(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)

>>Good morning, everyone. Welcome back to the Cube's coverage of snowflake summit 22 live from Caesar's forum in Las Vegas. Lisa Martin, here with Dave Valante. This is day three of our coverage. We've had an amazing, amazing time. Great conversations talking with snowflake executives, partners, customers. We're gonna be digging into data mesh with data.world. Please welcome John loins, the chief product officer. Great to have you on the program, John, >>Thank you so much for, for having me here. I mean, the summit, like you said, has been incredible, so many great people, so such a good time, really, really nice to be back in person with folks. >>It is fabulous to be back in person. The fact that we're on day four for, for them. And this is the, the solution showcase is as packed as it is at 10 11 in the morning. Yeah. Is saying something >>Yeah. Usually >>Chopping at the bit to hear what they're doing and innovate. >>Absolutely. Usually those last days of conferences, everybody starts getting a little tired, but we're not seeing that at all here, especially >>In Vegas. This is impressive. Talk to the audience a little bit about data.world, what you guys do and talk about the snowflake relationship. >>Absolutely data.world is the only true cloud native enterprise data catalog. We've been an incredible snowflake partner and Snowflake's been an incredible partner to us really since 2018. When we became the first data catalog in the snowflake partner connect experience, you know, snowflake and the data cloud make it so possible. And it's changed so much in terms of being able to, you know, very easily transition data into the cloud to break down those silos and to have a platform that enables folks to be incredibly agile with data from an engineering and infrastructure standpoint, data out world is able to provide a layer of discovery and governance that matches that agility and the ability for a lot of different stakeholders to really participate in the process of data management and data governance. >>So data mesh basically Jamma, Dani lays out the first of all, the, the fault domains of existing data and big data initiatives. And she boils it down to the fact that it's just this monolithic architecture with hyper specialized teams that you have to go through and it just slows everything down and it doesn't scale. They don't have domain context. So she came up with four principles if I may, yep. Domain ownership. So push it out to the businesses. They have the context they should own the data. The second is data as product. We're certainly hearing a lot about that today this week. The third is that. So that makes it sounds good. Push out the, the data great, but it creates two problems. Self-serve infrastructure. Okay. But her premises infrastructure should be an operational detail. And then the fourth is computational governance. So you talked about data CA where do you fit in those four principles? >>You know, honestly, we are able to help teams realize the data mesh architecture. And we know that data mesh is really, it's, it's both a process in a culture change, but then when you want to enact a process in a culture change like this, you also need to select the appropriate tools to match the culture that you're trying to build the process in the architecture that you're trying to build. And the data world data catalog can really help along all four of those axes. When you start thinking first about, let's say like, let's take the first one, you know, data as a product, right? We even like very meta of us from metadata management platform at the end of the day. But very meta of us. When you talk about data as a product, we track adoption and usage of all your data assets within your organization and provide program teams and, you know, offices of the CDO with incredible evented analytics, very detailed that gives them the right audit trail that enables them to direct very scarce data engineering, data architecture resources, to make sure that their data assets are getting adopted and used properly. >>On the, on the domain driven side, we are entirely knowledge graph and open standards based enabling those different domains. We have, you know, incredible joint snowflake customers like Prologis. And we chatted a lot about this in our session here yesterday, where, because of our knowledge graph underpinnings, because of the flexibility of our metadata model, it enables those domains to actually model their assets uniquely from, from group to group, without having to, to relaunch or run different environments. Like you can do that all within one day catalog platform without having to have separate environments for each of those domains, federated governance. Again, the amount of like data exhaust that we create that really enables ambient governance and participatory governance as well. We call it agile data governance, really the adoption of agile and open principles applied to governance to make it more inclusive and transparent. And we provide that in a way that Confederate across those means and make it consistent. >>Okay. So you facilitate across that whole spectrum of, of principles. And so what in the, in the early examples of data mesh that I've studied and actually collaborated with, like with JPMC, who I don't think is who's not using your data catalog, but hello, fresh who may or may not be, but I mean, there, there are numbers and I wanna get to that. But what they've done is they've enabled the domains to spin up their own, whatever data lakes, data, warehouses, data hubs, at least in, in concept, most of 'em are data lakes on AWS, but still in concept, they wanna be inclusive and they've created a master data catalog. And then each domain has its sub catalogue, which feeds into the master and that's how they get consistency and governance and everything else is, is that the right way to think about it? And or do you have a different spin on that? >>Yeah, I, I, you know, I have a slightly different spin on it. I think organizationally it's the right way to think about it. And in absence of a catalog that can truly have multiple federated metadata models, multiple graphs in one platform, I, that is really kind of the, the, the only way to do it, right with data.world. You don't have to do that. You can have one platform, one environment, one instance of data.world that spans all of your domains, enable them to operate independently and then federate across. So >>You just answered my question as to why I should use data.world versus Amazon glue. >>Oh, absolutely. >>And that's a, that's awesome that you've done now. How have you done that? What, what's your secret >>Sauce? The, the secret sauce era is really an all credit to our CTO. One of my closest friends who was a true student of knowledge graph practices and principles, and really felt that the right way to manage metadata and knowledge about the data analytics ecosystem that companies were building was through federated linked data, right? So we use standards and we've built a, a, an open and extensible metadata model that we call costs that really takes the best parts of existing open standards in the semantics space. Things like schema.org, DCA, Dublin core brings them together and models out the most typical enterprise data assets providing you with an ontology that's ready to go. But because of the graph nature of what we do is instantly accessible without having to rebuild environments, without having to do a lot of management against it. It's, it's really quite something. And it's something all of our customers are, are very impressed with and, and, and, and, you know, are getting a lot of leverage out of, >>And, and we have a lot of time today, so we're not gonna shortchange this topic. So one last question, then I'll shut up and let you jump in. This is an open standard. It's not open source. >>No, it's an open built on open standards, built on open standards. We also fundamentally believe in extensibility and openness. We do not want to vertically like lock you into our platform. So everything that we have is API driven API available. Your metadata belongs to you. If you need to export your graph, you know, instantly available in open machine readable formats. That's really, we come from the open data community. That was a lot of the founding of data.world. We, we worked a lot in with the open data community and we, we fundamentally believe in that. And that's enabled a lot of our customers as well to truly take data.world and not have it be a data catalog application, but really an entire metadata management platform and extend it even further into their enterprise to, to really catalog all of their assets, but also to build incredible integrations to things like corporate search, you know, having data assets show up in corporate Wiki search, along with all the, the descriptive metadata that people need has been incredibly powerful and an incredible extension of our platform that I'm so happy to see our customers in. >>So leasing. So it's not exclusive to, to snowflake. It's not exclusive to AWS. You can bring it anywhere. Azure GCP, >>Anytime. Yeah. You know where we are, where we love snowflake, look, we're at the snowflake summit. And we've always had a great relationship with snowflake though, and really leaned in there because we really believe Snowflake's