>>The cube presents, Dell technologies world brought to you by Dell. >>Hello. Welcome to the cube here at Dell tech world. I'm John furry host of the cube with Dave Alon here with Michael Dell, the CEO of Dell technologies cube alumni comes on every year. We have the cube here. It's been two years. Michael, welcome to the cube. Get to see you. >>Hey, John, Dave, great to be with you guys. Thanks for being here. Wonderful to be back here in Vegas with >>You. Well, great to be in person two years ago, we had the cue with the pandemic a lot's happened. We were talking end to end solutions here at Dell tech world in person two years ago, pandemic hits. Thank God you had all that supply for the, for the people having the remote remote end to work now back in person. What's it look like now with, with Dell tech end to end, the edge is important. What's the story, >>You know, edge is, is the physical world. And if you, if you step back from clouds and, you know, multi-cloud, you sort of think about what is the purpose of a cloud or a data center? Well, it's to take data out of the physical world and move it to this place, to somehow enhance it or do something with it and create business value and hopefully create better outcomes. Well, it turns out that, you know, increasingly a lot of that data is gonna stay in the physical world and all of those nodes are gonna be connected. They're gonna be intelligent and we're seeing it in manufacturing and retail and healthcare, transportation, logistics. We're seeing this rapidly intelligent edge being formed. And then of course, with the new networks, the 5g we're seeing, you know, all, all this develop. And so here on the show floor, we're showing a lot of those solutions, but our customers are, are highly engaged. And certainly we think that's a, a big, a big growth factor for the next decade. >>And it's been ING to watch the transformation of the it world and cloudification and the as service, uh, consumption model, which you guys are putting out there has been very successful, but cloud operations is more prominent now on premises and edge and cloud. So the combination of cloud on-premise and edge hardware matters more now than ever before Silicon advances, um, abstraction layers from modern cloud native applications are what people are focused on. What's the story that you cite to the CIOs saying, we're here to help you with that new architecture cloud multi-cloud on premise and edge. What's the main story for you guys with the customers? >>Well, you know, customers want to go faster, right? And they want to accelerate their transformation. And so they wanna shift more resources over to developers, to applications, to access their data, to create competitive advantage. And so we talk a lot about the value line and what are those things below the value line, where we can provide that as a service on a consumption based model and accelerate their transformation, kind of, you know, do for them what we've done inside our own business. And, you know, it's absolutely resonating. We're seeing great growth there. People continue to, to need the solutions, but as we can automate the management and deployment of infrastructure and make it super easy, it gives them a lot of cycles back. >>You know, Michael, my, the favorite part, my favorite part of your book was you were in, I think you were in his, in his home court, in his dining room at Carl Icahn's house. And you said, well, why don't you just buy the company? And then you'll do what you're doing. I I'll buy it back for cheaper. Now, thankfully, you didn't have to do that. Cuz you had an environment of low interest rates and you obviously took it into the other direction, added tremendous value, 101 billion in revenue last year, 17% revenue growth, which was out astounding. When you think about that, um, now we're entering a new chapter with VMware untethered of course you're the chairman of both companies. So how should we think about the new Dell what's next? >>Well, so look, we, we have some unbelievable core businesses, right? We have our client system business and we've all learned during these last two years, how incredibly important it is to enable and empower your workforce with the right tools in the remote and high hybrid work. And we're showing off all kinds of new innovations here. That's a huge business force continues to grow, continues to be super important. Then we have our ISG, the cloud data center, the network of the future, the edge, you know, the, the sort of epicenter of where we're embracing, consumption based business models. That's absolutely huge. Then we have these new, new businesses that we're building with telco with edge, put it all together. It's a 1.3 trillion Tam that we operate in, as you said, more than a hundred billion dollars last year. So there's plenty of room for us to continue to grow and, and expand. And you know, as we make this shift to outcomes, it's obviously more valuable for customers and that, you know, increases our opportunity, increases the, the value we can create for all our stakeholders. >>And number one, number one, share in PCs, by the way, congratulations, again, hit that milestone. All of our gamer, uh, fans in our discord want to know what's the hottest chips coming. What's the fastest machines. What, how's the monitors coming? They want faster, cheaper. What's the coolest, uh, monitors out there right now and, and machines. >>Well, uh, you know, what what's, what's amazing is the, the pace of innovation continues to improve. So whether it's in the GPU, the CPU, the, the resolution, I I'm pretty partial to our 41, uh, display 11 million pixels of fun. And look, I mean, we, we it's, it's, it's clear that people are more productive when they have large screens and all the performance is enabling photo realistic, uh, you know, uh, gaming and photo realistic, everything. And these are immersive experiences. And, you know, again, uh, what companies have figured out to bring it back to, to, to a little bit of business here, John, is that when you, uh, give people the right tools, they're more productive, they're more engaged and look, people are smart. They know what tools are available. And, you know, uh, the thing that actually is most representative of how a person thinks about the tools they have at their organization is actually the thing that's right in front of 'em. And so, you know, this ability for us to provide a pool set of solutions for organizations to keep their workforce productive, to run their applications and infrastructure securely anywhere they want. That's, that's a winning proposition. >>Michael trust was a big theme of your keynote yesterday. And when you acquired EMC and got VMware, it really changed the dynamic with regard to your ability to, into new parts of organizations. You became a much more strategic supplier. I, I would argue. And now with VMware as a separate company, do you feel like you have built up over the, you know, five or whatever years that muscle memory you kinda earn that trust. So how do you see the customer relationship with that regard to that integration that they, they loved the eco. So system competitors might not have loved it so much, but the customers really did love. In fact, the, the U S a, a gentleman yesterday kind of mentioned that, how do you see it? >>You know, customers, uh, are not as interested in the balance sheet and what you know, where different holdings are, what they, they want things to work together, right? And they want partnerships in ecosystems. And certainly, you know, with VMware, even before the combination, we had a powerful partnership. It obviously solidified in a super special way. And now we have this first and best relationship and I've remained the chairman of VMware and super excited about their future. But our ecosystem is incredibly broad. And you see that here in this show floor, and again, making things work together better and more effectively building these engineered solutions that allow people to very quickly deploy the kind of capabilities they want, whether it's, you know, snowflake now working with the on premise and the edge data and more of these, you know, multi-cloud, uh, eco of systems that are being built. It's not gonna be just one company >>You called the edge a couple years ago. You're really prominent in your, in your speeches. And your keynotes data also is a big theme. You mentioned data now, data engineering seems to be the hottest track of, of, of students graduating with data engineering skills, not data science, data engineering, large scale data as code concepts. So what's your vision now with data, how's that fitting into the solutions and the role of data, obviously data protection with cybersecurity data as code is becoming really part of that next big thing. >>Yeah. I mean, if, if you look at anything that is interesting in the world today, uh, at the center of it is data, right? Whether it's the blockchain or the defi or the AI drug discovery, or the autonomous vehicles or whatever you wanna do, there's data in, in, in the middle of that. And of course with that data, well, you've gotta manage it. You, you need compute engines, right? You need to be able to protect it, secure it. And, you know, that's kind of what we do, and we're not going to create all those solutions, but we are gonna be an enabling layer to allow that data to be accessed no matter, you know, where, where it is. And, and, and of course, you know, leading in storage continues to be a super important part of our business. Number one, larger than number two than number three, number four, combined, and, and most of number five as well, and, and growing share. And, and you saw today, the software defined innovations, allowing that, you know, data layer to exist across the edge, the colos, the OnPrem, and the public clouds >>Throughout a stat yesterday. I can't remember if it was a keynote of the analyst round table, but it was 9 million cell towers. And if I heard, right, you kinda look at those as potential data centers talk about that's >>Right. It it's actually 7 million, but, but probably will be 9 million and not, not too long, I don't have the update, but so yeah, the public clouds all together is about 600 data centers. They're about 7 million cellular base stations in the world. Every single one of those is becoming a, you know, multi access, edge compute node. And what are they putting in there? They're putting many data centers of compute and GPS and storage. And, you know, 5g is not about, uh, connecting people that was 4g and before 5g is about connecting things. And there are way more things than there are people, right? And, uh, you know, this, this, this edge is, is rapidly developing. You'll also have private 5g and you'll have, you know, again, embedded intelligence I believe is gonna be in everything this next decade is going to be about that intelligent, connected future, taking that data, turning it into useful outsides in insights and outcomes. And, you know, lots of new businesses will be existing. Businesses will be transformed and also disrupted. >>Yeah. I mean, I think that's so right on and not to pat ourselves on the back day, but we called that edge distributed computing a couple years ago on the cube. And that's, what's turning into the home with COVID you saw that become a workplace, basically compute center, these compute nodes, tying it together as we, what everyone's talking about right now. So as customers say, okay, I want to keep my operations steady, steady, and secure. How do I glue it together? How do I bring these compute node together? That seems to be the top question on, on top of people's minds. And they want it to be cloud native, which means they want it to run cloud-like and they want to connect these compute node together. That's a big discussion point. What's your view on, >>Well, you know, if you, if you sort of have a, a cloud here, a cloud there cloud everywhere, and you, you know, have lots of different Kubernetes frameworks, uh, and you've got, you know, everything is, is spread out, it's a disaster, right? And, and, and it's, it's a, it's a, it's a real challenge to manage all that. So what people are trying to do is create ruthless standardization. It's like, how do you drive cost out and get speed? It's ruthless standardization create consistent environments where you can operate the across all the different domains that, that you want. And so, uh, you know, this is what we're bringing together in, in, in the capabilities that we're delivering. >>And that chaos is great opportunity for you. Um, how are you feeling about VMware these days, new team, uh, give us the update there. >>Yeah. The team is doing well. You know, I think the tons message is resonating. You know, people want Kubernetes and, and, and container based apps, for sure. That's the main, you know, growth in, in, in, in, in new, in new workloads. Uh, but they also want it to work with what they have. Yeah. And they don't want it to be locked into one particular infrastructure. So software finding everything, making it run in all the public clouds, you know, we've had a great success with VxRail, you know, that, that absolutely continues. We have, uh, 200,000 plus nodes, 15,000 customers and growing, we have edge satellite nodes and we continue to work together in SD wan in software defined networking in VMware cloud foundation, uh, you know, expressed, uh, in, in, in all locations. >>You know, one of the things that we've been seeing with the trend towards, um, future of work, which is a big theme, here is a lot of managed services are popping up where the complexity is so ha high that customers want to manage services. Uh, and also the workforce of it's kind of changing. You got a younger generation coming in, how do you see that future of the workforce? The next level? It's not gonna be like, yesterday's it, it's gonna be distributed computing dashboard based. And then you've got these managed services, you know, need to have the training and expertise maybe to run something at scale. How do, how do you see that connecting? Cuz that seems to be another big trend people are talking about, Hey, it's complex someone manage it for me. And I want ease of views. I want the easy button in it. >>Yeah. Well we we've all been at this a while. So we can remember, you know, the beginnings of converged infrastructure and then hyperconverged, which wasn't that long go. And now we have consumption based business models. These are all along the trajectory of the easy button that you're talking about and customers really thinking about the value line, where are the things that really differentiate and add value for their business. And it's not below the value line in those infrastructure areas are creating that easy button with appliances, with consumption based models and allowing them to deploy the scarce resources. They have to the things that really drive their unique differe. And you know, if you look at our managed services flex on demand, all the sort of ancestors and predecessors of apex, those have been great businesses for us. And now with apex, we're kind of industrializing this and, and making it, you know, at scale for all >>Customers, you know, the three of us, we go back, we, we, our first interactions with you separately, we're in the nine. And then we reconnected in the 2012. I think it was Tarkin Mayer had a little breakout session with CIOs. You brought us to early on a Dell tech world in Austin. And of course it was, >>It was just Dell world. Then Dell >>Four, we had Dell tech, you and then EMC world in 2010 was our first cube. And now that's all come together here in Las Vegas. So, you know, it's been great. Uh, the three of us come together and so really appreciate that. Yeah. >>Awesome. Absolutely awesome. >>Well, you know, really appreciate you guys being here, the wonderful work you do in thank you in, you know, bringing out the, the, the stories and, and showing off and helping us show off the innovations that, you know, our team has been working on. You know, during the past year >>It's been great in conversations and, and on a personal note, it's been great to have, uh, chat with all the top people and your company. Appreciate it. Um, someone told me to ask you this question, I want to ask you, you, we've all seen waves of innovation cycles up and down. We're kind of on one. Now you're seeing an inflection point, this next gen, uh, computing and, and web three cultural shit F with workforces and distributed computing decentralization. You mentioned that DFI earlier, how do you see this wave coming? Cause we've seen cycles come and go.com. Bubble kind of looks the same as the web three NFTs and stuff. Now it seems to be Look different, but how do you see this next wave? Cuz looking back on all the other ones that you you have lived through and you rode >>Well. So, you know, the, the way I see it is is, uh, to some extent, these are like foundational layers that have to be built for the next phase to occur. And if you look at the sort of new companies that are being founded today, and we see a lot of those, you, you, you, you see'em, we invest in a bunch of 'em, you know, they're, they're not going and, and kind of redoing the old foundational layers, they're going deeply into vertical businesses and, and disrupting and adding value on top of those. And I think that's, that's really the, the point of, of technology, right? It's enabling human progress us in, in all fields, it's making us healthier. It's making us safer. It's making us more successful in everything that, that we as humans do. And so all these layers of technology are enabling further progress and I think it's absolutely gonna continue. It's all been super exciting. Yeah. You know, so far for the first several decades, but as I, as I believe it, it's, it's just a pre-game show. >>And it's clear your strategy is, is, is really building that foundation of a layer, hardening it, but making it flexible enough, anybody read your book, you're a technology, visionary. A lot of people put you in a, you know, finance bucket, but you can, you can see that you can connect the dots. And that's what you're doing with your foundation of layers. You that's where you're making the bets, isn't it? Uh, you don't can't predict the future. You've said that many times, but you can sort of see where it's going and be prepared for >>It. Well, you, you, you know, you think about any company in, in the industry or any public sector organization, right? Uh, they're, they're, they're wanting to evolve more quickly and transform more quick, more quickly. Right. And we can give them an infrastructure or set of tools, a set of capabilities to help them go faster. >>Yeah. And the other one thing in the eighties, when you started Dell and we were in college, there was no open source really then if look at the growth of open source, talk about those layers, open source, better Silicon GPS, faster, cheap >>More now and now we even have, uh, open source instruction sets for processors. So I mean the whole world's changing. It's exciting. You have people around the world working together. I mean, when you see our development teams, uh, whether they're in Israel or Ireland or Bangalore or Singapore, Hopton Austin, Silicon valley, you know, Taiwan, they're, they're all, they're all collaborating together and, you know, driving, driving innovation and, and, and our business is not that dissimilar from our customers >>Like great to have you in the queue. Great. To have a physical event. People are excited. I'm talking to people, Hey, haven't been back in Vegas in two years. Thanks for having this event. Great to see you. Thanks for coming on the cube. >>Absolutely. Thank you guys. >>Michael Dell here in the cube CEO of Dell technologies. I'm John far, Dave Volante. We'll be right back, more live coverage here at Dell tech world.

