>> Announcer: From around the globe. It's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome to theCUBEs coverage of IBM Think 2021. I'm Lisa Martin, joined next by Peter Morrow the VP of sales and marketing at IBM partner, TRM. Peter, welcome to the program. >> Hi, thank you. Happy to be here. >> Tell me a little bit about yourself and TRM before we dig in. >> Well, TRM is a long time business partner for IBM. We for about 30 years, specialize in helping IBM customers implement Maximo and have a lot of deep technology expertise and in Maximo and related software in the enterprise asset management industry. I'm the VP of sales and marketing, I've been at TRM for about 10 years and I'm proud to lead our talented sales teams and our sole mission is to help our customers get value out of their EAM solutions. And we're really excited about recent developments in AI and bringing value to a lot of our customers and their organization, and finally get ROI out of their long time investments in the EAM. >> Let's dig in a bit more to the IBM relationship. I know TRM is a gold partner, but talk to me about that and how TRM has leveraged that partnership with IBM to help your customers be successful. >> Sure. Now, we're a little bit of a unique partner with IBM for a long time. We've been pure resell and implementation services. And recently we've transitioned into an OEM relationship with IBM where we actually embed IBM products into broader TRM offerings. And this relationship that we have with IBM is really important as IBM is the dominant player in EAM and AI and hybrid cloud it's really a natural fit for us to leverage those really mature solutions and build on top of them TRMs deep expertise and the technology and the reliability side to offer more of an end to end solution to our customers. >> Got it. So the last year or so we've seen a lot of market dynamics. Talk to me about the EAM market what's going on there. What are some of the changes? >> Well, there's a couple of key changes that we see, one of the biggest changes I think that impacts our business and IBM is that customers really don't have an appetite for long expensive implementations of custom solutions. They're really looking for more turnkey solutions that have proven value already and very mature. They've already spent tens of millions of dollars implementing Maximo or related EAM solution. They really don't want to embark on this really expensive long journey to get to that next level. And so to meet this requirement we've been focused for the past couple of years on developing much more turnkey solutions. One of which is one that we call TRMs Maximo AAM, solution and that's built on Maximo, but it's also layered with IBM's new AI solution for Maximo customers. And we marry that with our deep reliability expertise and we're really excited about being able to roll it out in literally weeks instead of months or years for a lot of new customers. And that's a really short time to value and ROI is something that's pretty much unheard of until now in this industry. >> Talk to me about some of the advantages that your customers are getting like on a general level from AI, from hybrid cloud, from data. >> I mean, it's this really groundbreaking. What we're finding is that there's, until very recently AI was really not embraced as a practical solution to a lot of maintenance problems. You're looking at a community of pretty old school mindsets and maintenance and reliability where, it's a very, RCM is a very structured methodology for breaking down equipment and failure types and solutions, ways to predict those failures. And for a long time RCM specialists didn't really feel like AI was a solution that was the answer. And what we're finding is that, with the maturity level of IBM's products, it is now at a point where AI is a great fit and you take a experienced reliability specialist and you've armed them with AI tools like IBM's asset monitor and Maximo health and predict and you give them those tools and they can roll out predictive solutions that scale like really they've never had the chance to before. >> And talk to me about some of the adaptations that TRM has made in the last year or so as the market has been so much influx and so many dynamics going on, how have you adapted to that to really help those customers take advantage of the latest technologies? And for example, use AI. >> The big thing for us is recognizing that customers really aren't interested in a long expensive, drawn out solution. They recognize they have problems, but until you can come to table with something that they can digest in small bites. And that is at a price point that isn't over the top they're really happy staying with the status quo, at least until the solutions can be delivered and like that very bite-sized implementation programs. So we've spent a lot of time trying to make our solutions more turnkey, packaging up offerings in a way that you can start small, but all that effort you put in on a small scale, you can ramp up and scale enterprise wide without making a huge investment. And it doesn't take years to roll it out. You can really do something and make an impact within a couple of weeks or months, rather than you know, many months and years down the road. >> That time to value is key. Especially I think we've learned in the last year that real time is so essential for so many things. I'm just curious if any industries in particular that TRM and IBM are really helping transform like energy, for example, give me some examples of industries that are really moving forward with your technologies. >> It's really the classic asset intensive industries. Utilities are really big maximum users in there. They're the ones that, they've embraced Maximo for many, many years. They're hungry for innovative technology, but still cautious about moving forward on a large scale but we're able to get them excited with the programs that we're offering. And the same goes with oil and gas. That's another big user of Maximo, asset intensive organization and manufacturing definitely big Maximo users, all three have been very interested in moving forward with the AI for maintenance solutions that TRM and IBM are together bringing to the market. >> We summed up across the oil and gas, energy utilities, as you mentioned what are some of the biggest things that you hear in terms of demands from customers when you're in sales meetings? What are they looking for problems they need you to help solve? >> You know, it's honestly, it's the classic problems that we've been working with them for 20 years and really have they haven't been able to solve effectively where they're talking about critical assets that break down on expectedly, maintenance teams, running around doing a lot of maintenance on assets. That's in perfect health, making big promises on transforming maintenance, massively reducing maintenance budgets. And it really hasn't happened. And there's really been until now no real solution that solves those problems directly. And we believe the combination of AI and reliability engineering and in the key EAM fundamental principles is what's going to help our customer base really get value and really solve the problems that they really suffered from all these years. >> It's interesting that you say that it really they haven't been able to solve those problems but from a technological perspective the technology is there now to help them do that. What's the time window when you're talking with customers especially given the market dynamics that we're still living in, are they coming to you saying help us within a week, two weeks, we got to turn this around? >> I mean, the ones that are approaching this with an open mind, we can