>>Okay, welcome back everyone. This is the Cube's coverage here in Las Vegas. Back at events re Mars, Amazon re Mars. I'm your host, John fur with the cube. Mars stands for machine learning, automation, robotics, and space. It's great event brings together a lot of the industrial space machine learning and all the new changes in scaling up from going on the moon to, you know, doing great machine learning. And we've got two great guests here with kinda called lunar outpost, Justin Sears, CEO, Lauren, man. He's the co-founder and chief strategy officer lunar outpost. They're right next to us, watching their booth. Love the name, gentlemen. Welcome to the cube. >>Yeah. Thanks for having us, John. >>All right. So lunar outpost, I get the clues here. Tell us what you guys do. Start with that. >>Absolutely. So lunar outpost, we're a company based outta Colorado that has two missions headed to the moon over the course of the next 24 months. We're currently operating on Mars, which forest will tell you a little bit more about here in a second. And we're really pushing out towards expanding the infrastructure on the lunar surface. And then we're gonna utilize that to provide sustainable access to other planetary bodies. >>All right, far as teeing it up for you. Go, how cool is this? We don't, we wanna use every minute. What's the lunar surface look like? What's the infrastructure roads. You gonna pave it down. You what's going on. Well, >>Where we're going. No one has ever been. So, um, our first mission is going to Shackleton connecting Ridge on the south pole, the moon, and that's ripe to add infrastructure such as landing pads and other things. But our first Rover will be primarily driving across the surface, uh, exploring, uh, what the material looks like, prospecting for resources and testing new technologies. >>And you have a lot of technology involved. You're getting data in, you're just doing surveillance. What's the tech involved there. >>Yeah. So the primary technology that we're demonstrating is a 4g network for NOK. Um, we're providing them mobility services, which is basically like the old Verizon commercial. Can you hear me now? Uh, where the Rover drives farther and farther away from the Lander to test their signal strength, and then we're gonna have some other payloads ride sharing along with us for the ride >>Reminds me the old days of wifi. We used to call it war drive and you go around and try to find someone's wifi hotspot <laugh> inside the thing, but no, this is kind of cool. It brings up the whole thing. Now on lunar outpost, how big is the company? What's how what's to some of the stats heres some of the stats. >>Absolutely. So lunar outpost, 58 people, uh, growing quite quickly on track to double. So any of you watching, you want a job, please apply <laugh>. But with lunar outpost, uh, very similar to how launch companies provide people access to different parts of space. Lunar outpost provides people access to different spots on planetary bodies, whether it's the moon, Mars or beyond. So that's really where we're starting. >>So it's kinda like a managed service for all kinds of space utilities. If you kind of think about it, you're gonna provide services. Yeah, >>Absolutely. Yeah. It, it's definitely starting there and, and we're pushing towards building that infrastructure and that long term vision of utilizing space resources. But I can talk about that a little bit more here in a sec. >>Let's get into that. Let's talk about Mars first. You guys said what's going on with >>Mars. Absolutely. >>Yeah. So right now, uh, lunar outpost is part of the science team for, uh, Moxi, which is an instrument on the perseverance Rover. Yeah. Moxi is the first demonstration of space resource utilization on another planet. And what space resource utilization is basically taking resources on another planet, turning them into something useful. What Moxi does is it takes the CO2 from the atmosphere of Mars and atmosphere of Mars is mostly CO2 and it uses a process called solid oxide electrolysis to basically strip oxygen off of that CO2 to produce oh two and carbon monoxide. >>So it's what you need to self sustain on the surface. >>Exactly. It's not just sustaining, um, the astronauts, but also for producing oxygen for propellant. So it'll actually produce, um, it's a, it's a technology that'll produce a propellant for return rockets, um, to come back for Mars. So >>This is the real wildcard and all this, this, this exploration is how fast can the discoveries invent the new science to provide the life and the habitat on the surface. And that seems to be the real focus in the, in the conversations I heard on the keynote as well, get the infrastructure up so you can kinda land and, and we'll pull back and forth. Um, where are we on progress? You guys have the peg from one zero to 10, 10 being we're going, my grandmother's going, everyone's going to zero. Nothing's moving. >>We're making pretty rapid >>Progress. A three six, >>You know, I'll, I'll put it on an eight, John an >>Eight, I'll put it on >>Eight. This is why the mission force was just talking about that's launching within the next 12 months. This is no longer 10 years out. This is no longer 20 years away, 12 months. And then we have mission two shortly after, and that's just the beginning. We have over a dozen Landers that are headed to line surface this decade alone and heavy lift Landers and launchers, uh, start going to the moon and coming back by 2025. >>So, and you guys are from Colorado. You mentioned before you came on camera, right with the swap offices. So you got some space in Colorado, then the rovers to move around. You get, you get weird looks when people drive by and see the space gear. >>Oh yeah, definitely. So we have, um, you know, we have our facility in golden and our Nevada Colorado, and we'll take the vehicles out for strolls and you'll see construction workers, building stuff, and looking over and saying, what's >>Good place to work too. So you're, you're hiring great. You're doubling on the business model side. I can see a lot of demand. It's cheaper to launch stuff now in space. Is there becoming any rules of engagement relative to space? I don't wanna say verified, but like, you know, yet somehow get to the point where, I mean, I could launch a satellite, I could launch something for a couple hundred grand that might interfere with something legitimate. Do you see that on the radar because you guys are having ease of use so smaller, faster, cheaper to get out there. Now you gotta refine the infrastructure, get the services going. Is there threats from just random launches? >>It's a, it's a really interesting question. I mean, current state of the art people who have put rovers on other planetary bodies, you're talking like $3 billion, uh, for the March perseverance Rover. So historically there hasn't been that threat, but when you start talking about lowering the cost and the access to some of these different locations, I do think we'll get to the point where there might be folks that interfere with large scale operations. And that's something that's not very well defined in international law and something you won't really probably get any of the major space powers to agree to. So it's gonna be up to commercial companies to operate responsibly so we can make that space sustainable. And if there is a bad actor, I think it they'll weed themselves out over time. >>Yeah. It's gonna be of self govern, I think in the short term. Good point. Yeah. What about the technology? Where are we in the technology? What are some of the big, uh, challenges that we're overcoming now and what's that next 20 M stare in terms of the next milestone? Yeah, a tech perspective. >>Yeah. So the big technology technological hurdle that has been identified by many is the ability to survive the LUN night. Um, it gets exceptionally cold, uh, when the sun on the moon and that happens every 14 days for another, for, you know, for 14 days. So these long, cold lunar nights, uh, can destroy circuit boards and batteries and different components. So lunar outpost has invested in developing thermal technologies to overcome this, um, both in our offices, in the United States, but we also have opened a new office in, uh, Luxembourg in Europe. That's focusing specifically on thermal technologies to survive the lunar night, not just for rovers, but all sorts of space assets. >>Yeah. Huge. That's a hardware, you know, five, nine kind of like meantime between failure conversation, right. >><laugh> and it's, it gets fun, right? Because you talk five nines and it's such like, uh, you know, ingrained part of the aerospace community. But what we're pitching is we can send a dozen rovers for the cost of one of these historical rovers. So even if 25% of 'em fail, you still have eight rovers for the cost of one of the old rovers. And that's just the, economy's a scale. >>I saw James Hamilton here walking around. He's one of the legendary Amazonians who built out the data center. You might come by the cube. That's just like what they did with servers. Hey, if one breaks throw it away. Yeah. Why buy the big mainframe? Yeah. That's the new model. All right. So now about, uh, space space, that's a not space space, but like room to move around when you start getting some of these habitats going, um, how does space factor into the size of the location? Um, cuz you got the, to live there, solve some of the thermal problems. How do I live on space? I gotta have, you know, how many people gonna be there? What's your forecast? You think from a mission standpoint where there'll be dozens of people or is it still gonna be small teams? >>Yeah. >>Uh, what's that look like? >>I mean you >>Can guess it's okay. >>I mean, my vision's thousands of people. Yep. Uh, living and working in space because it's gonna be, especially the moon I think is a destination that's gonna grow, uh, for tourism. There's an insane drive from people to go visit a new destination. And the moon is one of the most unique experiences you could imagine. Yep. Um, in the near term for Artis, we're gonna start by supporting the Artis astronauts, which are gonna be small crews of astronauts. Um, you know, two to six in the near term. >>And to answer your question, uh, you know, in a different way, the habitat that we're actually gonna build, it's gonna take dozens of these robotic systems to build and maintain over time. And when we're actually talking, timelines, force talks, thousands of people living and working in space, I think that's gonna happen within the next 10 to 15 years. The first few folks are gonna be on the moon by 2025. And we're pushing towards having dozens of people living and working in space and by 2030. >>Yeah. I think it's an awesome goal. And I think it's doable question I'll have for you is the role of software in all this. I had a conversation with, uh, space nerd and we were talking and, and I said open sources everywhere now in the software. Yeah. How do you repair in space? Does you know, you don't want to have a firmware be down. So send down backhoe back to the United States. The us, wait a minute, it's the planet. I gotta go back to earth. Yeah. To get apart. So how does break fix work in space? How, how do you guys see that problem? >>So this one's actually quite fun. I mean, currently we don't have astronauts that can pick up a or change a tire. Uh, so you have to make robots that are really reliable, right. That can continuously operate for years at a time. But when you're talking about long-term repairs, there's some really cool ideas and concepts about standardization of some of these parts, you know, just like Lu knots on your car, right? Yeah. If everyone has the same Lu knots on their wheel, great. Now I can go change it out. I can switch off different parts that are available on the line surface. So I think we're moving towards, uh, that in the long >>Term you guys got a great company. Love the mission. Final question for both of you is I noticed that there's a huge community development around Mars, living on Mars, living on the moon. I mean, there's not a chat group that clubhouse app used, used to be around just kind of dying. But now it's when the Twitter spaces Reddit, you name it, there's a fanatical fan base that loves to talk about an engineer and kind of a collective intelligence, not, may not be official engineering, but they just love to talk about it. So there's a huge fan base for space. How does someone get involved if they really want to dive in and then how do you nurture that audience? How does that, is it developing? What's your take on this whole movement? It's it's beyond just being interested. It's it's become, I won't say cult-like but it's been, there's very, a lot of people in young people interested in space. >>Yeah. >>Yeah. There's, there's a whole, lots of places to get involved. There's, you know, societies, right? Like the Mar society there's technical committees, um, there's, you know, even potentially learning about these, you know, taking a space, resources master program and getting into the field and, and joining the company. So, um, we really, uh, thrive on that energy from the community and it really helps press us forward. And we hope to, uh, have a way to take everyone with us on the mission. And so stay tuned, follow our website. We'll be announcing some of that stuff soon. >>Awesome. And just one last, uh, quick pitch for you, John, I'll leave you with one thought. There are two things that space has an infinite amount of the first is power and the second is resources. And if we can find a way to access either of those, we can fundamentally change the way humanity operates. Yeah. So when you're talking about living on Mars long term, we're gonna need to access the resource from Mars. And then long term, once we get the transportation infrastructure in place, we can start bringing those resources back here to earth. So of course there are gonna be those people that sign up for that first mission out to Mars with SpaceX. But, uh, we'd love for folks to join on with us at lunar outpost and be a part of that kind of next leap accessing those resources. >>I love the mission, as always said, once in the cube, everything in star Trek will be invented someday. <laugh>, we're almost there except for the, the, uh, the transporter room. We don't have that done yet, but almost soon be there. All right. Well, thanks for coming. I, I really appreciate Justin for us for sharing. Great story. Final minute. Give a plug for the company. What are you guys looking for? You said hiring. Yep. Anything else you'd like to share? Put a plug in for lunar outpost. >>Absolutely. So we're hiring across the board, aerospace engineering, robotics engineering, sales marketing. Doesn't really matter. Uh, we're doubling as a company currently around 58 people, as we said, and we're looking for the top people that want to make an impact in aerospace. This is truly a unique moment. First time we've ever had continuous reliable operations. First time NASA is pushing really hard on the public private partnerships for commercial companies like ours to go out and create this sustainable presence on the moon. So whether you wanna work with us, our partner with us, we'd be excited to talk to you and, uh, yeah. Please contact us at info. Lunar outpost.com. >>We'll certainly follow up. Thanks for coming. I love the mission we're behind you and everyone else is too. You can see the energy it's gonna happen. It's the cube coverage from re Mars new actions happening in space on the ground, in the, on the moon you name it's happening right here in Vegas. I'm John furrier. Thanks for watching.