(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 theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCube and ETR, this is Breaking Analysis with Dave Vellante. >> When a piece of digital art sells for $69.3 million, more than has ever been paid for works, by Gauguin or Salvador Dali, making it created the third most expensive living artists in the world. One can't help but take notice and ask, what is going on? The latest craze around NFTs may feel a bit bubblicious, but it's yet another sign, that the digital age is now fully upon us. Hello and welcome to this week's Wikibon's CUBE insights, powered by ETR. In this Breaking Analysis, we want to take a look at some of the trends, that may be difficult for observers and investors to understand, but we think offer significant insights to the future and possibly some opportunities for young investors many of whom are fans of this program. And how the trends may relate to enterprise tech. Okay, so this guy Beeple is now the hottest artist on the planet. That's his Twitter profile. That picture on the inset. His name is Mike Winkelmann. He is actually a normal looking dude, but that's the picture he chose for his Twitter. This collage reminds me of the Million Dollar Homepage. You may already know the story, but many of you may not. Back in 2005 a college kid from England named Alex Tew, T-E-W created The Million Dollar Homepage to fund his education. And his idea was to create a website with a million pixels, and sell ads at a dollar for each pixel. Guess how much money he raised. A million bucks, right? No, wrong. He raised $1,037,100. How so you ask? Well, he auctioned off the last 1000 pixels on eBay, which fetched an additional $38,000. Crazy, right? Well, maybe not. Pretty creative in a way, way early sign of things to come. Now, I'm not going to go deep into NFTs, and explain the justification behind them. There's a lot of material that's been published that can do justice to the topic better than I can. But here are the basics, NFTs stands for Non-Fungible Tokens. They are digital representations of assets that exist in a blockchain. Now, each token as a unique and immutable identifier, and it uses cryptography to ensure its authenticity. NFTs by the name, they're not fungible. So, unlike Bitcoin, Ethereum or other cryptocurrencies, which can be traded on a like-for-like basis, in other words, if you and I each own one bitcoin we know exactly how much each of our bitcoins is worth at any point of time. Non-Fungible Tokens each have their own unique values. So, they're not comparable on a like-to-like basis. But what's the point of this? Well, NFTs can be applied to any property, identities tweets, videos, we're seeing collectables, digital art, pretty much anything. And it's really. The use cases are unlimited. And NFTs can streamline transactions, and they can be bought and sold very efficiently without the need for a trusted third party involved. Now, the other benefit is the probability of fraud, is greatly reduced. So where do NFTs fit as an asset class? Well, they're definitely a new type of asset. And again, I'm not going to try to justify their existence, but I want to talk about the choices, that investors have in the market today. The other day, I was on a call with Jay Po. He is a VC and a Principal at a company called Stage 2 Capital. He's a former Bessemer VC and one of the sharper investors around. And he was talking about the choices that investors have and he gave a nice example that I want to share with you and try to apply here. Now, as an investor, you have alternatives, of course we're showing here a few with their year to date charts. Now, as an example, you can buy Amazon stock. Now, if you bought just about exactly a year ago you did really well, you probably saw around an 80% return or more. But if you want to jump in today, your mindset might be, hmm, well, okay. Amazon, they're going to be around for a long time, so it's kind of low risk and I like the stock, but you're probably going to get, well let's say, maybe a 10% annual return over the longterm, 15% or maybe less maybe single digits, but, maybe more than that but it's unlikely that any kind of reasonable timeframe within any reasonable timeframe you're going to get a 10X return. In order to get that type of return on invested capital, Amazon would have to become a $16 trillion valued company. So, you sit there, you asked yourself, what's the probability that Amazon goes out of business? Well, that's pretty low, right? And what are the chances it becomes a $16 trillion company over the next several years? Well, it's probably more likely that it continues to grow at that more stable rate that I talked about. Okay, now let's talk about Snowflake. Now, as you know, we've covered the company quite extensively. We watched this company grow from an early stage startup and then saw its valuation increase steadily as a private company, but you know, even early last year it was valued around $12 billion, I think in February, and as late as mid September right before the IPO news hit that Marc Benioff and Warren Buffett were going to put in $250 million each at the IPO or just after the IPO and it was projected that Snowflake's valuation could go over $20 billion at that point. And on day one after the IPO Snowflake, closed worth more than $50 billion, the stock opened at 120, but unless you knew a guy, you had to hold your nose and buy on day one. And you know, maybe got it at 240, maybe you got it at 250, you might have got it at higher and at the time you might recall, I said, You're likely going to get a better price than on day one, which is usually the case with most IPOs, stock today's around 230. But you look at Snowflake today and if you want to buy in, you look at it and say, Okay, well I like the company, it's probably still overvalued, but I can see the company's value growing substantially over the next several years, maybe doubling in the near to midterm [mumbles] hit more than a hundred billion dollar valuation back as recently as December, so that's certainly feasible. The company is not likely to flame out because it's highly valued, I have to probably be patient for a couple of years. But you know, let's say I liked the management, I liked the company, maybe the company gets into the $200 billion range over time and I can make a decent return, but to get a 10X return on Snowflake you have to get to a valuation of over a half a trillion. Now, to get there, if it gets there it's going to become one of the next great software companies of our time. And you know, frankly if it gets there I think it's going to go to a trillion. So, if that's what your bet is then you know, you would be happy with that of course. But what's the likelihood? As an investor you have to evaluate that, what's the probability? So, it's a lower risk investment in Snowflake but maybe more likely that Snowflake, you know, they run into competition or the market shifts, maybe they get into the $200 billion range, but it really has to transform the industry execute for you to get in to that 10 bagger territory. Okay, now let's look at a different asset that is cryptocurrency called Compound, way more risky. But Compound is a decentralized protocol that allows you to lend and borrow cryptocurrencies. Now, I'm not saying go out and buy compound but just as a thought exercise is it's got an asset here with a lower valuation, probably much higher upside, but much higher risk. But so for Compound to get to 10X return it's got to get to $20 billion valuation. Now, maybe compound isn't the right asset for your cup of tea, but there are many cryptos that have made it that far and if you do your research and your homework you could find a project that's much, much earlier stage that yes, is higher risk but has a much higher upside that you can participate in. So, this is how investors, all investors really look at their choices and make decisions. And the more sophisticated investors, they're going to use detailed metrics and analyze things like MOIC, Multiple on Invested Capital and IRR, which is Internal Rate of Return, do TAM analysis, Total Available Market. They're going to look at competition. They're going to look at detailed company models in ARR and Churn rates and so forth. But one of the things we really want to talk about today and we brought this up at the snowflake IPO is if you were Buffet or Benioff and you had to, you know, quarter of a dollars to put in you could get an almost guaranteed return with your late in the game, but pre IPO money or a look if you were Mike Speiser or one of the earlier VCs or even someone like Jeremy Burton who was part of the inside network you could get stock or options, much cheaper. You get a 5X, 10X, 50X or even North of a hundred X return like the early VCs who took a big risk. But chances are, you're not one of these in one of these categories. So how can you as a little guy participate in something big and you might remember at the time of the snowflake IPO we showed you this picture, who are these people, Olaf Carlson-Wee, Chris Dixon, this girl Sono. And of course Tim Berners-Lee, you know, that these are some of the folks that inspired me personally to pay attention to crypto. And I want to share the premise that caught my attention. It was this. Think about the early days of the internet. If you saw what Berners-Lee was working on or Linus Torvalds, in one to invest in the internet, you really couldn't. I mean, you couldn't invest in Linux or TCP/IP or HTTP. Suppose you could have invested in Cisco after its IPO that would have paid off pretty big time, for sure. You know, he could have waited for the Netscape IPO but the core infrastructure of the internet was fundamentally not directly a candidate for investment by you or really, you know, by anybody. And Satya Nadella said the other day we have reached maximum centralization. The main protocols of the internet were largely funded by the government and they've been co-opted by the giants. But with crypto, you actually can invest in core infrastructure technologies that are building out a decentralized internet, a new internet, you know call it web three Datto. It's a big part of the investment thesis behind what Carlson-wee is doing. And Andreessen Horowitz they have two crypto funds. They've raised more than $800 million to invest and you should read the firm's crypto investment thesis and maybe even take their crypto startup classes and some great content there. Now, one of the people that I haven't mentioned in this picture is Camila Russo. She's a journalist she's turned into hardcore crypto author is doing great job explaining the white hot defining space or decentralized finance. If you're just at read her work and educate yourself and learn more about the future and be happy perhaps you'll find some 10X or even hundred X opportunities. So look, there's so much innovation going around going on around blockchain and crypto. I mean, you could listen to Warren Buffet and Janet Yellen who implied this is all going to end badly. But while look, these individuals they're smart people. I don't think they would be my go-to source on understanding the potential of the technology and the future of what it could bring. Now, we've talked earlier at the, at the start here about NFTs. DeFi is one of the most interesting and disruptive trends to FinTech, names like Celsius, Nexo, BlockFi. BlockFi let's actually the average person participate in liquidity pools is actually quite interesting. Crypto is going mainstream Tesla, micro strategy putting Bitcoin on their balance sheets. We have a 2017 Jamie diamond. He called Bitcoin a tulip bulb like fraud, yet just the other day JPM announced a structured investment vehicle to give its clients a basket of stocks that have exposure to crypto, PayPal allowing customers to buy, sell, and Hodl crypto. You can trade crypto on Robin Hood. Central banks are talking about launching digital currencies. I talked about the Fedcoin for a number of years and why not? Coinbase is doing an IPO will give it a value of over a hundred billion. Wow, that sounds frothy, but still big names like Mark Cuban and Jamaat palliate Patiala have been active in crypto for a while. Gronk is getting into NFTs. So it goes to have a little bit of that bubble feel to it. But look often when tech bubbles burst they shake out the pretenders but if there's real tech involved, some contenders emerge. So, and they often do so as dominant players. And I really believe that the innovation around crypto is going to be sustained. Now, there is a new web being built out. So if you want to participate, you got to do some research figure out things like how PolkaWorks, make a call on whether you think avalanche is an Ethereum killer dig in and find out about new projects and form a thesis. And you may, as a small player be able to find some big winners, but look you do have to be careful. There was a lot of fraud during the ICO. Craze is your risk. So understand the Tokenomics and maybe as importantly the Pump-a-nomics, because they certainly loom as dangers. This is not for the faint of heart but because I believe it involves real tech. I like it way better than Reddit stocks like GameStop for example, now not to diss Reddit. There's some good information on Reddit. If you're patient, you can find it. And there's lots of good information flowing on Discord. There's people flocking to Telegram as a hedge against big tech. Maybe there's all sounds crazy. And you know what, if you've grown up in a privileged household and you have a US Education you know, maybe it is nuts and a bit too risky for you. But if you're one of the many people who haven't been able to participate in these elite circles there are things going on, especially outside of the US that are democratizing investment opportunities. And I think that's pretty cool. You just got to be careful. So, this is a bit off topic from our typical focus and ETR survey analysis. So let's bring this back to the enterprise because there's a lot going on there as well with blockchain. Now let me first share some quotes on blockchain from a few ETR Venn Roundtables. First comment is from a CIO to diversified holdings company who says correctly, blockchain will hit the finance industry first but there are use cases in healthcare given the privacy and security concerns and logistics to ensure provenance and reduce fraud. And to that individual's point about finance. This is from the CTO of a major financial platform. We're really taking a look at payments. Yeah. Do you think traditional banks are going to lose control of the payment systems? Well, not without a fight, I guess, but look there's some real