(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)

(upbeat music) >> (Narrator) From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> Hello, welcome back to theCUBE's coverage of IBM Think 2021 virtual. Soon we'll be back in person in real life, but this year again it's a virtual conference. I'm John Furrier, your host of the cube for more cube coverage. We've got a great guest here, Debbie Vavangas, Global Garage Lead for IBM Services. Global Garage, great program. Debbie, great to see you. Thanks for coming on theCUBE. >> Thanks for having me. >> So, we've covered the Garage a lot on theCUBE in the past, and a success, everyone loves the Garage. Things are born in the Garage, entrepreneurship, innovation, has been kind of categorically known for, kind of, the Garage startup. >> Absolutely. >> But also, it's become known for, really, agility, which has been a cloud phenomenon, DevOps. Now we're seeing dev SecOps as a big trend this year with hybrid cloud. So, I've got to ask you, how is Garage doing with the pandemic? Obviously, I can almost imagine people at home kind of disrupted from the office, but maybe more creativity, maybe more energy online? What's going on with the Garage? How has your transformation journey been with COVID? >> Well, John, COVID has been the leveler for us all, right? There isn't a person who hasn't had some challenge or some complexity to And that includes our clients. And I'm incredibly proud to be able to say that IBM Garage, because it is so digitally native, when the COVID pandemic has struck around the world every single one of our Garages was able to switch to being virtual without fail, without a single days lost productivity. And that's hugely beneficial to clients who are on an incredibly time-sensitive journey. And so, we've seen as a result of COVID actually there are a huge acceleration in Garages, for two reasons. So, number one, from a virtualization perspective, actually it's much easier when everybodies together in the same space. So everybody's together virtually in the same space, and we've seen, you know, acceleration in our velocity, in our collaboration, because everybody is really learning how to work in that same space. But two, because of the pandemic, because of the pressure on our client's needs to make decisions fast, know not guess, really be focused on their outcomes, not just doing stuff, the Garage really plays to that objective for them. And so we've seen a huge rise, you know, we've gone from in 2019 to just a few hundred garages, to finishing 2020 with over two and a half thousand garages. And it being embedded across services and with the goal of being the primary way our clients experience it. So COVID has been a big accelerator. >> Sorry, Debbie, can you repeat the numbers again? I just want to capture that, I missed that. >> Sure, sure. >> I did a double take on the numbers. (Debbie laughs) >> So then, we finished 2019 with just under 300 garages, and we finished 2020 with just over two and a half thousand. So, we've had a huge growth, and it isn't just the number of garages, it's the range of garages and what we're serving with our clients, and how we're collaborating with our clients, and the topics we're unpacking that has really broadened. >> Yeah, I mean I covered, and we've reported on the Garage on theCUBE and also on www.siliconangle.com in the past things and through your news coverage, but that's amazing growth. I got to believe the tailwind from COVID and just the energy around it has energized you. I want to get your thoughts on that because, you know, what we've reported on in the past has been about design thinking, human-centered design, all of those beautiful things that come with cloud-scale, right? You know, you're moving faster, you're innovating, and so that's been kind of there. But what you're getting at with this growth is, and with COVID has proven, and again, we've been pointing this out, you're seeing the pattern, it's clear. Companies are either retrenching, okay, which is refactoring, redesigning, doing those things to kind of get ready to come out of COVID with a growth strategy, and you're seeing other companies build net new innovations. So, they're building new capabilities, because COVID's shown them, kind of pulled back the curtain if you will on where the action is. So, this means there's two threads going on. You've got, "Okay, I've got to transform my business, and I got to refactor', or 'Hey, we got net new business models'. These are kind of two different things and not mutually exclusive. What's your comment on that? >> And I think that my comment on it is that is the sweet spot that Garage comes into its own, right? You mentioned lots of things in there. You talked about design thinking, and agility, and, you know, these other buzzwords that are used all the time, and Garage of course is synonymous with those. Of course, Garage uses the best design thinking, and AGILE practices, and all of those things that absolutely call to what we do. DevOps, even through down to DesignOps. You know, we have the whole range depending on what the client objective is. But, I think what is really happening now is that innovation being something separate is no longer how to accelerate your outcomes, and your business outcomes. Regardless of whether that is in refactoring and modernizing your existing estate, or diversifying, creating new ecosystems, new platforms, new offerings. Regardless of what that is, you can't do it separate to your core business. I mean, it's a well known fact, John, right? Like 75% of transformation programs fail to deliver an impact to the business performance, right? And in the same period of time there's been huge cuts in innovation funding, and that's because for the same reason, because they don't deliver the impact to the business performance. And that's why Garage is unique, because it is entirely focused on the outcome, right? We're using user research, through design thinking of course, using agile to deliver it at speed, and all of those other things. But, it's focused on value, on benefits realization and driving to your outcome. And we do that by putting that innovation at the heart of your enterprise in order to drive that transformation, rather than it being something separate. >> Debbie, I saw you gave a talk called 'Innovation is Dead'. Obviously, that's a provocative title, that's an attention-getter. Tell me what you mean by that. Because it seems to be a setup. >> I mean, if the innovation is dead, >> Of course. was it with a question mark? Were you, kind of, trying to highlight that innovation is transformation? >> So, the full title was 'Innovation is dead and transformation is pointless'. And, of course, it's meant to be an eye-catching title so people show up and listen to my pitch rather than somebody else's. But, the reality is I mean it most sincerely, it's back to that stat. 75% of these transformation programs fail to deliver the impact, and I speculate that that is for a few reasons. Because, the idea itself wasn't a good one, or wasn't at the right time. Because, you were unable to understand what the measure of good looked like, and therefore just being able to create that path. And, in order to transform a company, you must transform the individuals within a company. And so that way of working becomes incredibly holistic. And it's those three things, that I think amongst the whole myriad of others, that are the primary reasons why those programs fail. And what Garage does, is it breaks that. By putting innovation at the heart of your enterprise, and by using data-driven value orchestration, that means that we don't guess where the value to be gained is, we know. It's no longer chucking ideas at the wall to see what sticks, it's meaningful research. This is my favorite quote from my dear friend, Courtney Noll, who says, "It's not about searching for the innovation needle in the proverbial haystack, it's using your research in order to de-risk your investment, and drive your innovation to enable your outcomes." And so, if you do innovation without a view to how it's going to yield your business outcomes, I agree, I fundamentally agree that it's pointless. >> Yeah, exactly. And, you know, of course we're on the writing side, we love titles like, 'Innovation is dead, long live innovation'. So, it's classic, you know, to get your attention. >> Exactly, exactly. And of course, what I really mean is that innovation is a separate entity. >> Totally. >> There's no longer relevance for a company to make sure they achieve their business outcomes. >> Well, this is what I wanted to just double-click on that with you on is that you look at transformation. You guys are essentially saying transformation meets innovation with the Garage philosophy, if I get that right. >> Yep >> And it's interesting, and we've experienced this here with theCUBE, we're theCUBE virtual, we're not at IBM Think, there is no physical game day like some of us normally do. >> Well, as you can see, I'm at my house. (Debbie laughs) And so, I was talking to a CEO and I said, "Hey, you guys are doing really, really good. We had to pivot with the cube", and he goes, "You guys did a good pivot yourself". He goes, "No, John, we did not pivot. We actually put our business on hold because of the pandemic. We actually created a line extension, so, technically, we're going to bring that business back when COVID has gone and come back to real life, so it's technically not a pivot, we're not pivoting our business, we've created new functionality." Through the innovations that they were doing. So, this is kind of like, this is the real deal here. Share your thoughts on that. >> To me, it's about people get so focused on the output that they lose track of the outcome, right? And so, be really clear on what you're doing, and why. And the outcomes can be really broad, so instead of saying, "We're all going to implement a new ERP, or build a new mobile app". That's not an outcome, right? What we should be saying is, "What we're trying to achieve is a 10 percent growth in net promoter score in China, right? In this group." Or whatever it is we were trying to achieve, right? Or, "We want to make a 25% reduction in our operating cost base by simplifying our estate". Whatever those outcomes are, that's the starting point, and then driving that to use as the vehicle for what is the right innovation, what is going to deliver that value, and fast, right? Garage delivers three to five times faster than other models and at a reduced delivery cost, and so it's all about that speed. Speed of decision, speed of insight, speed of culture and training, speed of new skills, and speed to outcomes. >> Well, Debbie, you did a great job, love what you're doing, and Garage has got a great model. Congratulations on the growth, love this intersection, or transformation meets innovation because innovation is transformation, and vice versa, this interplay going on there. >> Exactly. >> I think COVID has proven that. Let me dig into a little bit more about the garage, what's going on. How many practitioners do you guys have there now at IBM? You've got growth, are you adding more people in? Obviously, Virtual First, COVID, is there still centers of design? Take us through what's going on at Garage. >> Certainly, so like, I think I mentioned it right up front. Our goal is to make IBM Garage the primary way our clients experience us. We've proven in that it delivers higher value to our clients and they get a really rich and broad set of outcomes. And so, in order for us to deliver on that promise we have to be enabled across IBM to deliver to it, right? So, over the last 18 months or so we've had a whole range of training programs in Enable, we've had a whole badging and certification program, we have all the skills, and the pathways, and the career pathways to find. But Garage is for everybody, right? And so, it isn't about creating a select group that can do this across IBM. This is about making all of services capable. So, in 2020 we trained over 28,000 people, in all the different skills that are needed, from selling, to execution, to QA, to user research, whatever it is. And this year we're launching our Garage Skills Academy, which will take that across all of services and make it easily available. So, you know, we've got hundreds of thousands. >> And talk about the footprint on the global side, because, again, not to bring up global, but global is what is in your title. >> Yep. >> Companies need to be global, because now with virtual workforces you're seeing much more tapped creativity and ability to execute from global teams. How does that impact you? >> Well, so it's global in two perspectives, right? So, number one, we have Garages all around the world, right? It isn't just the market of, you know, our most developed nations in Americas and Europe, it is everywhere, we see it in all emerging markets. From Latin America, through to all parts of eastern Europe, which are really beginning to come into their own. So, we see all these different Garages at different scales and opportunities. So, definitely global from that image. But, what virtualization has also enabled is truly global teams. Because, it's really easy to go, "Oh, I need one of those. Okay, I need a supply chain expert, and I need an AI expert, and I need somebody who's got industry experience in whatever it is." And you can quickly gather them around the virtual table, you know, faster than you can in a physical table. But, we still leverage the global communities with those physical. >> It's an expert network. You have an expert network there at IBM. >> We have a huge network, yeah. And both within IBM, and of course a growing network of ecosystem partners that we continue to work with. >> Well, Debbie, I'm really excited. Congratulations on the growth. I'm looking forward to partnering with you on your ecosystem as that develops. I can almost imagine you must be getting a lot of outside IBM practitioners and experts coming in to collaborate in a social construct. >> Absolutely. >> It's a great program, thanks for sharing. >> My pleasure, it's been great to be here, thank you. >> Okay, IBM's Global Garage Lead, Debbie Vavangas, who's here on theCUBE with IBM Services. A phenomenon, it's a social construct that's helping companies with digital transformation. Intersecting, with innovation. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> (lively music) >> Narrator: From around the globe it's theCUBE, with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got a 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois is representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Paul: Thanks Lisa. >> Dominique: Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

>> Announcer: From around the globe, it's theCUBE, with digital coverage of IBM Think 2021, brought to you by IBM. >> Hey, welcome to theCUBE's coverage of IBM Think, the digital event experience. I'm your host, Lisa Martin, welcoming back to the program one of our CUBE alumn. Dominique Dubois joins me. She's the Global Strategy and Offerings Executive in the Business Transformation Services of IBM. Dominique, it's great to talk to you again. >> Hi Lisa, great to be with you today. >> So we're going to be talking about the theme of this interview. It's going to be the ROI of AI for business. We've been talking about AI emerging technologies for a long time now. We've also seen a massive change in the world. I'd love to talk to you about how organizations are adopting these emerging technologies to really help transform their businesses. And one of the things that you've talked about in the past, is that there's these different elements of AI for business. One of them is trust, right, the second is ease of use, and then there's this importance of data in all of these important emerging technologies that require so much data. How do those elements of AI come together to help IBM's clients be able to deliver the products and services that their customers are depending on? >> Yeah. Thank you, Lisa. So, when we look at AI and AI solutions with our clients, I think how that comes together is in the way in which we don't look at AI, or AI application solution, independently, right. We're looking at it and we're applying it within our customer's operations with respect to the work that it's going to do, with respect to the part of the operations and the workflow and the function that it sits in, right. So the idea around trust and ease of use and the data that can be leveraged in order to kind of create that AI and allow that AI to be self-learning and continue to add value really is fundamental around how we design and how we implement it within the workflow itself. And how we are working with the employees, with the actual humans, that are going to be touching that AI, right, to help them with new skills that are required to work with AI, to help them with what we call the new ways of working, right, 'cause it's that adoption that really is critical to get the use of AI in enterprises at scale. >> That adoption that you just mentioned, that's critical. That can be kind of table stakes. But what we've seen in the last year is that we've all had to pivot, multiple times, and be reactionary, or reactive, to so many things out of our control. I'm curious what you've seen in the last year in terms of the appetite for adoption on the employees front. Are they more willing to go, all right, we've got to change the way we do things, and it's probably going to be, some of these are going to be permanent? >> Yeah. Lisa, we've absolutely seen a huge rise in the adoption, right, or in the openness, the mindset. Let's just call it the mindset, right. It's more of an open mindset around the use of technology, the use of technology that might be AI backed or AI based, and the willingness to, and I will say, the willingness to try is really then what starts that journey of trust, right. And we're seeing that open up in spades. >> That is absolutely critical. It's just the willingness, being open-minded enough to go, all right, we've got to do this, so we've got to think about this. We don't really have any other choices here. Things are changing pretty quickly. So talk to me, in this last year of change, we've seen massive disruptions and some silver linings for sure, but I'd love to know what IBM and the state of Rhode Island have done together in its challenging time. >> Yeah, so, really interesting partnership that we started with the state of Rhode Island. Obviously, I think this year, there's been lots of things. One of them has been speed, so everything that we had to do has been with haste, right, with urgency. And that's no different than what we did with the state of Rhode Island. The governor there, Gina Raimondo, she took very swift action, right, when the pandemic started. And one of the actions she took was to partner with private firms, such as IBM and others, to really help get her economy back open. And that required a lot of things. One of them, as you mentioned, trust, right, was a major part of what the governor there needed with her citizenships, with her citizens, excuse me, in order to be able to open back up the economy, right. And so, a key pillar of her program, and with our partnership, was around the AI-backed solutions that we brought to the state of Rhode Island, so inclusive of contact tracing, inclusive of work that we had provided around AI-based analytics that allowed really the governor to speak to citizens with hard facts quickly, almost real time, right, and start to build that trust, but also competence, and competence was the main, one of the main things that was required during this pandemic time. And so, there were, through this, the AI-based solutions that we provided, which were, there were many pillars, we were able to help Rhode Island not only open their economy, but they were one of the only states that had their schools open in the fall, and as a parent, I always see that as a litmus, if you will, of how our state is doing, right. And so they opened in the fall, and they, as far as I know, have stayed open. And I think part of that was from the AI-based contact tracing, the AI-backed virtual, sorry, AI analytics, the analytics suite around infections and predictions and what we were able to provide the governor in order to make swift decisions and take action. >> That's really impressive. That's one of the challenges I've had living in California, is you (mumbles) you are going to be data-driven than actually be data-driven, but the technology, living in Silicon Valley, the technology is there to be able to facilitate that, yet there was such a disconnect, and I think that's, you bring up the word confidence, and customers need confidence, citizens need confidence, knowing that what we've seen in the last year has shown in a lot of examples that real time isn't a nice-to-have anymore, it's a requirement. I mean, this is clearly life-and-death situations. That's a great example of how a state came to IBM to partner and say, how can we actually leverage emerging technologies like AI to really and truly make real-time data-driven decisions that affect every single person in our state. >> Mm-hmm. Absolutely, absolutely! Really, really, I think, a great example of the public-private partnerships that are really popping up now, more and more so because of that sense of urgency and that need to build greater ecosystems to create better solutions. >> So that's a great example in healthcare, one that our government in public health, and I think everybody, it will resonate with everybody here, but you've also done some really interesting work that I want to talk about with AI-driven insights into supply chain. We've also seen massive changes to supply chain, and so many organizations having to figure out, whether they were brick-and-mortar only, changing that, or really leveraging technology to figure out where do we need to be distributing products and services, where do we need to be investing. Talk to me about Bestseller India, and what it is that you guys have done there with intelligent workflows to really help them transform their supply chain. >> Yeah, Bestseller India, really great, hugely successful fashion forward company in India, and that term fashion forward always is mind boggling to me because basically, these are clothing retailers who go from runway to store within a matter of days, couple of weeks, which always is just hugely impressive, right, just what goes into that. And when you think about what happens in a supply chain to be able to do that, the requirements around demand forecasting, what quantities, of what style, what design, to what stores, and you think about the India market, which is notoriously a difficult market, lots of micro-segments, and so very difficult to serve. And then you couple that what's been happening from an environmental sustainability perspective, right. I think every industry has been looking more about how they can be more environmentally sustainable, and the clothing industry is no different. And when, and there is a lot of impact, right, so a stat that really has hit home with me, right: 20% of all the clothes that are made globally goes unsold. That's all a lot of clothing, that's a lot of material, and that's a lot of environmental product that goes into creating it. And so, Bestseller India really took it to heart to become not only more environmentally sustainable, but to help itself and be digitally ready for things like the pandemic that ultimately hit. And they were in a really good position. And we worked with them to create something called Fabric AI. So Fabric AI is India's only, first and only, AI-based platform that drives their supply chain, so it drives not only their decisions on what design should they manufacture, but it also helps to improve the entire workflow of what we call design to store. And the AI-based solution is really revolutionary, right, within India, but I think it's pretty revolutionary globally, right, globally as well. And it delivered really big impact, so, reductions in the cost, right, 15-plus reduction in cost. It helped their top line, so they saw a 5% plus top line, but it also reduced their unsold inventory by 5% and more, right. They're continuing to focus on that environmental sustainability that I think is a really important part of their DNA, right, the Bestseller India's DNA. >> And it's one that so many companies and other industries can learn from. I was reading in that case study on Bestseller India on the IBM website that I think it was 40 liters of water to make a cotton shirt. And to your point about the percentage of clothing that actually goes unsold and ends up in landfills, you see there the opportunity for AI to unlock the visibility that companies in any industry need to determine what is the demand that we should be filling, where should it be distributed, where should we not be distributing things. And so I think it was an interesting kind of impetus that Bestseller India had about one of their retail lines or brands was dropping in revenue, but they had been able to apply this technology to other areas of the business and make a pretty big impact. >> Yeah, absolutely. So they had been been very fortunate to have 11 years of growth, right, in all of their brands. And then one of their brands kind of hit headwinds, but the CIO and head of supply chain at that time really had the foresight to be able to say, you know what, we're hitting a problem, one of our brands, but this really is indicative of a more systemic problem. And that problem was lack of transparency, lack of data-driven, predictive, and automation to be able to drive a more effective and efficient kind of supply chain in the end, so, really had the forethought to dive into that and fix it. >> Yeah. And now talk to me about IBM Garage Band, and how's that, how did that help in this particular case? >> Yeah. So, in order to do this, right, it was, they had no use of AI, no use of automation, at the time that we started this. And so to really not only design and build and execute on Fabric AI, but to actually focus on the adoption, right, of AI within the business, we really needed to bring together the leaders across many lines of businesses, IT and HR, right. And when you think about pulling all of these different units together, we used our IBM Garage approach, which really is, there are many attributes and many facets of the IBM Garage, but I think one of the great results of using our IBM Garage approach is being able to pull from across all those different businesses, all of which may have some different objectives, right, they're coming from a different lens, from a different space, and pulling them together around one focus mission, which for here was Fabric AI. And we were able to actually design and build this in less than six months, which I think is pretty dramatic and pretty incredible from a speed and acceleration perspective. But I think even more so was the adoption, was the way in which we had, through all of it, already been working with the employees 'cause it's really touched almost every part of Bestseller India, so really being able to work with them and all the employees to make sure that they were ready for these new ways of working, that they had the right skills, that they had the right perspective, and that it was going to be adopted. >> That, we, if we unpack that, if we had time, that can be a whole separate conversation because the important, the most important thing about adoption is the cultures of these different business units have to come together. You said you rolled this out in a very short period of time, but you also were taking the focus on the employees. They need to understand the value in it. why they should be adopting it. And changing that culture, that's a whole other separate conversation, but that's an, that's a very interesting and very challenging thing to do. I wish we had more time to talk about that one. >> Yeah. It really is an, that the approach of bringing everyone together, it makes it just very dynamic, which is what's needed when you have all of those different lenses coming together, so, yeah. >> It is, 'cause you get a little bit of thought diversity as well when we're using AI. Well, Dominic, thank you for joining me today. Talked to me about what you guys are doing with many different types of customers, how you're helping them to integrate emerging technologies to really transform their business and their culture. We appreciate your time. >> Well, thank you, Lisa. Thanks >> For Dominique Dubois, I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think, the digital event. (upbeat music)