>>Hello, everyone. This is not appropriate to lapse of America. I'm going to talk about the motivation. For zero knowledge goes back to the heart off, winding down identity, ownership, community and control. Much of photography exists today to support control communications among individuals in the one world. We also consider devices as extensions of individuals and corporations as communities. Here's hoping you're not fit in this picture. What defines the boundary off an individual is the ability to hold a secret with maybe, it says, attached to the ownership. Off some ethic, we want the ability to use the secret to prove ownership of this asset. However, giving up the secret itself essentially announced ownership since then, anybody else can do the same. Dear Knowledge gives us tools to prove ownership without revealing the secret. The notion of proving ownership off a digital object without revealing it sounds very paradoxical outside the model off. So it gives us a surprise when this motion was formalized and constructed by Goldwasser Miccoli and back off in the late eighties, we'll focus on the non interactive >>version of Siri, a knowledge our music in the >>stock, which was first developed by blow Tillman and Peggy, where the general it can span multiple rounds of communications music only allows a single message to be trusted. No, let's get into some technical details for musics. The objective of for music is to show that an object X, which you can think off as the public footprint, often asset, belonging clan and the language without revealing its witness. W, which you can think off as the Future Analytics team consists off three algorithms, video proof and very. The key generation process is executed by a trusted third party and the very opposite, resulting in a common >>random string, or steers, which is made public. The >>true vendor produces a proof by based on the CIA's X and the very fine with the checks. The proof against X and accepts or rejects music off course has to satisfy some properties. We needed to be correct, which basically says that when everyone follows the protocol correctly on, so we can expect, we need to be thought, which says that a false statement cannot be proven. The channel is a trickier properly to form this. How do we capture the intuition behind saying that the proof there is no knowledge of the witness. One way to capture that is to imagine their tools is the real world where the proof is calculated. Using the witness on there's a simulation worth where the proof is calculated without a witness. To make this possible, the simulator may have some extra information about the CIA's, which is independent off the objectives. The property then requires that it is not possible to effectively distinguish these words Now. It is especially challenging to construct music's compared to encryption signature schemes, in particular in signature schemes. The analog off the Hoover can use a secret, and in any case, the analog off the very fire can use a secret. But in is it's none of the crew layer and the verifier can hold a secret. Yeah, in this talk, I'm going to focus on linear subspace languages. This class is the basis of hardness. >>Assumptions like GH and deliver >>on has proved extremely useful in crypto constructions. This is how we express DD it and dealing as linear software. We will use additive notation on express the spirit logs as the near group actions on coop elements. You think the syntax we can write down Deitch on dealing Jupiter's very naturally a zoo witness sector times a constant electric so we can view the language as being penetrated by a constant language. Metrics really was hard by many groups in our instructions. What does it mean? S while uh, Standard group allows traditions and explain it off by in your group also allows one modification In such groups, we can state various in yourself facing elections. The DDN is the simplest one. It assumes that sampling a one dimensional space is indistinguishable from something full professional. The decisional linear assumption assumes the theme from tours is three dimensional spaces generalizing the sequence of Presumptions. The scaling the resumption asks to distinguish between gay damaged examples and full it and >>examples from a K plus one national space. >>Right, So I came up with a breakthrough. Is the construction in Europe 2008 in particular? There? Music for many years Off Spaces was the first efficient >>construction based on idiots and gear. Structurally, >>it consisted of two parts Our commitment to the witness Andre question proof part and going how the witness actually corresponds to the object. The number of elements in the proof is linear in the number >>of witnesses on the number of elements in the object. >>The question remains to build even shorter visits. The Sierras itself seemed to provide some scoop Rosa Russo fix. See how that works for an entire class of languages? Maybe there's a way to increase proof efficiency on the cost of having had Taylor Sierra's for each year. This is what motivates quality and after six, where we let the solace depend on the language itself. In particular, we didn't require the discrete logs of the language constants to generate this, Yes, but we did require this constant student generated from witness sample distributions. This still turns out to be sufficient for many applications. The construction achieved a perfect knowledge, which was universally in the sense that the simulator was independent. However, soundness is competition. So here's how the construction differed from roots high at a very high level, the language constants are embedded into the CIA s in such a way that the object functions as it's only so we end up not needing any separate commitment in the perfect sense. Our particular construction also needed fewer elements in the question proof, as there On the flip side, the CIA's blows up quadratic instead of constant. Let's get into the detail construction, which is actually present with this script. Let the language apparently trace by Giovanni tricks with the witness changing over time, we sat down and matrices >>D and B with appropriate damages. >>Then we construct the public series into what C. S. D is meant to be used. By the way. On it is constructed by >>multiplying the language matrix with D and being worse, Sierra's V is the part that is meant to be used by the very fair, and it is constructed using details be on be embedded in teaching. >>Now let's say you're asked to computer proof for a candidate X with fitness number we computed simply as a product of the witness with CSP. The verification of the truth is simply taking with the pairing off the candidate and the proof with the Sierras. Seeming threats is equal to zero. If you look carefully. Sierra's V essentially embedded in G to the kernel of the Matrix, owned by the language metrics here and so to speak. This is what is responsible for the correctness. The zero knowledge property is also straightforward, >>given the trapdoor matrices, D and B. Now, >>when corrected journalism relatively simple to prove proving illnesses strictly The central observation is that, given CSP, there is still enough entropy. >>India and me to >>random I seriously in particular Sierra's we Can we expand it to have an additional component with a random sample from the kernel allows it. This transformation is purely statistical. No, we essentially invented idiots are killing their talent in the era of kernel part in this transform sitting within show that an alleged proof on a bad candidate and we used to distinguish whether a subspace sample was used for a full space >>sample was used at the challenge. The need >>to have the kernel of the language in this city. That's the technical >>reason why we need the language to come from a witness. Sample. >>Uh, let's give a simple illustration >>of the system on a standard Diffie Hellman, which g one with the hardness assumption being idiot. >>So the language is defined by G one elements small D, E and F, with pupils off the phone due to the W. After that ugly, the CIA is is generated as follows example D and >>B from random on Compute Sierra speak as due to the day after the being verse and Sierra's V as G to do to do the big on day two of the video. The >>proof of the pupil >>detail that I do after the bill is computed using W. As Sierra Speed race to the party. I know that this is just a single element in the group. The verification is done by bearing the Cooper and the proof with the Sierras VMS and then checking in quality. The >>similar can easily compute the proof using trapdoors demand without knowing that what we are expecting. People leave a Peter's die and reduce the roof size, the constant under a given independent of the number of witnesses and object dimensions. Finally, at Cryptocurrency 14 we optimize the proof toe, one group >>element under the idiots. In both the works, the theorists was reduced to linear sites. The >>number of bearings needed for ratification was also industry in years. This is the crypto Ford in construction in action, the construction skeleton remains more or less the famous VR turkey. But the core observation was that many of the Sierras elements could were anomaly. Comite. While still >>maintaining some of this, these extra random items are depicted in red in this side. >>This round of combination of the Sierras elements resulted in a reduction of boat, Bruce says, as also the number of clearings required for education in Europe in 2015 kills, and we came up with a beautiful >>interpretation of skill sets based on the concept of small predictive hash functions. >>This slide is oversimplified but illustrated, wanting, uh, this system has four collecting >>puzzle pieces. The goodness of the language metrics okay again and a key Haider when >>the hidden version of the key is given publicly in the Sears. Now, when we have a good object, the pieces fit together nicely into detectable. However, when we have a bad object, the pieces no longer fit and it becomes >>infeasible to come up with convincing. Zero knowledge is demonstrable by giving the key to the simulator on observing that the key is independent of the language metrics. >>Through the years, we have extended >>enhanced not mind to be six system, especially with our collaborators, Masayuki Abby Koko Jr. Born on U. >>N. Based on your visits, we were able to construct very efficient, identity based encryption structure, resulting signatures >>public verifiable CCS, secure encryption, nine signatures, group signatures, authorities, key extremes and so on. >>It has also been gratifying to see the community make leaps and bounces ideas and also use queuing visits in practical limits. Before finishing off, I wanted to talk to you a little bit about >>some exciting activities going on Hyper ledger, which is relevant for photographers. Hyper >>Leisure is an open source community for enterprise. Great. It's hosted by the minute formation on enjoys participation from numerous industry groups. Uh, so difficult funded to efforts in Africa, we have versa, which is poised to be the crypto home for all. Blocking it and practice a platform for prospecting transactions are part of the legs on the slide here, >>we would love participation from entity inference. So >>that was a brief history of your analytics. Thanks for giving me the opportunity. And thanks for listening

>>Live from Barcelona, Spain. It's the Cube covering Cisco Live 2020 right to you by Cisco and its ecosystem partners. >>Welcome back to the Cube's live coverage here at Cisco Live 2020 in Barcelona, Spain. I'm jumpers student of cube coverage. We've got a lot of stuff going on in Cisco Multi cloud and cloud technology. Quantification of Cisco's happening in real time is happening right now. Cloud is here here to stay. We got two great guests unpack what's going on in cloud native and networking and applications as the modern infrastructure and software evolves. We got you. Gene Kim, global product marketing. Compute Storage at Cisco Global marketing manager and Rob Gori, senior director. Cloud Solution Marketing Guys come back. Thanks for coming back. Appreciate it. Great to see you Barcelona guys. So, Bobby, we've had multiple conversations and you see that from the sales force given kind of the the discussion in the motivation Cloud is big. It's here. It's here to stay. It's changing. Cisco AP I first week here in all the products, it's changing everything. What's the story now? What's going on? >>I would say you know the reason why we're so excited about the launch here in Barcelona is because this time it's all about the application of spirits. I mean, the last two years we've being announcing some really exciting stuff in the cloud space where I think about all the announcements with AWS is the Googles the azure, so the world. But this time it really boils down to making sure that is incredibly hyper distributive world. There is an application explosion. Ultimately, we will help for the right operation stools and infrastructure management tools to ensure that the right application experience will be guaranteed for the end customer. And that's incredibly important because at the end, what really really matters is that you will ensure the best possible digital experience to your customer. Otherwise, ultimately nothing's gonna work. And, of course, you're gonna lose your brand and your customers. >>One of the main stories that we're covering is the transformation of the industry. Also, Cisco and one of the highlights to me was the opening keynote. You had APP dynamics first, not networking. Normally it's like what's in the hood? Routers and the gear. No, it was about the applications. This is the story we're seeing. It's kind of a quiet unveiling. Its not get a launch, but it's evolving very quickly. Can you share what's going on behind this? All this? >>Absolutely. It's exactly along the lines of what I was saying a second ago, in the end that the reason why we're driving the announcement, if you want from the application experience side of the House, is because with Appdynamics, we already have very, very powerful application performance management, which it's evolving extremely rapidly. First of all, Appdynamics can correlate not just the application for four months to some technology, maybe eyes, but through actual business KP eyes. So app dynamics can give you, for instance, serial time visibility off, say, a marketing funnel conversion rates transactions that you're having in your in your business operation. Now we're introducing an incredibly powerful new capability that takes the bar to a whole new level. And that's the Appdynamics experience. Journey maps. What are those? It's actually the ability off, focusing not so much on front ends and back ends and the business performances, but really focusing on what the user is seen in front of his or her screen. And so what really matters is capturing the journey that given user of your application is being and understanding whether the experience is the one that you want to deliver or you have, like, a sudden drop off somewhere. And you know why this is important because in the end we've been talking about is the problem of the application, performance issues or performance. It could be a badly designed page. How do you know? And so this is a very precious information they were giving to application developers know, just through the idea. Ops, guys, that is incredibly gracious. >>Okay, you want to get this in. So you just brought up that journey. So that's part of the news. Just break down real quick. One minute what the news is. >>Yeah, so we have three components. The 1st 1 as you as you correctly pointed out, is really the introduction of the application. The journey maps, right. The experience journey maps. That's very, very important. The second he's way are actually integrating Appdynamics with the inter site. Actually, inter site the optimization manager, the workload optimization, workload, optimizer. And so because there is exchange of data between the two now, you are in a position to immediately understand whether you have an application problem. We have a worker problem for structure problem, which is after me, where you really need to do as quickly as you can. And thirdly, way have introduced a new version of our hyper flex platform, which is hyper converge flagship platform for Cisco with a fully containerized version, the tax free if you want as well, that is a great platform for containerized applications. >>So you do and what I've been talking to customers last few years. When they go through their transformational journey, there's the modernization they need to do. The pattern I've seen most successful is first, modernize the platform often HD I is, you know, an option for that. It really simplifies the environment, reduces the silos on, has more of that operational model that looks closer to what the cloud experience is. And then, if I've got a good platform, then I can modernize the applications on top of it. But often those two have been a little bit disconnected. It feels like the announcements now that they are coming together. What are you seeing? What're you hearing? How your solutions at solving this issue >>exactly. I mean, as we've been talking to our customers, a lot of them are going through a different application. Modernizations and kubernetes and containers is extremely important to them. And to build a container cloud on Prem is extremely one of their needs. And so there's three distinctive requirements that they've kind of talk to us about. A lot of it has to be ableto it's got to be very simple, very turnkey, fully integrated, ready to turn on the other. One is something that's very agile, right? Very Dev Ops friendly and the third being a very economic container cloud on prim. So as you mentioned, High Flex Application Platform takes our hyper converge system and build on top of it a integrated kubernetes platform to deliver a container as a service type capability. And it provides a full stack, fully supported element platform for our customers, and one of the best great aspects of it is it's all managed from inter site, from the physical infrastructure to the hyper converge layer to all the way to the container management. So it's very exciting to have that full stack management and inter site as well. >>It's great to see you, John and I have been following this kubernetes wave since the early early days. Fabio mentioned integrations with the Amazons and Googles of the world because, you know, a few years ago you talk to customers and they're like, Oh, well, I'm just going to build my own community. Nobody ever said that is easy now. Just delivering as a service seems to be the way most people want it. So if I'm doing it on Amazon or Google, they've got their manage service that I could do that or that there partners we're working with. So explain what you're doing to make it simpler in the data center environment. Because on Prem absolutely is a piece of that hybrid equation that customers need. >>Yes, so, essentially from the customer experience perspective, as I mentioned, very fairly turnkey right from the hyper flex application platform we're taking are happening for software were integrating a application virtualization layer on top of it analytics k VM based. And then on top of that, we're integrating the kubernetes stack on top of as well. And so, in essence, right? It's a fully curated kubernetes stack that has all the different elements from the networking from the storage elements and provide that in a very turnkey way. And as I mentioned, the inter site management is really providing that simplicity that customers need for that management. >>Fabio This is the previous announcements you've made with the public clouds. This just ties into those hybrid environments. That's exactly a few years ago. People like, Oh, is there going to be a distribution that wins in kubernetes? We don't think that's the answer, but still, I can't just move between kubernetes. You know seamlessly yet. But this is moving toward that >>direct. Absolutely. A lot of customers want to have a very simple implementation. At the same time, they weren't off course a multi cloud approach and I really care about marking the difference between multi cloud hybrid Cloud has been a lot of confusion. But if you think about a multi cloud is re routed into the business need or harnessing innovation from wherever it comes from, you know the different clouds capability from things, and you know what they do today. Tomorrow it could even change, so people want optionality, so they want a very simple implementation that's integrated with public cloud providers that simplifies their life in terms of networking, security and application of workload management. And we've been executing towards that goal so fundamentally simplify the operations of these pretty complex kind of hybrid apartments. >>And once you nail that operations on hybrid, that's where multi cloud comes in. That's really just a connection point. >>Absolutely, you know, you might know is an issue. So in order to fulfill your business, your line of business needs you. Then you have a hybrid problem, and you want to really kind of have a consistent production grade environment between things on Prem that you own and control versus things that you use and you want to control better. Now, of course, they're different school thoughts. But most of the customers who are speaking with really want to expand their governance and technology model right to the cloud, as opposed to absorb in different ways of doing things from each and every time. >>I want to unpack a little bit of what you said earlier about the knowing where the problem is, because a lot of times it's a point, the finger at the other first, it's the application promising the problem, so I want to get into that. But first I want to understand the hyper flex application platform. Eugene, if you could just share the main problem that you guys solve, what are some of the pain points that customers had? What problem does the AP solved? >>Yeah, as I mentioned, it's really the platform for our customers to modernize the applications on right, and it addresses those things that they're looking for as far as the economics right, really? The ability to provide a full stack container experience without having to, you know, but bringing any third party hyper visor licenses as well support costs that's well integrated. There you have your integrated, hyper converged storage capability. You have the cloud based