principles, particularly around cloud and being cloud native and the operating advantages that it affords companies that that's really aligned with what we do. And so snowflake was really the first of the cloud data catalogs that we ultimately or say the cloud data warehouses that we integrated with and to see them transition to building really out the data cloud has been awesome. >>Talk about how data world and snowflake enable companies like per lodges to be data companies. These days, every company has to be a data company, but they, they have to be able to do so quickly to be competitive and to, to really win. How do you help them if we like up level the conversation to really impacting the overall business? >>That's a great question, especially right now, everybody knows. And pro is a great example. They're a logistics and supply chain company at the end of the day. And we know how important logistics and supply chain is nowadays and for them and for a lot of our customers. I think one of the advantages of having a data catalog is the ability to build trust, transparency and inclusivity into their data analytics practice by adopting agile principles, by adopting a data mesh, you're able to extend your data analytics practice to a much broader set of stakeholders and to involve them in the process while the work is getting done. One of the greatest things about agile software development, when it became a thing in the early two thousands was how inclusive it was. And that inclusivity led to a much faster ROI on software projects. And we see the same thing happening in data analytics, people, you know, we have amazing data scientists and data analysts coming up with these insights that could be business changing that could make their company significantly more resilient, especially in the face of economic uncertainty. >>But if you have to sit there and argue with your business stakeholders about the validity of the data, about the, the techniques that were used to do the analysis, and it takes you three months to get people to trust what you've done, that opportunity's passed. So how do we shorten those cycles? How do we bring them closer? And that's, that's really a huge benefit that like Prologis has, has, has realized just tightening that cycle time, building trust, building inclusion, and making sure ultimately humans learn by doing, and if you can be inclusive, it, even, it even increases things like that. We all want to, to, to, to help cuz Lord knows the world needs it. Things like data literacy. Yeah. Right. >>So data.world can inform me as to where on the spectrum of data quality, my data set lives. So I can say, okay, this is usable, shareable, you know, exactly of gold standard versus fix this. Right. Okay. Yep. >>Yep. >>That's yeah. Okay. And you could do that with one data catalog, not a bunch of >>Yeah. And trust trust is really a multifaceted and multi multi-angle idea, right? It's not just necessarily data quality or data observability. And we have incredible partnerships in that space, like our partnership with, with Monte Carlo, where we can ingest all their like amazing observability information and display that in a really like a really consumable way in our data catalog. But it also includes things like the lineage who touch it, who is involved in the process of a, can I get a, a, a question answered quickly about this data? What's it been used for previously? And do I understand that it's so multifaceted that you have to be able to really model and present that in a way that's unique to any given organization, even unique within domains within a single organization. >>If you're not, that means to suggest you're a data quality. No, no supplier. Absolutely. But your partner with them and then that you become the, the master catalog. >>That's brilliant. I love it. Exactly. And you're >>You, you just raised your series C 15 million. >>We did. Yeah. So, you know, really lucky to have incredible investors like Goldman Sachs, who, who led our series C it really, I think, communicates the trust that they have in our vision and what we're doing and the impact that we can have on organization's ability to be agile and resilient around data analytics, >>Enabling customers to have that single source of truth is so critical. You talked about trust. That is absolutely. It's no joke. >>Absolutely. >>That is critical. And there's a tremendous amount of business impact, positive business impact that can come from that. What are some of the things that are next for data.world that we're gonna see? >>Oh, you know, I love this. We have such an incredibly innovative team. That's so dedicated to this space and the mission of what we're doing. We're out there trying to fundamentally change how people get data analytics work done together. One of the big reasons I founded the company is I, I really truly believe that data analytics needs to be a team sport. It needs to go from, you know, single player mode to team mode and everything that we've worked on in the last six years has leaned into that. Our architecture being cloud native, we do, we've done over a thousand releases a year that nobody has to manage. You don't have to worry about upgrading your environment. It's a lot of the same story that's made snowflake. So great. We are really excited to have announced in March on our own summit. And we're rolling this suite of features out over the course of the year, a new package of features that we call data.world Eureka, which is a suite of automations and, you know, knowledge driven functionality that really helps you leverage a knowledge graph to make decisions faster and to operationalize your data in, in the data ops way with significantly less effort, >>Big, big impact there. John, thank you so much for joining David, me unpacking what data world is doing. The data mesh, the opportunities that you're giving to customers and every industry. We appreciate your time and congratulations on the news and the funding. >>Ah, thank you. It's been a, a true pleasure. Thank you for having me on and, and I hope, I hope you guys enjoy the rest of, of the day and, and your other guests that you have. Thank you. >>We will. All right. For our guest and Dave ante, I'm Lisa Martin. You're watching the cubes third day of coverage of snowflake summit, 22 live from Vegas, Dave and I will be right back with our next guest. So stick around.

>>Hello. Nice to meet you. My name is yes. Um Escuela. I'm a professor in a university in Japan. So today I want to introduce my research. That title is a really time emotional detection using e g with riel time knowing the reduction. First of all, I want to introduce myself. My major is system identification and signal processing for large removed and by American signal process for owner off them. A common technique. It's most magical. Modern by you creation using this opportunity identification method. So today topic it's e easy modern by the Barriers Council with heavy notes. We call this technique the concept moody. Now what is a concept? I mean, the concept is Japanese world because studies are first in Japan. So consider is similar to emotion and sensibility, but quite different. The commercial nous sensibility is innate ability. The concert is acquired after birth, so concept is similar to how to be So we focus on this can see using the brain signals. As for the brain Sina, there is ah, many way to know the brain. For example, the optical leading X c T m i m e g e g optical topography um, function and my by using these devices, we have three areas off research, for example, like neural engineering area for obligation, including new market neuroscience area for understanding the mechanism a medically oil area for treatment. So but it's very important to use, depending on the purpose. So what did they can be obtained? Uh, in the case of e g, we can see the activity of neurons that scalp the case of in years so we can attain the river off oxygen bar Pratt The case off natural and safe Alagem we can see the activity of new uh, that contact is neck case off position. Martian topography. We can get activity off reception by the contact list. If we use that, I we can measure the amount of blood by the contractors. These devices are showing these figures. So our motivation is to get the concept question using their model by system identification where it's not removed on. The second motivation is to theorize that's simple and small cancer X election using the each information when we use the ever my the large scale and the expensive on binding. So it is unuseful. So we focus on the EEG because the e g iss Moscow inexpensive a non binding on to use. So we focus on the energy. So e g is actually a potential from the major from the scalp that detective data is translated to the pregnancy domain. And if you can see domain that their point to 44. We call the data death of it 4 to 6. We called a cedar with on 17. 