(lively music) >> From around the globe it's theCUBE, with digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome to theCUBE's coverage of IBM Think 2021, the digital event experience. I'm your host, Lisa Martin. I've got an alumni joining me and a brand new guest to the CUBE please welcome Paul Papas, the Global Managing Partner, for IBM Global Business Services, this is transformation services. Paul, welcome back to the virtual CUBE. >> Thanks Lisa great to be here with you today. And Dominique Dubois is here as well. She is the Global Strategy and Offerings Leader in business transformation services or BTS at IBM. Dominique, welcome to the program. >> Thanks Lisa, great to be here. So, we're going to be talking about accelerating business transformation with intelligent workflows. We're going to break through all that, but Paul we're going to start with you. Since we last got together with IBM, a lot has changed so much transformation, so much acceleration of transformation. Talk to me from your perspective, how have you seen the way that businesses running change and what some of the changes in the future are going to be? >> Well, you hit on two key words there Lisa and thanks so much for that question. Two key words that you hit on were change and acceleration. And that's exactly what we see. We were seeing this before the pandemic and if anything, with the pandemic did when things started started kind of spreading around the world late or early last year, around January, February timeframe we saw that word acceleration really take hold. Every one of our clients were looking for new ways to accelerate the change that they had already planned to adapt to this new, this new normal or this new abnormal, depending on how you view it. In fact, we did a study recently, an IBV study that's our Institute of Business Value and found that six out of 10 organizations were accelerating all of their transformation initiatives they had already planned. And that's exactly what we're seeing happening right now in all parts of the world and across all industries. This acceleration to transform. >> So, one of the things that we've talked about for years, Paul, before the pandemic was even a thing, is that there was a lot of perceived technical barriers in terms of like the tech maturity for organizations and employees being opposed to change. People obviously it can be a challenge. They're used to doing things the way they are. But as you just said, in that IBV survey, nearly 60% of businesses say we have to accelerate our transformation due to COVID, probably initially to survive and then thrive. Talk to me about some of those, those barriers that were there a little over a year ago and how businesses 60 plus percent of them have moved those out of the way. >> You know at IBM we've got 109 year history of being a technology innovation company. And the rate of pace of technical change is always increasing. It's something that we love and that we're comfortable with. But the rate and pace of change is always unsettling. And there's always a human element for change. And the human element is always the rate, the rate setter in terms of the amount of change that you can have in an organization. Our former chairman Ginni Rometty, used to say that growth and comfort cannot co-exist. And it's so true because changing is uncomfortable. It's unsettling. It can be, it can be nerve-racking. It can instill fear and fear can be paralyzing in terms of driving change. And what we also see is there's a disconnect, a lot of times and that IBV study that I was referring to before, we saw results coming back where 78% of executives feel that they have provided the training and enablement to help their employees transform to new required skills and new ways of working but only half of the people surveyed felt the same way. Similarly, we saw a disconnect in terms of companies feeling that they're providing the right level of health and wellness support during the pandemic. And only half of the employees responded back they feel that they're getting that level of support. So, the people change aspect of may doing a transformation or adapting to new circumstances is always the most critical component and always the hardest component. And when we talk about helping our clients do that in IBM that's our service as organization. That's the organization that Dominique Dubois are representing here today. I'm responsible for business transformation services within our organization. We help our clients adapt using new technologies, transforming the way they work, but also addressing the people change elements that could be so difficult and hitting them head on so that they can make sure that they can survive and thrive in a meaningful and lasting way in this new world. >> One of the hardest things is that cultural transformation regardless of a pandemic. So, I can't imagine I'd love to get one more thing, Paul from you before we head over to Dominique. IBM is on 109 year old organization. Talk to me about the IBM pledge. This is something that came up last year, huge organization massive changes last year, not just the work from home that the mental concerns and issues that people had. What did IBM do like as a grassroots effort that went viral? >> Yeah, so, it's really great. So, when the pandemic started, we all have to shift it, We all have to shift to working from home. And as you mentioned, IBM's 109 year old company, we have over 300,000 employees working in 170 countries. So, we had to move this entire workforce. It's 370,000 humans to working in a new way that many of which have never done before. And when we started experiencing, the minute we did that, within a few weeks, my team and I were talking Dominique is on my team and we were having conversations where we were feeling really exhausted. Just a few weeks into this and it was because we were constantly on Webex, we were constantly connected and we're all used to working really hard. We travel a lot, we're always with our clients. So, it wasn't that, you have a team that is adapting to like working more hours or longer hours, but this was fundamentally different. And we saw that with schools shutting down and lock downs happening in different of the world the home life balance was getting immediately difficult to impossible to deal with. We have people that are taking care of elderly parents, people that are homeschooling children, other personal life situations that everyone had to navigate in the middle of a pandemic locked at home with different restrictions on when you can go out and get things done. So, we got together as a group and we just started talking about how can we help? How can we help make life just a little bit easier for all of our people? And we started writing down some things that we would, we would commit to doing with each other. How we would address each other. And when that gave birth to was what we call the IBM Work From Home Pledge. And it's a set of principles, all grounded in the belief that, if we act this way, we might just be able to make life just a little bit easier for each other and it's grounded in empathy. And there are parts of the Plex that are pledging to be kind. Recognizing that in this new digital world that we're showing up on camera inside of everyone's home. We're guests in each other's homes. So, let's make sure that we act appropriately as guests at each other's home. So, if children run into the frame during the middle of a meeting or dog started barking during the middle of a meeting, just roll with it. Don't call out attention to it. Don't make people feel self-conscious about it. Pledged the support so your fellow IBM by making time for personal needs. So, if someone has to, do homeschooling in the middle of the day, like Dominique's got triplets she's got to do homeschooling in the middle of the day. Block that time off and we will respect that time on your calendar. And just work around it and just deal with it. There are other things like respecting that camera ready time. As someone who's now been on camera every day it feels like for the last 14 months we want to respect the time that people when they have their cameras off. And not pressure them to put their cameras on saying things like, Hey, I can't see you. There's no reason to add more pressure to everyone's life, if someone's camera's off, it's all for a reason. And then other things like pledging to checking on each other, pledging to set boundaries and tend to our own self-care. So, we published that as a group, we just again and we put it on a Slack channel. So it's kind of our communication method inside the company. It was just intended to be for my organization but it started going viral and tens of thousands of IBM members started taking, started taking the pledge and ultimately caught the attention of our CEO and he loved it, shared it with his leadership team, which I'm a part of. And then also then went on LinkedIn and publicly took the pledge as well. Which then also got more excitement and interaction with other companies as well. So, grassroots effort all grounded in showing empathy and helping to make life just a little bit easier for everyone. >> So important, I'm going to look that up and I'm going to tell you as a person who speaks with many tech companies a week. A lot of businesses could take a lead from that and it gets really important and we are inviting each other into our homes and I see you're a big Broadway fan I'll have to ask you that after we wrap (giggles) Dominique I don't know how you're doing any of this with triplets. I only have two dogs (Dominique laughs) but I'd love to know this sense of urgency, that is everywhere you're living it. Paul talked about it with respect to the acceleration of transformation. How from your lens is IBM and IBM helping customers address the urgency, the need to pivot, the need to accelerate, the need to survive and thrive with respect to digital transformation actually getting it done? >> Right, thanks Lisa, so true our clients are really needing to and ready to move with haste. That that sense of urgency can be felt I think across every country, every market, every industry. And so we're really helping our clients accelerate their digital transformations and we do that through something that we call intelligent workflows. And so workflows in and of themselves are basically how organizations get work done. But intelligent workflows are how we infuse; predictive properties, automation, transparency, agility, end to end across a workflow. So, pulling those processes together so they're not solid anymore and infusing. So, simply put we bring intelligent workflows to our clients and it fundamentally reinvents how they're getting work done from a digital perspective, from a predictive perspective, from a transparency perspective. And I think what really stands apart when we deliver this with our clients in partnership with our clients is how it not only delivers value to the bottom line, to the top line it also actually delivers greater value to their employees, to the customers, to the partner to their broader ecosystem. And intelligent workflows are really made up of three core elements. The first is around better utilizing data. So, aggregating, analyzing, getting deeper insight out of data, and then using that insight not just for employees to make better decisions, but actually to support for emerging technologies to leverage. So we talked about AI, automation, IOT, blockchain, all of these technologies require vast amounts of data. And what we're able to bring both on the internal and external source from a data perspective really underpins what these emerging technologies can do. And then the third area is skills. Our skills that we bring to the table, but also our clients deep, deep expertise, partner expertise, expertise from the ecosystem at large and pulling all of that together, is how we're really able to help our clients accelerate their digital transformations because we're helping them shift, from a set of siloed static processes to an end-to-end workflow. We're helping them make fewer predictions based on the past historical data and actually taking more real-time action with real time insights. So, it really is a fundamental shift and how your work is getting done to really being able to provide that emerging technologies, data, deep skills-based end to end workflow. >> That word fundamental has such gravity. and I know we say data has gravity being fundamental in such an incredibly dynamic time is really challenging but I was looking through some of the notes that you guys provided me with. And in terms of what you just talked about, Dominique versus making a change to a silo, the benefits and making changes to a spectrum of integrated processes the values can be huge. In fact, I was reading that changing a single process like billing, for example might deliver up to 20% improved results. But integrating across multiple processes, like billing, collections, organizations can achieve double that up to 40%. And then there's more taking the intelligent workflow across all lead to cash. This was huge. Clients can get 50 to 70% more value from that. So that just shows that fundamental impact that intelligent workflows can make. >> Right, I mean, it really is when we see it really is about unlocking exponential value. So, when you think about crossing end to end workflow but also, really enhancing what clients are doing and what companies are doing today with those exponential technologies from kind of single use the automation POC here and AI application POC here, actually integrating those technologies together and applying them at scale. When I think intelligent workflows I think acceleration. I think exponential value. But I also really think about at scale. Because it's really the ability to apply these technologies the expertise at scale that allows us to start to unlock a lot of that value. >> So let's go over Paul, in the last few minutes that we have here I want to talk about IBM garage and how this is helping clients to really transform those workflows. Talk to me a little bit about what IBM garage is. I know it's not IBM garage band and I know it's been around since before the pandemic but help us understand what that is and how it's delivering value to customers. >> Well, first I'm going to be the first to invite you to join the IBM garage band, Lisa so we'd love to have you >> I'm in. no musical experience required... >> I like to sing, all right I mean (laughs) We're ready, we're ready for. So, let me talk to you about IBM garage and I do want to key on two words that Dominique was mentioning speed and scale. Because that's what our clients are really looking for when they're doing transformations around intelligent workflows. How can you transform at scale, but do that with speed. And that really becomes the critical issue. As Dominique mentioned, there's a lot of companies that can help you do a proof of concept do something in a few weeks that you can test an idea out and have something that's kind of like a throw away piece of work that maybe proves a point or just proves a point. But even if it does prove the point at that point you'd have to restart a new, to try to get something that you could actually scale either in the production technology environment or scale as a change across an organization. And that's where IBM garage comes in. It's all a way of helping our clients co-create, co-execute and then cooperate, innovating at scale. So, we use methods like design thinking inside of IBM we've trained several hundred thousand people on design thinking methods. We use technologies like neural and other things that help our clients co-create in a dynamic environment. And what's amazing for me is that, the cause of the way we were, we were doing work with clients in a garage with using IBM garage in a garage environment before the pandemic. And one of our clients Frito-Lay of North America, is an example where we've helped them innovate at scale and speed using IBM garage over a long period of time. And when the pandemic hit, we in fact were running 11 garages across 11 different workflow areas for them the pandemic hit and everyone was sent home. So, we all instantly overnight had to work from home together with relay. And what was great is that we were able to quickly adapt the garage method to working in a virtual world. To being able to run that same type of innovation and then use that innovation at scale in a virtual world, we did that overnight. And since that time which happened, that happened back in March of last year throughout the pandemic, we've run over 1500 different garage engagements with all of our clients all around the world in a virtual, in a virtual environment. It's just an incredible way, like I said to help our clients innovate at scale. >> That's fantastic, go ahead Dominique. >> Oh, sorry, was just said it's a great example, we partnered with FlightSafety International, they train pilots. And I think a great example of that speed and scale right is in less than 12 weeks due to the garage methodology and the partnership with FlightSafety, we created with them and launched an adaptive learning solution. So, a platform as well as a complete change to their training workflow such that they had personalized kind of real-time next best training for how they train their pilots for simulators. So, reducing their cycle time but also improving the training that their pilots get, which as people who normally travel, it's really important to us and everyone else. So, just a really good example, less than 12 weeks start to start to finish. >> Right, talk about acceleration. Paul, last question for you, we've got about 30 seconds left I know this is an ecosystem effort of IBM, it's ecosystem partners, it's Alliance partners. How are you helping align right partner with the right customer, the right use case? >> Yeah, it's great. And our CEO Arvind Krishna has really ushered in this era where we are all about the open ecosystem here at IBM and working with our ecosystem partners. In our services business we have partnerships with all the major, all the major technology players. We have a 45 year relationship with SAP. We've done more SAP S 400 implementations than anyone in the world. We've got the longest standing consulting relationship with Salesforce, we've got a unique relationship with Adobe, they're only services and technology partner in the ecosystem. And we just recently won three, procedures Partner Awards, with them and most recently we announced a partnership with Celonis which is an incredible process execution software company, process mining software company that's going to help us transform intelligent workflows in an accelerated way, embedded in our garage environment. So, ecosystem is critical to our success but more importantly, it's critical to our client success. We know that no one alone has the answers and no one alone can help anyone change. So, with this open ecosystem approach that we take and global business services and our business transformation services organization, we're able to make sure that we bring our clients the best of everyone's capabilities. Whether it's our technology, partners, our services IBM's own technology capabilities, all in the mix, all orchestrated in service to our client's needs all with the goal of driving superior business outcomes for them. >> And helping those customers in any industry to accelerate their business transformation with those intelligent workloads and a very dynamic time. This is a topic we could keep talking about unfortunately, we are out of time but thank you both for stopping by and sharing with me what's going on with respect to intelligent workflows. How the incremental exponential value it's helping organizations to deliver and all the work that IBM is doing to enable its customers to be thrivers of tomorrow. We appreciate talking to you >> Thanks Lisa. >> Thank you >> For Paul Papas and Dominique Dubois I'm Lisa Martin. You're watching the CUBE's coverage of IBM Think the digital event experience. (gentle music)