management, and that's really developing. You provide that developer dev ops simplicity from that agility that they're looking for internally as well as for their production environments. And then the other aspect is the simplicity to manage all this right and the entire life cycle management >>as well. So it's the operational side of the hole in under the covers hobby on the application side where the problem is because this is where I'm a bit skeptical, Normal rightfully so. But I can see a problem where it's like Whose fault is it? Applications, problem or the network? I mean, it runs on where? Sears Workloads, Banking app. It's having trouble. How do you know where the problem is? And how do you solve that problem with what's going on for that specific issue? >>Absolutely. And you know, the name of the game here is breaking down this operational side, right? And I love what are appdynamics VP? GM Any? Whitaker said. You know, he has this terminology. Beast develops, which it may sound like an interesting acrobatics, but it's absolutely too. The business has to be part of this operational kind of innovation because, as you said, you know, developer just drops their containers and their code to the I T. Ops team, but you don't really know whether the problem a certain point is going to be in the code or in the application is actually deployed. Or maybe a server that doesn't have enough CPU. So in the end, it boils down to one very important thing. You have to have visibility, insights and take action at every layer of the stack. Instrumentation. Absolutely. There are players that only do it in their software overlay domain. The problem is, very often these kind of players assume they're underneath. Things are fine, and very often they're not. So in the end, this visibility inside in action is the loop that everybody's going after these days, too, Really get to the next. If you want a generational operation, where you gotta have a constant feedback loop and making it more faster and faster because in the end you can only win in the marketplace, right? So your I T ops, if you're faster than your competitors, >>will still still questioning the GM of APP Dynamics. Run, observe, ability. And he's like, No, it's not a feature, it's everywhere. So he's comment was observe. Abilities don't really talk about it because it's a big in. You agree with that? >>Absolutely. It has to be at every layer of the stack, and only if you have visibility inside an action through the entire stock, from the software all the way to the infrastructure level that you can solve the problems. Otherwise, the finger pointing quote unquote will continue, and you will not be able to gain the speed you need. >>Okay, so The question on my mind I want to get both of you guys could weigh in on this is that if you look at Cisco as a company, you got a lot going on. You guys huge customer base core routers to know applications. There's a lot going on a lot of a lot of complexity. You got I o. T. Security members talking about that. You got the WebEx rooms totally popular. It's got a lot of glam, too, and having the WebEx kind of, I guess, what virtual presence was telepresence kind of model. And then you get cloud. Is there a mind share within the company around how cloud is baked into everything? Because you can't do I ot edge without having some sort of cloud operational things. Stuff we're talking about is not just a division. It's kind of it's kind of threads everywhere across Cisco. What's the what's the mind share right now within the Cisco teams and also customers around cloud ification? >>Well, I would say it's it's a couple of dimensions. The 1st 1 is the cloud is one of the critical domains of this multi domain architecture. That, of course, is the cornerstone of Cisco's. The knowledge is strategy, right? If you think about it, it's all about connecting users to applications wherever they are and not just the users to the applications themselves. Like if you look at the latest US from I. D. C. 58% of workloads is heading to a public cloud, and the edge is like the data center is exploding many different directions. So you have this highly distributed kind of fabric. Guess what sits in between. All these applications and micro services is a secure network, and that's exactly what we're executing upon. Now that's the first kind of consideration. The second is if you look at the other civil line. Most of the Cisco technology innovation is also going a direction of absorbing cloud as a simplified way of managing all the components or the infrastructure. You look at the hyper flex. AP is actually managed by Inter site, which is a SAS kind of component. This journey started long time ago with Cisco Iraqi on then, of course, we have sass properties like WebEx. Everything else absolutely migrate borders. >>We've been reporting Eugene that five years ago we saw the movement where AP, eyes were starting to come in when you go back five years ago. Not a lot of the gear and stuff that Cisco had AP eyes. Now you got AP eyes building in all the new products that you see the software shift with you intent based networking to APP dynamics. It's interesting. It's you're seeing kind of the agile mindset. This is something you and I talk all the time. But agile now is the new model. Is it ready for customers? I mean, the normal enterprises still have the infrastructure and separated, and they're like, Okay, how do I bring it together? What do you guys see in the customer base? What's going on with that early adopters, Heavy duty hardcore pioneers out there. But you know, the general mainstream enterprise. Are they there yet? Have they had that moment of awakening? >>Yeah, I mean, I think they they are there because fundamentally, it's all about ensuring that application experience. And you could only ensure the application experience right by having your application teams and infrastructure teams work together. And that's what's exciting. You mentioned Ap eyes and what we've done. They were with APP dynamics, integrating with inner sight workload. Optimizer as you mentioned all the visibility inside in action and what APP Dynamics has provides. Provide that business and end user application performance experience. Visibility Inter site. It's giving you visibility on the underlining workload, and the resource is whether it's on prim in your private data center environment or in a different type of cloud providers. So you get that full stack visibility right from the application all the way down to the bottom and then inter site local optimizer is then also optimizing the resource is to proactively ensure that application experience. So before you know, if we talk about someone at a check out and they're about there's of abandonment because the function is not working, we're able to proactively prevent that and take a look at all that. So, you know, in the end, I think it's all about ensuring that application experience and what we're providing with APP Dynamics is for the application team is kind of that horizontal visibility of how that application performing and at the same time, if there's an issue, the infrastructure team could see exactly within the workload topology, where the issue is and entertain safely, whether it be manual intervention or even automatically our ops capability. Go ahead and provide that action so the action could be, you know, scaling out the VM that's on Prem or looking at new, different type of easy to template in the cloud. That's a very exciting about this. It's really the application experience is now driving and optimize the infrastructure in real >>time. And let me flip your question like, Do you even have a choice, John, when you think about in the next two years 50% more applications? If you're a large enterprise here, 5 to 7000 apps you have another 2 3000 applications just coming into into the and then 50% of the existing ones that are going to be re factor lifted and shifted the replace or retired by SAS application. It's just like a tsunami that's that's coming on you and oh, by the way, because again the micro services kind of effect the number of dependencies between all these applications is growing incredibly rapidly, Like last year, we were eight average interdependencies for applications. Now we have 20 so in Beijing imaginable happens as you are literally flooded with this can really you have to ensure that your application infrastructure fundamentally will get tied up as quickly as you can >>see. You and I have been talking for at least five years now, if not longer. Networking has been the key kind of last change over clarification. I would agree with you guys. I think last question because I wanted to get your perspective. But think about it. It's 13 years since the iPhone so mobile has shown people that mobile app can change business. But now you get the pressure of the networks. Bringing that pressure on the network or the pressure of the network to be better than programmable is the rise of video and data. I mean, you got mobile check now you got it. Video. I mean more people doing video now than ever before. Videos of consumer. Well, it's streaming. You got data? These two things absolutely forced customers to deal with it. >>But what really tipped the balance? John is actually the SAS effect is the cloud effect because, as you know, it's an I t. So the inflection points. Nothing gets a linear right. So once you reach a certain critical mass of cloud apps, and we're absolutely they're already all of a sudden your traffic pattern on your network changes dramatically. So why in the world are you continuing? Kind of, you know, concentrating all of your traffic in your data center and then going to the Internet. You have to absolutely open the floodgates at the branch level and as close to the users this possible, and that it implies a radical change of the >>way I would even add to that. And I think you guys are right on where you guys are going. It may be hard to kind of tease out with all the complexity with Cisco, but in the keynote, the business model shifts come from SAS. So you got all this technical stuff going on. You have the sass ification, or cloud changes the business models so new entrants can come in and existing players get better. So I think that whole business model conversation never was discussed at Cisco Live before in depth. Okay, run your business, connect your hubs campus move packets around Dallas applications in business model, >>but also the fact that there is increasing number off software capabilities and so fundamental. You want to simplify the life of your customers through subscription models that help the customer buying a using what they really need the right at any given point in time, all the way to having enterprise agreements. >>I also think that's about delivering these application experiences free for small, different experience. That's really what's differentiating you from your competitors, right? And so that's a different type of >>shift as well. Well, you guys have got a good That's a good angle on this cloud. I love it. I got to ask the question. What can we expect next from Cisco? More progression along cloud ification? What's next? >>Well, I would say we've been incredibly consistent, I believe in the last few years in executing on our cloud strategy, which again is sent around helping customers really gluing this mix, set off data centers and clouds to make it work as one right as much as possible. And so what we really deliver is networking security and application performance management, and we're integrating this more and more on the two sides of the equation, right? The data center side and the public cloud side and more more integrated in between all of these layers again, to fundamentally give you this operational capability to get faster and faster. We'll continue doing so and >>we'll get you set up before we came on camera that you were talking to sales teams. What are they? What's the vibe with sales team? They get excited by this. What's the >>oh yeah, feedback. And absolutely, from the inter site work optimizer and the app Dynamics side. It's very exciting for them. Switch the conversation they're having with their customers, really from that application experience and proactively ensuring it. And on the hyper flex application platform side, this is extreme exciting with providing a container cloud to our customers. And you know what's coming down is more and more capabilities for our customers to modernize the applications on hyper >>flex. You guys are riding a pretty big waves here at Cisco in a cloud way to get the i o t. Security wave. Great stuff. Thanks for coming in. Thanks for sharing the insights. Appreciate it. >>Thank you for having >>coverage here in Barcelona. I'm John. First, Minutemen back with more coverage. Fourth day of four days of cube coverage. Be right back after this short break. >>Yeah, yeah, yeah.

>> Live from Las Vegas It's theCUBE covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to theCUBE's live coverage of the AWS Executive Summit here at the Venetian in Las Vegas. I'm your host, Rebecca Knight. We have two guests for this segment. We have Mike Moore, Senior Principal at Accenture Research, and Andrew Wilson, Chief Information Officer at Accenture. Thank you both so much for returning to theCUBE. >> Good to see you as ever, Rebecca, and to be back in Las Vegas as well. >> Exactly, back in Sin City, right, here we are. So our topic is innovation. A buzzword that is so buzzy it's almost boring. Let's start the conversation with just defining innovation. What does innovation mean? >> An objective, a behavior, a way of working. To me, innovation is what we need to do with modern technology to enable the enterprise and the business world and be creative humans and to use disciplines which we didn't typically bring to work before. >> And is it creativity, or is there sort of logic and rationale too? >> I think there's logic and rationale. But there's also entertainment, fun, modern consumer-like experimentation, risk-taking, things of that nature. >> I think that a big key is actually striking a balance between creativity and logic and rationale and that's the really tricky bit, because you need to give your employees the license to be creative but within a certain set of boundaries as well. >> The rules of work have definitely changed, and behaviors that we encourage, even the clothes we wear, how we work, when we work, those are all characteristic of a more innovative, accepting diverse world, and a world that can keep up with the modern technology and the advancements and the announcements like we're hearing about here at re:Invent. >> It's the ultimate right brain, left brain behavior and activity. So Mike, you've done some research recently about the hallmarks of innovative companies, what they do differently from the ones that are not innovative, that are failing here, so tell our viewers a little bit about what you've found in your research. >> We surveyed 840 executives from a variety of different companies, different industries, different geographies, to understand their approach to innovation, and those who were doing it particularly well, and those maybe not so well. And around about 14 percent of our respondents were turning their investments in innovation into accelerated growth, and there were lots of different reasons for their success but three things really stood out. So first of all their outcome lacked in terms of the way they approach innovation, so they put a clear set of processes around their innovation activities, and then linked those to operational and financial performance metrics. They're also disruption minded, so they're not just pursuing incremental tweaks to their products and services, but their investing in disruptive technologies that could actually create entirely new markets. And then finally they're change orientated. They're not just using innovation to change their products and services, but also to fundamentally change the nature of their own organizations as a whole. >> So 14 percent are knocking it out of the park. Does that mean the rest of them are all laggards or are sort of some in the middle? What is the state of innovation in industry today, would you say, Andrew? >> I would say it's hugely variable by industry, geography, type of company, and individual instance of leader and culture, but I am sure that the most successful companies, those that are pivoting to the new, those that are imaginative, those that have recently arrived, all have that DNA that we're describing, all have that way of working, all have that ability to operate cleverly, intelligently, humorously, and at speed. I think innovation is very much characterized by something that can be fast-failed, do, step, move sideways, do again. The way of working has changed in modern enterprises. We as CIO's have to accept that. We have to speed up. We have to create the environment in where that productivity, where that creation can occur, and I think all of that's key. >> You keep mentioning this, the way of working has changed, and I think we all sort of know what you mean but explain a little bit what you're seeing. >> Experimentation, the ability to get more done with the resources that you have. So here we are at AWS re:Invent, cloud-based operations. Cloud gives you, gives me as a CIO the means to do more, more quickly, more rapidly, on a greater scale, in more places that I ever could have imagined in my old old-fashioned data senses. So the services we can consume, the data we can connect together, the artificial intelligence we can bring to it, the consumer-like experience. All of those things, which by the way, are drawing on innovative behaviors in their own right, are absolutely what the game is about now. >> How does AWS figure into your cloud transformation? >> Well for our cloud transformation at Accenture, AWS is one of the core cloud platform providers who power Accenture. We are nearly 95 percent in cloud. So as an organization that's very pronounced, and typically ahead of most organizations. But we sort of have to be, don't we? I mean, we have to be our own North Star. I can't sit here and explain the virtues of what Accenture can bring to a client's cloud transformation if we haven't already done it to ourselves. And by the way, that drew on innovative approaches, risk-taking approaches because over the last three years we've moved Accenture to the cloud. >> So I love how you said it, we are our own North Star, and other people would say we eat our own dog food, I mean that's just kind of more gross, but in terms of having experienced this transformation yourselves, how do you use what you've learned to help your companies transform as well? And make these moves, take these risks, what would you say to that? >> Well I think we keep an eye on the research with our colleagues there, they're our own North Star. I think we look at the ecosystem, we assess readiness for enterprise, security compliance, scale, availability, and then we also look and say, and what's ready for prime time in terms of Accenture scale, half a million people nearly. You bring all of those things together and it's a recipe, and that's why we consult our business, that's why we guide and educate and experiment and innovate together. And that's very much how we adopted cloud, it's very much how we do a number of other things, and the creative services we have. >> In terms of, let's get back to the research. So how do you, I mean as you said, the research is, as Andrew said, it's something that executive leaders are looking at to figure out what's actually happening in the market as well as what's happening within the organization itself. So how do you set your research agenda in terms of figuring out where you want to focus your time and energy and resources. >> Well I think we do it in a very similar way to in which we consult with clients, we speak to them. We talk to them about some of the key issues that they're facing and we always interview a series of executives and also academics to get their perspective at the start of their project. And that's something that we did in this particular instance and what we heard from many executives was that, to the point that Andrew was making before, the speed and scale of innovation today is happening at a completely different pace than in the past. So product cycle times are just diminishing in every single industry and as a consequence, executives now need to build new innovation units to make sure that they can respond to that changing market. So that's we wanted to explore through the research. >> So in this research, with the 14 percent doing it well, the 86 percent sort of either, somewhere on the spectrum of doing terribly or figuring things out, getting better, what are their pain points, and what's your advice to those companies? >> Well I think, and we take the positive spin on it in terms of what the companies are doing well, one of the points that Andrew was making before was how Accenture works with other partners to become more innovative itself. And that's something that we saw many of the high performing companies doing. So many of them were what we call networks powers. Not just innovating using their own resources, their own people, but their drawing on a broader ecosystem of partners to bring the very best products and services to their customers, and their spending not just on R and D internally but also on accelerators, incubators, technology based M and A, and actually their spending as much on inorganic innovation as they are on organic innovation. >> At Accenture we actually help our clients look for trap value, and what we mean by that is if an organization with a history, with a set of business processes, a set of technologies, and a set of disciplines and employees that have been successful and worked possibly for decades in that model, then they're going to be in some pretty tight guide rails. How do you innovate out of that, to deal with all of the destruction that's now available, good healthy disruption, that actually reveals the next level of efficiency, customer satisfaction, product creativity, and innovation in it's own right, so that's innovation in action, if you like. >> I want to ask, here we are at AWS re:Invent, Andy Jassy on the main stage this morning announcing a dizzying number of new products, services, and AWS, this is Amazon, this is a huge company that really seems to know how to innovate, and do it constantly, but is that is that, can every company be Amazon? You know what I'm saying? I mean, is this really possible and attainable? >> Is such a thing as innovation fatigue perhaps? >> Well, exactly, right! >> My view is that you have to find a way to make innovation a constant and a norm. It doesn't mean that you always will have to operate with the same ridiculous pace, but creativity and pace do go hand in hand to a point, but to be ahead, to stay ahead, and to lead an organization of technologists, who can comprehend all of these announcements, so you have to innovate in both how you lead and operate as well. It's not just your product, it's your behaviors, because there's just so much coming all the time. >> Right, and we've seen a number of large companies, not necessarily technology companies, but I'm thinking of Sears and Toys-R-Us, that have really, you've seen what can happen, the cautionary tales. >> Look at the attrition in the Fortune 500, and you can see how companies have a, a half life now, which perhaps is very different to 20 or 30 years ago. >> Right, right, exactly. Well, Mike and Andrew, thank you so much for coming on theCUBE. This was a really fascinating discussion. >> Thanks. >> Thank you, good to see you again. >> I'm Rebecca Knight, stay tuned for more of theCUBE's live coverage of the AWS Executive Summit. (techno music)

