(bright upbeat music) >> Well, hello everybody. John Walls is here on "the CUBE". Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK and for with us or with us. Now to talk about that is Lianne Anderton, who is in with the Intelligent Automation Garage Delivery Lead at the UK Department of Work and Pension. Lianne, good to see you today. Thanks for joining us here on "the CUBE". >> Hi, thanks for having me. >> And also with this us is Amar Narayan, who is a Manager Director at Accenture the AWS Business Group for the Lead in Health and Public Sector, also UK and Ireland. And Amar, I think, you and Lianne, are in the same location, Newcastle, I believe in the UK, is that right? >> Yeah, absolutely. Yep, yeah, we're, here in the northeast of UK. >> Well, thank you for being with us. I appreciate the time. Lianne, let's talk about what you do, the Department of Work and Pension, the famous DWP in England. You have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? >> Sure, so for the Department for Work and Pensions I think the pensions bit is fairly self explanatory so anybody who is over state pension age within the UK. for the work part of that we also deal with people of working age. So, these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work. And we also deal with health related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So, we're aware of a huge part of the UK government. >> All right, so say that number again. How many? >> 20 million claimants every year. >> Million with an M, right? >> Yeah. >> So, and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How, much traffic basically, are you seeing? >> An extraordinary amount? I'm not even, I don't think I even know that number. (Lianne laughing) >> Mind blowing, right? So, it's- >> A huge, huge amount. >> Mind blowing. >> Yeah, so, basically the we kind of keep the country going. So, you know, if the department for Work and Pensions kind of didn't exist anymore then actually it would cause an infinite number of problems in society. We, kind of help and support the people who need that. And, yeah, so we play a really vital role in kind of you know, social care and kind of public service. >> So, what was your journey to Accenture then? What, eventually led you to them? What problem were you having and how have you collaborated to solve that? >> So, in terms of how we work with Accenture. So, we had in around 2017 DWP was looking at a projected number of transactions growing by about 210 million which was, you know, an extraordinary amount. And, you know, I think as we've kind of covered everything that we do is on a massive scale. So, we as DWP as an organization we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, you know, after kind of various kind of paths and ideas of how we were going to do that, automation, was actually the answer. But the problem that we have with that is that we have, like many governments around the world, we have really older legacy systems. So, each of these benefits that we deal with are on legacy systems. So, whatever we were going to develop had to, you know, connect to all of these, it had to ingest and then process all of these pieces of data some of which, you know, given the fact that a lot of these systems have a lot of manual input you have data issues there that you have to solve and whatever we did, you know, as we've talked about in terms of volumes has to scale instantly as well. So, it has to be able to scale up and down to meet demand and, you know, and that down scaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes but also you've got to be able to downscale when when it's not needed. But we had nothing that was like that kind of helped us to meet that demand. So, we built our own automation platform, The Intelligent Automation Garage and we did that with Accenture. >> So Amar, I'd like you to chime in here then. So, you're looking at this client who has this massive footprint and obviously vital services, right? So, that's paramount that you have to keep that in mind and the legacy systems that Lianne was just talking about. So, now you're trying to get 'em in the next gen but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case did you get them to automation as the solution? >> Sure, so I think I think one of the interesting things, yeah as Lianne has sort of described it, right? It's effectively like, you know the department has to have be running all of the time, right? They can't, you know, they can't effectively stop and then do a bunch of IT transformation, you know it's effectively like, you know, changing the wheels of a jumbo jet whilst it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we were in and the client relationship we had was that we knew we had to it wasn't just a technology play, we couldn't just go, "All right, let's just put some new technology in." What we also needed to do was really sort of create a culture, an innovation culture, and go, "Well how do we think about the problems that we currently have and how do we think about solving them differently and in collaboration, right?" So, not just the, "Let's just outsource a bunch of technology for to, you know, to Accenture and build a bunch of stuff." So, we very carefully thought about, well actually, the unique situation that they're in the demands that the citizens have on the services that the department provide. And as Lianne mentioned, that technology didn't exist. So, we fundamentally looked at this in a different way. So, we worked really closely with the department. We said, Look, actually what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know all of a sudden had a hundred thousand pension claims that needed to be processed in a week that you could click your fingers and, you know in a physical world you'd have another building all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week you didn't need that you no longer needed that building that stuff or the machinery. And we wanted to replicate that in the virtual world. So, we started designing a platform we utilized and focused on using AWS because it had the scalability. And we thought about, how were we going to connect something as new as AWS to all of these legacy systems. How are we going to make that work in the modern world? How are we going to integrate it? How we going to make sure it's secure? And frankly, we're really honest with the client we said, "Look, this hasn't been done before. Like, nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people, I think we can do it." And, we've prototyped and we've built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative but most importantly not doing sort of proof of concepts that didn't go anywhere but building something that actually scaled. And I think that was really the real the start of what was has been the Garage. >> So, And Lianne, you mentioned this and you just referred to it Amar, about The Garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talked about it, what the needs are all this and that, but Lianne, I'll let you jump in first and Amar, certainly compliment her remarks, but what is the IAG, what's the... >> So, you know, I think exactly what kind of Amar, has said from a from a kind of a development point of view I think it started off, you know, really, really small. And the idea is that this is DWP, intelligent automation center of excellence. So, you know, it's aims are that, you know, it makes sure that it scopes out kind of the problems that DWP are are facing properly. So, we really understand what the crux of the problem is. In large organizations It's very easy, I think to think you understand what the problem is where actually, you know, it is really about kind of delving into what that is. And actually we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like using as Amar said, all the exciting new technology that we kind of have available to us. That kind of sensible solution as to what that should look like. We then build that sensible solution and we then, you know as part of that, we make sure that it scales to demand. So, something that might start out with, I dunno, you know a few hundred claimants or kind of cases going through it can quite often, you know, once that's that's been successful scale really, really quickly because as you know, we have 20 million claimants that come through us every year. So, these types of things can grow and expand but also a really key function of what we do is that we have a fully supported in-house service as well. So, all of those automations that we build are then maintained and you know, so any changes that kind of needed to be need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as sufficient, sorry, as efficient as possible. And what I mean by that is looking at, you know as new technologies come around and they can allow us to do things more effectively. So, it allows us to kind of almost do that that kind of continuous improvement ourselves. So, that's a huge part of what we do as well. And you know, I think from a size point of view I said this started off really small as in the idea was this was a kind of center of excellence but actually as automation, I think as Amar alluded to is kind of really started to embed in DWP culture what we've started to kind of see is the a massive expansion in the types of of work that people want us to do and the volume of work that we are doing. So, I think we're currently running at around around a hundred people at the moment and I think, you know we started off with a scrum, a couple of scrum teams under Amar, so yeah, it's really grown. But you know, I think this is here to stay within DWP. >> Yeah, well when we talk about automation, you know virtual and robotics and all this I like to kind of keep the human element in mind here too. And Amar, maybe you can touch on that in certain terms of the human factors in this equation. 'Cause people think about, you know, robots it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things Lianne wants to do down the road, you know, is a road for people basically? >> Oh yeah, absolutely. I think fundamentally what the department does is support people and therefore the solutions that we designed and built had to factor that in mind right? We were trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports. The department itself is a big organization, right? We're up to, we're talking between sort of 70 and 80,000 employees. So, how do we embed automation but also make the lives of the, of the DWP agents better as well? And that's what we thought about. So we said, "Well look, we think we can design solutions that do both." So, a lot of our automations go through a design process and we work closely with our operations team and we go, well actually, you know in processing and benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data and there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, "Well how do I best support this citizen?" And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those and then we really focused on making sure that the elements that required the human, the human input was made as user friendly and centric as we possibly could. So, if there's a really complex case that needs to be processed, we were able to present the information in a really digestible and understandable way for the agents so that they could make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in but also maintain the quality and if not improve, you know the accuracy of the claims processing that we had. >> So, how do you know, and maybe Lianne, you can address this. How do you know that it's successful on both sides of that equation? And, 'cause Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the, your side too. So, how do you, what's your measurement for success and what kind of like raw feedback do you get that says, "Okay, this has worked for both of our client bases, both our citizens and our employees?" >> Yeah, so we can look at this both from a a quantitative and a qualitative point of view as well. So, I think from a let take the kind figures first. So we are really hot on making sure that whatever automations we put in place we are there to measure how that automation is working what it's kind of doing and the impact that it's having from an operational point of view. So I think, you know, I think the proof of the fact that the Intelligent Automation Garage is working is that, you know, in the, in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over 2 million operational hours. I was going to say that again that's 2 million operational hours. And what that allows us to do as an organization those 2 million hours have allowed us to rather than people as Amar, said, cutting and pasting and doing work that that is essentially very time consuming and repetitive. That 2 million hours we've been able to use on actual decision making. So, the stuff that you need as sentient human being to make judgment calls on and you know and kind of make those decisions that's what it's allowed us as an organization to do. And then I think from a quality point of view I think the feedback that we have from our operational teams is, you know is equally as as great. So, we have that kind of feedback from, you know all the way up from to the director level about, you know how it's kind of like I said that freeing up that time but actually making the operational, you know they don't have an easy job and it's making that an awful lot easier on a day to day basis. It has a real day to day impact. But also, you know, there are other things that kind of the knock on effects in terms of accuracy. So for example, robot will do is exactly as it's told it doesn't make any mistakes, it doesn't have sick days, you know, it does what it says on the tin and actually that kind of impact. So, it's not necessarily, you know, counting your numbers it's the fact that then doesn't generate a call from a customer that kind of says, "Well you, I think you've got this wrong." So, it's all that kind of, these kind of ripple effects that go out. I think is how we measure the fact that A, the garage is working and b, it's delivering the value that we needed to deliver. >> Robots, probably ask better questions too so yeah... (Lianne laughing) So, real quick, just real quick before you head out. So, the big challenge next, eureka, this works, right? Amar, you put together this fantastic system it's in great practice at the DWP, now what do we do? So, it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DWP and say, "This is where I think we can jump to next." >> Yeah, so I think, what we've been able to prove as I say is that is scaled innovation and having the return and the value that it creates is here to stay, right? So, I think the next things for us are a continuous expand the stuff that we're doing. Keeping hold of that culture, right? That culture of constantly solving difficult problems and being able to innovate and scale them. So, we are now doing a lot more automations across the department, you know, across different benefits across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture driven architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're going to do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across for our citizens. But it's looking, you know, the future's looking bright. I think we've got a number of different backlogs of items to work on. >> Well, you've got a great story to tell and thank you for sharing it with us here on "the CUBE", talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture's doing to make 20 million lives plus, a lot simpler for our friends in England. You've been watching ""the CUBE"" the AWS Executive Summit sponsored by Accenture. (bright upbeat music)

(bright upbeat music) >> Well, hello everybody. John Walls is here on "the CUBE". Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK and for with us or with us. Now to talk about that is Lianne Anderton, who is in with the Intelligent Automation Garage Delivery Lead at the UK Department of Work and Pension. Lianne, good to see you today. Thanks for joining us here on "the CUBE". >> Hi, thanks for having me. >> And also with this us is Amar Narayan, who is a Manager Director at Accenture the AWS Business Group for the Lead in Health and Public Sector, also UK and Ireland. And Amar, I think, you and Lianne, are in the same location, Newcastle, I believe in the UK, is that right? >> Yeah, absolutely. Yep, yeah, we're, here in the northeast of UK. >> Well, thank you for being with us. I appreciate the time. Lianne, let's talk about what you do, the Department of Work and Pension, the famous DWP in England. You have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? >> Sure, so for the Department for Work and Pensions I think the pensions bit is fairly self explanatory so anybody who is over state pension age within the UK. for the work part of that we also deal with people of working age. So, these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work. And we also deal with health related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So, we're aware of a huge part of the UK government. >> All right, so say that number again. How many? >> 20 million claimants every year. >> Million with an M, right? >> Yeah. >> So, and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How, much traffic basically, are you seeing? >> An extraordinary amount? I'm not even, I don't think I even know that number. (Lianne laughing) >> Mind blowing, right? So, it's- >> A huge, huge amount. >> Mind blowing. >> Yeah, so, basically the we kind of keep the country going. So, you know, if the department for Work and Pensions kind of didn't exist anymore then actually it would cause an infinite number of problems in society. We, kind of help and support the people who need that. And, yeah, so we play a really vital role in kind of you know, social care and kind of public service. >> So, what was your journey to Accenture then? What, eventually led you to them? What problem were you having and how have you collaborated to solve that? >> So, in terms of how we work with Accenture. So, we had in around 2017 DWP was looking at a projected number of transactions growing by about 210 million which was, you know, an extraordinary amount. And, you know, I think as we've kind of covered everything that we do is on a massive scale. So, we as DWP as an organization we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, you know, after kind of various kind of paths and ideas of how we were going to do that, automation, was actually the answer. But the problem that we have with that is that we have, like many governments around the world, we have really older legacy systems. So, each of these benefits that we deal with are on legacy systems. So, whatever we were going to develop had to, you know, connect to all of these, it had to ingest and then process all of these pieces of data some of which, you know, given the fact that a lot of these systems have a lot of manual input you have data issues there that you have to solve and whatever we did, you know, as we've talked about in terms of volumes has to scale instantly as well. So, it has to be able to scale up and down to meet demand and, you know, and that down scaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes but also you've got to be able to downscale when when it's not needed. But we had nothing that was like that kind of helped us to meet that demand. So, we built our own automation platform, The Intelligent Automation Garage and we did that with Accenture. >> So Amar, I'd like you to chime in here then. So, you're looking at this client who has this massive footprint and obviously vital services, right? So, that's paramount that you have to keep that in mind and the legacy systems that Lianne was just talking about. So, now you're trying to get 'em in the next gen but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case did you get them to automation as the solution? >> Sure, so I think I think one of the interesting things, yeah as Lianne has sort of described it, right? It's effectively like, you know the department has to have be running all of the time, right? They can't, you know, they can't effectively stop and then do a bunch of IT transformation, you know it's effectively like, you know, changing the wheels of a jumbo jet whilst it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we were in and the client relationship we had was that we knew we had to it wasn't just a technology play, we couldn't just go, "All right, let's just put some new technology in." What we also needed to do was really sort of create a culture, an innovation culture, and go, "Well how do we think about the problems that we currently have and how do we think about solving them differently and in collaboration, right?" So, not just the, "Let's just outsource a bunch of technology for to, you know, to Accenture and build a bunch of stuff." So, we very carefully thought about, well actually, the unique situation that they're in the demands that the citizens have on the services that the department provide. And as Lianne mentioned, that technology didn't exist. So, we fundamentally looked at this in a different way. So, we worked really closely with the department. We said, Look, actually what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know all of a sudden had a hundred thousand pension claims that needed to be processed in a week that you could click your fingers and, you know in a physical world you'd have another building all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week you didn't need that you no longer needed that building that stuff or the machinery. And we wanted to replicate that in the virtual world. So, we started designing a platform we utilized and focused on using AWS because it had the scalability. And we thought about, how were we going to connect something as new as AWS to all of these legacy systems. How are we going to make that work in the modern world? How are we going to integrate it? How we going to make sure it's secure? And frankly, we're really honest with the client we said, "Look, this hasn't been done before. Like, nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people, I think we can do it." And, we've prototyped and we've built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative but most importantly not doing sort of proof of concepts that didn't go anywhere but building something that actually scaled. And I think that was really the real the start of what was has been the Garage. >> So, And Lianne, you mentioned this and you just referred to it Amar, about The Garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talked about it, what the needs are all this and that, but Lianne, I'll let you jump in first and Amar, certainly compliment her remarks, but what is the IAG, what's the... >> So, you know, I think exactly what kind of Amar, has said from a from a kind of a development point of view I think it started off, you know, really, really small. And the idea is that this is DWP, intelligent automation center of excellence. So, you know, it's aims are that, you know, it makes sure that it scopes out kind of the problems that DWP are are facing properly. So, we really understand what the crux of the problem is. In large organizations It's very easy, I think to think you understand what the problem is where actually, you know, it is really about kind of delving into what that is. And actually we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like using as Amar said, all the exciting new technology that we kind of have available to us. That kind of sensible solution as to what that should look like. We then build that sensible solution and we then, you know as part of that, we make sure that it scales to demand. So, something that might start out with, I dunno, you know a few hundred claimants or kind of cases going through it can quite often, you know, once that's that's been successful scale really, really quickly because as you know, we have 20 million claimants that come through us every year. So, these types of things can grow and expand but also a really key function of what we do is that we have a fully supported in-house service as well. So, all of those automations that we build are then maintained and you know, so any changes that kind of needed to be need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as sufficient, sorry, as efficient as possible. And what I mean by that is looking at, you know as new technologies come around and they can allow us to do things more effectively. So, it allows us to kind of almost do that that kind of continuous improvement ourselves. So, that's a huge part of what we do as well. And you know, I think from a size point of view I said this started off really small as in the idea was this was a kind of center of excellence but actually as automation, I think as Amar alluded to is kind of really started to embed in DWP culture what we've started to kind of see is the a massive expansion in the types of of work that people want us to do and the volume of work that we are doing. So, I think we're currently running at around around a hundred people at the moment and I think, you know we started off with a scrum, a couple of scrum teams under Amar, so yeah, it's really grown. But you know, I think this is here to stay within DWP. >> Yeah, well when we talk about automation, you know virtual and robotics and all this I like to kind of keep the human element in mind here too. And Amar, maybe you can touch on that in certain terms of the human factors in this equation. 