14 were called the Alfa Hour and 14 to 26. We called a better work in a conventional method we want if we want use the cats a deep sleep, we use that death of it in a case of light sleep we used a secretive and so but this is just only the sensible method. So we cannot use that for all the film Actuary accuracies under the 20%. So we need to define the situation original. So recall this technique council modeling. So these are the block diagram Kansi the concept What? So this field this part eyes for the noise, this part for the mathematical model. So we calculate this transfer function like this. This is a discrete time water, and, uh, this time, uh, is continuous time model. So then we really right this part Thio Discrete time water. So we cull Create, uh, this part us like this This'll first part on the second part is calculated by the party application so we can get this the argumentative model. So then that we were right this part by using that the transfer function transport formation. So we right this argument ID model like this. So the off about the inverse and better off the inverse is the point as this equation. So each the coefficient is corrugated by this equation on. But then we calculate a way too busy with beaver by using this because of a least squares algorithm. So we call this identification method the self joining identification method. Um, that this is an example of stories modeling. The first of all, we decide we gather the data like a story. It's moving. So we move the small beans, try to trade at 41 hour. So last 10 minutes we used as stories and we measure that culture soul for sliced levin Onda. We associate the egg and we measure the 8000 data. Uh, in 17 years we? Yeah, that's a 17 years. So in the case, off the simple, easy universes that there are many simply devices in the world like this so many of them the There we calculate the signal nodes. Lazio, The signal means the medical easy system on the each device made it sn Lazio. And we investigate 58 kinds off devices on almost off All devices are noise devices. So I'm also asked about to various parts more device that best. So my answer is anything. Our skill is, you know, processing on def. With love. Data can be obtained from the device. No, but what device? He may use the same result commission. Our novelty is level Signal processing on our system is structured by 17 years Data for one situation. So the my answer is what? Anything. So we applied this system to Arial product. We call this product concern Analyzer. In a concept analyzer, you can see the concept that right the our time a concept dinner influence Solis sickness concentration on like so that we combine that this can't say analyzer And the camera system We made the euro system your account so pretty show it this is in Eureka. Well, this is, uh, e g system and we can get can say by using the iPhone on the, uh, we combine the camera system by the iPhone camera and if the cancer is higher than the 6% 60% so automatically recorded like this. Mhm. So every time we wear the e g devices, we can see the no awareness, the constant way. That's so finally we combine the each off cancer. So like that this movie, so we can see the thes one days. Can't say the movie s Oh, this is a miracle. On the next example, it's neuro marketing using a constant analyzer. So this is a but we don't know what is the number one point. So then we analyze the deeds CME by using concert analyzer so we can get the rial time concept then that we can see the one by one situation like this. So this is the interest level and we can see the high interest like this. So the recorded a moment automatically on the next one is really application. The productive design. Ah, >>Japanese professor has come up with a new technology she claims can read minds, she says. The brainwave analysis system will help businesses better understand their customers, needs workers at a major restaurant chain or testing a menu item that is being developed. This device measures brain waves from the frontal lobes of people who try the product. An application analyzes five feelings how much they like something and their interest, concentration, stress and sleepiness. >>The >>new menu item is a cheese souffle topped with kiwi, orange and other fruit. The APP checks the reaction of a person who sees the souffle for the first time. Please open your eyes. When she sees the souffle, the like and interest feelings surge on the ground. This proves the desert is visually appealing. Now please try it. After the first bite, the like level goes up to 60. That shows she likes how the dessert tastes. After another bite, the like level reaches 80. She really enjoys the taste of the souffle. It scores high in terms of both looks and taste, but there's an unexpected problem. When she tries to scoop up the fruit, the stress level soars to 90. I didn't know where to put the spoon. I felt it was a little difficult to eat. It turned out it was difficult to scoop up the fruit with a small spoon. So people at the restaurant chain are thinking of serving this a flavor with a fork instead. Green well. How could be the difference with the device? We can measure emotional changes in minute detail in real time. This is a printing and design firm in Tokyo. >>It >>designs direct mail and credit card application forms. The company is using the brainwave analyzing system to improve the layout of its products. The idea is to make them easier to read during this test, The subject wears an eye tracking device to record where she's looking. In addition to the brainwave analyzing device, her eye movements are shown by the red dots on the screen. Stress levels are indicated on the graph on the left. Please fill out the form. This is a credit card application form. Right after she turns her eyes to this section, her stress levels shoots up. It was difficult to read as each line contained 60 characters, so they decided to divide the section in two, cutting the length of the lines by half 15 a Hong Kong. This system is very useful for us. We can offer differentiated service to our clients by providing science based solutions. The brain wave analyzed. >>Okay, uh, now the we construct a concert detection like this. Like this. Like concentration, interest sickness stories contain, like comfortable, uncomfortable. I'm present the rats emotion, deadly addictive case lighting, comfort, satisfaction and the achievement. So finally we conquer more presentation. So in this presentation, we introduce the our such we construct the council question Onda we demonstrate that c street signal processing and we apply the proposed method to Arial product. Uh, we named the constant riser. So this is the first in the world, that's all. Thank you so much.

>> 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. >> And welcome back here, San Francisco Moscow Centre, North John Walls along with John Troyer. We're live here on the Cuban Veum World 2019 and right now we're joined by Christmas. Reynolds, who's a product in court product management and Clyde on data service, is for Centurylink. It's good to see you, sir. Good to be here. Thank you. And And he's gonna tell us today why Milliseconds matter, right? You are. >> That is the goal. Your >> your subject of ah, coming presentation. Just about 45 minutes or so. But we'll get to that a little bit. First off, let's just paint the picture of centurylink your presence here quite obvious. But you know what your portfolio includes? There what you're up to, and maybe starting to hint a little bit about why milliseconds matter to you. >> Makes it so. Where a technology company, global in nature. A lot of our roots started with fiber connectivity. Basic networking service is I. P Service is. But over the years we've become far more of a nightie service company. So there was an acquisition of Savvas a long time ago that brought a lot of those capabilities to our company. And we've made more fold in acquisitions that have also bolster those capabilities. We have invested heavily in Security Service's recently and about two weeks ago we had an announcement that said, We're investing heavily an edge compute getting workloads closer to end users. And that's really where milliseconds matters. You want the performance of those applications to consumers or machinery or whatever it may be toe work effectively and work well. And sometimes that requires that those workloads air in close proximity to the end users. >> Would you bring up ej compute? We were just having this discussion before we started, John asked of you. Okay, What? How do you define the because of there A lot of different slices of that, right? Different interpretations, different definitions. So with that being said, how do you define and or at least in your mind, how do you separate edge or what's true edge? Yeah, >> good questions. I think he was John question, not mine. I chuckled time, so because there is no perfect answer. Uh, the broadest definition I've seen is that you have core, and you can think eight of us Azure. You can think where the big core cloud nodes are that are pretty central, maybe 50 milliseconds away from the end users. There's two intermediate edges, if you will, and this is where there are varying opinions. To me, there's really only one if you're within five milliseconds of where your end users are, I consider that to be a market edge. Some people say there's a closer edge that's in within a millisecond of the end users, but I just I personally have not seen the use cases come out yet that require that low of a late unsee that don't actually reside where the end users are so >> going. Well, that's, um, so that's, um, modules at a at a warehouse or ah, manufacturing facility. Is that what? Is that what you consider like an edge? Uh, media marketed? >> Yeah, in >> theirs. It's interesting if you have 10 manufacturing plants in a geographic area, or