(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)

(soft electronic music) >> Okay, we're back with Tim Yocum who is the Director of Engineering at InfluxData. Tim, welcome, good to see you. >> Good to see you, thanks for having me. >> You're really welcome. Listen, we've been covering opensource software on theCUBE for more than a decade and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on opensource, mobile, social platforms, key databases, and of course, InfluxDB. And InfluxData has been a big consumer and crontributor of opensource software. So my question to you is where have you seen the biggest bang for the buck from opensource software? >> So yeah, you know, Influx really, we thrive at the intersection of commercial services and opensource software, so OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services, templating engines. Our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants. And like you've mentioned, even better, we contribute a lot back to the projects that we use, as well as our own product InfluxDB. >> But I got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop, the innovations come so fast and furious, and as a software company, you got to place bets, you got to commit people, and sometimes those bets can be risky and not pay off. So how have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit, though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do is we fail fast and fail often; we try a lot of things. You know, you look at Kubernetes, for example. That ecosystem is driven by thousands of intelligent developers, engineers, builders. They're adding value every day, so we have to really keep up with that. And as the stack changes, we try different technologies, we try different methods. And at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's something that we just do every day. >> So we have a survey partner down in New York City called Enterprise Technology Research, ETR, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes, is one of the areas that is kind of, it's been off the charts and seen the most significant adoption and velocity particularly along with cloud, but really, Kubernetes is just, you know, still up and to the right consistently, even with the macro headwinds and all of the other stuff that we're sick of talking about. So what do you do with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS and the way we were running was a little bit like containers junior. Now we're running Kubernetes everywhere at AWS, Azure, Google cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code. So our developers can focus on delivering services not trying to learn the intricacies of Amazon, Azure, and Google, and figure out how to deliver services on those three clouds with all of their differences. >> Just a followup on that, is it now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and out to the edge, wherever. Is that correct? >> Yeah, so we've basically built more or less platform engineering is this the new, hot phrase. Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx cloud. >> And I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer, if I can use that term, are there specific attributes to InfluxDB or is it kind of just generally off-the-shelf PaaS? Is there any purpose built capability there that is value-add or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services, for instance, POSTGRES databases for metadata, perhaps. Get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code. that we can, as an SRE group, as an OPS team, that we can manage with very few people, really, and we can stamp out clusters across multiple regions in no time. >> So sometimes you build, sometimes you buy it. How do you make those decisions and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. We're looking for trade-offs that make sense. We really want to protect our customers' data, so we look for services that support our own software with the most up-time reliability and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team and of course, for our customers; you don't even see that. But we don't want to try to reinvent the wheel, like I had mentioned with SQL datasource for metadata, perhaps. Let's build on top of what of these three large cloud providers have already perfected and we can then focus on our platform engineering and we can help our developers then focus on the InfluxData software, the Influx cloud software. >> So take it to the customer level. What does it mean for them, what's the value that they're going to get out of all these innovations that we've been talking about today, and what can they expect in the future? >> So first of all, people who use the OSS product are really going to be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across over four billion series keys that people have stored, so there's a proven ability to scale. Now in terms of the opensource software and how we've developed the platform, you're getting highly available, high cardinality time-series platform. We manage it and really, as I had mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in realtime. We deploy to our platform every day, repeatedly, all the time. And it's that continuous deployment that allow us to continue testing things in flight, rolling things out that change, new features, better ways of doing deployments, safer ways of doing deployments. All of that happens behind the scenes and like we had mentioned earllier, Kubernetes, I mean, that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx cloud platform, you really are able to take advantage of new features immediately. We roll things out every day and as those things go into production, you have the ability to use them. And so in the then, we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let us do that for you. >> That makes sense. Are the innovations that we're talking about in the evolution of InfluxDB, do you see that as sort of a natural evolution for existing customers? Is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >> Yeah, it really is. It's a little bit of both. Any engineer will say, "Well it depends." So cloud-native technologies are really the hot thing, IoT, industrial IoT especially. People want to just shove tons of data out there and be able to do queries immediately and they don't want to manage infrastructure. What we've started to see are people that use the cloud service as their datastore backbone and then they use edge computing with our OSS product to ingest data from say, multiple production lines, and down-sample that data, send the rest of that data off to Influx cloud where the heavy processing takes place. So really, us being in all the different clouds and iterating on that, and being in all sorts of different regions, allows for people to really get out of the business of trying to manage that big data, have us take care of that. And, of course, as we change the platform, endusers benefit from that immediately. >> And so obviously you've taken away a lot of the heavy lifting for the infrastructure. Would you say the same things about security, especially as you go out to IoT at the edge? How should we be thinking about the value that you bring from a security perspective? >> We take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data that we store is kept private. It's, of course, always a concern, you see in the news all the time, companies being compromised. That's something that you can have an entire team working on which we do, to make sure that the data that you have, whether it's in transit, whether it's at rest is always kept secure, is only viewable by you. You look at things like software bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software and we do that, you know, as we use new tools. That's something, that's just part of our jobs to make sure that the platform that we're running has fully vetted software. And you know, with opensource especially, that's a lot of work, and so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip that they used to but that is really just part of a day in the life for folks like us that are building platforms. >> And that's key, especially when you start getting into the, you know, that we talk about IoT and the operations technologies, the engineers running that infrastrucutre. You know, historically, as you know, Tim, they would air gap everything; that's how they kept it safe. But that's not feasible anymore. Everything's-- >> Can't do that. >> connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R&D so you can focus on some of the other activities. All right, give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a two-lane approach, with Influx, with any time-series data. You've got a lot of stuff that you're going to run on-prem. What you had mentioned, air gapping? Sure, there's plenty of need for that. But at the end of the day, people that don't want to run big datacenters, people that want to entrust their data to a company that's got a full platform set up for them that they can build on, send that data over to the cloud. The cloud is not going away. I think a more hybrid approach is where the future lives and that's what we're prepared for. >> Tim, really appreciate you coming to the program. Great stuff, good to see you. >> Thanks very much, appreciate it. >> Okay in a moment, I'll be back to wrap up today's session. You're watching theCUBE. (soft electronic music)

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

(upbeat music) >> Okay, we're back with Tim Yoakum, who is the Director of Engineering at Influx Data. Tim, welcome. Good to see you. >> Good to see you. Thanks for having me. >> You're really welcome. Listen, we've been covering open source software on the Cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on open source, mobile social platforms, key databases, and of course Influx DB, and Influx Data has been a big consumer and contributor of open source software. So my question to you is where have you seen the biggest bang for the buck from open source software? >> So, yeah, you know, Influx, really, we thrive at the intersection of commercial services and open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service, from our core storage engine technologies to web services, templating engines. Our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants. And like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product, Influx DB. >> You know, but I got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop. The innovations come so fast and furious, and as a software company, you got to place bets, you got to, you know, commit people, and sometimes those bets can be risky and not pay off. How have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example. That ecosystem is driven by thousands of intelligent developers, engineers, builders. They're adding value every day. So we have to really keep up with that. And as the stack changes, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's something that we just do every day. >> So we have a survey partner down in New York City called Enterprise Technology Research, ETR, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes, is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity, particularly, you know, along with cloud. But really Kubernetes is just, you know, still up and to the right consistently, even with, you know the macro headwinds and all of the other stuff that we're sick of talking about. So what are you doing with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS, and the way we were running was a little bit like containers junior. Now we're running Kubernetes everywhere, at AWS, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers, and we can manage that in code. So our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google, and figure out how to deliver services on those three clouds with all of their differences. >> Just a follow up on that, is it, now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and up to the edge, you know, wherever. Is that, is that correct? >> Yeah, so we've basically built, more or less, platform engineering. This is the new hot phrase. You know, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on, and they only have to learn one way of deploying their application, managing their application. And so that just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx Cloud. >> Yeah, and I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer if I can use that term, are there specific attributes to Influx DB, or is it kind of just generally off the shelf PaaS? You know, is there any purpose built capability there that is value add, or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services for instance, Postgres databases for metadata perhaps, get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code that we can, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions in no time. >> So how, so sometimes you build, sometimes you buy it. How do you make those decisions, and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. We're looking for trade offs that make sense. You know, we really want to protect our customers' data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers, you don't even see that, but we don't want to try to reinvent the wheel. Like I had had mentioned with SQL data storage for metadata perhaps. Let's build on top of what these three large cloud providers have already perfected, and we can then focus on our platform engineering, and we can have our developers then focus on the Influx Data software, Influx Cloud software. >> So take it to the customer level. What does it mean for them? What's the value that they're going to get out of all these innovations that we've been been talking about today? And what can they expect in the future? >> So first of all, people who use the OSS product are really going to be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you. But then you want to scale up. We have some 270 terabytes of data across over 4 billion series keys that people have stored. So there's a proven ability to scale. Now, in terms of the open source software, and how we've developed the platform, you're getting highly available, high cardinality time series platform. We manage it, and really as I mentioned earlier, we can keep up with the state of the art. We keep reinventing. We keep deploying things in real time. We deploy to our platform every day repeatedly, all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change, new features, better ways of doing deployments, safer ways of doing deployments. All of that happens behind the scenes. And we had mentioned earlier Kubernetes, I mean that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx Cloud platform, you really are able to take advantage of new features immediately. We roll things out every day. And as those things go into production, you have the ability to use them. And so in the end, we want you to focus on getting actionable insights from your data instead of running infrastructure. You know, let us do that for you. >> And that makes sense, but so is the, are the innovations that we're talking about in the evolution of Influx DB, do you see that as sort of a natural evolution for existing customers? Is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >> Yeah, it really is. It's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are really the hot thing. IoT, industrial IoT especially, people want to just shove tons of data out there and be able to do queries immediately, and they don't want to manage infrastructure. What we've started to see are people that use the cloud service as their data store backbone, and then they use edge computing with our OSS product to ingest data from say multiple production lines and down-sample that data, send the rest of that data off to Influx Cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that, and being in all sorts of different regions allows for people to really get out of the business of trying to manage that big data, have us take care of that. And of course, as we change the platform, end users benefit from that immediately. >> And so obviously, taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IoT and the edge? How should we be thinking about the value that you bring from a security perspective? >> Yeah, we take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data we store is kept private. It's of course always a concern. You see in the news all the time companies being compromised. You know, that's something that you can have an entire team working on, which we do, to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You look at things like software bill of materials. If you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that's just part of our jobs, to make sure that the platform that we're running has fully vetted software. And with open source especially, that's a lot of work. And so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip than they used to. But that is really just part of a day in the life for folks like us that are building platforms. >> Yeah, and that's key. I mean, especially when you start getting into the, you know, we talk about IoT and the operations technologies, the engineers running that infrastructure. You know, historically, as you know, Tim, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >> Can't do that. >> connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R and D so you can focus on some of the other activities. All right. Give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a a two lane approach. With Influx, with any any time series data, you know, you've got a lot of stuff that you're going to run on-prem. What you mentioned, air gaping, sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want to entrust their data to a company that's got a full platform set up for them that they can build on, send that data over to the cloud. The cloud is not going away. I think a more hybrid approach is where the future lives, and that's what we're prepared for. >> Tim, really appreciate you coming to the program. Great stuff. Good to see you. >> Thanks very much. Appreciate it. >> Okay, in a moment, I'll be back to wrap up today's session. You're watching the Cube. (gentle music)

