>> Announcer: It's The Cube. Covering HPE Big Data Conference 2016. Now here are your hosts, Dave Vellante and Paul Gillin. >> Welcome back to Boston, everybody, this is The Cube, we're here live at HP's big data conference, hashtag seize the data. Steve Spear is here, he's an author, MIT professor, author of The High Velocity Edge, welcome to The Cube, thanks for coming on. >> Oh, thanks for having me. >> I got to tell you, following Phil Black, you were coming onstage, I have never heard you speak before, I said, "Oh, this poor guy," and you did awesome, you were great, you held the audience, so congratulations, you were very dynamic and he was unbelievable and you were fantastic, so. >> Today was second-worst speaking setup, one time I was on a panel where it was three admirals, a general, and then the other guy wearing a suit, I said, "Well at least another schmo in a suit," and his opening lines were, "You know, this reminds me, "when I was on the space shuttle and we were flying "to the Hubble," and I'm like, "A flipping astronaut, "I got to follow an astronaut?" So anyway, this was only a SEAL, there were a lot of them, there were far fewer astronauts, so that was easy. >> What I really liked about your talk is, first of all, you told the story of Toyota, which I didn't know, you may. >> No, my experience with Toyota was in the early '70s, I remember the Toyota sort of sweeping into the market but you talked about 20 years before it when they were first entering and how this really was a company that had a lot of quality problems and it was perceived as not being very competitive. >> Yeah, Toyota now people look at as almost, they just take for granted the quality, the productivity, they assume good labor relations and that kind of thing, it's non-unionized, not because the unions haven't tried to unionize, but the employees don't feel the need. And again, in the '50s, Toyota was absolutely an abysmal auto-maker, their product was terrible, their productivity was awful and they didn't have particularly good relations with the workforce either. I mean, it's a profound transformation. >> And you gave this test, in the 50s, I forget what it was, it was one-tenth the productivity of the sort of average automobile manufacturer and then they reached parity in '62, by '68 they were 2X, and by '73, they were off the charts. >> Right, right, right. >> Right, so amazing transformation and then you try to figure out how they did it and they couldn't answer, but they said, "We can show you," right? And that sort of led to your research and your book. >> Yeah, so the quick background is in some regards, this fellow Kenneth Bowen, who was my mentor and advisor when I was doing my doctorate, he could argue we were late to the game because people started recognizing Toyota as this paragon of virtue, high quality at low cost, and so that in the 1980s prompted this whole investigation and the term lean manufacturing came out of the realization that on any given day, Toyota and suppliers were making basically twice the product with half the effort and so you had this period of '85 to about '95 where there was this intense attempt to study Toyota, document Toyota, imitate Toyota, General Motors had a joint venture with Toyota, and then you have the mid-'90s and there's no second Toyota, despite all this investment, so we go to the Toyota guys and say, "Look, clearly if everyone is studying you, imitating you, "copying you, and they haven't replicated you, "they've missed something, so what is it?" And they say, "I'm sorry, but we can't tell you." And we said, "Well you got to be kidding, I mean, "you have a joint venture with your biggest competitor, "General Motors," and they said, "No, no, it's not that we wouldn't tell you, "we just actually don't know how to explain what we do "'cause most of us learn it in this very immersive setting, "but if you'd like to learn it, "you can learn it the way we do." I didn't realize at the time that it would be this Karate Kid wax-on, wax-off, paint-up, paint-down experience, which took years and years to learn and there are some funny anecdotes about it but even at the end, their inability to say what it is, so I went years trying to capture what they were doing and realizing I was wrong 'cause different things wouldn't work quite right, and I can tell you, I was on the Shinkansen with the guy who was my Toyota mentor and I finally said, "Mr. Oba, I think I finally "figured it out, it all boils down to these basic "approaches to seeing and solving problems." And he's looking over my cartoons and stuff and he says, "Well, I don't see anything wrong with this." (laughs) >> That was as good as it got. >> That was as good as it got, I was like, "Score, nothing wrong that he can see!" So anyway. >> But so if you talk about productivity, reliability, you made huge gains there, and the speed of product cycles, were the three knobs that Toyota was turning much more significantly than anybody else and then fuel efficiency came. >> Right, so if you start looking at Toyota and I think this is where people first got the attraction and then sort of the dismissive of, we don't make cars, so the initial hook was the affordable reliability, they could deliver a much higher-quality car, much more affordable based on their productivity. And so that's what triggered attention which then manifest itself as this lean manufacturing and its production control tools. What then sort of started to fall off people's radar is that Toyota not only stayed ahead on those dimensions but they added to the dimensionality of the game, so they started introducing new product faster than anybody else and then they introduced new brand more successfully so all the Japanese, Nissan, Honda, Toyota, all came out with a luxury version, but no one came out with Lexus other than Toyota. The Affinity and the Acura, I mean, it's nice cars, but it didn't become this dominant brand like the Lexus. And then in trying to hit the youth market, everyone tried to come up with, like Honda had the Element but nothing like the Scion, so then Toyota's, and that's much further upstream, a much more big an undertaking than just productivity in a factory. And then when it came time to this issue around fuel efficiency, that's a big technology play of trying to figure out how you get these hybridized technologies with a very very complex software engineering overlay to coordinate power flow in this thing and that, and everyone has their version of hybrid, but no one has it through six generations, 21 platforms, and millions of copies sold. So it didn't matter where you were, Toyota figured out how to compete on this value to market with speed and ease which no one else in their industry was replicating. >> You're talking about, this has nothing to do with operational efficiency, when you talk about the Scion for example, you're talking about tapping into a customer, into an emotional connection with your customer and being able to actually anticipate what they will want before they even know, how do you operationalize that? >> So I think, again, Toyota made such an impression on people with operational efficiency that a lot of their genius went unrecognized, so what I was trying to elaborate on this morning is that Toyota's operational efficiency is not the consequence of just more clever design of operations, like you have an algorithm which I lack and so you get to a better answer than I do, it was this very intense almost empathetic approach to improving existing operations, so you're working on something and it's difficult so we're perceptive of that difficulty and try to understand the source of that difficulty and resolve it, and just do that relentlessly about everything all the time, and it's that empathy to understand your difficulty which then becomes the trigger for making things better, so as far as the Scion comes in, what you see is the same notion of empathic design apply to the needs of the youth market. And the youth market unlike the folks who are, let's say at the time, middle-aged, was less about reliable affordability, but these were people who were coming of age during the Bannatyne era where, very fast mass customization or the iPod era, which was common Chassis but very fast, inexpensive personalization and the folks at Toyota said, "You know what, "the youth market, we don't really understand that, "we've been really successful for this older mid-market, "so let's try to understand the problems that the youth "are trying to solve with their acquisitions," and it turned out personalization. And so