disruption possibilities here. And just last comment from a government CIO says, we're going to wait until the big platform players they get into their software. And so that is happening Oracle, IBM, VMware, Microsoft, AWS Cisco, they all have blockchain initiatives going on, now by the way, none of these tech companies wants to talk about crypto. They try to distance themselves from that topic which is understandable, I guess, but I'll tell you there's far more innovation going on in crypto than there is in enterprise tech companies at this point. But I predict that the crypto innovations will absolutely be seeping into enterprise tech players over time. But for now the cloud players, they want to support developers who are building out this new internet. The database is certainly a logical place to support a mutable transactions which allow people to do business one-on-one and have total confidence that the source hasn't been hacked or changed and infrastructure to support smart contracts. We've seen that. The use cases in the enterprise are endless asset tracking data access, food, tracking, maintenance, KYC or know your customer, there's applications in different industries, telecoms, oil and gas on and on and on. So look, think of NFTs as a signal crypto craziness is a signal. It's a signal as to how IT in other parts of companies and their data might be organized, managed and tracked and protected, and very importantly, valued. Look today. There's a lot of memes. Crypto kitties, art, of course money as well. Money is the killer app for blockchain, but in the future the underlying technology of blockchain and the many percolating innovations around it could become I think will become a fundamental component of a new digital economy. So get on board, do some research and learn for yourself. Okay, that's it for today. Remember all of these episodes they're available as podcasts, wherever you listen. I publish weekly on wikibon.com and siliconangle.com. Please feel free to comment on my LinkedIn post or tweet me @dvellante or email me at david.vellante@siliconangle.com. Don't forget to check out etr.plus for all the survey action and data science. This is Dave Vellante for theCUBE Insights powered by ETR. Be well, be careful out there in crypto land. Thanks for watching. We'll see you next time. (soft music)

>> From the KIPP studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> IBM's new CEO has an opportunity to reset the direction of the company. Outgoing CEO Ginni Rometty, inherited a strategy that was put in place over two decades. It became fossilized in a lower-margin services-led model that she helped architect. Ginni spent a large portion of her tenure, shrinking the company so it could grow. But unfortunately, she ran out of time. For decades, IBM has missed opportunities to aggressively invest in the key waves that are now powering the tech economy. Instead, IBM really tried to balance investing innovation with placating Wall Street. We believe IBM has an opportunity to return to the Big Blue status that set the standard for the tech industry. But several things have to change, some quite dramatically. So we're going to talk about what it's going to take for IBM to succeed in this endeavor. Welcome to this special Wikibon CUBE Insights powered by ETR. In this breaking analysis, we're going to address our view of the future of IBM and try to accomplish three things. First, I want to review IBM's most recent earnings, the very first one under new CEO Arvind Krishna, and we'll discuss IBM's near-term prospects. Next, we'll look at how IBM got to where we are today. We want to review some of the epic decisions that it has made over the past several years and even decades. Finally, we'll look at some of the opportunities that we see for IBM to essentially remake itself and return to that tech titan that was revered by customers and feared by competitors. First, I want to look at the comments from new CEO Arvind Krishna. And let's try to decode them a bit. Arvind in the first earnings call that he held, and in interviews as well, and also internal memos, he's given some clues as to how he's thinking. This slide addresses a few of the key points. Arvind has clearly stated that he's committed to growing the IBM company, and of course, increasing its value. This is no surprise, as you know, every IBM CEO has been under pressure to do the same. And we'll look at that further a little later on in the segment. Arvind, also stated that he wants the company, he said it this way, "To lead with a technical approach." Now as we reported in January when Krishna was appointed to CEO. We're actually very encouraged that the IBM board chose a technical visionary to lead the company. Arvind's predecessors did not have the technical vision needed to make the bold decisions that we believe are now needed to power the company's future. As a technologist, we believe his decisions will be more focused on bigger tactical bets that can pay bigger returns, potentially with more risk. Now, as a point of just tactical commentary, I want to point out that IBM noted that it was doing well coming into the March month, but software deals especially came to a halt as customers focused on managing the pandemic and other parts of the business were okay. Now, this chart pulls some of the data from IBM's quarter. And let me make a few comments here. Now, what was weird here, IBM cited modest revenue growth on this chart, this was pulled from their slides. But revenue was down 2% for the quarter relative to last year. So I guess that's modest growth. Cloud revenue for the past 12 months, the trailing 12 months, was 22 billion and grew 23%. We're going to unpack that in a minute. Red Hat showed good growth, Stu Miniman and I talked about this last week. And IBM continues to generate a solid free cash flow. Now IBM, like many companies, they prudently suspended forward guidance. Some investors bristled at that, but I really have no problem with it. I mean, just way too much uncertainty right now. So I think that was a smart move by IBM. And basically, everybody's doing it. Now, let's take a look at IBM's business segments and break those down and make a few comments there. As you can see, in this graph, IBM's 17 plus billion dollar quarter comprises their four reporting segments. Cloud and cognitive software, which is, of course, its highest margin and highest growth business at 7%. You can see its gross margin is really, really nice. But it only comprises 30% of the pie. Services, the Global Business Services and GTS global technology services are low-growth or no growth businesses that are relatively low margin operations. But together they comprise more than 60% of IBM's revenue in the quarter and consistently throughout the last several years. Systems, by the way, grew nicely on the strength of the Z15 product cycles, it was up by 60% and dragged storage with it. But unfortunately power had a terrible quarter and hence the 4% growth. But decent margins compared to services of 50%. IBM's balance sheet looks pretty good. It took an advantage of some low rates recently and took out another $4 billion in corporate debt. So it's okay, I'm not too concerned about its debt related to the Red Hat acquisition. Now, welcome back to cloud at 22 billion for the past 12 months and growing at 23%. What, you say? That sounds very large, I don't understand. It's understandable that you don't understand. But let me explain with this next graphic. What this shows is the breakdown of IBM's cloud revenue by segment from fiscal year 19. As you can see, the cloud and cognitive segments, or segment which includes Red Hat comprises only 20% of IBM's cloud business. I know, kind of strange. Professional services accounts for 2/3 of IBM's Cloud revenue with systems at 14%. So look, IBM is defining cloud differently than most people. I mean, actually, that's 1% of the cloud business of AWS, Azure and Google Cloud come from professional services and on-prem hardware. This just doesn't have real meaning. And I think frankly, it hurts IBM's credibility as it hides the ball on cloud. Nobody really believes this number. So, I mean, it's really not much else I can say there. But look, why don't we bring in the customer angle, and let's look at some ETR data. So what this chart shows is the results of an ETR survey. That survey ran, we've been reporting on this, ran from mid March to early April. And more than 1200 respondents and almost 800 IBM customers are in there. If this chart shows the percentage of customers spending more on IBM products by various product segments that we chose with three survey samples April last year, January 2020, and the most recent April 2020 survey. So the good news here is the container platforms, OpenShift, Ansible, the Staples of Red Hat are showing strength, even though they're notably down from previous surveys. But that's the part of IBM's business that really is promising. AI and machine learning and cloud, they're right there in the mix, and even outsourcing and consulting and really across the board, you can see a pretty meaningful and respectable number or percent of customers are actually planning on spending more. So that's good, especially considering that the survey was taken right during the middle of the COVID-19 pandemic. But, if you look at the next chart, the net scores across IBM's portfolio, they're not so rosy. Remember, net score is a measure of spending momentum. It's derived by essentially subtracting the percent of customers that are spending less from those that are spending more. It's a nice simple metric. Kind of like NPS and ETR surveys, every quarter with the exact same methodology for consistency so we can do some comparisons over time series, it's quite nice. And you can see here that Red Hat remains the strongest part of IBM's portfolio. But generally in my experience as net scores starts to dip below 25% and kind of get into the red zone, that so called danger zone. And you can see many parts of IBM's portfolio are showing softness as we measure in net score. And even though you see here, the outsourcing and consulting businesses are up relative to last year, if you slice the data by large companies, as we showed you with Sagar Kadakia last week, that services business is showing deceleration, same thing we saw for Accenture, EY, Deloitte, etc. So here's the takeaway. Red Hat, of course, is where all the action is, and that's where IBM is going to invest in our opinion, and we'll talk a little bit more about that and drill into that kind of investment scenario a bit later. But what I want to do now is I want to come back to Arvind Krishna. Because he has a chance to pull off a Satya Nadella like move. Maybe it's different, but there are definite similarities. I mean, you have an iconic brand, a great company, that's in many technology sectors, and yes, there are differences, IBM doesn't have the recurring software revenue that Microsoft had, it didn't have the monopoly and PCs. But let's move on. Arvind has cited four enduring platforms for IBM, mainframes, services, middleware, and the newest hybrid cloud. He says that IBM must win the architectural battle for hybrid cloud. Now, I'm going to really share later what we think that means. There's a lot in that statement, including the role of AI in the edge. Both of which we'll address later on in this breaking analysis. But before we get there, I want to understand from a historical perspective where we think Arvind is going to take IBM. And to do that, we want to look back over the modern history of IBM, modern meaning of the post mainframe dominance era, which really started in 1993 when Louis Gerstner took over. Look, it's been well documented how Louis Gerstner pivoted into services. He wrote his own narrative with the book, "Who Says Elephants Can't Dance". And you know, look, you can't argue with his results. The graphic here shows IBM's rank in the fortune 500, that's the green line over time. IBM was sixth under Gerstner, today it's number 38. The blue area chart on the Insert, it shows IBM's market cap. Now, look, Gerstner was a hero to Wall Street. And IBM's performance under his tenure was pretty stellar. But his decision to pivot to services set IBM on a path that to this day marks company's greatest strength, and in my view, its greatest vulnerability. Name a product under the mainframes in which IBM leads. Again, middleware, I guess WebSphere, okay. But you know, IBM used to be the leader in the all important database market, semiconductors, storage servers, even PCs back in the day. So, I don't want to beat on this too much, I can say it's been well documented. And I said earlier, Ginni essentially inherited a portfolio that she had to unwind, and hence the steep revenue declines as you see here, and it's 'cause she had to jettison the so called non-strategic businesses. But the real issue is R&D, and how IBM has used it's free cash. And this chart shows IBM's breakdown of cash use between 2007 and 2019. Blue is cash return to shareholders, orange is research and development, and gray is CapEx. Now I chose these years because I think we can all agree that this was the period of tech defined by cloud. And you can see, during those critical early formative years, IBM consistently returned well over 50%, and often 60% plus of its free cash flow to shareholders in the form of dividends and stock buybacks. Now, while the orange appears to grow, it's because of what you see in this chart. The point is the absolute R&D spend really didn't change too much. It pretty much hovered, if you look back around 5 1/2 to $6 billion annually, the percentage grew because IBM's revenue declined. Meanwhile, IBM's competitors were spending on R&D and CapEx, what were they doing? Well, they were building up the cloud. Now, let me give you some perspective on this. In 2007 IBM spent $6.2 billion on R&D, Microsoft spent 7 billion that same year, Intel 5.8 billion, Amazon spent 800 million, that's it. Google spent 2.1 billion that year. And that same year, IBM returned nearly $21 billion to shareholders. In 2012 IBM spent $6.3 billion on R&D, Microsoft that year 9.8 billion, Intel 10 billion, Amazon 4.6 billion, less than IBM, Google 6.1 billion, about the same as IBM. That year IBM returned almost $16 billion to shareholders. Today, IBM spends about the same 6 billion on R&D, about the same as Cisco and Oracle. Meanwhile, Microsoft and Amazon are spending nearly $17 billion each. Sorry, Amazon 23 billion, and IBM could only return $7 billion to shareholders last year. So while IBM was returning cash to its shareholders, its competitors were investing in the future and are now reaping the rewards. Now IBM suspended