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. Welcome back to Las Vegas. You're watching the Cube's coverage of UI Path Forward. Five. We reach cruising altitude on day two. Christina Seacrest is here. She's the process Artificial intelligence and automation GPS automation leader at ey. And Bob PCIs, executive director for Intelligent Automation for the state of Tennessee. Folks, welcome to the cube. Thank you for Adam. >>Good >>To have you. Okay, I don't know if I messed up that title, Christina, but it's kind of interesting. You got process, you got ai, you got automation, you got gps. What's your role? >>I have a lot of rules, so thank you for that. Yeah, so my focus is first and foremost automation. So how do you get things like UI path into our clients, but also I focus specifically in our government and public sector clients. So sled specifically. So state local education. So that's why I'm here with the state of Tennessee. And then we also like to take it beyond automation. So how do you bring an artificial intelligence and all the technologies that come with that. So really full end to end spectrum of >>Automation. So Bob, when you think about the sort of the, the factors that are driving your organization of, how did you describe that, Those sort of external factors that inform your strategy. What, what's, what are the catalysts for how you determine to deploy technology? >>Well, it was primarily that we know tendency has a tendency to provide good customer service, but we want to get to a great status best in class, if you will. And we had an external advisory review where it said, Hey, you know, we could make automation to improve our customer experience. And so that was like a directive of the, the state leaders to go across the board and automate all processes statewide, starting with the 23 executive agencies. >>So where's the focus from that standpoint? Is it on just providing better interfaces to your constituents, your customers? Is it cutting costs or you actually have more budget to invest? Kind of a combination of >>Those? Yeah, so it's, it's really both qualitative and quantitative, right? So quantitative is where we're able to reduce hours and therefore we can redirect people to more less mundane work, if you will. And then qualitative is where we're able to reduce the errors, improve data quality, reduce cycle time for our citizens, you know, when they're making requests, et cetera. So it's, I think it's a combination of both of those quantitative and qualitative metrics that we are mandated in, in micromanaged, quite frankly to, to bring, make those >>Numbers. So I'm from Massachusetts, when I go to a a mass.gov website, I say, all this was done in the 1990s and you could just see where the different stovepipes were, were. But then every now and then you'll hit one and you'll say, Wow, okay, this is up to, it's such a great experience. And then the flip side of that is you want your employees to be happy and not have to do all this mundane work so you can retain the best people. You don't have to. So you're living that in, in state and, and local. So where did you start your automation journey? What role did EY play? Let's go. Yeah, >>Sure. So I, I, I think the thought for process automation was probably three or four years ago, but then we started the program about 18 months ago and there was a lot of, let's say behind the scenes work before we could bring EY in, you know, like what resources was I gonna have in, in the state that were gonna help me address all of the agency simultaneously, right? Cuz normally you'll see a project that'll do be more siloed across the state and say, we're gonna do this agency, we're gonna do this division. Well, you have 40 other agencies that are, you know, the momentum is it's just gonna fall, it wayside. So how we looked at it was let's blanket it and go across all 23 agencies at the same time, you know, identify common processes that are used across 40 divisions, for example, right? >>So, so what we basically did is we procured the software, you know, did the contracts, and then it was really about, I designed, I'm gonna say a multistream approach where they were, we could run multiple work streams, independent define all the architectures, required dev tests, production, the disaster recovery at the same time in parallel developed the center of excellence, the operation model, the processes, methodologies. And the third one was, let's go out to a few divisions, business administration, health, you know, health, human resources, and be able to do a process inventory to see what was there. And then based on that, there's all this theory of well let's do a proof of concept. Let's do a proof of technology, let's do apply. Well, the bottom line is rpa technology's been around for a long time. It's proven there's nothing to prove. But really what was important to prove before we decided to go, you know, full tilt was, you know, develop a proof of perceived business value. >>Are we gonna bring in the, the business value, the hours and the qu qualitative metrics that is expected by our ex executive team, The leadership, we were able to do that, you know, with the help of help of ey, we built out the prototypes and we got the green light to go forward, got ey to start, and then we just basically went pedal to the metal. We had our foundation already defined. We built up the architecture in less than one to two months. Now, in, in a public sector or private sector, it's just not heard of, right? But we have a tendency with EYs technical team, myself, we look around the, the road around the rock instead, the rock in the road, right? So we ended up coming up with a very unique, very easy to easy to handle architecture that was very scalable. And then were able to hit the ground running and deploy in production by December where head of >>Was EY involved in the whole, you know, dev test production, dr. Center of excellence, the, the process inventory or did you bring them in? Did you kind of do that internally then bring EY in for the proof of >>Value? EY was actually awarded the contract for soup to nuts, basically the first phase, which was those four work streams I told you about. And they worked with myself and the state of Tennessee infrastructure architecture teams. We needed to get these things defined and signed off the architecture so we could expedite getting them built out. And then they, and they basically ran all four work streams, you know, the process, inventory, the prototype, the, the proof of perceived business value, the building out the center of excellence, working with myself. And, and this wasn't just us in a, a vacuum, we ended up having to, I mean, I could do the strategy, I could do the technology and I could said the roadmap and all the good stuff, but we had to actually meet with a lot of the state or tendency organizations on change management. How do we end up putting this process or an automation in the middle of the, the normal traditional process, right? So there was a lot of interaction there and getting their feedback and then tweaking our operational model based on feedback from the state of Tennessee. So it was all very collective collaborative. I think that would be the keyword is collaborative and then building out everything. So then, and then we ended up going to the next way where they knew so much and we were, we had such a tight timeframe that we continued with ey. >>So Christina, Bob mentioned center of excellence a couple of times in the state of Tennessee, but then beyond state of Tennessee, other organizations you've worked with in this space, what's the relationship between center of excellence and this thing we've been hearing about over the last couple of days, the citizen developer has that been, has, has, has that been leveraged in the state of Tennessee? Bob, have you seen that leveraged in other places? Christina? What's that relationship look like? >>Yeah, so we don't leverage that, that model yet we have centralized model and there's reasons for that. So we don't end up having maverick's, runoff runoffs have one off, have, you know, have a a UI path version or down this division or have another RPA tool in another division, right? So then all of a sudden we're, we have a maintenance nightmare. Manageability nightmare. So we basically, you know, I I I negotiate an ELA with UI path, so therefore if anyone wants to go do another automation on another division, or they would basically follow our model, our design, our coe, our quality gates. We we're the gatekeepers to bring into production. >>Got it. Now, yeah. Now Christina, what's your perspective? Because I can imagine Nashville and Memphis might have very different ideas about a lot of things. Yeah. Little Tennessee reference there, but what, what, what about what, what about other places are you, are you seeing the citizen developer leveraged in, in some kinds of places more than others or >>What? Yeah. Yeah. And that's part of, because of the foundation we're building. Yeah. So we laid, you know, when, when Bob talks about the first phase of eight weeks, that was amazingly fast, even in that's ridiculous. Spoke about it to say you're gonna lay these four foundations. I was excited, like, I was like, wow, this, this is a very serious client. They wanna go fast and they wanna get that momentum, but the AUM was laid out so we could propel ourselves. So we are at 40 automations right now. We're in the works of creating 80 more automations in this next year. We'll be at 120 really quickly. The AUM is critical. And I will say at a client, I've, I've worked with over 50 clients on automation programs. The way state of Tennessee treats the aom and they abide by it, it is the living document of how you go and go fast. Got it. And the one thing I would say is it's also allowed us to have such immense quality. So I always talk about you put in forward, you put in another 80, we're at 98% uptime on all our automations, meaning they don't go down. And that's because of the AOM we set up. And the natural progression is going to be how do you take it to citizen developer? How do you take it to, we call, you know, process automation plus, >>But methodically, methodically, not just throwing it out at the beginning and, and hoping the chaos >>Works. Exactly. Exactly. And >>The ratio of of bots to automations, is that one to one or you have automation? Oh no, the single bot is doing multiple. So how many bots are you talking about? >>We're doing, Bob, you're gonna answer this better than I will, but the efficiency is amazing. We've been pushing that. >>So our ratio now, cause we have a high density architecture we put in is four bots, excuse me, four processes. The one bot and four bots, The one virtual machine EC two server. Right? So it's four to one, four to one. Now what we're going to get by next summer, we'll do more analysis. We'll probably get the six to one, six to one that's made serious shrinkage of our footprint from a machine, you know, management perspective from 60 down to seven right now we're gonna add the next chunk. We add another 80 automations in FIS gear 24. We're only gonna add two more bot, two more servers. Right? So that's only 10 running like close to 200 bucks. >>And, and is doing this on prem in the cloud? >>No, our, the architecture's fully >>Oh, cloud based >>Ct. Yeah. So we use UiPath SAS model. Yeah. Right. So that handles the orchestrator, the attended bots, all the other tooling you need automation hub, process minor et etc. Etc. Cetera. And then on the state side in aws we have, we use unattended bots, cert bots that have to go down into the legacy systems, et cetera. And they're sitting on EC two instances. >>Was there, was there a security not hole that you had to get through internally? What was that like? >>No, actually we, we, we were lock and step with the security team on this. I mean, there are some standards and templates and you know, what we had to follow, you know, but they're doing an assessment every single release, they do assessments on little bots, what systems it's activating or are accessing, et cetera. The data, because you have fedra data of FTI data, you know, in the public sector to make sure we're not touching it. >>Do you guys golf? >>I do, yeah. Not Well, yes, >>If you mean I I like golf but not don't golf well, but so you know what, what a mulligan is. If you had a Mulligan right, for the state of Tennessee, what'd you learn? What would you do differently? You know, what are some of the gotchas you see maybe Christina in, in other customers and then maybe specifically state of Tennessee, >>Right? I would say, you know, it is the intangibles. So when we talk about our clients that go fast and go big, like state of Tennessee, it's because that, that we call it phase zero that gets done that Bob did. It's about making sure you've got the sponsorship. So we've got executive sponsorship all the way up. You've got amazing stakeholder engagement. So you're communicating the value of what we're trying to do. And you're, you're showing them the value. We have been really focused on the return on investment and we'll talk a little bit about that, but it's how do you make sure that when you do, you know, states are different with those agencies, you have such an opportunity to maximize return on investment if you do it right, because you're not talking about automation in one agency, you're talking it across multiple agencies. We call that the multiplier effect. And that's huge. And if you understand that and how to actually apply that, the value you get is amazing. So I, I don't, I can't say there's a mulligan here, Bob, you may think of some, I know on other clients, if you don't line up your stakeholders and you don't set the expectations early on, you meander and you may get five, six automations in over the year. You know, when I go to clients and say, we're doing 40, we're doing 80, they're like, >>Wow, that's the, but that's the bottom line. Gotcha. Is if you, if you want to have an operational impact and have multiple zeros, you gotta go through that process that you said up front. >>Exactly. A >>Anything you do differently, Bob? >>Well, I I what I do differently, I mean, I think, I mean we, we did get executive sponsorship, you know, and in one area, but we still have to go out to all the 23 agencies and get, and bring awareness and kind of like set the hook to bring 'em in, right? Bring 'em to the, to the, to the lake. Right. And, and I think if, if it was more of a blanket top down, getting every agency to agree to, you know, in investigate automation, it would've been a lot easier. So we're, we're, we're getting it done. We've gone through 13 agencies already and less than a year, all of our releases are sprinkling across multiple agencies. So it's not like a silo. I'll look at that. Everyone at every agency is being impacted. So I think that's great. But I, I think our, our Mueller now is just trying to make sure we have enough backlog to do the next sprints. >>Is it, you know, the ROI on these initiatives is, is, is so clear and so fast. Is it self-funding? Is there gain sharing or do you just give business, give money back to the state and have to scramble for more? Do you get to, you know, get a lick off that cone? >>Unfortunately we don't, but I, I, I try to see if we could get some property like, nah, we don't do that. It's all cost, cost based. But, but our ROI is very attractive, I think for, for doing a whole state, you know, transformation. I think our ROI is three and a half to four years. Right. And that's pretty mind blowing. Even if you look at private sector or, I, I think some of the, the key things which people are noticing, even though we're in public sector, we're we are very nimble. This project is extremely nimble. We've had people come in, exactly, we need this, so we're gonna get penalized. Okay, knock it out in four hours, four days. Right? So it's that nimbleness that you just don't hear of even in private sector or public sector. And we're just able to do that for all the collaboration we do across ey, across myself and across all the other organizations that I, that I kind of drag along or what have, >>What do you, what do you, do you see any limits to the opportunities here? I mean, is this a decade long opportunity? Is you have that much runway >>Or that's just not my dna, so we're gonna, we're gonna probably do it like in four years, but Well, when >>You say do it, I mean, will you be done at that point? Or do you see the weight, >>Look at, you know, we could boil the ocean and I think this is one of the reasons why we're successful is we could boil the ocean and and be, it will be 10 attended 20 year program. Yeah. Okay. Or we looked at it, we had some of EY guys look at it and say, I said, what's the 25 80 rule? Meaning, you know, give me, So if we had 500 processes, tell me how many processes will gimme 80% of the hours. And it was 125, it was a 25 80 rule. I said, that's what we're doing it, we're doing, we're gonna do the 80% of the hours quantifiably. Now when we're done with that pass, then we'll have those other ones that are bringing 20% of the hours, that's when we might be bringing citizens in. That's what we're bringing state workers in. But at that same time, we will be going back in the wave and doing advanced ai. Right. Or advance ia, in other words. So right now we do rpa, ocr, icr, but you know, there's NL ml nps, there's virtual agents and stuff. So that's like the wave we're gonna do through the ones we've already gone through. Got it. Right. So it'll probably be a two or three wave or iterations. >>Cool. Guys, thanks so much for coming into the cube. Great story. Really appreciate you taking us through it. Thank you so much for having us. You're very welcome. All right, keep it right there. Dave Nicholson. The Dave ante. We back at UI path forward five from the Venetian in Las Vegas. Keep it right there.

>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.