if you look at the Scion, it wasn't necessarily a technically or technologically sophisticated quote-unquote sexy product, what it did was it leant itself towards very diverse personalization, which was the problem that the youth market was trying to solve. And you actually see, if I can go on this notion of empathic design, so you see this with the Lexus, so I think the conventional wisdom about luxury cars was Uber technology and bling it, throw chrome and leather and wood and when Toyota tried that initially, they took what was I guess now the Avalon, full-sized car, and they blinged it up and it was contradictory 'cause if you're looking for a luxury car, you don't go to a Toyota dealer, and if you go to a Toyota dealer and you see something with chrome and leather and wood veneer, you're like, you have dissonance. So they tried to understand what luxury meant from the American consumer perspective and again, it wasn't, you always wish you'd get this job, but they sent an engineering team to live in Beverly Hills for some months. (laughs) It's like, ooh, twist my arm on that one, right? But what they found was that luxury wasn't just the physical product, it was the respectful service around it, like when you came back to your hotel room, you walked in, people remembered your name or remembered that, oh we noticed that you used a lot of bath towels so we made sure there were extra in your room, that sort of thing, and if you look at the Lexus, and people were dismissive of the Lexus, saying, "It looks like slightly fancier Toyota, "but what's the big deal, it's not a Beamer or Mercedes." But that wasn't the point, it was the experience you got when you went for sales and service, which was, you got treated so nice, and again, not like hoity toity but you got treated respectfully, so anyway, it all comes back to this empathic design around what problem is the customer or someone inside a plan trying to solve. >> So Toyota and Volkswagen trying to vie for top market share but Toyota, as you say, has got this brand and this empathy that Volkswagen doesn't. You must get a lot of questions about Tesla. Thoughts on Tesla. >> Yeah, cool product, cool technology and time will tell if they're actually solving a real problem. And I don't mean to be dismissive, it's just not an area where I've spent a lot of time. >> And we don't really know, I mean, it's amazing and a software-defined automobile and autonomous, very difficult to predict, we're very tight on time. >> All the cool people seem to drive them though. >> Yeah, that's true. Last question I have is, what the heck does this have to do with analytics at a conference like this? >> Right, so you start thinking about the Toyota model, really, it's not that you can sit down and design something right, it's that you design things which you know deep-rooted in your DNA is that what you've designed is wrong, and that in order to get it right and actually much righter than anything else in the marketplace, what you need to do is understand what's wrong about it and so the experience of the user will help inform what's wrong, the worker rounds they do, the inconveniences they experience, the coping, the compensation they do, and that you can not only use that to help inform what's wrong, but then help shape your understanding of how to get to right, and so where all this fits in is that when you start thinking about data, well first of all, these are gigantic systems, right, which it's probably well-informed to think in terms of these systems are being designed by flawed human beings so the systems themselves have flaws, so it's good to be attentive to the flaws that are designed in it so you can fix them and make them more usable by your intended clientele. But the other thing is that these systems can help you gain much greater precision, granularity, frequency of sampling and understanding of where things are misfiring sooner than later, smaller than larger, so you can adjust and adapt and be more agile in shaping the experience. >> Well Steve, great work, thanks very much for coming on The Cube and sharing and great to meet you. >> Yeah likewise, thanks for having me. >> You're welcome. Alright, keep it right there, everybody, Paul and I will be back with our next guest, we're live from Boston, this is The Cube, we'll be right back. (upbeat music)

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

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante snowflake they love the stock at 400 and hated at 165 that's the nature of the business i guess especially in this crazy cycle over the last two years of lockdowns free money exploding demand and now rising inflation and rates but with the fed providing some clarity on its actions the time has come to really dig into the fundamentals of companies and there's no tech company that's more fun to analyze than snowflake hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we look at the action of snowflake stock since its ipo why it's behaved the way it has how some sharp traders are looking at the stock and most importantly what customer demand looks like the stock has really provided some great theater since its ipo i know people who got in at 120 before the open and i know lots of people who kind of held their noses and bought the stock on day one at over 300 a day when it closed at around 240 that first day of trading snowflake hit 164 this week it's all-time low as a public company as my college roommate chip simonton a long time trader told me when great companies trade at all times time lows because of panic it's worth taking a shot he did now of course the stock could go lower there's geopolitical risk and the stock with a 64 billion market cap is expensive for a company that's forecast to do around 2 billion in product revenue this year and remember i don't recommend stocks you shouldn't take my advice and my comments you got to do your own research but i have lots of data and i have opinions and i'm willing to share that with you stocks like snowflake crowdstrike z-scaler octa and companies like this are highly volatile when markets are moving up they're going to move up faster than the mean when they're declining they're going to drop more severely and that's clearly what's happened to snowflake so with a company like this you when you see panic selling you'll also see panic buying sometimes like we we've seen with this name it went from 220 to 320 in a very short period earlier snowflake put in a short-term bottom this week and many traders feel the issue was oversold so they bought okay but not everyone felt this way and you can see this in the headlines snowflake hits low but cloud stocks rise and we're going to come back to that is it a buy don't buy the dip buy the dip and what snowflake investors can learn from microsoft and from the street.com snow stock is sliding on the back of ill-conceived guidance and to that i would say that conservative guidance these days is anything but ill-conceived now let's unpack all this a bit and to do so i reached out to ivana delevska who has been on this program before she's with spear invest a female-led etf that goes deep into understanding supply chains she came on breaking analysis and laid out her thesis to buy the dip on snowflake this is a while ago she told me currently spear still likes snowflake and has doubled its position let me share her analysis she called out two drivers for the downside interest rates you know rising of course in snowflakes guidance which my own publication called weak in that previous chart that i just showed you so let's dig into that a bit snowflake guided for product revenues of 67 year on year which was below buy side expectations but i believe within sell side consensus regardless the guide was nuanced and driven by snowflake's decision to pass along price efficiencies to customers from optimizing processor price performance predominantly from aws's graviton too this is going to hit snowflakes revenue a net of about a hundred million dollars this year but the timing's not precise because it's going to hit 165 million but they're going to make up 65 million in increased demand frank slootman on the earnings call made this very clear he said quote this is not philanthropy this stimulates demand classic slootman the point is spear and other bulls believe that this will result in a gain for snowflake over the medium term and we would agree price goes down roi gets better you throw more projects at snowflakes customers going to buy more snowflake and when that happens and it gives the company an advantage as