'Cause people think about, you know, robots it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things Lianne wants to do down the road, you know, is a road for people basically? >> Oh yeah, absolutely. I think fundamentally what the department does is support people and therefore the solutions that we designed and built had to factor that in mind right? We were trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports. The department itself is a big organization, right? We're up to, we're talking between sort of 70 and 80,000 employees. So, how do we embed automation but also make the lives of the, of the DWP agents better as well? And that's what we thought about. So we said, "Well look, we think we can design solutions that do both." So, a lot of our automations go through a design process and we work closely with our operations team and we go, well actually, you know in processing and benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data and there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, "Well how do I best support this citizen?" And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those and then we really focused on making sure that the elements that required the human, the human input was made as user friendly and centric as we possibly could. So, if there's a really complex case that needs to be processed, we were able to present the information in a really digestible and understandable way for the agents so that they could make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in but also maintain the quality and if not improve, you know the accuracy of the claims processing that we had. >> So, how do you know, and maybe Lianne, you can address this. How do you know that it's successful on both sides of that equation? And, 'cause Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the, your side too. So, how do you, what's your measurement for success and what kind of like raw feedback do you get that says, "Okay, this has worked for both of our client bases, both our citizens and our employees?" >> Yeah, so we can look at this both from a a quantitative and a qualitative point of view as well. So, I think from a let take the kind figures first. So we are really hot on making sure that whatever automations we put in place we are there to measure how that automation is working what it's kind of doing and the impact that it's having from an operational point of view. So I think, you know, I think the proof of the fact that the Intelligent Automation Garage is working is that, you know, in the, in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over 2 million operational hours. I was going to say that again that's 2 million operational hours. And what that allows us to do as an organization those 2 million hours have allowed us to rather than people as Amar, said, cutting and pasting and doing work that that is essentially very time consuming and repetitive. That 2 million hours we've been able to use on actual decision making. So, the stuff that you need as sentient human being to make judgment calls on and you know and kind of make those decisions that's what it's allowed us as an organization to do. And then I think from a quality point of view I think the feedback that we have from our operational teams is, you know is equally as as great. So, we have that kind of feedback from, you know all the way up from to the director level about, you know how it's kind of like I said that freeing up that time but actually making the operational, you know they don't have an easy job and it's making that an awful lot easier on a day to day basis. It has a real day to day impact. But also, you know, there are other things that kind of the knock on effects in terms of accuracy. So for example, robot will do is exactly as it's told it doesn't make any mistakes, it doesn't have sick days, you know, it does what it says on the tin and actually that kind of impact. So, it's not necessarily, you know, counting your numbers it's the fact that then doesn't generate a call from a customer that kind of says, "Well you, I think you've got this wrong." So, it's all that kind of, these kind of ripple effects that go out. I think is how we measure the fact that A, the garage is working and b, it's delivering the value that we needed to deliver. >> Robots, probably ask better questions too so yeah... (Lianne laughing) So, real quick, just real quick before you head out. So, the big challenge next, eureka, this works, right? Amar, you put together this fantastic system it's in great practice at the DWP, now what do we do? So, it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DWP and say, "This is where I think we can jump to next." >> Yeah, so I think, what we've been able to prove as I say is that is scaled innovation and having the return and the value that it creates is here to stay, right? So, I think the next things for us are a continuous expand the stuff that we're doing. Keeping hold of that culture, right? That culture of constantly solving difficult problems and being able to innovate and scale them. So, we are now doing a lot more automations across the department, you know, across different benefits across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture driven architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're going to do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across for our citizens. But it's looking, you know, the future's looking bright. I think we've got a number of different backlogs of items to work on. >> Well, you've got a great story to tell and thank you for sharing it with us here on "the CUBE", talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture's doing to make 20 million lives plus, a lot simpler for our friends in England. You've been watching ""the CUBE"" the AWS Executive Summit sponsored by Accenture. (bright upbeat music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this CUBE Conversation. You know, theCUBE has been following the trends in the so-called big data space since 2010. And one of the things that we reported on for a number of years is the complexity involved in wrangling and making sense out of data. The allure of this idea of no schema on write and very low cost platforms like Hadoop became a data magnet. And for years, organizations would shove data into a data lake. And of course the joke was it was became a data swamp. And organizations really struggled to realize the promised return on their big data investments. Now, while the cloud certainly simplified infrastructure deployment, it really introduced a much more complex data environment and data pipeline, with dozens of APIs and a mind-boggling array of services that required highly skilled data engineers to properly ingest, shape, and prepare that data, so that it could be turned into insights. This became a real time suck for data pros, who spent 70 to 80% of their time wrestling data. A number of people saw the opportunity to solve this problem and automate the heavy lift of data, and simplify the process to adjust, synchronize, transform, and really prepare data for analysis. And one of the companies that is attacking this challenge is InfoWorks. And with me to talk about the evolving data landscape is Buno Pati, CEO of InfoWorks. Buno, great to see you, thanks for coming in. >> Well thank you Dave, thanks for having me here. >> You're welcome. I love that you're in Palo Alto, you come to MetroWest in Boston to see us (Buno laughs), that's great. Well welcome. So, you heard my narrative. We're 10 years plus into this big data theme and meme. What did we learn, what are some of the failures and successes that we can now build on, from your point of view? >> All right, so Dave, I'm going to start from the top, with why big data, all right? I think this big data movement really started with the realization by companies that they need to transform their customer experience and their operations, in order to compete effectively in this increasingly digital world, right? And in that context, they also realized very quickly that data was the key asset on which this transformation would be built. So given that, you look at this and say, "What is digital transformation really about?" It is about competing with digital disruption, or fending off digital disruption. And this has become, over time, an existential imperative. You cannot survive and be relevant in this world without leveraging data to compete with others who would otherwise disrupt your business. >> You know, let's stay on that for a minute, because when we started the whole big data, covering that big data space, you didn't really hear about digital transformation. That's sort of a more recent trend. So I got to ask you, what's the difference between a business and a digital business, in your view? >> That is the foundational question behind big data. So if you look at a digital native, there are many of them that you can name. These companies start by building a foundational platform on which they build their analytics and data programs. It gives them a tremendous amount of agility and the right framework within which to build a data-first strategy. A data-first strategy where business information is persistently collected and used at every level of the organization. Furthermore, they take this and they automate this process. Because if you want to collect all your data and leverage it at every part of the business, it needs to be a highly automated system, and it needs to be able to seamlessly traverse on-premise, cloud, hybrid, and multi-cloud environments. Now, let's look at a traditional business. In a traditional enterprise, there is no foundational platform. There are things like point tools for ETL, and data integration, and you can name a whole slew of other things, that need to be stitched together and somehow made to work to deliver data to the applications that consume. The strategy is not a data-first strategy. It is use case by use case. When there is a use case, people go and find the data, they gather the data, they transform that data, and eventually feed an application. A process that can take months to years, depending on the complexity of the project that they're trying. And they don't automate this. This is heavily dependent, as you pointed out, on engineering talent, highly skilled engineering talent that is scarce. And they have not seamlessly traversed the various clouds and on-premise environments, but rather fragmented those environments, where individual teams are focused on a single environment, building different applications, using different tools, and different infrastructure. >> So you're saying the digital native company puts data at the core. They organize around that data, as opposed to maybe around a bottling plant, or around people. And then they leverage that data for competitive advantage through a platform that's kind of table stakes. And then obviously there's cultural aspects and other skills that they need to develop, right? >> Yeah, they have an ability which traditional enterprises don't. Because of this choice of a data-first strategy with a foundational platform, they have the ability to rapidly launch analytics use cases and iterate all them. That is not possible in a traditional or legacy environment. >> So their speed to market and time to value is going to be much better than their competition. This gets into the risk of disruption. Sometimes we talk about cloud native and cloud naive. You could talk about digital native and digital naive. So it's hard for incumbents to fend off the disrupters, and then ultimately become disrupters themselves. But what are you seeing in terms of some of the trends where organizations are having success there? >> One of the key trends that we're seeing, or key attributes of companies that are seeing a lot of success, is when they have organized themselves around their data. Now, what do I mean by that? This is usually a high-level mandate coming down from the top of the company, where they're forming centralized groups to manage the data and make it available for the rest of the organization to use. There are a variety of names that are being used for this. People are calling it their data fabric. They're calling it data as a service, which is pretty descriptive of what it ends up being. And those are terms that are all sort of representing the same concept of a centralized environment and, ideally, a highly automated environment that serves the rest of the business with data. And the goal, ultimately, is to get any data at any time for any application. >> So, let's talk a little bit about the cloud. I mentioned up front that the cloud really simplified infrastructure deployment, but it really didn't solve this problem of, we talked about in terms of data wrangling. So, why didn't it solve that problem? And you got companies like Amazon and Google and Microsoft, who are very adept at data. They're some of these data-first companies. Why is it that the cloud sort of in and of itself has not been able to solve this problem? >> Okay, so when you say solve this problem, it sort of begs the question, what's the goal, right? And if I were to very simply state the goal, I would call it analytics agility. It is gaining agility with analytics. Companies are going from a traditional world, where they had to generate a handful of BI and other reporting type of dashboards in a year, to where they literally need to generate thousands of these things in a year, to run the business and compete with digital disruption. So agility is the goal. >> But wait, the cloud is all about agility, is it not? >> It is, when you talk about agility of compute and storage infrastructure. So, there are three layers to this problem. The first is, what is the compute and storage infrastructure? The cloud is wonderful in that sense. It gives you the ability to rapidly add new infrastructure and spin it down when it's not in use. That is a huge blessing, when you compare it to the six to nine months, or perhaps even longer, that it takes companies to order, install, and test hardware on premise, and then find that it's only partially used. The next layer on that is what is the operating system on which my data and analytics are going to be run? This is where Hadoop comes in. Now, Hadoop is inherently complex, but operating systems are complex things. And Spark falls in that category. Databricks has taken some of the complexity out of running Spark because of their sort of manage service type of offering. But there's still a missing layer, which leverages that infrastructure and that operating system to deliver this agility where users can access data that they need anywhere in the organization, without intensely deep knowledge of what that infrastructure is and what that operating system is doing underneath. >> So, in my up front narrative, I talked about the data pipeline a little bit. But I'm inferring from your comments on platform that it's more than just this sort of narrow data pipeline. There's a macro here. I wonder if you could talk about that a little bit. >> Yeah. So, the data pipeline is one piece of the puzzle. What needs to happen? Data needs to be ingested. It needs to be brought into these environments. It has to be kept fresh, because the source data is persistently changing. It needs to be organized and cataloged, so that people know what's there. And from there, pipelines can be created that ultimately generate data in a form that's consumable by the application. But even surrounding that, you need to be able to orchestrate all of this. Typical enterprise is a multi-cloud enterprise. 80% of all enterprises have more than one cloud that they're working on, and on-premise. So if you can't orchestrate all of this activity in the pipelines, and the data across these various environments, that's not a complete solution either. There's certainly no agility in that. Then there's governance, security, lineage. All of this has to be managed. It's not simply creation of the pipeline, but all these surrounding things that need to happen in order for analytics to run at-scale within enterprises. >> So the cloud sort of solved that layer one problem. And you certainly saw this in the, not early days, but sort of mid-days of Hadoop, where the cloud really became the place where people wanted to do a lot of their Hadoop workloads. And it was kind of ironic that guys like Hortonworks, and Cloudera and MapR really didn't have a strong cloud play. But now, it's sort of flipping back where, as you point out, everybody's multi-cloud. So you have to include a lot of these on-prem systems, whether it's your Oracle database or your ETL systems or your existing data warehouse, those are data feeds into the cloud, or the digital incumbent who wants to be a digital native. They can't just throw all that stuff away, right? So you're seeing an equilibrium there. >> An equilibrium between ... ? >> Yeah, between sort of what's in the cloud and what's on-prem. Let me ask it this way: If the cloud is not a panacea, is there an approach that does really solve the problem of different datasets, the need to ingest them from different clouds, on-prem, and bring them into a platform that can be analyzed and drive insights for an organization? >> Yeah, so I'm going to stay away from the word panacea, because I don't think there ever is really a panacea to any problem. >> That's good, that means we got a good roadmap for our business then. (both laugh) >> However, there is a solution. And the solution has to be guided by three principles. Number one, automation. If you do not automate, the dependence on skill talent is never going to go away. And that talent, as we all know, is very very scarce and hard to come by. The second thing is integration. So, what's different now? All of these capabilities that we just talked about, whether it's things like ETL, or cataloging, or ingesting, or keeping data fresh, or creating pipelines, all of this needs to be integrated together as a single solution. And that's been missing. Most of what we've seen is point tools. And the third is absolutely critical. For things to work in multi-cloud and hybrid environments, you need to introduce a layer of abstraction between the complexity of the underlying systems and the user of those systems. And the way to think about this, Dave, is to think about it much like a compiler. What does a compiler do, right? You don't have to worry about what Intel processor is underneath, what version of your operating system you're running on, what memory is in the system. Ultimately, you might-- >> As much as we love assembly code. >> As much as we love assembly code. Now, so take the analogy a little bit further, there was a time when we wrote assembly code because there was no compiler. So somebody had to sit back and say, "Hey, wouldn't it be nice if we abstracted away from this?" (both laugh) >> Okay, so this sort of sets up my next question, which is, is this why you guys started InfoWorks? Maybe you could talk a little bit about your why, and kind of where you fit. >> So, let me give you the history of InfoWorks. Because the vision of InfoWorks, believe it or not, came out of a rear view mirror. Looking backwards, not forwards. And then predicting the future in a different manner. So, Amar Arsikere is the founder of InfoWorks. And when I met him, he had just left Zynga, where he was the general manager of their gaming platform. What he told me was very very simple. He said he had been at Google at a time when Google was moving off of the legacy systems of, I believe it was Netezza, and Oracle, and a variety of things. And they had just created Bigtable, and they wanted to move and create a data warehouse on Bigtable. So he was given that job. And he led that team. And that, as you might imagine, was this massive project that required a high degree of automation to make it all come together. And he built that, and then he built a very similar system at Zynga, when he was there. These foundational platforms, going back to what I was talking about before digital days. When I met him, he said, "Look, looking back, "Google may have been the only company "that needed such a platform. "But looking forward, "I believe that everyone's going to need one." And that has, you know, absolute truth in it, and that's what we're seeing today. Where, after going through this exercise of trying to write machine code, or assembly code, or whatever we'd like to call it, down at the detailed, complex level of an operating system or infrastructure, people have realized, "Hey, I need something much more holistic. "I need to look at this from a enterprise-wide perspective. "And I need to eliminate all of this dependence on," kind of like the cloud plays a role because it eliminates some of the dependence, or the bottlenecks around hardware and infrastructure. "And ultimately gain a lot more agility "than I'm able to do with legacy methodology." So you were asking early on, what are the lessons learned from that first 10 years? And lot of technology goes through these types of cycles of hype and disillusionment, and we all know the curve. I think there are two key lessons. One is, just having a place to land your data doesn't solve your problem. That's the beginning of your problems. And the second is that legacy methodologies do not transfer into the future. You have to think differently. And looking to the digital natives as guides for how to think, when you're trying to compete with them is a wonderful perspective to take. >> But those legacy technologies, if you're an incumbent, you can't just rip 'em and throw 'em out and convert. You going to use them as feeders to your digital platform. So, presumably, you guys have products. You call this space Enterprise Data Ops and Orchestration, EDO2. Presumably you have products and a portfolio to support those higher layer challenges that we talked about, right? >> Yeah, so that's a really important question. No, you don't rip and replace stuff. These enterprises have been built over years of acquisitions and business systems. These are layers, one on top of another. So think about the introduction of ERP. By the way, ERP is a good analogy of to what happened, because those were point tools that were eventually combined into a single system called ERP. Well, these are point capabilities that are being combined into a single system for EDO2, or Enterprise Data Operations and Orchestration. The old systems do not go away. And we are seeing some companies wanting to move some of their workloads from old systems to new systems. But that's not the major trend. The major trend is that new things that get done, the things that give you holistic views of the company, and then analytics based on that holistic view, are all being done on the new platforms. So it's a layer on top. It's not a rip and replace of the layers underneath. What's in place stays in place. But for the layer on top, you need to think differently. You cannot use all the legacy methodologies and just say that's going to apply to the new platform or new system. >> Okay, so how do you engage with customers? Take a customer who's got, you know, on-prem, they've got legacy infrastructure, they don't want to get disrupted. They want to be a digital native. How do you help them? You know, what do I buy from you? >> Yeah, so our product is called DataFoundry. It is a EDO2 system. It is built on the three principles, founding principles, that I mentioned earlier. It is highly automated. It is integrated in all the capabilities that surround pipelines, perhaps. And ultimately, it's also abstracting. So we're able to very easily traverse one cloud to another, or on-premise to the cloud, or even back. There are some customers that are moving some workloads back from the cloud. Now, what's the benefit here? Well first of all, we lay down the foundation for digital transformation. And we enable these companies to consolidate and organize their data in these complex hybrid, cloud, multi-cloud environments. And then generate analytics use cases 10x faster with about tenth of the resource. And I'm happy to give you some examples on how that works. >> Please do. I mean, maybe you could share some customer examples? >> Yeah, absolutely. So, let me talk about Macy's. >> Okay. >> Macy's is a customer of ours. They've been a customer for about, I think about 14 months at this point in time. And they had built a number of systems to run their analytics, but then recognized what we're seeing other companies recognize. And that is, there's a lot of complexity there. And building it isn't the end game. Maintaining it is the real challenge, right? So even if you have a lot of talent available to you, maintaining what you built is a real challenge. So they came to us. And within a period of 12 months, I'll just give you some numbers that are just mind-blowing. They are currently running 165,000 jobs a month. Now, what's a job? A job is a ingestion job, or a synchronization job, or a transformation. They have launched 431 use cases over a period of 12 months. And you know what? They're just ramping. They will get to thousands. >> Scale. >> Yeah, scale. And they have ingested a lot of data, brought in a lot of DataSources. So to do that in a period of 12 months is unheard of. It does not happen. Why is it important for them? So what problem are they trying to solve? They're a retailer. They are being digitally disruptive like (chuckles) no one else. >> They have an Amazon war room-- >> Right. >> No doubt. >> And they have had to build themselves out as a omni-channel retailer now. They are online, they are also with brick and mortar stores. So you take a look at this. And the key to competing with digital disrupters is the customer experience. What is that experience? You're online, how does that meld with your in-store experience? What happens if I buy online and return something in a store? How does all this come together into a single unified experience for the consumer? And that's what they're chasing. So that was the first application that they came to us with. They said, "Look, let us go into a customer 360. "Let us understand the entirety "of that customer's interaction "and touchpoints with our business. "And having done so, we are in a position "to deliver a better experience." >> Now that's a data problem. I mean, different DataSources, and trying to understand 360, I mean, you got data all over the place. >> All over the place. (speaking simultaneously) And there's historical data, there's stuff coming in from, you know, what's online, what's in the store. And then they progress from there. I mean, they're not restricting it to customer experience and selling. They're looking at merchandising, and inventory, and fulfillment, and store operations. Simple problem. You order something online, where do I pull this from? A store or a warehouse? >> So this is, you know, big data 2.0, just to use a sort of silly term. But it's really taking advantage of all the investment. I've often said, you know, Hadoop, for all the criticism it gets, it did lower our cost of getting data into, you know, at least one virtual place. And it got us thinking about how to get insights out of data. And so, what you're describing is the ability to operationalize your data initiatives at scale. >> Yeah, you can absolutely get your insights off of Hadoop. And I know people have different opinions of Hadoop, given their experience. But what they don't have, what these customers have not achieved yet, most of them, is that agility, right? So, how easily can you get your insights off of Hadoop? Do I need to hire a boatload of consultants who are going to write code for me, and shovel data in, and create these pipelines, and so forth? Or can I do this with a click of a button, right? And that's the difference. That is truly the difference. The level of automation that you need, and the level of abstraction that you need, away from this complexity, has not been delivered. >> We did, in, it must have been 2011, I think, the very first big data market study from anybody in the world, and put it out on, you know, Wikibon, free research. And one of the findings was (chuckles) this is a huge services business. I mean, the professional service is where all the money was going to flow because it was so complicated. And that's kind of exactly what happened. But now we're entering, really it seems like a phase where you can scale, and operationalize, and really simplify, and really focus your attention on driving business value, versus making stuff work. >> You are absolutely correct. So I'll give you the numbers. 