communicate to them that a huge undertaking is not required. They can get started on a small project, select one critical asset and then begin to plug in some data, around that asset that they know is related to equipment failures. We can get that data connected with the maximum asset monitor and within weeks they begin monitoring that asset health. They do some anomaly detection and it does not require of big long-term investment. And so for somebody who is willing to keep an open mind about AI and really wants to give it a try, that sales cycle is very short. They're willing to get going relatively quickly. We do find that many organizations want to step back take it slow, assess other options. And for them that's that aligns more with the classic, big bang type of implementation project, where that takes months of planning, budget planning approvals. And that goes into that 12, 18 month sales cycle or project planning phase, that's fine. And at the end of that, it's a big project but we really do recommend starting small. It is definitely possible to see some early quick wins and then roll out on a larger scale. And frankly, you could have something deployed at scale within that entire period of planning that they would otherwise naturally do on their own. >> Take us out of here Peter, with some predictions, some thoughts, maybe a crystal ball on where you see the EAM market going, the rest of this year and what TRM is planning to do to help customers really leverage opportunities and growth. >> You know I really do believe we're at a tipping point where there's been a lot of anticipation leading up to the release of Maximo and the Maximo eight and the Maximo application suite. There's the AI apps that are in the suite like asset monitor and health and predict that they really are mature products. There's not, I think until now there's the customer base has viewed AI as more of a like a fantasy or science fiction as it relates to maintenance, but these products are real. And I think with a lot of spending having been on hold over the past year, there's a lot of interest in learning more trying to test the waters. I really think that we're going to see a lot of interest in predictive solutions, a lot of interest in IOT projects. And, we're in a position where we're ready to begin rolling these out and it's really exciting. >> Yeah, the maturation is there, the technology is the customer interest is there certainly the opportunities are there. Peter takes out with where can customers go to learn more information about your solutions? >> I mean the best places to check us out on our website, www.trmnet.com. We're also on LinkedIn. We do a lot of blogs. We do a lot of webinars, we're out in front and trying to make the market aware of our thought leadership and deep expertise in Maximo and EAM and in predictive solutions we've got a YouTube channel where we post demos and all of our webinars. So we're trying to push information out there happy to, we look forward to interacting with prospects and customers about how our solutions can impact them. >> Excellent, Peter, thanks for stopping by and sharing with us more about the TRM IBM relationship, the opportunities in the EAM market and the appetite for AI that your customers and very big industries are having. We appreciate your time. >> Thank you very much. I enjoyed it. >> Me too, for Peter Morrow, I'm Lisa Martin. You're watching theCUBEs coverage of IBM Think 2021. (upbeat music)

>>from around the globe, it's the cube with digital coverage of >>IBM. think 2021 >>brought to you by IBM. >>Welcome to the cubes coverage of IBM Think 2021. I'm lisa martin joined next by Peter morrow, the VP of sales and marketing at IBM partner TRM. Peter welcome to the program. >>I thank you. Happy to be here. >>Tell me a little bit about yourself and TRM before we dig in. >>Um Well Trn is a longtime business partner for IBM, we for about 30 years have specialized in helping IBM customers implement maximo and um have a lot of deep technology expertise and in maximo and and related um software and the enterprise asset management industry um On the VP of sales and marketing. I've been at TRM for about 10 years and I'm I'm proud to lead our talented sales teams and our sole mission is to help our customers get value out of their am solutions and and we're really excited about recent developments in Ai and and bringing value um to a lot of our customers and their organization and finally get our ally out of their longtime investments in the am. >>Let's dig in a bit more to the IBM relationship, I know TRM as a gold partner, but talk to me about that and how TRM has leveraged that partnership with IBM to help your customers be successful. >>Um sure, now, you know, we're a little bit of a unique partner with IBM for a long time, we've been pure resell and implementation services and and recently we've transitioned into an O E M relationship with IBM where we actually embed IBM products into broader TRM offerings. And I, you know, this, this relationship that we have with with IBM is really important as IBM is the, is the dominant player in the A, m and A I and and hybrid cloud. It's really a natural fit for us to leverage those really mature solutions and build on top of them. Uh, TRS deep expertise and the technology and the reliability side to offer more of an end to end solution to our customers. >>Got it. So the last year or so we've seen a lot of market dynamics. Talk to me about the E a M, market, what's going on there? What are some of the changes? >>Um well, there's a couple of key changes that that we see. Um one of the biggest changes I think that impacts our business and IBM is that customers really don't have an appetite for long expensive implementations of custom solutions. They're really looking for more turnkey solutions that have proven value already and and very mature. You know, they've already spent tens of millions of dollars implementing, you know, maximo or related E AM solution. They really don't want to embark on, you know, this really expensive long journey um to get to that next level. And so to me this requirement, we've been focused for the past couple years on developing much more turnkey solutions, one of which is is one that we call TRM maximo A. M solution and that's built on maximum, but it's also layered with IBM's new AI solution for maximo customers. And you know, we marry that with our deep reliability expertise and you know, we're really excited about being able to roll it out and in literally weeks instead of months or years for a lot of new customers and you know, that's a really short time to value. And our ally, it's something that's pretty much unheard of until now in this industry. >>Talk to me about some of the advantages that your customers are getting like on a general level from ai from hybrid cloud from data. >>Um I mean this is really groundbreaking. What we're finding is that there's, you know, until very recently, a I was really not embraced as a practical solution to a lot of maintenance problems. You know, you're looking at a community of of pretty old school mindsets and maintenance and reliability where you know, it's a very, you know, R. C. M is a very structured methodology for breaking down um equipment and failure types and and and solutions, you know, ways to predict those failures and you know, for a long time RCM specialists didn't really feel like a I was a solution that that was the answer. And what we're finding is that, you know, with the maturity level of IBMS products, it is now at a point where a I is a great fit and you take an experienced reliability specialist and you arm them with A. I tools like like IBM is asset monitor and maximum health and predict and you