maybe a better example is if you're a logistics company and you have sorting and distribution centers, you have multiple of those in an area that can all use the same compute as long as it's within five milliseconds, you can do the sorting lines and keep the machinery working. You can get routed into the rate vehicles for distribution. That's a good market edge. When you get all the way to that, the deep edge or on premise they think of an autonomous vehicle is a good example. There are certain things you're not gonna want to transmit and make driving decisions that don't reside on that vehicle. You don't want to crash into anyone. You need almost instantaneous decisions. And that would be the edge that intermediate one millisecond that sits between the two of those. I think it pushes one direction or the other. >> So Chris, here in the emerald 2019 obviously a lot of talking about cloud, but very specifics. This year. We have a lot of specifics around what Veum, where is doing Hybrid Cloud Israel and of course, hybrid cloud implies the network. And so one of the latest announcement from Centurylink is that you're providing via more cloud on AWS you're managing. You are able to help manage provide that as a managed service. I know you already do. Manage service is where you managing stuff in your data centers. But you could, I guess you can also manage workloads on prim and talk a little bit about that portfolio and how adding Veum VMC on AWS few more cloud nebulas adds to that. And then maybe we'll slide into the networking peace and how important that is. >> So we have AH, tool called Cloud Application Manager that has been built over the past handful of years that allows customers to deploy workloads to AWS toe azure and now to be emcee on AWS as well as private cloud environment. So maybe customers want to host those workloads on premise. Maybe it's regulatory compliance or whatever the reason may be. So we have a lot of experience of helping customers deploy those workloads, and then a lot of customers come to us and want to manage. I want us to manage the life cycle of those workloads, those air, the core capabilities. I think the reason that VMC on AWS is so compelling to customers is a lot of customers may not want to deal with the hardware refresh cycles that they do when it's their own private cloud environment or their own hardware stack. This gives them the opportunity to migrate those workloads and a relatively seamless fashion into an environment that is sitting in Maur of, ah, public cloud type model where it's it's Op X versus the Catholics in the headache. >> Go ahead. John was good, just in terms of so and so. Part of why you would work with Centurylink is you are experienced manage service provider. But also you have ah lot of the networking set up to do that efficiently, right? So maybe you talk about some of the workload is that you see going up there and some of the tools and, uh, performance folks can expect, >> Yeah, that's near the core part of my products that so near and dear to me for sure. We've developed a lot of capabilities over the last year and 1/2 around dynamic networking. So if you have your existing VM wear environment in your own data center, or maybe it's a private cloud that's managed by century link, we now have the ability for customers to go in and create net new connections, private network connections that have better Leighton see have better through putting performance between those environments and AWS or, in this case, VMC on AWS. And it allows customers to do a couple of things if they have their own environment and they're happy with it today. But it's not scaling, and they need to add more capacity. They could do that in the hybrid fashion in VMC on eight of us. If they're done with their existing environment hardware stack and they just want a forklift and move that into VMC on eight of us, they can create a big, large connection, push a ton of data over a few weeks, shut it down, and our building models and hourly billing models such that we're only charging them for as long as it's necessary. This gives them flexibility to manage where their workloads air sitting between those two locations as they see fit over time. >> So you're talking about all these new flexibilities new capabilities, much more agile systems being, I guess, interconnected with each other, right? But whether it's hybrid or whether it's multi cloud, whatever the case is, >> how you how to get >> everybody or everything that talk to each other in a way that works and provides, You know, the addresses, the Leighton see challenge, because to me, I'm again outside looking in. That's Ah, that's a big hurdle. As new capabilities get developed, new possibilities exists, but we gotta make it fast way, and we have to make sure they're they're speaking the same language. >> Yeah, it's a great question, and it is very challenging, and it is not all automated today as much as we would like. We have great integration to deploy workloads between environments. We've spent a ton of time from a networking standpoint of integrating with different cloud providers, and they each have their loan little nuances and to make it common between all of them takes a lot of time and effort. Where a lot of our focus is going in the next 12 months is how do you take those application, migration and management capabilities we have in one tool set? How do you marry that? With all of the dynamic networking capabilities and standardization across the cloud providers, we've done so the now it's not only are you moving network workloads, you're also creating the right underlying network to support those workloads in that multi cloud fashion well to capabilities we have. We just need to marry him up a little more clearly. >> I mean, what are you saying out there in the market with your customers? Multi Cloud Bright is perhaps another overused word like EJ. Are you seeing multi cloud portfolios? Are you seeing applications? Talk, actually use have data in one place, and and the and the computer and another. And obviously network becomes increasingly important if that's a reality today. But is that is that real, or is that still science fiction? >> It's becoming more riel so that there are a lot of customers. My pain, A lot of enterprises really bet big on one cloud provider because you have to build up the competency of capabilities inside your own shop and you become really good with working in Azure. Eight of us or Google or of'em were on the hunt. BP BMC Oh, the companies that are doing true multi cloud and using multiple cloud providers. Well, our companies that probably reside around here, so I won't say any of these specifically or doing this mutt. Companies like uber companies like Spotify companies that are born in the cloud that started with those core competencies will take the best of multiple cloud providers. So maybe the Big Data Analytics sitting in Google is most intriguing to them. But they love the tale of the storage cost. Price points on eight of us, and they love this. Ask spit in azure. They'll piece together components since they built it in a containerized fashion. And they take the best of what each cloud has to offer and into your point. The cloud providers air coming to centurylink and saying We need a better way to stitch together all of these different cloud environments because people, the cutting edge developers are pushing us in that direction. Now >> what about the the application network relationship? Um, changing is, you know, you see a shift there of some kind of as, uh, we're talking about, obviously a lot of new opportunities, a lot of developments, and so does that alter the dynamics of that relationship in any way >> It does, and it's the same conversations I just mentioned. Actually, that's driving it. I think today it is network engineers and network infrastructure. People reacting to applications not performing well are reacting to a software developers requested toe add this Google region or that VM wear on on AWS region over time. What's gonna happen, I believe, is their service mesh orchestration capabilities like SDO is a good example is the one Google is pushing hard and it would it allows people to do is from a rules driven perspective. I want my application to have these Leighton see requirements and you can't find me a network solution that is any worse than that. Or if you're seeing packet loss greater than 80% I want you to add more capacity to the network. It won't be humans the network engineers doing that. It's going to be application saying here are my criteria for me to work well, networks Let me see all the options I have out there now. I'm gonna go pick the best one and change it if I need you to make make myself work the way I need to. As an application. >> I love that that I've never connected Is Theo down as as an at, sir, as an APP service layer down to the network. Thank you. I just have a new I got a new thought. Eureka another reason >> why milliseconds matter. That's right. Hey, Chris. Thanks for the time. We appreciate that. I know this is a very busy time for you on. You do have a speaking engagements. We're gonna cut you loose for that. But thanks for spending time with us. And good luck. It centurylink appreciate it. Enjoyed it. Looking forward, Thio. More success. Back with more for Vimal. World 2019 after this short break right here on the Q.