55% of this industry is services. About 30% is software, and the rest is hardware. Break it down that way. 55%. So what's going on? People will buy a big data system. Call it Hadoop, it could be something in the cloud, it could be Databricks. And then, this is welcome to the world of SIs. Because at this point, you need these SIs to write code and perform these services in order to get any kind of value out of that. And look, we have some dismal numbers that we're staring at. According to Gardner, only 17% of those who have invested in Hadoop have anything in production. This is after how many years? And you look at surveys from, well, pick your favorite. They all look the same. People have not been able to get the value out of this, because it is too hard. It is too complex and you need too many consultants (laughs) delivering services for you to make this happen. >> Well, what I like about your story, Buno, is you're not, I mean, a lot of the data companies have pivoted to AI. Sort of like, we have a joke, ya know, same wine, new bottle. But you're not talking about, I mean sure, machine intelligence, I'm sure, fits in here, but you're talking about really taking advantage of the investments that you've made in the last decade and helping incumbents become digital natives. That sounds like it's at least a part of your mission here. >> Not become digital natives, but rather compete with them. >> Yeah, right, right. >> Effectively, right? >> Yep, okay. >> So, yeah, that is absolutely what needs to get done. So let me talk for a moment about AI, all right? Way back when, there was another wave of AI in the late 80s. I was part of that, I was doing my PhD at the time. And that obviously went nowhere, because we didn't have any data, we didn't have enough compute power or connectivity. Pretty inert. So here it is again. Very little has changed. Except for we do have the data, we have the connectivity, and we have the compute power. But do we really? So what's AI without the data? Just A, right? There's nothing there. So what's missing, even for AI and ML to be, and I believe these are going to be powerful game changers. But for them to be effective, you need to provide data to it, and you need to be able to do so in a very agile way, so that you can iterate on ideas. No one knows exactly what AI solution is going to solve your problem or enhance your business. This is a process of experimentation. This is what a company like Google can do extraordinarily well, because of this foundational platform. They have this agility to keep iterating, and experimenting, and trying ideas. Because without trying them, you will not discover what works best. >> Yeah, I mean, for 50 years, this industry has marched to the cadence of Moore's Law, and that really was the engine of innovation. And today, it's about data, applying machine intelligence to that data. And the cloud brings, as you point out, agility and scale. That's kind of the new cocktail for innovation, isn't it? >> The cloud brings agility and scale to the infrastructure. >> In low risk, as you said, right? >> Yeah. >> Experimentation, fail fast, et cetera. >> But without an EDO2 type of system, that gives you a great degree of automation, you could spend six months to run one experiment with AI. >> Yeah, because-- >> In gathering data and feeding it to it. >> 'Cause if the answer is people and throwing people at the problem, then you're not going to scale. >> You're not going to scale, and you're never going to really leverage AI and ML capabilities. You need to be able to do that not in six months, in six days, right, or less. >> So let's talk about your company a little bit. Can you give us the status, you know, where you're at? As their newly minted CEO, what your sort of goals are, milestones that we should be watching in 2020 and beyond? >> Yeah, so newly minted CEO, I came in July of last year. This has been an extraordinary company. I started my journey with this company as an investor. And it was funded by actually two funds that I was associated with, first being Nexus Venture Partners, and then Centerview Capital, where I'm still a partner. And myself and my other two partners looked at the opportunity and what the company had been able to do. And in July of last year, I joined as CEO. My partner, David Dorman, who used to be CEO of AT&T, he joined as chairman. And my third partner, Ned Hooper, joined as President and Chief Operating Officer. Ned used to be the Chief Strategy Officer of Cisco. So we pushed pause on the funding, and that's about as all-in as a fund can get. >> Yeah, so you guys were operational experts that became investors, and said, "Okay, we're going to dive back in "and actually run the business." >> And here's why. So we obviously see a lot of companies as investors, as they go out and look for funding. There are three things that come together very rarely. One is a massive market opportunity combined with the second, which is the right product to serve that opportunity. But the third is pure luck, timing. (Dave chuckles) It's timing. And timing, you know, it's a very very challenging thing to try to predict. You can get lucky and get it right, but then again, it's luck. This had all three. It was the absolute perfect time. And it's largely because of what you described, the 10 years of time that had elapsed, where people had sort of run the experiment and were not going to get fooled again by how easy this supposed to be by just getting one piece or the other. They recognized that they need to take this holistic approach and deploy something as an enterprise-wide platform. >> Yeah, I mean, you talk about a large market, I don't even know how you do a TAM, what's the TAM? It's data. (laughs) You know, it's the data universe, which is just, you know, massive. So, I have to ask you a question as an investor. I think you've raised, what 50 million, is that right? >> We've raised 50 million. The last round was led by NEA. >> Right, okay. You got great investors, hefty amount. Although, you know, in this day and age, you know, you're seeing just outrageous amounts being raised. Software obviously is a capital efficient business, but today you need to raise a lot of money for promotion, right, to get your name out there. What's your thoughts on, as a Silicon Valley investor, as this wave, I mean, get it while you can, I guess. You know, we're in the 10th year of this boom market. But your thoughts? >> You're asking me to put on my other hat. (Dave laughs) I think companies have, in general, raised too much money at too high a value too fast. And there's a penalty for that. And the down round IPO, which has become fashionable these days, is one of those penalties. It's a clear indication. Markets are very rational, public markets are very rational. And the pricing in a public market, when it's significantly below the pricing of in a private market, is telling you something. So, we are a little old-fashioned in that sense. We believe that a company has to lay down the right foundation before it adds fuel to the mix and grows. You have to have evidence that the machinery that you build, whether it's for sales, or marketing, or other go-to-market activities, or even product development, is working. And if you do not see all of those signs, you're building a very fragile company. And adding fuel in that setting is like flooding the carburetor. You don't necessarily go faster. (laughs) You just-- >> Consume more. >> You consume more. So there's a little bit of, perhaps, old-fashioned discipline that we bring to the table. And you can argue against it. You can say, "Well, why don't you just raise a lot of money, "hire a lot of sales guys, and hope for the best?" >> See what sticks? (laughs) >> Yeah. We are fully expecting to build a large institution here. And I use that word carefully. And for that to happen, you need the right foundation down first. >> Well, that resonates with us east coast people. So, Buno, thanks very much for comin' on theCUBE and sharing with us your perspectives on the marketplace. And best of luck with InfoWorks. >> Thank you, Dave. This has been a pleasure. Thank you for having me here. >> All right, we'll be watching, thank you. And thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see ya next time. (upbeat music fades out)

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by IBM Wear and its ecosystem partners. >> Welcome to our live coverage here in Mosconi North Lobby, Of'em World 2019. I'm John for a Student and a Volante celebrating our 10th VM World or 10 years of covering the M world. Dave's stew. What a run been Go back across Mosconi South 10 years ago with the green set. This is 10 years later. 10:10 p.m. World BMC Rule No longer the show, so that kind of folds in the Dell Technologies Man, The world's changed. Pat Nelson had just delivered his keynote as CEO Sanjay Poon and a CEO came on talk to customers stew. A lot of acquisitions, a lot of cloud native, a lot of cloud. 2.0, this is turning into VM. Wear 2.0, where vm zehr kind of only one part of the equation. So let's jump into the analysis, Dave. I mean, you put out some killer research on silken angle dot com, and we keep on dot com around customer spend still, we put out a lot of analysis on all the key trends that Vienna was playing into. Cloud two point. Oh, is what we're calling it. It's enterprise Cloud of fresh scale Day. What? What? What? What do you want? Your analysis, Latino >> John, when you go back. 10 VM Worlds ago, it was all about virtualization, completely changing the deployment dynamics. When when I first saw a VM deployed, I went, Oh, my God, This is gonna change everything. And it did. But while compared to now what's happening with cloud and a I we heard so much about five g. It was also the big, big difference in the ecosystem. Back when e. M. C owned VM wearing 2010 there was that sort of Chinese wall stew. You were working there, you know, just before that. And there wasn't a lot of, you know, swapping of I P, if you will. They were sort of treating them as unequal player to net app and everybody else out there. Tod Nielsen used to say, for every dollar spent on of'em were licensed, 15 spent an ecosystem. You don't hear that kind of narrative anymore, you hear we're crushing the HC. I vendor where number one basically a sort of backhand to Nutanix We heard on the on the keynote Very tight integration VX rail project Dimension So much, much tighter integration since Pat Tell Singer joined VM. Where from the emcee lots has changed >> will be a lot of research on reporting leading up to the show around Cloud two point. Oh, I'll see Dev. Ops is willing to home of the dimension on enterprise scale, the number of acquisitions of'em wears made and then, boom. They dropped two monsters on the table or the 11th hour pivotal for 2.7 billion carbon black for 2.1 billion. Lot of stories in those AK was other acquisitions, your analysis and how that played out today on the >> Kino. As Dave said when we started coming to this event back in 2010 you know, the virtual machine was the center of the universe. What were these servers that it lived on, how to storage and network and get fixed to be ableto live in that environment And the keynote. It was a lot of cloud, you know, John, we brought in a lot of the Cloud camp people that first year and some people were like, Why are we talking about Cloud? This is VM World, and we're like, Well, this is the future. And today we're not talking about V EMS at the center we're talking about containers were talking about cloud native applications, that multi cloud world absolutely something that pack l singer did. Front center actually felt it almost glossed over a little bit of the H C, I and NSX and all these wonderful things. Sure, there was some big del pieces in there. The M word cloud on Delhi emcee the Del Di are, you know, data protection, power protect, you know, into the VM where peace something that you definitely would not have seen under the old emcee Federation model. So Michael Dell, absolutely having his strong footprint here. Dave's done a lot of analysis talking about things like Pivotal getting pulled in and like so many different acquisitions, Pivotal came out of'em wear and, you know, carbon black Boston based companies so many different pieces here to get them talking about applications and where Veum, where the company sits in this multi cloud world where they're trying to be, you know, maintain their relationship with us. >> Let's get into the analysis on the whole ecosystems. I really want to dig into the work. Dave, you didn't and the team did. But let's go through the keynote first. So my personal opinion was it felt like, um, I'll give him a C plus Pat because it just didn't have a lot of meat. In my opinion, it felt like it was too much tech for good, although super important to have that mission driven stuff I think is really valuable as the market tends to look >> at tech >> as bad actors. I thought that was addressing. That was a positive thing, but it felt too much. I didn't see a lot of specifics. It felt do is and David, if they were hiding something, they were putting a lot of it didn't seem like there's a lot of substance coming out specifically around how Kubernetes was going to be impacted. Specifically, how Cooper is going to sit within the VM where ecosystem products specifically I just didn't feel like the product side was there. >> Well, you know what? I'll say it, John and General, I agree with you because Day one usually is here is the company vision. And if the vision is kubernetes, well, we've been hearing kubernetes for a bunch of years. Kubernetes is not the answer. Kubernetes is an enable ionizing technology job. Ada, who we up on stage? You know, we had him on the Cuban. He's like, look committed. This is not a magic layer. It's this thin layer that's gonna help us go between clouds. Getting into some of their future projects is something I usually would expect on Day two, the vision of V. M. Whereas a company, it feels like we're in that transition from who do you want a big tech for? Good? That that's great stuff. You know, Pat has a long history of talking about, you know, that moral compass that he has and wants the company to live. That which is a good change from many of the Silicon Valley companies. But, you know, I didn't get a strong feel for their vision and it was not >> a conservative. They didn't want to actually put a position down there because I think everyone in the hallway that I talked to wants to know how Cooper is gonna impact the sphere for instance, is gonna change the makeup of the sphere. And what's the impact on the product side the head that stat about bare metal being 8%. I was like, a little bit biased. Maybe there, So are they. They tiptoeing. Dave, you think? I mean, the spend numbers show that if you could just hold the line for 24 months and the new trends won't take away from that license, I mean, is it a tactical thing? Or do you think that here's the >> thing? I want to go back? I do want to give'em where? Props on one thing and you've used this term to If you go back to 8 4009 Paul Maritz talked about. We're building the software mainframe and passed them pretty consistent about that they used, they said, Any workload, any app? What's different today than back then is, he said, any workload, any up any cloud. Really. Cloud wasn't as much of a factor back then, but that vision has been fairly consistent it to you. Answer your question, Veum. We're spending remains strong, you know they're spending data that we shared with the GT R on silicon angle yesterday and today is that 41% of the VM were installed. Base is going to spend Maurine the second half of 2019 and only 7% are going to spend less. Okay, that's a real positive. But at the same time, the data clearly shows that cloud is negatively impacting VM wear spend and so that's a real threat. So multi club Pat said today technologists who Master Master Multi Cloud will own the next decade. He's talking to his audience. I'm not sure I agree with that. How much you're mastering Multi Cloud is what's gonna be the determining factor to own the next decade. >> Well, I'm stumped. Stick with my position. That multi cloud is not a reality. I think it's really more overhyped, and our actually just started to be hyped and probably will be then over hypes. And then seven years from now we'll start seeing multiple clouds truly interoperable. But I think multi cloud is we find on the Cuba simply enterprises have multiple vendors and multiple environments that happen to be those vendors have cloud, so I don't think it actually is an operating model yet. But again, just like on the Cube 2012 stew. We talked about hybrid Cloud. I called. I asked, yes. When was it a halfway house of the weigh station? He had a connection. >> So gassy. So, John, here's what I say. Number one is customers today absolutely have multiple clouds. But for multi cloud, to be a reality multi cloud must be greater than the sum of just the piece is that it's made up today and absolutely were not there. Today. VM wear has a strong reason why it should be at the center of that discussion. But they're gonna be right at loggerheads with Red Hat and Microsoft and Google and Cisco in that kind of debate at the multi cloud >> and we had, we had a story on our special report on silicon angle dot com. Check it out. It's called Coping With Multi Cloud. Were coping was by design. Coping as a mechanism used to deal with uncertainty. Coping strategies is what CEOs are going to deal with. But read that post. But in it I kind of see. I mean, I kind of agree and disagree. We have two perspectives, Dave developing. You want to get your thoughts butts do on this C I ose that come from a traditional I t background tend to like multi vendor things because they know they don't want lock. And they're afraid if you then swing to the progressive side si SOS, for instance, who are have a gun to their head in terms of security, they're all saying no, we're betting on one cloud and we'll have backup clouds, but our development staff is gonna build stacks. Have AP eyes, and we'll share those AP ice to our suppliers. Cloud vendors are saying Support our specs. So to spectrums the old school I t. Guys saying Multi vendor equals multi cloud. And then then, on the other end, See says to say, I'm gonna build technology and build a stack, exposed FBI's and let the clouds support my my tooling that not the other way around your thoughts. I >> pulled a quote in my piece That's on Silicon angle as well. From David. If lawyer and he was defining a hybrid multi cloud, he said, any application of application service can run on any note of the hybrid cloud without rewriting re compiling a re testing. My argument would be you're never gonna have that North Star without a high degree of homogeneity. And there's three examples of high degrees of homogeneity in hybrid Cloud. Today it's azure stack. It's clouded customer, and it's outposts. You're so this idea that we're gonna have this diverse set of clouds and yet they're all gonna run is one to me. I ask, Is it technically feasible? And is it Is it practical? >> Well, Steve, Steve Harry was on his Hey had announced the signal. FX has come. Portfolio can be sold on a big deal to split when he was on The Cube with me last week and he said one of them looking back on the 10 years that 1 may be M where great was virtual ization allowed for massive efficiencies and improvements without rewriting the apse. The question today's point is, is that a reality? Can what's next? So that that next gain that's not gonna require people to rewrite their APs >> well and that actually not rewriting the axes where VM or has its strength. Because, you know, I I made a joke during the keynote. It was like you have a V M insert magic. Congratulations. You now have a cloud workload because I just did. VM were cloud and it's the same app. But on the other hand, that's actually been my biggest dig on V M. Where is the long pole? In the tent and modernization is modernizing wraps. And that is that Tom Zoo that Veum were announced. They're taking bit Nami and pivotal because we do need to modernize the application. If you have an application, you've been running long enough that your users are complaining about it. We need to modernize that. VM wear has not been much of enabler of that pivotal. Yes, absolutely. That's what the cloud Foundry Labs, the pivotal Labs has been doing for years. It is a tough thing to do. That's what the developers we hear it Amazon. They're building new abs. I don't hear modern building new app at VM where, but they are moving in that >> direct question for you guys and John you in particular, but also used to as well followed AWS probably more closely than any two people I know, Pat said. Strength, lies and differences, not similarities. I've noted many differences in philosophy between A. W S and V M. where they're both winning in the market place. We know a divorce is growing much faster, but a divorce doesn't believe in multi cloud. A Devil's doesn't believe security is broken. That's that's VM wears narrative VM where says it wants to be the best infrastructure and develop our software company. That's kind of like eight of us is the platform for that. They both want to be the security cloud, and and VM were said today they have 10,000 cloud data centers, and I'm guessing that Andy Jassy wouldn't think that many of those data centers are cloud data centers. Your thoughts on the differences between between A. W S s philosophy and VM wears narrative. And can they both? Is there enough market for them both to win? >> Well, it's strikingly different. I mean, AWS is just in a breed of its own. VM wears hedging and playing there their bets. They're kind of putting, you know, bets on each horse, right? Interesting enough in the cloud thing. There was no mention of Google Cloud. I didn't see that mentioned there. Andi was speculation. Wouldn't Oracle be great partnering with Google? That's not a rumor. I'm just kind of put it out there. That would be a good combination partnership, given the Oracle's cloud is failing miserably, I think v M. Where because of the operating leverage in the enterprise, has that operational layer down to me, Amazon is the model, the future, because they are clearly born with a dev ops mindset. They have an environment where developers can build applications and they could operate. It scale with all the efficiencies of operations. So I think cloud to foreigners were calling. It is all about having developers and operational excellence without a lot of disruption or re platforming. So I think that's where the differences are. You have company that have toe have to work with this world of legacy applications, and that requires first lift and shift, which doesn't become attractive. Then you add containers on the game changes. So I think container ization really was, I think, the seminal moment in the shift where where you got kubernetes and containers. So let the enterprise cloud. Native guys get in and have an operational framework that takes advantage of the horsepower of public cloud, which is computing storage, which is why we think networking and security will be the absolute focus areas for Cloud two point. Oh, and Amazon is just dominating the depth and the ops. And I don't think anyone is coming close. >> I'd love to hear your thoughts, too, but I just got caught. I don't think Oracles Cloud is failing miserably. I think it's I wouldn't say it that way. I think their infrastructures of service is irrelevant and the cloud is all about SAS. But just, you know, that's what I think. Waken debate that somebody >> has been great for the Oracle customers. But in terms of all metrics in terms of public and enterprise, cloud with multiple environments nonstarter. >> So there's a bit of a schism out there if you talk to customers. There are many customers when they deploy in Public Cloud, although uses, you know, compute storage and, like the identity management and that's it. And they'll stop and I talkto you con many customers that are using kubernetes so that if they want to hit the eject button, but they're all on Amazon today, so it's not like they're all fleeing Amazon or doing it. But we talked to lots of developers that are deep in aws they're using those service is they're using Lambda and they're building it. So how deep will they go? And that's where I look at this VM we're offering. And it's if I'm gonna take the sphere and extend that with kubernetes. I saw Cuba. Well, um, actually in the Twitter stream said it is, you know, cloud lock in to Dato is what we get if we do that. Because the whole reason VM were originally created called Foundry. So they didn't have to take that entire V's fear colonel and put it everywhere. So it's a nice bridge. That van, where has the partnership they have with AWS is a great strategy. But I still think it is a bridge to an ultimate solution where they'll still use the M where the embers not going anyway. But that shift of where my application live in what service is I do is going to change a lot over the next 3 to 5. >> Let's not lose sight, Dave, of where we are in the industry. I mean, we're at VM World 2019. We go to reinvents coming up. We kind of live in a tech bubble in the sense that all this stuff is all kind of great skating to where the puck is gonna be. But the reality is in most I tea shops, and again, I use ceases as a proxy in my mind, because they're in the cutting edge of all the real critical nature of security, of the impact that harm that could happen to a company. So I look at sea. So she's more of a canary in the coal mine for trends than the nutritional CEO. At this point, most enterprises are just trying to rationalize kubernetes, generally speaking like never mind, like making a centerpiece of their entire architecture. They're looking at their existing environment saying, Hey, I got V EMS that did great for me. Serve a consolidation enabled more efficiency, not rewriting code. Now what? I gotta do kubernetes and do all this other stuff. How do I suspect my VM with kubernetes? Is it on bare metal? So I think we're way ahead right now. In the narrative, I think the reality is that people catch up. That's where the proof is gonna come into. That's why the customer survey numbers are interesting. >> Keep keep. Townsend is set on the Cube VM, where moves at the speed of the CEO, so they're not moving too far ahead of them, but they are key heating up with them. >> Let me share some data to share some data so you could go to Silicon Angle. Look at the V M World 2019 90 spending survey containers, Cloud NSX and pivotal its data from Enterprise Technology Research that we analyzed. There's no evidence right now that Container's air hurting VM wear. But then that was the narrative that containers are gonna kill the M where but long term. There's real threats there. So that's what the pivotal acquisition, at least in part was about. I want to address the pivotal acquisition cause we haven't dug into it a little bit a cz, Much as I'd like to see. There's really three things there. One pivotal was struggling. You look at the stock price, you look at their buying patterns, you know the stock was down that not even close to their original AIPO price, so they wanted to get out of the public eye right now would not be on that 30 day shot clock. The second is it's a hedge on containers. And the third is it's a financial scheme. I mean, I'll call it that VM wears paying $800 million in cash for an asset that's worth $4 billion. How can that be? Well, they already owned 15% of pivotal there. Give. They're exchanging stock. So their trade trading paper to Adele in exchange for Dell's 70% ownership in Pivotal. So they pick up this asset, and it's basically a forced migration by Michael Del, who controls 96% of the voting shares. So there's all kinds of inside nuance going on there that nobody's really talked about it a >> great deal for Of'em. Where and Michael Dell? It's >> a very good deal for VM wear and Michael Dell. >> Let's unpack that are rapidly. >> Just did the one piece on that, right, because kubernetes it was the elephant, the room that was damaging what Pivotal was doing. VM were made a couple of acquisitions VM where needs to react at, so it made sense to pull out back in. Even if it does go against some of the original mission, that Cloud Foundry and Pivotal had to be able to be that cloud native without that full strong time, >> it's all about building apse, right? It's all about enabling developers. >> Let's on that note. Let's go around the horn and talk about what we expect from the emerald this year. And then we'll kick off three days of wall to wall coverage. I'll start, I expect. And I'm not looking for is how VM wear and its ecosystem and who's really deep in the ecosystem, who's kind of independent and neutral, what they're doing with their containers and kubernetes play. Because I think the container revolution that was started with Dr Absolutely is very relevant to the C i o and the Sea. So so and then how they're using data in that in their applications. So you know how VM Way wants to position themselves on the control plane, how that fits in the NSX. I think containers in the container ization is going to change. I think bare metal is gonna be a super important topic in the next couple of years. Dio I'm kind of swinging back to the my feeling that you know, hyper convergence what it did for server storage networking back when you were calling those those moves. I think that kind of hyper convergence mentality is coming up the stack, and I think Containers and the Kubernetes Chess Board will will play out. >> I think if you my feelings, if you don't own a public cloud, you better convince your customers in your ecosystem that the future is in our definition of cloud, which is multi cloud. And that's what this VM world to me is all about. >> Yeah, you know, Veum wears taking their software state and trying to live in all of those cloud world. So you know, V. Amar has 600,000 customers and they want to be the ones to educate them on the kubernetes containers. You know you're at modernization, but there's a lot of other places customers can learn about this. No one understand where VM wear really adds value beyond all of those pieces, because all the cloud platforms have their kubernetes. >> A lot of other places, like the public cloud. That's where all the action >> exactly comes back down the cloud 2.0 Dev and ops developers and operations all come together with software. Thank you. Breaking it down here for three days. Wall to wall coverage here in Moscow north to set celebrating our 10th year covering VM World. Thanks for watching stay with us from or action after this short break.

