exits. Today it's a shadow of its former self and the glory days of storage will not return. Hello everyone, and welcome to this week's Wikibon CUBE Insights Powered by ETR. In this breaking analysis, we'll lay out our premise for what's happening in the storage industry, and share some fresh insights from our ETR partners, and data that supports our thinking. We've had three decades of tectonic shifts in the storage business. From the simplified history of this industry shows us there've been five major waves of innovation spanning five decades. The dominant industry model has evolved from what was first the mainframe centric vertically integrated business, but of course by IBM and it became a disintegrated business that saw between like 70 or 80 Winchester disk drive companies that rose and then fell. They served a booming PC industry in this way it was led by the likes of Seagate. Now Seagate supplied the emergence of an intelligent controller based external disc array business that drove huge margins for functions that while lucrative was far cheaper than captive storage from system vendors, this era of course was led by EMC and NetApp. And then this business was disrupted by a flash and software defined model that was led by Pure Storage and also VMware. Now the future of storage is being defined by cloud and intelligent data management is being led by AWS and a three letter company that we'll just call TBD, otherwise known as Jump Ball Incorporated. Now, let's get into it here, the impact of AWS cannot be overstated now while legacy storage players, they're sick and tired of talking about the cloud, the reality cannot be ignored. The cloud has been the most disruptive force in storage over the past 10 years, and we've reported on the spending impact extensively. But cloud is not the only factor pressuring the on-prem storage business, flash has killed what we call performance by spindles. In other words, the practice of adding more disk drives to keep performance from tanking. So much flash has been injected into the data center that that no longer is required. But now as you drill down into the cloud, AWS has been by far the most significant factor in our view. Lots of people talked about object storage before AWS, but there sure wasn't much spending going on, S3 changed that. AWS is getting much more aggressive about expanding its storage portfolio and its offerings. S3 came out in 2006 and it was the very first AWS service and then Elastic Block Service EBS came out a couple of years later, nobody really paid much attention. Well last fall at storage day, we saw AWS announce a number of services, many fire-related and this year we saw four new announcements of Amazon at re:Invent. We think AWS' storage revenue will surpass 8 billion this year and could be as high as 10 billion. There's not much data out there, but this would mean that AWS' storage biz is larger than that of a NetApp, which means AWS is larger than every traditional storage player with the exception of Dell. Here's a little glimpse of what's coming at the legacy storage business. It's a clip of the vice-president of AWS storage, her name is Mahlon Thompson Bukovec, watch this. Okay now, you may say Dave, what the heck does that have to do with anything? Yeah, I don't know, but as an older white guy, that's been in this business for awhile, I just think it's badass that this woman boxes and runs a business that we think is approaching $10 billion. Now let's take a quick look at the storage announcements AWS made at re:Invent. The company made four announcements this year, let me try to be brief, the first is EBS io2 Block Express Volumes, got to love the names. AWS was claims this is the first storage area network or sand for the cloud and it offers up to 256,000 IOPS and 4,000 megabytes per second throughput and 64 terabytes of capacity. Hey, sounds pretty impressive right, Well let's dig in a little bit okay, first of all, this is not the first sand in the cloud, at least in my view there may be others but Pure Storage announced cloud block store in 2019 at its annual accelerate customer conference and it's pretty comparable here. Maybe not so much in the speeds and feeds, but the concept of better block storage in the cloud with higher availability. Now, as you may also be saying, what's the big deal? The performance come on, we can smoke that we're on-prem vendor We can bury that. Compared to what we do, AWS' announcement is really not that impressive okay, let me give you a point of comparison there's a startup out there called VAST Data. Just there for you and closure with bundled storage and compute can do 400,000 IOPS and 40,000 megabytes per second and that can be scaled, so yeah, I get it. And AWS also announced that io2 two was priced at 20% less than previous generation volumes, which you might say is also no big deal and I would agree 20% is not as aggressive as the average price decline per gigabyte of any storage technology. AWS loves to make a big deal about its price declines, it's essentially following the industry trends but the point is that this feature will be great for a lot of workloads and it's fully integrated with AWS services meaning for example, it will be very convenient for AWS customers to invoke this capability for example Aurora and other AWS databases through its RDS service, just another easy button for developers to push. This is specially important as we see AWS rapidly