give them those tools and they can, they can roll out predictive solutions that scale like like really they've never had the chance to before >>and talk to me about some of the the adaptations that TRM has made in the last year or so as the market has been so much in flux and so many dynamics going on. How have you adapted to that to really help those customers take advantage of the latest technologies and for example use aI >>you know, well the big thing for us is recognizing that that customers really aren't interested in a long expensive, drawn out solution. You know, they recognize they have problems but until you can come to the table with something that they can digest in in small bites and that, is that a price point that isn't over the top there really happy staying with the status quo at least until the solutions, you know, can be delivered in like that, you know, very um bite sized um implementation program. So we've, we've spent a lot of time trying to make our solutions more turnkey, packaging up offerings in a way that you can start small, but all that effort you put in on a small scale, you can ramp up and scale enterprise wide without making a huge investment and it doesn't take years to roll it out. You can really do something and make an impact within a couple of weeks or months rather than many months and years down the road. >>That time to value is key, especially I think we've learned in the last year that that real time is so essential for so many things. I'm just curious of any industries in particular that TRM and IBM are really helping transform energy, for example, give me some examples of industries that are really moving forward with your technologies. >>It's really the classic asset intensive industries, utilities are really big maximum users and they're they're the ones that, you know, they um there embraced, they've embraced maximum for many, many years, they're hungry for innovative technology, but still, you know, cautious about moving forward on a large scale, but we're able to get them excited with the programs that we're offering and the same goes with oil and gas, that's another big user of maximum, you know, asset intensive organization and you know, manufacturing, you know, definitely big maximo users, all three have been, you know, very interested in moving forward with, you know, the ai for maintenance solutions that, you know, TRM and and IBM are together bringing to the market, >>You summed up, you know, across the oil and gas energy utilities, as you mentioned, what are some of the biggest things that you hear in terms of demands from customers when you're in sales meetings, what are they looking for problems they need you to help solve? >>You know, it's honestly, it's it's the classic problems that we've been working with them for 20 years and really have, they haven't been able to solve effectively, you know, where they're talking about, you know, critical assets that break down unexpectedly, you know, maintenance teams running around doing a lot of maintenance on assets that's in perfect health, um making big promises on transforming maintenance, you know, massively reducing maintenance budgets and it really hasn't happened. And there's really been until now no real solution that solves those problems directly. And we believe the combination of Ai and reliability engineering and in the key e A. M fundamental principles is what's going to help our customer base really get value and really solve the problems that they really suffered from all these years. >>It's interesting that you say that it really they haven't been able to solve those problems and but from a technological perspective, the technology is there now to help them do that. What's the time window when you're talking with customers, especially given the market dynamics that were still living in, are they coming to you saying help us? You know, within a week two weeks we've got to turn this around. >>I mean the ones that are approaching this with an open mind, you know, we can we can communicate to them that a huge undertaking is not required. They can they can get started on a small project, select one critical asset and then begin to plug in some data, um, you know, around that asset that they know is related to equipment failures. We can get that data connected with the maximum asset monitor and you know, within weeks they begin monitoring that asset health, They do some anomaly detection and it does not require a big long term investment. And so for somebody who is willing to keep an open mind about AI and and really wants to give it a try, you know, that sale cycle is very short. They're willing to get going relatively quickly. You know, we do find that many organizations want to step back, take it slow, assess other options. And for them, that's that aligns more with the classic, you know, big bang type of implementation project where that takes months of planning, you know, budget planning approvals and and that goes into that 12, 18 months sales cycle or project planning phase that, you know, that's fine. And at the end of that, you know, it's a big project but we really do recommend starting small, it is definitely possible to see some early quick winds and then roll out on a larger scale and you know, frankly you could have something deployed at scale within that entire period of planning that they would otherwise naturally do on their own. >>Take us out here peter with some predictions, some thoughts maybe a crystal ball on where you see the ea m market going the rest of this year and what TRM is planning to do to help customers really leverage opportunities and growth. >>You know, I really do believe we're at a tipping point where there's been a lot of anticipation leading up to um the release of maximo and the max maximum eight and the maximum application suite. There's the Ai apps that are in the sweet like asset monitor and health and predict that they really are mature products. There's not, you know, I think until now there's the customer base has, has viewed ai as more of like a fantasy or science fiction as it relates to two maintenance. But you know, these products are real and I think with a lot of spending having been on hold over the past year, there's a lot of interest in learning more, trying to test the waters. I really think that we're going to see a lot of interest in predictive solutions, a lot of interest in IOT projects and you know, we're in a position where we're ready to begin rolling these out and it's really exciting. >>Yeah, the maturation is there, the technology is the customer interest is there? Certainly the opportunities in there. Peter take us out with customers, go to learn more information about your solutions. >>I mean, the best places to check us out on our website, W W W dot TRM net dot com were also on linked in. We do a lot of blogs, we do a lot of webinars, you know, we're out in front and trying to make um, you know, the market aware of our thought leadership and deep expertise in, you know, maximo and e a M and and in predictive solutions. Um We've got a Youtube channel where we post demos and, you know, all of our webinars, so we're, you know, we're trying to push information out there, um you know, happy to, you know, we look forward to interacting with prospects and customers about how our solutions can impact them. >>Excellent. Peter thanks for stopping by and sharing with us more about the TRM IBM relationship, the opportunities in the A. M. Market and the appetite for AI that >>your customers and >>very big industries are having. We appreciate your time. >>Hey, thank you very much. I enjoyed it. >>Two for Peter Morrow. I'm Lisa Martin. You're watching the cubes coverage of IBM think 2021 Yeah.