>>The Cube presents UI Path Forward. Five. Brought to you by UI Path. >>Welcome back to The Cube's coverage of UI Path Forward. Five from Las Vegas. We're inside. The formerly was The Sands, now it's the Venetian Convention Center. Dave Nicholson. David, Deb. I've never seen it set up like this before. UI Path's. Very cool company. So of course the setup has to be cool, not like tons of concrete. James Furlong is here, the Vice President of Supply Chain Management and projects at Puma. And Derek Weimer is the CEO of Robo, who's an implementation partner, expert at Intelligent Automation. Folks, welcome to the Cube. Good to see you. Great to have you on. >>Thank you. It's a pleasure. >>So what's happening at Puma these days? I love your sneakers, but you guys probably do more than that, but let's tell us about, give us the update on Puma. >>Yeah, absolutely. Puma's one of the world's leading sports, sports brands. So we encompass all things sports. We do footwear, we do apparel, we do accessories. Cobra, Puma golf is underneath our umbrella as well. So we get the added benefit of having that category as well. And yeah, trade, trade all over the world and it's an exciting, exciting brand to be with. >>And di Robo Atlanta based really specialists in intelligent automation. That's pretty much all you do, is that right? >>Yeah, we are a pure play intelligence automation professional services firm. That's all we do. We're the world's largest firm that focuses only on automation headquarter in Germany, but with a large presence here in Americas. >>So we hear from a lot of customers. We've heard from like with the journey it started, you know, mid last decade, Puma James is just getting started. We April you mentioned. So take us through that. What was the catalyst as you're exiting the, the pandemic, the isolation economy we call it? Yeah. What was the catalyst tell, take us through the sort of business case for automation. >>Sure, absolutely. So Puma, our mission is forever faster. It's, it's our mantra and something we live and breathe. So naturally we have an intense focus on innovation and, and automation. So with that mindset, the way this all kicked off is that I had the opportunity to go into some of our distribution facility and I was unbelievably impressed with the automation that I saw there. So how automation augmented the employee workforce. And it was just very impressive to see that some of our state of the art technology and automation at the same time. Then I went back to the office with that excitement and that passion and I saw that we had the opportunity to take that to our employee base as well. We sort of lacked that same intense focus on how do we take automation and technology like I saw at the distribution facilities and bring it to our employees because picture a large workforce of talented, dedicated employees and they just couldn't keep up with the explosive growth who's seen explosive growth over the last couple of years and they just couldn't keep up with it. So I said that that's it. We need to, to take that same passion and innovation and enter in hyper hyper automation. So we went to the leadership team and no surprise they were all in. We went with them with the idea of bringing hyper automation, starting with RPA to, to our office employees. And they were in, they support innovation and they said, Great, what do you need? Really? Go for it. >>The first question wasn't how much, >>Actually the first question I will say that the funny part is, is they said, Well I like this, it sounds too good to be true. And because it, it really does. If you're new to it like we were and I'm pitching all the benefits that RPA could bring, it does sound too good to me. True. So they said, All right, you know, we trust you and, and go for it. What do you need? Resources, just let us know. So sure enough, I had a proof of concept, I had an idea, but now what? I didn't know where to go from there. So that's where we did some intensive research into software suppliers, but also implementation partners because now we knew what we wanted to do. We had excitement, we had leadership buy-in now, now what do I do? So this is when we entered our partnership to figure out, okay, help Puma on this journey. >>How'd you guys find each other? You know, >>Just intensive research and spoke with a lot of people here. Is there a lot of great organizations? But at the end of the day, they really supported everything that Houma stood for, what we're looking to do and had a lot of trust in the beginning and Dirk and his team and how he could help us on this journey. Yeah. >>Now James, your, your job title system for supply chain management. It is, but I understand that you have had a variety of roles within the organization. Now if we're talking about another domain, artificial intelligence, machine learning. Yeah. There's always this concept of domain expertise. Yeah. And how when you're trying to automate things in that realm, domain expertise is critical. Yeah. You have domain expertise outside of your job title. Yeah. So has that helped you with this journey looking at automation, being able to, being able to have insight into those other organizations? >>Yeah, absolutely. And I think when we were pitching it to the leadership team in the beginning, that enabled me to look at each one sitting at the table and saying, alright, and on the sales, on a commercial side, I was a head of sales for one of the trade channels. I could speak directly to him in the benefits it could have with not with tribal knowledge and with an expertise. So it wasn't something that, it was just, oh, that's supply chain. I could sit, you know, with the, our CFO and talk to him about the, the benefits for his group merchandising and legal so on. I was really able to kind of speak to each one of them and how it would support, because I had that knowledge from being blessed of 15 years experience at, at Puma. So yeah, I was able to take all of that and figure out how do I make sure not just supply chain benefits from rpa, but how does the whole organization benefit from not only RPA but the hyper automation strategy. >>So what's an engagement look like? You start, I presume you, you gotta do some type of assessment and, and you know, of some upfront planning work. Yeah. What does that look like? How, what's the starting point? Take us through that >>Journey. Yeah, so exactly. So the, the key when you're trying to get value from Intel automation is finding the right opportunities, right? And you can automate a lot of things, but which are the things that are gonna drive the most value and, and the value that actually matters to the company, right? So where are you trying to get to from a strategic level, your objectives and how do you actually use automation to help you get to there? So the first thing is, what are the opportunities gonna help you do that? And then once you identify, what we recommend is start with something that's gonna be, you know, accessible, small, You're gonna get a quick win. Cuz then the important thing is once you get that out there, you build the momentum and excitement in the organization that then leads to more and more. And then you build a proper pipeline and you and you get that the, the engagement. >>So what was that discovery like? Was it you fly up there and do a, a chalk talk? Or did you already know James, like where you wanted to focus? >>Yeah, I knew I had a solid proof of concept with the disruptions in supply chain we couldn't keep up with, with all the changes and supply. So right away I knew that I have a very substantial impact on the organization and it would be a solid proof of concept. It was something that not only would supply chain steal, but our customers would feel that we would be servicing them better. Our sales team, the commercial team, marketing impacted everybody. But at the same time it was tangible. I saw two people that just physically couldn't get their, their work done despite how talented and hardworking they were. So I, I was in on that proof of concept and then I just took that idea with some strong advice from Dirk and and his team on, okay, well how do I take that? But then also use that to evangelize through the organization. What are some pitfalls to avoid? Because as a proof of concept, they just told me it's too good to be true. I believe in it. So it was so important to me that it >>Was successful. >>It get your neck out. Oh, I sure was. Which is a little scary, but I had confidence that we would >>Do it. But your poc you had to have a systems view. Yes. Right? Cuz you were trying to, I think you, I'm inferring that you had two people working really hard, but they couldn't get their job done. Yeah, for sure. They were just sitting on their hands. Right. Waiting. Okay. So you kind of knew where the bottlenecks were. Yes. And that's what you attacked and or you helped James and her the team think through that or, >>Yeah, exactly. So, so a couple points you were asking about her domain model of knowledge earlier, and I think that's really key to the puma's success with it, is that they've come at it from a business point of view, what matters to the business. And at the point, you know, supply chain challenges, how do we use automation to address that? And then, you know, and then it's gonna, it's actually gonna, you know, pick opportunities that are gonna matter to the business. Yeah, >>Yeah. At the same time, we, we knew this could be a scary thing, right? If it's not done right, you know, automation definitely can, can take a, a wrong path. So what we relied on them for is tell us how to make this successful. We wanted structure, we wanted oversight, we wanted to balance that with speed and really, you know, developing our pipeline, but at the same time, tell us how to do this right? How do we set up a center, our first ever center of excellence? They help us set that up. Our steerco, our process definition documents are like, they really helped us add that structure to how to make this successful, sustainable and make sure that we were standing things up the right way versus launching into a strong proof, proof of concept. But then it's not gonna be scalable if we didn't really take their strong advice on how to make this something, you know, that had the right oversight, the right investment. So that was, that was key as >>Well for us. So when you looked at the POC and James was saying there were potential pitfalls, what were those pitfalls? Like what did you tell Puma, Hey, watch out for this, watch out for that. What was sort of the best advice there? >>Yeah, so I think one is understanding complexity, right? So a lot of opportunities sound good, but you want to make sure that it's, it's feasible with the right tool set. And also that you're not bit off too much in the beginning is really important. And so some of that is that bringing that expertise to say, Okay, yeah, look, that does something, a good process. You're gonna get value out. It's not gonna be overly complicated. It's a good place to start. And then also, I guess the thing too to mention is it's more than just a technology project. And that's the thing that we also really focus on is it's actually as much about the change management, it's much about, you know, what is the right story, the business case around it, the technology actually in a way is the easy part and it's all the stuff around it that really makes the POC effective, >>Obviously the process. Yeah. Been the people I presume getting to adopt, >>Right? And I think, again, with our, our brand mantra forever faster, we, we get that support that the buy-in from the top is is there from, from the beginning. So that's a benefit that some companies don't, they don't have, right? They have a little resistance maybe from the top. We're trying to get everyone's buy in it. And we had that. So we had, you know, the buy-in the engagement, we were ready to go. So now we just needed someone to kind of help us. >>One more if I may. Yeah, yeah. Gabe, six months in. Yes. That's the business impact that, can >>You tell you? That was tremendous. Yeah. >>Really already six months. Wow. >>Yeah, >>Absolutely. Cfo, CFO's dream. Yeah. >>And again, and, and we had a CFO change mid, mid project. So the new CFO comes in, not new to Puma, the same thing. Super, super smart guy. And I had to sit and again pitch, you know, pitch what it is and the support that I needed by way of investment. And he saw the results and he was all in, you know, what do you need, what's next? And instantly was challenging his departments, Why don't he got competitive, right? We're a competitive bunch, so why don't you know, you should have more in the pipeline. And he was, he was bought in. So there was that fear of a new CFO coming in and how do you show value? Because some of it is, it's very easy to show right away, You know, we were able to refocus those two full-time employees on, on higher value chain activity and you know, they're doing a tremendous job and they're, you know, they have the, the bot and the automation supporting them. So he saw that right away. And we can show him that. But he also understands, as does the whole leadership team, the concept of downstream impacts that you can't necessarily, you know, touch and, and put on paper. So he sees some, but then he also recognizes all the other upstream and downstream impacts that it's had and he's all in and supports whatever, whatever we need. >>Yeah. New CFOs like George Seaford taking over for bill walls. >>Yeah, exactly. Exactly. We >>Have, we have to keep showing results and it has to be sustainable. So that's, again, we'll rely on our partnership to say, okay, this is the beginning, you know, what's next? Keep us, you know, honest on oversight and, and any pitfalls that we should avoid because he's excited. But at the same time, we need to make sure that we sustain those results and, and show what's next. Now they all gotta taste to the apple and they're very eager to see what's next in, in, in this hyper automation journey. >>Well, Dirk, you've partnered on this journey, this specific journey with, with, with Puma. But from your perspective in the broader marketplace, what would be the perfect low hanging fruit opportunity that you would like to have somebody call you and say, Hey, we've got, we've got this perspective engagement with a client. What would be the, what would be the like, Oh yeah, that's easy, that's huge roi really quickly, What does that look like? >>Yeah, I think there's, there's a few areas, right? You know, one task automation RPA is a, is a really good entry point, right? Because it's, it's, it's not overly complex. It doesn't involve a lot of complicated technologies. And I'd say the, the usual starting areas, you know, you, you finance back office, you know, shared service, invoice processing, you know, payables is a very good opportunity area. HR is also an area I would look at, you know, in new, new employee onboarding process or you know, payroll, et cetera. And then supply chain is actually becoming more and more, more common, right? So those would be I guess, top three areas I would mention. And >>Then, and then kind of follow onto that, what's the tip of this sphere? What's the sort of emerging market Yeah. >>For >>This kind of technology? >>I think there's two things. One, it's taking a holistic into end view and leveraging multiple, you know, technology, you know, beyond just rpa, right? You know, intelligent document processing, iml, you know, bringing all this to bear to actually do a true digital transformation. That's, that's number one. And then I'd say the second is going from focusing on cost and efficiency to actually getting into the front office and how do you, how do you actually increase revenue? How do you increase margin? How do you actually, you know, help with that, that top line growth. I think that's really, and that's where you're leveraging technologies, you know, like the, the AI as an example to really help you understand how do you optimize. >>So James, that's, that becomes then an enterprise wide initiative. Yeah. That's, that's, is that your vision? Maybe maybe lay that out for >>Us a bit. Yeah, ab absolutely. The, the vision is now that we've seen what, what it can do, how do we take it from being managed by just, you know, supply chain and this proof of concept cuz I manage projects, but now it's bigger than just a supply chain project. And how do we sort of evangelize that through the whole organization And you know, they mentioned on main stage this, the creation of new jobs and, and roles and how a, a company might set out their strategic directive now is, is changing and evolving. So you know that that's our idea now and that what we'll need support next is how should we structure now for success. And so that it's across the whole enterprise. But that's, that's the vision for >>Sure. What worries you do, you worried about it like taking off and getting outta control and not being governed and so you have to be a little bit careful there. >>Yeah, for sure. That was really important to us. And we actually got to leverage a lot of heavy lifting that Puma Global had done at the same time that we were coming up and, and thinking of the idea of rpa. They were having the same thoughts and they did a lot of heavy lifting again, about not only the software providers but also what does the structure look like, the oversight, a center of excellence globally. So we were able to really leverage a lot of best practices and SOPs that they had set out and we were able to kind of leverage those, bring those to Puma North America so that we didn't face that fear cuz that would be a limiting factor for us. So because we were so disciplined and we could leverage the work that they had done, that fear wasn't, wasn't there. Now we have to stay, you know, on top of it. And as people get excited, how do you kind of mirror the excitement and with it at the same time that the oversight and not getting, you know, too, too big, too fast. So that's the balance that we'll, we'll work through now. It's a good problem to have. >>Well, exactly. It is super exciting. Great story. Congratulations on, on the success and good luck. Thank you. Yeah, you very much for coming to the, Yeah. Thank you. Thank you. All right. And thank you for watching. Keep it right there. Dave Nicholson Andante right back, the cube live from Las Vegas UI path forward. Five.