expanding its machine learning in AI capabilities with SageMaker, it's embedding ML into things like Redshift and driving analytics, so integration is very key for its customers. Now, is Amazon retail going to run its business on io2 volumes? I doubt it. I believe they're running on Oracle and they need much better performance, but this is a mainstream service for the EBS masses to tap. Now, the other notable announcement was EBS Gp3 volumes. This is essentially a service that lets let you programmatically set SLAs for IOPS and throughput independently without needing to add additional storage. Again, you may be saying things like, well atleast I remember when SolidFire let me do this several years ago and gave me more than 3000 IOPS and 125 megabytes per a second performance, but look, this is great for mainstream customers that want more consistent and predictable performance and that want to set some kind of threshold or floor and it's integrated again into the AWS stack. Two other announcements were made, one that automatically tiers data to colder storage tiers and a replication service. On the former, data migrates to tier two after 90 days of inaccess and tier three, after 180 days. AWS remember, they hired a bunch of folks out of EMC years ago and they put them up in the Boston Seaport area, so they've acquired lots of expertise in a lot of different areas I'm not sure if tiering came out of that group but look, this stuff is not rocket science, but it saves customers money. So these are tried and true techniques that AWS is applying but the important thing is it's in the cloud. Now for sure we'd like to see more policy options than say for example, a fixed 90 day or 180 day policy and more importantly we'd like to see intelligent tiering where the machine is smart enough to elevate and promote certain datasets when they're needed for instance, at the end of a quarter for comparison purposes or at the end of the year, but as NFL Hall of Fame Coach Hank Stram would have said, AWS is matriculating the ball down the field. Okay, let's look at some of the data that supports what we're saying here in our premise today. This chart shows spending across the ETR taxonomy. It depicts the net score or spending velocity for different sectors. We've highlighted storage, now don't put too much weight on the January data because the survey was just launched, but you can see storage continues to be a back burner item relative to some other spending priorities. Now as I've reported, CIOs are really focused on cloud, containers, container orchestration, automation, productivity and other key areas like security. Now let's take a look at some of the financial data from the storage crowd. This chart shows data for eight leading names in storage and we put storage in quotes because as we said earlier, the market is shifting and for sure companies like Cohesity and Rubrik, they're not positioning as storage players in fact, that's the last thing they want to do. Rather they're category creators around data management or intelligent data management but their inadjacency to storage, they're partnering with all the primary storage companies and they're in the ETR taxonomy. Okay, so as you can see, we're showing the year over year, quarterly revenue growth for the leading storage companies. NetApp is a big winner, they're growing at a whopping 2%. They beat expectations, but expectations were way down so you can see in the right most column upper right, we've added the ETR net score from October and net score of 10% says that if you ask customers, are you spending more or less with a company, there are 10% of the customers that are essentially spending more than are spending less, get into that a little further later. For comparison, a company like Snowflake, it has a net score approaching 70% Pure Storage used to be that high several years ago or high sixties anyway. So 10% is in the red zone and yet NetApp, is the big winner this quarter. Now Nutanix isn't really again a storage company, but they're an adjacency and they sell storage and like many of these companies, it's transitioning to a subscription pricing model, so that puts pressure on the income statement, that's why they went out and did a deal with Bain, Bain put in $750 million to help Bridge that transition so that's kind of an interesting move. Every company in this chart is moving to an annual recurring revenue model and that as a service approach is going to be the norm by the end of the decade. HPE's doing it with GreenLake, Dell has announced Apex, virtually every company is headed in this direction. Now speaking of HPE, it's Nimble business that has momentum, but other parts of the storage portfolio are quite a bit softer. Dell continues to see pressure on its storage business although VxRail is a bright spot. Everybody's got a bright spot, everybody's got new stuff that's growing much faster than the old stuff, the problem is the old stuff is much much bigger than the new stuff. IBM's mainframe storage cycle, well that's seems to have run its course, they had been growing for the last several quarters that looks like it's over. And so very very cyclical businesses here now as you can see, The data protection data management companies, they are showing spending momentum but they're not public so we don't have revenue data. But you got to wonder with all the money these guys have raised and the red hot IPO and tech markets, why haven't