>> Narrator: From Gillette Stadium in Foxborough, Massachusets, it's theCUBE, covering VTUG Winter Warmer 2018. Presented by SiliconANGLE. (upbeat music) >> I'm Stu Miniman and this is theCUBE's fifth year at the VTUG Winter Warmer. 2018 is the 12th year of this event, always love when we get to talk to some of the users at the conference which's why I'm really happy to introduce to our audience Sue Morrow, who is a network manager at United Methodist Homes. Thanks for joining me Sue. >> No problem. >> First, tell me a little bit about yourself and what brings you all the way from Upstate New York to come to the VTUG. >> Well, I like to go to conferences whenever I can continue my education in IT. I grew up with computers in my house in the '80s. My dad was a physics teacher and a scientist so we always had a Commodore 64 or an Amiga in our house, growing up, when most people had Atari, we had computers. >> Totally, so Commodore 64, classic. I myself was a Tandy Radioshack, the TRS-80 Model III. So, in a similar era. >> Yep, I actually took a basic coding class on a TRS-80 when I was around 10, I think. Anyway, grew up with computers and somehow stumbled into IT later in life. So, that's why I'm here. >> United Methodist Homes, tell us just a little bit about what the mission of the company is. >> United Methodist Homes is a longterm care corporation. We have four facilities, two in the Binghamton area and two in Northeastern Pennsylvania. We have all levels of care from nursing homes, skilled care, up to independent living, and everything in between. >> Okay, and as network manager, what's under your purview? >> Well, it's kind of a silly title, actually. In longterm care or in healthcare or nonprofits, as we are, you often wear many hats and so that's, sort of, a weird title for me, but I supervise our help desk which we serve centrally from our corporate office. We serve about 600 actual computer users and, all in total, about 1200 employees who interface with the technology, in some way. So, I supervise the help desk, I make sure our network is running well. IT has changed over the years so that we're now providing more of a service and making sure that everything is up and running, network-wise, for everyone instead of keeping our servers running all the time. >> Yeah, reminds me of the old saying, it was like oh, the network is the computer, things like that, so you've got both ends of it. >> Sue: Yes. >> What kind of things are you looking at from a technology standpoint when you come to event like this? Did you catch some of the keynotes this morning, there was a broad spectrum? >> Yes. >> What are the kind of things that you're digging in to and find interesting? >> Yeah, the keynotes are really interesting. I think the first one that I went to with Luigi and Chris was great just to, kind of, expand your thinking about your own career personally, and where you want to go with your life was really interesting. I also watched Randall do his coding which is completely outside of what I do everyday, but was fascinating. And then the last major keynote was fantastic. I think that from my perspective in my company, we're kind of small and we don't do a whole lot of, we don't run apps and things like that, so the things that we have ritualized is mostly storage, so I'm looking at better ways that we can manage our storage and stuff. Most of the applications that we run now are SAS applications hosted by somebody else and their cloud, or a public cloud, or wherever, so I'm not so much looking at the cloud technologies like more businesses are that are providing an application for their company. >> It sounds like cloud and SAS's being a part of the overall strategy, have you been seeing that dynamic change in your company? How does it impact what you're doing or is it just a separate organization. >> It's definitely been a shift in the last few years, we used to run all of our applications in-house. Longterm care has caught up now, with the hospitals, so we have our electronic medical record which is a hosted application, whereas, up until five years ago, that was an on-premises application that we hosted and had to run and maintain, and update and upgrade, and make sure was available. That is definitely been a shift, that everything is now hosted. So we just make sure that our network is up and running and support our users and all of their issues when they break things, flip their screens, drop something, provide hardware for them all that sorts of stuff. >> The constant pace of innovation change. On the news this week they were saying, okay, medical records on your iPhone is up for debate. Does regulation impact your day to day activities and what are some of the challenges in that area? >> Absolutely. One of the other things we have to do is interface with the providers. We have medical providers that come in from the outside and they need to access our EMR also, so we need to provide access for them on, sometimes, whatever device they bring in, which is not always compatible, so we have a whole other set of challenges there. Where we can manage our computers for our employees by pushing out policies and things that are required for the application. When someone comes in from the outside, it isn't, necessarily, setup right, so we have that other set of challenges, and regulation-wise, yes. The government is always pushing out new and updated regulations for healthcare and we have to keep on top of that too. Of course, we have HIPAA concerns and things like that, which is also comes into play when you're talking about cloud host, and any hosted application. We have to be concerned about HIPAA, as well. >> Yeah, wondering when I look at the space that you're in, the ultimate goal is you want the patients, the people at your company, be able to spend more time, help them, not be caught up in the technology of things. Could you, maybe, talk a little bit about that dynamic? >> Yeah, one of the things that I always say is, we need to give our employees the tools that they need to do their job most efficiently. A nurse needs to be ready to go at the beginning of her shift on her laptop, ready to pass meds, and when they can't remember their password or that computer isn't working, my team needs to work as quickly as we can to get them back to work. We serve our users, really. We're not there being all techy. They want us to fix them and get them back to work, and that's what we do. We put tools in their hands, any device that they need to make them more efficient. I try hard to provide a variety of devices, people have different preferences on how they do their work. Some people prefer a laptop, some people prefer to stand at a wall-mounted touchscreen and document, some people want to carry a tablet with them. I try to provide a range of devices so that they can have whatever suits them and makes them most comfortable to get their job done. >> Love that, it's not, necessarily, about the cool or trendier thing, it's about getting business done, helping, and in you're case, enabling your employees to really help the people that are there. Anything you want to highlight as to things you're excited to look at this show, or just technology in general? >> I'm just kind of here for the general nature of it. I enjoy the networking and getting to talk to people, and keeping current in what's happening in the industry and my career, so that's why I come. >> Alright, well Sue Morrow, really appreciate you coming, sharing with our audience. >> Absolutely. >> User groups like this, all about the users. Happy to have lots of them on the program, so big thanks to the VTUG group for bringing us some great guests. We'll be back with more coverage here. I'm Stu Miniman, you're watching theCUBE. (upbeat music)