(upbeat music) >> We're here on theCube covering AWS Storage Day 2022. Kevin Miller joins us. He's the vice president and general manager of Amazon S3. Hello, Kevin, good to see you again. >> Hey Dave, it's great to see you as always. >> It seems like just yesterday we were celebrating the 15th anniversary of S3, and of course the launch of the modern public cloud, which started there. You know, when you think back Kevin, over the past year, what are some of the trends that you're seeing and hearing from customers? What do they want to see AWS focus more on? What's the direction that you're setting? >> Yeah, well Dave, really I think there's probably three trends that we're seeing really pop this year. I think one just given the kind of macroeconomic situation right now is cost optimization. That's not a surprise. Everyone's just taking a closer look at what they're using, and where they might be able to pair back. And you know, I think that's a place that obviously S3 has a long history of helping customers save money. Whether it's through our new storage classes, things like our Glacier Instant Retrieval, storage class that we launched to reinvent last year. Or things like our S3 storage lens capability to really dig in and help customers identify where their costs are are being spent. But so certainly every, you know, a lot of customers are focused on that right now, and for obvious reasons. I think the second thing that we're seeing is, just a real focus on simplicity. And it kind of goes hand in hand with cost optimization, because what a lot of customers are looking for is, how do I take the staff that I have, and do more this year. Right, continue to innovate, continue to bring new applications or top line generating revenue applications to the market, but not have to add a lot of extra headcount to do that. And so, what they're looking for is management and simplicity. How do I have all of this IT infrastructure, and not have to have people spending a lot of their time going into kind of routine maintenance and operations. And so that's an area that we're spending a lot of time. We think we have a lot of capability today, but looking at ways that we can continue to simplify, make it easier for customers to manage their infrastructure. Things like our S3 intelligent tiering storage class, which just automatically gives cost savings for data that's not routinely accessed. And so that's a big focus for us this year as well. And then I think the last and probably third thing I would highlight is an emerging theme or it's been a theme, but really continuing to increase in volume, is all around sustainability. And you know, our customers are looking for us to give them the data and the assurances for them, for their own reports and their own understanding of how sustainable is my infrastructure. And so within AWS, of course, you know we're on a path towards operating with 100% renewable energy by 2025. As well as helping the overall Amazon goal of achieving net zero carbon by 2040. So those are some big lofty goals. We've been giving customers greater insights with our carbon footprint tool. And we think that, you know the cloud continues to be just a great place to run and reduce customer's carbon footprint for the similar you know, storage capacity or similar compute capacity. But that's just going to continue to be a trend and a theme that we're looking at ways that we can continue to help customers do more to aggressively drive down their carbon footprint. >> I mean, it makes sense. It's like you're partnering up with the cloud, you know, you did same thing on security, you know, there's that shared responsibility model, same thing now with ESG. And on the macro it's interesting Kevin, this is the first time I can remember where, you know it used to be, if there's a downturn it's cost optimization, you go to simplicity. But at the same time with digital, you know, the rush to digital, people still are thinking about, okay how do I invest in the future? So but let's focus on cost for a moment then we'll come back to sort of the data value. Can you tell us how AWS helps customers save on storage, you know, beyond just the price per terabyte actions that you could take. I mean I love that, you guys should keep doing that. >> Absolutely. >> But what other knobs are you turning? >> Yeah, right and we've had obviously something like 15 cost reductions or price reductions over the years, and we're just going to continue to use that lever where we can, but it's things like the launch of our Glacier Instant Retrieval storage class that we did last year at Reinvent, where that's now you know, 4/10ths of a cent per gigabyte month. For data that customers access pretty infrequently maybe a few times a year, but they can now access that data immediately and just pay a small retrieval fee when they access that data. And so that's an example of a new capability that reduces customer's total cost of ownership, but is not just a straight up price reduction. I mentioned S3 Intelligent-Tiering, that's another case where, you know, when we launch Glacier Instant Retrieval, we integrated that with Intelligent-Tiering as well. So we have the archive instant access tier within Intelligent-Tiering. And so now data that's not accessed for 90 days is just automatically put into AIA and and then results in a reduced storage cost to customers. So again, leaning into this idea that customers are telling us, "Just do, you know what should be done "for my data to help me reduce cost, can you just do it, "and sort of give me the right defaults." And that's what we're trying to do with things like Intelligent-Tiering. We've also, you know, outside of the S3 part of our portfolio, we've been adding similar kinds of capabilities within some of our file services. So things like our, you know elastic file service launched a one zone storage class as well as an intelligent tiering capability to just automatically help customers save money. I think in some cases up to 92% on their their EFS storage costs with this automatic intelligent tiering capability. And then the last thing I would say is that we also are just continuing to help customers in other ways, like I said, our storage lens is a great way for customers to really dig in and figure out. 'Cause you know, often customers will find that they may have, you know, certain data sets that someone's forgotten about or, they're capturing more data than they expected perhaps in a logging application or something that ends up generating a lot more data than they expected. And so storage lens helps them really zoom in very quickly on, you know this is the data, here's how frequently it's being accessed and then they can make decisions about use that data I keep, how long do I keep it? Maybe that's good candidates to move down into one of our very cold storage classes like Glacier Deep Archive, where they they still have the data, but they don't expect to need to actively retrieve it on a regular basis. >> SDL bromide, if you can measure it, you can manage it. So if I can see it, visualize it, that I can take actions. When you think about S3- >> That's right. it's always been great for archival workloads but you made some updates to Glacier that changed the way that we maybe think about archive data. Can you talk about those changes specifically, what it means for how customers should leverage AWS services going forward? >> Yeah, and actually, you know, Glacier's coming up on its 10 year anniversary in August, so we're pretty excited about that. And you know, but there's just been a real increase in the pace of innovation, I think over the last three or four years there. So we launched the Glacier Deep Archive capability in 2019, 2018, I guess it was. And then we launched Glacier Instant Retrieval of course last year. So really what we're seeing is we now have three storage classes that cover are part of the Glacier family. So everything from millisecond retrieval for that data, that needs to be accessed quickly when it is accessed, but isn't being accessed, you know, regularly. So maybe a few times a year. And there's a lot of use cases that we're seeing really quickly emerge for that. Everything from, you know, user generated content like photos and videos, to big broadcaster archives and particularly in media and entertainment segment. Seeing a lot of interest in Glaciers Instant Retrieval because that data is pretty cold on a regular basis. But when they want to access it, they want a huge amount of data, petabytes of data potentially back within seconds, and that's the capability we can provide with Glacier Instant Retrieval. And then on the other end of the spectrum, with Glacier Deep Archive, again we have customers that have huge archives of data that they be looking to have that 3-AZ durability that we provide with Glacier, and make sure that data is protected. But really, you know expect to access it once a year if ever. Now it could be a backup copy of data or secondary or tertiary copy of data, could be data that they just don't have an active use for it. And I think that's one of the things we're starting to see grow a lot, is customers that have shared data sets where they may not need that data right now but they do want to keep it because as they think about, again these like new applications that can drive top line growth, they're finding that they may go back to that data six months or nine months from now and start to really actively use it. So if they want that option value to keep that data so they can use it down the road, Glacier Deep Archive, or Glacier Flexible Retrieval, which is kind of our storage class right in the middle of the road. Those are great options for customers to keep the data, keep it safe and secure, but then have it, you know pretty accessible when they're ready to get it back. >> Got it, thank you for that. So, okay, so customers have choices. I want to get into some of the competitive differentiators. And of course we were talking earlier about cost optimization, which is obviously an important topic given the macro environment you know, but there's more. And so help us understand what's different about AWS in terms of helping customers get value from their data, cost reduction as a component of value, part of the TCO, for sure. But just beyond being a cloud bit bucket, you know just a storage container in the cloud, what are some of the differentiators that you can talk to? >> Yeah, well Dave, I mean, I think that when it comes to value, I think there's tremendous benefits in AWS, well beyond just cost reduction. I think, you know, part of it is S3 now has built, I think, an earned reputation for being resilient, for storing, you know, at massive scale giving customers that confidence that they will be able to scale up. You know, we store more than 200 trillion objects. We regularly peak at over 100 million requests per second. So customers can build on S3 and Glacier with the confidence that we're going to be there to help their applications grow and scale over time. And then I think that in all of the applications both first party and third party, the customers can use, and services that they can use to build modern applications is an incredible benefit. So whether it's all of our serverless offerings, things like Lambda or containers and everything we have to manage that. Or whether it's the deep analytics and machine learning capabilities we have to help really extract, you know value and insight from data in near real time. You know, we're just seeing an incredible number of customers build those kinds of applications where they're processing data and feeding their results right back into their business right away. So I'm just going to briefly mention a couple, like, you know one example is ADP that really helps their customers measure, compare and sort of analyze their workforce. They have a couple petabytes of data, something like 25 billion individual data points and they're just processing that data continuously through their analytics and machine learning applications to then again, give those insights back to their customers. Another good example is AstraZeneca. You know, they are processing petabytes and petabytes of genomic sequencing data. And they have a goal to analyze 2 million genomes over the next four years. And so they're just really scaling up on AWS, both from a pure storage point of view, but more importantly, from all of the compute and analytics capability on top that is really critical to achieving that goal. And then, you know, beyond the first party services we have as I mentioned, it's really our third party, right? The AWS partner network provides customers an incredible range of choice in off the shelf applications that they can quickly provision and make use of the data to drive those business insights. And I think today the APN has something like 100,000 partners over in 150 countries. And we specifically have a storage competency partner where customers can go to get those applications that directly work, you know, on top of their data. And really, like I said, drive some of that insight. So, you know, I think it's that overall benefit of being able to really do a lot more with their data than just have it sit idle. You know, that's where I think we see a lot of customers interested in driving additional value. >> I'm glad you mentioned the ecosystem, and I'm glad you mentioned the storage competency as well. So there are other storage partners that you have, even though you're a head of a big storage division. And then I think there's some other under the cover things too. I've recently wrote, actually have written about this a lot. Things like nitro and rethinking virtualization and how to do, you know offloads. The security that comes, you know fundamentally as part of the platform is, I think architecturally is something that leads the way in the industry for sure. So there's a lot we could unpack, but you've fundamentally changed the storage market over the last 16 years. And again, I've written about this extensively. We used to think about storage in blocks or you got, you know, somebody who's really good in files, there were companies that dominated each space with legacy on-prem storage. You know, when you think about object storage Kevin, it was a niche, right? It was something used for archival, it was known for its simple, get put syntax, great for cheap and deep storage, and S3 changed that. Why do you think that's happened and S3 has evolved, the object has evolved the way it has, and what's the future hold for S3? >> Yeah I mean, you know, Dave, I think that probably the biggest overall trend there is that customers are looking to build cloud native applications. Where as much of that application is managed as they can have. They don't want to have to spend time managing the underlying infrastructure, the compute and storage and everything that goes around it. And so a fully managed service like S3, where there's no provisioning storage capacity, there's, you know we provide the resiliency and the durability that just really resonates with customers. And I think that increasingly, customers are seeing that they want to innovate across the entire range of business. So it's not about a central IT team anymore, it's about engineers that are embedded within lines of business, innovating around what is critical to achieve their business results. So, you know, if they're in a manufacturing segment, how can we pull data from sensors and other instrumentation off of our equipment and then make better decisions about when we need to do predictive maintenance, how quickly we can run our manufacturing line, looking for inefficiencies. And so we've developed around our managed offerings like S3, we've just developed, you know, customers who are investing and executing on plans and you know transformations. That really give them, you know put digital technology directly into the line of business that they're looking for. And I think that trend is just going to continue. People sometimes ask me, well "I mean, 16 years, you know, isn't S3 done?" And I would say, "By no stretcher are we done." We have plenty of feedback from customers on ways that we can continue to simplify, reduce the kinds of things they need to do, when they're looking for example and rolling out new security policies and parameters across their entire organization. So raising the bar there, finding, you know, raising the bar on how they can efficiently manage their storage and reduce costs. So I think we have plenty of innovation ahead of us to continue to help customers provide that fully managed capability. >> Yeah I often say Kevin, the next 10 years ain't going to be like the last in cloud. So I really thank you for coming on theCube and sharing your insights, really appreciate it. >> Absolutely Dave, thanks for having me. >> You're welcome. Okay keep it right there for more coverage of AWS Storage Day 2022 in theCube. (calm bright music)