(electronic music) >> Announcer: From Boston, Massachusetts, it's theCUBE, covering Cloud Foundry Summit 2018. Brought to you by the Cloud Foundry Foundation. >> Welcome back, I'm Stu Miniman, and this is theCUBE's coverage of Cloud Foundry Summit 2018, here in Boston. Happy to welcome back to the program, Chen Goldberg, who's the Engineering Director at Google. Chen, thanks for joining me. >> Thank you, it's always a pleasure. >> So Chen, what are the big questions coming in? We talked to you at the KubeCon Show before. You know, Kubernetes, you know, Cloud Foundry, containers, serverless, all these things mashing up. You've been here at the show for about a day, what have you learned so far? How do all these kind of fit together in your mind? >> So actually, it was great being here for the last day, 24 hours so far, and just seeing how Cloud Foundry Community is really opening and welcoming influence from other communities in the cloud native space. And we see it in different ways. We see work that is being done on building some open standards, for example, and so working with the Cloud Foundry on things like OCI, the Open Container Initiative, and the CSI, which is the Container Storage Interface. But not only that, for example, we in Google have been working, last year, building Kubo, which then became the Cloud Foundry Container Runtime, and really bringing things together. And I think that's awesome because like any other technology, we need to know how we can take the best out of everything. And this is what really, user wants to know. They want to know that when they are making a decision or a choice of a technology, that technology can move with them forward. The last thing that we also see all of interest about the Open Service Broker and how you can really mesh things together with different platforms. >> Chen, I'm wondering if you can help us squint through this a little bit. And we've heard Google talking for a while about Open Cloud, and that means it doesn't beam all one source in the public cloud portability between clouds, public and private. Google's had many partnerships over the years with there. How do these pieces fit together in your mind? >> I think it all starts with what user wants. Okay, I always talk about the customer and what is their pain? And the pain, in reality is that they have a very complex environment, okay? They have on-prem. They want to use some of the cloud services. Sometimes they have some places, like we hear it from retail, they have some warehouses, that they don't have actually good connectivity, but they still want to serve, they still want to have the guild transformation. And, I think, that's the main thing that what we hear from users, that they want to have that flexibility over to run their business. Okay, because this is what they really have to do and they want to compete more effectively. So, think about that. The other piece which we hear about users is that they want to make sure like we talked about Cloud Foundry before. They want to make sure that the infrastructure they choose though, that the tools will allow them to evolve, and that can be in different ways. It can be about maybe having flexibility to choose different tools, but also not to be locked in to a specific vendor because that happened to them before, right? So, they want to make sure that they can continue and move forward because the technology we know today maybe, probably will change in the future. So, by having all of that together, that leads us to some of the pieces I've talked about in the keynote. And the first one is portability. We achieve it by open source. We believe in open source because it does bring the community together. We learn about users, partners. We have an amazing ecosystem. So that's one. The second piece is about its sensibility and this is where you can see how Cloud Foundry can actually integrate into Kubernetes. It's because of those extension points. We don't know where innovation will come from. What will be the next cool thing? And back in KubeCon, I talked about some serverless framework we see on top of Kubernetes. All of that is possible through those extensions. Open Service Broker is actually a combination of two. So Open Service Broker is an open standard. It allows you to consume services from different platforms. We saw, in the keynote, so Google is announcing, now in beta, the Google Managed Service Broker, supporting the Open Service Broker API. And you consume it out from any Kubernetes cluster that are using a catalog, service catalog. And it is available also through those extensions. So when we think about Open Hybrid Cloud, we think about that you can run it anywhere. And either you have interopabilities, so you can consume different tools and you can extend it and innovate on top of it. So that's our way of thinking. >> Yeah, I mean, we know the only thing that's constant in this industry today is change. >> Yes. >> One of the things we've been tracking is if I look at an application, it used to be I deploy an application, it takes me 12-18 months at least, and then, once I'm running it, gosh. Yeah, sure, were going to run it for three to five years but, no, no, actually, we're going to run it for 10-12 years. We're going to keep it longer. How does this kind of decomposability of applications and having things and more components? We talk about things like flexibility and speed but, you know, how do you hear from customers, really, from the application side of things? >> This is all about microservices? >> Yeah. >> Right? Just making sure that your application is architected in a way that allows you to change things. I think also that developers are now used to that cycle which is really fast. I'm talking about agility and how quickly you can deploy changes. You know, I keep talking with my engineering team, like don't get too attached (laughs) to anything because things do change and requirements change all the time, and if you're building your application right, you can do those changes. For example, again, going back to the Open Service Broker, you can use a service. First of all, maybe your own service, like your own SQL. But then you can use through a managed service like if you are running on G Key or having Cloud Foundry running on GCP, then you can use one of the managed services offered by Google. >> Okay, anything new you're hearing from users? What are some of their biggest challenges? What's exciting them these days? >> So it depends which user and also who you talk in that audience. Yeah, I think developers are still very excited about the opportunity and the different tools and open source and how quickly the technology is moving forward. When we talk with enterprise, they are very excited about consistency because it's hard. That complexity and managing all of it is really hard to train your operational teams and the developers on different tools. So they are very much concerned about that, their TCO. So they care about, of course, the cost of the infrastructure, but also the people. Right, we don't talk about how hard it is to train and change technology, technologies, all during a cultural change within an organization. So, they care about consistency and this is something that is really in the heart of the thing that we are building. So starting with Kubernetes, we talk about flexibility without compromising consistency. And you do it by building obstructions and letting everyone own a different piece. And there's always some excitement about Istio, in that sense, because what it allows you is to create an obstruction for managing services which is separated from the code that you build. So, let's say you want to, for example, deploy a new policy of access control to your services, you can do it through Istio, because you have proxies in front of all your services, regardless of what they run, by the way. You can have services on VMs, on Cloud Foundry, on a Google Kubernetes engine, or anything, anywhere else you actually would like to have them. And you have that consistent layer in front of all of them. You can do troubleshooting easier because you will have the same matrix and data and telometry on top of it. And so, moving into that direction, creating more obstructions that are creating less friction for the end-user, while still allowing just the platform to evolve, right? If you have this platform on top of it, you can still move services from running from one platform to another, but that person that is using the data, actually, their experience won't change. >> Alright, Chen, what should we be looking for from Google and Eureka's system for the rest of 2018? >> So, of course, we continue and invest a lot in Kubernetes and its ecosystem, and you can see it all the time. All the time, we are bringing more and more tools in open source, showing some of our best practices of how we manage development and production into the community. Some of it is in, like project, like developer experience project, like scaffold, and others that were announced in the last few months. So we will see more of those coming. And in some ways, it's also around the best practices. So, we have been delivering messages of how you should run your clusters or application more secured. And, of course, some of those offerings will be on GCP. But that's another area where we are heavily investing. We have a lot of experience and we are happy to share that. >> Well okay, last question I have for you, is the world becoming more Googly? Or is Google becoming more like the rest of the world? (Stu and Chen laugh) >> I want to say that the world becoming more Googly. (laughs) Being Googly means many things for people here in the, that maybe don't know what means. To me, being Googly is being nice and being kind, and also, being open to more ideas and that's what I would hope to see the world moving towards. But yes, but definitely Google, as part of it being Googly, is working, continuing to work with the community and get feedback, and that's great. >> Okay, well, Chen Goldberg, it's a pleasure to catch up with you again. We will have lots more Google content (Chen laughs) and Googly guests, not only here at the Cloud Foundry Summmit, we're going to be at KubeCon, Copenhagen, as well as KubeCon, Seattle, at the end of the year, and really excited to say that we will be at the Google Cloud Next Show >> Aww. >> this summer, so, look for lots more of theCUBE. >> Thank you, Chen, for joining me. >> That's exciting. >> I'm Stu Miniman. Thanks for watching theCUBE. (electronic music)