(upbeat techno music) >> Live from Las Vegas, it's theCUBE. Covering Boomi World 2018. Brought to you by Dell Boomi. >> Good afternoon, welcome back to theCUBE's continuing coverage of Boomi World 2018 from Las Vegas. I'm Lisa Martin with John Furrier and we're welcoming back to theCUBE one of our alumni Nima Badiey, Head of Technology Ecosystems from Pivotal. Nima, welcome back. >> Thank you for having me back. >> So Pivotal, part of the Dell technologies part of the companies, >> Yeah. >> You guys IPOd recently. And I did read that of the first half 2018, eight of the 10 tech IPOs were powered by Boomi. >> Well, I don't know about that specific. I know that tech IPOs are making a big comeback. We did IPO on the 20th of April, so we've passed out six-month anniversary if you can say. But it's been a distinct privilege to be part of the overall Dell family of businesses. I think what you have in Michael as a leader, who, he has a specific vision, but he's left the independent operating units to work on their own, to find their path through that journey, and to help each other as brethren, as like sisters and brothers. And the fact that Pivotal is here supporting Boomi. That Boomi is within our conference of supporting our customers that we're working together really speaks volumes. I think if you take a look at it, a lot of things happened this week, right? So a couple weeks ago, IBM's acquiring RedHat, this morning VMWare's acquiring Heptio. That's a solid signal that the enterprise transformation and adoption of cloud native model is really taking off. So the new middleware is really all about the cloud native polyglock, multiglock environment. >> And what's interesting, I want to get your thoughts on this because first of all congratulations on the IP, some are saying Pivotal's never going to go public, and they did, you guys were spectacular, great success. But what's going on now is interesting. We're hearing here at this show, as other shows is, cloud scale and data are really at the center of this horizontally scalable cloud poly proposition. Okay great, you mention Kubernetes and Heptio and VM where, that's all great. The question that is how do you compete when ecosystems become the most important thing. You worked at VMware you're at Pivotal. Dell knows ecosystems. Boomi's got an ecosystem. Partners, which is also suppliers and integrators. >> Yeah. >> They integrate and also developers. This is a key competitive advantage. What's your take on that here? >> So I think you touched on the right point. You compete because of your ecosystem, not despite your ecosystem. We can't be completely hedgemonic like Microsoft or Cisco or Amazon can afford to be. And I don't think customers really want that. Customers actually want choice. They want the best options but from a variety of sources. And that's why one of the reasons that we not only invest Dell ecosystem but also in Pivotal's own ecosystem is to cultivate the right technologies that will help our customers on that journey. And our philosophy's always find the leaders in the quadrant. The Cadillac vendors, the Lexus vendors onboard them and the most important thing you can do is, to ensure a pristine customer experience. We're not measuring whether feature A from one partner is better than feature B from another partner. We really don't care. What we care about is we can hand wire and automate what would have been a very manual process for customers, so that, let's say Boomi with Cloud Foundry works perfectly out of the box. So the customers doesn't have to go through and hire consultants and additional external resources just to figure out how two pieces of software should work together, they just should. So when they make that buying decision they know that the day after that buying decision, everything's going to be installed and their developers and their app dev teams and their ops teams can be productive. So that's the power of the ecosystem. >> Can you talk about the relationship between Pivotal and Boomi, because Boomi's been born in the Cloud as start up. Acquired eight years ago. You're part of the Dell Technologies family. VMware's VMware, we know about VMware doing great. You guys doing great. Now Boomi's out there. So how do they factor into and what's the relationship you have with them and how does that work, how do you guys work together? >> Perfect question. So, in my primary role at Pivotal is to manage all of our partner ecosystems, specifically the technology partners. And what I look for are any force multipliers. Any essentially ISVs who can help us accomplish more together than we could on our own. Boomi's a classic example of that. What do they enable? So take your classic customer. Classic customer has, let's say, 100 applications in inventory that they have built, managed, and purchased procured off from shelf-to-shelf components. And roughly 20 or 30% are newish, green field applications, perfect for the cloud native transformation. Most 80% of them or 70% are going to be older, ground field applications that will have to be refactored. But there's always going to be that 15% towards the end that's legacy mainframe. It can't be changed, you cannot afford to modernize it, to restructure it, to refactor it. You're going to have to leave it alone, but you need it. Your inventory systems are there. >> These are critical systems, those people who think legacy as outdated, but they're actually just valued. >> No, they're critically valuable. >> Yes. >> We just cannot be modernized. >> Bingo. >> So a partner like Boomi will allow you to access the full breadth of those resources without having to change them. So I could potentially put Boomi in front of any number of older business applications and effectively modernize them by bridging those older legacy systems with the new systems that I want to build. So let's do an example. I am the Gap and I want to build a new version of our in-store procurement system that runs on my iPhone, that I can just point to a garment and it will automatically put it in my, ya know, check out box. How do I do that? Well I can build all the intelligence. And I can use AI and functions and I can build everything it's out of containers, that's great. But I still have to connect to the inventory system. Inventory system... >> Which is a database. All these systems are out there. >> Somewhere, something. And my developers don't know enough about the old legacy database to be able to use it. But if I put a restful interface using Boomi in front of it and a business connector that's not older XML or kind of inflexible, whatever, solo gateways. Then I have enabled my developer to actually build something that is real. That is customer focused. It is appropriate for that market without being hamstrung by my existing legacy infrastructure. And now my legacy infrastructure is not an anchor that's holding me back. >> You had mentioned force, me and Lisa talk about this all the time on theCUBE, where that scenario's totally legit and relevant because in the old version of IT you have to essentially build inventory management into the new app. You'd have to essentially kill the old to bring in the new. I think with containers and cloud native has shown is you can keep the old and sunset it if you want on your own time table or keep it there and make it productive. Make the data exposeble, but you can bring the cool relevant new stuff in. >> Yeah. >> I think that is what I see and we see from customers, like OK cool, I don't have to kill the old. I'll take care of it on my own timetable versus a complete switching cost analysis. Take down a production system. >> Exactly. >> Build something new, will it work. Ya know cross your fingers. Okay, again and this is a key IT different dynamic. >> It is and it's a realization that there are things you can move and those are immutable. They're simply just monolithic that will never move. And you're going to work within those confines. You can have the best of both worlds. You can maintain your legacy applications. They're still fine, they run most of your business. And still invent the new and explore new markets and new industries and new verticals. And just new capabilities all through and through without having to touch in your back end systems. Without having to bring the older vendors in and say can you please modernize your stuff because my business is dependent and I am going to lose that. I'm going to become the new Sears, I going to become the new Woolworth or whoever. Blockbuster that has missed an opportunity to vector into a new way of delivering their services. >> When you're having customer conversations, Nima, I'm curious, talking with enterprise organizations who have tons of data, all the systems including the legacy, which I'm glad that you brought up that that's not just old systems. There's a lot of business critical, mission critical application running on 'em. Where do you start that conversation with the large enterprise, who doesn't want to become a Blockbuster to your point, and going this is the suite of applications we have, where do we start? Talk to us about that customer journey that you help enable. >> That's great 'cause in most cases the customers already know exactly what they want. It's not the what that you have to have the conversation around, it's the how do I get there. I know what I want, I know what I want to be, I know what I want to design. And it's how do I transform my business fundamentally do an app transformation, enterprise transformation, digital transformation? Where do I begin? And so, ya know, our perspective at Pivotal is, ya know, we're diehard adopters of agile methodology. We truly, truly believe that you can be an agile development organization. We truly believe in Marc Andreessen's vision of software eating the world. Which let's unpack what that means. It just means that if you're going to survive the next 10 years you have to fundamentally become a software company, right? So look at all the companies we work with. Are you an insurance company or are you delivering an insurance product through software? Are you a bank or are you delivering banking product through software? Well, when was the last time you talked to a bank teller? Or the atm, most of your banking's done online. Your computer or your mobile device. Even my check cashing, I don't have to talk to anyone. It's wonderful. Ford Motor Company, do they bend sheet metal and put wheels on it or are they a software company? Well consider that your modern pickup truck has... >> They're an IOT company now. (laughing) (crosstalking) Manufacturing lines. >> That's what's crazy. You have a 150 million lines of code in your pickup truck. Your car, your pickup truck, your whatever is more software than it is anything else. >> But also data's key. I want to get your thoughts since this is super important Michael Dell brought up on the keynote today here at Boomi World was, okay the data's got to stay in the car. I don't need to have a latency issue of hey, I need to know nanosecond results. With data, cloud has become a great use case. With multicloud on the horizon, some people are going to throw data in multiple clouds and that's clear use case, and everyone can see the benefits of that. How do you guys look at this? 'Cause now data needs to be addressable across horizontal systems. You mentioned the Gap and the Gap example. >> That's great, so, one of the biggest trends we see in data is really event streaming. Is the idea that the ability to generate data far out exceeds the ability to consume it. So, what if we treated data as just a river? And I'm going to cast my line and only pick up what I want out of that stream. And this is where CAFCA and companies like Solice and any venturing networks and spring cloud functions and spring cloud data are really coming into play, is acknowledgement that yes we are not in a world where we can store all of the data all the time and figure out what to do with it after the fact. We need timely, and timely is within milliseconds, if not seconds. Action taken on an event or data even coming through. So why don't we modernize around, ya know, that type of data structure and data event and data horizon. So that's one of the trends we see. The second is that there is no one database to rule them all anymore. I can't get away with having oracle and that's my be all, end all. I now have my ESQL and SQL and Mongo and Cassandra and Redis and any other number of databases that are form, fit and function specific for a utility and they're perfect for that. I see graph databases, I see key value stores, I see distributed data warehouse. And so my options as a developer, as a user is really expanding, which means the total types of data components that I can use are also expanding exponentially. And that gives me a lot more flexibility on the types of products that I can build and the services that I can ultimately deliver. >> And that highlights micro services trend, because you have now a multitude of databases, it's not the one database rules them all. They'll be literally thousands of database on censors, so micro service has become the key element to connect all these systems. >> All of it together. And micro services really a higher level of abstraction. So we started with virtual machines and then we went to containers and then we went to functions and micro services. It's on an upward trend necessarily as it is an expansion. Into different ways of being able to do work. So some of my work products are going to be very, very small. They can afford to be ephemeral, but there may be many of them. How do I manage a cluster of millions of these potential work loads? Backing off I can have an ephemeral applications that run inside of containers or I can have ridged fixed applications that have to run inside a virtual machines. I'm going to have all of them. What I need is a platform that delivers all of this for me without me having to figure out how to hand wire these bits and pieces from various different either proprietary or open source kits just to make it work. I'm going to need a 60 to 100 or 200 person team just to maintain this very bespoke thing that I have developed. I'll just pull it off the shelf 'cause this is a solved problem. Right, Pivotal has already solved this problem. Other companies have already solved this problem. Let me start there and so now I'm here. I don't have to worry about all this left over plumbing. Now I can actually build on top of my business. The analogy I'd use is you don't bring furniture with you every time you check into a hotel. And we're telling customers every time you want to move to a different city just for business meeting or for work trip we're going to build you a house and you need to furnish it. Well, that's ridiculous. I'm going to check into a hotel and my expectation is I can check out of any other room and they'll all be the same, it doesn't really matter what floor I'm on, what room I'm in. But they'll have the same facilities, the same bed, the same, ya know, restroom facilities. That's what I want. That's what containers are. Eventually all the services surrounding that hotel room experience will be micro services. >> And we're the work load, the people. >> And we are the work load and we're the most important thing, we are the application, you're right. >> I love that. That's probably best analogy I've heard of containers. Nima, thanks so much for stopping by theCUBE, joining John and me today. And talking to us about what's going on with Pivotal and how you guys are really helping as part of Dell business dramatically transform. >> Been my pleasure. Thank you both. >> Thank you. >> Thank you. Thank you for watching theCUBE. I'm Lisa Martin with John Furrier. We are in Las Vegas at Boomi World '18. Stick around, John and I will be right back with our next guest. (light techno music)

(upbeat music) >> Hi I'm Peter Burris. Welcome to another CUBE Conversation from our wonderful studios in beautiful Palo Alto, CA. Today we are going to be talking about new architectures, new disciplines required to really make possible the opportunities associated with digital business. And to do that, we've got Neil Vachharajani, who is the Technical Director at Pure Storage. Neil welcome to theCUBE. >> Thank you for having me, Peter. >> So Neil, we have spent a fair amount of time within Wikibon and within the CUBE community, talking a lot about what is digital business. So, give me a second, run something by ya, tell me if you agree. So we think that there is a difference between business and digital business. And specifically, we think that difference is, a digital business uses data assets differently, than a business does. Walmart beat Sears 'cause it used data differently. AWS is putting the pressure on Walmart, because it uses data differently. Or Amazon is putting the pressure on Walmart, because it uses data differently. So, that is at the centerpiece of a lot of these digital transformations. How are you using data to re-institutionalize your work, realign your resources, reestablish a new engagement model with your marketplace. Would you agree with that? >> Yeah, absolutely agree with that and I think a lot of it has to do with the volume of data, where the data is coming from. If you look at traditional business, it really was about just putting into computers what we used to do on paper. And digital business today I think is about generating huge volumes of data by really looking at every interaction we have no matter how small or how big. >> So, putting telemetry on as many things. So, IoT for machines, mobile for human beings, but it used to be as you said. It was a process, known process, unknown technology world for a long time. And now, these are data driven processes. We're actually using data to describe what their next best action should be, what the recommendation should be. >> That's right. >> So, as we think about this, you know, businesses has been around for a long time. There's this notion of evidence based management, which is the idea that we use data differently, from the boardroom all the way down to the drivers. How does a business start to bring forward the discipline required to really make possible this data driven world. >> Well you know I think the first thing is, to really recognize why does this new paradigm shift changes things? And I think in the old world, if you looked at a piece of data, you actually could articulate all the way from the boardroom down to the stockroom every use of the data. And that meant that you could build a lot of siloed applications and that wasn't a big deal. You got your money's worth out of the data. So for example, recording transactions in store number 17. >> That's right. But in the new world, you actually don't know what the value of the data is ahead of time. Right. You're, in some sense, you're trying to capture a lot of data and then use technology to correlate it with things, mix and mash, mix and match, mash it up, and then drive business decisions that you didn't even know you were making a decision a few weeks ago and that means that you can't really lock up your data, you can't constrain it, because that's going to limit your possibilities. It's going to limit your ROI on that data. >> Yeah, we like to say that data as an asset is different from all other assets, because it is inherently sharable, reusable, it doesn't follow the laws of scarcity. And so, in many respects what the IT organization has had to do is find new ways to privatize that data through things like security, but as you're saying, they don't want to introduce technologies that artificially constrain derivative and future uses of that data. >> And I think, that's where, really the big architectural shift is happening in the data center. Because if you look traditionally, we have siloed the data and it wasn't like this intentional thing that we want to put it into a silo. But that's how we packaged our applications and that's how we deployed our applications. And now, we need a new discipline inside the data center, that makes the data available, lets people put policies on it. Like security policies. But then also makes it available for the innovators all throughout the company to get access to that data. You know, we're trying to crystallize this whole philosophy into something we refer to as the data-centric architecture. Where data is at the center, people have access to the data, and then there's just applications all around it that are all hitting this common pool of data and doing different things, driving new business processes. >> Now, you're talking not about a physical pool of data, but rather a logical pool of data. Data is stil going to be very distributed, right? >> Well you know, data gets generated in a distributed way, data is very large. I think it would be a bit naive to be able to point to one rack and one data center and say all your data center is going to be right here in this one rack. >> Or in one cloud. >> Or in one cloud for that matter. But just from a philosophical perspective, you do want to pull your data out of anything that is, like you said a minute ago, that's constraining it. So, I think, one really good example of this is when we went, quote unquote, web scale, we saw a lot of applications