these guys gone public? The answer has to be that they're either not ready or maybe their a numbers weren't where they want them to be, maybe they're not predictable enough, maybe they don't have their operational act together or maybe they need to you get that in order, some combination of those factors is likely. They'll tell you, they'll give other answers if you ask them, but if they had their stuff together they'd be going out right now. Now here's another look at the spending data in terms of net score, which is again spending velocity. The ETR here is measuring the percent of respondents that are adopting new, spending more, spending flat, spending less or retiring the platform. So net score is adoptions, which is the lime green plus the spending more, which is the forest green. Add those two and then subtract spending less, which is the pink and then leaving the platform, which is the bright red, what's left over is net score. So, let's look at the picture here, Cohesity leads all players in the storage taxonomy, the ETR storage taxonomy, again they don't position that way, but that's the way the customers are answering. They've got 55% net score which is really solid and you can see the data in the upper right-hand corner, it's followed by Nutanix. Now they're really not again in the scope of Pure play storage play but speaking of Pure, its net score has come down from its high of 73% in January, 2016. It's not going to climb back up there, but it's going to be interesting to see if Pure net scorecard rebound in a post COVID world. We're also watching what Pure does in terms of unifying file and object and how it's fairing in cloud and what it does with the Portworx acquisition which is really designed to bring forth a new programming model. Now, Dell is doing fine with VxRail, but VSAN is well off its net score highs which we're in the 60% plus range a couple of years ago, VSAN is definitely been a factor from VMware, but again that's come off its highs, HPE with Nimble still has some room to improve, I think it actually will I think that these figures that we're showing here they're are somewhat depressed by the COVID factor, I expect Nimble is going to bounce back in future surveys. Dell and NetApp are the big leaders in terms of presence or market share in the data other than VMware, 'cause VMware has a lot of instances, it's software defined that's why they're so prominent. And with VMware's large share you'd expect them to have net scores that are tepid and you can see a similar pattern with IBM. So Dell, NetApp, tepid net scores as is IBM because of their large market share VMware, kind of a newer entry into the play and so doing pretty well there from a net score standpoint. Now Commvault like Cohesity and Rubrik is really around intelligent data management, trying to go beyond backup into business recovery, data protection, DevOps, bringing that analytics, bringing that to the cloud, we didn't put Veeam in here and we probably should have. They had pre-COVID net scores well in to the thirties and they have a steadily increasing share of the market, so we expect good things from Veeam going forward. They were acquired earlier this year by Insight, capital private equity firm. So big changes there as well, that was their kind of near-term exit maybe more to come. But look, it's all relative, this is a large and mature market that is moving to the cloud and moving to other adjacencies. And the core is still primary storage, that's the main supreme prerequisite and everything else flows from there, data protection, replication, everything else. This chart gives you another view of the competitive landscape, it's that classic XY chart it plots net score in the vertical axis and market share on the horizontal axis, market share remember is a measure of presence in the dataset. Now think about this from the CIO's perspective, they have their on-prem estate, got all this infrastructure and they're putting a brick wall around their core systems. And what do they want out of storage for that class of workload? They want it to perform consistently, they want it to be efficient and they want it to be cost-effective, so what are they going to do? they're going to consolidate, They're going to consolidate the number of vendors, they're going to consolidate the storage, they're going to minimize complexity, yeah, they're going to worry about the blast radius, but there's ways to architect around that. The last thing they want to worry about is managing a zillion storage vendors this business is consolidating, it has been for some time, we've seen the number of independent storage players that are going public as consolidated over the years, and it's going to continue. so on-prem storage arrays are not giving CIOs the innovation and strategic advantage back when things like storage virtualization, space efficient snapshots, data de-duplication and other storage services were worth maybe taking a flyer on a feature product like for example, a 3PAR or even a Data Domain. Now flash gave the CIOs more headroom and better performance and so as I said earlier, they're not just buying spindles to increase performance, so as more and more work gets pushed to the cloud, you're seeing a bunkering in on these large scale mission-critical workloads. As you saw earlier, the legacy storage market is consolidating and has been for a while as I just said, it's essentially becoming a managed decline