>> live from London, England. It's the queue covering a ws summat. London twenty nineteen, Brought to you by Amazon Web services, >> come to the A. W s summit in London's Excel Center. I'm Susanna Street, and David Aunty is my co host today on the Cube. This means so much to talk about here at the summit today to do with machine learning and a I. And I'm really pleased to say that we have to really key people here to discuss this. We've got time. Tom Summerfield, who is head off commerce, a foot asylum on also Richard Potter, who is the CEO of Peak. Now you guys have really formed a partnership. Haven't you put asylum? Is a leisure wear really? Retailer started in bricks and mortar stores. Really moved online on Peak is a pioneer for artificial intelligence. System's really well to get together. What What sparked? Really your demands. Ready for their services, Tom. >> Yeah, well, so way knew that we needed to be doing something with data on A and we didn't really know exactly what it would be Way were interested in personalization, but then also in a bigger picture, like a wider digital transformation piece for the business where well established bricks, a martyr business, but then a fast growing online business. And we're interested to know how way could harness the momentum of the stores to help the digital side of the business and also vice versa. On we thought data would be the key, and we ended up having a conversation with the guys at Peak, and that's exactly what we've been able to do. Actually, on the back of that deliver, we're delivering a hyper personal experience for our consumers Now. >> I was one of the statue that I notice when looking into what you be doing, a twenty percent increase in email revenue. So that's quite remarkable, Really. So Richard, tell us, you know how you're able to do this? What kind of services that you lean on? T make those kind of result. >> It's a combination of a lot of things, really. You know, you obviously need people who know what they're doing from a returning a business perspective. Married with technical experts, data science algorithms, data. Um, I think specifically how we've done it is a pig's built, a fairly unique A I system that becomes almost like the central brain within our customers. Businesses on off that algorithms help automate certain business processes and deliver tangible uplifts in business performance like the twenty eight percent uplifting sales here, Um, in order to do it. So it's quite a long journey, I suppose. The outlook we took when we started collaborating was was that if we could deliver that hyper personalized shopping experience, we were always going to be ableto show customers the right product at the right time. And if we were doing that that we would lead Toa High brand engagement, higher loyalty higher on higher lifetime values of customers. And that's and that's what's shown to be the case in silent example. >> Yeah, definitely that echo that. You know that the high profits hypothesis wass If you can show the right custom of the right product at the right time, then their purchase frequency average order Volumetrics all start to move positively and ultimately than affecting their long term engagement with our brand, which increases revenue on also delivers a more, you know, a frictionless consumer experience, hopefully for the customer, >> because I suppose your experience is the same. So many companies out there they're sitting on this huge pile of data, yet they don't know how to best optimize that data. When did you first realize, Richard that there was this kind of gap in the market for Pete to grow? >> Yeah, I think data and analytics have come on a bit of a journey away from common sense reporting tio more advanced analytics. But when you get a I and machine learning what you're talking about, his algorithms being our self learning make predictions about things that actually fundamentally changes the way businesses can operate on DH. And in this case, a great example is you know, we're sending hyper personalized marketing communications, Teo, every single for silent customer. They don't realize necessarily that they are tailored to them, but they just become more relevant. But it doesn't require a digital marketing to create every single one of those campaigns or emails and even trigger the sending of those materials. Brain takes care of that. It can automate it. And what the marketer needs to do is it's faded, engaging content and set up digital campaigns. And then and then and then you're left with this capability where eyes saying you might be a market for this product. Let's let's send you something that might appeal to you on DH that just gives that gives a marketing team scale. And then, as we move into other use cases like in the supply chain for film and delivery of product the same thing the team's just get huge scale out of letting algorithms do those things for them. Andi, I suppose the realization for us that there was that gap in the market was just that you can see the out performance of certain cos you can see that Amazon attributes five percent of their sales to their machine learning recommendation systems. I think Netflix says eighty five percent of all content is consumed >> because it's Al Burns. Andi. Companies >> like that can harness machine learning to such a great degree. How does how did howto other businesses do it? Who can't access that talent pool of Silicon Valley or along the global? You know, the global talent leaders in tech and that's that's where we had the insight that his peak way could create a company that gave our custom is that that technology and that capability Teo deliver that same kind results that the Amazon and Netflix >> so before the Internet brand's had all the power you could price however you wanted if you overprice, nobody even even knew. And the Internet was sort of like the revenge of the consumer. Aye, aye, And data now gives the brands the ability to learn more about its customers. But you have to be somewhat careful, don't you? Because their privacy concerns obviously DPR etcetera. So you have to have a value proposition for the customer, as you were saying, which they made are you know that machine is providing these offers, but they get value out of it. So how do you guys think about that in terms of experience for the customer? And how do you draw that balance? >> I think from my angle, that Richard touch on a couple of bits there to do it scale first and foremost across the entire alarm on Thai network of consumers is killer element to it. But to deliver that personal experience, I think consumers nowadays are so they're more expectant of this. Really. We would have considered it innovation a couple of years ago, but now actually it's expected, I think, from the consumer. So it's actually in the name ofthe You have to move forward to stand still. So but way think where we're right at the front of this at the moment. And we're really looking now how we optimize the journey for the consumer so that actually we know if we're from some transactional data that we have in a little bit of over behavioral data that, you know, we're really conscious of the whole GDP, our peace and stuff, and that's really, really relevant and super important. Andi, I'm pleased to say that you know, we have that. We know that by a peek, it's completely on lock down from that perspective as >> well. Where did the data's where the data source of comfort. You mentioned some transaction data. Where is the other day to come from using show social data and behavioral data? Where does that come? >> So those elements of social data, some of it is a little bit black box. You can't always access it, and that's a GDP, our peace there, and rightly so. Actually, in some cases we have a loyalty scheme which allows us to understand our Kashima's better in our bricks and mortar retail, which is really cool that we've got some of that transactional data on a customer level from the stars. We know that some people in our sector maybe don't have that, so that so that allows us to complete sort of single customer view, which then we can aggregate in peaks brain, then transaction data on the website in the app and bits off browsing, you know, just within our own network. You know where customs potentially being and reacted with somethin. A piece of content. Janet within the website, that's that's how we build that view. >> Do you think this is the way that more bricks and more two stores Khun survive? Because so many are closing in high streets up down the UK and in other countries because simply they're not really delivering what the customer wants? >> Yeah, I think so. We rich now. Both feel quite strongly now that wear so onto this now a little bit. It's a really As as our relationship for the two businesses has evolved, it's become clearer and clearer that actually we've armed with this. You know this data, our fingertips, we can actually breathe fresh life into the stores, and it's in the eye of proper true Omnichannel retailing way. Don't mind where the cost consumer spends the money. We just need to be always on in a connected environment so that A Z said before pushing the right product at the right time. And when they're when they're in market, we turn up the mark the message a little bit. But then understanding when they're not in market and maybe to back off him and maybe we warn them what with a little bit of a different type of message then and actually we're trapped with one challenge ourselves to send but less better marketing communications to our consumers. But absolutely that store piece is now, so we tail back. Our store opening strategy is a business to focus more on the digital side of things, but now it's possible that way might open some more stores now, but it will be with a more reform strategy of wet, wet where, why we need to do that? >> Isn't this ironic? The brick and mortar marketplaces getting disrupted by online retailers, obviously Amazons, that big whale in the marketplace, and your answer to that is to use Amazons, cloud services and artificial intelligence to pave the way for your future. Yeah, I mean, that's astounding when you think about >> me. Yeah, this sort of unified commerce approach, Tio, you know, there's a place in the world for shops. It's like it's not Romance isn't completely dead and going shopping. It turns out, you know so on. Actually, yeah, we're using honesty in the eight of us, but we'LL hire our friends at Peak. Yeah, it's it's some irony there. I think it's really cool. >> And that decision that you made obviously wasn't made made lightly. But you saw the advantages of working with the clouds outweighing the potential trade offs of competition. >> Yeah, I mean, that's not that was never really, really no, I'm certainly not know. I think this is something that is happening, that data, and on harnessing it in a safe, responsible, effective way, I believe, is the future of all commerce. So >> that as far as security is concerned because, of course, we have had data breaches your customers, credit card details, access. How do you ensure that it's as secure as possible in the way that you you you choose the services I think >> that come that just comes down to best practice infrastructure on the way we look at it, a peak is there's no bear tools in the world to do that, then the same technologies that Amazon themselves use. It's to do with how you configure those services until ls to make it secure, you know, And if you have an unsecure open database on a public network, of course that's not secure. But you could have the same thing in your own infrastructure, and it wouldn't be secure. So I think the way we look at it is exactly the same thing on actually, being in the Amazon prime for us gives us a greater comfort, particularly in terms of co location of date centers and like making sure that our application fails over into different locations. It gives us infrastructure we couldn't afford otherwise, and then on top of that, we get all these extra pieces of technology that can make us even more secure than we could do. Otherwise we'd have to wait, have to employ an army of infrastructure engineers, and we don't have to do that because we run on Yes. >> Okay, so we were able to eliminate all that heavy lifting. That same goes. You've got this corpus of data. I'm interested in how long it took to get through. A POC trained the models how much data science was involved. How much of a heavy lift was that? Yeah, well, I think for >> us we better be pretty rapid. Actually, we started working together in January last year, so we're only just sort of year into that. >> And in that faith in that entire >> sofa length of of our relationship, we've gone from high for personalizing digital campaigns to recommendation systems on a website to now optimizing customer acquisition on social media and then finally into the supply chain and optimizing demand and so on it. And I think there's >> a lot of reasons >> why we've been able to do it quickly. But that's fundamental to the technologies that the peak is built. There's two. There's two sides to it. Our technologies cut out a lot of the friction so way didn't run a proof of concept. We were able to just pick it up, run with it and deliver value. And that's to do with I think, the product that peak is built. But then you obviously need a a customer who's who's going on a transformation journey and is hungry to make that make that stick in London on. Then when the two come together, >> I think that it's an interesting point that, though, because while suite for asylum, we always I always say it's that we're not. We're not massive, but we're not tiny, but it's the sort place you Khun turn upon a Monday and say, I've had an idea about something and we're not doing it by Friday. That's That's a nice, agile culture. It can create some drama as well. Possibly. I think it's really straightforward to get straight into it. And I think this is where some of the bigger, um, sleepier high street retailers that Amar, fixed in a in a brick from our world, needs to not be too afraid to come out and start embracing it, because I think some of them are trying now. I think it might be a little bit late for some now, but it's just it's just it just wasn't that hard really to get going >> and you've seen the business results, can you share any measurements? or quantification. We've >> got a really a really good one that we're just talking about at the moment. Actually, Way were able to use segmentation tools within within the peak brain Teo to use them on Social than Teo. Create lookalike audiences. So Facebook Custom tools, Right? We'LL help you create audiences that it thinks you're the right buyer. It's complex algorithms itself, but we almost took a leap ahead of their algorithms by fire, our algorithms uploading our own segments to create a more sophisticated lookalike audience. We produced a row US results or return on that spend. People are not familiar with that of eight thousand four hundred percent, which Wei would normally be happy as a business, we've sort of seven, eight hundred percent. If you're running that that we've say on AdWords campaign or something like that, that's quite efficient campaign. So it's at zero. We were a bit like it felt like it's a mistake that, you >> know that is >> not the right, >> Yeah, but not so that's super cool. And that's really that's really opened our eyes to the potential of punishing that the, you know, our sort of piquet I brain to then bring it onto Social on. Do more outward. Advertise on there. >> So moving the goal post meant that your teeth are really high school. Thank you. Thank you very much for telling us all about that time someone feels on which floor. Sir. Thank you for joining me and David Auntie here at the eight of US Summit in London. Merchant to come on the King.

>> live from London, England. Q. Covering A Ws summat. London twenty nineteen Brought to you by Amazon Web services >> to the A. W s Summit in London's Excel Center home. Susanna Street and David is my co host today on the Cube. They mean so much to talk about here at the summit today to do with machine learning an A I and I'm really pleased to say that we have to really key people here to discuss this. But we've got some Tom Summerfield who is head off commerce, a foot asylum on also Richard Potter, who is the CEO of Peak. Now, you guys have really formed a partnership, haven't you? Foots asylum is a leisure wear really. Retailer started in bricks and mortar stores. Really moved online on Peak has been a pioneer for artificial intelligence systems really well to get together. What what comes? Sparked Really your demands ready for their services, Tom? >> Yeah, well, so way knew that we needed to be doing something with data on A and we didn't really know exactly what it would be way were interested in personalization, but then also in a bigger picture, like a wider digital transformation piece for the business where well established bricks, a martyr business but a fast grow in online business. And we're interested to know how we could harness the momentum of the stores to help the digital side of the business and also vice versa. On we thought data would be the key, and we ended up having a conversation with the guys at Peak, and that's exactly what we've been able to do. Actually, on the back of that deliver, we're delivering a hyper personal experience for our consumers Now. >> I was one of the statue that I notice when looking into what you be doing, a twenty percent increase in email revenue. So that's quite remarkable, really. So Richard, tell us you how you're able to do this. What kind of services that you lean on? T make those kind of result. >> It's a combination of a lot of things, really. You know, you obviously need people who know what they're doing from a returning a business perspective. Married with technical experts, data science algorithms, data, I think specifically how we've done is picks built a fairly unique A I system that becomes almost like the central brain within our customers businesses on off that algorithms help automate certain business processes and deliver tangible uplifts in business performance like the twenty eight percent up lift in sales here, Um, in order to do it. So it's quite a long journey, I suppose. The outlook we took when we started collaborating was was that if we could deliver that hyper personalized shopping experience, we were always going to be ableto show customers the right product at the right time. And if we were doing that that we would lead Tio Hi brand engagement, higher loyalty, higher on higher lifetime values of customers. And that's and that's what's shown to be the case in a silent example. >> Yeah, definitely that echo that. You know that the hypothesis hypothesis, wass. If you can show the right custom of the right product at the right time, then their purchase frequency average order Volumetrics all starts move positively and ultimately than affecting their long term engagement with our brand, which increases revenue on also delivers a more, you know, a frictionless consumer experience, hopefully for the customer, >> because I suppose your experience is the same. So many companies out there they're sitting on this huge pile of data, yet they don't know how to best optimize that data. When did you first realize, Richard that there was this kind of gap in the market for Pete to grow? >> Yeah. I think data and analytics have come on a bit of a journey away from common sense reporting Thio more advanced analytics. But when you get a I and machine learning what you're talking about, his algorithms being our self learning make predictions about things, and that actually fundamentally changes the way businesses can operate on DH. And in this case, a great example is you know, we're sending hyper personalized marketing communications, Teo every single foot silent customer. Um, they don't realize necessarily that they are tailored to them, but they just become more relevant. But it doesn't require a digital marketed to create every single one of those campaign or emails and even triggered the sending of those materials. The brain takes care of that. It can automate it. And what the marketer needs to do is feed it engaging content and set up digital campaigns. And then and then and then you're left with this capability where eyes saying you might be a market for this product. Let's let's send you something that might appeal to you on DH that just gives that gives a marketing team scale. And then, as we move into other use cases like in the supply chain for film and delivery of product the same thing that teams just get huge scale out of letting algorithms do those things for them. Andi, I suppose the realization for us that there was that gap in the market was just that you can see the out performance of certain cos you can see that Amazon attributes thirty five percent of their sales to their machine learning recommendation systems. I think Netflix says eighty five percent of all content is consumed >> in prison. It's Al Burns. Andi. Companies >> like that can harness machine learning to such a great degree. How does how do you know howto other businesses do it? Who can't access that talent pool of Silicon Valley or along the global? You know, the global talent leaders in tech and that's that's what we have. The insight that is Peak Way could create a company that gave our system is the that technology and that capability Teo deliver that same kind results that the Amazon and Netflix >> So before the Internet Yeah. Brand's had all the power you could price however you wanted if you overprice, nobody even even knew. And the Internet was sort of like the revenge of the consumer. Aye, aye. And data. How gives the brands the ability to learn more about its customers. But you have to be somewhat careful, don't you? Because your privacy concerns, obviously. Gpr etcetera. So you have to have a value proposition for the customer, as you were saying, which they may not even know that machine is providing these offers. Yeah, but they get value out of it. So how do you guys think about that in terms of experience for the customer? And how do you draw that balance? >> I think from my angle that Richard touch on a couple of bits there to do it scale first and foremost across the entire all on on Thai network of consumers is killer element to it. But to deliver that personal experience, I think consumers nowadays are so they're more expectant of this. Really? We would have considered it innovation a couple of years ago, but now Actually, it's expected, I think, from the consumer. So it's actually in the name ofthe You have to move forward to stand still. So but way Think we're We're right at the front of this at the moment. And we're really looking now how we optimize the journey for the consumer so that actually we know if we're from Simpson transactional data that we have in a little bit of over behavioral data that, you know, we're really conscious of the whole GDP, our peace and stuff, and that's really, really relevant and super important. Andi, I'm pleased to say that you know, we have that backed by a peek. It's completely on lock down from that perspective as >> well. Where do the data's where the data source of comfort. You mentioned some transaction data. Where is the other data come from? Using show social data and behavioral data? Where does that come >> with these elements of social data? Some of it is a little bit black box, so you can always access it. And that's the GPR piece there. And rightly so. Actually, in some cases we have a loyalty scheme which allows us to understand our Kashima's better in our bricks and mortar retail, which is really cool that we've got some of that transactional data on a customer level from the store's way know that some people in our sector maybe don't have that, so that so that allows us to complete sort of single customer view, which then we can aggregate in peaks brain, then transaction data on the website in the app and bits off browsing, you know, just within our own network. But you know where customers potentially being and reactive of somethin, a piece of content on journeys within the website, That's that's how we build that view. >> Do you think this is the way that more bricks and more two stores Khun survive? Because so many are closing in high streets up down that you can in other countries, because simply they're not really delivering what the customer wants? >> Yeah, I think so. Rich Now, both feel quite strongly now that wear something to this now a little bit. It's a really As as our relationship for the two businesses has evolved, it's become clearer and clearer that actually we've armed with this. You know this data, our fingertips, we can actually breathe fresh life into the stores, and it's in the eye of proper true omnichannel retailing way. Don't mind where the cost consumer spends the money. We just need to be always on in a connected environment. So that way said before pushing the right product at the right time. And when that when they're in market, we turn up the mark the message a little bit. But then understanding when they're not in market and maybe to back off him and maybe we warn them what with a little bit of a different type of message then and actually we're trapped would want to challenge ourselves to send but less better marketing communications to our consumers. But absolutely that store piece is now, so we tail back. Our store opening strategy is a business to focus more on the digital side of things, but now it's possible that way might open some more stores now, but it will be with a more reform strategy of wet, wet where, why we need to do that? >> Isn't this ironic? The brick and mortar marketplaces getting disrupted by online retailers, obviously Amazons, that big whale in the marketplace and your answer to that is to use Amazon's cloud services and artificial intelligence to pave the way for your future. Yeah, I mean, that's astounding when you think about >> coming. >> Yeah, sort of unified commerce approach, Tio. You know, there's a place in the world for shops. It's like it's not Romance isn't completely dead and going shopping Friends out, you know so on. Actually, yeah, we're using honest in the eight of us, but we'LL hire our friends at Peak. Yeah, it's it's some irony there. I think it's really cool. >> And that decision that you made obviously made made lightly. But you saw the advantages of working with the clouds outweighing the potential trade offs of competition. >> Yeah, I mean, that's not that was never really, really no, I'm certainly not know. I think this is something that is happening, that data, and on harnessing it in a in a safe, responsible, effective way, I believe, is the future of all commerce. So >> that as far as security is concerned because, of course, we have had data breaches. Yeah, customers, credit card details, access. How do you ensure that it's as secure as possible in the way that you you you choose the services. I think >> that come that just comes down to best practice infrastructure on the way we look at it, a peak is there's no bear tools in the world to do that, then the same technologies that Amazon themselves use. It's to do with how you configure those services until ls to make it secure. You know, if you have an unsecure open database on a public network, of course that's not secure. But you could have the same thing in your own infrastructure, and it wouldn't be secure. So I think the way we look at it is exactly the same thing on actually, being in the Amazon plan for us gives us a greater comfort, particularly in terms of co location of data centres, like making sure that our application fails over into different locations. It gives us infrastructure we couldn't afford otherwise, and then on top of that, we get all these extra pieces of technology that can make us even more secure than we could do. Otherwise we'd have to wait, have to employ an army of infrastructure engineers, and we don't have to do that because we run on Yes, >> okay, so we were able to eliminate all that heavy lifting. Same goes. You've got this corpus of data. I'm interested in how long it took to get through. A POC trained the models how much data science was involved. How much of a heavy lift was that? Yeah, well, I think for us >> we better be pretty rapid. Actually, we start working together in January last year, so we're only just sort of year into that. >> And in that faith in that entire >> sofa length of of our relationship, we've gone from high for personalizing digital campaigns to recommendation systems on a website to now optimizing customer acquisition on social media and then finally into the supply chain and optimizing demand. And so on and on. I think there's a lot of reasons why we've been able to do it quickly, but that's fundamental to the technologies that that peak is built. There's two. There's two sides to it. Our technologies cut out a lot of the friction so way didn't run a proof of concept. We were able to just pick it up, run with it and deliver value. And that's to do with I think, the product that peak is built. But then you obviously need a a customer who's who's going on a transformation journey and is hungry to make that make that stick in London on. Then when the two come together, >> I think that it's an interesting point that, though, because while suite for asylum, we always I always say it's that we're not. We're not massive, but we're not tiny, but it's the sort place you Khun turn upon a Monday and say, I've had an idea about something and we're not doing it by Friday. That's That's a nice, agile culture. It can create some drama as well. Possibly. I think it's really straightforward to get straight into it. And I think this is where some of the bigger, um, sleepier high street retailers that Amar fixed in a in a brick Samara world need to not be too afraid to come out and start embracing it. Because I think some of them are trying now. I think it might be a little bit late for some now, but it's it was just it was just wasn't that hard really to get going here >> and you've seen the business results. Can you share any measurements or quantification. We've >> got a really a really good one that we're just talking about at the moment. Actually, Way were able to use segmentation tools within within the peak brain, too to use them on social than Teo. Create lookalike audiences. So Facebook custom tools, right? We'LL help you create audiences that it thinks will be wrapped pirates complex algorithms itself. But we almost took a leap ahead of their algorithms by fire, our algorithms uploading our own segments to create a more sophisticated lookalike audience. We produced a row US results or return on that spend People are not familiar with that of eight thousand four hundred percent which we we would normally be happy as a business. We've sort of seven, eight hundred percent. If you're running that that we've say on AdWords campaign or something like that, that's quite efficient campaign. So it's at zero. We were a bit like it felt like it's a mistake that, you >> know, that is >> not the right Yeah, but not so that's super cool. And that's really that's really opened our eyes to the potential of punishing that the, you know, our sort of piquet I brain to then bring it onto Social on. Do more outward. Advertise on there. >> So moving the goal post meant that your teeth have really high school. Thank you. Thank you very much for telling us all about that time someone feels on Richard for so thank you for joining me and David Auntie here at the age of Lou s summit in London. Merchant to come on the King.