its stock buybacks after the Red Hat deal, which is good, in my opinion. Buybacks have been a poor use of cash for IBM, in my view. Recently, IBM raised its dividend by a penny. It did this so it could say that it has increased its dividend 25 years in a row. Okay, great, not expensive. So I'm glad that that investors were disappointed with that move. But since 2007, IBM has returned more than $175 billion to shareholders. And somehow Arvind has to figure out how to tell Wall Street to expect less while he invests in the future. So let's talk about that a little bit. Now, as I've reported before, here is the opportunity. This chart shows data from ETR. It plots cloud landscape and is a proxy for multi-cloud and hybrid cloud. It plots net score or spending momentum on the y-axis, and market share, which really isn't market share, as we've talked about, it's a measure of pervasiveness in the data set, that's plotted on the x-axis. So, the point is, IBM has presence, it's pervasive in the marketplace, Red Hat and OpenShift, they have relevance, they have momentum with higher net scores. Arvind's opportunity is to really plug OpenShift into IBM's, large install base, and increase Red Hat's pervasiveness, while at the same time lifting IBM momentum. This, in my view, as Stu Miniman and I reported last week at the Red Hat Summit, puts IBM in a leading position to go after multi and hybrid cloud and the edge. So let's break that down a little bit further. When Arvind talks about winning the architectural battle for hybrid cloud, what does he mean by that? Here's our interpretation. We think IBM can create the de facto standard for cloud and hybrid cloud. And this includes on-prem, public cloud, cross clouds, or multi cloud, and importantly, the edge. Here's the opportunity, is to have OpenShift run natively, natively everywhere, on-premises in the AWS cloud, in the Azure Cloud, GCP, Alibaba, and the IBM Cloud and the Oracle Cloud, everywhere natively, so we can take advantage of the respective services within all those clouds. Same thing for on-prem, same thing for edge opportunities. Now I'll talk a little bit more about that in a moment. But what we're talking about here is the entire IT stack running natively, if I haven't made that point on OpenShift. The control plane, the security plane, the transport, the data management plane, the network plane, the recovery plane, every plane, a Red Hat lead stack with a management of resources is 100% identical, everywhere the same cloud experience. That's how IBM is defining cloud. Okay, I'll give them a mulligan on that one. IBM can be the independent broker of this open source standard covering as many use cases and workloads as possible. Here's the rub, this is going to require an enormous amount of R&D. Just think about all the startups that are building cloud native services and imagine IBM building or buying to fill out that IT stack. Now I don't have enough time to go in too deep to all other areas, but I do want to address the edge, the opportunity there and weave in AI. Beyond what I said above, which I want to stress, the points I made above about hybrid, multi-cloud include edge, the edge is a huge opportunity. But IBM and in many other, if not most other traditional players, we think are kind of missing the boat on that. I'll talk about that in a minute. Here's the opportunity, AI inference is going to run at the edge in real-time. This is going to be incredibly challenging. We think about this, a car running inference AI generates a billion pixels per second today, in five years, it'll be 15 times that. The pressure for real-time analysis at the edge is going to be enormous, and will require a new architecture with new processing models that are likely going to be ARM-based in our opinion. IBM has the opportunity to build end-to-end solutions powered by Red Hat to automate the data pipeline from factory to data center to cloud and everywhere. Anywhere there's instruments, IBM has an opportunity to automate them. Now rather than toss traditional Intel-based IT hardware over the fence to the edge, which is what IBM and most people are doing right now, IBM can develop specialized systems and make new silicon investments that can power the edge with very low cost and efficient systems that process data in real-time. Hey look, I'm out of time, but some other things I want you to consider, IBM transitioning to a recurring revenue model. Interestingly, Back to the Future, right? IBM used to have a massive rental revenue stream before it converted that base to sales. But if Arvind can recreate a culture of innovation and win the day with developers via its Red Hat relationships, as I said recently, he will be CEO of the decade. But he has to transform the portfolio by investing more in R&D. He's got to convince the board to stop pouring money back to investors for a number of years, not just a couple of quarters and do Whatever they have to do to protect the company from corporate raiders. This is not easy, but with the right leader, IBM, a company that has shown resilience through the decades, I think it can be done. All right, well, thanks for watching this episode of the Wikibon CUBE Insights powered by ETR. This is Dave Vellante. And don't forget, these episodes are available as podcasts, wherever you listen, I publish weekly on siliconangle.com, where you'll find all the news, I publish on wikibon.com which is our research site. Please comment on my LinkedIn posts, check out etr.plus, that's where all the data lives. And thanks for watching everybody. This is Dave Vellante for Breaking Analysis, we'll see you next time. (soft music)

(upbeat music) >> Hey welcome back everybody. Jeff Frick here with theCUBE. I wish I could give you my best John Miller impersonation but I'm just not that good. But we are at Oracle Park, home of the San Francisco Giants. We haven't really done a show here since 2014, so we're excited to be back. Pretty unique event, it's called Sports Tech Tokyo World Demo Day. About 25 companies representing about 100 different companies really demonstrating a bunch of cool technology that's used for sports as well as beyond sports, so we're excited to have one of the companies here who's demoing their software today, or their solution I should say. It's Albert Ng, he's the founder and CEO of Misapplied Sciences. Albert, great to see you. >> Great to see you, thank you for having me. >> So Misapplied Sciences. Now I want to hear about the debates on that name. So how did that come about? >> Yeah, so I used to work part time for Microsoft, at Microsoft Research, and one of the groups I worked for was called the Applied Sciences group. And so it was a little bit of a spin on that and it conveys the way that we come up with innovations at our company. We're a little bit more whimsical as a company that we take technologies that weren't intended for the ways that we apply them and so we misapply those technologies to create new innovations. >> Okay, so you're here today, you're showing a demo. So what is it? What is your technology all about? And what is the application in sports, and then we'll talk about beyond sports. >> Yeah, so Misapplied Sciences, we came up with a new display technology. Think like LED video wall, digital signage, that sort of display. But what's unique about our displays, is you can have a crowd of people, all looking at the same display at the same time, yet every single person sees something completely different. You don't need to have any special glasses or anything like that. You look at your displays with your naked eyes, except everyone gets their own personalized experience. >> Interesting. So how is that achieved? Obviously, we've all been on airplanes and we know privacy filters that people put on laptops so we know there's definitely some changes based on angle. Is it based on the angles that you're watching it? How do you accomplish that and is it completely different, or I just see a little bit of difference here, there, and in other places? >> Sure, so at the risk of sounding a little too technical, it's in the pixel technology that we developed itself. So each of our pixels can control the color of light that it sends in many different directions. So one time a single pixel can emit green light towards you, whereas red light towards the person sitting right next to you, so you perceive green, whereas the person right next to you perceives red at the same time. We can do that at a massive scale. So our pixels can control the color of light that they send between tens of thousands, up to a million different angles. So using our software, our processors on our back end, we can control what each of our pixels looks like from up to a million different angles. >> So how does it have an edge between a million points of a compass? That's got to be, obviously it's your secret sauce, but what's going on in layman's terms? >> Yeah, so it's a very sophisticated technology. It's a full stack technology, as we call it. So it's everything from new optics to new high performance computing. We had to develop our own custom processor to drive this. Computer vision, software user interfaces, everything. And so this is an innovation we can up with after four and a half years in stealth mode. So we started the company in late 2014, and we were all the way completely in stealth mode until middle of last year. So about four years just hardcore doing the development work, because the technology's very sophisticated. And I know when I say this, it does sound a little impossible, a little bit like science fiction, so we knew that. So now we have our first product coming on the market, our first public installation later next year and it's going to be really exciting. >> Great. So, obviously you're not going to have a million different feeds, 'cuz you have to have a different feed I would imagine, for each different view, 'cuz you designate this is the view from point A. This is the view from point B. Use feed A, use feed B. I assume you use something like that 'cuz obviously the controller's a big piece of the display. >> Exactly, so a lot of the technology underneath the hood is to reduce the calculations, or the rendering required from a normal computer, so you can actually drive our big displays that can control hundreds of different views using a normal PC, just using our platform. >> So what's the application. You know obviously it's cute and it's fun and I told you it's a dog, no it's a cat as you said, but what are some of the applications that you see in sports? What are you going to do in your first demo that you're putting out? >> Yeah, so what the technology enables is finally having personalized experiences when in a public environment, like a stadium, like an airport, like a shopping mall. So let me give an airport example. So imagine you go up to the giant flight board and instead of a list of a hundred flights, you see only your own flight information in big letters so you can see it from 50 feet away. You can have arrows that light your path towards your particular gate. The displays could let you know exactly how many minutes you have to board, and suggest places for you to eat and shop that are convenient for you. So the environment can be tailored just for you and you're not looking down at a smart phone, you're not wearing any special glasses to see everything that you want to see. So that ability to personalize a venue stretch, is to every single public venue, even in the stadium here, imagine the stadium knowing whether you're a home team fan or away team fan or your fantasy players. You can see it all on the jumbotron or any of the displays that are in the interstitial areas. We can have the entire stadium come alive just for you and personalize it. >> Except you're not going to have 10,000 different feeds, so is there going to be some subset of infinite that people are driving in terms of the content side? >> Mhmm. >> So on your first one, you're first installation, what's that installation going to be all about? >> The first installation is going to be at an airport, I can't see right now publicly where it's going to be or when it's going to be or what partner. But the idea is to be able to have a giant flight board that you only see your own flight information, navigating you to your particular gate. You know when you're at an airport, or any other public venue like a stadium, a lot of times you feel like cow in a herd, right? And it's not tailored for you in any way. You don't have as good of an experience. So we can personalize that for you. >> All right, Misapplied Sciences. Oh I'll come down and take a look at the booth a little bit later. And thanks for taking a few minutes. Good luck on the adventure. I look forward to watching it unfold. >> Appreciate it, thank you so much. >> All right, he's Albert I'm Jeff. You're watching theCUBE. We're at Oracle Park, on the shores of McCovey Cove. Thanks for watching, we'll see ya next time. >> Thank you. (upbeat music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello everyone, welcome to Palo Altos Cube Studios for CUBE Conversation. I'm John Furrier, co-host of theCUBE. We're with Kevin Ackroyd, CEO of Cision, CUBE Alumni. He's been on before. Building one of the most compelling companies that's disrupting and changing the game in Comms, advertising, PR, with Cloud technologies. Kevin, great to see you again, thanks for coming in. >> Likewise John, It's really good to be back. >> So, we haven't chatted in two years. You've been busy. Our last conversation was the beginning of 2017. Cision's done a lot of interesting things. You've got a lot of M and A under your belt. You're putting this portfolio together with Cloud technologies. Really been interesting. I really got to say I think you cracked the code on I think a new reality, a new economic reality. Also new capabilities for comms folks. Congratulations. >> Thank you, it's been a fun ride. >> So give us the update. So two years since we talked, how many deals, companies have you bought? What's the headcount, what's the revenue? Give us an update. >> In the four years, 12 acquisitions, seven of which have happened since I've been here. Up to 4,500 employees in over 40 countries. Customer count has grown to over 50,000 customers globally. Revenue's kind of gone from 500s to just shy of 800 million. A lot of leadership changes, and as you just mentioned, pretty seismic change, finally. We've certainly been the catalyst and the cattle prod for that seismic change around tech, data, measurement and analytics finally becoming mature and adopted inside this line of business like the Chief Communication Officer, the earn media folks. To say that they were not tech savvy a few years ago would be an understatement. So, a lot's been going on. >> Yeah, and certainly the trend is your friend, in my opinion, for you. But I think the reality is not yet upon people's general mindset. It's coming quickly, so if you look at some of the big trends out there. Look at fake news, look at Facebook, look at the Google effect. Elizabeth Warren wants to break up Big Tech, Amazon. Cloud computing, in that time period that you were, prior to just going to Cision, you had Oracle Cloud, done a lot of great things on the Marketing Cloud side. But the timing of Cloud computing, the timing of how media has changed. There's not many journalists anymore. We had Andy Cunningham, a legendary industry veteran, formerly of Cunningham Communications. He did the PR for Steve Jobs. You said, there's no more journalists, a few left, but you got to tell your story direct to the consumer. >> You do. >> This is now a new marketing phenomenon. This is a tailwind for you at Cision because you guys, although put these cubbies together, have a unique vision around bringing brand value advertising at PR economics. >> Yeah, that's a good way to put it. >> Tell us the vision of Cision and specifically the shift that's happening. Why are you guys important? What wave are you riding? >> So, there's a couple shifts, John. You and I have talked about this in previous programs There's this shift of the line of business, having to work in a whole bunch of non-integrated point solutions. The CFO used to live in 17 different applications from 17 vendors. That's all squished together. Now I buy from one Cloud platform, right, from Oracle or SAP. Same thing happened in Human Capital Management. 