(bright music) >> Welcome to this next session of AWS Storage Day. I'm your host, Dave Vellante of theCUBE. And right now we're going to explore how to simplify and evolve your data lake backup disaster recovery and analytics in the cloud. And we're joined by Kevin Miller who's the general manager of Amazon S3. Kevin, welcome. >> Thanks Dave. Great to see you again. >> Good to see you too. So listen, S3 started as like a small ripple in the pond and over the last 15 years, I mean, it's fundamentally changed the storage market. We used to think about storage as, you know, a box of disc drives that either store data in blocks or file formats and then object storage at the time it was, kind of used in archival storage, it needed specialized application interfaces, S3 changed all that. Why do you think that happened? >> Well, I think first and foremost, it's really just, the customers appreciated the value of S3 and being fully managed where, you know, we manage capacity. Capacity is always available for our customers to bring new data into S3 and really therefore to remove a lot of the constraints around building their applications and deploying new workloads and testing new workloads where they know that if something works great, it can scale up by a 100x or a 1000x. And if it doesn't work, they can remove the data and move on to the next application or next experiment they want to try. And so, you know, really, it's exciting to me. Really exciting when I see businesses across essentially every industry, every geography, you know, innovate and really use data in new and really interesting ways within their business to really drive actual business results. So it's not just about building data, having data to build a report and have a human look at a report, but actually really drive the day-to-day operations of their business. So that can include things like personalization or doing deeper analytics in industrial and manufacturing. A customer like Georgia-Pacific for example, I think is one of the great examples where they use a big data lake and collect a lot of sensor data, IoT sensor data off of their paper manufacturing machines. So they can run them at just the right speed to avoid tearing the paper as it's going through, which really just keeps their machines running more and therefore, you know, just reduce their downtime and costs associated with it. So you know, it's just that transformation again, across many industries, almost every industry that I can think of. That's really what's been exciting to see and continue to see. I think we're still in the really early days of what we're going to see as far as that innovation goes. >> Yeah, I got to agree. I mean, it's been pretty remarkable. Maybe you could talk about the pace of innovation for S3. I mean, if anything, it seems to be accelerating. How Kevin, does AWS, how has it thought about innovation over the past decade plus and where do you see it headed? >> Yeah, that's a great question Dave, really innovation is at our core as part of our core DNA. S3 launched more than 15 years ago, almost 16 years old. We're going to get a learner's permit for it next year. But, you know, as it's grown to exabytes of storage and trillions of objects, we've seen almost every use case you can imagine. I'm sure there's a new one coming that we haven't seen yet, but we've learned a lot from those use cases. And every year we just think about what can we do next to further simplify. And so you've seen that as we've launched over the last few years, things like S3 Intelligent Tiering, which was really the clouds first storage class to automatically optimize and reduce customer's costs for storage, for data that they were storing for a long time. And based on, you know, variable access patterns. We launched S3 Access Points to provide a simpler way to have different applications operating on shared data sets. And we launched earlier this year S3 Object Lambda, which really is, I think, cool technology. We're just starting to see how it can be applied to simplify serverless application development. Really the next wave, I think, of application development that doesn't need, not only is the storage fully managed, but the compute is fully managed as well. Really just simplify that whole end to end application development. >> Okay, so we heard this morning in the keynote, some exciting news. What can you tell us, Kevin? >> Yeah, so this morning we launched S3 Multi-Region Access Points and these are access points that give you a single global endpoint to access data sets that can span multiple S3 buckets in different AWS regions around the world. And so this allows you to build these multi-region applications and multi-region architectures with, you know, with the same approach that you use in a single region and then run these applications anywhere around the world. >> Okay. So if I interpret this correctly, it's a good fit for organizations with clients or operations around the globe. So for instance, gaming, news outlets, think of content delivery types of customers. Should we think about this as multi-region storage and why is that so important in your view? >> Absolutely. Yeah, that is multi-region storage. And what we're hearing is seeing as customers grow and we have multinational customers who have operations all around the world. And so as they've grown and their data needs grow around the world, they need to be using multiple AWS regions to store and access that data. Sometimes it's for low latency so that it can be closer to their end users or their customers, other times it's for regions where they just have a particular need to have data in a particular geography. But this is really a simple way of having one endpoint in front of data, across multiple buckets. So for applications it's quite easy, they just have that one end point and then the data, the requests are automatically routed to the nearest region. >> Now earlier this year, S3 turned 15. What makes S3 different, Kevin in your view? >> Yeah, it turned 15. It'll be 16 soon, you know, S3 really, I think part of the difference is it just operates at really an unprecedented scale with, you know, more than a hundred trillion objects and regularly peaking to tens of millions of requests per second. But it's really about the resiliency and availability and durability that are our responsibility and we focus every single day on protecting those characteristics for customers so that they don't have to. So that they can focus on building the businesses and applications that they need to really run their business and not worry about the details of running highly available storage. And so I think that's really one of the key differences with S3. >> You know, I first heard the term data lake, it was early last decade. I think it was around 2011, 2012 and obviously the phrase has stuck. How are S3 and data lakes simpatico, and how have data lakes on S3 changed or evolved over the years? >> Yeah. You know, the idea of data lakes, obviously, as you say, came around nine or 10 years ago, but I actually still think it's really early days for data lakes. And just from the standpoint of, you know, originally nine or 10 years ago, when we talked about data lakes, we were looking at maybe tens of terabytes, hundreds of terabytes, or a low number of petabytes and for a lot of data lakes, we're still seeing that that's the kind of scale that currently they're operating at, but I'm also seeing a class of data lakes where you're talking about tens or hundreds of petabytes or even more, and really just being used to drive critical aspects of customer's businesses. And so I really think S3, it's been a great place to run data lakes and continues to be. We've added a lot of capability over the last several years, you know, specifically for that data lake use case. And we're going to continue to do that and grow the feature set for data lakes, you know, over the next many years as well. But really, it goes back to the fundamentals of S3 providing that 11 9s of durability, the resiliency of having three independent data centers within regions. So the customers can use that storage knowing their data is protected. And again, just focus on the applications on top of that data lake and also run multiple applications, right? The idea of a data lake is you're not limited to one access pattern or one set of applications. If you want to try out a new machine learning application or something, do some advanced analytics, that's all possible while running the in-flight operational tools that you also have against that data. So it allows for that experimentation and for transforming businesses through new ideas. >> Yeah. I mean, to your point, if you go back to the early days of cloud, we were talking about storing, you know, gigabytes, maybe tens of terabytes that was big. Today, we're talking about hundreds and hundreds of terabytes, petabytes. And so you've got huge amount of information customers that are of that size and that scale, they have to optimize costs. Really that's top of mind, how are you helping customers save on storage costs? >> Absolutely. Dave, I mean, cost optimization is one of the key things we look at every single year to help customers reduce their costs for storage. And so that led to things like the introduction of S3 Intelligent Tiering, 10 years ago. And that's really the only cloud storage class that just delivers the automatic storage cost savings, as data access patterns change. And, you know, we deliver this without performance impact or any kind of operational overhead. It's really intended to be, you know, intelligent where customers put the data in. And then we optimize the storage cost. Or for example, last year we launched S3 Storage Lens, which is really the first and only service in the cloud that provides organization-wide visibility into where customers are storing their data, what the request rates are and so forth against their storage. So when you talk about these data lakes of hundreds of petabytes or even smaller, these tools are just really invaluable to help customers reduce their storage costs year after year. And actually, Dave I'm pleased, you know, today we're also announcing the launch of some improvements to S3 Intelligent Tiering, to actually further automate the cost savings. And what we're doing is we're actually removing the minimum storage duration. Previously, Intelligent Tiering had a 30 day minimum storage duration, and we're also eliminating our monitoring and automation charge for small objects. So previously there was that monitoring and automation charge applied to all objects independent of size. And now any object less than 120 kilobytes is not charged at that charge. So, and I think some pretty critical innovations on Intelligent Tiering that will help customers use that for an even wider set of data lake and other applications. >> That's three, it's ubiquitous. The innovation continues. You can learn more by attending the Storage Day S3 deep dive right after this interview. Thank you, Kevin Miller. Great to have you on the program. >> Yeah, Dave, thanks for having me. Great to see you. >> You're welcome, this is Dave Vellante and you're watching theCUBE's coverage of AWS Storage Day. Keep it right there. (bright music)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of IBM Think 2021. Brought to you by IBM. >> Welcome back to IBM Think 2021. This is "theCUBE's" ongoing coverage, where we go out to the events, in this case, of course, virtually, to extract the signal from the noise. And now we're going to talk about the shifts in customer employee experiences and channels. The past year, obviously, has exposed gaps in both of those areas. The shift to digital channels, something that hit every industry. If you weren't a digital business, you were out of business. So, there's huge demand for better, a.k.a. less frustrating, and hopefully superior, customer experiences. That's never been higher. It puts a lot of pressure on companies and their marketing departments to deliver. And with me to talk about these trends are two great guests. Alex Shootman, the General Manager of Adobe Workfront. Alex was CEO of Workfront, which Adobe acquired last year. And Matthew Candy, Global Managing Director of IBM iX. Gentleman, welcome. Thanks for coming on. >> Thanks for having us. >> Great to be here. >> Matt, let's start with you. Maybe you could talk to the shifts that I talked about earlier, in the past year, and customers' expectations, and how they changed, and how you guys responded. >> Yes, so, Dave, I mean, it's been, my goodness, what a year, right? If we'd gone back and thought, we never would have seen this coming. And certainly, I guess, for the clients, I run the digital customer experience business, the services business, here at IBM, and certainly, we have been very busy helping clients, across just about every industry, accelerate their digital transformation efforts. And I think what has been absolutely clear, is digital, mobile, all of these ways of engaging with customers through channels, has been an absolutely critical way in which businesses have kept going, and survived over this time. And certainly, we've seen that transformation accelerate, right? And companies shifting from face-to-face interactions from a B2B sales perspective, into a kind of online B2B commerce, et cetera. So, really it's become digital by default. And I think customers really demanding personalized experiences, and wanting to make sure that these companies really know you in how they deal with you. >> You know Matt, I mean, our business, you think about our business, it was predominantly going out to events, live events, and then overnight, our entire industry had to shift to virtual. And what it was is, you had all these physical capabilities, and people trying to shove it into virtual, and it was really hard. There was a lot of unknowns, and really different. I imagine there's some parallels within marketing organizations. And I wonder if you could talk, Matt, about what kind of barriers you saw about delivering those kind of digital interactions and experiences. >> Yes. So, I guess, we've seen kind of five core challenges that companies have been facing. So, firstly, around volume and velocity of content. So, as we're putting more demand into organizations, right, for more content at a greater pace, right, this causes challenges for companies in terms of being able to get content out there, and surface it through their digital channels, right? Whether that's kiosks, or voice, web, mobile, et cetera. And that pace is not slowing down. Second thing is this demand for personalization. So, as companies and individuals are touching through all of these touch points across marketing sales and service, the need to be able to interact in the right way, showing that you know me, using personal data to match the right offer at the right time, critically important. Thirdly, the martech stack, right. Across many of these organizations, this explosion in marketing technology over the last 10 years has been absolutely incredible. And so one of the big challenges companies have is how we tie all of these different components of the stack together, to build this seamless experience. Fourth challenge, right? Additional communication channels. So, as we need more content and personalization, and we've got to join up across all these different systems, how do we make this consistent across all of these channels, right, whether it's digital or physical, is a true test of many organizations' ability to respond. And the fifth point is the coordination needed across departments within companies. And so, how the marketing department deals with legal, with regulatory approvals, with sales. How they go out to their agency partners. And this has certainly got a lot more complex across geographies, and across boundaries, within companies and outside. And so we see, absolutely, this need to put in place, basically, the marketing system of record that helps manage this. And this is where we see huge opportunity together with Adobe. >> Yeah, so, Alex, maybe you could talk about this a little bit. I mean, you guys are well-known for deep expertise and leadership, and orchestrating marketing workflows and the like. Matt talked about the martech stack. What's your take on this? And how are IBM iX and Adobe Workfront working together? >> What has occurred in response to what Matt talked about, is that companies started realizing that work was a tier one asset inside the marketing team. You know, they looked at, if you go back in time, and you look at financials in a company, people thought, "Wow, this is really important to us. We should put a system in place to manage financials." They realized their customers were really important, so they said, "We should put a system in place to manage our customers." People are important. They bought Workday to make sure that they could manage their people. And all of this complexity that Matt talked about caused enterprises to realize that the work of marketing was as important as some of those other activities in the organization. And so they started investing in a marketing system of record, like Workfront. >> You know, that's interesting. Just a quick aside. I mean, if you think about a lot of the problems we have in data and big data, typical to talk about stovepipe. You just mentioned three examples, finance, HR, and now marketing, where we've contextualized the system. In other words, the domain experts, the people in finance, and HR, and marketing, they're the ones who know the data the best. They don't have to go, necessarily, to some big data team, and data scientist, and all this stuff. They know what they want and they know it. And that's really what you guys are serving in your streamlining. This notion, Alex, of a marketing system of record is really interesting. I mean, it's relatively new, isn't it? So, why does it matter so much to marketers? >> Yeah, if you think about it, we've been able to serve 3,000 enterprises around the globe. We serve all 10 of the top 10 brands. Half of the Fortune 100. And what has created the need for the new, if you think about it, are the challenges that start arising when you implement the concepts that Matt talked about. Consider one of the largest private credit card issuers on the planet. And you think about delivering that personalized experience all the way to an end customer. You've got a private credit card issuer. They do business with hundreds of thousands of companies. Their account managers are interacting with those companies, and all of that lands back on a marketing organization that has to jointly plan promotions with those companies to drive the private credit card business. That marketing team needs visibility to the work that's happening. Or consider a major medical manufacturer who's trying to get medical products out the door. And the marketing team is trying to coordinate with the product team, with the regulatory team, with the supply chain team, with the legal team. And they're trying to orchestrate all of that work, so that they can get products out the door more quickly. Or maybe a financial services organization that's also trying to get new products out the door, and they're trying to get all the approval about the content that goes with those products, and it's all about speed to market. That's what's creating the need for the new, as you phrased it, Dave. >> Yes. Excellent. Thank you. So, then Matt, paint a picture. A lot of people may not be familiar with IBM iX. Maybe how you guys... You got creators, you got deep expertise in this area. So, maybe talk about where you add value, and how you work with Adobe. >> Okay, so IBM iX, so, we sit within the services business at IBM. As you said, Dave, right, we have designers, experienced strategists, engineers, basically able to deliver kind of end-to-end digital and customer experience solution, right from the creative, all the way through to the technology platforms, and the operations. Adobe is one of our key strategic partners across IBM, and certainly within my part of the business. And so, we couldn't have been more delighted when Workfront joined Adobe, through the acquisition there. So, we already had a strong relationship with the Workfront team. And so now seeing that as part of the Adobe platform and family there, really opens up massive opportunities. We're working with several major airlines, automotive companies, retailers, using Adobe technology to transform the customer