(bright symphony music) >> Hello, I'm John Furrier, the founder of SiliconANGLE Media and co-host of theCUBE. We're here in Paulo Alto at our studio here. I'm joining with Joseph Jacks, the founder and general partner of OSS Capital. Open Source Software Capital, is what OSS stands for. He's also the founder of KubeCon which now is part of the CNCF. It's a huge conference around Kubernetes. He's a cloud guy. He knows open source. Very well respected in the industry and also a great guest and friend of theCUBE, CUBE alumni. Joseph, great to see you. Also known as JJ. JJ, good to see you. >> Thank you for having me on again, John. >> Hey, great to have you come on. I know we've talked many times on theCUBE, but you've got some exciting news. You got a new firm, OSS Capital. Open Source Software, not operational support like a telco, but this is an investment opportunity where you're making investments. Congratulations. >> Thank you. >> So I know you can't talk about some of the specifics on the funds size, but you are actually going to go out, talk to entrepreneurs, make some equity investments. Around open source software. What's the thesis? How did you get here, why did you do it? What's motivating you, and what's the thesis? >> A lot of questions in there. Yeah, I mean this is a really profoundly huge year for open source software. On a bunch of different levels. I think the biggest kind of thing everyone anchors towards is GitHub being acquired by Microsoft. Just a couple of weeks ago, we had the two huge hadoop vendors join forces. That, I think, surprised a lot of people. MuleSoft, which is a big opensource middleware company, getting acquired by Salesforce just a year after going public. Just a huge outcome. I think one observation, just to sort of like summarize the year 2018, is actually, starting in January, almost on sort of like a monthly basis, we've observed a major sort of opensource software company outcome. And sort of kicking off the year, we had CoreOS getting acquired by Red Hat. Brandon and Alex, the founders over there, built a really interesting company in the Kubernetes ecosystem. And I think in February, Al Fresco, which is an open source content portal taking privatization outcome from a private equity firm, I believe in March we had Magento getting acquired by Adobe, which an open source based CMS. PHP CMS. So just a lot of activity for significant outcomes. Multibillion dollar outcomes of commercial open source companies. And open source software is something like 20 years old. 20 years in the making. And this year in particular, I've just seen just a huge amount of large scale outcomes that have been many years in the making from companies that have taken lots of venture funding. And in a lot of cases, sort of partially focused funding from different investors that have an affinity for open source software and sort of understand the uniqueness of the open source model when it's applied to business, when it's applied to company building. But more sort of opportunistic and sort of affinity oriented, as opposed to a pure focus. So that's kind of been part of the motivation. I'd say the more authentically compelling motivation for doing this is that it just needs to exist. This is sort of a model that is happening by necessity. We're seeing more and more software companies be open source software companies. So open source first. They're built in a distributed way. They're leveraging engineers and talent around the world. They're just part of this open source kind of philosophy. And they are fundamentally kind of commercial open source software companies. We felt that if you had a firm basically designed in a way to exclusively focus on those kind of companies, and where the firmware actually backed and supported by the founders of the largest commercial open source companies in the world before sort of the last decade. That could actually deliver a lot of value. So we've been sort of blogging a little bit about this. >> And you wrote a great post on it. I read about open source monetization. But I think one of the things I'm seeing as well that supports your thesis, and I like to get your reaction to it because I think this is something that's not really talked about, but open source is still young. I mean, you go back. I remember the days when we used to have to hide in the shadows to get licenses and pirate stuff and do all those crazy stuff. But now, it's only a couple decades away. The leaders that were investing were usually entrepreneurs that've been successful. The Rob Bearns, the Amar Wadhwa, the guy that did Spring. All these different open source. Linux, obviously, great success story. But there hasn't any been any institutional. Yeah, you got benchmark, other things, done some investments. A discipline around open source. Where open source is now table stakes in all software development. Cloud is scaling, scaling out globally. There's no real foc- There's never been a firm that's been focused on- Just open source from a commercial, while maintaining the purity and ethos of open source. I mean, is that. >> You agree? >> That's true. >> 100%, yeah. That's been the big part of creating the firm is aligning and solving for a pure focused structure. And I think what I'll say abstractly is this sort of venture capital, venture style approach to funding enterprise technology companies, software companies in general, has been to kind of find great entrepreneurs and in an abstract way that can build great technology companies. Can bring them to market, can sell them, and can scale them, and so on. And either create categories, or dominate existing categories, and disrupt incumbents, and so on. And I think while that has worked for quite a while, in the venture industry overall, in the 50, 60 years of the venture industry, lots of successful firms, I think what we're starting to see is a necessary shift toward accounting for the fundamental differences of opensource software as it relates to new technology getting created and going, and new software companies kind of coming into market. So we actually fundamentally believe that commercial open source software companies are fundamentally different. Functionally in almost every way, as compared to proprietary closed source software companies of the last 30 years. And the way we've sort of designed our firm and we'll about ten people pretty soon. We're just about a month in. We're growing the team quickly, but we're sort of a small, focused team. >> A ten's not focused small, I mean, I know venture firms that have two billion in management that don't have more than 20 people. >> Well, we have portfolio partners that are focused in different functional areas where commercial open source software companies have really fundamental differences. If you were to sort of stack rank, by function, where commercial open source software companies are really fundamentally different, sort of top to bottom. Legal would be, probably, the very top of the list. Right, in terms of license compliance management, structuring all the sort of protections and provisions around how intellectual property is actually shipped to and sold to customers. The legal licensing aspects. The commercial software licensing. This is quite a polarizing hot topic these days. The second big functional area where we have a portfolio partner focused on this is finance. Finance is another area where commercial open source software companies have to sort of behaviorally orient and apply that function very, very differently as compared to proprietary software companies. So we're crazy honored and excited to have world experts and very respected leaders in those different areas sort of helping to provide sort of different pillars of wisdom to our portfolio companies, our portfolio founders, in those different functional areas. And we provide a really focused kind of structure for them. >> Well I want to ask you the kind of question that kind of bridges the old way and new way, 'cause I definitely see you guys definitely being new and different, which is good. Or as Andy Jassy would say, you can be misunderstood for a while, but as you become successful, people will start understanding what you do. And that's a great example of Amazon. The pattern with success is traditionally the same. If we kind of encapsulate the difference between open source old and new, and that is you have something of value, and you're disrupting the market and collecting rents from it. Or revenue, or profit. So that's commercial, that's how businesses run. How are you guys going to disrupt with open source software the next generation value creation? We know how value's created, certainly in software that opensource has shown a path on how to create value in writing software if code is value and functionality's value. But to commercialize and create revenue, which is people paying something for something. That's a little bit different kind of value extraction from the value creation. So open source software can create value in functionality and value product. Now you bring it to the market, you get paid for it, you have to disrupt somebody, you have to create something. How are you looking at that? What's the vision of the creation, the extraction of value, who's disrupted, is it greenfield new opportunities? What's your vision? >> A lot of nuance and complexity in that question. What I would say is- >> Well, open source is creating products. >> Well, open source is the basis for creating products in a different kind of way. I'll go back to your question around let's just sort of maybe simplify it as the value creation and the value capture dynamics, right? We've sort of written a few posts about this, and it's subtle, but it's easy to understand if you look at it from a fundamental kind of perspective. We actually believe, and we'll be publishing research on this, and maybe even sort of more principled scientific, perhaps, even ways of looking at it. And then blog posts and research. We believe that open source software will always generate or create orders of magnitude more value than any constituent can capture. Right, and that's a fundamental way of looking at it. So if you see how cloud providers are capturing value that open source creates, whether it's Elasticsearch, or Postgres, or MySQL or Hadoop. And then commercial open source software companies that capture value that open source software creates, whether it's companies like Confluent around Kafka, or Cloudera around Hadoop, or Databricks around Apache Spark. Or whether it's the creators of those projects. The creators of Spark and Hadoop and Elasticsearch, sometimes many of them are the founders of those companies I mentioned, and sometimes they're not. We just believe regardless of how that sort of value is captured by the cloud providers, the commercial vendors, or the creators, the value created relative to the value captured will always be orders and orders of magnitude greater. And this is expressed in another way, which this may be easier to understand, it's a sort of reinforcing this kind of assertion that there's orders of magnitude value created far greater than what can be captured. If you were to do a survey, which we're currently in the process of doing, and I'm happy to sort of say that publicly for the first time here, of all the commercial open source software companies that have projects with large significant adoption, whether, say for example, it's Docker, with millions of users, or Apache Hadoop. How many Hadoop deployments there are. How many customers' companies are there running Hadoop deployments. Or it may be even MySQL. How many MySQL installations are there. And then you were to sort of survey those companies and see how many end users are there relative to how many customers are paying for the usage of the project. It would probably be something like if there were a million users of a given project, the company behind that project or the cloud provider, or say the end user, the developer behind the project, is unlikely to capture more than, say, 1% or a couple percent of those end users to companies, to paying companies, to paying customers. And many times, that's high. Many times, 1% to 2% is very high. Often, what we've seen actually anecdotally, and we're doing principled research around this, and we'll have data here across a large number of companies, many times it's a fraction of 1%. Which is just sort of maybe sometimes 10% of 1%, or even smaller. >> So the practitioners will be making more money than the actual vendors? >> Absolutely right. End users and practitioners always stand to benefit far greater because of the fundamental nature of open source. It's permissionless, it's disaggregated, the value creation dynamics are untethered, and it is fundamentally freely available to use, freely available to contribute to, with different constraints based on the license. However, all those things are sort of like disaggregating the creating of technology into sort of an unbounded network. And that's really, really incredible. >> Okay, so first of all, I agree with your premise 100%. We've seen it with CUBE, where videos are free. >> And that's a good thing. All those things are good. >> And Dave Vellante says this all the time on theCUBE. And we actually pointed this out and called this in the Hadoop ecosystem in 2012. In fact, we actually said that on theCUBE, and it turned out to be true, 'cause look at Hortonworks and Cloudera had to merge because, again, the market changed very quickly >> Value Creation. >> Because value >> Was created around them in the immediate cloud, etc. So the question is, that changes the valuation mechanisms. So if this true, which we believe it is. Just say it is. Then the traditional net present value cash flow metric of the value of the firm, not your firm, but, like, if I'm an open source firm, I'm only one portion of the extraction. I'm a supplier, and I'm an enabler, the valuation on cash flow might not be as great as the real impact. So the question I have for you, have you thought about the valuation? 'Cause now you're thinking about bigger construct community network effects. These are new dynamics. I don't think anyone's actually crunched a valuation model around this. So if someone knew that, say for example, an open source project created all this value, and they weren't necessarily harvesting it from a cash flow perspective, there might be other ways to monetize it. Have you though about that, and what's your reaction to that concept? 'Cause capitalism would kind of shake down the system. 'Cause why would someone be motivated to participate if they're not capturing any value? So if the value shifts, are they still going to be able to participate? You follow the logic I'm trying to- >> I definitely do. I think what I would say to that is we expect and we encourage and we will absolutely heavily invest in more business model innovation in the area of open source. So what I mean by that is, and it's important to sort of qualify a few things there. There's a huge amount of polarization and lack of consensus, lack of industry consensus on what it actually means to have or implement an open source based business model. In fact there's a lot of people who just sort of point blankedly assert that an opensource business model does not exist. We believe that many business models for monetizing and commercializing open source exist. We've blogged and written about a few of them. Their services and training and support. There's open core, which is very effective in sort of a spectrum of ways to implement open core. Around the core, you can have a thin crust or a thick crust. There's SAS. There are hardware based distribution models, things like Sourcefire, and Cumulus Networks. And there are also network based approaches. For example, project called Storj or Stor-J. Being developed and run now by Ben Golub, who's the former CEO of Docker. >> CUBE alumni. >> Ben's really great open source veteran. This is a network, kind of decentralized network based approach of sort of right sizing the production and consumption of the resource of a storage based open source project in a decentralized network. So those are sort of four or five ways to commercializing value, however, four or five ways of commercializing value, however what we believe is that there will be more business model innovation. There will be more developments around how you can better capture more, or in different ways, the value that open source creates. However, what I will say though, is it is unrealistic to expect two things. It is unrealistic and, in fact, unfair to expect that any of those constituents will contribute back to open source proportional to the value that they received from it, or the benefit, and I'm actually paraphrasing Doug Cutting there, who tweeted this a couple of years ago. Very profoundly deep, wise tweet, which I very strongly agree with. And it is also unrealistic to expect a second thing, which is that any of those constituents can capture a material portion of the value that open source creates, which I would assert is many trillions of dollars, perhaps tens of trillions of dollars. It's really hard to quantify that. And it's not just dollars in economic sense, it's dollars in productivity time saved, new markets, new areas, and so on. >> Yeah, I think this is interesting, and I think that we'll be an open book at that. But I will say that what I've observed in looking through all these CUBE interviews, I think that business model innovation absolutely is something that is an IP. >> We need it. Well, it's now intellectual property, the business model isn't, hey I went to business school, learned this at Babson or Harvard, I learned this business model. We're going to do SAS premium. Okay, I get that. There's going to be very interesting new innovations coming, and I think that's the new IP. 'Cause open source, if it's community based, there's going to be formulas. So that's going to be really inter- Okay, so now let's get back to actual funding itself. You guys are doing early stage. Can you take us through the approach? >> We're very focused on early stage, investing, and backing teams that are, just sort of welcoming the idea of a commercial entity around their open source project. Or building a business fundamentally dependent on an open source project or maybe even more than one. The reason for that is this is really where there's a lot of structural inefficiency in supporting and backing those types of founders. >> I think one of the things with ... is with that acquisition. They were pure on the open source side, doing a great job, didn't want to push the business model too hard because the open source, let's face it, you got people like, eh, I don't want to get caught on the business side, and get revenue, perverse incentives might come up, or fear of incentives that might be different or not aligned. Was a great a value. >> I think so. >> So Red Hat got a steal on that one. But as you go forward, there's going to be certainly a lot more stuff. We're seeing a lot of it now in CNCF, for instance. I want to get your thoughts on this because, being the co founder of KubeCon, and donating it to the CNCF, Kubernetes is the hottest thing on the planet, as we talked about many years ago. What's your take on that, now? I see exciting things happening. What is the impact of Kubernetes, in your opinion, to the world, and where do you see that evolving rapidly, and where is the focus here as the people should be paying attention to? >> I think that Kubernetes replaces EC2. Kubernetes is a disaggregated API for distributed computing anywhere. And it happens to be portable and able to run on any kind of computer infrastructure, which sort of makes it like a liquid disaggregated EC2-like API. Which a lot of people have been sort of chasing and trying to implement for many years with things like OpenStack or Eucalyptus. But interestingly, Kubernetes is sort of the right abstraction for distributed computing, because it meets people where they are architecturally. It's sort of aligned with this current movement around distributed systems first designs. Microservices, packaging things in small compartmentalized units. >> Good for integrating of existing stuff. >> Absolutely, and it's very composable, un-opinionated architecturally. So you can sort of take an application and structure it in any given way, and as long as it has this sort of isolation boundary of a container, you can run it on Kubernetes without needing to sort of retrofit the architecture, which is really awesome. I think Kubernetes is a foundational part of the next kind of computing paradigm in the same way that Linux was foundational to the computing paradigm that gave rise to the internet. We had commodity hardware meeting open source based sort of cost reduction and efficiency, which really Linux enabled, and the movement toward scale out data center infrastructure that supported the Internet's sort of maturity and infrastructure. I think we're starting to see the same type of repeat effect thanks to Kubernetes basically being really well received by engineers, by the cloud providers. It's now the universal sort of standard for running container based applications on the different cloud providers. >> And think having the non-technical opinion posture, as you said, architectural posture, allows it to be compatible with a new kind of heterogeneous. >> Heterogeneity is critical. >> Heterogeneity is key, 'cause it's not just within the environment, it's also within each vendor, or customer has more heterogeneity. So, okay, now that's key. So multi cloud, I want to get your thoughts on multi cloud, because now this goes into some of things that might build on top of if Kubernetes continues to go down the road that you say it does. Then the next question is, stateful applications, service meshes. >> A lot of buzz words. A lot of buzz words in there. Stateful application's real because at a certain point in time, you have a maturity curve with critical infrastructure that starts to become appealing for stateful mission critical storage systems, which is typically where you have all the crown jewels of a given company's infrastructure, whether it's a transactional system, or reading and writing core customer, or financial service information, or whatever it is. So Kubernetes' starting to hit this maturity curve where people are migrating really serious mission critical storage workloads onto that platform. And obviously we're going to start to see even more critical work loads. We're starting to see Edge workloads because Kubernetes is a pretty low footprint system, so you can run it on Edge devices, you can even run it on microcontrollers. We're sort of past the experimental, you know, fun and games was Raspberry Pi, sort of towers, and people actually legitimately doing real world Edge kind of deployments with Kubernetes. We're absolutely starting to see multi-geo, multi-replication, multi-cloud sort of style architectures becoming real, as well. Because Kubernetes is this API that the industry's agreeing upon sufficiently. We actually have agreement around this sort of surface area for distributed system style computing that if cloud providers can actually standardize on in a way that lets application specific vendors or new types of application deployment models innovate further, then we can really unlock this sort of tight coupling of proprietary services inside cloud providers and disaggregate it. Which is really exciting, and I forget the Netscape, Jim Barksdale. Bundling, un-bundling. We're starting to see the un-bundling of proprietary cloud computing service API's. Things like Kinesis, and ALB and ELB and proprietary storage services, and these other sticky services get un-bundled because of two big things. Open source, obviously, we have open source alternative data paths. And then we have Kubernetes which allows us to sort of disaggregate things out pretty easily. >> I want to hear your thoughts, one final concept, before we break, 'cause I was having a private conversation with three people besides myself. A big time CIO of a company that if I said the name everyone would go, oh my god, that guy is huge, he's seen it all going back many, many ways. Currently done a lot of innovation. A hardcore network chip guy who knows networking, old school infrastructure. And then a cloud native application founder who knows a lot about software development and is state-of-the-art cloud native. So cloud native, all experienced, old-school, kind of about my age, a cloud native app developer, a big time CIO, and a chip networking kind of infrastructure guy. And we're talking, and one thing that came