they continue to build their moat it's a longer term bet on cloud and data which are good bets now some of this could also be competitive pressures there have been you know studies that are out there from competitors attacking snowflakes pricing and price performance and they make comparisons oracle's been pretty aggressive as have others but so far the company's customers continue to consume now at a very fast rate now on on this front what can we learn from microsoft that applies to snowflake that's the headline here from benzinga so the article quoted a wealth manager named josh brown talking about what happened to microsoft after the dot-com bubble burst and how they quadrupled earnings over the next decade and the stock went sideways suggesting the same thing could happen to snowflake now i'd like to make a couple of comments here first at the time microsoft was a 23 billion dollar company and it had a monopoly and was already highly profitable steve ballmer became the ceo of microsoft right after the dot-com bubble burst and he hugged onto windows for dear life and lived off of microsoft's pc software monopoly microsoft became an extremely profitable and remarkably uninteresting caretaker of a pc in on-prem software estate during balmer's tenure so i just don't see the comparison as relevant snowflake you know they're going to make struggle for other reasons but that one didn't really resonate with me what's interesting is this chart it poses the question do cloud and data markets behave differently it's a chart that shows aws growth rates over time and superimposes the revenue in the red in q1 2018 aws generated 5.4 billion dollars in revenue and that was growing at the time at nearly a 50 rate now that rate as you can see decelerated quite significantly as aws grew to a 50 billion dollar run rate company that down below where you see it bottoms now it makes sense right law of large numbers you can't keep growing that fast when you get that big well oops look what happened in 2021 aws's growth rate bottoms in the high 20s and then rockets back up to 40 this past quarter as aws surpasses a 70 billion dollar run rate so you have to ask is cloud different is data different is cloud data different or data cloud different let's put it in the snowflake parlance can cloud because of its consumption model and the speed of innovation and ecosystem depth and breadth enable snowflake to exhibit lots of variability in its growth rates versus a say progressive and somewhat linear decline as the company grows revenue which is what you would expect historically and part of the answer relates to its market size here's a chart we've shared before with some additions it's our version of snowflake's total available market they're tam which snowflake's version that that blue data cloud thing superimposed on the right it shows the various layers of market opportunity that we came up with that that snowflake and others we think have in front of them emerging from the disruption of legacy data lakes and data warehouses to what snowflake refers to as its data cloud we think about the data mesh concept and decentralized data architectures with domain ownership and data product and service builders as consistent with snowflake's data cloud vision where snowflake data stores are nodes they're just simply discoverable nodes on the mesh you could have you know data bricks data lakes you know s3 buckets on that mesh it doesn't matter they can be discovered they can be shared and of course they're governed in a federated model now in snowflake's model it's all inside the snowflake data cloud that's fine then you'll go to the out years it gets a little fuzzy you know from edge locations and ai inference it becomes massive and decision making occurs in real time where machines and machine data take over the world instead of you know clicks and keystrokes sounds out there but it's real and how exactly snowflake plays there at this point is unclear but one thing's for sure there'll be a lot of data and it's going to find its way into snowflake you know snowflake's not a real-time engine it's an analytical system it's moving into the realm of data science and you know we've talked about the need for you know semantic layer between those those two worlds of analytics and data science but expanding the scope further out we think that snowflake is a big role to play in this future and the future is massive okay check you got the big tam now as someone that looks at companies through a fundamentals prism you've got to look obviously at the markets in the tan which we just did but you also want to understand customers and it's not hard to find snowflake customers capital one disney micron alliance sainsbury sonos and hundreds of other companies i've talked to snowflake customers who have also been customers of oracle teradata ibm neteza vertica serious database practitioners and they tell me it's consistent soulflake is different they say it's simpler it's more agile it's less complicated to secure and it's disruptive to their traditional ways of doing data management now of course there are naysayers i've spoken to a number of analysts that feel snowflake is deficient in areas like workload management and course complex joins and it's too specialized in a world where we're seeing the convergence of analytics and transactional workloads our own david floyer believes that what oracle is doing with mysql heatwave is radically disruptive to many of the database architectures and blows away anything out there and he believes that snowflake and the likes of aws are going to have to respond now this the other criticism here is that snowflake is not architected for real-time inference where a lot of that edge activity is is going to happen it's a multi-hundred billion dollar market and so look snowflake has a ton of competition that's the other thing all the major cloud players have very capable and competitive database platforms even though they all partner with snowflake except oracle of course but companies like databricks and have garnered tons of vc other vc funded companies have raised billions of dollars to do this kind of elastic consumption based separate compute from storage stuff so you have to always keep an open mind and be aware of potential blind spots for these companies but to the criticisms i would say look snowflake they got there first and watch their ecosystem it's a real key to its continued success snowflake's not going to go it alone and it's going to use its ecosystem partners to expand its reach and accelerate the network effects and fill those gaps and it will acquire its stock is valuable so it should be doing that just as it did with streamlit a zero revenue company that it bought for 800 million dollars in stock and cash just recently streamlit is an open source python library that gets snowflake further deeper into that data science space that data brick space and look watch what snowflake is doing with snowpark it's an api library for processing data and building data intensive applications we've talked about snowflake essentially being becoming the super cloud and building this sort of path-like layer across clouds rather than trying to do it all themselves it seems snowflake is really staring at the api economy and building its ecosystem to plug those holes so let's come back to the customers here's a chart that shows snowflakes customer spending momentum or net score on the the top line that's the vertical axis and pervasiveness in the data or market share and that bottom brown line snowflake has unprecedented net scores and held them up for many many quarters as you can see here going back you know a couple years all leading to its expanded market penetration and measured as pervasiveness of so-called market share within the etr survey it's not like idc market share it's pervasiveness in the data set now i'll say this i don't see how this is sustainable i've been waiting for this to moderate i wouldn't be surprised to see snowflake come back to earth a little bit i think they'll clearly still be highly elevated based on the data that i've seen but but i could see in in one or more of the etr surveys this year this starting to moderate as they get they get big it's just it has to happen um but i would again expect them to have a high spending velocity score but i think we're going to see snowflake you know maybe porpoise a bit here meaning you know it moderates it comes back up it's just really hard to sustain this piece of momentum and higher train retain and scale without absorbing some some