experiences that they have. Putting in place new digital platforms, and new ways of engaging with those customers. But what is absolutely clear, as Alex was talking about, this need for a marketing systems of record, as this landscape becomes more complex, as the velocity of change increases the need to not just focus on the customer experience, and how a customer interacts with the brand, but the need to get the workflows and the processes within the organization that sit behind that, organized, executing in the correct way, in an efficient way, in order to make sure that you can deliver on that customer promise. And so this is absolutely critical, effectively, to drive this kind of workflow improvement, the productivity improvement, and put intelligence and automation into these processes, across the organization. So there is, certainly, we believe a huge opportunity together in the market, to help clients transform, and to deliver the value in this space. >> Got it. Alex, maybe you can just, at a high level, share some examples of how Adobe, and drawing on your experiences from Workfront, how you've helped companies where they had to get content out, they had to automate the processes, and the outcomes that you saw, that you hope to share with other clients. >> You know what, what Matt's talking about is the need for intelligent workflows within a marketing organization. Because a marketing organization is trying to solve one of two challenges. Either they're trying to be more efficient because they can't get more resources to do the work that they need to do, or they're trying to operate with speed. And so what our breakthrough thinking was, Dave, in terms of solving these problems, and then I'll give an example, is the realization that while it seemed like work should be different in different enterprises, ultimately, all work has five elements to it. The first thing is, you decide to do something, or I ask you to do something. So, we have to have the strategic planning around the intake of work. Then we have to plan out the work. Then we actually have to execute the work. We have to understand who's doing what. We have to have transparency to whether or not that work is getting done, or if people need help in that work. Then that work needs to be approved by somebody. And then finally, especially in marketing, then we have to actually deliver that work to a technology like ADM, where we're going to publish it on the web. So, if you take the case of a major financial, a financial company that serves consumers, that financial company is constantly bringing new products to market. Now, if you're bringing new products to market, if you think about the United States, you have to make sure that you have supported the regulatory approval that's necessary for a product. So, that product has to be able to go to the right investor. That product, if it's in a certain state, has to have oversight to it. So, now you're a marketing team, in a financial services organization that's supporting getting new product to market, and in a particular customer, it used to take 'em 63 days to go through all of the approvals necessary to just get content out the door. Now that they are effectively intaking the work, planning the work, executing the work reviewing the work, and delivering the work digitally, that's down to eight days. >> And with the martech platform, you have the data. So, you know what content you want to get out, and you can make decisions much better. I mean, my big takeaway is, you got the art of marketing, and those with the marketing DNA, I don't have that gene, but it's intersecting with the science and automation, and the data, and the workflows, and driving efficiency, and ultimately driving results and revenue. So, that's my big takeaway from this conversation, but Alex, maybe you could give us your takeaway, and then Matt, you can bring us home. >> Yeah. I mean, my takeaway is in this new economy, marketing is a tier one corporate activity. Marketing is a peer activity to manufacturing, to distribution, to sales, and to finance. And every one of those disciplines are managed with a system. Marketing needs its own system, because it's as important as any other organization. And so to me, Dave, it's no more complicated than that. That marketing is now as important as every other function. And it needs to be managed as every other function. And Workfront is the application that marketing manages the workflows, and the business of marketing. >> All right, Matt. Give us your final thoughts, please. >> Yep, no. My final thought, building on what Alex said, so, we've put together a joint point of view with Adobe, and with Workfront, called "Intelligent Content Transformation," right. That is our strategic framework to help clients accelerate on this journey, both of delivering these amazing customer outcomes, but how we transform the processes within the marketing organization. And I think that yes, you can continue to focus on delivering amazing digital experiences for customers, and it's absolutely critical, and that's critical to revenue growth, but actually, what's also critical, is to drive efficiency in these workflows across the enterprise, right? And that is not only going to enable the revenue growth, it's going to enable you to deliver on that promise. But it's also going to result in significant cost and efficiency improvements for these companies, by focusing on marketing in the same way as we have done for procurement transformation, supply chain transformation, finance transformation, HR transformation, right? There's a lot of effort gone into the efficiency of those workflows. We've got to do the same for marketing. So, massive opportunity, Dave, massive. >> It is massive. Every company has to, in some way, shape, or form, put high-quality content in front of their customers to engage with them. Gentlemen, thanks so much for coming on "theCUBE." Really appreciate your time. >> Yeah, thanks for having us. >> All right- >> See you again. >> And thank you everybody for watching. This is Dave Vellante for "theCUBE." You're watching IBM Think 2021, the virtual edition. We'll be right back. (bright music) ♪ Da, de, de, da, da, de, da, la ♪ (bright music)

>>from around the globe. It's the >>cube presenting >>active de que intelligent automation for data quality brought to you by Iota Ho >>Welcome to the sixth episode of the I. O. Tahoe data automation series. On the cube. We're gonna start off with a segment on how to accelerate the adoption of snowflake with Glenn Grossman, who is the enterprise account executive from Snowflake and yusef khan, the head of data services from Iota. Gentlemen welcome. >>Good afternoon. Good morning, Good evening. Dave. >>Good to see you. Dave. Good to see you. >>Okay glenn uh let's start with you. I mean the Cube hosted the snowflake data cloud summit in November and we heard from customers and going from love the tagline zero to snowflake, you know, 90 minutes very quickly. And of course you want to make it simple and attractive for enterprises to move data and analytics into the snowflake platform but help us understand once the data is there, how is snowflake helping to achieve savings compared to the data lake? >>Absolutely. dave. It's a great question, you know, it starts off first with the notion and uh kind of, we coined it in the industry or t shirt size pricing. You know, you don't necessarily always need the performance of a high end sports car when you're just trying to go get some groceries and drive down the street 20 mph. The t shirt pricing really aligns to, depending on what your operational workload is to support the business and the value that you need from that business? Not every day. Do you need data? Every second of the moment? Might be once a day, once a week through that t shirt size price and we can align for the performance according to the environmental needs of the business. What those drivers are the key performance indicators to drive that insight to make better decisions, It allows us to control that cost. So to my point, not always do you need the performance of a Ferrari? Maybe you need the performance and gas mileage of the Honda Civic if you would just get and deliver the value of the business but knowing that you have that entire performance landscape at a moments notice and that's really what what allows us to hold and get away from. How much is it going to cost me in a data lake type of environment? >>Got it. Thank you for that yussef. Where does Io Tahoe fit into this equation? I mean what's, what's, what's unique about the approach that you're taking towards this notion of mobilizing data on snowflake? >>Well, Dave in the first instance we profile the data itself at the data level, so not just at the level of metadata and we do that wherever that data lives. So it could be structured data could be semi structured data could be unstructured data and that data could be on premise. It could be in the cloud or it could be on some kind of SAAS platform. And so we profile this data at the source system that is feeding snowflake within snowflake itself within the end applications and the reports that the snowflake environment is serving. So what we've done here is take our machine learning discovery technology and make snowflake itself the repository for knowledge and insights on data. And this is pretty unique. Uh automation in the form of our P. A. Is being applied to the data both before after and within snowflake. And so the ultimate outcome is that business users can have a much greater degree of confidence that the data they're using can be trusted. Um The other thing we do uh which is unique is employee data R. P. A. To proactively detect and recommend fixes the data quality so that removes the manual time and effort and cost it takes to fix those data quality issues. Uh If they're left unchecked and untouched >>so that's key to things their trust, nobody's gonna use the data. It's not trusted. But also context. If you think about it, we've contextualized are operational systems but not our analytic system. So there's a big step forward glen. I wonder if you can tell us how customers are managing data quality when they migrate to snowflake because there's a lot of baggage in in traditional data warehouses and data lakes and and data hubs. Maybe you can talk about why this is a challenge for customers. And like for instance can you proactively address some of those challenges that customers face >>that we certainly can. They have. You know, data quality. Legacy data sources are always inherent with D. Q. Issues whether it's been master data management and data stewardship programs over the last really almost two decades right now, you do have systemic data issues. You have siloed data, you have information operational, data stores data marks. It became a hodgepodge when organizations are starting their journey to migrate to the cloud. One of the things that were first doing is that inspection of data um you know first and foremost even looking to retire legacy data sources that aren't even used across the enterprise but because they were part of the systemic long running operational on premise technology, it stayed there when we start to look at data pipelines as we onboard a customer. You know we want to do that era. We want to do QA and quality assurance so that we can, And our ultimate goal eliminate the garbage in garbage out scenarios that we've been plagued with really over the last 40, 50 years of just data in general. So we have to take an inspection where traditionally it was E. T. L. Now in the world of snowflake, it's really lt we're extracting were loading or inspecting them. We're transforming out to the business so that these routines could be done once and again give great business value back to making decisions around the data instead of spending all this long time. Always re architect ng the data pipeline to serve the business. >>Got it. Thank you. Glenda yourself of course. Snowflakes renowned for customers. Tell me all the time. It's so easy. It's so easy to spin up a data warehouse. It helps with my security. Again it simplifies everything but so you know, getting started is one thing but then adoption is also a key. So I'm interested in the role that that I owe. Tahoe plays in accelerating adoption for new customers. >>Absolutely. David. I mean as Ben said, you know every every migration to Snowflake is going to have a business case. Um uh and that is going to be uh partly about reducing spending legacy I. T. Servers, storage licenses, support all those good things um that see I want to be able to turn off entirely ultimately. And what Ayatollah does is help discover all the legacy undocumented silos that have been built up, as Glenn says on the data estate across a period of time, build intelligence around those silos and help reduce those legacy costs sooner by accelerating that that whole process. Because obviously the quicker that I. T. Um and Cdos can turn off legacy data sources the more funding and resources going to be available to them to manage the new uh Snowflake based data estate on the cloud. And so turning off the old building, the new go hand in hand to make sure those those numbers stack up the program is delivered uh and the benefits are delivered. And so what we're doing here with a Tahoe is improving the customers are y by accelerating their ability to adopt Snowflake. >>Great. And I mean we're talking a lot about data quality here but in a lot of ways that's table stakes like I said, if you don't trust the data, nobody's going to use it. And glenn, I mean I look at Snowflake and I see obviously the ease of use the simplicity you guys are nailing that the data sharing capabilities I think are really exciting because you know everybody talks about sharing data but then we talked about data as an asset, Everyone so high I to hold it. And so sharing is is something that I see as a paradigm shift and you guys are enabling that. So one of the things beyond data quality that are notable that customers are excited about that, maybe you're excited about >>David, I think you just cleared it out. It's it's this massive data sharing play part of the data cloud platform. Uh you know, just as of last year we had a little over about 100 people, 100 vendors in our data marketplace. That number today is well over 450 it is all about democratizing and sharing data in a world that is no longer held back by FTp s and C. S. V. S and then the organization having to take that data and ingested into their systems. You're a snowflake customer. want to subscribe to an S and P data sources an example, go subscribe it to it. It's in your account there was no data engineering, there was no physical lift of data and that becomes the most important thing when we talk about getting broader insights, data quality. Well, the data has already been inspected from your vendor is just available in your account. It's obviously a very simplistic thing to describe behind the scenes is what our founders have created to make it very, very easy for us to democratize not only internal with private sharing of data, but this notion of marketplace ensuring across your customers um marketplace is certainly on the type of all of my customers minds and probably some other areas that might have heard out of a recent cloud summit is the introduction of snow park and being able to do where all this data is going towards us. Am I in an ale, you know, along with our partners at Io Tahoe and R. P. A. Automation is what do we do with all this data? How do we put the algorithms and targets now? We'll be able to run in the future R and python scripts and java libraries directly inside Snowflake, which allows you to even accelerate even faster, Which people found traditionally when we started off eight years ago just as a data warehousing platform. >>Yeah, I think we're on the cusp of just a new way of thinking about data. I mean obviously simplicity is a starting point but but data by its very nature is decentralized. You talk about democratizing data. I like this idea of the global mesh. I mean it's very powerful concept and again it's early days but you know, keep part of this is is automation and trust, yussef you've worked with Snowflake and you're bringing active D. Q. To the market what our customers telling you so far? >>Well David the feedback so far has been great. Which is brilliant. So I mean firstly there's a point about speed and acceleration. Um So that's the speed to incite really. So where you have inherent data quality issues uh whether that's with data that was on premise and being brought into snowflake or on snowflake itself, we're able to show the customer results and help them understand their data quality better Within Day one which is which is a fantastic acceleration. I'm related to that. There's the cost and effort to get that insight is it's a massive productivity gain versus where you're seeing customers who've been struggling sometimes too remediate legacy data and legacy decisions that they've made over the past couple of decades, so that that cost and effort is much lower than it would otherwise have been. Um 3rdly, there's confidence and trust, so you can see Cdos and see IOS got demonstrable results that they've been able to improve data quality across a whole bunch of use cases for business users in marketing and customer services, for commercial teams, for financial teams. So there's that very quick kind of growth in confidence and credibility as the projects get moving. And then finally, I mean really all the use cases for the snowflake depend on data quality, really whether it's data science, uh and and the kind of snow park applications that Glenn has talked about, all those use cases work better when we're able to accelerate the ri for our joint customers by very quickly pushing out these data quality um insights. Um And I think one of the one of the things that the snowflake have recognized is that in order for C. I. O. Is to really adopt enterprise wide, um It's also as well as the great technology with Snowflake offers, it's about cleaning up that legacy data state, freeing up the budget for CIA to spend it on the new modern day to a state that lets them mobilise their data with snowflake. >>So you're seeing the Senate progression. We're simplifying the the the analytics from a tech perspective. You bring in Federated governance which which brings more trust. Then then you bring in the automation of the data quality piece which is fundamental. And now you can really start to, as you guys are saying, democratized and scale uh and share data. Very powerful guys. Thanks so much for coming on the program. Really appreciate your time. >>Thank you. I appreciate as well. Yeah.