>> live from the Congress Centre in London, England. It's the queue at M I t. And the digital economy. The second machine Age Brought to you by headlines sponsor M I T. >> Everybody, welcome to London. This is Dave along with student men. And this is the cube. The cube goes out, we go to the events. We extract the signal from the noise. We're very pleased to be in London, the scene of the first machine age. But we're here to talk about the second Machine age. Andrew McAfee and Erik Brynjolfsson. Gentlemen, first of all, congratulations on this fantastic book. It's been getting great acclaim. So it's a wonderful book if you haven't read it. Ah, Andrew, Maybe you could hold it up for our audience here, the second machine age >> and Dave to start off thanks to you for being able to pronounce both of our names correctly, that's just about unprecedented. In the history of this, >> I can probably even spell them. Whoa, Don't. So, anyway, welcome. We appreciate you guys coming on and appreciate the opportunity to talk about the book. So if you want to start with you, so why London? I mean, I talked about the first machine age. Why are we back here? One of the >> things we learned when we were writing the book is how big deal technological progress is on the way you learn that is by going back and looking at a lot of history and trying to understand what bet the curve of human history. If we look at how advanced our civilizations are, if we look at how many people there are in the world, if we look at GDP per capita around the world, amazingly enough, we have that data going back hundreds, sometimes thousands of years. And no matter what data you're looking at, you get the same story, which is that nothing happened until the Industrial Revolution. So for us, the start of the first machine machine age for us, it's a real thrill to come to London to come to the UK, which was the birthplace of the Industrial Revolution. The first machine age to talk about the second. >> So, Eric, I wonder if you could have with two sort of main vectors that you take away from the book won is that you know, machines have always replaced humans and maybe doing so at a different rate of these days. But the other is the potential of continued innovation, even though many people say Moore's law is dead. You guys have come up with sort of premises to how innovation will continue to double. So boil it down for the lay person. What should we think about? Well, sure. >> I mean, let me just elaborate on what you just said. Technology's always been destroying jobs, but it's also always been creating jobs, you know, A couple centuries ago, ninety percent of Americans worked in agriculture on farms in nineteen hundred is down to about forty one percent. Now is less than two percent. All those people didn't simply become unemployed. Instead, new industries were invented by Henry Ford, Steve Jobs, Bill Gates. Lots of other people and people got rather unemployed, became redeployed. One of the concerns is is, Are we doing that fast enough? This time around, we see a lot of bounty being created by technology. Global poverty rates are falling. Record wealth in the United States record GDP per person. But not everyone's participating in that. Not even when sharing the past ten fifteen years, we've actually to our surprise seem median income fall that's income of the person the fiftieth percentile, even though the overall pie is getting bigger. And one of the reasons that we created the initiative on the digital economy was to try to crack that, not understand what exactly is going on? How is technology behaving differently this time around in earlier eras and part that has to do with some of the unique characteristics of eventual goods? >> Well, your point in the book is that normally median income tracks productivity, and it's it's not this time around. Should we be concerned about that? >> I think we should be concerned about it. That's different than trying to stop for halt course of technology. That's absolutely not something you >> should >> be more concerned about. That way, Neto let >> technology move ahead. We need to let the innovation happen, and if we are concerned about some of the side effects or some of the consequences of that fine, let's deal with those. You bring up what I think is the one of most important side effects to have our eye on, which is exactly as you say when we look back for a long time, the average worker was taking home more pay, a higher standard of living decade after decade as their productivity improved. To the point that we started to think about that as an economic law, your compensation is your marginal productivity fantastic what we've noticed over the past couple of decades, and I don't think it's a coincidence that we've noticed this, as the computer age has accelerated, is that there's been a decoupling. The productivity continues to go up, but the wage that average income has stagnated. Dealing with that is one of our big challenges. >> So what you tell your students become a superstar? I mean, not everybody could become a superstar. Well, our students cats, you know, maybe the thing you know they're all aspired to write. >> A lot of people focus on the way that technology has helped superstars reach global audiences. You know, I had one student. He wrote an app, and about two or three weeks, he tells me, and within a few months he had reached a million people with that app. That's something that probably would have been impossible a couple of decades ago. But he was able to do that because he built it on top of the Facebook platform, which is on top of the Internet and a lot of other innovations that came before. So in some ways it's never been easier to become a superstar and to reach literally not just millions, but even billions of people. But that's not the only successful path in the second machine age. There's also other categories where machines just aren't very good. Yet one of the ones that comes to mind is interpersonal skills, whether that's coaching or underst picking up on other cues from people nurturing people carrying for people. And there are a whole set of professions around those categories as well. You don't have to have some superstar programmer to be successful in those categories, and there are millions of jobs that are needed in those categories for to take care of other P people. So I think there's gonna be a lot of ways to be successful in the second machine age, >> so I think >> that's really important because one take away that I don't like from people who've looked at our work is that only the amazing entrepreneurs or the people with one forty plus IQ's are going to be successful in the second machine age. That's it's just not correct. As Eric says, the ability to negotiate the ability Teo be empathetic to somebody, the ability to care for somebody machines they're lousy of thes. They remain really important things to do. They remain economically valuable things >> love concern that they won't remain louse. If I'm a you know, student listening, you said in your book, Self driving cars, You know, decade ago, even five years ago so it can happen. So how do we predict with computers Will and won't be good at We >> basically don't. Our track record in doing that is actually fairly lousy. The mantra that I've learned is that objects in the future are closer than they appear on the stuff that seem like complete SciFi. You're never goingto happen keeps on happening now. That said, I am still going to be blown away the first time I see a computer written novel that that that works, that that I find compelling, that that seems like a very human skill. But we are starting to see technologies that are good at recognizing human emotions that can compose music that can do art paintings that I find pretty compelling. So never say never is another. >> I mean right, right. If if I look some of the examples lately, you know, basic news computers could do that really well. IBM, you know, the lots of machine can make recipes that we would have never thought of. Very things would be creative. And Ian, the technology space, you know, you know, a decade ago computer science is where you tell everybody to go into today is data scientists still like a hot opportunity for people to go in And the technology space? Where, where is there some good opportunity? >> Or whether or not that's what the job title on the business card is that going to be hot being a numerous person being ableto work with large amounts of data input, particular being able to work with huge amounts of data in a digital environment in a computer that skills not going anywhere >> you could think of jobs in three categories is ready to technology. They're ones that air substitutes