(serene music) >> From around the globe, it's theCUBE, with digital coverage of IBM Think 2021, brought to you by IBM. >> Hey, welcome to theCUBE's coverage of IBM Think, the digital event experience. I'm your host, Lisa Martin, welcoming back to the program one of our CUBE alumn. Dominique Dubois joins me. She's the Global Strategy and Offerings Executive in the Business Transformation Services of IBM. Dominique, it's great to talk to you again. >> Hi Lisa, great to be with you today. >> So we're going to be talking about the theme of this interview. It's going to be the ROI of AI for business. We've been talking about AI emerging technologies for a long time now. We've also seen a massive change in the world. I'd love to talk to you about how organizations are adopting these emerging technologies to really help transform their businesses. And one of the things that you've talked about in the past, is that there's these different elements of AI for business. One of them is trust, right, the second is ease of use, and then there's this importance of data in all of these important emerging technologies that require so much data. How do those elements of AI come together to help IBM's clients be able to deliver the products and services that their customers are depending on? >> Yeah. Thank you, Lisa. So, when we look at AI and AI solutions with our clients, I think how that comes together is in the way in which we don't look at AI, or AI application solution, independently, right. We're looking at it and we're applying it within our customer's operations with respect to the work that it's going to do, with respect to the part of the operations and the workflow and the function that it sits in, right. So the idea around trust and ease of use and the data that can be leveraged in order to kind of create that AI and allow that AI to be self-learning and continue to add value really is fundamental around how we design and how we implement it within the workflow itself. And how we are working with the employees, with the actual humans, that are going to be touching that AI, right, to help them with new skills that are required to work with AI, to help them with what we call the new ways of working, right, 'cause it's that adoption that really is critical to get the use of AI in enterprises at scale. >> That adoption that you just mentioned, that's critical. That can be kind of table stakes. But what we've seen in the last year is that we've all had to pivot, multiple times, and be reactionary, or reactive, to so many things out of our control. I'm curious what you've seen in the last year in terms of the appetite for adoption on the employees front. Are they more willing to go, all right, we've got to change the way we do things, and it's probably going to be, some of these are going to be permanent? >> Yeah. Lisa, we've absolutely seen a huge rise in the adoption, right, or in the openness, the mindset. Let's just call it the mindset, right. It's more of an open mindset around the use of technology, the use of technology that might be AI backed or AI based, and the willingness to, and I will say, the willingness to try is really then what starts that journey of trust, right. And we're seeing that open up in spades. >> That is absolutely critical. It's just the willingness, being open-minded enough to go, all right, we've got to do this, so we've got to think about this. We don't really have any other choices here. Things are changing pretty quickly. So talk to me, in this last year of change, we've seen massive disruptions and some silver linings for sure, but I'd love to know what IBM and the state of Rhode Island have done together in its challenging time. >> Yeah, so, really interesting partnership that we started with the state of Rhode Island. Obviously, I think this year, there's been lots of things. One of them has been speed, so everything that we had to do has been with haste, right, with urgency. And that's no different than what we did with the state of Rhode Island. The governor there, Gina Raimondo, she took very swift action, right, when the pandemic started. And one of the actions she took was to partner with private firms, such as IBM and others, to really help get her economy back open. And that required a lot of things. One of them, as you mentioned, trust, right, was a major part of what the governor there needed with her citizenships, with her citizens, excuse me, in order to be able to open back up the economy, right. And so, a key pillar of her program, and with our partnership, was around the AI-backed solutions that we brought to the state of Rhode Island, so inclusive of contact tracing, inclusive of work that we had provided around AI-based analytics that allowed really the governor to speak to citizens with hard facts quickly, almost real time, right, and start to build that trust, but also competence, and competence was the main, one of the main things that was required during this pandemic time. And so, there were, through this, the AI-based solutions that we provided, which were, there were many pillars, we were able to help Rhode Island not only open their economy, but they were one of the only states that had their schools open in the fall, and as a parent, I always see that as a litmus, if you will, of how our state is doing, right. And so they opened in the fall, and they, as far as I know, have stayed open. And I think part of that was from the AI-based contact tracing, the AI-backed virtual, sorry, AI analytics, the analytics suite around infections and predictions and what we were able to provide the governor in order to make swift decisions and take action. >> That's really impressive. That's one of the challenges I've had living in California, is you (mumbles) you are going to be data-driven than actually be data-driven, but the technology, living in Silicon Valley, the technology is there to be able to facilitate that, yet there was such a disconnect, and I think that's, you bring up the word confidence, and customers need confidence, citizens need confidence, knowing that what we've seen in the last year has shown in a lot of examples that real time isn't a nice-to-have anymore, it's a requirement. I mean, this is clearly life-and-death situations. That's a great example of how a state came to IBM to partner and say, how can we actually leverage emerging technologies like AI to really and truly make real-time data-driven decisions that affect every single person in our state. >> Mm-hmm. Absolutely, absolutely! Really, really, I think, a great example of the public-private partnerships that are really popping up now, more and more so because of that sense of urgency and that need to build greater ecosystems to create better solutions. >> So that's a great example in healthcare, one that our government in public health, and I think everybody, it will resonate with everybody here, but you've also done some really interesting work that I want to talk about with AI-driven insights into supply chain. We've also seen massive changes to supply chain, and so many organizations having to figure out, whether they were brick-and-mortar only, changing that, or really leveraging technology to figure out where do we need to be distributing products and services, where do we need to be investing. Talk to me about Bestseller India, and what it is that you guys have done there with intelligent workflows to really help them transform their supply chain. >> Yeah, Bestseller India, really great, hugely successful fashion forward company in India, and that term fashion forward always is mind boggling to me because basically, these are clothing retailers who go from runway to store within a matter of days, couple of weeks, which always is just hugely impressive, right, just what goes into that. And when you think about what happens in a supply chain to be able to do that, the requirements around demand forecasting, what quantities, of what style, what design, to what stores, and you think about the India market, which is notoriously a difficult market, lots of micro-segments, and so very difficult to serve. And then you couple that what's been happening from an environmental sustainability perspective, right. I think every industry has been looking more about how they can be more environmentally sustainable, and the clothing industry is no different. And when, and there is a lot of impact, right, so a stat that really has hit home with me, right: 20% of all the clothes that are made globally goes unsold. That's all a lot of clothing, that's a lot of material, and that's a lot of environmental product that goes into creating it. And so, Bestseller India really took it to heart to become not only more environmentally sustainable, but to help itself and be digitally ready for things like the pandemic that ultimately hit. And they were in a really good position. And we worked with them to create something called Fabric AI. So Fabric AI is India's only, first and only, AI-based platform that drives their supply chain, so it drives not only their decisions on what design should they manufacture, but it also helps to improve the entire workflow of what we call design to store. And the AI-based solution is really revolutionary, right, within India, but I think it's pretty revolutionary globally, right, globally as well. And it delivered really big impact, so, reductions in the cost, right, 15-plus reduction in cost. It helped their top line, so they saw a 5% plus top line, but it also reduced their unsold inventory by 5% and more, right. They're continuing to focus on that environmental sustainability that I think is a really important part of their DNA, right, the Bestseller India's DNA. >> And it's one that so many companies and other industries can learn from. I was reading in that case study on Bestseller India on the IBM website that I think it was 40 liters of water to make a cotton shirt. And to your point about the percentage of clothing that actually goes unsold and ends up in landfills, you see there the opportunity for AI to unlock the visibility that companies in any industry need to determine what is the demand that we should be filling, where should it be distributed, where should we not be distributing things. And so I think it was an interesting kind of impetus that Bestseller India had about one of their retail lines or brands was dropping in revenue, but they had been able to apply this technology to other areas of the business and make a pretty big impact. >> Yeah, absolutely. So they had been been very fortunate to have 11 years of growth, right, in all of their brands. And then one of their brands kind of hit headwinds, but the CIO and head of supply chain at that time really had the foresight to be able to say, you know what, we're hitting a problem, one of our brands, but this really is indicative of a more systemic problem. And that problem was lack of transparency, lack of data-driven, predictive, and automation to be able to drive a more effective and efficient kind of supply chain in the end, so, really had the forethought to dive into that and fix it. >> Yeah. And now talk to me about IBM Garage Band, and how's that, how did that help in this particular case? >> Yeah. So, in order to do this, right, it was, they had no use of AI, no use of automation, at the time that we started this. And so to really not only design and build and execute on Fabric AI, but to actually focus on the adoption, right, of AI within the business, we really needed to bring together the leaders across many lines of businesses, IT and HR, right. And when you think about pulling all of these different units together, we used our IBM Garage approach, which really is, there are many attributes and many facets of the IBM Garage, but I think one of the great results of using our IBM Garage approach is being able to pull from across all those different businesses, all of which may have some different objectives, right, they're coming from a different lens, from a different space, and pulling them together around one focus mission, which for here was Fabric AI. And we were able to actually design and build this in less than six months, which I think is pretty dramatic and pretty incredible from a speed and acceleration perspective. But I think even more so was the adoption, was the way in which we had, through all of it, already been working with the employees 'cause it's really touched almost every part of Bestseller India, so really being able to work with them and all the employees to make sure that they were ready for these new ways of working, that they had the right skills, that they had the right perspective, and that it was going to be adopted. >> That, we, if we unpack that, if we had time, that can be a whole separate conversation because the important, the most important thing about adoption is the cultures of these different business units have to come together. You said you rolled this out in a very short period of time, but you also were taking the focus on the employees. They need to understand the value in it. why they should be adopting it. And changing that culture, that's a whole other separate conversation, but that's an, that's a very interesting and very challenging thing to do. I wish we had more time to talk about that one. >> Yeah. It really is an, that the approach of bringing everyone together, it makes it just very dynamic, which is what's needed when you have all of those different lenses coming together, so, yeah. >> It is, 'cause you get a little bit of thought diversity as well when we're using AI. Well, Dominic, thank you for joining me today. Talked to me about what you guys are doing with many different types of customers, how you're helping them to integrate emerging technologies to really transform their business and their culture. We appreciate your time. >> Well, thank you, Lisa. Thanks >> For Dominique Dubois, I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think, the digital event. (upbeat music)

>> Live from San Francisco, it's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to Moscone North, this is IBM Think 2019. You're watching theCUBE, I'm Stu Miniman, and we're going to dig into a segment talking about the cognitive enterprise. And helping me through that, I have one returning guest and one new guest to theCUBE, so furthest away from me, the returning guest is Jesus Mantas, who is the managing partner strategy for the digital platforms and innovation in the IBM Global business services. Jesus, welcome back. >> Thank you >> A little bit of a mouthful on the title. And Mani Dasgupta, CMO of the same group, the IBM Global business services. Thanks so much both for joining us. Alright, so cognitive enterprise. We're going to play a little game here first. Buzzword Bingo here, you know, can we talk about, what cognitive is, where you can't say AI, ML, platform, or enterprise in there. So do we start with the CMO first? >> Sure, I can go. Cognitive enterprise, those are two bing bing right there. What's your core competitive advantage, is what I would say. As a company, do you know why you exist? And once you get to that, how do you then take it to your clients, in a way that would help you grow, and sustain growth in the future. That truly is the future of a smart business, what we call the cognitive enterprise. >> So, Jesus, data is something we talk about a lot, at all the shows, we hear all the tropes about it's the new oil, the rocket fuel that are going to drive companies. You've got strategy and innovation in your title, I'd love you to build off as to where this cognitive enterprise fits in to those big trends of AI that we were talking about. Jinny was just on the keynote stage, talking about Watson, talking about all those pieces, so where does that fit with some of these megawaves that we're talking about. >> I think it's the way that we define this new, smarter organizations that use data to the fullest extent. And I think the way that we define it is, one is this reuse of data, your own data, the external data, and the way you aggregate it, the way that you apply AI or other things to use that. But the technology itself is a means to an end, it's not the end, so these organizations change the way the work flows, and they also train people to make sure that they understand how to operate in a world where they have more information and they can make better decisions with that data that they could before. All of that is what we are labeling. It's more than digital, it's more than AI. It is this concept of a cognitive enterprise. It's a smarter way to do what a company does. >> Okay, I'd love if you could give us a little bit of a compare, contrast. You know, the wave of big data was, there's massive amounts of data, we're going to allow the business practitioner, to be able to leverage that data. Was a great goal, unfortunately when we did research, at least half the time it wasn't really panning out there. Doesn't mean we didn't learn good things, and there weren't lots of great tools and business value generated out there. So, give us, you know, what's the same and what's different, as to this new wave. >> This is how do you make that data work for you, really. It is about, when you talk of data, you think of data that's out there, but 80% of the data today, is owned by you. And by you, I mean a business, right, you own your customers' data, you know your customer better than anybody else. So what do you really do with it? And we are at an inflection point right now, where these technologies that you just talked about, be it blockchain, be it internet of things, be it AI. You can truly bring the power of these technologies, to start making sense of that data that you own, and use it to create, what we call, your competitive advantage, your business platform. So, think about it, I can break it down. Would you just be a retailer of clothes? Or, would you be a fashion expert? And which one would have long-term success for you? Or if you think of a completely different industry, would you be an insurance provider, you sell insurance products, or would you be a risk management expert? That decision to be who you want to be, is really at the heart of the cognitive enterprise, and what we are proposing to the clients here. >> Alright, help frame for us your group, where that fits in. IBM sells hardware, software, has a huge services organization. What are the deliverables and the services and products involved in your group? >> Sure, we are the services organization of IBM, and one of the core reasons why we exist is to help our clients solve their toughest business problems. And so, if you think about it, you think about it as different puzzle pieces, but they don't quite always fit together. We exist to sharpen the edges, to sometimes round the edges, make it customized, make it right for you, so that at the end of the day, you're able to deliver results for your customers and be closer to them than ever before. >> The balance we look at in this multi-cloud world, it'd be nice if you have a little bit more standardization, but of course we know when we talk with businesses, every company is different and is challenging. So, where are the architectural engagements? What are the design criteria? Where is some of the hard work your group gets involved in? >> Yeah, I think we've been spending a lot of work and a lot of time on understanding how to get clients, most clients have done a lot of experimentation. But they rarely figured out how to get that experimentation into real production, at scale, with impact. So that's where we've spent a lot of the time. Fundamentally it has to do with, not only understanding Agile as a method, but being able to combine that with taking that journey all the way through to production, actually integrating with compliance requirements that, if you're in a regulated industry, you have to do, and do that in a way that doesn't become a digital island. I think what we have learned is, when companies see this big divide between, that's the legacy world and that's the new world you can never put those two together. So we came up with this concept of IBM Garage, which is the way in which our team, the services side, can actually bring it all together, and it gets massively enhanced and improved, with technology like containers, like Kubernetes, because now you can actually open up architectures, without reinventing them, and connect them with new technology, and do that synchronously. So you can basically be modernizing your legacy, you can be creating new innovation, in the form of new platforms, but you can do it at the same time, and as you do that through cycles, you also change the skillsets that you have in your company, because if you don't change that skillset, you're always going to have a problem scaling. That's what we do, that's what we help the clients do. >> Yeah, skillsets are so critical. Something we've been hearing over and over is, that whole digital transformation, this isn't some 18 to 24 month going to deploy some software, bring in a lot of consultants, they go and do it, hopefully it works and then they walk away. We're talking about much faster time frames, usually agile methodology, talk about skillset-changing. How do we help customers move fast and accelerate, because that's really the faster, faster, faster, it's just one of those driving things we hear. >> I was talking to one of the clients this morning, and what she said is, it's so helpful to have a framework, just to know where to start, and also to know, sometimes it's there in their mind, but they want to see it in front of them, how to break a problem down into smaller components, so that you can get to value faster, so we have actually a seven-step process, of the cognitive enterprise. So we start with, what is your core platform? In fact, Jesus coined this term, he calls it the digital Darwinism. Do you want to talk about the digital Darwinism, Jesus? >> Yeah, I think it reflects very well this urgency. In the analog world when most businesses are based on how clients choose you based on proximity, based on convenience, based on brand, based on trust, based on price. Even if you're not great at it, you have enough friction in an analog world, that the clients will keep coming. All of us and more of our things that we do every day, are in our phone, and they are digitally accessible, all of that friction disappears, and what happens then is, the people that are very good at something becomes, everybody goes to them, and the people that are not the best. I call it, they either thrive or they die very quickly. So in the digital world, being really good at something is a lot more important than in the analog world. You can survive being average in the analog world. Once you get to the digital world, it's transparent. Everyone will know, you're the best, you're not the best, and nobody would pick you if you're not the best, so it's really important to reconfigure yourself, and understand the trust and your brand, understand how digitally you translate what you are, and then make sure that your clients will keep choosing you in a digital world as much as they were choosing you in an analog world. >> I tell you, that resonates really well with me. The old line you used to hear is, if you want to get something done, give it to someone who's really busy, because they will usually figure out a way to do it. I spent a handful of years in my career doing operations, and what I did when I was in operations, when I talked to people in IT, is tell me next quarter and next year, do you think you're going to have more or less work more data to deal with, more thing thing, and of course the answer is, we all know that pace of change is the only thing that's constant in this industry. So, if I don't figure out how I automate, change, or get rid of the stuff that I'm not good at, we're just going to continue to be buried. Are there commonalities that you see, as success factors or how do you help measure, what are some key KPIs that customers walk out of, when they go through an engagement like this? >> Yeah, just carrying on from where Jesus left off, the second step is very close to what you were just saying. It's about the data and how you're using that data. So some of the key success factors would be, what is the output of it, and it's not in the proof of concept phase anymore. It is real-time, it is big, people are doing it at a grand scale. I think, Jesus, maybe we take it through the seven steps, and then the key success criteria comes right at you, right after that. So after you do the workflow, after you do the data for internal competitive advantage, we go to the next step, which is all about workflows. You want to talk a bit about that? >> Yeah, I think one of the advantages that artificial intelligence brings to companies is, the fact that you can now, I mean as a human, there is only so much data that you can ingest. There is a limit, and most businesses try to optimize what that is and how you make decisions. But, artificial intelligence becomes this aid that will read and summarize things for you. So now you can take into account, into workflows, massive amounts of information, to optimize, or even not having to do things you had to do before, at a scale that, as a human you cannot do. This idea of inserting AI into workflows is the real idea. I think we talk a lot about AI as a technology, but that's just a means to an end. The end is a workflow that is embedded with blockchain, with AI, with IOT, and then people that are trained to engage with those workflows, so you actually change the output. And I think that's the big idea, that step of, it is workflow that is embedded with AI, it's not just about the technology, it's the combination of the main industry, and the technology that actually creates that >> And where does it sit, right? Where does it sit? Your tech choices, the architecture choices are also important. And we joked about this, like if you really like Netflix, and you're watching something and something is coming up after three seconds, how does it know what you really like? But it does, but think about this. This wouldn't be possible on a 1950s television set. So you've got to think about what's your tech platform of choice, how do you upgrade that, and what's the architecture look like? >> I want to give you both the final word. Lots of users here at the show. What are you most excited about? Give us an insight on some of the conversations you've been having already. >> Amazing conversations so far. The really aha-moment was, people really like to share within their peer set, so this morning I was at the business exchange, and people were having conversations, but just to bounce it off someone, who is facing the same issues that you do, across different industries, was a really aha-moment, and we have the IBM Garage actually right behind us on the other side of Moscone. We set it up so that clients can come in, and unpack their problems, and we helped them think it through, used design thinking, help them think it through. We are hoping in the next couple of days, we get lots of brilliant ideas, come from the sessions like that, and really putting the customer at the core of what you want to do. >> It's a recurring theme of all the client conversations, this idea of, they all want the speed and agility of a startup at the strength and scale of an enterprise. That's what they're asking us, as the services organization of IBM, to do is, help us not just experiment, that was good before, not good enough now. Help us do that with agility, with new technologies, but we want it to mean something at scale, globally implement it, create an impact. And I think again, the way in which hybrid multi-cloud can play into that, the way in which IBM Garage can combine the legacy world with the new world and moving people into new platforms is a really exciting method and approach that is resonating a lot with clients. >> Really appreciate you both sharing updates and absolutely as you painted a picture, just as in 1950 we didn't have the tools to run Netflix, now in 2019, we have the tools for customers to be able to help build the cognitive enterprise and not only test but get into real-world deployment at a speed that was really unheralded before today. Thanks so much for joining. We'll be back with more coverage here from IBM Think 2019. I'm Stu Miniman, and thanks for watching theCUBE. (upbeat techno music)