>>from around the globe. It's the Cube with digital coverage of workplace next made possible by Hewlett Packard Enterprise. >>Hello, everyone. We're here covering workplace next on the Cube For years, you know, we've talked about new ways to work, and it was great thought exercise. And then overnight the pandemic heightened the challenges of creating an effective work force. Most of the executives that we talked to in our survey say that productivity actually has improved since the work from Home Mandate was initiative. But, you know, we're talking not just about productivity, but the well being of our associates and managing the unknown. We're going to shift gears a little bit now. We've heard some interesting real world examples of how organizations are dealing with the rapid change in workplace, and we've heard about some lessons to take into the future. But now we're going to get more practical and look at some of the tools that are available to help you navigate. The changes that we've been discussing and with me to talk about these trends related to the future of work are are are Qadoura, who's the vice president of worldwide sales and go to market for Green Lake at HP Sadat Malik is the VP of I O t and Intelligent Edge at HP and Satish Yarra Valley is the global cloud and infrastructure practice Head at Whip Probe guys welcomes. Good to see you. Thanks for coming on. >>Thanks for having us. >>You're very welcome. Let me start with Sadat. You're coming from Austin, Texas here. So thank you. Stay crazy. As they say in Austin, for the uninitiated, maybe you could talk a little bit about h p E point. Next. It's a strategic component of H p. E. And maybe tell us a little bit about those services. >>Thank you so much for taking the time today. Appreciate everybody's participation here. So absolutely so point Next is HP Services on. This is the 23,000 strong organization globally spread out, and we have a very strong ecosystem of partners that be leveraged to deliver services to our customers. Um, our organization differentiates itself in the market by focusing on digital digital transformation journeys for our customers. For customers looking toe move to a different way off, engaging with its customers, transforming the way its employees work, figuring out a different way off producing the products that it sells to. His customers are changing the way it operationalize these things. For example, moving to the cloud going to a hybrid model, we help them achieve any of these four transformation outcomes. So point next job is toe point. What is next in this digital transformation journey and then partner with our customers to make that happen? So that's what we do. >>Thank you for that. I mean, obviously, you're gonna be seeing a lot of activity around workplace with shift from work from home, changes in the network changes in security. I mean the whole deal. What are some of your top takeaways that you can share with our audience? >>Yeah, they're >>so a lot has been happening in the workplace arena lately. So this is not new, right? This is not something that all of a sudden side happening when Kobe 19 hit, uh, the digital workplace was already transforming before over 19 happened. What over 19 has done is that it has massively accelerated the pace at which this change was happening. So, for example, right remote work was already there before over 19. But now everybody is working remotely so, in many ways, the solution that we have for remote work. They have been strained to appoint, never seen before. Networks that support these remote work environments have been pushed to their limits. Security was already there, right? So security was a critical piece off any off the thinking, any of the frameworks that we had. But now security is pivotal and central. Any discussion that we're having about the workplace environment data is being generated all across the all across the environment that we operated, right? So it's no longer being generated. One place being stored. Another. It's all over the place now. So what Kobe, 19 has done is that the transformation that was already underway in the digital workplace, it has taken that and accelerated it massive. The key take away for me is right that we have to make sure that when we're working with our customers, our clients, we don't just look at the technology aspect of things. We have to look at all the other aspect as well the people in the process aspect off this environment. It is critical that we don't assume that just because the technology is there to address these challenges that I just mentioned. Our people and our processes would be able to handle that as well. We need to bring everybody along. Everybody has different needs, and we need to be able to cater to those needs effectively. So that's my biggest take away. Make sure that the process and the people aspect of things was hand in glove with the technology that we were able to bring to bear here. >>Got it. Thank you. So, ah, let's go to San Francisco, bringing our war to the conversation. You're one of your areas of focus is is HP Green Lake. You guys were early on with the as a service model. Clearly, we've seen Mawr interest in cloud and cloud like models. I wonder if you could just start by sharing. What's Green Lake all about? Where does it fit into this whole workplace? Next, Uh, conversation that we're having? >>Yeah, absolutely. Um HP Green lake effectively is the cloud that comes to your data center to your Coehlo or to your edge, right? We saw with Public Cloud. The public cloud brought a ton of innovations, um, into the sort of hyper scale model. Now, with HP. What we've done is we've said, Look, customers need this level of innovation and this level of, you know, pay as you go economics the, you know, management layer the automation layer not just in a public cloud environment, but also in our customers data center or to the other potential edges or Coehlo scenarios. And what we've done is we've brought together Asada just mentioned the best of our point next services our software management layer as well as H. P. E s rich portfolio of hardware to come together to create that cloud experience. Um, of course, we can't do this without the rich ecosystem around us as well. And so everything from you know, some of our big S I partners like we bro, who also have the virtual desktop expertise or virtual desk that then come together to start helping us launch some of these new workloads supported cloud services such as D. D i eso for my perspective, v. D. I is the most important topic for a lot of our customers right now, especially in sectors like financial services, um, advanced engineering scenarios and health care where they need access to those, uh to their data centers in a very secure way and in a highly cost optimized way as well. >>Well, okay. Thank you. And then let's let's bring in, uh, petition talk a little bit about the ecosystem. I mean, we're pro. That's really kind of your wheelhouse. We've been talking a lot on the cube about moving from an industry of point products to platforms and now ecosystem innovation, Uh, are are mentioned VD I we saw that exploding eso teach. Maybe you could weigh in here and and share with us what you're seeing in the market and specifically around ecosystem. >>As we all know, the pandemic has redefined the way we collaborate to support this collaboration. We have set up huge campuses and office infrastructure In summary, our industry has centralized approach. Now, the very premise of the centralization bringing people together for work has changed. This evolving workspace dynamics have triggered the agency to reimagine the workspace strategy. CEO, CEO S and C H R ose are all coming together to redefine the business process and find new ways off engaging with customers and employees as organizations embrace work from home for the foreseeable future. Customer need to create secure by design workspaces for remote working environments. With the pro virtual disk platform, we can help create such seamless distal workspaces and enable customers to connect, collaborate and communicate with ease from anywhere securely. They're consistent user experience. Through this platform led approach, we are able to utter the market demands which are focused on business outcomes. >>Okay, and this is the specifics of this hard news that you're talking about Video on demand and Citrix coming together with your ecosystem. H p E were pro and again, the many partners that you work with is that correct? >>Well, actually, Dave, we see a strong playoff ecosystem partners coming together to achieve transformative business outcomes. As Arbor said earlier, HP and Wipro have long standing partnership, and today's announcement around HP Green Lake is an extension off this collaboration, where we provide leverage HP Green Leg Andre Pro, which elders platform to offer video as a service in a paper user model. Our aim is to enable customers fast track there. It is still works based transformation efforts by eliminating the need to support upfront capital investments and old provisioning costs while allowing customers to enjoy the benefit off compromise, control, security and compliance. Together, we have implemented our solution across various industry segments and deliver exceptional customer experiences by helping customer businesses in their workspace. Transformation journeys by defining their workspace strategy with an intelligent, platform led approach that enables responsiveness, scalability and resilience. It's known that Wipro is recognized as a global leader in the distal workspace and video I, with HP being a technology leader, enabling us with high level of program ability on integration capabilities. We see tremendous potential to jointly address the industry challenges as we move forward. >>Excellent. Uh, sad. I wanna come back to you. We talk a lot about the digital business, the mandate for digital business, especially with the pandemic. Let's talk about data. Earlier this year, HP announced the number of solutions that used data to help organizations work more productively safely. You know, the gamut talk about data and the importance of data and what you guys were doing there specifically, >>Yeah, that's a great question. So that is fundamental to everything that we're doing in the workplace arena, right? So from a technology perspective that provides us with the wherewithal to be able to make all the changes that we want to make happen for the people in the process side of things. So the journey that we've been on this past year is a very interesting one. Let me share with the audience a little bit of what's been going on on the ground with our customers. Um, what's what's been happening in the field? So when the when Kobe 19 hit right, a lot of our customers were subjected to these shutdown, which were very pervasive, and they had to stop their operations. In many cases, they had to send their employees home. So at that point, HB stepped in the point. Next organization stepped in and helped these customers set up remote work out options, which allowed them to keep their businesses going while they handle these shutdowns. Fast forward. Six months and the shutdown. We're starting to get lifted and our customers were coming back to us and saying to us that Hey, we would now like to get a least a portion off our workforce back to the normal place of work. But we're concerned that if we do that, it's gonna jeopardize their safety because off the infection concerned that were there. So what we did was that we built a cities or five solutions using various types of video analytics and data analysis analysis technologies that allowed these customers to make that move. So these five solutions, uh, let me walk, walk our customers and our clients and audience through those. The first two of these solutions are touchless entry and fever detection. So this is the access control off your premise, right? So to make sure that whoever is entering the building that's in a safe manner and any infection concerned, we stop it at the very get go once the employees inside the workplace, the next thing that we have is a set of two solutions. What one is social distance tracing and tracking, and the other one is workplace alerting. What these two solutions do is that they use video analytics and data technology is to figure out if there is a concern with employees adhering to the various guidelines that are in place on alerting the employees and the employers if there is any infringement happening which could risk overall environment. Finally, we realized right that irrespective off how much technology and process we put in place. Not everybody will be able to come into the normal place of work. So what we have done is that the first solution that we have is augmented reality and visual remote guidance. This solution uses a our technologies allow. People were on site to take advantage of the expertise that resides offsite to undertake complex task task, which could be as complex as overhauling a machine on ah factory floor using augmented reality where somebody off site who's an expert in that machine is helping somebody on site data has become central to a lot of the things that we do. But as I said, technology is one aspect of things. So ultimately the people process technology continuum has to come together to make these solutions real for our customers. >>Thank you, Arwa. We just have just about 30 seconds left and I wonder if you could close on. We're talking about cloud hybrid. Uh, everybody's talking about hybrid. We're talking about the hybrid workplace. What do you see for the for the future over the next 2345 years? >>Absolutely. And I think you're right, Dave. It is, ah, hybrid world. It's a multi cloud world. Ultimately, what our customers want is the choice and the flexibility to bring in the capabilities that drive the business outcomes that they need to support. And that has multiple dimensions, right? It's making sure that they are minimizing their egress costs, right. And many of our on Prem solutions do give them that flexibility. It is the paper use economics that we talked about. It is about our collective capability as an ecosystem to come together. You know, with Citrix and NVIDIA with R s I partner we pro and the rich heritage of HP es services as well as hardware to bring together these solutions that are fully managed on behalf of our customers so that they can focus their staff their i t capabilities on the products and services they need to deliver to their customers. >>Awesome. Guys, I wish we had more time. We got to go day volonte for the cube. Keep it right there. Lots of great more content coming your way. >>Yeah,