out, I want to get you thoughts on this, he says, so what's going on with DevOps, how do you see this service mesh, is a stay for (mumbles) on top of the stack, no stacks, horizontally scalable. And the comment that came out was storage and networking have had this relationship with everything since day one. Network moves a packet from point A to point B, and nothing happens in between, maybe some inspection. And storage goes from here now to the then, because you store it. He goes, that premise moves up the stacks, so then the cloud native guy goes, well that's what's happening up at the top, there's a lot of moving things around, workloads and or services, provisioning services, and then from now to then state. In real time. And what dawned on the next conversation the CIO goes, well this is exactly our challenge. We have under the hood infrastructure being programmable, >> We're having some trouble with the connection. Please try again. >> My phone's calling me. >> Programmable connections. >> So you got the programmable on the top of the stack too, so the CIO said, that's exactly the problem we're trying to solve. We're trying to solve some of these network storage concepts now at an application level. Your thoughts to that. >> Well, I think if I could tease apart everything you just said, which is profound synthesis of a lot of different things, I think we've started to see application logic leak out of application code itself into dedicated layers that are really good at doing one specific thing. So traditionally we had some crud style kind of behavioral semantics implemented around business logic. And then, inside of that, you also had libraries for doing connectivity and lookups and service discovery and locking and key management and encryption and coordination with other types of applications. And all that stuff was sort of shoved into the single big application binary. And now, we're starting to see all those language runtime specific parts of application code sort of crack or leak out into these dedicated, highly scalable, Unix philosophy oriented sort of like layers. So things like Envoy are really just built for the sort of nervous system layer of application communication fabric up and down the layer two through layer seven sort of protocol transport stack, which is really profound. We're seeing things like Vault from Hashicorp handle secure key storage persistence of application dedication, authorization, metadata and information to sort of access different systems and end points. And that's a dedicated sort of stateful layer that you can sort of fragment out and delegate sort of application specific functionality to, which is really great for scalability reasons. And on, and on, and on. So we've started to see that, and I think one way of looking at that is it's a cycle. It's the sort of bundling and un-bundling aspect. >> One of the granny level services are getting a really low level- >> Yeah, it's a sort of like bundling and un-bundling and so we've got all this un-bundling happening out of application code to these dedicated layers. The bundling back may happen. I've actually seen a few Bay Area companies go like, we're going back to the monolith 'cause it actually gives us lots of efficiencies in things that we though were trade offs before. We're actually comfortable with a big monorepo, and one or two core languages, and we're going to build everything into these big binaries, and everyone's going to sort of live in the same source code repository and break things out through folders or whatever. There's a lot of really interesting things. I don't want to say we're sort of clear on where this bundling, un-bundling is happening, but I do think that there's a lot of un-bundling happening right now. And there's a lot of opportunity there. >> And the open source, obviously, driving it. So final question for you, how many deals have you done? Can you talk a little bit about the firm? And exciting things and plans that you have going forward. >> Yeah, we're going to be making a lot of announcements over the next few months, and we're, I guess, extremely thrilled. I don't want to say overwhelmed, 'cause we're able to handle all of the volume and inquiries and inbound interest. We're really honored and thrilled by the reception over the last couple weeks from announcing the firm on the first of October, sort of before the Hortonworks Cloudera merger. The JFrog funding announcement that week. The Elastic IPO. Just a lot of really awesome things happened that week. This is obviously before Microsoft open sourced all their patents. We'll be announcing more investments that we've made. We announced our first one on the first of October as well with the announcement of the firm. We've made a good number of investments. We're not able to talk to much about our first initiative, but you'll hear more about that in the near future. >> Well, we're excited. I think it's the timing's perfect. I know you've been working on this kind of vision for a while, and I think it's really great timing. Congratulations, JJ >> Thank you so much. Thanks for having me on. >> Joesph Jacks, also known as JJ, founder and general partner of OSS Capital, Open Source Software Capital, co founder of KubeCon, which is now part of the CNCF. A real great player in the community and the ecosystem, great to have him on theCUBE, thanks for coming in. I'm John Furrier, thanks for watching. >> Thanks, John. (bright symphony music)

(orchestral music) >> Hello everyone. Welcome to the special CUBE conversation here in theCUBE's Palo Alto studio. I'm John Furrier. Where conversation around venture capital, entrepreneurship, crypto currencies, block chain, and more, Jonathan Ebinger our friend with BRV, formerly Blue Run Ventures, but BRV for short, sounds better, welcome to theCUBE. >> Thanks John, looking forward to it. >> Great to see you, we've known each other for a long time and you've been a great investor, your firm has done a lot of great stuff, deals are really famous deals, but also you dig into the companies and you really stand by your portfolio companies, but you've also done a lot of work in China. >> Yes. >> So you have a good landscape of what's going on. What's the, what's going on in China? >> Well China is really expanding in ways which we had not foreseen when we first started investing there almost 15 years ago. We were really active for five to 10 years, investing in companies that initially were considered copycat companies, you can't really use that term anymore. In fact what's happening more and more, you're seeing Chinese ideas coming to the United States. Businesses like We Chat are being copied as fast as they can, you're seeing Snapchat, Messenger and so forth, they're quickly trying to amalgamate as many assets as they can within their viewership much like we're seeing in a lot of the other Chinese analogs over there. It's exciting to see, it's very much an arms race. >> It's been interesting to watch. We were at the Ali Baba Cloud Conference last year, at the end of last year, it's interesting the innovation and entrepreneurial thirst has really changed. If you go back just 10 years ago when you guys were first getting in there, I remember the conversations were what's going on in China, it's very developmental but what's going on 10 years ago, they are dominating the mobile space, they're mobile usage is really much different makeup in how they do startups, the apps. How much of that has influenced some of their success just the demand? >> Always on, location always available, it opens up a whole new level of communication services. The idea of the larger screen format, people used to think in the United States, these large devices coming out of Korea first and then China, we thought these would never play in the United States, now Apple 10, larger screen size, it makes sense, it's mobile first right from the get go for a now billion plus users. >> So BRV, how many active portfolio companies do you guys have and what's the profile that you're looking for for entrepreneurs, what are some of the kind of companies? >> We're about 45 active companies right now. We're putting about, we're putting money in about 10 new companies a year at this point. We have a very disciplined approach of investing in Series A style companies, Series A of course means a lot of different things to people, but generally, we like to put $3 to $5 million to work early on and then follow on. >> How much do take for that, just a third? >> Typical in the 20%-25% range. There's a lot of companies out there that still fit that profile. Of course you're seeing some super sized Series A's that happen, we don't play in those but for the traditional software companies, evaluations are really right in our sweet spot. >> How big is the fund now, just what's the number in terms of capital? >> We're in fund six, we're just over $150 million. >> And you got to save some for follow on rounds. >> Exactly. >> Talk about the changes in venture capital because what's interesting, I had a conversation with Greg Sands with Costanoa Ventures, another great investor, formerly I think the first employee of Netscape I think or the business plan. Great guy, he talked about the dynamics of, you don't need that much cash anymore because if you can get unit economic visibility into what the business is working, you can do so much more with that and I'm calling it the hourglass effect, you get through that visibility, you're in control, you own your own destiny, versus the old Silicon Valley model which seems to be fading away, which is hey, what do you need? $40 million, or here's $100 million. That really limits your exit options and sometimes you can drown in your own capital. Talk about that dynamic. >> You're seeing the $40 million rounds with businesses that are much more capital intensive and that's coming back in vogue now but for the most part, I agree with what Greg's saying and this whole advent of seed funds and super seed funds and angel funds and so forth has been really great for the traditional series A investor. A lot of that early fundamental and foundational work is being done and then when the series A comes, it's more about expansion so we're effectively getting what was a Series B type stage company now we're investing in Series A. We're saying hey, this product works, there's product market fit, let's put dollars to work to really grow the market. >> So you're saying Series B was a kind of prove the business model, shifted down to the A because the cost to get there is lower and hence that's opened up a seed round lower in numbers, so it just shifts down a little bit. >> It really has, it really has and that plays into our sweet spot. We really like working on business models, distribution strategies, things like that. >> And what kind of startups do you want to invest in? What are some of the categories? >> Love financial services, we like health tech, we're doing education, we're really pretty omnivorous when it comes to the sector. What we're looking for is really businesses that are using data, real time data to disrupt the numbers. >> So you're not sector driven, you're disruption oriented. >> That's right. >> Okay let's talk about disruption, my favorite trend. Obviously I love the China dynamic because you're not sure what it is, but it's really doing well so you can't ignore it and they're innovative and they're hustling hard and they've got massive numbers. Block chain, we're super excited about, we love crypto, we think it's the biggest wave coming out there, so a lot of my smart, entrepreneurial friends are jumping on their surfboards literally and jumping out into those waves and there's a lot of action there. At the same time, people are saying, stay away from that crypto thing, it's a scam. Kind of a different perspective, what's your thoughts on that? >> If you look at, you separate the cryptocurrencies from block chain, I think it becomes a lot more clear. Block chain is for real. Tracking provenance on transactions, real estate transactions, multinational transactions, makes a lot of sense, dovetails nicely with security, so there's a real business there. You saw the announcement with IBM and Mersk the other day, what they are taking enterprise level block chain into their whole supply chain. I think that's really important. We have a company in the category called pay stand which is doing the same sort of thing with smaller size businesses, just accelerating the whole process on accounts receivable, taking working capital. >> And they're doing block chain for that? >> Yes block chain is an option, we're not forcing people onto block chain, but the idea of hey, let's give people more cost effective ways to transact, get rid of the paper checks, get rid of the invoicing and just join the modern world, much like you use Venmo if you and I are going to exchange money. >> That's pay stand, that's one of your hot companies. >> Yeah it is, absolutely. >> So are they using block chain or not? >> They are, yes. >> Okay, because it's a physical asset, it's kind of a supply chain thing? >> They use it to track the funds themselves, unlike a credit card where you have to pay a big fee or ACH which you can't really get proof of funds, with their block chain technology, you can be sure that you have the funds available and you get it instantly. >> Let's talk about use cases that you think out there, I'd like you to just weigh in on use cases for block chain that a mainstream person that's not in the tech business would understand, because they say, is it real or not? I agree block chain is legit, what are some use cases that would highlight that? >> I think if you've ever been involved in real estate, bought a home, things like that, just tracking title insurance, you're going all the way back if you live in California, you're going all the way back to pre-statehood days, you have to track the provenance of that land all the way through. You're paying title insurance, title insurance is a business you don't really need if you have accurate provenance tracking through block chain. I think that's one most of us can understand. Obviously bills of weighting with things coming over on ships. That's natural and right now things get held up in port because people are trying to find a clipboard before you can sign off on who, is this bill of weighting actually clean, that stuff can be done automatically with 2D barcodes, block chain usage. >> Certainly with perishable goods too, we learned that with IBM's example. >> Sure. >> Okay let's get into the hot companies you got going on. Name some of the hot investments that you've done. >> Sure, well I talked about pay stand a minute ago, really excited about them, another one we really like is a company called aerobotics. I know you're a fan of autonomous flying. If you think about drones and everyone knows DJI and they're a great company, that's one to one, one person flying one drone, that's not scalable obviously, it scales at one to one. With autonomous flying, you can have a whole army of drones out doing your business, whether they're doing site exploration, checking for chemical spills, looking at traffic and so forth. The company is now operating in three continents, it's just, if you think about what a drone is, effectively it's a flying cell phone. It's a cell phone that goes around, takes pictures, transmits data back, we know something about cell phones at BRV, we've been investing in this category for a long time so when we say aerobotics come along, we said this is just a natural extension of real time data, cellular technology, and location based services. >> You guys don't get a lot of credit as much as you should, in my opinion on that, you guys were very early on the mobile, mobile connectivity side and mobile footprint and device and software. That's playing well into the hottest trend that we see, that's not the sexiest trend, that's IOT. >> Absolutely. >> Because drones are certainly, industrial IOT is a big one. Instrumenting physical plants, equipment, and IOT in general the edge of the network. What's your thoughts on IOT and how would you, how do you see that evolving? It's more than just the edge of the network issue, it's bigger. >> It is, well of course the devices and sensors are important. I think a lot of that's been commoditized. The business that we've been seeing develop and there's a lot of folks, they've moved from analytics of the web to analytics of IOT, so there's a lot of interesting companies coming in the analytic space. We're not playing in that as much, we tend to like to invest in companies that are big enough that you need to have analytics for them. We like companies that have proprietary control of analytics versus necessarily running analytics for company X. >> So you're not poopooing IOT per se, just that from an investment thesis standpoint, it's not on your radar yet. >> That's right, they're either too capital intensive for us as a firm or you're basically managing someone else's data. I want to be in companies that we're managing our own data for a proprietary advantage. >> That's really what I was going to get to next, the role of data driven, so we've lived in dupe world, theCUBE started in 2010 in the offices of Cloud Air actually and people don't know the history and it's been interesting, Hadoop was supposed to save the world, the data, but it really started the data trend, the data driven trend, Mike Olsen, Amar Omadala and the team over there really nailed it but it didn't turn into be just Hadoop, it's everything so we're seeing that now become a bumper sticker, data driven marketer, I'm a data driven executive, I'm a data driven interviewer, all that stuff, what does it actually mean? What does data driven mean to you? >> Data is, there's big data and then there's actionable data obviously people talk about exhaust, the data coming off, we really got started with, as you know, we were investors in Waze, awful lot of data coming out of your cell phone, extracting just the important pieces of it are really what's important. We're investors in a company called Cabbage which looks at every transaction a small business makes to determine their credit worthiness. It's really the science. People talk about data scientists, what do they actually do? What they're actually doing is separating out the wheat from the chaff because it's just a crush of data. I saw your interview with Andy Jazzy to other day from AWS, the amount of data that's being stored, it's almost unfathomable but the important people. >> They have a lot of data. You'd like to invest in them now. >> Exactly, but that's really the thing, it's being able to separate the good data from the bad. >> You look at Amazon, I was talking to Jesse and he didn't really go there because he was kind of on message but when I talked with Swami who runs the AI group over there, we were talking about, I said to him straight up, I'm like, you're running a lot of workloads on your cloud, I'm sure you have data on those workloads. Just the impact of what they could do with that data. This is the virtuous cycle that their business model is made up of, but it's changing the game for what they can become. The thing that we're seeing in the data world is, sometimes the outcome might not be what you think because if you can use the data effectively, it's a competitive advantage, not a department. >> Right and you have to really stay true to your commitment to data. What we've seen happen is when companies, if you've been around for 10 years or so, you start to trust your gut, that's important, but it can also not lead you to see obvious conclusions because the world changes. >> And also committing to data also means from a practitioner's standpoint, investing in the tech, investing in things to be data driven, not just to say it. >> Exactly. >> Okay so what's the future for you guys? What are you looking at next year, what are some of the things you'd like to accomplish for investment opportunities, besides getting all the hot deals, you did Waze, that was an amazing deal, one of my favorite products, how did that go down? How many people passed on Waze? >> I don't know how many people passed, but we were lucky, they wanted to bring us in to the initial syndicate, they wanted to have some folks who understood. >> But it wasn't that obvious though at the beginning. What was the original pitch? >> The initial pitch was that they were going to have folks have the dash devices, the product would sit on your dashboard and they were going to be using it to map Eastern Europe because Eastern Europe was just coming into the Western world and they didn't really have good roads and good maps. We thought, that's interesting but they probably also don't have smartphones, so why don't we come across the Atlantic and let's make this thing work in the US and then from there, the rest took off country by country we were the number one navigation app in I think 150 countries at one point. >> What's the biggest thing that you've learned over the past few years in the industry that's different now I mean obviously there's some context that I'll share which is obviously the big cloud players are becoming bigger, scale's a big thing, you got Google, you got Microsoft and Amazon, you've got Facebook's out there as well. Then you get the political climate. You go to Washington D.C. and New York, Silicon Valley is not really talked highly about these days on the hill in Washington, yet GovCloud is completely changing the game of how the government is going to work with massive innovations and efficiencies, literally overnight, it's almost weird. >> It is and it isn't. If you look at it through a longer term horizon, Silicon Valley is again at the forefront, we're really the first ones with more transparency in the industry, all the different movements which are really important and all the conversations that are happening are important and they're happening here first. I think you're starting to see a ripple effect, you're seeing it going through entertainment, you're going to see it in the government, industry after industry I think is going to start to have to be more open as Silicon Valley has led the way on that. >> That's a great point. Take a minute to describe the folks out there watching that aren't from here, what is Silicon Valley about in your opinion? >> Silicon Valley is, of course it's more than a mindset, but folks who are here are here on purpose. They come here intentionally. There are very few people that I know who were born and raised here, so they're coming here because they want to be part of a shared ethos around success, around success, around shared values and competition so it's a very healthy environment, I came, I used to live in Washington D.C. and I couldn't be happier to be 3000 miles away. >> If you're a technology entrepreneur, this is where all the sports and action is, as I always say, we always love sports analogies. Okay, I got to ask you about the VC situation around ICOs, initial coin offerings are being talked about as an alternative to fundraising, there's some security options on token sales as a utility, the SEC has started to put some guidelines down on what that looks like, but the general sentiment is, it's a new way to raise money and some people are doing private rounds with venture capital and doing token sales through ICOs. You see some hybrids, but for the most part, the hard core I don't want to say right or left wing, is there a wing of the political spectrum, but the hard core ICO guys are like, this is all about disrupting the VC community and you're a VC, so you got to take that a little bit personal but the point is, what do you think about that? Is that talked about? >> I think that's good salesmanship. The VC industry such as it is, you can fit every VC into one section of Stanford stadium. There just aren't that many VCs to really go after. We're a small group of folks. I think that going after maybe disrupting the way folks are raising money through Kickstarter and things like that, that's all great. We're not going to stop it, we're going to embrace it. I think that there's plenty of different ways to raise capital, I have no compunction about those things. >> Do you think it's more of a democratization trend or a new asset class, so you don't see it disrupting the VCs per se, but if it's only a handful of VCs that could fit into Stanford Stadium, for instance, then certainly there's more options, it's a dilution. >> I think you look at it as it's just an alternative financing method, do I take debt, do I take equity, do I take venture, do I take friends and family? It's just one more arrow in the quiver of the