move into direct attached storage, to dive deep into a technology. And that was great if you wanted to only come in the front door and access the data through the application that was managing that das. But, if you wanted to do anything else, you were kind of stuck. >> So as to summarize this point, we're moving from a world in which data is a place to data is a service. >> That's right. >> Have I got that right? >> That's absolutely right. I mean, the way I like to think about it is that data and storage need to really be different things and storage's job is to give you access to the data. Storage in its own right, you know, doesn't solve a business problem. It's the data that solves the business problem. Storage is the vehicle that gets you there. And so I think it's pretty exciting that there's new technologies that are coming out, or that honestly are here, that are enabling that. Things like Flash and NVMe, and you know, it's futures. >> Well let's talk about that because what, the observation that I made to clients for quite some time is that if you go back, disk, was a great technology for persisting data. So again, Store number 17, transaction at a certain time. It's already occurred, we have to record it. So, we record it, we persisted on disk. Now what we are trying to do is we're utilizing technologies that are inherently structured to deliver data so that we can have the data be very distributed, but still look at it from a logical standpoint. And have that data be delivered to a lot of applications whether that is local and as long as we don't undermine basic physics perhaps further away. But even more importantly, deliver it to different roles, different, same day of being delivered to developers, same day to being different, delivered to a new application. What are some of those core technologies that are going to be necessary to do this? You mentioned NVMe, let's start there. >> Yeah, if I just back up a little bit right, that in some sense, even that recording the data workflow that you talked about, we made disk work. But it was actually a pretty challenging media and so we put in a lot of optimizations and things in place, because we said, we know the usage pattern. And if we know the usage pattern, we know how to organize our data. And so as a step one, like the transformation that I think is, in pretty full swing these days was moving from disk to flash. And that was a huge transformation, because it meant that random access to the data was just as performant as this carefully crafted sequential access. That meant you could start accepting unknown workloads into your applications, but you were still stuck behind this very serial, very antiquated SCSI protocol. And NVMe is now bringing a lot more parallels, to play. And that's going to help us to drive things like just simple, plain old data center. Stuff like density, and performance density, and power, and that kind of thing. So, that's sort of step one in terms of the technology that you can package all of this stuff in a pretty dense package and put petabytes of storage with enough I/O to actually access that data. If that's the key that you can have pedabytes, but you can only have one I out for each gig, well you're not going to get a lot out of that data. >> So, just to stop right there, and that leads to a world, in which as long as your disciplined and architected, you do not have to know what workloads are going to access that data near term. >> Well, you know, that's only step one, right. >> Right. >> Because the other challenge is that very few people access storage directly, right. We hide this behind databases, and we hide this behind a whole bunch of other technologies. Now, those technologies might have have their own limitations in place. But we have a lot or really rich things we can do at the storage level to present the same data out multiple frontends. And so the simplest idea is, we don't have one copy of a database, we often will have the transactional database that's using, recording those transactions, but then we'll have an analytics copy of the database and now we need to keep the two of those things in sync. And this is where the discipline and the architecture really comes into place. And we kind of have a lot of that figured out for things like relational databases and best practices there. But in the meantime, the world also moved over to the new world of Node-SQL databases, Queue's, Kafka. Things of that nature. And those, brought direct attached storage as the best practice. And so I think where the discipline comes in and where some of the new technologies that we're talking about right now are: How do you bring those old disciplines that we figured out, on let's say the relational world, how you bring that to bear on the new technologies that are meeting the scale requirements that we have today? >> Well one of the more important workloads that are going to require scale is, for example, AI. So, how are we going to organize some of these technologies, add them to these new disciplines, to be able to make some of these AI workloads run really, really fast. >> You know, I think a lot of this really comes down to pulling the storage out and putting it into it's own tier. And so, Pure Storage has an offering which is called AIRI, which is packaging DGX and Video DGX boxes with FlashBlades. And we say, hey you don't need a whole bunch of direct attached storage which is siloing your data, you can go put it into this common shared pool. And I think that on, you know, the other side the house, our FlashArray business is doing something really similar with NVMe, the FlashArray/X is essentially commoditizing NVMe. It's saying, everybody has access to this high performance density. And looking into the future with technologies like NVMe over Fabric, what we're really saying is your apps that used to use direct attached storage, there's no reason why they can't go to a sand based architecture that offers rich data services and not compromise one iota on latency. >> Or access or any other number of activities as well. So we've got NVMe, NVMe over Fabric, Flash, new approaches for thinking about packaging some of these things. Are there any other technologies that you envision on the horizon that are going to be really important to customers and that Pure is going to take advantage of. >> Yeah, you know, I really think that the other thing is once you collect all this stuff, you need a way to tame the beast. You need a way to deploy your applications. You need a way to catalog everything. And honestly, things like Kubernetes and container orchestration is becoming this platform where you deploy all of this stuff. And some of the assumptions that are baked into that, really go back and tie in nicely with those other technologies. In particular, they assume that I can schedule this compute wherever I want and I have access to the data. So in that way of having a fabric if you will between your compute and your data is essential. And it's just another reason why siloing things off into particular units of compute is just really the architecture of the past. And the architecture going forward is going to be to logically centralize. And maybe put some smarts at that other layer, saying, hey if this data is in the public cloud, let me schedule up there. But if this data is in my data center, let me schedule the compute down there. But then not having to worry about the micro decisions about, does it have to be in this rack or, you know, or on this particular physical node. All your data is accessible. >> But increasingly, we're going to do things that move the compute both physically as well as logically closer to the data. >> You know, 100%. Right. But it's at what scale? That you really want to get the data center right. Your compute should be running in the correct data center. >> Or the center of data right? >> Or the center of data, right, you know. Get it in the right spot, but then you don't want to have to worry about all the other micro constraints. You don't want, you know, if you look on the networking side of the world, Leaf Spy networks are all about say, hey look they're really is a uniform fabric for networking. We're trying to do the same thing in storage and just say, look, the storage is so performant, there's no reason to silo. You can run your compute where ever you want. If you've got a good networking fabric and you've got a good storage fabric, the end of the day, all your data is accessible, to whatever new application you envision. And you just, there's no reason why you have to lock it up. You mentioned security before. You know, you should absolutely be able to orchestrate things like taking a snapshot of your data, putting it through, masking, or whatever anonymization you need to make it safely accessible to new applications and innovators inside of your company to drive that digital business. >> Yes, and we like to talk about moving from a world that is focused on infrastructure, taking cost out, making it static, by removing all uncertainty to a world where we've no workloads, and elastic capacity, or elastic scale to a plastic world. Where plastic, using of the physicals, you know, the physic sense is unknown workload, unknown scale. And just making sure that we have the option to use data any way we want as much as possible in the future. >> And I think that that's why you see the rise of service catalogs and self service coming up in IT, it's that plasticity that you have the brightest minds in your company trying to figure out what to do, and you don't want to have infrastructure be this bottleneck that's causing everything to go slower. Or for people to say no. You just always want to say, yes. And that's where I think it's always exciting to see, see these technologies, NVMe, come out and say, we've now got the performance to say yes. NVMe over Fabric to say there's no compromise over latency. And then honestly, having this stuff packaged in things like FlashArray/X, where the CIO or the CFO, doesn't complain about breaking the bank as well. Because now these technologies are the status quo. They're the standard. There's no premium for them. And if anyone is trying to charge you that premium, you should really, you know, ask them why. This is the new architecture, this should be, this should be, what, the only thing you offer >> Right. >> In some sense >> Yeah, we're bringing all these new technologies into economic envelope that IT has to be in for business today. >> That's right, and you know, you look at something like flash memory, right. It's not a new technology. I remember in college having a flash card to put into like a digital camera in the early days of digital cameras. But for it to make it into the data center, the thing that was critical was that economic aspect of it. So it's not just about being on the bleeding edge of technology, but it's packaging that in a way that's actually palatable for the entire C-Suite to consume inside your organization. >> And I remember my disk pack that I carried around in college from the PDP system that we had to use. (laughter) Alright, Neil Vachharajani, Technical Director of Pure Storage talking about the relationship between new technologies, data centeric architectures, and digital business. Thanks very much for being on theCUBE. >> Thanks so much Peter. >> And once again, I'm Peter Burris, you've been participating in another CUBE conversation. 'Til we talk again. (upbeat music)

>> Hi I'm Peter Burris. Welcome to another Wikibon Action Item. (upbeat music) We're once again broadcasting from theCube's beautiful Palo Alto, California studio. I'm joined here in the studio by George Gilbert and David Floyer. And then remotely, we have Jim Kobielus, David Vellante, Neil Raden and Ralph Finos. Hi guys. >> Hey. >> Hi >> How you all doing? >> This is a great, great group of people to talk about the topic we're going to talk about, guys. We're going to talk about the notion of de-risking digital business. Now, the reason why this becomes interesting is, the Wikibon perspective for quite some time has been that the difference between business and digital business is the role that data assets play in a digital business. Now, if you think about what that means. Every business institutionalizes its work around what it regards as its most important assets. A bottling company, for example, organizes around the bottling plant. A financial services company organizes around the regulatory impacts or limitations on how they share information and what is regarded as fair use of data and other resources, and assets. The same thing exists in a digital business. There's a difference between, say, Sears and Walmart. Walmart mades use of data differently than Sears. And that specific assets that are employed and had a significant impact on how the retail business was structured. Along comes Amazon, which is even deeper in the use of data as a basis for how it conducts its business and Amazon is institutionalizing work in quite different ways and has been incredibly successful. We could go on and on and on with a number of different examples of this, and we'll get into that. But what it means ultimately is that the tie between data and what is regarded as valuable in the business is becoming increasingly clear, even if it's not perfect. And so traditional approaches to de-risking data, through backup and restore, now needs to be re-thought so that it's not just de-risking the data, it's de-risking the data assets. And, since those data assets are so central to the business operations of many of these digital businesses, what it means to de-risk the whole business. So, David Vellante, give us a starting point. How should folks think about this different approach to envisioning business? And digital business, and the notion of risk? >> Okay thanks Peter, I mean I agree with a lot of what you just said and I want to pick up on that. I see the future of digital business as really built around data sort of agreeing with you, building on what you just said. Really where organizations are putting data at the core and increasingly I believe that organizations that have traditionally relied on human expertise as the primary differentiator, will be disrupted by companies where data is the fundamental value driver and I think there are some examples of that and I'm sure we'll talk about it. And in this new world humans have expertise that leverage the organization's data model and create value from that data with augmented machine intelligence. I'm not crazy about the term artificial intelligence. And you hear a lot about data-driven companies and I think such companies are going to have a technology foundation that is increasingly described as autonomous, aware, anticipatory, and importantly in the context of today's discussion, self-healing. So able to withstand failures and recover very quickly. So de-risking a digital business is going to require new ways of thinking about data protection and security and privacy. Specifically as it relates to data protection, I think it's going to be a fundamental component of the so-called data-driven company's technology fabric. This can be designed into applications, into data stores, into file systems, into middleware, and into infrastructure, as code. And many technology companies are going to try to attack this problem from a lot of different angles. Trying to infuse machine intelligence into the hardware, software and automated processes. And the premise is that meaty companies will architect their technology foundations, not as a set of remote cloud services that they're calling, but rather as a ubiquitous set of functional capabilities that largely mimic a range of human activities. Including storing, backing up, and virtually instantaneous recovery from failure. >> So let me build on that. So what you're kind of saying if I can summarize, and we'll get into whether or not it's human expertise or some other approach or notion of business. But you're saying that increasingly patterns in the data are going to have absolute consequential impacts on how a business ultimately behaves. We got that right? >> Yeah absolutely. And how you construct that data model, and provide access to the data model, is going to be a fundamental determinant of success. >> Neil Raden, does that mean that people are no longer important? >> Well no, no I wouldn't say that at all. I'm talking with the head of a medical school a couple of weeks ago, and he said something that really resonated. He said that there're as many doctors who graduated at the bottom of their class as the top of their class. And I think that's true of organizations too. You know what, 20 years ago I had the privilege of interviewing Peter Drucker for an hour and he foresaw this, 20 years ago, he said that people who run companies have traditionally had IT departments that provided operational data but they needed to start to figure out how to get value from that data and not only get value from that data but get value from data outside the company, not just internal data. So he kind of saw this big data thing happening 20 years ago. Unfortunately, he had a prejudice for senior executives. You know, he never really thought about any other people in an organization except the highest people. And I think what we're talking about here is really the whole organization. I think that, I have some concerns about the ability of organizations to really implement this without a lot of fumbles. I mean it's fine to talk about the five digital giants but there's a lot of companies out there that, you know the bar isn't really that high for them to stay in business. And they just seem to get along. And I think if we're going to de-risk we really need to help companies understand the whole process of transformation, not just the technology. >> Well, take us through it. What is this process of transformation? That includes the role of technology but is bigger than the role of technology. >> Well, it's like anything else, right. There has to be communication, there has to be some element of control, there has to be a lot of flexibility and most importantly I think there has to be acceptability by the people who are going to be affected by it, that is the right thing to do. And I would say you start with assumptions, I call it assumption analysis, in other words let's all get together and figure out what our assumptions are, and see if we can't line em up. Typically IT is not good at this. So I think it's going to require the help of a lot of practitioners who can guide them. >> So Dave Vellante, reconcile one point that you made I want to come back to this notion of how we're moving from businesses built on expertise and people to businesses built on expertise resident as patterns in the data, or data models. Why is it that the most valuable companies in the world seem to be the ones that have the most real hardcore data scientists. Isn't that expertise and people? >> Yeah it is, and I think it's worth pointing out. Look, the stock market is volatile, but right now the top-five companies: Apple, Amazon, Google, Facebook and Microsoft, in terms of market cap, account for about $3.5 trillion and there's a big distance between them, and they've clearly surpassed the big banks and the oil companies. Now again, that could change, but I believe that it's because they are data-driven. So called data-driven. Does that mean they don't need humans? No, but human expertise surrounds the data as opposed to most companies, human expertise is at the center and the data lives in silos and I think it's very hard to protect data, and leverage data, that lives in silos. >> Yes, so here's where I'll take exception to that, Dave. And I want to get everybody to build on top of this just very quickly. I think that human expertise has surrounded, in other businesses, the buildings. Or, the bottling plant. Or, the wealth management. Or, the platoon. So I think that the organization of assets has always been the determining factor of how a business behaves and we institutionalized work, in other words where we put people, based on the business' understanding of assets. Do you disagree with that? Is that, are we wrong in that regard? I think data scientists are an example of reinstitutionalizing work around a very core asset in this case, data. >> Yeah, you're saying that the most valuable asset is shifting from some of those physical assets, the bottling plant et cetera, to data. >> Yeah we are, we are. Absolutely. Alright, David Foyer. >> Neil: I'd like to come in. >> Panelist: I agree with that too. >> Okay, go ahead Neil. >> I'd like to give an example from the news. Cigna's acquisition of Express Scripts for $67 billion. Who the hell is Cigna, right? Connecticut General is just a sleepy life insurance company and INA was a second-tier property and casualty company. They merged a long time ago, they got into health insurance and suddenly, who's Express Scripts? I mean that's a company that nobody ever even heard of. They're a pharmacy benefit manager, what is that? They're an information management company, period. That's all they do. >> David Foyer, what does this mean from a technology standpoint? >> So I wanted to to emphasize one thing that evolution has always taught us. That you have to be able to come from where you are. You have to be able to evolve from where you are and take the assets that you have. And the assets that people have are their current systems of records, other things like that. They must be able to evolve into the future to better utilize what those systems are. And the other thing I would like to say-- >> Let me give you an example just to interrupt you, because this is a very important point. One of the primary reasons why the telecommunications companies, whom so many people believed, analysts believed, had this fundamental advantage, because so much information's flowing through them is when you're writing assets off for 30 years, that kind of locks you into an operational mode, doesn't it? >> Exactly. And the other thing I want to emphasize is that the most important thing is sources of data not the data itself. So for example, real-time data is very very important. So what is your source of your real-time data? If you've given that away to Google or your IOT vendor you have made a fundamental strategic mistake. So understanding the sources of data, making sure that you have access to that data, is going to enable you to be able to build the sort of processes and data digitalization. >> So let's turn that concept into kind of a Geoffrey