(funky electronic music) >> From Phoenix, Arizona, theCUBE, at Catalyst Conference. Here's your host, Jeff Frick. (music muffles) >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in Phoenix, Arizona, at the Girls in Tech Catalyst Conference. About 4,000, or excuse me, 400 people, kind of a small conference, fourth year, growing in size. Going to be back in the Bay Area next year. Wanted to come down, check it out, always like to get, you know, kind of early on some of these conferences and really see what's going on. And we're really excited for our next guest, Monique Morrow, the CTO of New Frontiers Engineering inside of Cisco, welcome. >> Thank you very much, it's a pleasure to be here. >> So we've had a ton of Cisco guests on over the years, but I never heard the New Frontiers Engineering title, so what is New Frontiers Engineering? >> So New Frontiers is exactly what you think. You can imagine it's really forward thinking in terms of technology and research. This combinatorial intersection, if you will, with economics, and what could be potential portfolio for the future business of the company, so that's what I look at. You know, that's a special position, I could say, because you really want to make sure that you're not too far out to your core business, and you care about your core business always. >> Right, I was going to ask, how much of it's kind of accelerating the core versus, you know, kind of green field? I know, you know, we've had some of the team from the UCS group, and, you know, it's a growing business inside of Cisco, not really kind of core, what you think about, in terms of core switches, and stuff. It's servers, and a data center for structure beyond just the network. Is that some of the stuff that you guys look at? To go, kind of out on new branches? >> Well, certainly cloud, so data centers, with that is cloud computing, and then you've got mobile, and you have video. I would also say you have cyber security, internet of things, very, very important business analytics. So that's core business. And it could be accelerating what we have, but it also could be creating a new business opportunity. So the modus operandi, or the modality, if you will, is not to steer too far away from your core, the network does count. Software is going to be very, very important for us, service is absolutely important. So, you know, it's really steering the ship mid way, in such a way that you de risk what you're doing as you look forward. >> If only McNeely had said the cloud is the computer, (laughing) the network is the computer, right? >> So true. (laughing) >> So I want to touch base on your talk, Changing the Landscape of the Digitized World. >> Yes, yeah. >> What was that all about? >> So, you know, setting the landscape, there are several points that I wanted to make during that presentation, and really, to fire up the audience. One is that 51% of the global population are women, and women do count. That is change is extremely, it is exponential, probably always has been. That this is all about how do you keep your skills up at the end of the day? This is all about it is never too late to understand what's happening out there, and hear the skills buckets. So cyber security, analytics, what you do with data, mobility, collab, collaboration is probably the 21st century currency in anything that we're going to do because we're so global. The notion of what you do with other components here, not only the internet of things. And with the internet of things, you've got interesting aspects with privacy and how you handle privacy, privacy engineering, privacy by design, and all kinds of modality of cyber security. Because, you know, companies and customers are very concerned about ransomware, so think about phishing attacks. And I would say that that's just a start. >> Right, right. >> But, you have to juxtapose that with critical thinking skills, and something that we call T skills. It's interdisciplinary skill sets that are going to be asked for in this century, along with intergenerational teaming. So it's not just about working with millennials, but it's about working with people who've been in the business, it's the power of the and here, and that's really, really the focus. >> We're going to run out of time way too early, I already know this. But there's so many things you just touched on, specifically back to your skills comment. What's interesting is the technology is changing so fast, it's the new skills that are the kind of the driving new programming language, that you're almost in an advantage if you don't kind of have the legacy behind you. Because everyone is learning all these new languages, and these new ways to do things, that didn't exist just a short time ago. >> Well, coding is fundamental. I think that coding is going to be fundamental, but you can learn new programming languages if you learn at least the fundamentals of coding. What's really, really important is to be able to pivot your skills sets in such a way that you are keeping up with it. It's never, ever too late. Once you have a knowledge of a particular language, or a knowledge of a particular algorithm, or a way something works, you're going to be able to learn anything. My message was it's never too late. You can start to learn now. >> Right. >> So that's really important. >> And then the other piece on the T skills, again, the IOT's is a giant bundle that we could jump into for a long time. But, you know, as the machines start to take more and more of the low level work, and increasingly the mid level, and the higher level, it is incumbent on a person to really start to bring some context, bring some relative scale, bring, you know, a lot softer skills to help influence that activity in the correct way. >> Interdisciplinary skills are the ask for the 21st century. So for example, I was just at the school of, I was actually on a strategic advisory board for the School of Computer Science, a particular university here in the United States, and one of the asks was not only have the skill set of computer science, but oh, by the way, go take an improvisational class at their school of fine arts. So to have the ability to communicate, because communication skills are the number one skills that companies and enterprises are looking for. So interdisciplinary skills, big currency for the 21st century. >> Well that's interesting, 'cause I wonder how aggressively that communications message is weaved into, kind of, your classic STEM conversation. >> They are, well, they are very much weaved into the classic STEM conversation, and I would say it's STEAM, because you have to put A for art there. >> Well, there you go. (laughing) Fixed. >> So, to the classic conversation, you can be a savant in a particular science, but if you don't have the ability, and this is with enterprises essentially, to communicate and to be able to work in teams, it's going to be a dead end for you to come into the enterprise. So it's really, really important to have those skill sets. >> Yeah, so I want to shift gears a little bit. >> Sure. >> 'Cause not only do you have your day job at Cisco-- >> Yeah. >> But you're involved in a lot of, kind of, advocacy. >> Yes. >> So tell the audience some of the work that you're doing there. >> Yes, I mean, so one of the areas that I really care about is advocating for women, and women creating technology, women who were actually in technologies, so there is also the UN component of that. I think that's very, very important, tech policy component for it. The UN women's organization received the lowest budget of all of the UN, so getting more, remember the context, 51% of the worlds population are women, and so we have to go up, and down, and across the pyramids. And so we need that, that's the level of advocacy that I'm involved in, not only from a company and an industry perspective, but also from a UN related perspective, and a standard setting perspective. Because it is about about the power of the and, and our ultimate goal is to achieve gender neutraility, I think, at the end of the day. I recall one thing is that there are 17 UN sustainable goals that were contented and approved, really, by the United Nations this past September. Number one is ending poverty, number five is achieving gender equality. >> It's just those are such big problems, just, you know, you look at hunger. >> Yes. >> And it just seems this continual battle to try to make improvement, make improvement, make improvement, and yet we're continued to be surrounded, probably within blocks of