entrepreneur, I think you have to be smart about it because thinking that you're going to get the same level of attention from an investor in your ICO that you are going to get from a series A investor who owns 20% of your company, those are two very different value propositions. >> So you see a lot of pitches and sometimes, you have to say no a lot and that's the way the game is, but a lot of times, you want the best deals. But the founders' side of the table, they're looking at the VC, I need money. So that's one of the options, what they really want is a value added partner, so what's your current take on what that means these days? Sometimes it means a firm, sometimes it means a partner, sometimes it means the community. How are you guys looking at BRV as value add versus the worst case scenario which is value subtract, you just want to have that be positive. >> I see that written about venture too. >> I know, some people experienced it. >> I think it helps that we've been around now for almost 20 years, we got started in '98 so you have to look at our body of work and the continuum of investments and founders and CEOs and CTOs that we've invested in. There's hundreds and hundreds of people who have taken money from BRV, and so that's one of the real positives about this current state we're in is that there's so much transparency. The fact that we are, I like to think we're good actors and have been for a long time, that comes out, now through our words but through the words of. >> What would they say about you guys? What would your entrepreneurs say about BRV? >> Aside from using buzzwords like value add, they say, they know their industry, they're not afraid to ask for help, they try to call problems when they see it, things like that. >> You stand by your companies. >> Absolutely. >> Awesome, well what's your favorite trend that you're personally interested in? >> I think you have to go after health care right now. It is just such a big market right now. People have been nibbling all different sides of it right now, there's been folks who are trying to expedite processing, there's actual innovations happening on the medical side, I think there is just, technology is just now starting to get into that, technology has gotten into education. >> How about the startup you guys funded that's related to the health care field. >> Yes, we're in a company called Hello Heart which is really at the confluence of a number of trends. It starts off, what Hello Heart is, it's a personal blood pressure cuff for you as an employee of a big company, more and more companies are starting to self insure. If you're a big enough company, 10,000 plus employees or even fewer, you're going to want to self insure to save money but also, your employees get very much more comfortable with you as an employer, you care about my well being, so it's a very virtuous cycle for the employees. >> So companies themselves insuring their own employees. >> Absolutely. >> They have to be super big, this company. >> This is just one component of a self insured business. You also, of course you still have access to doctors and stuff, I'm not making the pitch for being self insured as a company, I'm just saying that. >> But that's a trend. >> It's absolutely a trend and you're seeing a lot of what I would call point solutions stepping in, whether it's psychiatric, whether it's opioid help, whether it's working on heart conditions, these are all different point solutions which are being amalgamated together to help companies which are self insuring. >> So is Hello Heart for consumers or for business? >> It's sold to businesses but individual employees have it so they can keep track of their blood pressure. >> But I can't buy one if I wanted one? >> Not today, but I'll make sure I can get one to you. >> I need one, get all of our employees instrumented. >> Exactly. >> Drug tested all that stuff going on. People worry about the privacy, that's something I would be concerned with, putting. >> That's taken a really fast pendulum swing. A few years ago, Generation X was privacy, there is no privacy, the default was, location is always on, that's just flipped 180 degrees in the last few years. >> Well Jonathan, thanks for coming into this CUBE conversation, I want to ask you one final question, one thing we're passionate about is women in tech and underserved minorities, obviously Silicon Valley has to do a better job, it's out on the table, and it's working but we're still seeing a lot more work to be done, we're seeing titles not being at the right level, but pay's getting there in some places but titles aren't, some paying still below for women, still a lot more to do, what are you guys doing for the women in tech trend, how are you guys looking at that? Certainly it's a sensitive topic these days, but more importantly, it's one that's super important to society. >> It is, I think like a lot of things that have long term value, it's really about your actions versus your words, so our firm has two out of the five investment professionals are female, one of the last three CEO's we've founded is a female CEO, we have technologists, we have marketing people, we have CEO's that are females it's very much of a cross the board, sex, race and so forth. >> You guys are indiscriminate, a good deal's a good deal. >> Exactly right. >> It's about making money, VC's are in the business of making money, a lot of people don't understand, you guys have a job to do but you do a good job. >> We're in the business of making money but our investors for the most part are not for profits. Large universities, our biggest investor is the Red Cross, so when we do well, the Red Cross does well and the country does well. >> You're mission driven at this point. >> Exactly. >> Is that by design or is that just, your selection? >> We're delighted with our LP's, it's important that we have synergies aside from just finances with our investors. >> That's super well, I appreciate you coming on, I think it's super great that you're tying society benefits into money making and entrepreneurship, great stuff Jonathan Ebinger here on theCUBE, BRV check them out, great VC firm here in Silicon Valley. It's a CUBE conversation, we're talking about startups and entrepreneurship I'm John Furrier, thanks for watching. (dramatic music)

(Rhythm music) >> Narrator: Live, from Munich, Germany, it's The Cube. Coverage, DataWorks Summit Europe, 2017. Brought to you by Hortonworks. >> Okay, welcome back everyone. We are live in Munich, Germany for DataWorks 2017, formally known as Hadoop Summit. This is The Cube special coverage of the Big Data world. I'm John Furrier my co-host Dave Vallente. Two days of live coverage, day one wrapping up. Now, Dave, we're just kind of reviewing the scene here. First of all, Europe is a different vibe. But the game is still the same. It's about Big Data evolving from Hadoop to full open source penetration. Puppy's now public in markets Hortonworks, Cloudera is now filing an S-1, Neosoft, Talon, variety of the other public companies. Alteryx. Hadoop is not dead, it's not dying. It certainly is going to have a position in the industry, but the Big Data conversation is front and center. And one thing that's striking to me is that in Europe, more than in the North America, is IOT is more centrally themed in this event. Europe is on the Internet of Things because of the manufacturing, smart cities. So this is a lot of IOT happening here, and I think this is a big discovery certainly, Hortonworks event is much more of a community event than Strata Hadoop. Which is much more about making money and modernization. This show's got a lot more engagement with real conversations and developers sessions. Very engaging audience. Well, yeah, it's Europe. So you've go a little bit different smaller show than North America but to me, IOT, Internet of Things, is bringing the other cloud world with Big Data. That's the forcing function. And real time data is the center of the action. I think is going to be a continuing theme as we move forward. >> So, in 2010 John, it was all about 'What is Hadoop?' With the middle part of that decade was all about Hadoop's got to go into the enterprise. It's gone mainstream in to the enterprise, and now it's sort of 'what's next?' Same wine new bottle. But I will say this, Hadoop, as you pointed out, is not dead. And I liken it to the early web. Web one dot O it was profound. It was a new paradigm. The profundity of Hadoop was that you could ship five megabytes of code to a petabyte of data. And that was the new model and that's spawned, that's catalyzed the Big Data movement. That is with us now and it's entrenched, and now you're seeing layers of innovation on top of that. >> Yeah, and I would just reiterate and reinforce that point by saying that Cloudera, the founders of this industry if you will, with Hadoop the first company to be commercially funded to do what Hortonworks came in after the fact out of Yahoo, came out of a web-scale world. So you have the cloud native DevOps culture, Amar Ujala's at Yahoo, Mike Olson, Jeff Hammerbacher, Christopher Vercelli. These guys were hardcore large-scale data guys. Again, this is the continuation of the evolution, and I think nothing is changed it that regard because those pioneers have set the stage for now the commercialization and now the conversation around operationalizing this cloud is big. And having Alan Nance, a practitioner, rock-star, talking about radical deployments that can drop a billion dollars at a cost savings to the bottom line. This is the kind of conversations we're going to see more of this is going to change the game from, you know, "Hey, I'm the CFO buyer" or "CIO doing IT", to an operational CEO, chief operating officer level conversation. That operational model of cloud is now coming into the view what ERP did in software, those kinds of megatrends, this is happening right now. >> As we talk about the open, the people who are going to make the real money on Big Data are the practitioners, those people applying it. We talked about Alan Nance's example of billion dollar, half a billion dollar cost-savings revenue opportunities, that's where the money's being made. It's not being made, yet anyway with these public companies. You're seeing it Splunk, Tableau, now Cloudera, Hortonworks, MapR. Is MapR even here? >> Haven't seen 'em. >> No I haven't seen MapR, they used to have pretty prominent display at the show. >> You brought up point I want to get back to. This relates to those guys, which is, profitless prosperity. >> Yeah. >> A term used for open source. I think there's a trend happening and I can't put a finger on it but I can kind of feel it. That is the ecosystems of open source are now going to a dimension where they're not yet valued in the classic sense. Most people that build platforms value ecosystems, that's where developers came from. Developer ecosystems fuel open source. But if you look at enterprise, at transformations over the decades, you'd see the successful companies have ecosystems of channel partners; ecosystems of indirect sales if you will. We're seeing the formation, at least I can start seeing the formation of an indirect engine of value creation, vis-à-vis this organic developer community where the people are building businesses and companies. Shaun Connolly pointed to Thintech as an example. Where these startups became financial services businesses that became Thintech suppliers, the banks. They're not in the banking business per se, but they're becoming as important as banks 'cuz they're the providers in Thintech, Thintech being financial tech. So you're starting to see this ecosystem of not "channel partners", resell my equipment or software in the classic sense as we know them as they're called channel partners. But if this continues to develop, the thousand flower blooming strategy, you could argue that Hortonworks is undervalued as a company because they're not realizing those gains yet or those gains can't be measured. So if you're an MBA or an investment banker, you've got to be looking at the market saying, "wow, is there a net-present value to an ecosystem?" It begs the question Dave. >> Dave: It's a great question John. >> This is a wealth creation. A rising tide floats all boats, in that rising tide is a ecosystem value number there. No one has their hands on that, no one's talked about that. That is the upshot in my mind, the silver-lining to what some are saying is the consolidation of Hadoop. Some are saying Cloudera is going to get a huge haircut off their four point one billion dollar value. >> Dave: I think that's inevitable. >> Which is some say, they may lose two to three billion in value, in the IPO. Post IPO which would put them in line with Hortonworks based on the numbers. You know, is that good or bad? I don't think it's bad because the value shifts to the ecosystem. Both Cloudera and Hortonworks both play in open source so you can be glass half-full on one hand, on the haircut, upcoming for Cloudera, two saying "No, the glass is half-full because it's a haircut in the short-term maybe", if that happens. I mean some said Pure Storage was going to get a haircut, they never really did Dave. So, again, no one yet has pegged the valuation of an ecosystem. >> Well, and I think that is a great point, personally I think, I've been sort of racking my brain, will this Big Data hike be realized. Like the internet. You remember the internet hyped up, then it crashed; no one wanted to own any of these companies. But it actually lived up to the hype. It actually exceeded the hype. >> You can get pet food online now, it's called amazon. [Co-Hosts Chuckle Together] All the e-commerce played out. >> Right, e-commerce played out. But I think you're right. But everybody's expecting sort of, was expecting a similar type of cycle. "Oh, this will replace that." And that's now what's going to happen. What's going to happen is the ecosystem is going to create a flywheel effect, is really what you're saying. >> Jeff: Yes. >> And there will be huge valuations that emerge out of this. But today, the guys that we know and love, the Hortonworks, the Clouderas, et cetera, aren't really on the winners list, I mean some of their founders maybe are. But who are the winners? Maybe the customers because they saw a big drop in cost. Apache's a big winner here. Wouldn't ya say? >> Yeah. >> Apache's looking pretty good, Apache Foundation. I would say AWS is a pretty big winner. They're drifting off of this. How about Microsoft and IBM? I mean I feel in a way IBM is sort of co-opted this Big Data meme, and said, "okay, cognitive." And layered all of it's stuff on top of it. Bought the weather company, repositioned the company, now it hasn't translated in to growth, but certainly has profitability implications. >> IBM plays well here, I'll tell you why. They're very big in open source, so that's positive. Two, they have huge track record and staff dealing with professional services in the enterprise. So if transformation is the journey conversation, IBM's right there. You can't ignore IBM on this one. Now, the stack might be different, but again, beauty is in the eye of the beholder because depending on what work clothes you have it depends. IBM is not going to leave you high and dry 'cuz they have a really you need for what they can do with their customers. Where people are going to get blindsided in my opinion, the IBMs and Oracles of the world, and even Microsoft, is what Alan Nance was talking about, the radical transformation around the operating model is going to force people to figure out when to start cannibalizing their own stacks. That's going to be a tell sign for winners and losers in the big game. Because if IBM can shift quickly and co-op the megatrends, make it their own, get out in front of that next wave as Pat Gelsinger would say, they could surf that wave and then tweak, and then get out in front. If they don't get behind that next wave, they're driftwood. It really is all about where you are in the spectrum, and analytics is one of those things in data where, you've got to have a cohesive horizontal strategy. You got to be horizontally scalable with data. You got to make data freely available. You have to have an abstraction layer of software that will allow free movement of data, across systems. That's the number one thing that comes out of seeing the Hortonwork's data platform for instance. Shaun Connolly called it 'connective tissue'. Cloudera is the same thing, they have to start figuring out ways to be better at the data across the horizontal view. Cloudera like IBM has an opportunity as well, to get out in front of the next wave. I think you can see that with AI and machine learning, clearly they're going to go after that. >> Just to finish off on the winners and losers; I mean, the other winner is systems integrators to service these companies. But I like what you said about cannibalizing stacks as an indicator of what's happening. So let's talk about that. Oracle clearly cannibalizing it's stacks, saying, "okay, we're going to the red stack to the cloud, go." Microsoft has made that decision to do that. IBM? To a large degree is cannibalizing it's stack. HP sold off it's stack, said, "we don't want to cannibalize our stack, we want to sell and try to retool." >> So, your question, your point? >> So, haven't they already begun to do that, the big legacy companies? >> They're doing their tweaking the collet and mog, as an example. At Oracle Open World and IBM Interconnect, all the shows we, except for Amazon, 'cuz they're pure cloud. All are taking the unique differentiation approach to their own stuff. IBM is putting stuff that's relate to IBM in their cloud. Oracle differentiates on their stack, for instance, I have no problem with Oracle because they have a huge database business. And, you're high as a kite if you think Oracle's going to lose that database business when data is the number one asset in the world. What Oracle's doing which I think is quite brilliant on Oracle's part is saying, "hey, if you want to run on premise with hardware, we got Sun, and oh by the way, our database is the fastest on our stuff." Check. Win. "Oh you want to move to the cloud? Come to the Oracle cloud, our database runs the fastest in our cloud", which is their stuff in the cloud. So if you're an Oracle customer you just can't lose there. So they created an inimitability around their own database. So does that mean they're going to win the new database war? Maybe not, but they can coexist as a system of records so that's a win. Microsoft Office 365, tightly coupling that with Azure is a brilliant move. Why wouldn't they do that? They're going to migrate their customer base to their own clouds. Oracle and Microsoft are going to migrate their customers to their own cloud. Differentiate and give their customers a gateway to the cloud. VVMware is partnering with Amazon. Brilliant move and they just sold vCloud Air which we reported at Silicon Angle last night, to a French company recently so vCloud Air is gone. Now that puts the VMware clearly in bed with Amazon web services. Great move for VMware, benefit to AWS, that's a differentiation for VMware. >> Dave: Somebody bought vCloud Air? >> I think you missed that last night 'cuz you were traveling. >> Chuckling: That's tongue-in-cheek, I mean what did they get for vCloud Air? >> OVH bought them, French company. >> More de-levering by Michael. >> Well, they're inter-clouding right? I mean de-leveraging the focus, right? So OVH, French company, has a very much coexisted... >> What'd they pay? >> ... strategy. It's undisclosed. >> Yeah, well why? 'Cuz it wasn't a big number. That's my point. >> Back to the other cloud players, Google. I think Google's differentiating on their technology. Great move, smart move. They just got to get, as someone who's been following them, and you know, you and I both love an enterprise experience. They got to speak the enterprise language and execute the language. Not through 19 year olds and interns or recent smart college grads ad and say, "we're instantly enterprise." There's a dis-economies of scale for trying to ramp up and trying to be too heavy on the enterprise. Amazon's got the same problem, you can't hire sales guy fast enough, and oh by the way, find me a sales guy that has ten 15 years executive selling experience to a complex strategic sales, like the enterprise where you now have stakeholders that are in multiple roles and changing roles as Alan Nance pointed out. So the enterprise game is very difficult. >> Yup. >> Very very difficult. >> Well, I think these dupe startups are seeing that. None of them are making money. Shaun Connolly basically said, "hey, it used to be growth they would pay for growth, but now their punishing you if you don't have growth plus profitability." By the way, that's not all totally true. Amazon makes no money, unless stock prices go through the roof. >> There is no self-service, there is no self-service business model for digital transformation for enterprise customers today. It doesn't exist. The value proposition doesn't resinate with customers. It works good for Shadow IT, and if you want to roll out G Suite in some pockets of your organization, but an ad-sense sales force doesn't work in the enterprise. Everyone's finding that out right now because they're basically transforming their enterprise. >> I think Google's going to solve their problem. I think Google has to solve their problem 'cuz... >> I think they will, but to me it's, buy a company, there's a zillion company out there they could buy tomorrow that are private, that have like 300 sales people that are senior people. Pay the bucks, buy a sales force, roll your stuff out and start speaking the language. I think Dianne Green gets this. So, I think, I expect to see Google ... >> Dave: Totally. >> do some things in that area. >> And I think, to you're point, I've always said the rich get richer. The traditional legacy companies, they're holding servant in this. They waited they waited they waited, and they said, "okay now we're going to go put our chips on the table." Oracle made it's bets. IBM made it's bets. HP, not really, betting on hardware. Okay. Fine. Cisco, Microsoft, they're all making their bets. >> It's all about bets on technology and profitability. This is what I'm looking at right now Dave. We talked about it on our intro. Shaun Connolly who's in charge of strategy at Hortonworks clarified it that clearly revenue, losing money is not going to solve the problem for credibility. Profitability matters. This comes back to the point we've said on The Cube multiple years ago and even just as recently as last year, that the world's flipping back down to credibility. Customers in the enterprise want to see credibility and track record. And they're going to evaluate the suppliers based upon key fundamentals in their business. Can they make money? Can they deliver SLAs? These are going to be key requirements, not the shiny new toy from Silicon Valley. Or the cool machine learning algorithm. It has to apply to their product, their value, and they're going to look to companies on the scoreboard and say, "are you profitable?" As a proxy for relevance. >> Well I want to keep it, but I do want to, we've been kind of critical of some of the Hadoop players. Cloudera and Hortonworks specifically. But I want to give them props 'cuz you remember well John, when the legacy enterprise guys started coming into the Hadoop market they all said that they had the same messaging, "we're going to make Hadoop enterprise ready." You remember that well, and I have to say that Hortonworks, Cloudera, I would say MapR as well and the ecosystem, have done a pretty good job of making Hadoop and Big Data enterprise ready. They were already working on it very hard, I think they took it seriously and I think that that's why they are in the mix and they are growing as they are. Shaun Connolly talked about them being operating cashflow positive. Eking out some plus cash. On the next earnings call, pressures on. But we want to see, you know, rocket ships. >> I think they've done a good job, I mean, I don't think anyone's been asleep at the switch. At all, enterprise ready. The questions always been "can they get there fast enough?" I think everyone's recognized that cost of ownership's down. We still solicit on the OpenStack ecosystem, and that they move right from the valley properties. So we'll keep an eye on it, tomorrow we'll be checking in. We got a great day tomorrow. Live coverage here in Munich, Germany for DataWorks 2017. More coverage tomorrow, stay with us. I'm John Furrier with Dave Vallente. Be right back with more tomorrow, day two. Keep following us.