Moore kind of strategy bromide. At the end of the day you look at your value proposition and then what activities are central to that value proposition and what data is thrown off by those activities and what data's required by those activities. >> Right, both internal-- >> We got that right? >> Yeah. Both internal and external data. What are those sources that you require? Yes, that's exactly right. And then you need to put together a plan which takes you from where you are, as the sources of data and then focuses on how you can use that data to either improve revenue or to reduce costs, or a combination of those two things, as a series of specific exercises. And in particular, using that data to automate in real-time as much as possible. That to me is the fundamental requirement to actually be able to do this and make money from it. If you look at every example, it's all real-time. It's real-time bidding at Google, it's real-time allocation of resources by Uber. That is where people need to focus on. So it's those steps, practical steps, that organizations need to take that I think we should be giving a lot of focus on. >> You mention Uber. David Vellante, we're just not talking about the, once again, talking about the Uberization of things, are we? Or is that what we mean here? So, what we'll do is we'll turn the conversation very quickly over to you George. And there are existing today a number of different domains where we're starting to see a new emphasis on how we start pricing some of this risk. Because when we think about de-risking as it relates to data give us an example of one. >> Well we were talking earlier, in financial services risk itself is priced just the way time is priced in terms of what premium you'll pay in terms of interest rates. But there's also something that's softer that's come into much more widely-held consciousness recently which is reputational risk. Which is different from operational risk. Reputational risk is about, are you a trusted steward for data? Some of that could be personal information and a use case that's very prominent now with the European GDPR regulation is, you know, if I ask you as a consumer or an individual to erase my data, can you say with extreme confidence that you have? That's just one example. >> Well I'll give you a specific number on that. We've mentioned it here on Action Item before. I had a conversation with a Chief Privacy Officer a few months ago who told me that they had priced out what the fines to Equifax would have been had the problem occurred after GDPR fines were enacted. It was $160 billion, was the estimate. There's not a lot of companies on the planet that could deal with $160 billion liability. Like that. >> Okay, so we have a price now that might have been kind of, sort of mushy before. And the notion of trust hasn't really changed over time what's changed is the technical implementations that support it. And in the old world with systems of record we basically collected from our operational applications as much data as we could put it in the data warehouse and it's data marked satellites. And we try to govern it within that perimeter. But now we know that data basically originates and goes just about anywhere. There's no well-defined perimeter. It's much more porous, far more distributed. You might think of it as a distributed data fabric and the only way you can be a trusted steward of that is if you now, across the silos, without trying to centralize all the data that's in silos or across them, you can enforce, who's allowed to access it, what they're allowed to do, audit who's done what to what type of data, when and where? And then there's a variety of approaches. Just to pick two, one is where it's discovery-oriented to figure out what's going on with the data estate. Using machine learning this is, Alation is an example. And then there's another example, which is where you try and get everyone to plug into what's essentially a new system catalog. That's not in a in a deviant mesh but that acts like the fabric for your data fabric, deviant mesh. >> That's an example of another, one of the properties of looking at coming at this. But when we think, Dave Vellante coming back to you for a second. When we think about the conversation there's been a lot of presumption or a lot of bromide. Analysts like to talk about, don't get Uberized. We're not just talking about getting Uberized. We're talking about something a little bit different aren't we? >> Well yeah, absolutely. I think Uber's going to get Uberized, personally. But I think there's a lot of evidence, I mentioned the big five, but if you look at Spotify, Waze, AirbnB, yes Uber, yes Twitter, Netflix, Bitcoin is an example, 23andme. These are all examples of companies that, I'll go back to what I said before, are putting data at the core and building humans expertise around that core to leverage that expertise. And I think it's easy to sit back, for some companies to sit back and say, "Well I'm going to wait and see what happens." But to me anyway, there's a big gap between kind of the haves and the have-nots. And I think that, that gap is around applying machine intelligence to data and applying cloud economics. Zero marginal economics and API economy. An always-on sort of mentality, et cetera et cetera. And that's what the economy, in my view anyway, is going to look like in the future. >> So let me put out a challenge, Jim I'm going to come to you in a second, very quickly on some of the things that start looking like data assets. But today, when we talk about data protection we're talking about simply a whole bunch of applications and a whole bunch of devices. Just spinning that data off, so we have it at a third site. And then we can, and it takes to someone in real-time, and then if there's a catastrophe or we have, you know, large or small, being able to restore it often in hours or days. So we're talking about an improvement on RPO and RTO but when we talk about data assets, and I'm going to come to you in a second with that David Floyer, but when we talk about data assets, we're talking about, not only the data, the bits. We're talking about the relationships and the organization, and the metadata, as being a key element of that. So David, I'm sorry Jim Kobielus, just really quickly, thirty seconds. Models, what do they look like? What are the new nature of some of these assets look like? >> Well the new nature of these assets are the machine learning models that are driving so many business processes right now. And so really the core assets there are the data obviously from which they are developed, and also from which they are trained. But also very much the knowledge of the data scientists and engineers who build and tune this stuff. And so really, what you need to do is, you need to protect that knowledge and grow that knowledge base of data science professionals in your organization, in a way that builds on it. And hopefully you keep the smartest people in house. And they can encode more of their knowledge in automated programs to manage the entire pipeline of development. >> We're not talking about files. We're not even talking about databases, are we David Floyer? We're talking about something different. Algorithms and models are today's technology's really really set up to do a good job of protecting the full organization of those data assets. >> I would say that they're not even being thought about yet. And going back on what Jim was saying, Those data scientists are the only people who understand that in the same way as in the year 2000, the COBOL programmers were the only people who understood what was going on inside those applications. And we as an industry have to allow organizations to be able to protect the assets inside their applications and use AI if you like to actually understand what is in those applications and how are they working? And I think that's an incredibly important de-risking is ensuring that you're not dependent on a few experts who could leave at any moment, in the same way as COBOL programmers could have left. >> But it's not just the data, and it's not just the metadata, it really is the data structure. >> It is the model. Just the whole way that this has been put together and the reason why. And the ability to continue to upgrade that and change that over time. So those assets are incredibly important but at the moment there is no way that you can, there isn't technology available for you to actually protect those assets. >> So if I combine what you just said with what Neil Raden was talking about, David Vallante's put forward a good vision of what's required. Neil Raden's made the observation that this is going to be much more than technology. There's a lot of change, not change management at a low level inside the IT, but business change and the technology companies also have to step up and be able to support this. We're seeing this, we're seeing a number of different vendor types start to enter into this space. Certainly storage guys, Dylon Sears talking about doing a better job of data protection we're seeing middleware companies, TIBCO and DISCO, talk about doing this differently. We're seeing file systems, Scality, WekaIO talk about doing this differently. Backup and restore companies, Veeam, Veritas. I mean, everybody's looking at this and they're all coming at it. Just really quickly David, where's the inside track at this point? >> For me it is so much whitespace as to be unbelievable. >> So nobody has an inside track yet. >> Nobody has an inside track. Just to start with a few things. It's clear that you should keep data where it is. The cost of moving data around an organization from inside to out, is crazy. >> So companies that keep data in place, or technologies to keep data in place, are going to have an advantage. >> Much, much, much greater advantage. Sure, there must be backups somewhere. But you need to keep the working copies of data where they are because it's the real-time access, usually that's important. So if it originates in the cloud, keep it in the cloud. If it originates in a data-provider, on another cloud, that's where you should keep it. If it originates on your premise, keep it where it originated. >> Unless you need to combine it. But that's a new origination point. >> Then you're taking subsets of that data and then combining that up for itself. So that would be my first point. So organizations are going to need to put together what George was talking about, this metadata of all the data, how it interconnects, how it's being used. The flow of data through the organization, it's amazing to me that when you go to an IT shop they cannot define for you how the data flows through that data center or that organization. That's the requirement that you have to have and AI is going to be part of that solution, of looking at all of the applications and the data and telling you where it's going and how it's working together. >> So the second thing would be companies that are able to build or conceive of networks as data. Will also have an advantage. And I think I'd add a third one. Companies that demonstrate perennial observations, a real understanding of the unbelievable change that's required you can't just say, oh Facebook wants this therefore everybody's going to want it. There's going to be a lot of push marketing that goes on at the technology side. Alright so let's get to some Action Items. David Vellante, I'll start with you. Action Item. >> Well the future's going to be one where systems see, they talk, they sense, they recognize, they control, they optimize. It may be tempting to say, you know what I'm going to wait, I'm going to sit back and wait to figure out how I'm going to close that machine intelligence gap. I think that's a mistake. I think you have to start now, and you have to start with your data model. >> George Gilbert, Action Item. >> I think you have to keep in mind the guardrails related to governance, and trust, when you're building applications on the new data fabric. And you can take the approach of a platform-oriented one where you're plugging into an API, like Apache Atlas, that Hortonworks is driving, or a discovery-oriented one as David was talking about which would be something like Alation, using machine learning. But if, let's say the use case starts out as an IOT, edge analytics and cloud inferencing, that data science pipeline itself has to now be part of this fabric. Including the output of the design time. Meaning the models themselves, so they can be managed. >> Excellent. Jim Kobielus, you've been pretty quiet but I know you've got a lot to offer. Action Item, Jim. >> I'll be very brief. What you need to do is protect your data science knowledge base. That's the way to de-risk this entire process. And that involves more than just a data catalog. You need a data science expertise registry within your distributed value chain. And you need to manage that as a very human asset that needs to grow. That is your number one asset going forward. >> Ralph Finos, you've also been pretty quiet. Action Item, Ralph. >> Yeah, I think you've got to be careful about what you're trying to get done. Whether it's, it depends on your industry, whether it's finance or whether it's the entertainment business, there are different requirements about data in those different environments. And you need to be cautious about that and you need leadership on the executive business side of things. The last thing in the world you want to do is depend on data scientists to figure this stuff out. >> And I'll give you the second to last answer or Action Item. Neil Raden, Action Item. >> I think there's been a lot of progress lately in creating tools for data scientists to be more efficient and they need to be, because the big digital giants are draining them from other companies. So that's very encouraging. But in general I think becoming a data-driven, a digital transformation company for most companies, is a big job and I think they need to it in piece parts because if they try to do it all at once they're going to be in trouble. >> Alright, so that's great conversation guys. Oh, David Floyer, Action Item. David's looking at me saying, ah what about me? David Floyer, Action Item. >> (laughing) So my Action Item comes from an Irish proverb. Which if you ask for directions they will always answer you, "I wouldn't start from here." So the Action Item that I have is, if somebody is coming in saying you have to re-do all of your applications and re-write them from scratch, and start in a completely different direction, that is going to be a 20-year job and you're not going to ever get it done. So you have to start from what you have. The digital assets that you have, and you have to focus on improving those with additional applications, additional data using that as the foundation for how you build that business with a clear long-term view. And if you look at some of the examples that were given early, particularly in the insurance industries, that's what they did. >> Thank you very much guys. So, let's do an overall Action Item. We've been talking today about the challenges of de-risking digital business which ties directly to the overall understanding of the role of data assets play in businesses and the technology's ability to move from just protecting data, restoring data, to actually restoring the relationships in the data, the structures of the data and very importantly the models that are resident in the data. This is going to be a significant journey. There's clear evidence that this is driving a new valuation within the business. Folks talk about data as the new oil. We don't necessarily see things that way because data, quite frankly, is a very very different kind of asset. The cost could be shared because it doesn't suffer the same limits on scarcity. So as a consequence, what has to happen is, you have to start with where you are. What is your current value proposition? And what data do you have in support of that value proposition? And then whiteboard it, clean slate it and say, what data would we like to have in support of the activities that we perform? Figure out what those gaps are. Find ways to get access to that data through piecemeal, piece-part investments. That provide a roadmap of priorities looking forward. Out of that will come a better understanding of the fundamental data assets that are being created. New models of how you engage customers. New models of how operations works in the shop floor. New models of how financial services are being employed and utilized. And use that as a basis for then starting to put forward plans for bringing technologies in, that are capable of not just supporting the data and protecting the data but protecting the overall organization of data in the form of these models, in the form of these relationships, so that the business can, as it creates these, as it throws off these new assets, treat them as the special resource that the business requires. Once that is in place, we'll start seeing businesses more successfully reorganize, reinstitutionalize the work around data, and it won't just be the big technology companies who have, who people call digital native, that are well down this path. I want to thank George Gilbert, David Floyer here in the studio with me. David Vellante, Ralph Finos, Neil Raden and Jim Kobelius on the phone. Thanks very much guys. Great conversation. And that's been another Wikibon Action Item. (upbeat music)

(upbeat music) >> Announcer: Live from San Jose, it's theCUBE presenting Big Data Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partner. >> What am I going to spend time, next 15, 20 minutes or so, talking about. I'm going to answer three things. Our research has gone deep into where are we now in the big data community. I'm sorry, where is the big data community going, number one. Number two is how are we going to get there and number three, what do the numbers say about where we are? So those are the three things. Now, since when we want to get out of here, I'm going to fly through some of these slides but again there's a lot of opportunity for additional conversation because we're all about having conversations with the community. So let's start here. The first thing to know, when we think about where this is all going is it has to be bound. It's inextricably bound up with digital transformation. Well, what is digital transformation? We've done a lot of research on this. This is Peter Drucker who famously said many years ago, that the purpose of a business is to create and keep a customer. That's what a business is. Now what's the difference between a business and a digital business? What's the business between Sears Roebuck, or what's the difference between Sears Roebuck and Amazon? It's data. A digital business uses data as an asset to create and keep customers. It infuses data and operations differently to create more automation. It infuses data and engagement differently to catalyze superior customer experiences. It reformats and restructures its concept of value proposition and product to move from a product to a services orientation. The role of data is the centerpiece of digital business transformation and in many respects that is where we're going, is an understanding and appreciation of that. Now, we think there's going to be a number of strategic capabilities that will have to be built out to make that possible. First off, we have to start thinking about what it means to put data to work. The whole notion of an asset is an asset is something that can be applied to a productive activity. Data can be applied to a productive activity. Now, there's a lot of very interesting implications that we won't get into now, but essentially if we're going to treat data as an asset and think about how we could put more data to work, we're going to focus on three core strategic capabilities about how to make that possible. One, we need to build a capability for collecting and capturing data. That's a lot of what IoT is about. It's a lot of what mobile computing is about. There's going to be a lot of implications around how to ethically and properly do some of those things but a lot of that investment is about finding better and superior ways to capture data. Two, once we are able to capture that data, we have to turn it into value. That in many respects is the essence of big data. How we turn data into data assets, in the form of models, in the form of insights, in the form of any number of other approaches to thinking about how we're going to appropriate value out of data. But it's not just enough to create value out of it and have it sit there as potential value. We have to turn it into kinetic value, to actually do the work with it and that is the last piece. We have to build new capabilities for how we're going to apply data to perform work better, to enact based on data. Now, we've got a concept we're researching now that we call systems of agency, which is the idea that there's going to be a lot of new approaches, new systems with a lot of intelligence and a lot of data that act on behalf of the brand. I'm not going to spend a lot of time going into this but remember that word because I will come back to it. Systems of agency is about how you're going to apply data to perform work with automation, augmentation, and actuation on behalf of your brand. Now, all this is going to happen against the backdrop of cloud optimization. I'll explain what we mean by that right now. Very importantly, increasingly how you create value out of data, how you create future options on the value of your data is going to drive your technology choices. For the first 10 years of the cloud, the presumption is all data was going to go to the cloud. We think that a better way of thinking about it is how is the cloud experience going to come to the data. We've done a lot of research on the cost of data movement and both in terms of the actual out-of-pocket costs but also the potential uncertainty, the transaction costs, etc, associated with data movement. And that's going to be one of the fundamental pieces or elements of how we think about the future of big data and how digital business works, is what we think about data movement. I'll come to that in a bit. But our proposition is increasingly, we're going to see architectural approaches that focus on how we're going to move the cloud experience to the data. We've got this notion of true private cloud which is effectively the idea of the cloud experience on or near premise. That doesn't diminish the role that the cloud's going to play on