racing against machine. They're ones that air compliments that are using technology under ones that just aren't really affected yet by technology. The first category you definitely want to stay away from. You know, a lot of routine information processing work. Those were things machines could do well, >> prepare yourself as a job. Is that for a job as a payroll clerk? There's a really bad wait. >> See that those jobs were disappearing, both in terms of the numbers of employment and the wages that they get. The second category jobs. That compliment data scientist is a great example of that or somebody who's AP Writer or YouTube. Those are things that technology makes your skills more and more valuable. And there's this huge middle category. We talked earlier about interpersonal skills, a lot of physical task. Still, where machines just really can't touch them too much. Those are also categories that so far hell >> no, I didnt know it like middle >> school football, Coach is a job. It's going to be around a human job. It's going to be around for a long time to come because I have not seen the piece of technology that can inspire a group of twelve or thirteen year olds to go out there and play together as a team. Now Erik has actually been a middle school football coach, and he actually used a lot of technology to help him get good at that job, to the point where you are pretty successful. Middle school football coach >> way want a lot of teams games, and part of it was way could learn from technology. We were able to break down films in ways that people never could've previously at the middle school level. His technology's made a lot of things much cheaper. Now then we're available. >> So it was learning to be competitive versus learning how to teach kids to play football. Is that right? Or was a bit? Well, actually, >> one of the most important things and being a coach is that interpersonal connection is one thing I liked the most about it, and that's something I think no robot could do. What I think it be a long, long time. If ever that inspiring halftime speech could be given by a robot >> on getting Eric Gipper bring the Olsen Well, the to me, the more, most interesting examples I didn't realise this until I read your book, is that the best chess player in the world is not a computer, it's a computer and a human. That's what those to me. It seemed to be the greatest opportunities for innovative way. Call a >> racing with machines, and we want to emphasize that that's what people should be focusing. I think there's been a lot of attention on how machines can replace humans. But the bigger opportunities how humans and machines could work together to do things they could never have been done before in games like chess. We see that possibility. But even more, interestingly, is when they're making new discoveries in neuroscience or new kinds of business models like Uber and others, where we are seeing value creation in ways that was just not possible >> previously, and that chess example is going to spill over into the rest of the economy very, very quickly. I think about medicine and medical diagnosis. I believe that work needs to be a huge amount, more digital automated than it is today. I want Dr Watson as my primary care physician, but I do think that the real opportunities we're going to be to combine digital diagnosis, digital pattern recognition with the union skills and abilities of the human doctor. Let's bring those two skill sets together >> well, the Staton your book is. It would take a physician one hundred sixty hours a week to stay on top of reading, to stay on top of all the new That's publication. That's the >> estimate. And but there's no amount of time that watching could learn how to do that empathy that requires to communicate that and learn from a patient so that humans and machines have complementary skills. The machines are strong in some categories of humans and others, and that's why a team of humans and computers could be so >> That's the killer. Since >> the book came out, we found another great example related to automation and medicine in science. There's a really clever experiment that the IBM Watson team did with team out of Baylor. They fed the technology a couple hundred thousand papers related to one area of gene expression and proteins. And they said, Why don't you predict what the next molecules all we should look at to get this tart to get this desired response out on the computer said Okay, we think these nine are the next ones that are going to be good candidates. What they did that was so clever they only gave the computer papers that had been published through two thousand three. So then we have twelve years to see if those hypotheses turned out to be correct. Computer was batting about seven hundred, so people say, didn't that technology could never be creative. I think coming up with a a good scientific hypothesis is an example of creative work. Let's make that work a lot more digital as well. >> So, you know, I got a question from the crowd here. Thie First Industrial Revolution really helped build up a lot of the cities. The question is, with the speed and reach of the Internet and everything, is this really going to help distribute the population? Maur. What? The digital economy? I don't I don't think so. I don't think we want to come to cities, not just because it's the only waited to communicate with somebody we actually want to be >> face to face with them. We want to hang out with urbanization is a really, really powerful trend. Even as our technologies have gotten more powerful. I don't think that's going to revert, but I do think that if you if you want to get away from the city, at least for a period of time and go contemplate and be out in the world. You can now do that and not >> lose touch. You know, the social undistributed workforce isn't gonna drive that away. It's It's a real phenomenon, but it's not going to >> mean that cities were going >> to be popular. Well, the cities have two unique abilities. One is the entertainment. If you'd like to socialize with people in a face to face way most of the time, although people do it online as well, the other is that there's still a lot of types of communication that are best done in person. And, in fact, real estate value suggests that being able to be close toe other experts in your field. Whether it's in Silicon Valley, Hollywood, Wall Street is still a valuable asset. Eric and I >> travel a ton not always together. We could get a lot of our work done via email on via digital tools. When it comes time to actually get together and think about the next article or the next book, we need to be in the same room with the white bored doing it. Old school >> want to come back to the roots of innovation. Moore's law is Gordon Mohr put forth fiftieth anniversary next week, and it's it's It's coming to an end in terms of that actually has ended in terms of the way it's doubling every eighteen months, but looks like we still have some runway. But you know, experts can predict and you guys made it a point you book People always underestimate, you know, human's ability to do the things that people think they can't do. But the rial innovation is coming from this notion of combinatorial technologies. That's where we're going to see that continued exponential growth. What gives you confidence that that >> curve will continue? If you look at innovation as the work, not of coming up with some brand new Eureka, but as putting together existing building blocks in a new and powerful way, Then you should get really optimistic because the number of building blocks out there in the world is only going up with iPhones and sensors and banned weapon and all these different new tools and the ability to tap into more brains around the world to allow more people to try to do that recombination. That ability is only increasing as well. I'm massively optimistic about innovation, >> yet that's a fundamental break from the common attitude. We hear that we're using up all the low hanging fruit, that innovation. There's some fixed stock of it, and first we get the easy innovations, and then it gets harder and harder to innovate. We fundamentally disagree with that. You, in fact, every innovation we create creates more and more building blocks for additional innovations. And if you look historically, most of the breakthroughs have been achieved by combining previously existing innovations. So that makes me optimistic that we'LL have more and more of those building blocks going >> forward. People say that we've we've wrung all of the benefit out of the internal combustion engine, for example, and it's all just rounding error. For here. Know a completely autonomous car is not rounding error. That's the new thing that's going to change. Our lives is going to change our cities is going to change our supply chains, and it's making a new, entirely new use case out of that internal combustion. >> So you used the example of ways in the book, Really, you know, their software, obviously was involved, but it really was sensors and it was social media. And we're mobile phones and networks, just these combinations of technologies for innovation, >> none of which was an invention of the Ways team, none of which was original. Theyjust put those elements together in a really powerful way. >> So that's I mean, the value of ways isn't over. So we're just scratching the surface, and we could talk about sort of what you guys expect. Going forward. I know it's hard to predict well, another >> really important thing about wages in addition to the wake and combined and recombined existing components. It's