friction and some head woods that's going to slow you down but back to the aws growth example it's entirely possible that we could see a similar dynamic with snowflake that you saw with aws and you kind of see it with salesforce and servicenow very successful large entrenched entrenched companies and it's very possible that snowflake could pull back moderate and then accelerate that growth even though people are concerned about the moderated guidance of 80 percent growth yeah that's that's the new definition of tepid i guess i look i like to look at other some other metrics the one that really called you know my my my attention was the remaining performance obligations this last quarter rpo snowflakes is up to something like 2.6 billion and that is a forward-looking indicator of of future revenues so i want to i'd like to see that growing and it's growing at a fast pace so you're going to see some ups and downs with snowflake i have no doubt but i think things are still looking pretty solid for the company growth companies like snowflake and octa and z scalar those other ones that i mentioned earlier have probably been repriced and refactored by investors while there's always going to be market and of course geopolitical risk especially in these times fundamentals matter you've got huge market well capitalized you got a leadership position great products and strong customer adoption you also have a great team team is something else that we look for we haven't touched on that but i'll leave you with this thought everyone knows about frank slootman mike scarpelli and what they've accomplished in their years of working together that's why the stock you know in ipo was was so overvalued they had seen these guys do it before slootman just documented in all this in his book amp it up which gives great insight into the history of of that though you know that pair and and the teams that they've built the companies that they've built how he thinks about building companies and markets and and how you know total available markets super important but the whole philosophy and culture that that he's building in his management style but you got to wonder right how long is this guy going to keep going what keeps him motivated you know i asked him that one time here's what he said why i mean are you in this for the sport what's the story here uh actually that that's not a bad way of characterizing it i think i am in it uh you know for the sport uh you know the only way to become the best version of yourself is to be uh to be under the gun and uh you know every single day and that's that's certainly uh what we are it sort of has its own rewards building great products building great companies uh you know regardless of you know uh what the spoils may be uh it has its own rewards and i i it's hard for people like us to get off the field and uh you know hang it up so here we are so there you have it he's in it for the sport how great is that he loves building companies and that my opinion that's how frank slootman thinks about success it's not about money money's the byproduct of success as earl nightingale would say success is the progressive realization of a worthy ideal i love that quote building great companies building products that change the world changing people's lives with data and insights creating jobs creating life-altering wealth opportunities not for himself but for thousands of employees and partners i'd say that's a pretty worthy ideal and i hope frank slootman sticks with it for a while okay that's it for today thanks to stephanie chan for the background research she does for breaking analysis alex meyerson on production kristen martin and cheryl knight on social with rob hoff on siliconangle and thanks to ivana delevska of spear invest and my friend chip symington for the angles from the money side of things remember all these episodes are available as podcasts just search breaking analysis podcast i publish weekly on wikibon.com and siliconangle.com and don't forget to check out etr.plus for all the survey data you can reach me at devolante or david.velante siliconangle.com and this is dave vellante for cube insights powered by etrbsafe stay well and we'll see you next time [Music] you

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante by the dip has been been an effective strategy since the market bottomed in early march last year the approach has been especially successful in tech and even more so for those tech names that one were well positioned for the forced march to digital i sometimes call it i.e remote work online commerce data centric platforms and certain cyber security plays and two already had the cloud figured out the question on investors minds is where to go from here should you avoid some of the high flyers that are richly valued with eye-popping multiples or should you continue to buy the dip and if so which companies that capitalized on the trends from last year will see permanent shifts in spending patterns that make them a solid long-term play hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we shine the spotlight on three companies that may be candidates for a buy the dip strategy and it's our pleasure to welcome in ivana delevco who's the chief investment officer and founder of spear alpha a new research-centric etf focused on industrial technology ivana is a long-time equity analyst with a background in both long and short investing ivana welcome to the program thanks so much for coming on thanks for having me david yeah it's really our pleasure i i want to start with your etf and give the folks a bit more background about you first you know we gotta let people know i'm not an investment pro i'm not an advisor i don't make stock recommendations i don't sell investments so you got to do your own research i have a lot of data so happy to share it but you got to understand your own risks you of course yvonne on the other hand you do offer investment services and so people before investing got to carefully review all the available available investment docs understand what you're getting into before you invest now with that out of the way ivana i have some stats up here on this slide your spear you're a newly launched female lead firm that does deep research into the supply chain we're going to talk about that you try to uncover as i understand it under-appreciated industrial tech firms and some really pretty cool areas that we list here but tell us a little bit more about your background and your etf so thanks for having me david my background is in industrial research and industrial technology investments i've spent the past 15 years covering this space and what we've seen over the past five years is technology changes that are really driving fundamental shifts in industrial manufacturing processes so whether this is 5g connectivity innovation in the software stack increasing compute speeds all of these are major technological advancements that are impacting uh traditional manufacturers so what we try to do is assess speak to these firms and assess who is at the leading and who is at the lagging end of this digital transformation and we're trying to assess what vendors they're using what processes they're implementing and that is how we generate most of our investment ideas okay great and and we show on the bottom of of this sort of intro slide if you will uh so one of the processes that you use and one of the things that that is notable a lot of people compare you uh to kathy woods are investments when you came out uh i think you use a different process i mean maybe there are some similarities in terms of disruption but at the bottom of this slide it shows a mckinsey sort of graphic that that i think informs people as to how you really dig into the supply chain from a research standpoint is that right absolutely so for us it's all about understanding the supply chain going deep in the supply chain and gather data points from primary sources that we can then translate into investment opportunities so if you look at this mckinsey graph uh you will see that there is a lot of opportunity to for these companies to transform themselves both on the front end which means better revenue better products and on their operation side which means lower cost whether it's through better operations or through better processes on the the back end so what we do is we will speak to a traditional manufacturing company and ask them okay well what do you use for better product development and they will give us the name of the firms and give us an assessment of what's the differences