>> Live from San Francisco. Its The Cube. Covering IBM Think 2019. Brought to you by, IBM. >> Okay, welcome back everyone, live here in The Cube here in San Francisco, exclusive coverage of IBM Think 2019. I'm John Furrier and Stu meeting next guest is Calline Sanchez, Vice President of IBM Systems Labs Services. New role for you, welcome back to the cube. >> Yes. Thank you for asking me back. >> So the new role, Vice President of the Systems Lab Services. Sounds super cool, sounds like you got a little lab in there, a little experimentation >> yeah think of it as a sandbox for geeks worldwide. And what that means is we enable high performance computing deployments as well as what we do with blockchain and also artificial intelligence. >> So its a play ground for people that want to do some big things, solve big problems, what are some of the things that you offer, just take us through how it works. Do I just jump in online, is it a physical location? What's it like ? In 2018 9000 plus engagements worldwide in 123 countries. So to net it out is, it's not necessarily a single lab or a single garage, we have multiple locations and skills worldwide to enable these engagements. >> How big is the organization roughly? Its over a thousand folks, consultants who are smart and capable. >> We had a conversation yesterday with Jamie Thomas, talking about, from a super computer stand point, now IBM's reclaimed the top couple of positions there and from a research stand point, David Floyer from our team has been talking for years about how HPC architectures are really going to permeate what happens in the industry and I think about distributed architectures, it all seems to go back to what people in the HPC environment lived in. You've got background in that, you worked for one of the big labs, explain how this has come from something some government lab used to do to something that now many more companies around the globe are leveraging. >> Before IBM I worked at Sandia National Laboratories and the reason why I chose to work with these awesome skills worldwide in lab services is that I wanted to be part of the cool group, so to speak. So they were doing work in deployments with Oak Ridge National Laboratories and also Laurence Lilvermore. So you'll hear (inaudible) with Laurence Livermore speak on stage about some of the relevance associated with high performance computing and why were number 1. So, to get to our question it's cool to be back online with what I could say, high performance computing deployment. We are the mechanics so to speak in this organization. Similar to what we do with formula 1, people who put on the tires, add the air and also enable the cars to move around. Well without them, guess what? Things don't move around. >> So you guys work on the high performance systems, you got quantum coming around the corner, you got AI front and center so you guys are like the hot shots. You come in, you build solutions with what's in the tool chest, if you will with IBM, is that right ? >> correct You're 100% correct. I will say it in my mind, we make things real. We deploy and implement strategic technologies worldwide for the benefit of our end users and we do that also with our partners. >> Give an example of an engagement you guys have had that's notable, that's worth sharing. >> Recently, this was a really exciting area a Smarter Cities with Kazakhstan. And so heres this independent city that works on basically AI for filming things whether its a security thing recognizing certain faces, deployments associated with weapons etc. And they were able to secure safety based on the film, films that they've taken, those assets. Now the other aspect is managing safer traffic. So even the president of Kazakhstan felt it was extremely relevant that we helped him deploy and he comes back to one of our European leaders saying, hey we need more of this and we want it to be extensive, we want to scale this opportunity. >> Talk about the philosophy's you guys are deploying because it sounds like its a... you said sandbox, when I think sandbox I think you do prototypes, I'm thinking about cool stuff, building solutions and that kind of brings this whole entrepreneurial creation mindset. Do you guys have like a design thinking methodology, is there things you're bringing to the table what else is involved besides the sandbox? >> You are correct. We have a very key component of design thinking. There's a CTO that reports to me directly who leads our overall design thinking and so that's a key component of what we do worldwide. Now as far as... We also enable incubation of technologies. So it's like what we intend to do with IBM Cube, What we intend to do with blockchain on system Z. So with these things we have garages worldwide to deploy or incubate the technology. >> What's the coolest thing you've worked on so far? Or the team's worked on? >> That's really hard to say 'cause there's so much. >> It's like picking a favorite child. >> Yeah, it's like I have way too many. So I was - >> You mention blockchain. I like blockchain. Blockchain, are you in healthcare, is it more, is there certain industries that are popping out for you guys? >> So healthcare is an example but I have seen it in the telecom area as well as other industries in general. So we have 11 industries in which we serve. >> How about AI? We're always trying to understand where customers are, how they're really moving things forward, to understand that that HPC architecture is a foundational layer for many customers to help deploy AI. Where are customers starting to make progress ? Give us some of the vibe you're feeling from customers out there. >> So its exciting with AI right now because we have Power Vision that allows us as any of us to actually exploit, utilize and play with, so to speak. So from my perspective that is what's nice, is that you can enable opportunities with the consumer market and learn. Similar to what we do with, and for instance, I am jumping around here, IMB Cube. Where users can actually become a user and start evaluating algorithms in order to enable this really amazing technology as in IB Cube. >> That was always the promise of big date, is that we should be able to leverage our data and get the average business user to do it. So it sounds like AI will continue that trend. >> Correct. So in prior rule, I talked to all of you about big data storage, right and replication. So now what's amazing about the conversations is that they've transcended. Its like, here you're looking to manage these large data warehouses, when, what do you do with the data? How's it monetized, how is it used in order to solution what's possible. >> What is the goal of the organization, next 6 months, year, what's the charter, what's your key performance indicators, how do you guys measure success, client engagements, onboarding people, what is the business objectives? >> So we look at the number of engagements, we also look at educational services worldwide for instance I will be in Cairo, Egypt next week to work on specific things that are going on in Mia in order to enable this next growth market so to speak. What in addition we do to measure ourselves, utilization, classic services organization view of the world. So we also evaluate what we can do with revenue, profit and our understanding of growth and we really believe the focus is these growth technologies. >> Is there a criteria if I wanted to get involved, just say I am a customer, prospect, wow, I really want to get into this design thinking, got these labs, coolest labs services, I want to play with the cutting edge technologies, how do I get involved? Is there a criteria open to all or how does it work? >> In addition to IBM Systems Labs Services, I have technical universities and we actually run technical universities worldwide for end users, clients as well as what we do with partners and IBMers. And this is important because we're able to then discuss, talk, collaborate with SME's across multiple areas of technology. So its a very good question and very important that I mention the technical universities. >> Are there certifications along that line? What are some of the hot skill sets that people are looking to learn about ? >> It circles right back to your last question, AI. With regards to how we certify folks as well as we, in essence, they get enough training in boot camps in order to get badges. >> So their certification, they just pass the touring test and then they're okay. >> correct. Well. (laughs) I don't know about the touring test so to speak. >> So is there a website on IBM.com, is there like a URL as in like labservices.ibm.com? >> I personally like the look at twitter where you can do a search on IBM Lab Services or Tech U. >> Tech U. And screening, how big is that focus, used a lot of video, is it collaborative tooling is it face to face, virtual, how do you guys do the training, all the above? >> Unfair, I was going to say all of the above. (laughs) It depends. (laughs) Giving that classic response, our favorite is video blogs. What we can do in social media with the YouTube channels etc. to get our opinions or our voice out with regards to key technologies. >> Well great, make sure you let us know what those channels are and we'll promote them, get that metadata out there, of course The Cube loves to collaborate. And thanks for coming on and sharing. >> I appreciate it and I will definitely take a sticker and put it on my laptop. >> Calline Sanchez, Vice President of the new IBM Systems Lab Services. A lot of opportunities to get in the worldwide sandbox and put the sluices together from blockchain to cutting edge AI. Your live coverage here at San Francisco at IBM Think, I'm (inaudible) stay with us for more coverage after this short break. (lively music)

>> Live from San Francisco, it's theCUBE covering the IBM Think 2019, brought to you by IBM. >> Welcome to the redone Moscone Center here in San Francisco. I'm Stu Miniman with my co-host, Dave Vellante. You're watching theCUBE's wall to wall coverage of IBM Think 2019. >> Happy to welcome back to the program, a CUBE alumni Stephanie Trunzo, who's the Global Head of IBM Cloud Garage. >> Stephanie, thanks for joining us again. >> Yeah, yeah, great to be here. >> Good to see you. >> So, you're one of the IBM boomerangs. >> So you've worked for IBM before >> Indeed, that's right. and you're back now. So tell us a little about, we've had some interviews about the IBM Cloud Garage but tell us about your role, what you are doing. >> So I was with IBM for 13 years. >> I left and started a company called Point Source. >> We were a business partner and we did a lot of work in mobile and digital transformation and I sold that company and I kind of thought, "Well what's next?" and this opportunity presented itself. >> And it's perfect because the Cloud Garage is taking a new approach to how we interact with our clients from an IBM perspective and a lot of it is very similar to what we did at Point Source which is take this digital transformation, digital agency approach to looking at business outcomes first. >> Yeah, so one of our favorite topics, you know, cause it's a buzzword for a few years but when we talked to companies, I mean it's real. >> A few years back it was like, right, >> I'm doing a mobile app. I'm doing things like that. >> Bring us inside. It's a spectrum and every company is different but tell us what digital transformation means to the costumers that you're working with and how IBM and the Cloud Garage is helping them along that journey >> Yeah >> You know it's funny that you say that. Digital transformation can feel like a buzzword, right? >> And I think it's because there's so many things that are broader than just digital about transformation. So we talk in the Cloud Garage about guided transformation as a way of helping our clients not only think about how do they take Legacy applications, how do they take a new modern approach to their technology? How do they apply digital to processes that they already have in place? >> But also think about culture, new ways of working. >> Those aren't necessarily digital topics but we think about it as a guided transformation approach, meaning, can we teach along the way? >> So we're not just helping our clients see rapid outcomes and develop MVPs but are we helping them also learn along the way? >> So clients are really looking for people to help them, coach them on making decisions, bring expertise to the table so that they also have sustainable frameworks and you know, they're skilling people up in these new modern technologies as well. >> So digital transformation, of course, it is the buzzword of the day but every CEO you talk to is trying to get digital transformation right. >> So, what do you think some of the common ways in which people are pursuing the right path of digital transformation and maybe the question is what's perhaps some of the mistakes that people are making? >> Yeah, yeah, so I think if we think about it from the side of some challenges or mistakes or you know maybe missteps that people have along the way, is probably not spending enough time focusing on users, you know, taking the time to take a real outside-in approach. What is necessary to interact with your clients differently? >> What are the new capabilities that you could be offering? But instead of just daydreaming about all of the cool stuff that technology could do, really grounding it in an understanding of what your users want, what your users need, the data that will help inform those decisions. So I think that that's one misstep, is that people get excited about new technologies and so often it's like a solution looking for a problem and so we try to help make sure that we're really identifying business outcomes and what are the things that they want to test, to learn more so it's real iterative learning. >> And I think something you said is also really important, getting it right. >> What does that mean? >> Getting it right, it's a journey, it's this evolution so I'm not sure you ever hit a stage were you say, "Ah-ha, I've done it." (laughing) But more you can identify milestones where you can learn and apply that learning to keep evolving. >> Yeah. Often when we talk to users, the long pole to tent that transformation is that application portfolio. There's some stuff that can move pretty quick and we've seen that happen in the industry but boy, there's some stuff that I shoved it into VM and I kept it running five or 10 years longer than I should. >> How are companies doing along that line? >> How do we help get, because that's one of the challenges for users is, "Ugh, I have to use this horrible application." >> Yes. >> "That just can't move this at the speed that we need it to." >> Yeah, so when I talk with clients about this, one of the things that we often discuss is that you look backwards at your legacy architectures or your systems, like, core systems that take forever to migrate and often they were architected with time, not intention, right? So one microdecision after another took place over 10, 15, 20 years and your architecture, it reflects that. So I think that Cloud offers this really unique opportunity to look at your architecture going forward with an intentional mindset. So, kind of resetting the clock on all those architectural decisions that have accrued over a time. And I think that one of the aspects of getting people moving, even on the sticky projects, is breaking it down to consumable pieces. So one of the things we do in the Cloud Garage is help our clients figure out how to identify an actionable MVP. A minimum viable product that we can show quick success against. They've got a hypothesis they need to test. Let's just take one application, let's take one work load, and let's move that and see what happens. So we're going to do that learning, we're going to test that hypothesis and that starts you down a path that's a little quicker. >> How do I engage with the IBM Cloud Garage? >> If I'm interested, how do I get started? Is it a set of services? How does it all work? >> Yes, so we have 15 locations globally so they're built for purpose, built for activity spaces around the world. You can come into one of those spaces and we can do a tour, we can do a framing workshop which helps identify business opportunities, that first piece, the first step in the journey and get you moving really quickly. >> We also will do a couple different kinds of models if one of those locations doesn't work for a client or isn't a good geographical location. We'll also do pop-up Garages where we'll go to the client and work directly onsite with them. >> We've heard a lot about how Cloud fits into a lot of the digital transformation? >> What I haven't heard as much, but I would expect IBM is doing is how AI fits into that activity. >> Absolutely. Yeah, so in fact, I kind of lump that all together, to be honest, because part of the journey is identifying, again, if you're starting from business outcomes, you're working back to the technology solution so maybe the objective is to, you're in insurance industry and you need to develop policy quotes quicker. In order to develop that solution, that might necessarily involve us figuring out how to not only get their core systems to clouds so that they can extract data faster but also get more intelligent about underwriting processes so they can get quotes out quickly. So all of those technologies come into our process almost as a subplot to the business outcome that we're trying to drive for our clients. >> How much do you get involved in helping with the data strategy specifically? I mean, we think of the innovation sandwich that is data plus machine intelligence plus Cloud for scale, how involved are you in the data strategy? >> Is that part of the initiative? >> Absolutely. In fact, I think there's a really great symbiotic relationship and we see this pattern really often where clients will come to us because they want to do some application modernization as a starting point. >> As soon as we get into that conversation, you realize you actually need to modernize your data strategy as well. So there's a cyclical relationship and either entry point ends up involving the other, so if you're modernizing your data, what are you doing with it? You're probably surfacing it in an application, now we're back into an application discussion again. So we do definitely get involved in that and in fact, we have several offerings that are specifically geared towards data and analytics. >> Stephanie, about how long is a typical engagement? >> Is there an ending point or are there follow-ups that you have to make sure you're tweaking ... >> It never ends. It never ends. Yeah. (laughing) >> So, a typical engagement is we would start with the framing workshop I mentioned to identify the business opportunity. Design thinking workshop to take that business opportunity. Take all these great big ideas that people come up with and funnel it into something that's actionable. >> So take all the big ideas then and turn it into the one that we're going to pursue. >> And then an MVP workshop where we co-create with the client so we're teaching those skills, pair programming and working directly with them and a product owner to develop an MVP, test that hypothesis. And at the end, sometimes the MVP is something that is ready to roll in to production. >> Sometimes the MVP is something that leads to a learning that produces a second MVP. >> A typical engagement, end to end, for us, is probably around three months to get that first MVP and that's a pretty rapid pace to go the whole way from, and sometimes it's just as short as three weeks. So it just depends on the scope. But to go the whole way from identifying an opportunity and to testing it and having a real results, it's pretty fast. >> Are there specific KPI's that the customer can usually have coming out of that? >> Three months. That's a great window. >> You used to think about these engagements that used to roll out. >> Three years! >> It used to be more. >> Yeah, exactly. >> Yeah, so we do look at... >> It really depends on what it is that they're trying to achieve. But we do define success criteria upfront. Those success criteria then are the things that we're testing as part of the MVP process. And so at the end, you will have actionable results. You'll have information that you've learned from as a result of developing that MVP. >> Sometimes it's something like understanding whether certain security protocols internally can be met with moving a workload in a certain way. Sometimes it's actually about user conversion. So it could be a marketing goal. >> It really depends on what they're trying to achieve. >> Where do you want to see this go? I mean, obviously, you're riding the waves. Digital transformation, AI, data. Where do you see this going over the next two to five years? >> Yeah. So I think some of the fascinating things that we've been doing and the Garage is a great place because so much innovation is happening there. Our clients are kind of testing boundaries. So we get to see a lot of the pretty far, out-there things. >> We've had projects with blockchain tracking fish in streams like a farm to table scenario but marry that with Watson image recognition so we can tell what the fish is and digitally imprint an ID on it. The sky's the limit on the kinds of things that we can come up with and build an MVP for. But I think some of the stuff that I would see in the next few years is really more around what I'll say ambient computing. We're adding additional senses, it's no longer just sight. Now we have so much voice. >> There's all of these other ways that we are interacting in context. >> And so I think we're going to keep exploring this kind of ambient notion of the things that are going on around us, whether that's data, artificial intelligence, and forming things, and then incorporating that into how technology interacts with consumers, users, et cetera. >> You're really taking the notion of digital transformation to the next level. >> That's right. >> Say, sensing. >> Exactly. >> Acting on behalf of the brand. >> That's right. >> Injecting intelligence layer- >> You got it. >> Into that all. >> Exactly. >> Nice. >> Yeah. >> Alright. Stephanie, there's tons of users here at the show. Are there customer stories that people get to hear throughout the week? >> What highlights? >> Yeah, definitely. So, we really are big on storytelling because it's the easiest way to understand these things. Some of these technologies are difficult, you know. They're intense concepts. >> So we have a lot of our clients come and share their stories onstage. There's a keynote on Thursday where we're talking about how to take an idea to MVP and we've got several clients joining us to talk about the Cloud Garage and how we actually impacted their business so, yeah. >> Alright. Well, Stephanie, we really appreciate all the updates on IBM Cloud Garage. >> Yeah, absolutely. >> Congratulations. >> Thanks for having me back! Five years. Great. >> Alright. Well, we always love to tell the stories of what's happening at all the big shows. Help extract the signal from the noise. From Dave Vallente, I'm Stu Miniman. >> We'll be right back. Thanks for watching theCube.