business where RnD is going to increasingly get squeezed and go to other areas, both from the vendor community and on the buy-side where they're investing on things like cloud, containers and in building new layers in their business and of course the DX, the Digital Transformation. I mentioned VAST Data before, it is a company that's growing and another company that's growing is Infinidat and these guys are traditional storage on-prem models they don't bristle If I say traditional they're nexgen if you will but they don't own a cloud, so they were selling to the data center. Now Infinidat is focused on petabyte scale and as they say, they're growing revenues, they're having success consolidating storage that thing that I just talked about. Ironically, these are two Israeli founder based companies that are growing and you saw earlier, this is a share shift the market is not growing overall the part of that's COVID, but if you exclude cloud, the market is under pressure. Now these two companies that I'm mentioning, they're kind of the exception to the rule here, they're tiny in the grand scheme of things, they're really not going to shift the market and their end game is to get acquired so they can still share, but they're not going to reverse these trends. And every one on this chart, every on-prem player has to have a cloud strategy where they connect into the cloud, where they take advantage of native cloud services and they help extend their respective install bases into the cloud, including having a capability that is physically proximate to the cloud with a colo like an Equinix or some other approach. Now, for example at re:Invent, we saw that AWS has hybrid strategy, we saw that evolving. AWS is trying to bring AWS to the edge and they treat the data center as just another edge note, so outposts and smaller versions of outposts and things like local zones are all part of bringing AWS to the edge. And we saw a few companies Pure, Infinidant, Veeam come to mind that are connecting to outpost. They saw the Qumulo was in there, Clumio, Commvault, WekaIO is also in there and I'm sure I'm missing some so, DM me, email me, yell at me, I'm sorry I forgot you but you get the point. These companies that are selling on-prem are connecting to the cloud, they're forced to connect to the cloud much in the same way as they were forced to join the VMware ecosystem and try to add value, try to keep moving fast. So, that's what's going on here, what's the prognosis for storage in the coming year? Well, where've of all the good times gone? Look, we would never bet against data but the days of selling storage controllers that masks the deficiencies of spinning disc or add embedded hardware functions or easily picking off a legacy install base with flash, well, those days are gone. Repatriation, it ain't happening it's maybe tiny little pockets. CIOs are rationalizing their on-premises portfolios so they can invest in the cloud, AI, machine learning, machine intelligence, automation and they're re-skilling their teams. Low latency high bandwidth workloads with minimal jitter, that's the sweet spot for on-prem it's becoming the mainframe of storage. CIOs are also developing a cloud first strategy yes, the world is hybrid but what does that mean to CIOs? It means you're going to have some work in the cloud and some work on-prem, there's a hybrid We've got both. Everything that can go to the cloud, will go to the cloud, in our opinion and everything that can't or shouldn't won't. Yes, people will make mistakes and they'll "repatriate" but generally that's the trend. And the CIOs they're building an abstraction layer to connect workloads from an observability and manageability standpoint so they can maintain control and manage lock-in risk, they have options. Everything that doesn't go to the cloud will likely have some type of hybridicity to it, the reverse won't likely be the case. For vendors, cloud strategies involve supporting your install basis migration to the cloud, that's where they're going, that's where they want to go, they want your help there's business to be made there so enabling low latency hybrids in accommodating subscription models, well, that's a whole another topic, but that's the trend that we see and you rethink the business that you're in, for instance, data management and developing an edge strategy that recognizes that edge workloads are going to require new architecture and that's more efficient than what we've seen built around general purpose systems, and wow, that's a topic for another day. You're seeing this whole as a service model really reshape the entire cultures in the way in which the on-prem vendors are operating no longer is it selling a box that has dramatically marked up controllers and disc drives, it's really thinking about services that could be invoked in the cloud. Now remember, these episodes are all available as podcasts, wherever you listen, just search Breaking Analysis podcasts and please subscribe, I'd appreciate that checkout etr.plus for all the survey action. We also publish a full report every week on wikibon.com and siliconangle.com. A lot of ways to get in touch. You can email me at david.vellante@siliconangle.com. you could DM me @dvellante on Twitter, comment on our LinkedIn posts, I always appreciate that. This is Dave Vellante for theCUBE Insights Powered by ETR. Thanks for watching everyone stay safe and we'll see you next time. (upbeat music)