between the competitors why they like one versus the other so then we're gonna take the data and we will put it into our financial model and we'll understand the broader market for it um the addressable market the market share that the company has and will project the growth so for these higher growth stocks that that you cover the main alpha generation uh potential here is to understand what the amount of growth these companies will generate over the next 10 to 20 years so it's really all about projecting growth in the next three years in the next five years and where will growth ultimately settle in in the next 10 to 20 years love it we're gonna have a fun conversation because today we're going to get into your thesis for cooper snowflake and z scalar we're going to bring in some of our own data some of our data from etr and and why you think these companies may be candidates for long-term growth and and be buy the dip stock so to do that i hacked up this little comparison slide we're showing here i do this for context our audience knows i'm not a cfa or a valuation expert but we like to do simple comparisons just to give people context and a sense of relative size growth and valuation and so this chart attempts to do that so what i did is i took the most recent quarterly revenue for cooper snowflake and z scalar multiplied it by four to get a run rate we included servicenow in the table just for baseline reference because bill mcdermott as we've reported aspires to make service now the next great enterprise software company alongside with salesforce and oracle and some of the others and and all these companies that we list here that through the three here they aspire to do so in their own domain so we're displaying the market cap from friday morning september 10th we calculated a revenue run rate multiple and we show the quarterly revenue growth and what this data does is gives you a sense of the three companies they're well on their way to a billion dollars in revenue it underscores the relationship between revenue growth and valuation snowflake being the poster child for that dynamic savannah i know you do much more detailed financial analysis but let's talk about these companies in order maybe start with koopa they just crushed their quarter i mean they blew away consensus on the top line what else about the company do you like and why is it on your by the dip list so just to back up david on valuation these companies investors either directly or indirectly value on a dcf basis and what happened at the beginning of the year as interest rates started increasing people started freaking out and once you plug in 100 basis points higher interest rate in your dcf model you get significant price downside so that really drove a lot of the pullback at the beginning of the year right now where we stand today interest rates haven't really moved all that significantly off the bot of the bottom they're still around the same levels maybe a little bit higher but those are not the types of moves that are going to drive significant downside in this stock so as things have stabilized here a lot of these opportunities look pretty attractive on that basis so koopa specifically came out of our um if you go back to that uh the chart of like where the opportunities lie in um in across the manufacturing uh um enterprise koopa is really focused on business pen management so they're really trying to help companies reduce their cost uh and they're a leader in the space uh they're unique uh unique in that they're cloud-based so the feedback we've been hearing from from our companies that use it jetblue uses it train technologies uses it the feedback we've been hearing is that they love the ease of implementation so it's very easy to implement and it drives real savings um savings for these companies so we see in our dcf model we see multiple years of this 30 40 percent growth and that's really driving our price target yeah and we can i can confirm that i mean i mean just anecdotally you know you know we serve a lot of the technology community and many of our clients are saying hey okay you know when you go to do invoicing or whatever you work with procurement it's koopa you know this is some ariba that's kind of the legacy which is sap we'll talk about that a little later but let's talk about snowflake um you know snowflake we've been tracking them very closely we know the management there we've watched them through their last two companies now here and have been following that company early on since since really 2015. tell us why you like snowflake um and and maybe why you think it can continue its rapid growth thanks david so first of all i need to compliment you on your research on the company on the technology side so where we come in is more from understanding where our companies can use soft snowflake and where snowflake can add value so what we've been hearing from our companies is the challenge that they're facing is that everybody's moving to the cloud but it's not as simple as just send your data to the cloud and call aws and they're gonna generate more revenue for your solve your cost problem so what we've been hearing is that companies need to find tools that are easy to use where they can use their own domain expertise and just plug and play so um ansys is one of the companies we covered the dust simulation they've found snowflake to be an extremely useful tool in sales lead generation and within sales crm systems have been around for a while and they're they've really been implemented but analyzing sales numbers is something that is new to this company some some of our companies don't even know what their sales are even when they look back after the quarter is closed so tools like this help um companies do easy analytics and therefore drive revenue and cost savings growth so we see really big runway for for this company and i think the most misunderstood part about it is that people view it as a warehousing data warehousing play while this is all about compute and the company does a good job separating the two and what our their customers like or like the companies that we cover like about it is that it can lower their compute costs um and make it much easier much more easily manageable for them great and we're going to talk about more about each of these companies but let's talk about z-scaler a bit i mean z-scaler is a company we've been very excited about and identified them kind of early on they've definitely benefited from the move to cloud generally and specifically the remote work uh situation with the cyber threats etc but tell us why you like z-scaler so interestingly z-scaler um we like the broader security space um the broader cyber security space and interestingly our companies are not yet spending to the level that is commensurate with the increase in attack rate so we think this is a trend that is really going to accelerate as we go forward um my own board 20 of the time on the last board meeting was spent on cyber security what we're doing and this is a pretty simple operation that that we're running here so you can imagine for a large enterprise with thousands of people all around the world um needing to be on a single simple system z-scaler really fits well here very easy to implement several of our industrial companies use it siemens uses it ge uses it and they've had great great experience with it excellent i just want to take a quick look at how some of these names have performed over the last year and and what if anything this data tells us this is a chart comparing the past 12 months performance of of those four companies uh that we just talked about and we added in you know servicenow z scalar as you can see has outperformed the other despite your commentary on discounted cash flow snowflake is underperformed really precisely for the reasons that you mentioned not to mention the fact that it was pretty highly valued and you can see relative to the nas but it's creeping back lately after very strong earnings even though the stock dropped after it beat earnings because the street wants the cfo to say to guide even higher than maybe as mike scarpelli feels is prudent and you can see cooper has also underperformed relatively speaking i mean it absolutely destroyed consensus this week the stock went up but it's been off with the the weaker market this week i know you like to take a longer term view but but anything you would add here yeah so interestingly both z-scaler and koopa were in the camp of as we went into earnings expectations were already pretty high because few of