>> Live from Seattle, Washington it's theCUBE covering KubeCon and CloudNativeCon North America 2018 brought to you by Red Hat the Cloud Native Computing Foundation and it's Ecosystem Partners. >> Well, everyone welcome back to theCube's live coverage here in Seattle for KubeCon and CloudNativeCon 2018. I'm John Furrier with Stu Miniman. Three days of coverage around the Cloud Native growth, around the Ecosystem around open source, and the role of micro servers in the cloud. Our next guest is Roland Barcia who's the IBM Distinguished Engineer for IBM's Hybrid Cloud. Welcome to theCube. >> Thank you, glad to be here. >> Thanks for joining us. Being a Distinguished Engineer of IBM is a pretty big honor so congratulations. >> Thank you. >> it means you got technical chops so we can get down and dirty if we want to. >> Sure. >> I want to get your take on this because a lot of companies in IT are transforming and then that's been called digital transformation, it's happening and cloud has developed scale. And the wish list if you had the magic wand that could make things do better is actually happening. Supernetting's actually creating some goodness that if you had the magic wand, if I asked that question three years ago, if you had a magic wand what would an environment look like? Seamless operations around the cloud, so it's kind of happening. How are you guys positioned for this? Talk about the IBM cloud, what you're doing here, and how you see this cloud native market exploding. It's almost 8,000 people here up from 4,000 last year. >> Yeah, that's a great question I think. I work a lot with our enterprise clients. I'm part of what's called the IBM Cloud Garage, so I'm very customer facing. And often times, we're seeing that there is different paces of a journey. And so for example, I worked with a client that started building a cloud native application. They built about 60 micro services. And at the end of that, they were deploying it as one job which means they defeated the whole purpose of micro service architecture. And so what we really need to think about is an end to end journey. I think the developers are probably the more modern role in an enterprise, but we're starting to see modernization of an operations team for example, and adopting culture, and cutting down the walls of IT organizational groups into mixed squads, adopting something like a Spotify model. And I think a lot of the challenges in adopting kubernetes is really in cultural aspects and in enterprise. Does that make sense? >> Yeah. And because network guys are different than the app guys, and now they have policy knobs on kubernetes they can play with. Network guys love policy. >> Yeah, and they're fighting over ownership, right? >> Roland indeed. We look at that modernization, the application modernization really is that long home intent. And what we hear here is you need to be able to meet customers where they are. Sure, there's some stuff they're building shiny and new and have the developers, but enterprises have a lot of application and therefore there's a grand spectrum. What do you hear from customers? What's the easy part and where's the parts they're getting stuck? >> Yeah, so I think the easy part is writing the application. I think where they're getting stuck is really scaling it to the enterprise, doing the operations, doing the DevOps. I always tell people that a modernization journey might be better started by taking a certain class of applications like middleware where we have a WebSphere heritage from IBM, and saying why don't we take a look at containerizing that. We've built tools like Transformation Advisor that'll scan your WebSphere applications and tell you what do you need to change in that middleware application to make it behave well in a containerized platform. Then from there, you build your DevOps engine, your DevOps pipeline and you really start to get your operations teams going in delivering containers, delivering applications as containers. And then getting your policies and your standards in place. Then you can start opening up around innovation and start really driving towards building cloud native new applications in addition to that. >> One of those areas we've been talking about in the industry for decades is automation. The conversation's a little bit different these days. Maybe you can bring us up to speed about what's different than say it was earlier days. >> Yeah, I think IT organizations have always done a bit of automation. I think they write scripts, they automate builds. I think the mantra that I use is automate everything, right? Organizations need to really start to automate in a new way. How I deliver containers, but delivering the app is not enough. I need to automate all levels of testing in a modern way. Test driven development is big. At the IBM Cloud Garage, we have something we call the IBM Cloud Garage Method which really takes a set of practices like test driven development, pair programming, things out of lean startup, extreme programming, and really start to help enterprises adopt those practices. So I say why can't we automate end to end performance testing in the pipeline, and functional testing, and writing them early and in the beginning of projects? That way, as I'm deploying containers which are very dynamic, along with configuration, and along with policy you're testing it continuously. And I think that level of automation is what we need to get to. >> Talk about security as well 'cause security's one of those things where it's got to be baked in upfront. You got to think about it holistically. It's also now being pulled out of IT, it's more of a board function because the risk management is one hack you could get crushed. And so you got to have security. And the container there's a security boundary issue, so it's important. >> Last week we met with an insurance company. We did a workshop. And they walked us through all the compliant steps that they need to go through today. How they do it with traditional middleware and virtual machines and hardware and it was a very, what I'm going to say governance driven process. And so a lot of checks and balances, stop don't move forward, which is really the industry for developing and innovating is going the opposite way: self service and enabling. And there's a lot of risk with that. And so what we're really trying to do with technology is like Multicloud Manager, technology we have around multicluster, management is how do I do things like I want to check which clusters are Hipaa compliant and which ones are out. How do i force that policy? >> That's smart. >> Now that everything is software driven, software developed, there's an opportunity to really automate those checks. >> So your point automate everything. >> Yeah, I want to automate everything. >> Governance is a service. (laughing) >> Yeah, that's right. And actually, that can help get away from error prone human checks where they had all these tons of documents of all different policies they have to go through can now be automated in a seamless way. >> So compliance and governance could be a stumbling block or it can be just part of the software. That's what you're getting at here. >> That's right, that's what I'm getting at. I think the transition is look at it as an opportunity now that everything is software driven, use software disciplines that developers are used to in those security roles and those CSO roles, etc. >> So I want to ask you a question. So one of the things we're seeing obviously with the cloud is it's great for certain things, and then on premises it has latency issues. We saw Amazon essentially endorse this by saying RDS on VMware on premises. They announced Outpost had reinvent oh, latency. Things aren't moving into the cloud as fast. So you're going to see this hybrid environment. So hybrids, we get that, it's been around, check. No real discussion other than it's happening. The real trend is multicloud, right? >> That' right. >> And so multicloud is just a modern version of the word multi vendor about the client server days. So systems were a multi vendor man choice. This is a fundamental thing. It's not so much about multicloud as it is about choice. How do you guys see that? You are in an environment where you have a lot of customers who don't have one cloud, so this is a big upcoming trend in 2019. >> Most of our clients have at least five different clouds that they deal with, whether it be an IaaS, a PaaS, a SaaS base solution. What we're seeing as a trend is we talked about on premise and private and enterprise is I think is 80% of workloads are still in the data center. And so they want to build that private cloud environment as a transitionary point to public, but what we're seeing across the multicloud space is I'm going to say a new integration space. So if you really think 15 years ago, SOA and enterprise service bosses in a very centralized fashion, I think there's a new opportunity for integration across clouds and on-prem in a more decentralized way. So I think integration is kind of the next trend that we're seeing in this multicloud space because the new applications that we're seeing with cognitive data AI are mixing data sources from multiple clouds and on-prem and needing to control that in a hybrid control plane is key. >> It's funny, the industry always talks about these buzzwords, multicloud. If we're talkin' about multicloud, then it's a problem. The idea of infrastructure as code it's not even use the word multicloud. I mean, if you think about it, if you're programming the infrastructure and enabling the stuff under the covers, why even talk about cloud? It should be automated, so that's the future state, but in reality, that's kind of what enterprisers are tryin' to think about. >> They are, and I think it's a tension between innovation and moving fast and control, right? The enterprisers want to move fast, but they want to make sure that they don't break security protocol, that they don't break resiliency that they're maybe have used to with their existing customers and applications. I do think the challenge is how operations teams and management teams start to act like developers to get to that point. And I think that's part of the journey. >> Open source obviously a big part of this show, and that's open source, people contribute upstream It's great stuff. IBM is a big contributor, and it'll be even more when Red Hat gets into the mix. So upstream's great, but as you got 8,000 people here, you're startin' to see people talkin' about business issues, and other things. One of the downstream impacts of this conference being so open source centric is the IT equation and then just the classic developer. So you have multiple personas now kind of interacting. You got the developer, you got the IT architect, cloud architect pro whatever, and then you got the open source community members. Melting pot: good, challenges, thoughts? >> So I think it's so developers love that, right? I think from an enterprise perspective, there are issues. We're seeing a lot of our clients with our private cloud platform ask us to build out what's called air gapped environment which is how do I build up an open source style ecosystem within my enterprise. So things like getting an artifactory registry or a Docker registry or whatever type of registry where I get certified, open source packages in my enterprise that I've gone and done security vulnerability scans with, or that I've made sure that I look at every layer from the Linux kernel all the way up to whatever software is included. So what we're seeing is how do I open the aperture a bit, but do it in a more responsible fashion I think is the key. >> Yeah, and that's for stability, right? So Stu, one of things I've been talkin' about and want to get your thoughts on this role is that you got the cloud as a scalable system then one of the things that's being discussed in Silicon Valley now for the first time, we've been sitting on theCube for years, is the cloud's a system. It's just some architecture, it's network distributing, computing, art paradigm, all that computer science has been around for awhile, right? >> Yes, yes. >> So if you've been a systems person whether hardware or whatever, operating systems, you get cloud. But also you got the horizontal specialism of applications that are using machine learning and data and applications which is unique on top. So you have the collision of those two worlds. This is kind of a modern version of two worlds that we used to call systems and apps, but they're happening in a real dynamic way. What's your thoughts on this? Because you got the benefits of horizontally scalable cloud and you now have the ability to power that so we're seeing things like AI, which has been around for a long, long time, have a renaissance because now you got a lot of compute. >> That's right, and I think data is the real big challenge we're seeing with a lot of our clients. They have a lot of it in their enterprise, they don't want to unlock it all right away. We recently did what's called IBM Cloud Private for Data, in which we brought in a set of technologies around our AI, our Watson core to really start leveraging some of those tools in a private manner. And then what we're seeing is a lot of applications that are moving to the cloud have a data drag. It might start as something as simple as caching data and no SQL databases, but very quickly they want to learn a lot more about that data. So we're seeing that mix happening all the time. >> We've had it, we've had someone say in theCube ML's the new SQL. >> Yeah. >> Because you're starting to see SQL abstraction layers are a beautiful thing if they're connected. So I want to get your thoughts on this because everyone's kind of in discovery mode right now. Learning, there's a lot of education. I mean, we're talkin' about real, big time players. Architects are becoming cloud architects. Sysadmins are becoming operators for large infrastructure scale. You see network guys goin' wait a minute, if I don't get on the new network programmable model I'm going to be irrelevant. So a lot of persona changes in the enterprise. How are you guys handling that with customers? I know you guys have the expert program. Comment on that dynamic. >> I think what we're doing is we use the IBM Cloud Garage to bring in practices like the Spotify method where we start pushing things like >> What's the Spotify method? >> Spotify method is a way of doing kind of development where rather than having your disciplines of architects, development, operations, we're now splitting teams, let's say functionally, where I have mixed disciplines in a squad and maybe saying hey, the person building the account team has an SRE, an ops guy, a dev guy all within their same squad. And then maybe have guilds across disciplines, right? And so what we do at the Garage is we bring 'em in to one of the Garages. We have four team locations worldwide. Maybe do your first project. Then we build enablement and education around that, bring it back to the enterprise and start making that viral. And that's what we're doing in the IBM Cloud Garage. >> So not a monolithic thing, breakin' it down, integrating multiple disciplines, kind of like a playlist. >> Yeah, that's right. And I think the best way to do it is to practice it, right, in action. Let's pick a project rather than talking about it. >> If I had to ask you in 2019, what is the IT investment going to look like with kubernetes impact? How does kubernetes change the IT priorities and investments for an enterprise? >> Yeah, so I think you'll see kubernetes become a vehicle for enterprises to deliver content. So one, the whole area around helm and other package managers as a way to bundle software. I think as people build more clusters, multicluster management is going to be the big trend of how do I deal now with clusters that I have in public cloud and private cloud, all different clouds? And I think that integration layer that I talked about where what does modern integration look like across kubernetes based applications. >> Someone asked me last week at Reinvent hey, can't we just automate kubernetes? And then I was like, well it's kind of automated now. What's your thoughts on that? >> So I think when someone asks a question what does it mean to automate that I think the kubernetes stack really sits on top of IaaS infrastructure. And so for example, our IBM Cloud Private you can run it on zLinux or Power. And we have a lot of IBM folks that run multi architecture clusters. And therefore, they still need a level of automating how I create clusters over IaaS and there's technologies like Terraform and others that help with that, but then there's also automating standing up the DevOps stack, automating deployment of the applications over that stack. And I think they mean automating how I use kubernetes in an environment. >> So 2019, the year of programmability and automation creating goodness around kubernetes. >> Yeah, absolutely, >> Roland, thanks for comin' >> Thank you, it was great. >> on theCube, thanks for that smart insight. TheCube coverage here, day two winding down. We got day three tomorrow. This is theCube covering KubeCon and CloudNativeCon 2018. We'll be right back with more day two coverage after this short break. (happy electronic music)

(upbeat music) >> Announcer: Live from Las Vegas it's The Cube, covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. This is The Cube, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host Peter Burris, Moe Abdulla is here. He's the vice president of Cloud Garage and Solution Architecture Hybrid Cloud for IBM and Tim Davis is here, Data Analytics and Cloud Architecture Group and Services Center of Excellence IBM. Gentlemen, welcome to The Cube. >> Glad to be here. >> Thanks for having us. >> Moe, Garage, Cloud Garage, I'm picturing drills and wrenches, what's the story with Garage? Bring that home for us. >> (laughs) I wish it was that type of a garage. My bill would go down for sure. No, the garage is playing on the theme of the start-up, the idea of how do you bring new ideas and innovate on them, but for the enterprises. So what two people can do with pizza and innovate, how do you bring that to a larger concept. That's what The Garage is really about. >> Alright and Tim, talk about your role. >> Yeah, I lead the data and analytics field team and so we're really focused on helping companies do digital transformation and really drive digital and analytics, data, into their businesses to get better business value, accelerate time to value. >> Awesome, so we're going to get into it. You guys both have written books. We're going to get into the Field Guide and we're going to get into the Cloud Adoption Playbook, but Peter I want you to jump in here because I know you got to run, so get your questions in and then I'll take over. >> Sure I think so obvious question number one is, one of the biggest challenges we've had in analytics over the past couple of years is we had to get really good at the infrastructure and really good at the software and really good at this and really good at that and there were a lot of pilot failures because if you succeeded at one you might not have succeeded at the other. The Garage sounds like it's time to value based. Is that the right way to think about this? And what are you guys together doing to drive time to value, facilitate adoption, and get to the changes, the outcomes that the business really wants? >> So Tim you want to start? >> Yeah I can start because Moe leads the overall Garage and within the Garage we have something called the Data First Methodology where we're really driving a direct engagement with the clients where we help them develop a data strategy because most clients when they do digital transformation or really go after data, they're taking kind of a legacy approach. They're building these big monolithic data warehouses, they're doing big master data management programs and what we're really trying to do is change the paradigm and so we connect with the Data First Methodology through the Garage to get to a data strategy that's connected to the business outcome because it's what data and analytics do you need to successfully achieve what you're trying to do as a business. A lot of this is digital transformation which means you're not only changing what you're doing from a data warehouse to a data lake, but you're also accelerating the data because now we have to get into the time domain of a customer, or your customer where they may be consuming things digitally and so they're at a website, they're moving into a bank branch, they go into a social media site, maybe they're being contacted by a fintech. You've got to retain an maintain a digital relationship and that's the key. >> And The Garage itself is really playing on the same core value of it's not the big beating the small anymore, it's the fast beating the slow and so when you think of the fast beating the slow, how do you achieve fast? You really do that by three ways. So The Garage says the first way to achieve fast is to break down the problem into smaller chunks, also known as MVPs or minimum viable product. So you take a very complex problem that people are talking and over-talking and over engineering, and you really bring it down to something that has a client value, user-centered. So bring the discipline from the business side, the operation side, the developers, and we mush them together to center that. That's one way to do fast. The second way-- >> By the way, I did, worked with a client. They started calling it minimum viable outcomes. >> Yes, minimum viable outcomes means what product and there's a lot of types of these minimum viable to achieve, we're talking about four weeks, six weeks, and so on and so forth. The story of American Airlines was taking all of their kiosk systems for example and really changing them both in terms of the types of services they can deliver, so now you can recheck your flights, et cetera, within six week periods and you really, that's fast, and doing it in one terminal and then moving to others. The second way you do fast is by understanding that the change is not just technology. The change is culture, process, and so on. So when you come to The Garage, it's not like the mechanic style garage where you are sitting in the waiting room and the mechanic is fixing your car. Not at all. You really have some sort of mechanical skills and you're in there with me. That's called pair programming. That's called test-driven, these types of techniques and methodologies are proven in the industry. So Tim will sit right next to me and we'll code together. By the time Tim goes back to his company, he's now an expert on how to do it. So fast is achieving the cultural transformation as well as this minimum viable aspect. >> Hands on, and you guys are actually learning from each in that experience, aren't you? >> Absolutely. >> Oh