where we're sitting now, with people that are not getting enough to eat. So how does education compare to that, or how tightly are they intertwined? And then, within education, is STEAM a leading edge? Is STEAM, you know, kind of a way to break through, and get more education? How does STEAM fit within the education broader? >> Oh, well, it's, (chuckling) it's all intertwined. >> I told you we weren't going to have enough time. (laughing) >> Yeah, so, it's all, it's really all intertwined at the end of the day. It's what is taught at what age group, it depends on whether you're in a developing country or a developed country. So we're, you know, in the United States advocating, and most of other countries advocating that technology STEAM be really taught at a very early age, you know, primary school. If you get skill sets really broadened and developed at and early age, you also develop the capacity to actually be able to work, or to be able to create, and to be able to add to your household. And if you're in a village, to be able to do some very creative things, too, because of what you're dealing with. So think about connecting here's the bigger problem that we, as an industry, want to solve. That is connecting one to two billion people on the internet in the next several years, and they're not going to be in North America, and they're not going to be in Europe. They're going to be in Africa. They're going to be in other countries of the world, and so we need to think creatively, working with people on the ground, learning from them, and not being techno, what was told to me, not to be techno colonialist at the same time. Because there's some very interesting solutions that are coming out of the countries that we could actually tap into. >> Right, and just to wrap, not that you don't have enough to do in your day job, (chuckling) or your global advocacy, but you're also a very prolific writer. >> Yes, I'm a, well, a prolific writer, and I'm so proud to have coauthored three books this year. one that is already out, is Disrupting Unemployment. The other two will be out in June, which is Inner Cloud Interoperability with our three other coauthors. And the third book, which I'm almost most proud of, is The Internet of Women Accelerating Cultural Change, and that will be out on June 30th of this year. >> You're a busy lady. >> Busy. (chuckling) >> Alright, well, Monique, thanks for taking a few minutes-- >> Thank you. >> Out of your busy day. You probably could've written another couple chapters-- (chuckling) >> In the 20 minutes that we've had together. I really appreciate the time. I look forward to really kind of looking for where your guys imprint starts coming out of the Cisco machine on the back and with the products. So thank you very much-- >> Thank you. >> For all your work. >> Well, it's a pleasure to be here. >> Absolutely. Jeff Frick, here at the Girls in Tech Catalyst Conference in Phoenix, Arizona. Thanks for watching. (funky electronic music)

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back to San Francisco, everybody. My name is David Lantz. I'm here with my co host John Troia. This is Day three of V M World 2019 2 sets. >> This is >> our 10th year at the M. World Cube is the leader in live enterprise tech coverage. Marry on Blondie is here. He's the C m o and chief evangelist that 10 tree by DDN Yes, sir. He's joined by Graham Breezes The Field CTO at 10 Tree also by DDN Recent acquisition jets Great to see you. >> Likewise, as they say, we're back. I like I like to call it a hibernation in the sense that people may have not known where did Ian or 10 Trias and Tension by Dede and, as the name implies, were acquired a year ago at the M World August 31st of 2018. And in the year since, we've been ableto invest in engineering support, my joining the company in marketing to take this solution, we've been able to save thousands of customers millions of man hours and bring it to a larger number of users. Way >> first saw 10 tree, we said, Wow, this is all about simplification. And Jonah Course you remember that when you go back to the early early Dick Cube days of of'em World, very complex storage was a major challenge. 10 Tree was all about simplifying that. Of course, we know DDN as well is the high performance specialist and have worked with those guys for a number of years. But take >> us >> back Married to the original vision of 10 Cherie. Is that original vision still alive? How was it evolved? >> Well, I'd say that it's, ah, number one reason why we're a part of the DD and family of brands because, as, ah, portfolio company, they're looking good. Bring technologies. I'm the marketing guy for our enterprise or virtual ization audience, and the product sets that cover high performance computing have their own audience. So for me, I'm focused on that. Graham's also focused on that, and, uh, really what continues to make us different today is the fact we were designed to learn from the beginning to understand how virtual machines end to end work with infrastructure. And that's really the foundation of what makes us different today. The same thing, right? >> So from the very beginning we were we were built to understand the work clothes that we service in the data center. So and that was virtual machines. We service those on multiple hyper visors today in terms of being able to understand those workloads intrinsically gives us a tremendous capability. Thio place. I owe again understanding that the infrastructure network storage, hyper visor, uh, weaken view that end end in terms of a latent a graph and give customers and insight into the infrastructure how it's performing. I would say that we're actually extending that further ways in terms of additional workload that we're gonna be able to take on later this year. >> So I know a lot >> of storage admits, although I I only play one on >> TV, but, uh, no, consistently >> throughout the years, right? 10 tree user experiences that is the forefront there. And in fact, they they often some people have said, You know what? I really want to get something done. I grab my tent Reeboks and so it can't talk. Maybe some examples of one example of why the user experience how the user experiences differ or why, why it's different. >> I'll start off by saying that I had a chance being new to the company just two weeks to meet a lot of 10 tree users. And prior to taking the job, I talkto us some folks behind the scenes, and they all told me the same thing. But what I was so interested to hear is that if they didn't have 10 tree, they'd otherwise not have the time to do the automation work, the research work, the strategy work or even the firefighting that's vital to their everyday operations. Right? So it's like, of course, I don't need to manage it. If I did, I wouldn't be able to do all these other things. And I think that's it. Rings true right that it's hard to quantify that time savings because people say, 0 1/2 of it. See, that's really not much of the greater scheme of things. I don't know. 1/2 50. Working on strategic program is a huge opportunity. Let's see >> the value of 10 tree to our end users and we've heard from a lot of them this week actually spent a fantastic event hearing from many of our passionate consumers. From the very beginning. We wanted to build a product that ultimately customers care about, and we've seen that this week in droves. But I would say the going back to what they get out of it. It's the values and what they don't have to do, so they don't have to carve up ones. They don't have to carve up volumes. All they have to do is work with the units of infrastructure that air native to their environment, v ems. They deal with everything in their environment from our virtual machine perspective, virtual machines, one thing across the infrastructure. Again, they can add those virtual machines seamlessly. They can add those in seconds they don't have toe size and add anything in terms of how am I gonna divide up the storage coming in a provisional I Oh, how am I going to get the technical pieces right? Uh, they basically just get place v EMS, and we have a very simplistic way to give them