their competitors reported very strong results so this scalar yesterday their revenue growth was was pretty strong the stock is down today uh and the reason is because people were kind of caught up a little bit in the noise of this quarter growth is 57 last quarter it was 60 like is this a deceleration we don't see it as that at all and the company brought up one point that i thought was extremely interesting which is as their deal sizes are getting larger it takes a little longer time for them to see the revenue come through so it takes a little bit of time to for you to see it into from billings into into revenue same thing with cooper very strong earnings report but i think expectations were already pretty high going into it uh given the service now and um and anna plan as well reported strong results so i think it's all about positioning so we love these setups where you can buy the deep in on this opportunity where like people get caught up in um short-term noise and and it creates good entry points excellent i i want to bring in some data from our partner etr and see if you have any comments ivana so what we're showing here is a two-dimensional chart we like to show this uh very frequently it's based on a survey of between a thousand and fifteen hundred chief information officers and technology buyers every quarter this is from their most recent july survey the vertical axis shows net score which is a measure of spending momentum i mean this it measures the net percentage of customers in the survey that are spending more on a particular product or platform in other words it essentially subtracts the percentage of customers spending less from those spending more which yields a net score it's more granular than that but basically that's what it does the horizontal axis is market share or pervasiveness in the data set it's not revenue market share like you get from idc it's it's a mention market share and now that red dotted line at the 40 percent mark on the vertical represents an elevated level in other words anything above 40 percent we consider notable and we've plotted our three by the dip companies and included some of their competitors for context and you can see we added salesforce servicenow and oracle and that orange ellipse because they're some of the bigger names in the software business so let's take these in alphabetical order ivana starting with koopa in the blue you can see we plotted them next to sap's ariba and you can see cooper has stronger spending momentum but not as much presence in the market so to me my influence is oh that's an opportunity for them to steal share more modern technology you know more facile and of course oracle has products in this space but the oracle dot includes all oracle products not just the procurement stuff but uh maybe your thoughts on this absolutely i love this chart i think that's your spot on this would be the same way i would interpret the chart where um increased spending momentum is is a sign of the company providing products that people like and we we expect to see cooper's share grow market share grow over time as well so let's come back to the chart and i want to i want to really point out the green ellipse this is the data zone if you will uh and we're like a broken record on this program with snowflake has performed unbelievably well in net score and spending momentum every quarter the dtr has captured enough end sample in its survey holding near or above 80 percent its net score consistently is has been up there and we've plotted data bricks in that zone it's been expected right that data bricks is going to do an ipo this year late last month company raised 1.6 billion in a private round so i guess that was either a strategy to delay the ipo or raise a bunch more cash and give late investors a low risk bite at the apple you know pre-ipo as we saw with snowflake last year what we didn't plot here are some of snowflake's biggest competitors ivana who also happen to be their partners most notably the big cloud players all who have their own database offerings aws microsoft and google now you've said snowflake is much more than a database company i wonder if you could add some color here yeah that's a very good point david uh basically the the driver of the thesis in snowflake is all about acceleration and spending and what we are seeing is the customers that are signed up on their platform today they're not even spending they're probably spending less than five percent of what they can ultimately spend on this product and the reason is because they don't yet know what the ultimate applications are for this right so you're gonna start with putting the data in a format you can use and you need to come up with use cases or how are you actually going to use this data so back to the example that i gave with answers the first use case that they found was trying to optimize leads there could be like 100 other use cases and they're coming up with with those on a daily basis so i would expect um this score to keep keep uh keep up pretty high or or go even higher as we as people figure out how they can use this product you know the buy-the-dip thesis on snowflake was great last quarter because the stock pulled back after they announced earnings and when we reported we said you know mike the the company see well cleveland research came out remember they got the dip on that and we looked at the data and we said mike scarpelli said that you know we're going to probably as a percentage of overall customers decelerate the net net new logos but we're going deeper into the customer base and that's exactly what's happening with with snowflake but okay let's bring up the slide again last but not least the z scaler we love z scalar we named z scaler in 2019 as an emerging four-star security company along with crowdstrike and octa and we said these three should be on your radar and as you see we've plotted z scalar with octa who with its it's its recent move into to converging identity and governance uh it gets kind of interesting uh we plotted them with palo alto as well another cyber security player that we've covered extensively we love octa in addition to z-scaler we great respect for palo alto and you'll note all of them are over that 40 percent line these are disruptors they're benefiting well not so much palo alto they're more legacy but the the other two are benefiting from that shift to work from home cloud security modern tech stack uh the acquisition that octa-made of of of auth0 and again z scalar cloud security getting rid of a lot of hardware uh really has a huge tailwind at its back if on a zscaler you know they've benefited from the huge my cloud migration trend what are your thoughts on the company so i actually love all three companies that are there right and the point is people are just going to spend more money whether you are on the cloud of the cloud the data centers need more security as well so i think there is a strong case to be made for all three with this scaler the upside is that it's just very easy to use very easy to implement and if you're somebody that is just setting up infrastructure on the cloud there is no reason for you to call any other competitor right with palo alto the case there is that if you have an established um security platfor if you're on their security platform the databa on the data center side uh they they did introduce through several acquisitions a pretty attractive cloud offering as well so they've been gaining share as well in the space and and the company does look pretty attractive on valiation basis so for us cyber security is really all about rising tide lifts all boats here right so you can have a pure play like this scaler uh that benefits from the cloud but even somebody like palo alto is pretty well positioned um to benefit yeah we think so too over a year ago we reported on the valuation divergence between palo alto and fortinet fortinet was doing a better job moving to the cloud and obviously serves more of a mid-market space palo alto had some go-to-market execution challenges we said at the time they're going to get through those and when we talk to chief information security officers palo alto is like the gold standard they're the thought leader they want to work with them but at the same time they also want to participate in some of these you know modern cloud stacks so i we agree there's plenty of room for all three um just to add a bit more color and drill into the spending data a little bit more this slide here takes that net score and shows the progression since january 