industry or doesn't say that Amazon and AWS and Microsoft Azure and all the other options are not important. They're crucially important but it means we have to start thinking architecturally about how we're going to create value of data out of data and recognize that means that it, we have to start envisioning how our organization and infrastructure is going to be set up so that we can use data where it needs to be or where it's most valuable and often that's close to the action. So if we think then about that very quickly because it's a backdrop for everything, increasingly we're going to start talking about the idea of where's the workload going to go? Where's workload the dog going to be against this kind of backdrop of the divorce of infrastructure? We believe that and our research pretty strongly shows that a lot of workloads are going to go to true private cloud but a lot of big data is moving into the cloud. This is a prediction we made a few years ago and it's clearly happening and it's underway and we'll get into what some of the implications are. So again, when we say that a lot of the big data elements, a lot of the process of creating value out of data is going to move into the cloud. That doesn't mean that all the systems of agency that build or rely on that data, the inference engines, etc, are also in a public cloud. A lot of them are going to be distributed out to the edge, out to where the action needs to be because of latency and other types of issues. This is a fundamental proposition and I know I'm going fast but hopefully I'm being clear. All right, so let's now get to the second part. This is kind of where the industry's going. Data is an asset. Invest in strategic business capabilities to appreciate, to create those data assets and appreciate the value of those assets and utilize the cloud intelligently to generate and ensure increasing returns. So the next question is well, how will we get there? Now. Right now, not too far from here, Neil Raden for example, was on the show floor yesterday. Neil made the observation that, as he wandered around, he only heard the word big data two or three times. The concept of big data is not dead. Whether the term is or is not is somebody else's decision. Our perspective, very simply, is that the notion is bifurcating. And it's bifurcating because we see different strategic imperatives happening at two different levels. On the one hand, we see infrastructure convergence. The idea that increasingly we have to think about how we're going to bring and federated data together, both from a systems and a data management standpoint. And on the other hand, we're going to see infrastructure or application specialization. That's going to have an enormous implication over next few years, if only because there just aren't enough people in the world that understand how to create value out of data. And there's going to be a lot of effort made over the next few years to find new ways to go from that one expertise group to billions of people, billions of devices, and those are the two dominant considerations in the industry right now. How can we converge data physically, logically, and on the other hand, how can we liberate more of the smarts associated with this very, very powerful approach so that more people get access to the capacities and the capabilities and the assets that are being generated by that process. Now, we've done at Wikibon, probably I don't know, 18, 20, 23 predictions overall on the role that or on the changes being wrought by digital business. Here I'm going to focus on four of them that are central to our big data research. We have many more but I'm just going to focus on four. The first one, when we think about infrastructure convergence we worry about hardware. Here's a prediction about what we think is going to happen with hardware and our observation is we believe pretty strongly that future systems are going to be built on the concept of how do you increase the value of data assets. The technologies are all in place. Simpler parts that it more successfully bind specifically through all its storage and network are going to play together. Why, because increasingly that's the fundamental constraint. How do I make data available to other machines, actors, sources of change, sources of process within the business. Now, we envision or we are watching before our very eyes, new technologies that allow us to take these simple piece parts and weave them together in very powerful fabrics or grids, what we call UniGrid. So that there is almost no latency between data that exists within one of these, call it a molecule, and anywhere else in that grid or lattice. Now again, these are not systems that are going to be here in five years. All the piece parts are here today and there are companies that are actually delivering them. So if you take a look at what Micron has done with Mellanox and other players, that's an example of one of these true private cloud oriented machines in place. The bottom line though is that there is a lot of room left in hardware. A lot of room. This is what cloud suppliers are building and are going to build but increasingly as we think about true private cloud, enterprises are going to look at this as well. So future systems for improving data assets. The capacity of this type of a system with low latency amongst any source of data means that we can now think about data not as... Not as a set of sources that have to be each individually, each having some control over its own data and sinks woven together by middleware and applications but literally as networks of data. As we start to think about distributing data and distributing control and authority associated with that data more broadly across systems, we now have to think about what does it mean to create networks of data? Because that, in many respects, is how these assets are going to be forged. I haven't even mentioned the role that security is going to play in all of this by the way but fundamentally that's how it's likely to play out. We'll have a lot of different sources but from a business standpoint, we're going to think about how those sources come together into a persistent network that can be acted upon by the business. One of the primary drivers of this is what's going on at the edge. Marc Andreessen famously said that software is eating the world, well our observation is great but if software's eating the world, it's eating it at the edge. That's where it's happening. Secondly, that this notion of agency zones. I said I'm going to bring that word up again, how systems act on behalf of a brand or act on behalf of an institution or business is very, very crucial because the time necessary to do the analysis, perform the intelligence, and then take action is a real constraint on how we do things. And our expectation is that we're going to see what we call an agency zone or a hub zone or cloud zone defined by latency and how we architect data to get the data that's necessary to perform that piece of work into the zone where it's required. Now, the implications of this is none of this is going to happen if we don't use AI and related technologies to increasingly automate how we handle infrastructure. And technologies like blockchain have the potential to provide a interesting way of imagining how these networks of data actually get structured. It's not going to solve everything. There's some people that think the blockchain is kind of everything that's necessary but it will be a way of describing a network of data. So we see those technologies on the ascension. But what does it mean for DBMS? In the old way, in the old world, the old way of thinking, the database manager was the control point for data. In the new world these networks of data are going to exist beyond a single DBMS and in fact, over time, that concept of federated data actually has a potential to become real. When we have these networks of data, we're going to need people to act upon them and that's essentially a lot of what the data scientist is going to be doing. Identifying the outcome, identifying the data that's required, and weaving that data through the construction and management, manipulation of pipelines, to ensure that the data as an asset can persist for the purposes of solving a near-term problem or over whatever duration is required to solve a longer term problem. Data scientists remain very important but we're going to see, as a consequence of improvements in tooling capable of doing these things, an increasing recognition that there's a difference between a data scientist and a data scientist. There's going to be a lot of folks that participate in the process of manipulating, maintaining, managing these networks of data to create these business outcomes but we're going to see specialization in those ranks as the tooling is more targeted to specific types of activities. So the data scientist is going to become or will remain an important job, going to lose a little bit of its luster because it's going to become clear what it means. So some data scientists will probably become more, let's call them data network administrators or networks of data administrators. And very importantly as I said earlier, there's just not enough of these people on the planet and so increasingly when we think about again, digital business and the idea of creating data assets. A central challenge is going to be how to create the data or how to turn all the data that can be captured into assets that can be applied to a lot of different uses. There's going to be two fundamental changes to the way we are currently conceiving of the big data world on the horizon. One is well, it's pretty clear that Hadoop can only go so far. Hadoop is a great tool for certain types of activities and certain numbers of individuals. So Hadoop solves problems for an important but relatively limited subset of the world. Some of the new data science platforms that we just talked about, that I just talked about, they're going to help with a degree of specialization that hasn't been available before in the data world, will certainly also help but it also will only take it so far. The real way that we see the work that we're doing, the work that the big data community is performing, turned into sources of value that extend into virtually every single corner of humankind is going to be through these cloud services that are being built and increasingly through packaged applications. A lot of computer science, it still exists between what I just said and when this actually happens. But in many respects, that's the challenge of the vendor ecosystem. How to reconstruct the idea of packaged software, which has historically been built around operations and transaction processing, with a known data model and an unknown or the known process and some technology challenges. How do we reapply that to a world where we now are thinking about, well we don't know exactly what the process is because the data tells us at the moment that the actions going to be taking place. It's a very different way of thinking about application development. A very different way of thinking about what's important in IT and very different way of thinking about how business is going to be constructed and how strategy's going to be established. Packaged applications are going to be crucially important. So in the last few minutes here, what are the numbers? So this is kind of the basis for our analysis. Digital business, role of data is an asset, having an enormous impact in how we think about hardware, how do we think about database management or data management, how we think about the people involved in this, and ultimately how we think about how we're going to deliver all this value out to the world. And the numbers are starting to reflect that. So why don't you think about four numbers as I go through the two or three slides. Hundred and three billion, 68%, 11%, and 2017. So of all the numbers that you will see, those are four of the most important numbers. So let's start by looking at the total market place. This is the growth of the hardware, software, and services pieces of the big data universe. Now we have a fair amount of additional research that breaks all these down into tighter segments, especially in software side. But the key number here is we're talking about big numbers. 103 billion over the course of next 10 years and let's be clear that 103 billion dollars actually has a dramatic amplification on the rest of the computing industry because a lot of the pricing models associated with, especially the software, are tied back to open source which has its own issues. And very importantly, the fact that the services business is going to go through an enormous amount of change over the next five years as service companies better understand how to deliver some of these big data rich applications. The second point to note here is that it was in 2017 that the software market surpassed the hardware market in big data. Again, for first number of years we focused on buying the hardware and the system software associated with that and the software became something that we hope to discover. So I was having a conversation here in theCUBE with the CEO of Transwarp which is a very interesting Chinese big data company and I asked what's the difference between how you do things in China and how we do things in the US? He said well, in the US you guys focus on proof of concept. You spend an enormous amount of time asking, does the hardware work? Does the database software work? Does the data management software work? In China we focus on the outcome. That's what we focus on. Here you have to placate the IT organization to make sure that everybody in IT is comfortable with what's about to happen. In China, were focused on the business people. This is the first year that software is bigger than hardware and it's only going to get bigger and bigger over time. It doesn't mean again, that hardware is dead or hardware is not important. It's going to remain very important but it does mean that the centerpiece of the locus of the industry is moving. Now, when we think about what the market shares look like, it's a very fragmented market. 60%, 68% of the market is still other. This is a highly immature market that's going to go through a number of changes over the next few years. Partly catalyzed by that notion of infrastructure convergence. So in four years our expectation is that, that 68% is going to start going down pretty fast as we see greater consolidation in how some of these numbers come together. Now IBM is the biggest one on the basis of the fact that they operate in all these different segments. They operating the hardware, software, and services segment but especially because they're very strong within the services business. The last one I want to point your attention to is this one. I mentioned earlier on, that our expectation is that the market increasingly is going to move to a packaged application orientation or packaged services orientation as a way of delivering expertise about big data to customers. Splunk is the leading software player right now. Why, because that's the perspective that they've taken. Now, perhaps we're a limited subset. It's perhaps for a limited subset of individuals or markets or of sectors but it takes a packaged application, weaves these technologies together, and applies them to an outcome. And we think this presages more of that kind of activity over the course of the next few years. Oracle, kind of different approach and we'll see how that plays out over the course of the next five years as well. Okay, so that's where the numbers are. Again, a lot more numbers, a lot of people you can talk to. Let me give you some action items. First one, if data was a core asset, how would IT, how would your business be different? Stop and think about that. If it wasn't your buildings that were the asset, it wasn't the machines that were the asset, it wasn't your people by themselves who were the asset, but data was the asset. How would you reinstitutionalize work? That's what every business is starting to ask, even if they don't ask it in the same way. And our advice is, then do it because that's the future of business. Not that data is the only asset but data is a recognized central asset and that's going to have enormous impacts on a lot of things. The second point I want to leave you with, tens of billions of users and I'm including people and devices, are dependent on thousands of data scientists that's an impedance mismatch that cannot be sustained. Packaged apps and these cloud services are going to be the way to bridge that gap. I'd love to tell you that it's all going to be about tools, that we're going to have hundreds of thousands or millions or tens of millions or hundreds of millions of data scientists suddenly emerge out of the woodwork. It's not going to happen. The third thing is we think that big businesses, enterprises, have to master what we call the big inflection. The big tech inflection. The first 50 years were about known process and unknown technology. How do I take an accounting package and do I put on a mainframe or a mini computer a client/server or do I do it on the web? Unknown technology. Well increasingly today, all of us have a pretty good idea what the base technology is going to be. Does anybody doubt it's going to be the cloud? We got a pretty good idea what the base technology is going to be. What we don't know is what are the new problems that we can attack, that we can address with data rich approaches to thinking about how we turn those systems into actors on behalf of our business and customers. So I'm a couple minutes over, I apologize. I want to make sure everybody can get over to the keynotes if you want to. Feel free to stay, theCUBE's going to be live at 9:30. If I got that right. So it's actually pretty exciting if anybody wants to see how it works, feel free to stay. Georgia's here, Neil's here, I'm here. I mentioned Greg Terrio, Dave Volante, John Greco, I think I saw Sam Kahane back in the corner. Any questions, come and ask us, we'll be more than happy. Thank you very much for, oh David Volante. >> David: I have a question. >> Yes. >> David: Do you have time? >> Yep. >> David: So you talk about data as a core asset, that if you look at the top five companies by market cap in the US, Google, Amazon, Facebook, etc. They're data companies, they got data at the core which is kind of what your first bullet here describes. How do you see traditional companies closing that gap where humans, buildings, etc at the core as we enter this machine intelligence era, what's your advice to the traditional companies on how they close that gap? >> All right. So the question was, the most valuable companies in the world are companies that are well down the path of treating data as an asset. How does everybody else get going? Our observation is you go back to what's the value proposition? What actions are most important? what's data is necessary to perform those actions? Can changing the way the data is orchestrated and organized and put together inform or change the cost of performing that work by changing the cost transactions? Can you increase a new service along the same lines and then architect your infrastructure and your business to make sure that the data is near the action in time for the action to be absolute genius to your customer. So it's a relatively simple thought process. That's how Amazon thought, Apple increasingly thinks like that, where they design the experience and they think what data is necessary to deliver that experience. That's a simple approach but it works. Yes, sir. >> Audience Member: With the slide that you had a few slides ago, the market share, the big spenders, and you mentioned that, you asked the question do any of us doubt that cloud is the future? I'm with Snowflake, I don't see many of those large vendors in the cloud and I was wondering if you could speak to what are you seeing in terms of emerging vendors in that space. >> What a great question. So the question was, when you look at the companies that are catalyzing a lot of the change, you don't see a lot of the big companies being at the leadership. And someone from Snowflake just said, well who's going to lead it? That's a big question that has a lot of implications but at this point time it's very clear that the big companies are suffering a bit from the old, from the old, trying to remember what the... RCA syndrome. I think Clay Christensen talked about this. You know, the innovators dilemma. So RCA actually is one of the first creators. They created the transistor and they held a lot of original patents on it. They put that incredible new technology, back in the forties and fifties, under the control of the people who ran the vacuum tube business. When was the last time anybody bought RCA stock? The same problem is existing today. Now, how is that going to play out? Are we going to see a lot of, as we've always seen, a lot of new vendors emerge out of this industry, grow into big vendors with IPO related exits to try to scale their business? Or are we going to see a whole bunch of gobbling up? That's what I'm not clear on but it's pretty clear at this point in time that a lot of the technology, a lot of the science, is being done in smaller places. The moderating feature of that is the services side. Because there's limited groupings of expertise that the companies that today are able to attract that expertise. The Googles, the Facebooks, the AWSs, etc, the Amazons. Are doing so in support of a particular service. IBM and others are trying to attract that talent so they can apply it to customer problems. We'll see over the next few years whether the IBMs and the Accentures and the big service providers are able to attract the kind of talent necessary to diffuse that knowledge into the industry faster. So it's the rate at which that the idea of internet scale computing, the idea of big data being applied to business problems, can diffuse into the marketplace through services. If it can diffuse faster that will have both an accelerating impact for smaller vendors, as it has in the past. But it may also again, have a moderating impact because a lot of that expertise that comes out of IBM, IBM is going to find ways to drive in the product faster than it ever has before. So it's a complicated answer but that's our thinking at this point time. >> Dave: Can I add to that? >> Yeah. (audience member speaking faintly) >> I think that's true now but I think the real question, not to not to argue