yeah. >> And then sharing, yeah. >> I would also say I would think that there's one more thing for both of you guys and that is increasingly as business acknowledges that data is an asset unlike traditional systems approaches where we built a siloed application, this server, that database manager, this data model, that application and then we do some integration at some point in time, when you start with this garage approach, data-centric approach, figure out how that works, now you have an asset that can be reused in a lot of new and interesting ways. Does that also factor into this from a speed aspect? >> Yeah it does. And this is a key part. We have something called data science experience now and we're really driving pilots through The Garage, through the data first method to get that rapid engagement and the goal is to do sprints, to do 12 to 20 week kind of sprints where we actually produce a business outcome that you show to the business and then you put it into production and we're actually developing algorithms and other things as we go that are part of the analytic result and that's kind of the key and behind that, you know the analytic result is really the, kind of the icing on the cake and the business value where you connect, but there's a whole foundation underneath that of data and that's why we do a data topology and the data topology has kind of replaced the data lake, replaces all that modeling because now we can have a data topology that spans on premise, private cloud, and public cloud and we can drive an integrated strategy with the governance program over that to actually support the data analytics that you're trying to drive and that's how we get at that. >> But that topology's got to tie back to the attributes of the data, right? Not the infrastructure that's associated with it. >> It does and the idea of the topology is you may have an existing warehouse. That becomes a zone in the topology, so we aren't really ripping and replacing, we're augmenting, you know, so we may augment an on premise warehouse that may sit in a relational database technology with a Hadoop environment that we can spin up in the cloud very rapidly and then the data science applications and so we can have a discovery zone as well as the traditional structured reporting and the level of data quality can be mixed. You may do analytic discovery against raw data versus where you have highly processed data where we have extreme data quality for regulatory reporting. >> Compared to a god box where everything goes through some pipe into that box. >> And you put in on later. >> Yes. >> Well and this is the, when Hadoop came out, right, people thought they were going to dump all their data into Hadoop and something beautiful was going to happen right? And what happened is everybody created a lot of data swamps out there. >> Something really ugly happened. >> Right, right, it's just a pile of data. >> Well they ended up with a cheaper data warehouse. >> But it's not because that data warehouse was structured, it has-- >> Dave: Yeah and data quality. >> All the data modeling, but all that stuff took massive amounts of time. When you just dump it into a Hadoop environment you have no structure, you have to discover the structures so we're really doing all the things we used to do with data warehousing only we're doing it in incremental, agile, faster method where you can also get access to the data all the way through it. >> Yeah that makes sense. >> You know it's not like we will serve new wine before its time, you know you can. >> Yeah, yeah, yeah, yeah. >> You know, now you can eat the grapes, you can drink the wine as it's fermenting, and you can-- >> No wrong or right, just throw it in and figure it out. >> There's an image that Tim chose that the idea of a data lake is this organized library with books, but the reality is a library with all the books dumped in the middle and go find the book that you want. >> Peter: And no Dewey Decimal. >> And, exactly. And if you want to pick on the idea that you had earlier, when you look at that type of a solution, the squad structure is changing. To solve that particular problem you no longer just have your data people on one side. You have a data person, you have the business person that's trying to distill it, you have the developer, you have the operator, so the concept of DevOps to try and synchronize between these two players is now really evolved and this is the first time you're hearing it, right at The Cube. It's the Biz Data DevOps. That's the new way we actually start to tell this. >> Dave: Explain that, explain that to us. >> Very simple. It starts with business requirements. So the business reflects the user and the consumer and they come with not just generics, they come with very specific requirements that then automatically and immediately says what are the most valuable data sources I need either from my enterprise or externally? Because the minute I understand those requirements and the persistence of those requirements, I'm now shaping the way the solution has to be implemented. Data first, not data as an afterthought. That's why we call it the data first method. The developers then, when they're building the cloud infrastructure, they really understand the type of resilience, the type of compliance, the type of meshing that you need to do and they're doing it from the outside. And because of the fact that they're dealing with data, the operation people automatically understand that they have to deal with the right to recovery and so on and so forth. So now we're having this. >> Makes sense. You're not throwing it over the wall. >> Exactly. >> That's where the DevOps piece comes in. >> And you're also understanding the velocity of data, through the enterprise as well as the gaps that you have as an enterprise because you're, when you go into a digital world you have to accumulate a lot more data and then you have to be able to match that and you have to be able to do identity resolution to get to a customer to understand all the dimensions of it. >> Well in the digital world, data is the core, so and it's interesting what you were saying Moe about essentially the line of business identifying the data sources because they're the ones who know how data affects monetization. >> Yes. >> Inder Paul Mendari, when he took over as IBM Chief Data Officer, said you must from partnerships with the line of business in order to understand how to monetize, how data contributes to the monetization and your DevOps metaphor is very important because everybody is sort of on the same page is the idea right? >> That's right. >> And there's a transformation here because we're working very close with Inder Paul's team and the emergence of a Chief Data Officer in many enterprises and we actually kind of had a program that we still have going from last year which is kind of the Chief Data Officer success program where you can help get at this because the classic IT structure has kind of started to fail because it's not data oriented, it's technology oriented, so by getting to a data oriented organization and having a elevated Chief Data Officer, you can get aligned with the line of business, really get your hands on the data and we prescribe the data topology, which is actually the back cover of that book, shows an example of one, because that's the new center of the universe. The technologies can change, this data can live on premise or in the cloud, but the topology should only change when your business changes-- (drowned out) >> This is hugely important so I want to pick up on something Ginny Rometti was talking about yesterday was incumbent disruptors. And when I heard that I'm like, come on no way. You know, instant skeptic. >> Tim: And that's what, that's what it is. >> Right and so then I started-- >> Moe: Wait, wait, discover. >> To think about it and you guys, what you're describing is how you take somebody, a company, who's been organized around human expertise and other physical assets for years, decades, maybe hundreds of years and transform them into a data oriented company-- >> Tim: Exactly. >> Where data is the core asset and human expertise is surrounding that data and learn to say look, it's not an, most data's in silos. You're busting down those silos. >> Exactly. >> And giving the prescription to do that. >> Exactly, yeah exactly. >> I think that's what Tim actually said this very, you heard us use the word re-prescriptive. You heard us use the word methodology, data first method or The Garage method and what we're really starting to see is these patterns from enterprises. You know, what works for a startup does not necessarily translate easily for an enterprise. You have to make it work in the context of the existing baggage, the existing processes, the existing culture. >> Customer expectations. >> Expectations, the scale, all of those type dimensions. So this particular notion of a prescription is we're taking the experiences from Hertz, Marriott, American Airlines, RVs, all of these clients that really have made that leap and got the value and essentially started to put it in the simple framework, seven elements to those frameworks, and that's in the adoption, yeah. >> You're talking this, right? >> Yeah. >> So we got two documents here, the Cloud Adoption Playbook, which Moe you authored, co-authored. >> Moe: With Tim's help. >> Tim as well and then this Field Guide, the IBM Data and Analytic Strategy Field Guide that Tim you also contributed to this right? >> Yeah, I wrote some of it yeah. >> Which augments the book, so I'll give you the description of it too. >> Well I love the hybrid cloud data topology in the back. >> That's an example of a topology on the back. >> So that's kind of cool. But go ahead, let's talk about these. >> So if you look at the cover of that book and piece of art, very well drawn. That's right. You will see that there are seven elements. You start to see architecture, you start to see culture and organization, you start to see methodology, you start to see all of these different components. >> Dave: Governance, management, security, emerging tech. >> That's right, that really are important in any type of transformation. And then when you look at the data piece, that's a way of taking that data and applying all of these dimensions, so when a client comes forward and says, "Look, I'm having a data challenge "in the sense of how do I transform access, "how do I share data, how to I monetize?," we start to take them through all of these dimensions and what we've been able to do is to go back to our starting comment, accelerate the transformation, sorry. >> And the real engagement that we're getting pulled into now in many cases and getting pulled right up the executive chains at these companies is data strategy because this is kind of the core, you've got to, so many companies have a business strategy, very good business strategies, but then you ask for their data strategy, they show you some kind of block diagram architecture or they show you a bunch of servers and the data center. You know, that's not a strategy. The data strategy really gets at the sources and consumption, velocity of data, and gaps in the data that you need to achieve your business outcome. And so by developing a data strategy, this opens up the patterns and the things that we talk to. So now we look at data security, we look at data management, we look at governance, we look at all the aspects of it to actually lay this out. And another thought here, the other transformation is in data warehousing, we've been doing this for the past, some of us longer than others, 20 or 30 years, right? And our whole thing then was we're going to align the silos by dumping all the data into this big data warehouse. That is really not the path to go because these things became like giant dinosaurs, big monolithic difficult to change. The data lake concept is you leave the data where it is and you establish a governance and management process over top of it and then you augment it with things like cloud, like Hadoop, like other things where we can rapidly spin up and we're taking advantage of things like object stores and advanced infrastructures and this is really where Moe and I connect with our IBM Club private platforms, with our data capabilities, because we can now put together managed solutions for some of these major enterprises and even show them the road map and that's really that road map. >> It's critical in that transformation. Last word, Moe. >> Yeah, so to me I think the exciting thing about this year, versus when we spoke last year, is the maturity curve. You asked me this last year, you said, "Moe where are we on the maturity curve of adoption?" And I think the fact that we're talking today about data strategies and so on is a reflection of how people have matured. >> Making progress. >> Earlier on, they really start to think about experimenting with ideas. We're now starting to see them access detailed deep information about approaches and methodologies to do it and the key word for us this year was not about experimentation or trial, it's about acceleration. >> Exactly. >> Because they've proven it in that garage fashion in small places, now I want to do it in the American Airlines scale, I want to do it at the global scale. >> Exactly. >> And I want, so acceleration is the key theme of what we're trying to do here. >> What a change from 15, 20 years ago when the deep data warehouse was the single version of the truth. It was like snake swallowing a basketball. >> Tim: Yeah exactly, that's a good analogy. >> And you had a handful of people who actually knew how to get in there and you had this huge asynchronous process to get insights out. Now you guys have a very important, in a year you've made a ton of progress, yea >> It's democratization of data. Everyone should, yeah. >> So guys, really exciting, I love the enthusiasm. Congratulations. A lot more work to do, a lot more companies to affect, so we'll be watching. Thank you. >> Thank you so much. >> Thank you very much. >> And make sure you read our book. (Tim laughs) >> Yeah definitely, read these books. >> They'll be a quiz after. >> Cloud Adoption Playbook and IBM Data and Analytic Strategy Field Guide. Where can you get these? I presume on your website? >> On Amazon, you can get these on Amazon. >> Oh you get them on Amazon, great. Okay, good. >> Thank you very much. >> Thanks guys, appreciate it. >> Alright, thank you. >> Keep it right there everybody, this is The Cube. We're live from IBM Think 2018 and we'll be right back. (upbeat electronic music)

(electronic music) >> Announcer: Live from Las Vegas, it's theCUBE, covering Interconnect 2017. Brought to you by IBM. >> Welcome back to Interconnect 2017 everybody, this is theCUBE, the leader in live tech coverage. Russ Kennedy is here. He's the Vice President of Product Strategy and Customer Success at IBM. Russ, good to see you again. >> Good to see you, Dave. >> So Russ, of course, you and I have known each other for years. >> Yes. >> From the Cleversafe. You guys came in from the Cleversafe acquisition-- >> Right. >> A phenomenal move for you guys. Great exit, awesome move for IBM. >> Yep. >> So we're now well over a year in. >> Umm-hmm. >> So the integration, you've been long past Blue Washing (laughing) you're now in, and you're integrating with other services. >> Right. >> You're embedded in the cloud, still selling on prem-- >> Right. >> Hybrid messaging, so give us the update. What's happening at Interconnect? >> Sure, well, thanks for having me on. >> Dave: You're welcome! >> It's great to see you again. And you're absolutely right. Things have been moving very rapidly since the acquisition. It's about 15 months since we've been part of IBM now. And we still have a very robust on prem business that was our heritage in the Cleversafe days, but now that we're part of IBM we're well entrenched in the cloud. We've got cloud services, object storage services in the cloud, and a variety of different flavors there. We announced a couple of new things this week that I think are very exciting for clients. I'm sure we'll get into that as we go through this discussion. And we have a hybrid combination, so if clients want to have some of their data on prem, some of their data in the cloud, we offer that hybridity as well. And I think that's very exciting for enterprises that are looking to figure out where their workloads run best, and be able to have that flexibility to move things back and forth if they need to. >> We were talking off-camera, I remember I was saying to you, Cleversafe was one of Wicky-Bon's first clients-- >> Umm-hmm. >> Back when we were tiny-- >> Umm-hmm. >> And you guys were just getting started and-- >> Right. >> I remember we were working with you guys, and sort of talking about some positioning and things like that, and I remember saying, Look, it's going to cloud! >> Russ: Right, right, right. >> It's all going there. And at the time, it was like, you guys were saying, Yeah, we think so, too, but it's just not here yet (laughing). >> Right. >> (laughing) And we're a small startup you got-- >> Yeah. >> And so, you have the conviction of belief that it's going to happen, but at the same time you have to survive-- >> Sure, sure! >> And you got investors and it's... >> Yep. >> But the growth of unstructured data and then all of a sudden the combination of that, plus cloud happened. And then boom that was a huge tailwind. >> Right. >> Talk about that. >> Right, right, no, you're exactly right. In the early days it was very, very difficult to get people to understand the value of object storage and understand the value of cloud. And we were out there pioneering discussions around this concept, but we knew that the wave was going to happen. The growth of unstructured data was already obvious. You had music services, you had video services, everything going online. People wanting to distribute information and share information, and so you knew that the wave was coming. It took a little bit longer than I think everybody thought. I think certainly success in other public cloud services like Amazon and Microsoft kind of helped drove that as well. But we were certainly there with leading technology, and as soon as people started to realize the benefits of object storage for storing large, unstructured data objects, it just took off. >> Well, you know, too, the cloud progression was really interesting. >> Umm-hmm. >> You're right. Amazon sort of popularized it. >> Yep. >> And then the downturn in 2007, 2008, caused a lot of CFOs to say, Hey, let's try this cloud thing. >> Exactly. >> And then they came out of it-- >> Russ: Exactly, yep. >> And said, Hey, this cloud thing's actually really cool. >> Russ: Umm-hmm, umm-hmm. >> Now, let's operationalize it (laughing). >> Right. >> And go mainstream. And so, and now you've got this big discussion going on around data value, right. >> Russ: Of course. >> Everybody's talking about the value of data and what it means-- >> Russ: Sure, sure. >> And moving conversations up the stack away from sort of bit slicing and-- >> Right, right (laughing). >> Object stores-- >> Yeah, exactly. >> And ups the data value. >> You're exactly right. >> What are you seeing here? >> I think that's another new interesting area that we're getting into. It's the value of information, and I think what's driven that is the tools and the technologies that are now available to analyze data in variety of formats, right. The whole analysis and analytics capability that exist in the marketplace today is giving organizations a reason to take a look at their data, and to leverage their data, and to use their data, to drive business outcomes, to be more competitive, to be more agile, to be more flexible. So they're using the information. They have tools now that can give them insight into all kinds of things, their own data, external sources of data, new data that's being generated through applications and those kinds of things. All that can come together and analysis can go on top of it, to give people really quick insights into how to drive their business. And I think that's the really exciting part about being part of IBM's cloud because IBM has all those tools. >> We've been having conversations now for... It's well over several months and going into years-- >> Umm-hmm. >> Where the CIO's not so much thinking about storage, and certainly not worried about the media. >> Right. >> But definitely talking about what services can I tap to enhance the value of my data? >> Sure. >> How do I monetize, not necessarily data itself, but how does data contribute to the monetization of my company? >> Umm-hmm. >> And you guys fit into that. >> Sure. >> So maybe talk about that a little bit-- >> Sure, well, we talked to clients all the time about the value of the data, regardless of what industry you're in, financial services, healthcare, manufacturing, all of those types of organizations have information and it's information that can help them be more productive. It can help them be more agile. It can help them win in the marketplace. All they need to do is open it up and use it, leverage it, analyze it, look at it, look at it from a variety of different sources, and it can help them do a lot of things more efficiently, so we talked to clients all the time about the value of data. Storage is certainly something that makes that value realizable, and it's the interfaces between applications and tools that make the data usable. And we open that up to clients with our storage system very easily, whether it's on prem or it's in the cloud, and that's what they like. Now, we heard David Kenny on stage the other day-- >> Umm-hmm. >> He announced IBM Cloud Object Storage Flex-- >> Yes. >> And he said, We do have a marketing department, and yes, they did come up with that name. (laughing) A funny tongue-in-cheek moment. >> Yes, yes. >> But talk about Flex. What is it? And why is it relevant? >> So a lot of clients that we've engaged with recently have talked about... They love the cloud model. They certainly love the simplicity and the ease of growth and those kinds of things that cloud gives them. But they're a little confused about the pricing and they're worrying about whether they're paying too much for the workload that they have in the cloud. So we designed Flex as a way to look at storing data. First of all, it's a very low cost entry point for storing the data. And then it's designed for data where the workload may be unpredictable. It may be cold for some period of time, and then it may become very active for a period of time, and then go back to being cold again. What Flex does is it ensures that you don't overpay when you actually utilize that data, when it's very active, very hot, maybe you're running some sort of analytics against that data. Maybe it's some sort of cognitive recognition analytics process that you're running against the