2019 and you can see a snowflake just incredible in terms of its ability to maintain that elevated net score as we talked about and the table on the insert it shows you the number of responses and all three of these companies have been getting more mentions over time but snowflake and z scale are now both well over 100 n in the survey each quarter and the other notable piece here and this is really important you can see all three are coming out of the isolation economy with the spending uptick nice upticks shown in the most recent survey so that's again another positive but i want to close ivana with kind of making the bull and bear case and have you address really the risks to the buy the dip scenario so look there are a lot of reasons to like these companies we talked about them cooper they've got earnings momentum you know management on the call side had very strong end market demand this the stock you know has underperformed the nasdaq you know this year snowflake and zscaler they also have momentum snowflake get this enormous tam uh although they were punished for not putting a hard number on it which is ridiculous in my opinion i mean the thing is it's huge um the investors were just kind of you know wanting a little binky baby blanket but they all have modern tech in the cloud and really importantly this shows in the etr surveys you know the momentum that they have so very high retention is the other point i wanted to make the very very low churn of these companies however cooper's management despite the blowout quarter they gave kind of underwhelming guidance they've cited headwinds uh they've with the the the lamisoft uh migration to their cloud platform snowflake is kind of like price to perfection so maybe that's an advantage because every every little negative news is going to going to cause the company to dip but it's you know it's pretty high value because salutman and scarpelli everybody expects them to surpass what happened at servicenow which was a rocket ship and it could be all argued that all three are richly priced and overvalued so but ivana you're looking out as you said a couple of years three years maybe even five years how do you think about the potential downside risks in in your by the dip scenario you buy every dip you looking for bigger dips or what's your framework there so what we try to do is really look every quarter the company reports is there something that's driving fundamental change to the story or is it a one-off situation where people are just misunderstanding what the company is reporting so in the case we kind of addressed some of the earnings that that were reported but with koopa we think the man that management is guiding conservatively as they should so we're not very concerned about their ability to execute on on the guidance and and to exceed the guidance with snowflake price to perfection that's never a good idea to avoid a stock uh because it just shows that there is the company is doing a great job executing right so um we are looking for reports like the cleveland report where they would be like negative on the stock and that would be an entry point uh for us so broadly we apply by the deep philosophy but not not if something fundamentally changes in the story and none of these three are showing any signs of fundamental change okay we're going to leave it right there thanks to my guest today ivana tremendous having you would love to have you back great to see you thank you david and def you definitely want to check out sprx and the spear etf now remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you do is search breaking analysis podcasts you can always connect with me on twitter i'm at d vallante or email me at david.vellante at siliconangle.com love the comments on linkedin don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>> lie for Las Vegas. It's the queue covering VM World twenty eighteen, brought to you by IBM Wear and its >> ecosystem partners. Welcome back to Las Vegas were here of'Em World twenty eighteen Number. We've heard from BM, where for many years is you know, they've got five hundred thousand customers this morning on stage. Pat Gelsinger said that now over fifteen thousand of those customers are using v san. Hi, I'm Stew Minutemen. With me is John Troyer. We're gonna dig into a little bit of a V San discussion here, joining the first time guests on the program. We have Ernie China, who's the director of the San Worldwide product marketing with the M where and also Brent Collins, who's a global practice director with W. W. T. Who is part of the distribution channel partner of V M wear and many others in the ecosystem. Gentlemen, thanks so much for joining us. Thank you very much for having us. All right, Ernie, let's start charging V san. Uh, I would be San was first announced. I said this was the rising tide that will really lift on launch what we called hyper converge infrastructure. Right? Number of interesting announcement. Maybe, you know, give us a thumbnail of what >> happened. S so there's great. So we actually made some big announcements. First of all, we talked about how the moment, um, we've had two to your point right. Huge amount of adoption by our customers, especially elope sphere customers adopting descend on the marketplace today and then we kind of added to a lot of the things they like about the sand by announcing a few things around the product with a current update one which basically prevented two categories of capabilities. One is some management capabilities and make it much easier to administer to manage a visa and deployment. So being able to, uh, recapture any type of capacity that's not being used, for instance is a great thing for administrators are trying to manage also when they're doing trying to do any trouble shooting or trying to do any management. They also have some great trouble shooting capabilities that we announced this well. And then I think, for tea and of the other partners Way also announced new incentives to allow them to be more profitable, especially as they start selling more visa and compared to traditional storage. Some great ways for them to be profitable with Decenas. Well, >> all right, we've We've had a few guest from W W t on our program in the nine years we've been doing it, but it helped. The company I know has gone through a lot of changes, just like everybody else in this industry. So I want you to talk about the visa and stuff, but give us for a second, you know? W w a d T. How should we think of W W T? How do you differentiate from your peers in the marketplace? >> Yes, so I think you know W Vt. Is about a ten and a half billion dollar technology integration firm. We started off a little bit smaller, so a lot of if you haven't been around in awhile. We've grown quite a bit in the last few years but really have built a company or in around speed. So it's, uh, how do we help customers get things moving a lot faster? So it's speed to an informed decision with our advanced technology center. So it's, uh, it's about a two hundred million dollars playground that we used for everything from demos to proof of concept. So what we call lab is a service which is a longer term proof of concept. Um, we also have an integration facility. So we take that informed decision we make maybe a blueprint, So talk a lot about the sand, but it's indifferent consumption models. We might package that together with servers, top Iraq, switching in the rack and really stamp that out multiple times for larger clients. So we take that to the immigration facility, and we also have a world class global supply chains. So we started as a supply chain company. Not a lot of people know that, but you take that, you prove it out, you run it through immigration facility, and then you put it anywhere in the world. It's a really powerful set of capabilities for big customers. >> Well, Brit, I wanted to ask specifically around Lisa. Uh, and you talked about the consumption models. One of the interesting parts about the sand, right. You can roll your own, take the software, roll your own piece and ready notes, or buy it from, you know, from somebody already fully assembled in baked. What do you mean? You're obviously you're working with customers at a range of sizes and use cases. But I mean, can you talk about what? What in twenty eighteen, whether some of the common consumption models to people you know? Are you pulling it all together with the full rack and rolling it in? Or how what people look to W. W. T. And as a V San partner for >> sure, It's a great question. So we actually get all