22 things squished into the Cloud, one from Workday, right. Same thing happened, you had 25 different things for sales and service. That all squished together, into one CRM in the Cloud, I buy from Salesforce, right. And our last rodeo, the early part of this stack, it was me and Adobe battling it out for the right to go squish the entire the LUMAscape into a marketing cloud, right, so there could be one ring to rule them all for the CMO. So, it happens in every single category. It just hasn't had over here, happened on the earned media side and the Chief Communications Officer. So, bringing the tech stack so that now we are for the CCO what Adobe is for the CMO what Salesforce is for the CRO, Workday is for the CHRO. That has to happen. You can't do, you can't manage it this way without sophisticated tech, without automation, without integration, you can't do it. The second thing that had to happen, especially in marketing and advertising, they all figured out how to get revenue credit. Advertising was a slow single-digit CAGR industry for 50 years. And then something happened. After 5% CAGR for 50 years, and then something happened over the next 10 years. Digital paid went from like 15 billion to 150 billion. And what happened is that old, I know half my advertising is wasted on this one half. That went bye-bye. Now I know immediately, down to the page, down the ad unit, down to this, exactly what worked, right. When I was able to put Pixels on ads, John, you'd go to that page, Pixel would go on you, It would follow you around If you ended up putting something in the e-commerce shop that ad got credit. I'm not saying that's right, I'm just saying that's how the entire-- >> But that's how the infrastructure would let you, allowed you, it enabled you to do that. Then again, paid advertising, paid search, paid advertising, that thing has created massive value in here. >> Massive value. But my buyer, right, so the person that does the little ad on the most regional tech page got credit. My buyer that got Bob Evans, the Cloud King, to write an article about why Microsoft is going to beat AWS, he's a credible third party influencer, writing objectively. That article's worth triple platinum and has more credibility than 20,000 Microsoft sales reps. We've never, until Cision, well let's Pixel that, let's go figure out how many of those are the target audience. Let's ride that all the way down to the lead form that's right. Basically it's super simple. Nobody's ever tracked the press releases, the articles or any of the earned media content, the way people have tracked banner ads or e-commerce emails. Therefore this line of business never get revenue credit. It stayed over here in the OpEx pile where things like commerce and advertising got dumped onto the revenue pile. Well, you saw the crazy investment shift. So, that's really the more important one, is Comms is finally getting quantified ROI and business's attribution like their commerce and advertising peers for the first time ever in 2018 via what Cision's rolled out. That's the exciting piece. >> I think, I mean, I guess what I hear you saying is that for the first time, the PR actually can be measured, similar to how advertising >> You got it. >> Couldn't be measured then be measured. Now PR or communications can be measured. >> They get measured the same way. And then one other thing. That ad, that press release, down to the business event. This one had $2 million dollars of ad spend, this one had no ad spend. When it goes to convert, in CRM or it goes to convert on a website, this one came from banner ad, this one came from credible third party content. Guess which one, not only had zero ad spend instead of $2 million in ad spend. Guess which one from which source actually converts better. It's the guy that chose to read credible third-party article. He's going to convert in the marketing system way better that somebody who just clicked on the ad. >> Well certainly, I'm biased-- >> So all the way down the funnel, we're talking about real financial impact based on capturing earned media ID, which is pretty exciting. >> Well, I think the more exciting thing is that you're basically taking a value that is unfunded quote by the advertising firm, has no budget basically, or thin budgets, trying to hit an organic, credible outlet which is converting in progression to a buyer, an outcome. That progression is now tracked. But let's just talk about the economics because you're talking about $2 million in spend, it could be $20 million. The ratio between ad spend and conversion to this new element you mentioned is different. You're essentially talking about the big mega trend, which is organic content. Meaning connecting to sources. >> That's right. >> That flow. Of course, we believe and we, at the Cube, everyone's been seeing that with our business. Let's talk about that dynamic because this is not a funded operationalized piece yet, so we've been seeing, in the industry, PR and comms becoming more powerful. So, the Chief Communication Officer isn't just rolling out press releases, although they have to do that to communicate. You've got medium posts now, you've got multiple channels. A lot of places to put the story. So the Chief Communication Officer really is the Chief Storyteller Officer, Not necessarily the CMO. >> Emphatically. >> The Martech Stack kind of tracking. So talk about that dynamic. How is the Chief Communication Officer role change or changing? Why is that important and what should people be thinking about, if they are a Chief Communication Officer? >> You know, it's interesting. There's a, I'm just going to call it an actual contradiction on this front. When you and I were getting out of our undergrad, 7 out of 10 times that CCO, the Chief Communication Officer, worked for the CEO and 30% of time other. Yet the role was materially narrow. The role has exploded. You just said it pretty eloquently. This role has really exploded and widened its aperture. Right now though 7 out of 10 of them actually do work for the CMO, which is a pretty interesting contradiction. And only 30% of them work for the CEO. Despite the fact that from an organizational stand point, that kind of counter intuitive org move has been made. It doesn't really matter because, so much of what you just said too, you was in marketing's purview or around brand or around reputation or around telling the story or around even owning the key assets. Key assets isn't that beautiful Budweiser frog commercial they played on Super Bowl anymore. The key assets are what's getting done over in the communications, in part. So, from a storytelling standpoint, from an ownership of the narrative, from a, not just a product or a service or promotion, but the whole company, the whole brand reputation, the goodwill, all of that is comms. Therefore you're seeing comms take the widest amount of real estate around the boardroom table than they've ever had. Despite the fact that they don't sit in the chair as much. I mentioned that just because I find it very interesting. Comms has never been more empowered, never had a wider aperture. >> But budget wise, they're not really that loaded up with funding. >> And to my earlier point, it's because they couldn't show. Super strategic. Showing ROI. >> So, showing ROI is critical. >> Not the quality of clippings. >> It was the Maslow of Hierarchy of Needs if you can just show me that I put a quarter in and I got a dollar out. Like the ads and the e-commerce folks do. It simply drives the drives me. >> So take us through some of those analytics because people who know about comms, the old school comms people who are doing this, they should really be thinking about what their operation is because, can I get an article in the Wall Street Journal? Can Silicon Angle write about us? I've got to get more clippings. That tend to be the thing. Did we get the press release out on time? They're not really tied into some of the key marketing mix pieces. They tend to be kind of a narrow scope. Those metrics were pretty clear. What are the new metrics? What's the new operational playbook.? >> Yeah, we call those Vanity Metrics. I cared about theoretical reach. Hey, Yahoo tells me I reached 222 billion people, so I plug in 222 billion people. I reached more people than there are on the planet with this PR campaign. I needed to get to the basic stuff like how many people did I actually reach, number one. But they don't, they do theoretical reach. They work in things like sentiment. Well, I'm going to come up with, 100 reporters wrote about me. I'm going to come up with, how many of them I thought were positive, negative, neutral. Sentiment analysis, they measure number of reporters or hits versus their competitors and say, Proctor and Gamble rolled out this diaper product, how did I do this five days? How much did Proctor and Gamble diapers get written about versus Craft diapers versus Unilever's. Share a voice. Not irrelevant metrics. But not metrics the CEO and the CFO are going to invest in. >> Conversion to brand or sales, those kind of things? >> They never just never existed. Those never existed. Now when we can introduce the same exact metrics that the commerce and the ad folks do and say, I can tell you exactly how many people. I can tell you exactly who they were, demographic, firmographic, lifestyle, you name it. I can tell you who the audience is you're reaching. I can tell you exactly what they do. When those kind of people read those kind of articles or those kind of people read those kind of press releases, they go to these destinations, they take these behaviors. And because I can track that all the way down to whatever that success metric is, which could be a lead form if I'm B2B for pipe. It could be a e-commerce store from B2C. It could be a rating or review or a user generation content gourd. It could be a sign up and register, if I'm trying to get database names. Whatever the business metric is. That's what the commerce and the ad people do all day every day. That's why they are more funded than ever. The fact that press releases, articles, tweets, blogs, the fact that the earned media stuff has never been able to do those things is why they just continue to suffer and have had a real lack of investment prices going on for the last 20 year. >> Talk about the trend around-- >> It's simple stuff. >> I know, if you improve the ROI, you get more budget. >> It really is that simple. >> That's been the challenge. I think PR is certainly becoming, comms is becoming more powerful. People know I talk about it all the time. I think comms is the new CMO I think command and control and organic content work together in the organic. We've seen it first hand in our business. But, it's an issue of tech savviness and also vision. A lot of people just are uncomfortable shifting to the new realities. >> That's for sure. >> What are some of the people tech savvy look at when they look at say revamping comms platform or strategy versus say old school? >> I'll give you two answers on that, John. Here is one thing that is good for us, that 7 out of 10 to the CCOs work for the CMO. Because when I was in this seat starting to light that fire under the CMO for the first time, which was not that long ago, and they were not tech savvy, and they were not sophisticated. They didn't know how to do this stuff either. That was a good 10 year journey to get the CMO from not sophisticated to very sophisticated. Now they're one of the more sophisticated lines of business in the world. But that was a slog. >> So are we going to see a Comms Stack? Like Martech, ComTech. >> ComTech is the decision communication Cloud, is ComTech. So we did it. We've built the Cloud stack. Again like I said, just like Adobe has the tech stack for marketing, Cision has the tech stack for comms, and we've replicated that. But because the CCO works for the CMO and the CMO's already been through this. Been through this with Ad Techs, been through this with MarTech, been through this with eCommerce, been through this with Web. You know, I've got a three or four year sophistication path this time just because >> The learnings are there >> The company's already done it everywhere else. The boss has already done it everywhere else. >> So the learnings are there from the MarTech so it's a pretty easy leap to take? >> That's exactly right. >> It's just-- >> How CommTech works is shocking. Incredibly similar to how MarTech and AdTech work. A lot of it is the same technology, just being applied different. >> That's good news >> So, the adoption curve for us is a fantastic thing. It's a really good thing for us that 70% of them work for CMOs because the CMO is the most impatient person on the planet, to get this over because the CMO is sick of doing customer journeys or omni channel across just paid and owned. They recognize that the most influential thing to influence you, it's not their emails, it's not their push notifications, It's not their ads. It's recognizing which credible third-party