data. It makes it very usable, but yet, you're not paying too much to access that data. So Flex is designed for those kinds of uneven, varied workloads, or workloads where it's very cold for some period of time and very hot. Traditional tiers are designed for hot workloads, mid-level workloads, and very cold workloads. Flex actually covers the whole gamut, and it ensures that you're not paying too much for storing and using your data. >> So that's a problem that people have because-- >> Umm-hmm. >> They don't really understand how to optimize cost-- >> Right, right. >> If they don't understand their workloads. >> Right. >> They get the cloud bill at the end of the month. They go, Whoa-- >> Yep, exactly. >> What just happened? >> Exactly. >> It's complicated for people, there's a lot of times it's different APIs for different services. >> Russ: Sure, sure. >> So talk a little bit more about how customers... How you see customers deploying that and what it's going to mean to... >> Sure. >> What's the business impact? >> Yeah, no it's a great question. So Flex, first of all, you only have to remember four numbers. There's a number to store the data, a cost to store the data, a cost to retrieve the data, a cost for what we call Class A Operations, which are write operations and then Class B, which are read operations. Four numbers you have to remember. You know that you're not going to pay over a certain amount, regardless of how often you use the data, so it's very simple for people to understand. It's one set of numbers. It doesn't matter what the workload is. You know you're not going to be overcharged for that workload. >> You set a threshold. >> Exactly, you set a cap, you set a threshold. >> Yeah. >> And you're not going to pay over that amount, so it's very simple for them to utilize. Then, so they start to use it, and let's say that over a six-month period of time they start to understand their workload, and they know it's a very active workload. They can then change that data into maybe our standard tier, and actually even save more money because it's consistent, it's predictable when it's active, they'll actually lower their cost. And we're very open with clients about that because we want to take away that complexity of using the storage, and certainly the complexity of billing, like you talked about. And give clients a very easy transition into the cloud, and make sure that they can use it and leverage it the way they need to be more productive. >> So the key to that is transparency. >> Russ: Yes, absolutely. >> And control. And that's an elastic sort of dial-up, dial-down-- >> Absolutely. >> As you need it. >> Russ: Very, very much so. Yes, definitely. >> I wanted to ask you, so we've been obviously watching... IBM made the SoftLayer acquisition, it was like, Okay, we're going to buy this bare metal hosting company. >> Umm-hmm. >> And then they bring in Bluemix, and then they start bringing in applications. >> Yes, yes. >> And then all of a sudden it's like, IBM does what IBM does (laughing), and boom! Now, you've got this machine going. >> Yes. >> And so, several acquisitions that are relevant here, Aspera. >> Yes. >> Clearleap. >> Yes. >> UStream fits there because we know Ustream because we broadcast on UStream-- >> Russ: Yes, yes, uh-huh. >> And, of course, Cleversafe. >> Umm-hmm. >> Are you beginning to leverage those acquisitions and potentially others through Bluemix-- >> Yes. >> To create services and new value for clients? >> Yeah, so we're fully integrated with all those technologies, right, the object storage system through our APIs. Every single one of those technologies can leverage and utilize the storage system underneath. I'll give you an example, Aspera, as you mentioned, a very, prominent product in the marketplace. I think just about every company in media and entertainment and certainly any company that's dealing with unstructured data objects knows and uses Aspera. They have a service now in the cloud where you can actually move data very rapidly over their protocol, into the cloud, and then store it in the object storage system. That's easy, that's simple. That makes it easy to start to leverage cloud. UStream the same way, Clearleap the same way. All of this comes together in Bluemix. Bluemix is the glue, so to speak, so if you're developing new applications you have all of the Bluemix tools that you can use, and then you got all these technologies that are integrated, including the object storage system, which is the foundation, everything's going to... All the data's going to reside in an object storage system. That makes it all usable for clients, very simple, very easy. They have a whole portfolio of things that they can do. And it's all tied together through APIs. It's very, very nice-- >> And has that opened up when you're small startup... (laughing) You don't have all these resources-- >> Right. >> How has it opened up new opportunities for you guys? >> So we see a lot of new startups coming on board, and taking advantage of the storage system-- >> Right. >> And all the different services that sit on top. Many companies today are born on the cloud, or they're new applications that are being born on the cloud, and so, they have access to, not only infrastructure, like you said within Bluemix, they also have access to other services, video services, high-speed data transfer services, object storage services. So they're able to take advantage of all those different services, build applications very quickly. Another thing that's interesting about IBM, they have this concept, you may have heard of it, this Bluemix Garage concept-- >> Dave: Yeah, I have. >> Which is a rapid deployment, rapid application development, using design thinking and agile methodologies, to quickly develop a minimum viable product that now uses object storage as part of the services, right. So as a new client, you can come in, sit in the Bluemix Garage, work on the application, and have some really rapid prototyping going on, and leverage the storage system underneath. And that gets you started, gets you going. I can see a lot of new applications coming to market through that same-- >> So they're like seven garages, is that right around the world? >> Russ: Yes, yes. Yeah, they're around the world. And so, I didn't realize... So Cleversafe's a fundamental part of that, in the object storage. >> It is now. And we just announced it this week at Interconnect, but it is now. >> So what does that mean? So I go in and I can... It's basically a set of... Sets of best practices-- >> Correct, correct. >> And accelerance and-- >> Right. And obviously in the cloud world, you need a place to place your data, right. So the integration with Cloud Object Storage, Cleversafe now called Cloud Object Storage is now all part of that, so it's integrated into the app dev that's going on in those garages. And we're excited about that because I think we'll see a lot of new technologies coming through that methodology, and certainly ones that leverage our storage technology, for sure. >> What's it been like to go from relatively small Illinois-based startup. (laughing) And now you're in IBM. >> Right. >> What was the integration like (laughing)? Are you on the rocket ship now? You were kind of on it before, but now it's like, steep part of the S-curve-- >> Sure. >> With all these global resources. Describe that. >> Well, I think the biggest part that's happened to us as an organization is exposure to a number of different accounts that we as a small company may not have had access to, certainly in certain industries, IBM's in every part of the world, in every industry, and that exposure from IBM's go to market has been very, very exciting for us. And certainly, global now, right. As Cleversafe, we were only in North America and Europe, for example, and now we're all over the world, or had the chance to be all over the world, so that's been really exciting. And then on top of that the whole integration into the cloud, right, because IBM's cloud business unity is the one that drove the acquisition of Cleversafe because they wanted the technology in the cloud. And now that we're there, we can offer storage services, object storage services as a foundation to anyone all over the world. And I think that's really exciting, and it's the exposure to all kinds of different businesses that's been exciting since we've been part of-- >> Yeah, and the speed at which you can get to that object store as a service as opposed to-- >> Absolutely. >> As opposed to saying, Okay, knocking on-- >> Yes. >> All the cloud doors, (laughing) And, hey, do you want to buy my cloud? And like, Well, you know we got our own, or whatever it is. >> Right, right. >> And now it's just boom global-- >> It's shortened that sale cycle tremendously, right. People are up and running in a few days now, or even a few hours, whereas before it may take months or, even quarters, to get started. You can get started now just by going to the portal, signing up for object storage services, starting to write data into the cloud, starting to leverage these other services that we walked about. It's very simple-- >> And the commentorial effects of what we were talking about before with, like Aspera and UStream, and so fourth-- >> Russ: Umm-hmm, umm-hmm. >> Give you the ability to add even new services. IBM 's always been very good at-- >> Yes. >> Acquisitions. >> Yes. >> We forget that sometimes IBM... (laughing) >> Acquisitions are always hard-- >> Yeah. >> But we've been fortunate we've had a lot of support and a lot help in getting integrated into the various businesses, And I think it's been a good journey. >> So what should we look for? What kind of milestones? Can you show a little leg on futures (laughing)? What should we be paying attention to? >> Well, we're going to continue to do what clients are asking us to do. We're going to develop features and functions, both on prem and in the cloud. We're going to integrate with a lot of different technologies, both IBM technologies and other company technologies. You may have seen our announcements with NetApp and VERITAS this week. >> Yeah. >> So we're going to continue to expand our integration with other technologies that exist in the marketplace because that's what clients want. They want solutions. They want end-to-end solutions, both on on prem and in the cloud. So we're focused on that. We're going to continue to do that. We'll certainly integrate with other IBM services as they come to market in the cloud. That's a really exciting thing, so we're going to continue to focus on driving success for our clients. And that's exciting. >> Oh! Russ, belated congratulations on the acquisition, and going through the integration. I'm really happy for you guys, and excited for your future. Thanks for coming on theCUBE. >> Thank you. >> You're welcome. >> Thank you, Dave. >> Alright, keep right there everybody. We'll be back with our next guest. This is theCUBE, we're live from Interconnect 2017. Be right back! (electronic music)

>> Voiceover: Live from Las Vegas, it's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We are live in Las Vegas for IBM InterConnect 2017. This is IBM's Cloud show and, now, data show. This is theCUBE's coverage. I'm John Furrier with my cohost, Dave Vellante. Our next guest is Meg Swanson, VP of Marketing for Bluemix, the whole kit and caboodle, SoftLayer of Bluemix. Now you get to watch some data platform, IOT. The Cloud's growing up. How you doing? Good to see you again. >> It's good. Good to see you guys. Every time we get together, it's just huge growth. Every time, every month to month. Under Bluemix, we've pulled together infrastructure. The area that was called SoftLayer. And because we had developers that absolutely you need a provision down to bare metal servers, all the way up to applications. So we pulled the infrastructure together with the developer services, together with our VMware partnership, all in a single console. Continuing to work on, with clients, on just having a unified experience. That's why we have it under the Bluemix brand. >> You knew us when we were just getting theCUBE started. We knew you when you were kicking off the developer program, with Bluemix, was announced here in theCUBE. Seems like 10 dog years ago, which is about 50 years, no, that was, what, four years ago now? Are you four years in? >> I think so. Yeah, 'cause I remember running from the Hakkasan club, we had just ended a virtual reality session, and I had to run, and then I sat down, and we started immediately talking about Bluemix 'cause we just launched it. >> So here's the update. You guys have been making a lot of progress, and we've been watching you. It's been fantastic, 'cause you really had to run fast and get this stuff built out, 'cause Cloud Native, it wasn't called Cloud Native back then, it was just called Cloud. But, essentially, it was the Cloud Native vision. Services, microservices, APIs, things, we've talked about that. What's the progress? Give us the update and the status, and where are you? >> Yeah, obviously just massive growth in services and our partners. When you look at, we had Twitter up with us today, we've had continual growth in the technology partners that we bring to bear, and then also definitely Cloud Native. But then also helping clients that have existing workloads and how to migrate. So, massive partnerships with VMware. We also just announced partnership with Intel HyTrust on secure cloud optimization. When we first met, we talked so much about you're going to win this with an ecosystem. And the coolest thing is seeing that pay off every day with the number of partners that we've been so blessed to have coming to us and working together with us to build out this ecosystem for our clients. >> And what's the differentiator, because what's happening now is you're starting to see the clear line of sight from the big cloud players. You have you guys, you have Oracle, you see Microsoft, you see SAP, you all got the version of the cloud. And it's not a winner-take-all market, it's a multi-cloud world, as we're seeing. Certainly open-source is driving that. How do you guys differentiate, and is it the same message? What's new in terms of IBM's differentiators? What's the key message? >> That we're absolutely staying core to the reason we went into this business. We are looking at, what are the challenges that our clients are looking to solve? How do we build out the right solutions for them? And look at the technologies they're using today, and not have them just forklift everything to a public cloud, but walk with them every step of the way. It's absolutely been about uncovering the partnerships between on-premises and the Cloud, how you make that seamless, how you make those migrations in minutes versus hours and days. The growth that we've seen is around helping clients get to that journey faster, or, if they're not meant to go fully public Cloud, that's okay, too. We've been absolutely expanding our data centers, making sure we have everything lined up from a compliance standpoint. Because country to country, we have so many regulations that we need to make sure we're protecting our clients in. >> I want to ask you, and David Kenny referenced it a little bit today, talked about we built this for the enterprise, it didn't stem out of a retailer or a search. I don't know who he was talking about, but Martin Schroeter, on the IBM earnings call, said something that I want to get your comment on, and if we can unpack a little bit. He said, "Importantly, we've designed Watson "on the IBM Cloud to allow our clients "to retain control of their data and their insights, "rather than using client data "to educate a central knowledge graph." That's a nuance, but it's a really big statement. And what's behind that, if I can infer, is use the data to inform the model, but we're not going to take your data IP and give it to your competitors. Can you explain that a little bit, and what the philosophy is there? >> Yeah, absolutely. That is a core tenet of what we do. It's all about clients will bring their data to us to learn, to go to school, but then it goes home. We don't keep client data, that's critical to us that everything is completely within the client's infrastructure, within their data privacy and protection. We are simply applying our cognitive, artificial intelligence machine learning to help them advance faster. It's not about taking their insights in learning and fueling them into our Cloud to then resell to other teams. That, absolutely, it's great that you bring up that very nuanced point, but that's really important. In today's day and age, your data is your lifeblood as a company, and you have to trust where it's going, you have to know where it's going, and you have to trust that those machine learnings aren't going to be helping other clients that are possibly on the same cloud. >> Is it your contention that others don't make that promise, or you don't know, or you're just making that promise? >> We're making that promise. It's our contention that the data is the client's data. You look at the partnerships that we've made throughout Cloud, throughout Watson, it's really companies that have come to us to solve problems. You look at the healthcare industry, you look at all these partnerships that we have. Everything that we've built out on the IBM Cloud and within Watson has been to help advance client cases. You rarely see us launching something that's completely unique to IBM that hasn't been built together with a client, with a partner. Versus, there are other companies out there in this market where they're constantly providing infrastructure to run their own business, maybe their own retail store, and their own search engine. And they will continue to do that, and they absolutely should, but at the end of the day, when you're a client, what do you want to do? Are you trying to build somebody else's business, or do you want someone who's going to be all in on your business and helping you advance everything that you need to do. >> Well, it seems like the market has glombed on to public data plus automation. But you're trying to solve a harder problem. Explain that. >> When you look at the clients that we're working with and the data that we're working with, it's not just information that's out there to work in a sandbox environment and it's available to anyone, baseball statistics or something that's just out there in the wild. Every client engagement we're in, this is their critical data. You look at financial services. We just launched the great financial services solutions for developers. You look at those areas, and, oh my word, you cannot share that data, yet those clients, you look at the work we're doing with H&R Block, you have to look at, that is absolutely proprietary data, but how do we send in cognitive to help us learn, to help teach it, help teach them alongside, for the H&R Block example, the tax advisor. So we're helping them make their business better. It's not as if we ingested all of the tax data to then run a tax solution service from IBM. It's a nuance, but it's an important nuance of how we run this company. >> So seven years ago, I met this guy, and he said, the 2010 John, you said, "Data is the new development kit." And I was like, "What are you talking about?" But now we see this persona of data scientist and data engineer and the developer persona evolving. How are you redefining the developer? >> Yeah, it's a great point, because we see cognitive artificial intelligence machine learning development in developers really emerging strong as a career path. We see data scientists, especially where as you're building out any application, any solution, data is at the core. So, you had it 10 years ago, right? (laughs) >> (mumbles) But I did pitch it to Dave when I first met him in 2010. No, but this is the premise, right? Back then, web infrastructure, web scale guys were doing their own stuff. The data needs to be programmable. We've been riffing on this concept, and I want to get your thoughts on this. What DevOps was for infrastructurous code, we see a vision in our research at Wikibon that data as code, meaning developers just want to program and get data. They don't want to deal with all the under-the-hood production, complicated stuff like datasets, the databases. Maybe the wrangling could be done by another process. There's all this production heavy lifting that goes on. And then there's the creativity and coolness of building apps. So now you have those worlds starting to stabilize a bit. Your thoughts and commentary on that vision? >> Yeah, that's absolutely where it has been heading and is continuing to head. And as you look at all the platforms that developers get to work in right now. So you have augmented reality, virtual reality are not just being segmented off into a gaming environment, but it's absolutely mainstream. So you see where developers absolutely are looking for. What is a low-code environment for? I'd say more the productivity. How do I make this app more productive? But when it comes to innovation, that's where you see, that's where the data scientist is emerging more and more every day in a role. You see those cognitive developers emerging more and more because that's where you want to spend all your time. My developers have spent the weekend, came back on Monday, and I said, "What'd you do?" "I wrote this whole Getting Started guide "for this Watson cognitive service." "That's not your job." "Yeah, but it's fun." >> Yeah, they're geeking out on the weekends, having some beer and doing some hackathons. >> It's so exciting to see. That's where, that innovation side, that's where we're seeing, absolutely, the growth. One of the partnerships that we announced earlier today is around our investment in just that training and learning. With Galvanize. >> What was the number? How much? >> 10 million dollars. >> Evangelizing and getting, soften the ground up, getting people trained on cognitive AI. >> Yeah, so it's really about making an impactful investment in the work that we started, actually a couple years ago when we were talking, we started building out these Garages. The concept was, we have startup companies, we starting partnering with Galvanize, who has an incredible footprint across the globe. And when you look at what they were building, we started embedding our developers in those offices, calling them Garages because that is your workshop. That's where you bring in companies that want to start building applications quickly. And you saw a number of the clients we had on stage today consistently, started in the Garage, started in the Garage, started in the Garage. >> Yeah, we had one just on theCUBE earlier. >> Yeah, exactly, so they start with us in the Garage. And then we wanted to make sure we're continuing to fuel that environment because it's been so successful for our clients. We're pouring into Galvanize and companies in training, and making sure these areas that are really in their pioneering stages, like artificial intelligence, cognitive, machine learning. >> On that point, you bring up startups and Garage, two-prong question. We're putting together, I'm putting together an enterprise-readiness matrix. So you have startups who are building on the Cloud, who want to sell to the enterprise. And then you have enterprises themselves who are adopting Hybrid Cloud or a combination of public, private. What does enterprise-readiness mean to you guys? 