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a cube conversation. >> Everyone welcome to this cube conversation here in the Palo Alto cube studios. I'm John Furrier your host of theCUBE, with a great guest here, Shanthi Vigneshwaran, who is with the Office of Strategic programs in the Center for Drug Evaluation and Research within the US Food and Drug Administration, FDA, is the Informatica Intelligent Disrupter of the Year award. Congratulations, Shanthi welcome to this cube conversation. Thanks for joining me. >> Thank you for having me. >> Congratulations on being the Informatica Intelligent Disrupter of the year award. Tell us more about the organization. I see FDA everyone's probably concerned these days making sure things going faster and faster, more complex, more things are happening. Tell us about your organization and what you work on. >> FDA is huge, our organization is Center for Drug Evaluation research. And its core mission is to promote public health by ensuring the availability of safety and effective drugs. For example any drugs you go and buy it in the pharmacy today, Our administration helps in trying to approve them and make sure it's so in term of quality and integrity of the marketed products in the industry. My office is specifically Office of strategic programs whose mission is to transform the drug regulatory operations with the customer focus through analytics and informatics. They work towards the advancement for the CDERs public health mission. >> What are some of the objectives that you guys have? What are some things you guys have as your core top objectives of the CDER, the drug research group? >> The core objectives is we wanted to make sure that we are promoting a safe use of the marketed drugs. We want to make sure there's the availability of the drugs that are going to the patients are effective. And also the quality of the drugs that are being marketed are able to protect public health. >> What are some of the challenges that you guys have to take in managing the pharmaceutical safety, because I can only imagine certainly now that supply chains, tracing, monitoring, drug efficacy, safety, all these things are happening. What are some of the challenges in doing all this? >> In our office there are challenges in three different areas. One is the drug regulation challenges because as drugs are being more advanced and as there are more increasingly complex products, and there are challenging in the development area of the drugs, we wanted to make sure here we have a regulation that supports any advancement in science and technology. The other thing is also Congress is actually given new authorities and roles for the FDA to act. For example the Drug Quality and Security Act, which means any drug that's they want to track and trace all the drugs that goes to the public is they know who are the distributors, who are the manufacturers. Then you have the 21st Century Cures Act, and also the CARES Act package which was recently assigned, which also has a lot of the OTC drug regulatory modernization. Then there's also the area of globalization because just as disease don't have any borders, Product safety and quality are no longer on one country. It's basically a lot of the drugs that are being manufactured are overseas and as a result we wanted to make sure there are 300 US ports. And we want to make sure the FDA regulated shipments are coming through correctly to proper venues and everything is done correctly. Those are some the challenges we have to deal with. >> So much going on a lot of moving purchase as people say, there's always drug shortages, always demand, knowing that and tracking it. I can only imagine the world you're living in because you got to be innovative, got to be fast, got to be cutting edge, got to get the quality right. Data is super critical. And can you share take a minute to explain some of the data challenges you have to address and how you did that. Because I mean I could almost just my mind's blown just thinking about how you live it every day. Can you just share some of those challenges that you had to address and how did you do? >> Some of the key challenges we actually see is we have roughly 170,000 regulatory submissions per year. There are roughly 88,000 firm registration and product listing that comes to us, and then there are more than 2 million adverse event reports. So with all these data submissions and organization as such as us we need it, we have multiple systems where this data is acquired and each has its own criteria for validating the data. Adding to it are internal and external stakeholders also want certain rules and the way the data is being identified. So we wanted to make sure there is a robust MDM framework to make sure to cleanse and enrich and standardize the data. So that it basically make sure the trust and the availability and the consistent of the data, is being supplied to published to the CDER regulatory data users. >> You guys are dealing with- >> Otherwise like it's almost to give them a 360 degree view of the drug development lifecycle. Through each of the different phases, both pre market which is before the drug hits the market, and then after it hits the market. We still want to make sure the data we receive still supports a regulatory review and decision making process. >> Yeah, and you got to deliver a consumer product to get people at the right time. All these things have to happen, and you can see it clearly the impacts everyday life. I got to ask you that the database question 'cause the database geek inside of me is just going okay. I can only imagine the silos and the different systems and the codes, because data silos is big document. We've been reporting on this on theCUBE for a long time around, making data available automation. All these things have to happen if there's data availability. Can you just take one more minute talk about some of the challenges there because you got to break down the silos at the same time you really can't replace them. >> That's true. What we did was we did leave it more of us I mean, step back like seven years ago, when we did the data management. We had like a lot of silo systems as well. And we wanted to look at we wanted to establish a, we knew we wanted to establish a master data management. So we took a little bit more of a strategic vision. And so what we ended up saying is identifying what are the key areas of the domain that will give us some kind of a relationship. What are the key areas that will give us the 360 degree lifecycle? So that's what we did. We identified the domains. And then we took a step back and said and then we looked at what is the first domain we wanted to tackle. Because we know what are these domains are going to be. And then we were like, okay, let's take a step back and say which is the domain we do it first that will give us the most return on investment, which will make people actually look at it and say, hey, this makes sense. This data is good. So that's what we ended up looking at. We looked at it as at both ends. One is from a end user perspective. Which is the one they get the benefit out of and also from a data silo perspective which is the one data domains that are common, where there's duplication that we can consolidate. >> So that's good. You did the work up front. That's critical knowing what you want to do and get out of it. What were some of the benefits you guys got out of it. From an IT standpoint, how does that translate to the business benefits? And what was achieved? >> I think the benefits we got from the IT standpoint was a lot of the deduplication was not theirs. Which basically means like a lot of the legacy systems and all of the manual data quality work we had to do we automated it. We had bots, we also had other automation process that we actually put into work with Informatica, that actually helped us to make sure it's the cost of it actually went for us considerably. For example it used to take us three days to process submissions. Now it takes us less than 24 hours to do it, for the users to see the data. So it was a little bit more, we saw the, we wanted to look at what are the low hanging fruits where it's labor intensive and how can we improve it. That's how we acted there. >> What are some of the things that you're experiencing? I mean, like, we look back at what it was before, where it is now? Is it more agility, you more responsive to the changes? Was it an aspirin? Was it a complete transformation? Was some pain reduced? Can you share just some color commentary on kind of before the way it was before and then what you're experiencing now? >> So for us, I think before, we didn't know where the for us, I mean, I wouldn't say we didn't know it, when we have the data, we looked at product and it was just product. We looked at manufactured they were all in separate silos. But when we did the MDM domain, we were able to look at the relationship. And it was very interesting to see the relationship because we now are able to say is. for example, if there is a drug shortage during due to hurricane, with the data we have, we can narrow down and say, Hey, this area is going to be affected which means these are the manufacturing facilities in that area , that are going to be not be able to function or impacted by it. We can get to the place where the hurricane tracks we use the National Weather Service data, but it helps us to narrow down some of the challenges and we can able to predict where the next risk is going to be. >> And then before the old model, there was either a blind spot or you were ad hoc, probably right? Probably didn't have that with you. >> Yeah, before you were either blind or you're doing in a more of a reactionary not proactively. Now we are able to do a little bit more proactively. And even with I mean drug shortages and drug supply chain are the biggest benefit we saw with this model. Because, for us the drug supply chain means linking the pre and post market phases that lets us know if there's a trigger and the adverse events, we actually can go back to the pre market side and see where the traceability is who's at that truck. What are all the different things that was going on. >> This is one of the common threats I see in innovation where people look at the business model and data and look at it as a competitive advantage, in this case proactivity on using data to make decisions before things happen, less reactivity. So that increases time. I mean, that would probably you're saying, and you get there faster, if you can see it, understand it, and impact the workflows involved. This is a major part of the data innovation that's going on and you starting to see new kinds of data whereas has come out. So again, starting to see a real new changeover to scaling up this kind of concept almost foundationally. What's your thoughts just as someone who's a practitioner in the industry as you start to get this kind of feelings and seeing the benefits? What's next, what do you see happening because you haven't success. How do you scale it? What how do you guys look at that? >> I think our next is we have the domains and we actually have the practices that we work. We look at it as it's basically data always just changes. So we look at is like what are some of the ways that we can improve the data? How can we take it to the next level. Because now they talk about power. They are also warehouse data lakes. So we want to see is how can we take these domains and get that relationship or get that linkages when there is a bigger set of data that's available for us. What can we use that and it actually we think there are other use cases we wanted to explore and see what is the benefit that we can get a little bit more on the predictability to do like post market surveillance or like to look at like safety signals and other things to see what are the quick things that we can use for the business operations. >> It's really a lot more fun. You're in there using the data. You're seeing the benefits and real. This is what clouds all about the data clouds here. It's scaling. Super fun to talk about and excited. When you see the impacts in real time, not waiting for later. So congratulations. You guys have been selected and you receive recognition from Informatica as the 2020, Intelligent Disrupter of the year. congratulations. What does that mean for your organization? >> I think we were super excited about it. But one thing I can say is when we embarked on this work, like seven years ago, or so, problem was like we were trying to identify and develop new scientific methods to improve the quality of our drugs to get that 360 degree view of the drug development lifecycle. The program today enables FDA CDER to capture all the granular details of data we need for the regulatory data. It helps us to support the informed decisions that we have to make in real time sometimes or and also to make sure when there's an emergency, we are able to respond with a quick look at the data to say like, hey this is what we need to do. It also helps the teams. It recognizes all the hard work. And the hours we put into establishing the program and it helped to build the awareness within FDA and also with the industry of our political master data management is. >> It's a great reward to see the fruits of the labor and good decision making I'm sure it was a lot of hard work. For folks out there watching, who are also kind of grinding away in some cases, some cases moving faster. You guys are epitome of a supply chain that's super critical. And speed is critical. Quality is critical. A lot of days critical. A lot of businesses are starting to feel this as part of an integrated data strategy. And I'm a big proponent. I think you guys have have a great example of this. What advice would you have for other practitioners because you got data scientists, but yet data engineers now who are trying to architect and create scale, and programmability, and automation, and you got the scientists in the the front lines coming together and they all feed into applications. So it's kind of a new things go on. Your advice to folks out there, on how to do this, how to do it right, the learnings, share. >> I think the key thing I, at least for my learning experience was, it's not within one year you're going to accomplish it, It's kind of we have to be very patient. And it's a long road. If you make mistakes, you will have to go back and reassess. Even with us, with all the work we did, we almost went back a couple of the domains because we thought like, hey, there are additional use cases how this can be helpful. There are additional, for example, we went with the supply chain, but then now we go back and look at it and say like, hy, there may be other things that we can use with the supply chain not just with this data, can we expand it? How can we look at the study data or other information so that's what we try to do. It's not like you're done with MDM and that is it. Your domain is complete. It's almost like you look at it and it creates a web and you need to look at each domain and you want to come back to it and see how it is you have to go. But the starting point is you need to establish what are your key domains. That will actually drive your vision for the next four or five years. You can't just do bottom up, it's more of like a top down approach. >> That's great. That's great the insight. And again, it's never done. I mean, it's data is coming. It's not going away. It's going to be integrated. It's going to be shared. You got to scale it up. A lot of hard work. >> Yeah. >> Shanthi thank you so much for the insight. Congratulations on your receiving the Disrupter of the Year Award winner for Informatica. congratulations. Intelligence >> Yeah, thank you very much for having me. Thank you. >> Thank you for sharing, Shanthi Vigneshswaran is here, Office of Strategic programs at the Center for Drug Evaluation and Research with the US FDA. Thanks for joining us, I'm John Furrier for theCUBE. Thanks for watching. (soft music)