Yeah, I'm Aala, They're the co-founder back to back. This is the cube silicon angle.com, Silicon angle dot TV's production of the cube, our flagship telecasts. We go out to the event. That was a great conversation. I was really just, just cool. I could have, we could have probably hit on a few more things, obviously well read. Awesome. Co-founder of Cloudera a. You were, you did a good job teaming up with that co-founder, huh? Not bad on the cube, huh? He's not bad on the cube, isn't he? He, >>He reads the internet. >>That's what I'm saying. >>Anything is going on. >>He's a cube star, you know, And >>Technology. Jeff knows it. Yeah. >>We, we tell you, I'm smarter just by being in Cloudera all those years. And I actually was following what he was saying, Sad and didn't dust my brain. So, Okay, so you're back. So we were talking earlier with Michaels and about the relational database thing. So I kind of pick that up where we left off with you around, you know, he was really excited. It's like, you know, hey, we saw that relational database movement happen. He was part of that. Yeah, yeah. That generation. And then, but things were happening or kind of happening the same way in a similar way, still early. So I was trying to really peg with him, how early are we, like, so, you know, as the curve, you know, this is 1400, it's not the Javit Center yet. Maybe the Duke world, you know, next year might be at the Javit Center, 35,000 just don't go to Vegas. So I'm trying to figure out where we are on that curve. Yeah. And we on the upwards slope, you know, down here, not even hitting that, >>I think, I think, I think we're moving up quicker than previous waves. And actually if you, if you look for example, Oracle, I think it took them 15, 20 years until they, they really became a mature company, VM VMware, which started about, what, 12, 13 years ago. It took them about maybe eight years to, to be a big company, met your company, and I'm hoping we're gonna do it in five. So a couple more years. >>Highly accelerated. >>Yes. But yeah, we see, I mean, I'm, I'm, I've been surprised by the growth. I have been, Right? I've been told, warned about enterprise software and, and that it takes long for production to take place. >>But the consumerization trend is really changing that. I mean, it seems to be that, yeah, the enterprises always last. Why the shorter >>Cycle? I think the shorter cycle is coming from having the, the, the, the right solution for the right problem at the right time. I think that's a big part of it. So luck definitely is a big part of this. Now, in terms of why this is changing compared to a couple of dec decades ago, why the adoption is changing compared to a couple of decades ago. I, I think that's coming just because of how quickly the technology itself, the underlying hardware is evolving. So right now, the fact that you can buy a single server and it has eight cores to 16 cores has 12 hards to terabytes. Each is, is something that's just pushing the, the, the, the limits what you can do with the existing systems and hence making it more likely for new systems to disrupt them. >>Yeah. We can talk about a lot. It's very easy for people to actually start a, a big data >>Project. >>Yes. For >>Example. Yes. And the hardest part is, okay, what, what do I really, what problem do I need to solve? How am I gonna, how am I gonna monetize it? Right? Those are the hard parts. It's not the, not the underlying >>Technology. Yes, Yes, that's true. That's true. I mean, >>You're saying, eh, you're saying >>Because, because I'm seeing both so much. I'm, I'm seeing both. I'm seeing both. And like, I'm seeing cases where you're right. There's some companies that was like, Oh, this Hadoop thing is so cool. What problem can I solve with it? And I see other companies, like, I have this huge problem and, and, and they don't know that HA exists. It's so, And once they know, they just jump on it right away. It's like, we know when you have a headache and you're searching for the medicine in Espin. Wow. It >>Works. I was talking to Jeff Hiba before he came on stage and, and I didn't even get to it cuz we were so on a nice riff there. Right. Bunch of like a musicians playing the guitar together. But like he, we talked about the it and and dynamics and he said something that I thoughts right. On money and SAP is talking the same thing and said they're going to the lines of business. Yes. Because it is the gatekeeper that's, it's like selling mini computers to a mainframe selling client servers from a mini computer team. Yeah. >>There's not, we're seeing, we're seeing both as well. So more likely the, the former one meaning, meaning that yes, line of business and departments, they adopt the technology and then it comes in and they see there's already these five different departments having it and they think, okay, now we need to formalize this across the organization. >>So what happens then? What are you seeing out there? Like when that happens, that mean people get their hands on, Hey, we got a problem to solve. Yeah. Is that what it comes down to? Well, Hadoop exist. Go get Hadoop. Oh yeah. They plop it in there and I what does it do? They, >>So they pop it into their, in their own installation or on the, on the cloud and they show that this actually is working and solving the problem for them. Yeah. And when that happens, it's a very, it's a very easy adoption from there on because they just go tell it, We need this right now because it's solving this problem and it's gonna make, make us much >>More money moving it right in. Yes. No problems. >>Is is that another reason why the cycle's compressed? I mean, you know, you think client server, there was a lot of resistance from it and now it's more much, Same thing with mobile. I mean mobile is flipped, right? I mean, so okay, bring it in. We gotta deal with it. Yep. I would think the same thing. We, we have a data problem. Let's turn it into an >>Opportunity. Yeah. In my, and it goes back to what I said earlier, the right solution for the right problem at the right time. Like when they, when you have larger amounts of unstructured data, there isn't anything else out there that can even touch what had, can >>Do. So Amar, I need to just change gears here a minute. The gaming stuff. So we have, we we're featured on justin.tv right now on the front page. Oh wow. But the numbers aren't coming in because there's a competing stream of a recently released Modern Warfare three feature. Yes. Yes. So >>I was looking for, we >>Have to compete with Modern Warfare three. So can you, can we talk about Modern Warfare three for a minute and share the folks what you think of the current version, if any, if you played it. Yeah. So >>Unfortunately I'm waiting to get back home. I don't have my Xbox with me here. >>A little like a, I'm talking about >>My lines and business. >>Boom. Water warfares like a Christmas >>Tree here. Sorry. You know, I love, I'm a big gamer. I'm a big video gamer at Cloudera. We have every Thursday at five 30 end office, we, we play Call of of Beauty version four, which is modern world form one actually. And I challenge, I challenge people out there to come challenge our team. Just ping me on Twitter and we'll, we'll do a Cloudera versus >>Let's, let's, let's reframe that. Let team out. There am Abalas company. This is the geeks that invent the future. Jeff Haer Baer at Facebook now at Cloudera. Hammerer leading the charge. These guys are at gamers. So all the young gamers out there am are saying they're gonna challenge you. At which version? >>Modern Warfare one. >>Modern Warfare one. Yes. How do they fire in? Can you set up an >>External We'll >>We'll figure it out. We'll figure it out. Okay. >>Yeah. Just p me on Twitter and We'll, >>We can carry it live actually we can stream that. Yeah, >>That'd be great. >>Great. >>Yeah. So I'll tell you some of our best Hadooop committers and Hadoop developers pitch >>A picture. Modern Warfare >>Three going now Model Warfare three. Very excited about the game. I saw the, the trailers for it looks, graphics look just amazing. Graphics are amazing. I love the Sirius since the first one that came out. And I'm looking forward to getting back home to playing the game. >>I can't play, my son won't let me play. I'm such a fumbler with the Hub. I'm a keyboard controller. I can't work the Xbox controller. Oh, I have a coordination problem my age and I'm just a gluts and like, like Dad, sorry, Charity's over. I can I play with my friends? You the box. But I'm around big gamer. >>But, but in terms of, I mean, something I wanted to bring up is how to link up gaming with big data and analysis and so on. So like, I, I'm a big gamer. I love playing games, but at the same time, whenever I play games, I feel a little bit guilty because it's kind of like wasted time. So it's like, I mean, yeah, it's fun and I'm getting lots of enjoyment on it makes my life much more cheerful. But still, how can we harness all of this, all of these hours that gamers spend playing a game like Modern Warfare three, How can we, how can we collect instrument, all of the data that's coming from that and coming up, for example, with something useful with predicted. >>This is exactly, this is exactly the kind of application that's mainstream is gaming. Yeah. Yeah. Danny at Riot G is telling me, we saw him at Oracle Open World. He's up there for the Java one. He said that they, they don't really have a big data platform and their business is about understanding user behavior rep tons of data about user playing time, who they're playing with. Yeah, Yeah. How they want us to get into currency trading, You know, >>Buy, I can't, I can't mention the names, but some of the biggest giving companies out there are using Hadoop right now. And, and depending on CDH for doing exactly that kind of thing, creating >>A good user experience >>Today, they're doing it for the purpose of enhancing the user experience and improving retention. So they do track everything. Like every single bullet, you fire everything in best Ball Head, you get everything home run, you do. And, and, and in, in a three >>Type of game consecutive headshot, you get >>Everything, everything is being Yeah. Headshot you get and so on. But, but as you said, they are using that information today to sell more products and, and, and retain their users. Now what I'm suggesting is that how can you harness that energy for the good as well? I mean for making money, money is good and everything, but how can you harness that for doing something useful so that all of this entertainment time is also actually productive time as well. I think that'd be a holy grail in this, in this environment if we >>Can achieve that. Yeah. It used to be that corn used to be the telegraph of the future of about, of applications, but gaming really is, if you look at gaming, you know, you get the headset on. It's a collaborative environment. Oh yeah. You got unified communications. >>Yeah. And you see our teenager kids, how, how many hours they spend on these things. >>You got play as a play environments, very social collaborative. Yeah. You know, some say, you know, we we're saying, what I'm saying is that that's the, that's the future work environment with Skype evolving. We're our multiplayer game's called our job. Right? Yeah. You know, so I'm big on gaming. So all the gamers out there, a has challenged you. Yeah. Got a big data example. What else are we seeing? So let's talk about the, the software. So we, one of the things you were talking about that I really liked, you were going down the list. So on Mike's slide he had all the new features. So around the core, can you just go down the core and rattle off your version of what, what it means and what it is. So you start off with say H Base, we talked about that already. What are the other ones that are out there? >>So the projects that we have right there, >>The projects that are around those tools that are being built. Cause >>Yeah, so the foundational, the foundational one as we mentioned before, is sdfs for storage map use for processing. Yeah. And then the, the immediate layer above that is how to make MAP reduce easier for the masses. So how can, not everybody knows how to learn map, use Java, everybody knows sql, right? So, so one of the most successful projects right now that has the highest attach rate, meaning people usually when they install had do installed as well is Hive. So Hive takes sequel and so Jeff Harm Becker, my co-founder, when he was at Facebook, his team built the Hive system. Essentially Hive takes sql so you don't have to learn a new language, you already know sql. And then converts that into MAP use for you. That not only expands the developer base for how many people can use adu, but also makes it easier to integrate Hadoop through all DBC and JDBC integrated with BI tools like MicroStrategy and Tableau and Informatica, et cetera, et cetera. >>You mentioned R too. You mentioned R Program R >>As well. Yeah, R is one of our best partnerships. We're very, very happy with them. So that's, that's one of the very key projects is Hive assisted project to Hive ISS called Pig. A pig Latin is a language that ya invented that you have to learn the language. It's very easy, it's very easy to learn compared to map produce. But once you learn it, you can, you can specify very deep data pipelines, right? SQL is good for queries. It's not good for data pipelines because it becomes very convoluted. It becomes very hard for the, the human brain to understand it. So Pig is much more natural to the human. It's more like Pearl very similar to scripting kind of languages. So with Peggy can write very, very long data pipelines, again, very successful projects doing very, very well. Another key project is Edge Base, like you said. So Edge Base allows you to do low latencies. So you can do very, very quick lookups and also allows you to do transactions. So you can do updates in inserts and deletes. So one of the talks here that had World we try to recommend people watch when the videos come out is the Talk by Jonathan Gray from Facebook. And he talked about how they use Edge Base, >>Jonathan, something on here in the Cube later. Yeah. So >>Drill him on that. So they use Edge Base now for many, many things within Facebook. They have a big team now committed to building an improving edge base with us and with the community at large. And they're using it for doing their online messaging system. The live mail system in Facebook is powered by Edge Base right now. Again, Pro and eBay, The Casini project, they gave a keynote earlier today at the conference as well is using Edge Base as well. So Edge Base is definitely one of the projects that's growing very, very quickly right now within the Hudu system. Another key project that Jeff alluded to earlier when he was on here is Flum. So Flume is very instrumental because you have this nice system had, but Hadoop is useless unless you have data inside it. So how do you get the data inside do? >>So Flum essentially is this very nice framework for having these agents all over your infrastructure, inside your web servers, inside your application servers, inside your mobile devices, your network equipment that collects all of that data and then reliably and, and materializes it inside Hado. So Flum does that. Another good project is Uzi, so many of them, I dunno how, how long you want me to keep going here, But, but Uzi is great. Uzi is a workflow processing system. So Uzi allows you to define a series of jobs. Some of them in Pig, some of them in Hive, some of them in map use. You can define a series of them and then link them to each other and say, only start this job when these other jobs, two jobs finish because I'm waiting for the input from them before I can kick off and so on. >>So Uzi is a very nice framework that will will do that. We'll manage the whole graph of jobs for you and retry things when they fail, et cetera, et cetera. Another good project is where W H I R R and where allows you to very easily start ADU cluster on top of Amazon. Easy two on top of Rackspace, virtualized environ. It's more for kicking off, it's for kicking off Hadoop instances or edge based instances on any virtual infrastructure. Okay. VMware, vCloud. So that it supports all of the major vCloud, sorry, all of the me, all of the major virtualized infrastructure systems out there, Eucalyptus as well, and so on. So that's where W H I R R ARU is another key project. It's one, it's duck cutting's main kind of project right now. Don of that gut cutting came on stage with you guys has, So Aru ARO is a project about how do we encode with our files, the schema of these files, right? >>Because when you open up a text file and you don't know how to what the columns mean and how to pars it, it becomes very hard to work for it. So ARU allows you to do that much more easily. It's also useful for doing rrp. We call rtc remove procedure calls for having different services talk to each other. ARO is very useful for that as well. And the list keeps going on and on Maha. Yeah. Which we just, thanks for me for reminding me of my house. We just added Maha very recently actually. What is that >>Adam? I'm not >>Familiar with it. So Maha is a data mining library. So MAHA takes some of the most popular data mining algorithms for doing clustering and regression and statistical modeling and implements them using the map map with use model. >>They have, they have machine learning in it too or Yes, yes. So that's the machine learning. >>So, So yes. Stay vector to machines and so on. >>What Scoop? >>So Scoop, you know, all of them. Thanks for feeding me all the names. >>The ones I don't understand, >>But there's so many of them, right? I can't even remember all of them. So Scoop actually is a very interesting project, is short for SQL to Hadoop, hence the name Scoop, right? So SQ from SQL and Oops from Hadoop and also means Scoop as in scooping up stuff when you scoop up ice cream. Yeah. And the idea for Scoop is to make it easy to move data between relational systems like Oracle metadata and it is a vertical and so on and Hadoop. So you can very simply say, Scoop the name of the table inside the relation system, the name of the file inside Hadoop. And the, the table will be copied over to the file and Vice and Versa can say Scoop the name of the file in Hadoop, the name of the table over there, it'll move the table over there. So it's a connectivity tool between the relational world and the Hadoop world. >>Great, great tutorial. >>And all of these are Apache projects. They're all projects built. >>It's not part of your, your unique proprietary. >>Yes. But >>These are things that you've been contributing >>To, We're contributing to the whole ecosystem. Yes. >>And you understand very well. Yes. And >>And contribute to your knowledge of the marketplace >>And Absolutely. We collaborate with the, with the community on creating these projects. We employ committers and founders for many of these projects. Like Duck Cutting, the founder of He works in Cloudera, the founder for that UIE project. He works at Calera for zookeeper works at Calera. So we have a number of them on stuff >>Work. So we had Aroon from Horton Works. Yes. And and it was really good because I tell you, I walk away from that conversation and I gotta say for the folks out there, there really isn't a war going on in Apache. There isn't. And >>Apache, there isn't. I mean isn't but would be honest. Like, and in the developer community, we are friends, we're working together. We want to achieve the, there's >>No war. It's all Kumbaya. Everyone understands the rising tide floats, all boats are all playing nice in the same box. Yes. It's just a competitive landscape in Horton. Works >>In the business, >>Business business, competitive business, PR and >>Pr. We're trying to be friendly, as friendly as we can. >>Yeah, no, I mean they're, they're, they're hying it up. But he was like, he was cool. Like, Hey, you know, we know each other. Yes. We all know each other and we're just gonna offer free Yes. And charge with support. And so are they. And that's okay. And they got other things going on. Yes. But he brought up the question. He said they're, they're launching a management console. So I said, Tyler's got a significant lead. He kind of didn't really answer the question. So the question is, that's your core bread and butter, That's your yes >>And no. Yes and no. I mean if you look at, if you look at Cloudera Enterprise, and I mentioned this earlier and when we talked in the morning, it has two main things in it. Cloudera Enterprise has the management suite, but it also has the, the the the support and maintenance that we provide to our customers and all the experience that we have in our team part That subscription. Yes. For a description. And I, I wanna stress the point that the fact that I built a sports car doesn't mean that I'm good at running that sports car. The driver of the car usually is much better at driving the car than the guy who built the car, right? So yes, we have many people on staff that are helping build had, but we have many more people on stuff that helped run Hado at large scale, at at financial indu, financial industry, retail industry, telecom industry, media industry, health industry, et cetera, et cetera. So that's very, very important for our customer. All that experience that we bring in on how to run the system technically Yeah. Within these verticals. >>But their strategies clear. We're gonna create an open source project within Apache for a management consult. Yes. And we sell support too. Yes. So there'll be a free alternative to management. >>So we have to see, But I mean we look at the product, I mean our products, >>It's gotta come down to product differentiation. >>Our product has been in the market for two years, so they just started building their products. It's >>Alpha, It's just Alpha. The >>Product is Alpha in Alpha right now. Yeah. Okay. >>Well the Apache products, it is >>Apache, right? Yeah. The Apache project is out. So we'll see how it does it compare to ours. But I think ours is way, way ahead of anything else out there. Yeah. Essentially people to try that for themselves and >>See essentially, John, when I asked Arro why does the world need Hortonwork? You know, eventually the answer we got was, well it's free. It needs to be more open. Had needs to be more open. >>No, there's, >>It's going to be, That's not really the reason why Warton >>Works. >>No, they want, they want to go make money. >>Exactly. We wasn't >>Gonna say them you >>When I kept pushing and pushing and that's ultimately the closest we can get cuz you >>Just listens. Not gonna >>12 open source projects. Yes. >>I >>Mean, yeah, yeah. You can't get much more open. Yeah. Look >>At management >>Consult, but Airs not shooting on all those. I mean, I mean not only we are No, no, not >>No, no, we absolutely >>Are. No, you are contributing. You're not. But that's not all your projects. There's other people >>Involved. Yeah, we didn't start, we didn't start all of these projects. Yeah, that's >>True. You contributing heavily to all of them. >>Yes, we >>Are. And that's clear. Todd Lipkin said that, you know, he contributed his first patch to HPAC in 2008. Yes. So I mean, you go back through the ranks >>Of your people and Todd now is a committer on Edge base is a committer on had itself. So on a number >>Of you clearly the lead and, and you know, and, but >>There is a concern. But we, we've heard it and I wanna just ask you No, no. So there's a concern that if I build processes around a proprietary management console, Yes. I'm gonna end up being locked into that proprietary management CNA all over again. Now this is so far from ca Yes. >>Right. >>But that's a concern that some people have expressed. And, and, and I think one of the reasons why Port Works is getting so much attention. So Yes. >>Talk about that. It's, it's a very good, it's a very good observation to make. Actually, >>There there is two separate things here. There's the platform where all the data sets and then there's this management parcel beside the platform. Now why did we make the management console why the cloud didn't make the management console? Because it makes our job for supporting the customers much more achievable. When a customer calls in and says, We have a problem, help us fix this problem. When they go to our management console, there is a button they click that gives us a dump of the state, of the cluster. And that's what allows us to very quickly debug what's going on. And within minutes tell them you need to do this and you to do that. Yeah. Without that we just can't offer the support services. There's >>Real value there. >>Yes. So, so now a year from, But, but, but you have to keep in mind that the, the underlying platform is completely open source and free CBH is completely a hundred percent open source, a hundred percent free, a hundred percent Apache. So a year from now, when it comes time to renew with us, if the customer is not happy with our management suite is not happy with our support data, they can, they can go to work >>And works. People are afraid >>Of all they can go to ibm. >>The data, you can take the data that >>You don't even need to take the data. You're not gonna move the data. It's the same system, the same software. Every, everything in CDH is Apache. Right? We're not putting anything in cdh, which is not Apache. So a year from now, if you're not happy with our service to you and the value that we're providing, you can switch. There is no lock in. There is no lock. And >>Your, your argument would be the switching costs to >>The only lock in is happiness. The only lock in is which >>Happiness inspection customer delay. Which by, by the way, we just wrote a piece about those wars and we said the risk of lockin is low. We made that statement. We've got some heat for it. Yes. And >>This is sort of at scale though. What the, what the people are saying, they're throwing the tomatoes is saying if this is, again, in theory at scale, the customers are so comfortable with that, the console that they don't switch. Now my argument was >>Yes, but that means they're happy with it. That means they're satisfied and happy >>With it. >>And it's more economical for them than going and hiding people full-time on stuff. Yeah. >>So you're, you're always on check as, as long as the customer doesn't feel like Oracle. >>Yeah. See that's different. Oracle is very, Oracle >>Is like different, right? Yeah. Here it's like Cisco routers, they get nested into the environment, provide value. That's just good competitive product strategy. Yes. If it they're happy. Yeah. It's >>Called open washing with >>Oracle, >>I mean our number one core attribute on the company, the number one value for us is customer satisfaction. Keeping our people Yeah. Our customers happy with the service that we provide. >>So differentiate in the product. Yes. Keep the commanding lead. That's the strategist. That's the, that's what's happening. That's your goal. Yes. >>That's what's happening. >>Absolutely. Okay. Co-founder of Cloudera, Always a pleasure to have you on the cube. We really appreciate all the hospitality over the beer and a half. And wanna personally thank you for letting us sit in your office and we'll miss you >>And we'll miss you too. We'll >>See you at the, the Cube events off Swing by, thanks for coming on the cube and great to see you and congratulations on all your success. >>Thank >>You. And thanks for the review on Modern Warfare three. Yeah, yeah. >>Love me again. If there any gaming stuff, you know, I.