content you read, getting them into that, so that they're influencing you. >> It's kind of like Google PageRank in the old days. This source is more relevant than that one, give it more weight. >> And now all of a sudden if I have my Cision ID, I can plug in the more weight stuff under your profile. I want to let him go across paid and owned too, I materially improve the performance of the paid and owned because I'm putting in the really important signal versus what's sitting over there in the DMP or the CDP, which is kind of garbage. That's really important. >> I really think. >> I thinks you've got a home run here. I think you've really cracked the code on this. I think you are absolutely right on the money with comms and CommsTech. I see it all the time. In my years of experiences, it's so obvious. Then again, the tailwind is that they've been through the MarTech. The question I have for you is cultural shift. That's a big one. So, I'm out evangelizing all the time about the CUBE Cloud and some of the things we're doing. I run into the deer in the headlights on one side, what do you mean? And then people like, I believe, I totally understand. The believers and the non believers. What's the cultural shift? Because some chief comms op, they're very savvy, progressive, we've got to make the shift. How do they get the ship to turn? What are some of the cultural challenges? >> And boy is that right. I felt the same thing, getting more doing it with the CMO. A lot of people kept their head in the sand until they got obsoleted. They didn't know. Could they not see the train coming? They didn't want to see the train coming. Now you go look at the top 100 CMOs in the world today. Pretty different bunch than who those top 100 CMOs were 10 years ago. Really different bunch. History's repeating itself over here too. You've got the extremely innovative CCOs that are driving that change and transformation. You've got the deer in the headlight, okay, I know I need to do this, but I'm not sure how, and you do have your typical, you know, nope, I've got my do not disturb sign and police tape over my office. I won't even let you in my door. I don't want to hear about it. You've got all flavors. The good news is we are well past the half point where the innovators are starting actually to deploy and show results, the deer in the headlights are starting to innovate, and these folks are at least opening up the door and taking down some tape. >> Is there pressure on the agency side now? A lot of agencies charge a lot of monthly billings for these clients, the old school thing. Some are trying to be progressive and do more services. Have you seen, with the Cision Cloud and things that you're doing, that you're enabling, those agencies seem to be more productive? >> Yes. >> Are the client's putting pressure on those agencies so they see more value? Talk about the agency dynamic. >> That's also a virtuous cycle too, right? That cycle goes from, it's a Bell Curve. At the beginning of the bell curve, customers have no clue about the communications. They go to their agencies for advice. So, you have to educate the agencies on how to say nice things about you. By the time you're at the Bell Curve, the client's know about the tech or they've adopted the tech, and the agencies realize, oh, I can monetize the hell out of this. They need strategy and services and content and creative and campaign. This is yet another good old fashioned >> High gross profit. >> A buck for the tech means six bucks for me as the service agency. At the bottom, over here, I'll never forget this when we did our modern marketing experiences, Erik, the CMO of Clorox said, hey, to all you agencies out there, now that we're mature, you know, we choose our our agency based on their fluency around our tech stack. So it goes that violently and therefore, the agencies really do need to try to get fluent. The ones that do, really reap rewards because there is a blatant amount of need as the line of business customer tries to get from here to here. And the agency is the is the very first place that that customer is going to go to. >> So, basically the agency-- >> The customer has first right of refusal to go provide these services and monetize them. >> So, the agency has to keep up. >> They certainly do. >> Because, if the game gets changed by speed, it's accelerated >> If they keep up, yup. >> Value is created. If they don't have their running shoes on, they're out. >> If they keep up and they stay fluent, then they're going to be great. The last thing back in the things. We've kind of hit this. This is one of those magic points I've been talking about for 20 years. When the CFO or the CEO or the CMO walk down to the CCOs office and say, where are we on this, 'cause it's out in the wild now, there are over 1200 big brands doing this measurement, Cision ID, CommsTech stuff. It's getting written about by good old fashioned media. Customer says, wow, I couldn't do this for 50 years, now I am, and look what I just did to my Comms program. That gets read. The world's the same place as it always has been. You and I read that. We go down to our comms department and say, wow, I didn't know that was possible, where are we on this? So the Where Are We On This wave is coming to communications, which is an accelerant. >> It's an accountability-- >> Now it's accountability, and therefore, the urgency to get fluent and changed. So now they're hiring up quantums and operations and statisticians and database people just like the marketers did. The anatomy of a communications department is starting to like half science half art, just like happened in marketing. Whereas before that, it was 95% art and 5% science. But it's getting to be 50/50. >> Do you have any competition? >> We have, just like always. >> You guys pretty much have PR Newswire, a lot of big elements there. >> We do. >> You've got a good foothold. >> This is just an example. Even though Marketo is part of Adobe, giant. And Eloqua is part of Oracle, giant and Pardot is part of Salesforce. You've got three goliaths in marketing automation. Hubspot's still sticking around. PeerPlay, marketing Automation. You can just picture it. CRM giants, Microsoft and Salesforce have eaten the world Zendesk's still kicking around. It's a little PeerPlay. That equivalent exists. I have nobody that's even one fifth as big as I am, or as global or complete. But I do have some small, point specific solution providers. They're still hanging out there. >> The thing is, one, first you're a great leader. You've seen the moving on the marking tech side. You've got waves of experience under your belt. But I think what's interesting is that like the Web 1.0, having websites and webpages, Web 2.0 and social networks. That was about the first generation. Serve information, create Affiliate programs, all kind of coded tracking. You mentioned all that. I over-simplified it, but you get the idea. Now, every company needs a new capability. They need to stand up media infra structure. What does that mean? They're going to throw a podcast, they're going to take their content, put them into multiple channels. That's a comms function. Now comms is becoming the new CMO-like capability in this earned channel. So, your Cloud becomes that provisioning entity for companies to stand up capabilities without waiting. Is that the vision? >> You've nailed it. And that is one of the key reasons why you have to have a tech stack. That's a spot on one, another one. Early in my career, the 20 influences that mattered, they were all newspaper reporters or TV folks. There was only 20 of them. I had a Rolodex. so I could take each one of them out for a three Martini lunch, they'd write something good about me. >> Wish is was that easy now. >> Now, you have thousands of influencers across 52 channels, and they change in real time, and they're global in nature. It's another example of where, well, if you don't automate that with tech and by the way. >> You're left behind. >> If you send out digital content they talk back to you in real time. You have to actually not only do influencer identification, outreach and curation, you've got to do real time engagement. >> There's no agility. >> There's none. >> Zero agility. >> None, exactly. >> There's no like Dev Ops mindset in there at all. >> Then the speed with which, it's no longer okay for comms to call the agency and say, give me a ClipBook, I've got to get it to my CEO by Friday. That whole start the ClipBook on Tuesday, I've got to have the ClipBook, the physical ClipBook on the CEO as an example. Nope, if I'm not basically streaming my senior executives in real time, curated and analyzed as to what's important and what it means, I can't do that without a tech stack. >> Well, Andy Cunningham was on the Cube. >> This whole thing has been forced to get modernized by cloud technology and transformation >> Andy Cunningham, a legend in the comms business who did all Steve Jobs comms, legend. She basically said on The Cube, it's not about waiting for the clips to create the ClipBook, create your own ClipBook and get it out there. Then evaluate and engage. This is the new command and control with digital assets. >> Now, it's become the real-time, curated feed that never stops. It sure as hell better not. Because comms is in trouble if it does. >> Well this is a great topic. But let's have you in this, I can go deep on this. I think this is a really important shift, and you guys are the only ones that are on it at this level. I don't think the Salesforce and the Adobe yet, I don't think they're nimble enough to go after this wave. I think they're stuck on their wave and they're making a lot of money. >> You know John, paid media and owned media. The Google Marketing Cloud, that SAP Marketing Cloud, Adobe, Oracle, Salesforce Marketing Clouds. They don't do anything in earned. Nothing. This is one of the reasons I jumped because I knew this needed to happen. But, you know, they're also chasing much bigger pots of money. Marketing and Advertising is still a lot more money. We're working on it to grow the pie for comms. But, bottom line is, they're chasing the big markets as I was at Oracle. And they're still pretty much in a violent arms race against each other. Salesforce is still way more focused on what Adobe's doing. >> You're just on a different wave. >> So, we're just over here doing this, building a billion dollar cloud leader, that is mission critical to everyone of their customers. They're going to end up being some pretty import partners to us, because they've been too focused on the big arms race against each other, in paid and owned and have not had the luxury to even go here. >> Well I think this wave that you're on is going to be really big. I think they don't see it, in my opinion, or can't get there. With the right surfboard, to use a surfing analogy, there's going to be a big wave. Thanks for sharing your insights. >> Absolutely. >> While you're here, get the plug in for Cision. What's going on, what's next? What's the big momentum? Get the plug in for the company. What are you guys still going to do? >> Plugin for the company. The company has acquired a couple of companies in January. You might see, one of which is Falcon. Basically Falcon is one of the big four in the land of Hootsuite, Sprinklr, Spredfast. Cloud companies do this. Adobe has Creative Cloud, Document Cloud, Parking Cloud. Salesforce has Sales Cloud, Service Cloud, Marketing Cloud. Cision has just become a multi cloud company. We now have the Cision Social Cloud and the Cision Communications Cloud. And we're going to go grab a couple hundred million dollars of stuff away from Sprinklr, Hootsuite and collapse social into this. Most of social is earned as well. So, look for a wing spread, into another adjacent market. I think that's number one. Then look for publishing of the data. That's probably going to be the most exciting thing because we just talked about, again our metrics and capabilities you can buy But, little teaser. If we can say, in two months here's the average click through on a Google ad, YouTube ad, a banner ad, I'll show it to you on a Blog, a press release, an article. Apples to apples. Here is the conversion rate. If I can start becoming almost like an eMarketer or publisher on what happens when people read earned, there's going to be some unbelievable stats and they're going to be incredibly telling, and it's going to drive where are we on that. So this is going to be the year. >> It's a new digital advertising format. It's a new format. >> That's exactly right. >> It's a new digital advertising format. >> And its one when the CEO understands that he or she can have it for earned now, the way he's had it for marketing and advertising, that little conversation walking down the hall. In thousands of companies where the CCO or the VP of PR looks up and the CEO is going where are we on that? That's the year that that can flip switches, which I'm excited about. >> Every silo function is now horizontally connected with data, now measured, fully instrumented. The value will be there and whoever can bring the value gets the budget. That's the new model. Kevin Ackroyd, CEO of Cision, changing the game in the shift around the Chief Communications Officer and how that is becoming more tech savvy. Really disrupting the business by measuring earned media. A big wave that's coming. Of course, it's early, but it's going to be a big one. Kevin, thanks for coming on. >> My pleasure, John, thank you. >> So, CUBE conversation here in Palo Alto Thanks for watching. >> Thanks John. (upbeat music)