of the above. So we focus on the enterprise space, the larger clients and they a lot of them want custom solution. So we go prove out whatever they want toe have in there and again to the model I talked about earlier. We stamp that out and put it in their data centers. Now, some of them wanted in a V San rule your own. These others want VX rail, and then others want a full stack like the extract std. See, so way I see it for different use cases, right? So we look at it, uh, the sand in and of itself is an easy button, but when you package it in with the reference architecture, it's even easier for people to go, go roll that out and support different models. Whether that's VD, I or general purpose, virtual ization or even enterprise applications so way really like the the ability to customize that, depending on what the customer is looking for. >> All right, Ernie, which one of these things are is everything g et that we've talked about here, you know, how are their customers that have lined up and done? Some of these may be unpacked for us a little bit as toe. What's hitting the door? The door Which one's already rolled out as toe? What piece of those? You know, >> things have hit the door already. They're already out for a lot of customers. A lot of customers, actually, as we do a lot of times have them tested out beforehand. So many customers are already actually using a lot of these capabilities today. Some of them are actually being at the show, talking about some of the things that we've done here. Yeah, >> so what? One thing I love both of you to give us some commentary on when we look at the difference between kind of my data center and the public cloud is the public cloud. Nobody calls you and says, Hey, what version of eight of us are azure? You running right, as opposed to we know the history with these here. It's like, Well, what version of easier? Well, you know, I've got my little lab. Yeah, they're they're testing, you know, six, six, six, seven things like that. But I still got that five five deployment that you no way have plans, but it's gonna take awhile. How does the sensitive into that picture? And, you know, how do you help customers stay on the rev Upgrader to you once you want to sell it or you kind of done and they deal with GM wear? How does that dynamic work? >> I think the value The channel is really making it easier for customers to buy easier Teo deploy and easier to manage. So we do all the above. I think one of the things that we were talking about earlier is I think people look at cloud is the easy button and it is. But there's an interim step there. So for customers to say, Hey, I want it easy. You have the option to do it on Prem as well as in the clouds. So it's really, you know, when I look at my business, it's I'm in the computation of data management business, and V San fits into the really that data management side. The different question when you incorporate Cloud is it's not a question of, you know, Are we still doing the same thing? It's it's Where is it? How do I buy it? So I really like h c. I think it's it really is that interim easy button for people that say I want the simplicity a cloud, but I wanted on prom So >> we're going to get a commentary on kind >> of the management from the manager perspective. We're making it very, very easy for customers to go from whatever version they need to. Obviously the fact that we have all these great new features coming in, it really gets sense a lot of customers to want to move to the latest capabilities, but in general, for them, for customers who make it really easy for them to be able to move up to whatever whatever level they need to >> Nice Brian, I wanted to ask you talk about H I V and the easy. But in a couple of years ago, when a chai architectures we're just coming in, it was The industry always has a little bit black and white. It's either gonna destroy everything or save everything, and it's gonna be one hundred percent one way or the other. Turns out, you know, there's a mix of use cases for traditional storage as well as a C I What? What do you particularly like a CZ use cases for for bee san, uh, your customer base that you roll out? When do you When do you really say you know what? You should really look at this hyper converged infrastructure that we can build you first is, um or, you know, a traditional, bigger, different ray. Bigger array, separate array, sort of storage. >> I'LL give you the answer to everybody hates, which is It depends, right? So I think you know the sands a great platform, and we see it for a lot of different use cases. A lot of it depends on you know, what's the customer looking to do, what's there, what investments that they already made and then where is the fifth best? So from a technical perspective, I mean, I think we all know that general purpose virtual ization of Edie I make their great use cases were v san, but we're starting to see that creep into other use cases. So you start here and then you could say, Well, you know what? I'm refreshing over here. Maybe it makes more sense to take a look at something different a same time. Some people say, Hey, maybe a traditional storage array still makes sense for us, so we kind of see it both ways. But again, as people turn Estes comment as people start with the sand, they try it out. The simplicity, the ease of management and the cost effectiveness they really look at it and also the integration. We don't talk about a lot, but the integration with all the other virtualization tools makes it really easy all in. So because of some reasons why people might take a hard look at that versus a traditional storage array. >> Just add to that start off with media. Everyone was in that particular use case. Then remote office came in, so the edge was a big one that started to grow. Now, majority of our use cases around business Critical lapse. Most of the customers Air sequel Oracle. They're starting to deploy their so actually expanding quite a bit. And the nice thing, actually for partners is in many cases, the services are now starting to catch up. As you start going to these, this is critical APS. Actually, the services get bigger. So going back to that hole profitability element, it makes it more profitable foreigners as well. Right? >> Front V san isn't just a standalone product or, you know it is, obviously is always with the hyper visor. But it's an important piece of the Via MacLeod Foundation is W W t involved in any of those solutions that you know, Uh, yeah, way Don't do anything. Okay? Yeah, that's real straight for itself. But somewhere, cloud Is that something You talk to your customers about her, >> Of course Way. Do all of the above and you know, significant investments in the cloud in general. So a lot of over finding actually not to pivot too hard off of what we're talking about today. But you know, when we look at, uh, v r automation, for example, a lot of customers of purchase it but aren't taken advantage all the different features. So when we look at the entire stack, part of our methodology is working with customers to figure out, what do you have? And then how do we deploy and help you take advantage of what you have and then come back to the real question, which is? Let's take a holistic view out of all of those things and figure out how to maximize it. So it might be vee are a might be vey san. But in general, let's style those things together into something that makes more sense as a platform for what that customers looking to do. >> Okay, Ernie, wanna give you the final word? You know a lot of pieces. People are always trying to do it through. What's one thing that people should look at from the sand that they might have missed? >> So I think, from from the sand perspective, as they start to look at modernizing their infrastructure, they started looking at this whole idea of digital business and how we've become more agile, I think, actually, kind of pivoting a litte bit off. The point that was just made decent is a great way to get started, so it's a great way to be able to bring in some of those capabilities. And then it's a great start than to bring in the rest of our portfolio. That really adds a whole stack solutions. I really make that a reality. And along the way we make it very easy for especially the Spear customers to be able to deploy that >> real important point. Thank you, Ernie. China Front. Collins. Appreciate all the updates and, uh, the user perspective for John Troyer on student and back with lots more content from Veum World twenty eighteen. Thanks for watching the cue. Thank you.