Ah, visualization into that because we understand that virtual machine and what it takes to service. It comes right back to them in terms of time savings that are tremendous in terms of that. >> So let's deal with the elephant in the room. So, so 10 tree. We've talked about all the great stuff in the original founding vision. But then I ran into some troubles, right? And so what? How do you deal with that with customers in terms of just their perception of what what occurred you guys did the eye poets, et cetera, take us through how you're making sure customers are cool with you guys. >> I'm naturally, glass is half full kind of guy from previous, uh, times on the Cube. The interesting thing is, not a lot of people actually knew. Maybe we didn't create enough brand recognition in the past for people to even know that there was a transition. There were even some of our customers. And Graham, you can pile on this that because they don't manage the product every day because they don't have to. It's kind of so easy they even for gotten a lot about it on don't spend a lot of time. I'd say that the reason why we are able to continue. Invest today a year after the acquisition is because retaining existing customers was something that was very successful, and to a lot of them, you can add comments. It wasn't easy to switch to something. They could just switch to something else because there's no other product, does these automatic things and provides the predictive modeling that they're used to. So it's like what we switched to so they just kept going, and to them, they've given us a lot of great feedback. Being owned by the largest private storage company on planet Earth has the advantages of strong source of supply. Great Leverett reverse logistics partnerships with suppliers as a bigger company to be able to service them. Long >> trial wasn't broke, so you didn't need to fix it. And you were ableto maintain obviously a large portion of that customer base. And what was this service experience like? And how is that evolving? And what is Dede and bring to the table? >> So, uh, boy DD and brings so many resources in terms of bringing this from the point when they bought us last year. A year ago today, I think we transition with about 40 people in the company. We're up about 200 now, so Ah, serious investment. Obviously, that's ah have been a pretty heavy job in terms of building that thing back up. Uh, service and support we've put all of the resource is the stated goal coming across the acquisition was they have, ah, 10. Tree support tender by DNC would be better than where 10 tree support was. We fought them on >> rate scores, too. So it's hard to go from there. Right? And >> I would say what we've been doing on that today. I mean, in terms of the S L. A's, I think those were as good as they've ever been from that perspective. So we have a big team behind us that are working really hard to make sure that the customer experience is exactly what we want. A 10 tree experience to be >> So big messages at this This show, of course, multi cloud kubernetes solving climate change, fixing the homeless problem in San Francisco. I'm not hearing that from you guys. What's what's your key message to the VM world? >> Well, I personally believe that there's a lot of opportunity to invest in improving operations that are already pretty darn stable, operating these environments, talking to folks here on the floor. These new technologies you're talking about are certainly gonna change the way we deploy things. But there's gonna be a lot of time left Still operating virtualized server infrastructure and accelerating VD I deployments to just operationalized things better. We're hoping that folks choose some new technologies out there. I mean, there's a bill was a lot of hype in past years. About what technology to choose. We're all flash infrastructure, but well, I'd liketo for the say were intelligent infrastructure. We have 10 and 40 get boards were all flash, but that's not what you choose this. You choose this because you're able to take their operations and spend more your time on the apse because you're not messing around with that low level infrastructure. I think that there's a renaissance of, of, of investment and opportunity to innovate in that space into Graham's point about going further up the stack. We now have data database technology that we can show gives database administrators the direct ability to self service their own cloning, their own, staging their own operations, which otherwise would be a complex set of trouble tickets internally to provision the environment. Everyone loves to self service. That's really big. I think our customers love. It's a self service aspect. I see the self service and >> the ability to d'oh again, not have to worry about all the things that they don't have to do in terms of again not having to get into those details. A cz Morrow mentioned in terms of the database side, that's, ah, workload, the workload intelligence that we've already had for virtual machines. We can now service that database object natively. We're going to do sequel server later this year, uh, being ableto again, being able to see where whether or not they've got a host or a network or a storage problem being able to see where those the that unit they're serving, having that inside is tremendously powerful. Also being able the snapshot to be able to clone to be able thio manage and protect that database in a native way. Not having to worry about, you know, going into a console, worrying about the underlying every structure, the ones, the volumes, all the pieces that might people people would have to get involved with maybe moving from, like, production to test and those kinds of things. So it's the simplicity, eyes all the things that you really don't have to do across the getting down in terms of one's the volumes, the sizing exercises one of our customers put it. Best thing. You know, I hear a lot of things back from different customer. If he says the country, the sentry box is the best employee has >> I see that way? Reinvest, Reinvest. I haven't heard a customer yet that talks about reducing staff. Their I t staff is really, really critical. They want to invest up Kai throw buzzword out there, Dev. Ops. You didn't mention that it's all about Dev ops, right? And one thing that's interesting here is were or ah, technology that supports virtual environments and how many software developers use virtual environments to write, test and and basically developed programmes lots and being able to give those developers the ability to create new machines and be very agile in the way they do. Their test of is awesome and in terms of just taking big amounts of data from a nap, if I can circling APP, which is these virtual machines be ableto look at that on the infrastructure and more of her copy data so that I can do stuff with that data. All in the flying virtualization we think of Dev Ops is being very much a cloud thing. I'd say that virtual ization specifically server virtualization is the perfect foundation for Dav ops like functionality. And what we've been able to do is provide that user experience directly to those folks up the stacks of the infrastructure. Guy doesn't have to touch it. I wanted to pull >> a couple of threads together, and I think because we talked about the original vision kind of E m r centric, VM centric multiple hyper visors now multi cloud here in the world. So what >> are you seeing >> in the customers? Is that is it? Is it a multi cloud portfolio? What? What are you seeing your customers going to in the future with both on premise hybrid cloud public. So where does where does 10 tree fit into the storage portfolio? >> And they kind of >> fit all over the map. I think in terms of the most of the customers that we have ultimately have infrastructure on site and in their own control. We do have some that ultimately put those out in places that are quote unquote clouds, if you will, but they're not in the service. Vendor clouds actually have a couple folks, actually, that our cloud providers. So they're building their own clouds to service customers using market. What >> differentiates service is for serving better d our offerings because they can offer something that's very end end for that customer. And so there's more. They monetize it. Yeah, and I think those type of customers, like the more regional provider or more of a specialty service provider rather than the roll your own stuff, I'd say that Generally speaking, folks want tohave a level of abstraction as they go into new architecture's so multi cloud from a past life I wrote a lot about. This is this idea that I don't have to worry about which cloud I'm on to do what I'm doing. I want to be able to do it and then regards of which clouded on it just works. And so I think that our philosophy is how we can continue to move up the stack and provide not US access to our analytics because all that analytic stuff we do in machine learning is available via a P I We have ah v r o plug in and all that sort of stuff to be able allow that to happen. But when we're talking now about APS and how those APS work across multiple, you know, pieces of infrastructure, multiple V EMS, we can now develop build a composite view of what those analytics mean in a way that really now gives them new inside test. So how can I move it over here? Can I move over here? What's gonna happen if I move it over here over there? And I think that's the part that should at least delineate from your average garden variety infrastructure and what we like to call intelligent infrastructure stopping that can, Actually that's doing stuff to be able to give you that data because there's always a way you could do with the long way. Just nobody has time to do with the long way, huh? No. And I would actually say that you >> know what you just touched on, uh, going back to a fundamental 10 tree. Different churches, getting that level of abstraction, right is absolutely the key to what we do. We understand that workload. That virtual machine is the level of abstraction. It's the unit infrastructure within a virtual environment in terms of somebody who's running databases. Databases are the unit of infrastructure that they want to manage. So we line exactly to the fundamental building blocks that they're doing in those containers, certainly moving forward. It's certainly another piece we're looking. We've actually, uh I think for about three years now, we've been looking pretty hard of containers. We've been waiting to see where customers were at. Obviously Of'em were put. Put some things on the map this week in terms of that they were pretty excited about in terms of looking in terms of how we would support. >> Well, it certainly makes it more interesting if you're gonna lean into it with someone like Vienna where behind it. I mean, I still think there are some questions, but I actually like the strategy of because if I understand it correctly of Visa, the sphere admin is going to see the spear. But ah ah, developers going to see kubernetes. So >> yeah, that's kind of cool. And we just want to give people an experience, allows them to self service under the control of the I T department so that they can spend less time on infrastructure. Just the end of the I haven't met a developer that even likes infrastructure. They love to not have to deal with it at all. They only do it out. It assessed even database folks They love infrastructural because they had to think about it. They wanted to avoid the pitfalls of bad infrastructure infrastructures Code is yeah, way we believe in that >> question. Go to market. Uh, you preserve the 10 tree name so that says a lot. What's to go to market like? How are you guys structuring the >> organizational in terms of, ah, parent company perspective or a wholly owned subsidiary of DDN? So 10 tree by DDN our go to market model is channel centric in the sense that still a vast majority of people who procure I t infrastructure prefer to use an integrator or reseller some sort of thing. As far as that goes, what you'll see from us, probably more than you did historically, is more work with some of the folks in the ecosystem. Let's say in the data protection space, we see a rubric as an example, and I think you can talk to some of that scene where historically 10 Tree hadn't really done. It's much collaboration there, but I think now, given the overall stability of the segment and people knowing exactly where value could be added, we have a really cool joint story and you're talking about because your team does that. >> Yeah, so I would certainly say, you know, in terms of go to market Side, we've been very much channel lead. Actually, it's been very interesting to go through this with the channel folks. It's a There's also a couple other pieces I mentioned you mentioned some of the cloud provider. Some of those certainly crossed lines between whether they're MSP is whether they are resellers, especially as we go to our friends across the pond. Maybe that's the VM it'll Barcelona discussion, but some of those were all three, right? So there are customer their service providers there. Ah ah, channel partner if you want terms of a resellers. So, um, it's been pretty interesting from that perspective. I think the thing is a lot of opportunity interview that Certainly, uh, I would say where we're at in terms of, we're trying to very much. Uh, we understand customers have ecosystems. I mean, Marco Mitchem, the backup spaces, right? Uh, customers. We're doing new and different things in there, and they want us to fit into those pieces. Ah, and I'd certainly say in the world that we're in, we're not tryingto go solve and boil the ocean in terms of all the problems ourselves we're trying to figure out are the things that we can bring to the table that make it easier for them to integrate with us And maybe in some new and novel, right, >> So question So what's the number one customer problem that when you guys hear you say, that's our wheelhouse, we're gonna crush the competition. >> I'll let you go first, >> So I'd say, you know, if they have a virtualized environment, I mean, we belong there. Vermin. Actually, somebody said this bed is the best Earlier again. Today in the booze is like, you know, the person who doesn't have entries, a person who doesn't know about 10 tree. If they have a virtual environment, you know, the, uh I would say that this week's been pretty interesting. Lots of customer meetings. So it's been pretty, pretty awesome, getting a lot of things back. But I would say the things that they're asking us to solve our not impossible things. They're looking for evolution's. They're looking for things in terms of better insights in their environment, maybe deeper insights. One of the things we're looking to do with the tremendous amount of data we've got coming back, Um, got almost a million machines coming back to us in terms of auto support data every single night. About 2.3 trillion data points for the last three years, eh? So we're looking to make that data that we've gotten into meaningful consumable information for them. That's actionable. So again, again, what can we see in a virtual environment, not just 10 tree things in terms of storage of those kinds of things, but maybe what patches they have installed that might be affecting a network driver, which might affect the certain configuration and being able to expose and and give them some actionable ways to go take care of those problems. >> All right, we gotta go marry. I'll give you. The last word >> stated simply if you are using virtual, is a Shinto abstract infrastructure. As a wayto accelerate your operations, I run the M where, if you have ah 100 virtual machine, 150 virtual machines, you could really benefit from maybe choosing a different way to do that. Do infrastructure. I can't say the competition doesn't work. Of course, the products work. We just want hope wanted hope that folks could see that doing it differently may produce a different outcome. And different outcomes could be good. >> All right, Mario Graham, Thanks very much for coming to the cubes. Great. Thank you so much. All right. Thank you for watching John Troy a day Volante. We'll be back with our next guest right after this short break. You're watching the cube?