>> Narrator: Live, from Washington D.C., it's theCUBE. Covering .conf2017, brought to you by Splunk. >> Well, welcome back to .conf2017. Here we are at Splunk's annual get together, with Dave Vellante, I'm John Walls. We are live in the Walter Washington Convention Center, in beautiful Washington D.C. I say that, proud to be a native. Actually raised here, lived here, fly the flag here. >> Wow. >> This is my place, Dave. >> Listen, I love this city. >> I do too. >> I love coming down here. Lots to do, my son's down here, so. >> But if we weren't here, where should we be, maybe on the deck of a Carnival cruise line ship right now? >> That would be good. >> I would like that. >> I would love to have theCUBE on the deck of a Carnival >> Maybe, maybe Ruel Waite can swing that. What do you think? Ruel Waite joins us. He is the manager of delivery and support for Carnival. And you got room for two on the next ship out of Miami? >> Listen, man, for you guys anything. >> I love that. Alright, you're hired. >> I can make it happen. >> Outstanding. Alright Ruel, thanks for being here with us. >> No problem. >> On theCUBE, glad to have you, and here at the show as well. Alright, so let's talk about first off, Splunk. What are you doing? Let's back up, in terms of what you do. Your core responsibilities and then we'll get into Splunk story after that. >> Yeah, so I manage the support operation for our ecommerce platform, as well as for the guest facing ship board application. So the ecommerce platforms is where you go and purchase your cabin on the web. You would also be able to purchase your show excursions, your spa treatments, as well. Or we have an e-retail site where if you have a friend who's sailing you can buy a bottle of champagne and have it in their room for when they get there. So all those purchasing perks now that we support on the ecommerce platform. And then the guest facing application, Shipboard, we're talking 'about the mobile application where guests chat and interact with each other or plan their day. We're talking about the Pixels application where guests are purchase their photos that they take throughout their cruise. And their some facial recognition stuff there as well. And the iTV that's in your room. So we have a separate, many different sort of applications that fit under that portfolio. >> Let's talk about the data. >> Yes. >> A lot of data that you just created. >> Right? >> Yup. >> What's the data pipeline look like, where does Splunk fit? >> We Splunk as much as we can and we're continuing to build that as we go. Our application logs are Splunk, everything we produce from the application. Also our performance metrics from our servers and our data and our network, and all those systems, we Splunk that because that's critical for us to triage issues that occurring. Because our operation is about monitoring what's happening, it's about resolving issues as quickly as possible, and it's about communicating to our business. So those three things are data essential to all of that. So we need to get as much as we can and we need to be able to get insights into it. >> Can you talk about where you started, you had mentioned off camera about four years ago, and how you've been able to inject automation into your processes and just take us through your journey. >> Yeah, so we started a few years ago with Splunk, and it was primarily a triage tool for us. So an incident would occur, we'd try to get it, and look at some logs, figure out what's going on. And as we've evolved it's become more of a proactive alerting tool for us, it's become a communication tool, a collaborative tool, for us. You know, we leverage things like the ITSI, right. That allows us to understand the base line behavior of our system. Once we base line that then we can understand the spikes, we can understand when things are changing, and that allows us to react and quickly identify things, defects in our system, things that are occurring, and resolve them. So once we kind of got our legs around okay, we get how to use Splunk to find stuff, now let's figure out how to get Splunk to tell us stuff. >> Okay. >> Right? And now once Splunk is telling us stuff, let's figure out how we tell the business that stuff. So that's kind of how we the journey we've had with Splunk. >> And Splunk's in that thread the whole way? >> The whole way. >> So from, >> The whole. >> So, ultimately then, right now what are you putting into practice that you didn't have available >> Yeah, sure. >> two, three years ago? >> Yeah sure, so one of the challenges we had was, with a typical ecommerce site you have several layers of the application, right. You have your web server, you have caching infrastructure, you have a database server, yet we have a mainframe reservation system as well. So there are several things involved with supporting all those different platforms. Now when we have an incident, it's sometimes challenging to, you know you get somebody on the phone, you're like hey what are you seeing over there on the mainframe side? Well I see this error occurring. Oh and the database side they're telling you okay, we're seeing some sort of timeout here, but we're not sure if it's related to the same thing you're talking about. And we didn't have a way to tie it together. But by using Splunk Transactions what we decided to do was we decided to log the session ID, the web servers session ID across all our layers, right, and push that through, and that allows us to tie those transactions together across those layers. And now when we have an incident we're able to, when we're talking to the mainframe we're saying hey guy, hey go look at this. And he say here's what I'm seeing. >> You can isolate it? >> We can isolate it, we can pull it together, and it's really helpful. >> So will you get to the point, or you were trying to get to the point, where you can automate the remediation? Or is that something you don't want to do 'cause you want humans involved? >> You know, automation is good. And whatever we can automate we try to do that. At this point we're not automating the resolution through Splunk at this time, but what we are doing is we are providing the on call, or the engineer that are responding with as much information as we can in order to have them quickly flip that switch. So if we have an alert that we know, hey this issue requires a recycle of an application pool, or some kind of other action like that, we can put that in our Splunk alert. And we say hey we're seeing this issue occur. That email and that text message that goes out actually tells the engineer that these are the suggested actions that you can take in order to quickly resolve this issue. >> Ruel, what are you hearing from the business side? What are the business drivers and how is that effecting what you're doing in IT generally, and specifically with data and Splunk? >> Okay so from business side we're looking at most bookings is the one of the major metrics that we look at. And our guest experience. So and on the web that means the site needs to be available, it needs to perform, and it needs to work. So what we really are trying to do with Splunk is understand those issues that are impacting our guests on the booking side. What that means is we need to know how well we're converting. And if we're looking at homepage performance, and we can now tell hey if our homepage loads in five seconds verses three seconds, there are how many fewer people make it to our payment page, which is huge for us. So that's something that we really try to hone in on. And it really helps us to collaborate with the business and understand, really, what is the revenue impact of these IT metrics that we're spitting out. >> But there could be other factors involved in that too, >> Yes. >> other variables, right? >> There are. >> You can't just you know this is, but you have enough of a track record the are a couple reasons to say okay, five seconds means this, we get a 30% conversion rate. We get three seconds, man, we got 'em hello, and, now we have a 50%, whatever. >> Yeah, but that is where, what I'm excited about at the conference is the machine learning capabilities that we've been hearing about. 'Cause that will allow us to then model how those different factors that go into when someone goes from the homepage to payment, you're totally right. There's several things that go into that. And what we want to be able to model, hey, on a normal day here's our guest behavior, whether we have a sale, how do our guests behavior differently, or on a Monday night at eight PM what is the behavioral trend. So it's all important to us. And getting the data behind it and being able to model that is going to be really key for us. >> Connect the dots for me on >> Yes. >> how you use machine learning, and how will that affect the business? You'll make different offers at different times, or? >> So what I mean is if I understand how guests behave I will know if I'm having an issue on the site. If there's something happening that's impacting their ability to book. 'Cause sometimes you do a release, you do your quality control, and then you go home, everything looks good. And sometimes hours later, sometimes days later unfortunately, something pops up that you introduced during that release. And understanding what that baseline is, right. So what Splunk has allowed us to do is say okay, here's what normal behavior is. And we're trying to grow this more, but what we've been using ITSI to say here's what that behavior really is. Based on what we kind of know are the metrics around booking. Here's what that behavior is. And we do a release and we see a spike, a change, and now we're able to say wait a minute, we never saw this error before. This error never existed in our system at any point. That was definitely something that was introduced right here in this release, we need to go ahead and resolve this as well. And sometimes you get some false positives there, if your development team is doing change the way they log a little bit you might get a spike. But that's cool because you get to go in immediately and figure out what those changes are, and you get a comfort level that you kind of understand how your system works. >> Let me ask you another question. You got some experience with Splunk. >> Yes. >> Obviously, you were just working with them. What, in your mind, is on their to do list? What do you want to see out of them? Doug, if I'm Doug. Tell me, where should I go, what should I do. >> What do I want Splunk to do. >> Any gripes, give me the good, the bad, and the ugly. >> For me, it's performance, performance, performance. I want to see my queries run as quickly as possible. I want to see things fast. I want to hit the button and it happens right away. Now obviously that's not going to, that's not realistic. But I like what some of the things that Splunk are doing. You look at the new metrics index that they've been talking about the last two days. So they've now isolated your time serious data and they're able to optimize the searches on time serious data seperate from your application logs. So, you know, your CPUs, your memory consumption, that data is not the same as your logging an error, or logging that a booking was created, or something like that. Those are kind of two different things. So they have kind of decoupled that and they're saying anything that's time serious I'm going to put it over here. And I'm going to optimize that query, and then you can handle your other logs separately. But the additional benefit of that is then you can take your time serious and you can look at a CPU spike and then you can take your event data and overlay it on top. And then you can see, hey wait a minute, this event is what caused that spike. So that's where the cool is. >> I think they call that mstats. Is that right, mstats? >> Yes, it's mstats, yes. >> How 'about the stuff that you saw this week in the keynotes, particularly today was the product stuff. A lot of security obviously. Anything that you've seen here at the show that excites you, that you really said alright, I got to have that, I got to learn more? >> Yeah, so the ITSI event analytics really seems like something's going to be cool for us. As I've said before, we utilize ITSI internally. So we put together a glass table that's shows us here are all the different components and the hierarchy of things. And when this goes red it effects these other layers. And it's really cool. But what they've added in is the ability to click a button and drill in to those components and then you have a view of hey, here are the events associated with that. That's really cool because now you're triaging in one place, now you get to the problem really quick. And you can emote directly into your Splunk queries. It really allows what we're looking for is just to resolve issues as quickly as possible. >> And you're describing, if I understand this correctly, you can visualize the dependencies, and you can take remedial action or identify, inform the business what to expect. >> Exactly. >> Be much more proactive, that's what people are talking about. >> Yeah, yeah. And we found that one of the surprising things we found with Splunk is that our business are users of Splunk as well, right. So it's always an IT tool, it's something that only the geeks are going to look at. And then all of a sudden you present a dashboard to a business user and they go ah. That's pretty, right. And then all of a sudden they want it more than you do. So that's what makes it great right, 'cause you can present the data however you want and you can put it in a way that different audiences can consume. And so it becomes a platform that goes across the organization, which is really, really cool. >> John: But your bottom line's all speed right? >> Yes, yeah. >> Take care of my problems faster, get my customer faster, deliver faster, come on Splunk. >> Come on, let's go. >> We want to go. >> Brings the weekend faster. >> Right, right. >> Get more sleep, get more sleep. >> Ruel, thanks for being with us. >> Oh. >> We appreciate that. >> And, we'll talk about the cruise. Leonard Nelson, our producer over here already said book him for a massage, the presidential suite. He wants one night, and then the champagne buffet please. >> It's done. >> Fast internet, though. >> Yeah. >> Fast internet, yeah. It's done. >> Alright. We're simple people, we don't need all that, but we'll talk later. >> Alright man, appreciate it, thank you. >> Thank you for being with us. Ruel Waite joining us from Carnival. Back with more from Splunk, .conf2017. 2015, where did that come from? 2017, it's been a long day. (upbeat music)