with Dave but this is part of what we do. The real question is how is that knowledge going to diffuse into the enterprise broadly? Because Airbnb, I doubt is going to get into the business of providing services. (audience member speaking faintly) So I think that the whole concept of community, partnership, ecosystem is going to remain very important as it always has and we'll see how fast those service companies that are dedicated to diffusing knowledge, diffusing knowledge into customer problems actually occurs. Our expectation is that as the tooling gets better, we will see more people be able to present themselves truly as capable of doing this and that will accelerate the process. But the next few years are going to be really turbulent and we'll see which way it actually ends up going. (audience member speaking faintly) >> Audience Member: So I'm with IBM. So I can tell you 100% for sure that we are, I hired literally 50 data scientists in the last three months to go out and do exactly what you're saying. Sit down with clients and help them figure out how to do data science in the enterprise. And so we are in fact scaling it, we're getting people that have done this at Google, Facebook. Not a whole lot of those 'cause we want to do it with people that have actually done it in legacy fortune 500 Companies, right? Because there's a little bit difference there. >> So. >> Audience Member: So we are doing exactly what you said and Microsoft is doing the same thing, Amazon is actually doing the same thing too, Domino Data Lab. >> They don't like they're like talking about it too much but they're doing it. >> Audience Member: But all the big players from the data science platform game are doing this at a different scale. >> Exactly. >> Audience Member: IBM is doing it on a much bigger scale than anyone else. >> And that will have an impact on ultimately how the market gets structured and who the winners end up being. >> Audience Member: To add too, a lot of people thought that, you mentioned the Red Hat of big data, a lot of people thought Cloudera was going to be the Red Hat of big data and if you look at what's happened to their business. (background noise drowns out other sounds) They're getting surrounded by the cloud. We look at like how can we get closer to companies like AWS? That was like a wild card that wasn't expected. >> Yeah but look, at the end of the day Red Hat isn't even the Red Hat of open source. So the bottom line is the thing to focus on is how is this knowledge going to diffuse. That's the thing to focus on. And there's a lot of different ways, some of its going to diffuse through tools. If it diffuses through tools, it increases the likelihood that we'll have more people capable of doing this in IBM and others can hire more. That Citibank can hire more. That's an important participant, that's an important play. So you have something to say about that but it also says we're going to see more of the packaged applications emerge because that facilitates the diffusion. This is not, we haven't figured out, I don't know exactly, nobody knows exactly the exact shape it's going to take. But that's the centerpiece of our big data researches. How is that diffusion process going to happen, accelerate, and what's the resulting structure going to look like? And ultimately how are enterprises going to create value with whatever results. Yes, sir. (audience member asks question faintly) So the recap question is you see more people coming in and promising the moon but being incapable of delivering because they are, partly because the technology is uncertain and for other reasons. So here's our approach. Or here's our observation. We actually did a fair amount of research on this. When you take a look at what we call a approach to doing big data that's optimized for the costs of procurement i.e. let's get the simplest combination of infrastructure, the simplest combination of open-source software, the simplest contracting, to create that proof of concept that you can stand things up very quickly if you have enough expertise but you can create that proof of concept but the process of turning that into actually a production system extends dramatically. And that's one of the reasons why the Clouderas did not take over the universe. There are other reasons. As George Gilbert's research has pointed out, that Cloudera is spending 53, 55 % of their money right now just integrating all the stuff that they bought into the distribution five years ago. Which is a real great recipe for creating customer value. The bottom line though is that if we focus on the time to value in production, we end up taking a different path. We don't focus as much on whether the hardware is going to work and the network is going to work and the storage can be integrated and how it's going to impact the database and what that's going to mean to our Oracle license pool and all the other things that people tend to think about if they're focused on the technology. And so as a consequence, you get better time to value if you focus on bringing the domain expertise, working with the right partner, working with the appropriate approach, to go from what's the value proposition, what actions are associated with a value proposition, what's stated in that area to perform those actions, how can I take transaction costs out of performing those actions, where's the data need to be, what infrastructure do I require? So we have to focus on a time to value not the time to procure. And that's not what a lot of professional IT oriented people are doing because many of them, I hate say it, but many of them still acquire new technology with the promise to helping the business but having a stronger focus on what it's going to mean to their careers. All right, I want to be really respectful to everybody's time. The keynotes start in about five minutes which means you just got time. If you want to stay, feel free to stay. We'll be here, we'll be happy to talk but I think that's pretty much going to close our presentation broadcast. Thank you very much for being an attentive audience and I hope you found this useful. (upbeat music)

>> Welcome back everybody. Jeff Frick here with theCube. We're in the Palo Alto studio talking about customer journeys today. And we're really excited to have professional, who's been doing this for a long time, he's Jeff Weidener, he's an Information Management Professional at this moment in time, and still, in the past and future, Jeff Welcome. >> Well thank you for having me. >> So you've been playing in the spheres for a very long time, and we talked a little bit before we turned the cameras on, about one of the great topics that I love in this area is, the customer, the 360 view of the customer. And that the Nirvana that everyone says you know, we're there, we're pulling in all these data sets, we know exactly what's going on, the person calls into the call center and they can pull up all their records, and there's this great vision that we're all striving for. How close are we to that? >> I think we're several years away from that perfect vision that we've talked about, for the last, I would say, 10, 10 to 15 years, that I've dealt with, from folks who were doing catalogs, like Sears catalogs, all the way to today, where we're trying to mix and match all this information, but most companies are not turning that into actionable data, or actionable information, in any way that's reasonable. And it's just because of the historic kind of Silo, nature of all these different systems, I mean, you know, I keep hearing about, we're gonna do it, all these things can tie together, we can dump all the data in a single data lake and pull it out, what are some of the inhibitors and what are some of the approaches to try to break some of those down? >> Most has been around getting that data lake, in order to put the data in its spot, basically try and make sure that, do I have the environment to work in? Many times a traditional enterprise warehouse doesn't have the right processing power, for you, the individual, who wants to do the work, or, doesn't have the capacity that'll allow you to just bring all the data in, try to ratify it. That's really just trying to do the data cleansing, and trying to just make some sense of it, cause many times, there aren't those domain experts. So I usually work in marketing, and on our Customer 360 exercise, was around, direct mail, email, all the interactions from our Salesmaker, and alike. So, when we look at the data, we go, I don't understand why the Salesmaker is forgetting X, of that behavior that we want to roll together. >> Right. >> But really it's finding that environment, second is the harmonization, is I have Bob Smith and Robert Smith, and Master Data Management Systems, are perhaps few and far between, of being real services that I can call as a data scientist, or as a data worker, to be able to say, how do I line these together? How can I make sure that all these customer touchpoints are really talking about the same individual, the company, or maybe just the consumer? >> Right. >> And finally, it is in those Customer 360 projects getting those teams to want to play together, getting that crowdsourcing, either to change the data, such as, I have data, as you mentioned around Chat, and I want you to tell me more about it, or I want you to tell me how I can break it down. >> Right, right. >> And if I wanna make changes to it, you go, we'll wait, where's your money, in order to make that change. >> Right, right. >> And there's so many aspects to it, right. So there's kind of the classic, you know, ingest, you gotta get the data, you gotta run it through the processes you said did harmonize it to bring it together, and then you gotta present it to the person who's in a position at the moment of truth, to do something with it. And those are three very very different challenges. They've been the same challenges forever, but now we're adding all this new stuff to it, like, are you pulling data from other sources outside of the system of record, are you pulling social data, are you pulling other system data that's not necessarily part of the transactional system. So, we're making the job harder, at the same time, we're trying to give more power to more people and not just the data scientists. But as you said I think, the data worker, so how's that transformation taking place where we're enabling more kind of data workers if you will, that aren't necessarily data scientists, to have the power that's available with the analytics, and an aggregated data set behind them. >> Right. Well we are creating or have created the wild west, we gave them tools, and said, go forth and make, make something out of it. Oh okay. Then we started having this decentralization of all the tools, and when we finally gave them the big tools, the big, that's quote unquote, big data tools, like the process, billings of records, that still is the wild west, but at least we're got them centralized with certain tools. So we were able to do at least standardize on the tool set, standardize on the data environment, so that at least when they're working on that space, we get to go, well, what are you working on? How are you working on that? What type of data are you working with? And how do we bring that back as a process, so that we can say, you did something on Chat Data? Great! Bob over here, he likes to work with that Chat data. So that, that exposure and transparency because of these centralization data. Now, new tools are adding on top of that, data catalogs, and putting inside tools that will make it so that you actually tell, that known information, all-in-one wiki-like interface. So we're trying to add more around putting the right permissions on top of that data, cataloging them in some way, with these either worksheets, or these information management tools, so that, if you're starting to deal with privacy data, you've got a flag, from, it's ingest all the way to the end. >> Right. >> But more controls are being seen as a way that a business is improving its maturity. >> Yeah. Now, the good news bad news is, more and more of the actual interactions are electronic. You want it going to places, they're not picking up the phone as much, as they're engaging with the company either via web browser or more and more a mobile browser, a mobile app, whatever. So, now the good news is, you can track all that. The bad news is, you can track all that. So, as we add more complexity, then there's this other little thing that everybody wants to do now, which is real-time, right, so with Kafka and Flink and Spark and all these new technologies, that enable you to basically see all the data as it's flowing, versus a sampling of the data from the past, a whole new opportunity, and challenge. So how are you seeing it and how are you gonna try to take advantage of that opportunity as well as address that challenge in your world. >> Well in my data science world, I've said, hey, give me some more data, keep on going, and when I have to put on the data sheriff hat, I'm now having to ask the executives, and our stakeholders, why streaming? Why do you really need to have all of this? >> It's the newest shiny toy. >> New shiny toy! So, when you talk to a stakeholder and you say, you need a shiny toy, great. I can get you that shiny toy. But I need an outcome. I need a, a value. And that helps me in tempering the next statement I give to them, you want streaming, so, or you want real time data, it's gonna cost you, three X. Are you gonna pay for it? Great. Here's my shiny toy. But yes, with the influx of all of this data, you're having to change the architecture and many times IT traditionally hasn't been able to make that, that rapid transition, which lends itself to shadow IT, or other folks trying to cobble something together, not to make that happen. >> And then there's this other pesky little thing that gets in the way, in the form of governance, and security. >> Compliance, privacy and finally marketability, I wanna give you a, I want you to feel that you're trusting me, in handling your data, but also that when I respond back to you, I'm giving you a good customer experience so called, don't be creepy. >> Right, right. >> Lately, the new compliance rule in Europe, GDPR, a policy that comes with a, well, a shotgun, that says, if there are violations of this policy, which involves privacy, or the ability for me to be forgotten, of the information that a corporation collects, it can mean four percent of a total company's revenue. >> Right. >> And that's on every instance, that's getting a lot of motivation for information governance today. >> Right. >> That risk, but the rules are around, trying to be able to say, where did the data come from? How did the data flow through the system? Who's touched that data? And those information management tools are mostly the human interaction, hey what are you guys working on? How are you guys working on it? What type of assets are you actually driving, so that we can bring it together for that privacy, that compliance, and workflow, and then later on top of that, that deliverability. How do you want to be contacted? How do you, what are the areas that you feel, are the ways that we should engage with you? And of course, everything that gets missed in any optimization exercise, the feedback loop. I get feedback from you that say, you're interested in puppies, but your data set says you're interested in cats. How do I make that go into a Customer 360 product. So, privacy, and being, and coming at, saying, oh, here's an advertisement for, for hippos and you go, what do you know about me that I don't know? >> Wrong browser. >> So you chose Datameer, along the journey, why did you choose them, how did you implement them, and how did they address some of these issues that we've just been discussing? >> Datameer was chosen primarily to take on that self-service data preparational layer from the beginning. Dealing with large amounts of online data, we move from from taking the digital intelligence tools that are out there, knowing about browser activities, the cookies that you have to get your identity, and said, we want the entire feed. We want all of that information, because we wanna make that actionable. I don't wanna just give it to a BI report, I wanna turn it into marketing automation. So we got the entire feed of data, and we worked on that with the usual SQL tools, but after a while, it wasn't manageable, by either, all of the 450 to 950 columns of data, or the fact that there are multiple teams working on it, and I had no idea, what they were able to do. So I couldn't share in that value, I couldn't reuse, the insights that they could have. So Datameer allowed for a visual interface, that was not in a coding language, that allowed people to start putting all of their work inside one interface, that didn't have to worry about saving it up to the server, it was all being done inside one environment. So that it could take not only the digital data, but the Salesforce CRN data, marry them together and let people work with it. And it broadened on the other areas, again allowing it that crowdsourcing of other people's analytics. Why? Mostly because of the state we are in around IT, is an inability to change rapidly, at least for us, in our field. >> Right. >> That my, the biggest problem we had, was there wasn't a scheduler. We didn't have the ability to get value out of our, on our work, without having someone to press the button and run it, and if they ran it, it took eight hours, they walked away, it would fail. And you had no, you had to go back and do it all over again. >> Oh yeah. >> So Datameer allows us to have that self-service interface, that had management that IT could agree upon, to let us have our own lab environment, and execute our work. >> So what was the results, when you suddenly give people access to this tool? I mean, were they receptive, did you have to train them a lot, did some people just get it and some people just don't, they don't wanna act on data, what was kind of the real-world results of rolling this out, within the population? Real-world results allowed us to get ten million dollars in uplift, in our marketing activities across multiple channels. >> Ten million dollars in uplift? How did you measure that? >> That was measured through the operating expenses, by one not sending that work outside, some of the management, of the data, is being, was sent outside, and that team builds their own models off of them, we said, we should be able to drink our own champagne, second, it was on the uplift of a direct mail and email campaign, so having a better response rate, and generally, not sending out a bunch of app store messages, that we weren't needing too. And then turning that into a list that could be sent out to our email and direct mail vendors, to say, this is what we believe, this account or contact is engaged with on the site. Give those a little bit more context. So we add that in, that we were hopefully getting and resonating a better message. >> Right. >> In, and where did you start? What was the easiest way to provide an opportunity for people new to this type of tooling access to have success? >> Mostly it was trying to, was taking pre-doctored worksheets, or already pre-packaged output, and one of the challenges that we had were people saying well I don't wanna work in a visual language, while they're users of tools like Tableau or Clicks, and others that are happy to drag-and-drop in their data, many of the data workers, the tried-and-true, are saying, I wanna write it in SQL. >> Mm hm. >> So, we had to give at least that last mile, analytical data set to them, and say, okay. Yeah, go ahead and move it over to your SQL environment, move it over into the space that you feel comfortable and you feel confident to control, but let' come on back and we'll translate it back to, this tool, we'll show you how easy it was, to go from, working with IT, which would take months, to go and doing it on yourself, which would take weeks, and the processing and the cost of your Siloed, shadowed IT environment, will go down in days. We're able to show them that, that acceleration of time to market of their data. >> What was your biggest surprise? An individual user, an individual use case, something that really you just didn't see coming, that's kind of a pleasant, you know the law of unintended consequences on the positive side. >> That's was such a wide option, I mean honestly, beginning back from the data science background, we thought it would just be, bring your data in, throw it on out there, and we're done. We went from, maybe about 20 large datasets of AdTech and Martech, and information, advertising, technology, marketing technology, data, to CRMM formation, order activity, and many other categories, just within marketing alone, and I think perhaps, the other big ah-ha moment was, since we brought that in, of other divisions data, those own teams came in, said, hey, we can use this too. >> Right. >> The adoption really surprised me that it would, you would have people that say, oh I can work with this, I have this freedom to work with this data. >> Right right. >> Well we see it time and time again, it's a recurring theme of all the things we cover, which is, you know a really, big piece of the innovation story, is giving, you know, more people access to more data, and the tools to actually manipulate it. So that you can unlock that brain power, as opposed to keeping it with the data scientists on Mahogany Row, and the super-big brain. So, sounds like that really validates that whole hypothesis. >> I went through reviewing hands-on 11 different tools, when I chose Datameer. This was everything from, big name companies, to small start-up companies, that have wild artificial intelligence slogans in their marketing material, and we chose it mostly because it had the right fit, as an end-to-end approach. It had the scheduler, it had the visual interface, it had the, enough management and other capabilities that IT would leave us alone. Some of the other products that we were looking at gave you, Pig-El-Lee to work with data, will allow you to schedule data, but they never came all together. And for the value we get out of it, we needed to have something altogether. >> Right. Well Jeff, thanks for taking a few minutes and sharing your story, really appreciate it, and it sounds like it was a really successful project. >> Was! >> All right. He's Jeff Weidener, I'm Jeff Frick, you're watching theCube from Palo Alto. Thanks for watching.