'Cause you guys have a lot of experience. Google next, they said, "We're enterprising." They're really not. They're not ready yet, but they're going that way. You guys are there. What is enterprise-readiness? >> Yeah, and I see a lot of companies have ambitions to do that, which is what we need them to do. 'Cause as you mentioned, it's a multi-cloud environment for clients, and so we need clouds to be enterprise-ready. And that really comes down to security, compliance, scalability, multiple zones. It comes down to making sure you don't have just five developers that can work on something, but how do you scale that to 500? How do you scale that to 500,000? You've got these companies that you have to be able to ensure that developers can immediately interact with each other. You need to make sure that you've got the right compliance by that country, the data leaving that country. And it's why you see such a focus from us on industry. Because enterprise-grade is one thing. Understanding an industry top to bottom, when it comes to cloud compliance is a whole other level. And that's where we're at. >> It's really hard. Most people oversimplify Cloud, but it's extremely difficult. >> It is, 'cause it's not just announcing a healthcare practice for Cloud doesn't mean you just put everybody in lab coats and send out new digital material. It is you have to make sure you've got partnerships with the right companies, you understand all the compliance regulations, and you've built everything and designed it for them. And then you've brought in all the partner services that they need, and you've built that in a private and a public cloud environment. And that's what we've done in healthcare, that's what we're doing in finance, you see all the work we're doing with Blockchain. We are just going industry by industry and making sure that when a company comes to us in an industry like retail, or you saw American Airlines on stage with us today. We're so proud to be working with them. And looking at everything that they need to cover, from regulation, uptime, maintenance, and ensuring that we know and understand that industry and can help, guide, and work alongside of them. >> In healthcare and financial services, the number of permutations are mind-boggling. So, what are you doing? You're pointing Watson to help solve those problems, and you're codifying that and automating that and running that on the Cloud? >> That's a part of it. A part of it is absolutely learning. The whole data comes to school with us to learn, and then it goes back home. That's absolutely part of it, is the cognitive learning. The other part of it is ensuring you understand the infrastructure. What are the on-premises, servers that that industry has? How many transactions per second, per nanosecond, are happening? What's the uptime around that? How do you make sure that what points you're exposing? What's the security baked into all of that? So, it's absolutely, cognitive is a massive part of it, but it is walking all the way through every part of their IT environment. >> Well, Meg, thanks for spending the time and coming on theCUBE and giving us the update. We'll certainly see you out in the field as we cover more and more developer events. We're going to be doing most, if not all, of the Linux foundation stuff. Working a lot with Intel and a bunch of other folks that you're partnering with. So, we'll see you guys out at all the events. DockerCon, you name it, they're all there. >> We'll be there, too, right with them. >> Microservices, we didn't even get to Kubernetes, we could have another session on containers and microservices. Meg Swanson, here inside theCUBE, Vice President of Bluemix Marketing. It's theCUBE, with more coverage after this short break. Stay with us, more coverage from Las Vegas. (techno music)

(mumbling) >> Voiceover: Covering Sapphire now. Headline sponsored by SAP HANA Cloud, the leader in platform as a service. With support from Console Inc., the cloud internet company. Now, here are your hosts, John Furrier and Peter Burris. >> We are here live at SAP Sapphire. This is SiliconANGLE Media's The Cube. It's our flagship program. We go out to the events and extract the signal to noise and want to do a shoutout to our sponsors SAP HANA Cloud and Console Inc. at console cloud, connecting the clouds together. I'm John Furrier with my co-host Peter Burris. Our next guest is Reggie Jackson, winner, athlete, tech athlete now, entrepreneur, overall great guy, and a cube alumni. Four years ago, we interviewed him here at SAP Sapphire. Welcome back, Reggie, to The Cube. Thanks for coming on. John, thank you very much. It's good to be here with old friends. We were havin' a little conversation about baseball there, but good to see you guys. Yeah, and obviously, the baseball, we were just talkin' about the whole fisticuffs and the glee of the grand slam walk-off. >> Reggie: Good stuff, good stuff. >> It's a good pivot point in some of the things that you're workin' on in here, the conversations in the tech world, which is social media and that notion of celebrating in a world of Instagram and Snapchat and social media. Certainly, ya flip the bat, the views go up. But then, baseball has these (laughing) unwritten rules, right. So does corporations. And so we're now a new era. Is baseball safe now with these unwritten rules and should they maintain those, certain things that have kept the game in balance? But yet with social media, the players are their own brand. And you certainly were a brand, even back in your day, which is a pioneer. What's your thoughts on that? >> You know John, Peter, I don't like the idea of someone going out of their way to promote their brand. Some of the great brands to me in history, Babe Ruth, Ty Cobb, the great Jim Brown, Joe Montana, Michael Jordan. And Michael Jordan would be a prominent example where technology and TV enhanced who he was. And he had someone behind him to enhance his brand, Nike, Phil Knight, who was a real pioneer. I'm not so in favor, I'm not in favor at all of someone manufacturing themselves as a brand. And I hear players talk about their brand and about trying to create something. If you're great, if you deserve it, I don't think Stephen Curry works on his brand. I think he works on bein' a great player. I think he works on bein' a great teammate. I think he does his best to maximize his skill set. And he's nothing but a gentleman along the way. He'll celebrate with joy once in awhile, with the Curry moves, which we've come to recognize. But for guys that talk about the manufacturing of their brand, there's something about it that's manufactured. It's not real, it's false. And I don't like it. I think it's okay, the Snapchats and the Google+ and all of the stuff, Twitter and Facebook and all that stuff, all of the things that go along with trying to create some hubbub, etc. I'm okay with that. >> So you're saying if it's not deserved. People are overplaying their hand before earning it. >> A lot of it, John, a lot of it. Joe Montana didn't work on his brand, he was great. Jim Brown didn't work on his brand, he was great. I don't want to use Jimmy Brown. I want to use Montana because even young people today will know Joe Montana. Tom Brady, Peyton Manning, they're not about their brand. They're about being classy, being great, being part of a team, being a leader, presenting themselves as something that's respected in the NFL, across the United States. Go ahead, Pete. >> So even though it's cheaper to get your name out there, you still believe in let your performance speak for itself. >> You got to be real about it. Ya got to be who you are. If you're not a great player, get out of the way. Get out of the space. So manufacturing your brand. I played with the Yankees. I was in the era of Cosell and Billy Martin and George Steinbrenner. We won championships with the team. I was part of something that helped me become recognized. And so in our era, the Sandy Koufax's became brands because they were associated with greatness around them. They stood out and so they earned that tremendous brand. >> We were just watching Graig Nettles gettin' taken out by George Brett in that big game and also the pine tar, we kind of gettin' some good laughs at it. You look at the balance of personalities. Certainly, Brett and Nettles and your team and you had a great personality, winning championships. Worked together as a team. And so I want to ask you that question about the balance, about the in baseball, certainly, the unwritten rules are a legacy and that has worked. And now in a era of personalities, in some cases, people self-promoting themselves, people are questioning that. Your thoughts on that because that applies to business too 'cause tech athletes or business athletes have a team, there are some unwritten rules. Thoughts on this baseball debate about unwritten rules. >> Pete and John, I'll try to correlate it between some tech giants that have a brand. I just left a guy with a brand, Bill McDermott, that runs SAP. Even Hasso, the boss. The face now of SAP is Bill McDermott. Dapper, slender, stylish, bright. It comes across well. So maintaining that brand, to me, relates to SAP, bills a great image for it. He's stylish, he's smooth, he's smart. He's about people. He presents himself with care. So that is a brand. I don't think it's manufactured. That's who he is in real life. If you take a look, and I'll go back to Steph Curry because that name resonates and everyone recognize it. That style of cool, that style of control, that style of team and care. And he presents to us all that he cares about us, the fan, his team, his family. And so those are things and I think you can go from the tech world. Bill Gates had a brand. Brilliant, somewhat reclusive, concerned about the world, concerned about the country, concerned about his company. And so that resonated it Microsoft because that's who he really was. Some of the people today don't really recognize that Jobs was thrown out of Apple. He was pushed out. All of his brilliance, which was marketing. And the gentleman there that really was the mind for the company, Steve Wozniak, happens to be here at SAP Sapphire. Today, I think he speaks. But those brands were real, not manufactured. And so, in today's world, I think you can manufacture a brand. And then all of a sudden, it'll crumble. It'll go away in the future. But the great brands of whether it's Jackie Robinson or whether it's Jack Welch or whether it's George Steinbrenner and the Yankee brand, those brands were real. They were not manufactured. Those guys were eccentric. They were brilliant. Go ahead. >> And also, they work hard. And I want to point out a comment you made yesterday here at the event. You were asked a question up on stage about that moment when you hit the home runs. I think we talked about it last time. I don't necessarily want to talk about the home runs. But you made a comment I'd like you to expand on and share with the audience. 'Cause you said, "I worked hard," but that day during warm-ups, you had batting practice. You made a comment that you were in the zone. So working hard and being great as it leads up to that. But also, in the moment, 'cause that's a theme these days, in the moment, being ready and prepared. Share your thoughts on what you meant by you had a great batting practice and you just felt it. >> I'm going to take it to what you say is in the moment. I remember when I was talkin' about it yesterday, which you reference to, when I had such a fantastic batting practice. I walked by a coupla sports writers in that era. Really well-known guys, Dave Anderson, New York Times. I can't think of his name right now, but it'll come to me, of the Daily News. It was like hey man. >> John: You were rockin' it out there. >> I kind of hope I didn't leave it out here. (laughing) That was in the moment and at the same time, >> I mean, you were crushing it. >> Yes, when the game started, I got back in that moment. I got back in what was live, what was now, what was going on. Certainly, I think our world now with the instant gratification of sending out a message or tweeting to someone or whatever certainly in the moment is about what our youth is and who we are today as a country, as a universe. >> But you didn't make that up. You worked hard, but you pulled it together in the moment. >> A comment with that is I went and did something with ESPN earlier this year in San Francisco, in Oakland with Stephen Curry. They said, "Reggie, we want ya to come up "and watch his practice, his pre-game." And it was very similar to your batting practice, where people come out and watch, etc. And so I was looking forward to it and I like to go to the games about an hour and a half or two hours early so I can see warm-up and see some of the guys and say hello. And I got a chance to watch Steph Curry. I know his dad. And happened to be the first time I went this year, the dad, Carolina, the Panthers were in town. Not the Panthers. Come on, help me, help me, help me. >> Peter: The Wizards? >> No, no, no, the Carolina. >> Peter: Carolina Panthers. >> The Carolina Hornets. >> John: Hornets. >> Were there and I know his dad, Dell Curry. And we talked a little bit. But then, Steph came out and I watched him. And I watched the dribbling exhibition. I watched the going between the legs and behind the back and the fancy passing, etc. And I watched the shots, the high-arcing threes, the normal trajectory threes, the high shots off the backboard and things like that that he did. The left-handed shots, the right-handed shots. And the guy asked me what I thought of the show. And I said, "Well, it's a cool show, "but I'm going to see all that tonight." And me watching him, the behind the backs, the between the legs, the passes, the high-arching shots from three, the high-arching touches off the glass. He does all that. >> John: He brought it into the game. >> Yeah, I said so, (laughing) >> Peter: That is his game. >> It's not a show, but that's his game. >> So Reggie, you did an interesting promotion, Reggie's Garage, where you bought a virtual reality camera and you created a really nice show of your garage demonstrating your love >> Reggie: 360. >> Peter: of cars, 360. Talk a little bit about that. And then if ya get a second, imagine what baseball's going to be like as that technology becomes available and how some of the conversation that we're having about authenticity, the fan coming into the game. >> An experience. >> Is going to change baseball. Start with the garage and how that went and then how ya think that's going to translate into baseball, if you've had any thoughts on that. >> In the technology that was used, certainly I enjoyed it. While I was doing it, I noticed where the cameras were in different spots. There was one on the floor of my car. There was one in the backseat. And then there was someone following us as closely as they could. But you could see everything. You'd see the shift and you could see my feet. It was like you were with me. When we did the 360 inside the garage as well, you could listen to me and then you could use your finger and spin around. And they had these special headset and special glasses that you could look around, just with your headset on, and see all around the room. Behind you, in front of you. And so it's an experience that I think is going to become part of who we are as a nation, who we are as a people watching television, that you're going to really feel like you're in the room. I think it's going to be exciting. And I think it's going to be fun. And when you're talking about products, when you're talking about my website, if you will, with the focus on automotive parts, where a guy can go in and shop and get any part he wants for a vehicle, you really can build a complete car from my website. You can buy a frame. You can buy body parts. You can buy a horn, an engine, brakes, tires, grills, turn signals, the whole nine yards. And it gives you an experience through 360 video of really walking into the store, walking into the building, walking into the stadium and looking around to see the hot dog stand, see the dugout, see the pitcher and the hitter, to see the parts in the garage, to see the cars and take a look and view at everything that's there. >> How are players going to react to havin' the fans virtually right there with them? >> I don't think it bothers you. I don't think ya notice. I don't think they'll show anything that will affect the player that he's going to be concerned about. I think you'd have to be sensitive if they start microphoning, start micing up and then the looseness of the language would impact. So I don't think they'll go that far. But I do think the more that you can see, the more attractive the game becomes, the more interested that you can get people. When I broadcast baseball for ABC back in the 80's, I always tried to broadcast for the lady of the house, while she worked, while she cooked the meal, she didn't have time to think about a backup slider or the fastball that painted the outside corner, the changeup, etc., the sinker. I tried to broadcast for her interpretation so I could attract another fan to the game. So I think that the technology and the viewing that you'll see from behind home plate, from under the player's feet while he's running down the bases and the slides and things of that nature, Pete, I think are going to be exciting for the fan and it'll attract more fans, attract a new type of television it's going to produce, etc. So it's exciting. >> Reggie, thanks for comin' on The Cube again. Appreciate your time. I ask ya final two questions that I want to get your thoughts on. One is obviously the cars. Reggie's Garage is goin' great. And you shared with us last time on The Cube, it's on YouTube, about you when you grew up and decide football and baseball. But when you were growin' up, what was your favorite car? What was that car that you wanted that was out of reach? That car that was your hot rod? And then the second question is, we'll get to the second question. Answer that one first. What was you dream car at the time? How did ya get >> Reggie: The dream car >> John: hooked on this? >> at the time. I had a '55 Chevrolet that I bought from a buddy by the name of Ronny Fog. I don't even know if he's still around anymore. Out of Pennsylvania. I had $300 and my dad gave me $200. I'd saved up mine from workin' for my dad. But my dream car was I went to school with a guy named Wayne Gethman and another guy named Irwin Croyes. I don't know Wayne Gethman anymore. But from the age of 16, I reengaged with Irwin Croyes, who happens to be a business investing type guy in the city of Philadelphia, right where we're still from. He's a car collector. And he drove a '62 Corvette and so did Wayne Gethman. And I always wanted one. And I now happen to have four. (laughing) >> He who get the most toys wins. Final question, 'cause you're such a legend and you're awesome and you're doin' so much work. And you're very active, engaged, appreciate that. Advice to young athletes coming up, whether they're also in business or a tech athlete or a business athlete. But the sports athletes today got travel ball, you got all this stuff goin' on. The idols like Stephen Curry are lookin' great. Great role models now emerging. What advice do you give them? >> John's got a freshman in high school. I got a junior in high school. What would ya say to 'em? >> You know, I'll tell ya. When you're young, the people you want to listen to are Mom and Dad. No one, and I'll say this to any child from the age of eight or nine years old, five, six years old to 17, 18, 19, 20, all the way up, now my daughter's 25. All the way up to the end of your parents' days. No one cares for you more than your mother or your father. Any parent, whether it's a job or whether their success in life, number one in that man or woman, mom or dad, number one in their life is their children. And so for kids, I say if there's any person you're going to listen to for advice in any path you want to walk down, it's the one that your parents talk to you about or how they show you. That is what I would leave as being most important. For kids, anything, idea that you have that you believe you can do, whether it's the athlete like Stephen Curry that has created shots and done things on the basketball court that he envisioned, that he thought about. Or whether it's the next Steve Jobs who happens to be Mark Zuckerman, who I don't know Mark is 30 years old yet. >> John: He just turned 30. >> It's an idea. He's born around the same time. He's born this week. His birthday is in this week. My birthday's tomorrow. >> John: Happy birthday. >> But thank you. Anything that you can think of in today's world of technology. With places like Silicon Valley where they take dreams and create foundations for them. I had a dream about a website that would sell automotive parts and you could go to my site and buy anything for your car. We've got about 75,000 items now. We'll get to 180,000 in a few months. We'll get to a half a million as soon as my technology is ready for it. But we have things to pay attention to and look into and issues to make sure that we iron out that aren't there for our consumer, for ease of navigation, ease of consumption and purchasing. Any idea that you have, take time to dream. It's much more so than taking time to dream when I was a young kid. Because my father would say, "Stop daydreamin' "and wastin' time." >> John: Get to work. >> Reggie: In today's world, for our children, I say take time to create a vision or to create something new. And go to someone that's in the tech world and they'll figure out a way of helping you manifest it into something that's a reality. >> Listen to your parents, kids. And folks out there, dream, build the foundation, go for it. Reggie Jackson, congratulations for being a Cube alumni again, multi-return. >> Peter: Thank you very much. >> John: Appreciate it. Congratulate on all your continued success. You're a legend. Great to have you on. And thanks so much for comin' on The Cube. >> Peter: And happy 70th birthday. >> John, Pete, always a pleasure. >> John: Happy birthday. >> Thank you very much. >> Have some cake for Reggie. It's The Cube, live here in Orlando. Bringin' all the action here on The Cube. I'm John Furrier with Peter Burris with Reggie Jackson. We'll be right back. (electronic music)