[Music] [Music] okay we're back after that nice break here from knowledge we're here in Las Vegas at the Aria hotel this is service now's big customer conference about 4,000 folks here mostly customers most of the content at this event comes from customers its practitioners talking to practitioners which is quite rare actually at these conferences I'm Dave Volante everybody thanks for watching with wiki Bond org I'm here with my co-host Jeff Frick this is Silicon angles the cube we go to these events we extract the signal from the noise we love to bring you tech athletes and Fred ludie is here he is a tech athlete he's the founder of ServiceNow he started this platform around 2003 Fred welcome to the cube thank you very much so we really want to hear the story you know but we've been asked to sort of hold that off because we got another segment with you tomorrow but I just I have to ask you I mean seeing how this conference and ServiceNow as an organization has grown you just must be so thrilled in particular with the customer enthusiasm <Fred>  you know fundamentally I've got a personality flaw and I call it a kindergarten mentality I want to see my art on their refrigerator and the only way you can do that is by making somebody happy and so to see these people here with the excitement the enthusiasm and the smiles on their faces really is satisfying that kindergarten mentality cakes oh good stuff we were talking about that earlier Jeff had not seen the cakes before and was was quite amazed today no I think that's an industry-first actually good well be yeah announcements today you know that's if so you guys had some you're gonna transform an organization you got to have mobile I mean the whole world to go on mobile five billion devices and and growing what you guys announced today <Fred> well we announced the ability to run all of our applications on the iPad and you know I think people's reasonable expectations these days are that they should be able to manage anything anywhere anytime using the device that they currently have now I I like to think of an iPad as something that you use when you're pretending to be attending a meeting or when you're pretending to be watching TV with your family and when you are pretending to do that it'd be nice if very efficiently and very effectively you could manage whatever you needed to manage to get your job done and so today what we've announced is the ability to run everything that ServiceNow has on that iPad  <Dave> yeah I mean it seems to mobile is basically a fundamental delivery model and maybe even the main delivery model going forward wouldn't it be I <Fred> I think it will be a main delivery model and it's a it's a user interface that that requires complete rethinking about how you're going to do things you know for the longest time we we looked at screens with 24 by 80s you know these character screens and then we got big pixel monitors and then we got bigger pixeled monitors and we got very accurate Mouse's and everything got small and got hovers you've got you know this massive amount of data and now the form factor is completely shrunk and you're looking at this as my major input device so how am I going to get you know everything I used to do with a mouse where I'm hovering over things to see what they do or I'm touching you know 16 by 16 pixels which you by the way you can't hit with your fingernail how am I going to get all of that stuff how am I gonna be able to work with all that stuff using only my thumb or thumbs so how are you specifically taking advantage of that smaller form factor and you know the feature sets that you see in things like iPad <Fred> well I think it's a matter of rethinking so we're trying to get the user to be to be able to accomplish their task by doing considerably less work and one of the things that our system is actually very comprehensive it's very big and we create in the browser and our first user interface it was really created in 2005 we treat all the elements of the system equally so now what we've done in the in the mobile which I think is very unique it does MySpace I mean Facebook doesn't have this Lincoln doesn't have this we know exactly what you do as a user and we remember those things that you do edit of Li and so we're able to create shortcuts or we're able to remember the system is able to remember what you do and then very quickly present you back with those tasks which are repetitive so we're trying to simultaneously compress the information and reduce the interactions yeah so that doesn't sound trivial it sounds like there's some secret sauce behind that talk about that a little bit <Fred> well it's not trivial and it's a there there is secret sauce but it does it just requires you to rethink and for me you know if you if you read the jobs biography there were a couple of interesting things in their number one when he met dr. land they had both agreed that everything that had been invented was going to be invented had already been invented right the other thing that they that they pretty much agreed on are what job said and a quote that I've used for years is that great artists copy good artists copy and great artists steal and I've been a thief all my life I just I'm gonna admit it right here it's not on camera live and so what we do is we go ahead and take a look at who's doing this great Amazon is doing it great Zappos is doing it great asan is doing it great you know we and we capture those ideas and then what they meant by great artists steal is that you take them and you reformulate them for the task that you're trying to solve for the problem that you're trying to solve and the rich the artist won't they probably the original artist probably won't even recognize that as their work but yet they're they're deeply inspirational to us an artist so do you fancy yourself as a bit of  <Fred> well I think it's interesting  down down the road and you know to I was watching the Bellagio fountains create something like that if you think about the physics and the art that had to go into that to create that beautiful masterpiece you know it's not just a painting right think about the physics that goes on to shoot something seven its water seven hundred feet in the air and then cut it off instantly and have that all choreographed I mean it's phenomenal amount of engineering but it took also a phenomenal amount of art just to make that interesting so that we were we actually stood there in rapt amazement of you know look how all this is choreographed so yes I do in fact I don't think I take exception to the term engineering software engineering I don't think we haven't progressed to the point where this is an engineering this is this is an art this is a craft you know it's something that people practice and we try to get better at it and better at it and better at it but I don't think it's anywhere near an engineering discipline <Jeff> yeah the other interesting from the jobs book that I never really got until I read the book was like the iPod shuffle because when I first saw the iPod shuffle and you can't do anything you can't manage your playlists on it you all you can do is change songs I don't get it and then in reading the book as you just said you know what is what is it you're trying to accomplish with that form factor right and don't just automatically try to replicate what you can do a one form factor to another form factor but really rethink what's that application and it sounds like you're kind of taking advantage of that opportunity as you take the app to the mobile space into the iPad specifically to rethink what is the best use case for that platform you'll see tomorrow the iPad was really  <Fred> that's right and as as the inspirational first step that we're taking toward a totally mobile app and just like the Apple evolution of building all of this note wonderful new capabilities into iOS and then bringing them back into OS X we're going to be doing the same thing so you'll see tomorrow on stage not only in an iPad app but you will see a native iOS app running and you'll see that it does even more things than the iPad app does and much faster it's a wonderful user experience and those those notions will be also coming back into the browser etc the same way that apples been bringing a lot of the capabilities of iOS back onto OS X <Dave> I was talking to an IT practitioner last month at a large grocer and I asked him what's your what's your biggest challenge what excites you the most and he said the same thing he said both of X what's my biggest challenge is embracing all this pressure from my users for mobile and that's what excites me the most because I have a mobile addict I got in it pulls out all those devices so how do you see this announcement within your user base changing you know the lives of IT  prose.    <Fred> well it'll you know technology since the dawn of time has been used really for two things it's been it's been used to streamline make make tasks more efficient and more streamlined and it's been used to create business differentiators and so our our product really is about process and moving process through an organization and so we want to streamline that as much as possible so if I can we do things like change management change management has multiple levels of approval if I can get it to the point where a manager can pull his phone out of his pocket and do five approvals between meetings he's become significantly more efficient right the changes are going to be done in a more timely fashion and the bottom line improves it's as simple as that <Dave> yeah it's interesting we were those of you watching no we were earlier the today broadcasting from sa P sapphire event and if you go to sapphire are you here to to get huge doses of two things one is Hana of course which is there in memory database but the other is mobile he's all you hear and it's interesting to hear you guys talk about the ERP of IT and your si PE they know the poster child for ERP and all their customers are going to mobile whether it's retail manufacturing you know across the supply chain and so it sounds like you've got sort of similar mentality but more focused obviously with it within IT but of course now you're also reaching beyond IT do you see you're a mobile app a push going beyond the IT community <Fred> yeah absolutely you know our underlying all of our applications we have a platform that say it's a forms based workflow platform that's really purpose-built for something that we would characterize as a service service relationship management so pretty much any request response fulfillment type workflow can be handled by our platform and what our customers have done over the years is create different applications that help them streamline that workflow typically that workflow is handled by by people creating a spreadsheet emailing it to somebody else having a TA back perhaps they built a Lotus Notes app but yes everything that that that or I will say that our platform usage has been expanded by our customers sometimes beyond our wildest dreams and and we love it so you talked about you know some of the greatest artists we stole rights of and so now you guys put up this platform I've said a number of times today it's not trivial to it to actually get a CMDB working in the way that you wanted to get it to work so now you've had this platform out for quite some time your successes started to you know you get a lot of press people are starting to see it do you worry sometimes that people gonna say okay I can do that too I'm gonna I'm gonna you know rip it off what gives you confidence that you can stay ahead of those those thieves out there <Fred> well I have great confidence in that you know we have a very broad base of applications that are very deep in functionality but if that's really something that you want to happen yeah because you want some young people with fresh new ideas to try to unseat you because they will come at the come at this from a completely different perspective and a completely different angle and they will do things that you never thought of and so the race is then on are they going to become more relevant than me or am I going to be inspired by their ideas incorporate them into our platform and stay ahead of them see welcome that all right absolutely welcome back yeah we we wouldn't be where we are today if Edison and Bell weren't weren't the jobs and gates of their time I mean they had just and I think jobs and gates as well right they had this great rivalry that really caused technology to move ahead a lot faster than when it was just I be am selling mainframes and so you need those rivalries you need that you need that competition you know I'm I'm watching these young guys from asana it's a great little platform for for tasking and you know they came out of Facebook they have a very Facebook mentality and they have phenomenal ideas and believe me guys from asana I'm watching you those are just that's where great ideas come from >> <Dave> Wow we always like to say we love sports analogies here in the cube and Jeff your kids are into sports well as our mind you always want to see and play that more competitive you know environment it sounds like Fred you have the same philosophy yes very much so yeah excellent all right Fred well listen we really appreciate you coming by now you come back Fred's gonna be back again tomorrow we're gonna go through the story of service now that's why we really didn't touch up on it and in any kind of detail today but to it but but but Fred actually started the company we give him a little preview Fred so you started the company really not to go solve an IT service management problem right you came up with this sort of idea this platform and and then you you that was really the first application that you developed right up a step in for that oh great you see give us a little tidbit we're gonna back >> every day I wake up that's all I really >><Fred> I've been a programmer now for 40 years want to do why do I program because I want somebody to take a look at the technology that I build and say hey that's pretty helpful I like that I can use they're gonna put that in my fridge fridge so the real strategy behind the company was to build some software that somebody wanted that hopefully they would pay me so I could build more software that was the entire strategy and so you know on one hand I love technology and on the other hand it really irritates me when it makes me feel stupid or it makes other people feel stupid so what I wanted to do was to create an enterprise platform that people could use and they would feel empowered they could walk up and use it like they'd walk up and use an ATM like they'd walk up and buy something from Amazon etc so a completely you know consumer eyes thought process and then that was the thought process really in O 3 and no 4 and then what we do really figured out was that a platform is a very hard sale you know it's tough to convince somebody that they should take this it'd be like selling you an Intel processor and telling you can do anything you want right I want to solve a business problem and so we decided to go after the ITSM space first it was a space that was very underserved very lucrative and and growing significantly <Dave> amazing so so join us tomorrow we're gonna Fred back on and we're going to here this story the founding story of ServiceNow and how we got to where we are today so Fred thanks very much for coming on and sharing the news and I'm gonna change it all by tomorrow good all right so so keep it right there I will be up next we've got Douglas Leone coming on which is a partner at Sequoia Capital and and and one of the better-known DC's out in the valley so so keep it right there will be back with Doug just in a minute this is ServiceNow this is the cube this is 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