available for free on my phone, and GPS would've cost hundreds of dollars a few years ago, and it wouldn't have been nearly as good at ways. And in a decade before that, it would have been infinitely expensive. You couldn't get it at any price, and this is a really important phenomenon. The digital economy that is underappreciated is that so much of what we get is now available at zero cost. Our GDP measures are all the goods and services they're bought and sold. If they have zero price, they show up is a zero in GDP. >> Wikipedia, right? Wikipedia, but that just wait here overvalue ways. Yeah, it doesn't. That >> doesn't mean zero value. It's still quite valuable to us. And more and more. I think our metrics are not capturing the real essence of the digital economy. One of the things we're doing at the Initiative initiative, the addition on the usual economy is to understand better what the right metrics will be for seeing this kind of growth. >> And I want to talk about that in the context of what you just said. The competitiveness. So if I get a piece of fruit disappears Smythe Digital economy, it's different. I wonder if you could explain that, >> and one of the ways it's different will use waze is an example here again, is network effects become really, really powerful? So ways gets more valuable to me? The more other ways er's there are out there in the world, they provide more traffic information that let me know where the potholes and the construction are. So network effects lead to really kind of different competitive dynamics. They tend to lead toward more winner, take all situations. They tend to lead toward things that look more not like monopolies, and that tends to freak some people out. I'm a little more home about that because one of the things we also know from observing the high tech industries is that today's near monopolist is yesterday's also ran. We just see that over and over because complacency and inertia are so deadly, there's always some some disruptor coming up, even in the high tech industries to make the incumbents nervous. >> Right? Open source. >> We'LL open source And that's a perfect example of how some of the characteristics of goods in the digital economy are fundamentally different from earlier eras and microeconomics. We talk about rival and excludable goods, and that's what you need for a competitive equilibrium. Digital goods, our non rival and non excludable. You go back to your micro economics textbook for more detail in that, but in essence, what it means is that these goods could be freely coffee at almost zero cost. Each copy is a perfect replica of the original that could be transmitted anywhere on the planet almost instantaneously, and that leads to a very different kind of economics that what we had for the previous few hundred years, >> or you don't work to quantify that. Does that sort of Yeah, wave wanted >> Find the effect on the economy more broadly. But there's also a very profound effects on business and the kind of business models that work. You know, you mentioned open source as an example. There are platform economics, Marshall Banal Stein. One of the experts in the field, is speaking here today about that. Maybe we get a chance to talk about it later. You can sometimes make a lot of money by giving stuff away for free and gaining from complimentary goods. These are things that >> way started. Yeah, Well, there you go. Well, that would be working for you could only do that for a little >> while. You'll like you're a drug dealer. You could do that for a little while. And then you get people addicted many. You start charging them a lot. There's a really different business model in the second machine age, which is just give stuff away for free. You can make enough off other ancillary streams like advertising to have a large, very, very successful business. >> Okay, I wonder if we could sort of, uh, two things I want first I want to talk about the constraints. What is the constraints to taking advantage of that? That innovation curve in the next day? >> Well, that's a great question, and less and less of the constraint is technological. More and more of the constraint is our ability as individuals to cope with change and said There's a race between technology and education, and an even more profound constraint is the ability of our organisations in our culture to adapt. We really see that it's a bottleneck. And at the MIT Sloan School, we're very much focused on trying to relieve those constraints. We've got some brilliant technologists that are inventing the future on the technology side, but we've got to keep up with our business. Models are economic systems, and that's not happening fast enough. >> So let's think about where the technology's aren't in. The constraints aren't and are. As Eric says, access to technology is vanishing as a constraint. Access to capital is vanishing as a constraint, at least a demonstrator to start showing that you've got a good idea because of the cloud. Because of Moore's law and a small team or alone innovator can demonstrate the power of their idea and then ramp it up. So those air really vanishing constraints are mindset, constraints, our institutional constraints. And unfortunately, increasingly, I believe regulatory constraints. Our colleague Larry Lessing has a great way to phrase the choice, he says, With our policies, with our regulations, we can protect the future from the past, or we could protect the past from the future. That choice is really, really write. The future is a better place. Let's protect that from the incumbents in the inertia. >> So that leads us to sort of some of the proposals that you guys made in terms of how we can approach this. Good news is, capitalism is not something that you're you're you're you're very much in favor of, you know, attacking no poulet bureau, I think, was your comments on DH some of the other things? Actually, I found pretty practical, although not not likely, but practical things, right? Yes, but but still, you know, feasible certainly, certainly, certainly intellectually. But what have you seen in terms of the reaction to your proposals? And do you have any once that the public policy will begin to shape in a way that wages >> conference that the conversation is shifting. So just from the publication date now we've noticed there's a lot more willingness to engage with these ideas with the ideas that tech progress is racing ahead but leaving some people behind in more people behind in an economic sense over time. So we've talked to politicians. We've talked to policy makers. We've talked to faint thanks. That conversation is progressing. And if we want to change our our government, you want to change our policies. I think it has to start with changing the conversation. It's a bottom out phenomenon >> and is exactly right. And that's really one of the key things that we learned, you know well, we talked to our political science friends. They remind us that in American other democracies, leaders are really followers on. They follow public opinion and the people are the leaders. So we're not going to be able to get changes in our policies until we change the old broad conversation. We get people recognizing the issues they're underway here, and I wouldn't be too quick to dismiss some of these bigger changes we describe as possible the book. I mean, historically, there've been some huge changes the cost of the mass public education was a pretty radical idea when it was introduced. The concept of Social Security were recently the concept of marriage. Equality with something I think people wouldn't have imagined maybe a decade or two ago so you could have some big changes in the political conversation. It starts with what the people want, and ultimately the leaders will follow. >> It's easy to get dismayed about the logjam in Washington, and I get dismayed once in a while. But I think back a decade ago, if somebody had told me that gay marriage and legal marijuana would be pretty widespread in America, I would have laughed in their face. And, you know, I'm straight and I don't smoke dope. I think these were both fantastic developments, and they came because the conversation shifted. Not not because we had a gay pot smoker in the white. >> Gentlemen, Listen, thank you very much. First of all, for running this great book, well, even I got one last question. So I understand you guys were working on your topic for you next, but can you give us a little bit of, uh, some thoughts as to what you're thinking. What do we do? We tip the hand. Well, sure, I think that >> it's no no mystery that we teach in a business school. And we spent a lot of time interacting with business leaders. And as we've mentioned in the discussion here, there have been some huge changes in the kind of business models that are successful in the second machine age. We want to elaborate on those describe nuts what were seeing when we talk to business leaders but also with the economic theory says about what will and what? What won't work. >> So second machine age was our attempt it like a big idea book. Let's write the Business guide to the Second Machine Age. >> Excellent. First of all, the book is a big idea. A lot of big ideas in the book, with excellent examples and some prescription, I think, for moving forward. So thank you for writing that book. And congratulations on its success. Really appreciate you guys coming in the Cube. Good luck today and we look forward to talking to in the future. Thanks for having been a real pleasure. Keep right. Everybody will be right back. We're live from London. This is M I t E. This is the cube right back