>> Announcer: Live from Madrid, Spain. It's theCube covering HPE Discover Madrid 2017. Brought to you by Hewlett-Packard Enterprise. >> And we're back at HPE Discover Madrid 2018. This is theCube, the leader in live tech coverage. I'm Dave Vellante, with my co-host Peter Burris. This is day two of the event. Johannes Koch is here, he's the Vice President and Managing Director of Central Eastern Europe, Middle East and Africa for Hewlett-Packard Enterprise, and he's joined by Ali Saleh, he's the Senior Vice President and Chief Commercial Officer at GE Digital for Middle East and Africa. Gentlemen, thanks so much for coming to theCube. >> Appreciate it. Thank you. >> Thank you for having us. >> Johannes, let's start off with you. GE, HPE, what are you guys all about, what are you doing together? Talk about the partnership and the alliance. >> So, you know, it started actually one month ago, I suppose and it was meetings that we had with General Electric to understand the customer requirements in cybersecurity, and what we figured is in this world of IoT, Internet of Things there is an increased requirement for security. And there was, from our perspective, lots of solutions out there but it's quite difficult for customers to understand the landscape and who to turn to. And we also figured that in this world, nobody can serve every requirement of a customer, so this is how we figured out with GED that we have a joint interest here, to serve in the Central/Eastern European and then mainly in the Middle East and Africa part our customer base. And this is how it started and I think what I can say is it has accelerated incredibly during the last two months since we signed the joint agreement. We've been building a channel, we've been having lots of meetings with customers and built a really nice pipeline in the meantime, also I think here the show reflects an incredible interest by our customers. So I think we are in a very good state at the moment having a lot of interest, probably all the key customers in our region having this on their agenda. >> Ali, maybe you could just describe the situation, the industrial expansion if you will in Middle East and Africa, what you guys are seeing in terms of the big trends, and what the opportunity looks like. >> Well thank you. You know, GE has been in the Middle East, Africa, for over 80 years in some countries, and we have deep relationships on industrial side, whether it's power, oil and gas, aviation, healthcare and others. And our customers are thinking a lot about cost, quality, and access, and productivity is top of mind, and they've discovered that their industrial assets are smart and capable but the data are not being collected. So when we collaborate with ecosystem of partners, and we fetch the data and get connected, and get insights from the machine to make them able to make the right decision at the right time and then it drives optimization. This is top of mind. They want to see how they can do better for less. >> Okay, so the customers at GE Digital, the customers are going digital, they have all these devices, instruments, machines, and they're moving in a new direction you guys are trying to lead in. What are the challenges that they're facing, what are they asking your help on, what are the big problems that they're trying to solve? >> So, everyone wants to talk about productivity and calls out. The challenge is that not everyone is ready for digital transformation. Some do not feel there is a burning platform, and those that are ready when they feel there is a burning platform, they don't have a plan, they don't have a playbook. So it's important that we collaborate and help our partners and customers understand their current state and heat map and desired state and pinpoint to quick wins so that they get it and they see incremental improvement. And asset performance management has been an easy way for us to say, "Your asset is underutilized "compared to your industrial entitlement "you can do 10x better," and this gets their attention, and this is where we see the power of one in the industrial age is relevant, one percent. In our market, in the world free market, when we talk to them about one or two percent productivity they laugh at us. They say, "Talk to us about ten or twenty." Because there has been a lot of gap in productivity and efficiency. >> Are you able to, I mean, it's only been nine months, but are you able to start to see any kind of customer results at this point? Do you have some examples even early wins with customers? >> So to be exact, the start of the relationship in a formal way-- >> Was it, what'd you say? >> --is two months. >> I thought I heard nine months. >> No, no, we started our first conversations and until it was over, it came to the agreement-- >> So it was brand new, in terms of... >> It is really brand new and I think what we can say is, I think we have 180 partners already engaged. We have probably more than 500 customer contacts in the region already so with large accounts. And we have a pipeline that is multi-million dollar in size. So we're expecting the first close within our first quarter, which ends at January 2018. I think there's no question that there is a big market opportunity out there, right? And I think the show here, I think for me, even accelerated things, because I think in the past, digital transformation was sort of limited to a few industries, we always give travel industry, we take banking sometimes but here, I think what became transparent to many of the customers that we had here, that there is no industry that is sort of immune against what is happening out there and specifically also that the sensors and the devices out there require special attention. And I think with the, specifically on the OT side, we have a solution now with GED that we can basically roll out across our territory. >> So I wanna talk about three things very quickly, I'm gonna lay this out and ask you if in fact this is going to catalyze that much more attention. Number one is a lot of the industry in the Middle East and Africa are natural resource industries, where the historical ways of doing things have been relatively inefficient. So there's a lot of opportunity to use IoT and related things to bring more efficiency, better practices, overall resource management. Number two is, that the technology's now capable of doing that in places where you don't necessarily have the best infrastructure. Aruba technology, for wireless, some of the other things are now possible, that adds to it. And number three, we've seen some recent steps in liberalizing some of the countries that have the most opportunity to do some things differently. You know, Robert Mugabe, no longer in Zimbabwe, the new prince in Saudi Arabia talking about liberalizing things. Are you seeing these come together in a way that would encourage people to think new ways, do new things, use information perhaps differently than it did before? What do you think, is this a confluence, is this a moment? >> Well I agree with you, and absolutely. Today, our customers and partners in region are more ready than before, and they're pulling hard. And when we put our act together as an ecosystem of partners we make it easier on them to make the right decision. When we talk productivity, productivity comes from people, from process operation, from industrial entitlement. And when we talk about the digital thread that brings it all together whether we look at the culture and vision and mission and people utilization, look at the process defined or not, and how we can optimize it, look at the industrial entitlement, and tell them, "Compared to your peers, "this is where you can be." We have their attention. And with the push from the government for productivity and utilization and do more for less, this is becoming top of mind, everyone is talking about it. So, when we partner together and we say, "This is the playbook, this is how you can start, "and this is the edge to cloud solution in a secure way." And we link it to the industrial entitlement, and let's underline industrial, because when we speak the healthcare language and the power language and the oil and gas language we get their attention. >> Excellent, so there's an increasing interest, and you anticipate that there's going to be new action with their pocketbooks. >> Johannes: Yes. And I would add, I think we, this is not an easy marketplace but you can have some outstanding projects. And we have, in the region, you may have heard about, there was in the private investment fund, when the crown prince did announce the NEOM project. Or, we have in Dubai Smart City as a project, with the city of Dubai which are all projects that probably would not happen in Western Europe. So there is potential, there are bigger things happening, and I think there is also an understanding that this is a way how to leapfrog, to your point, to leapfrog technology. And I think that is what can happen. What we need to be careful of is where to invest, because there are lots of ideas out there, and to understand what are the real things, and what are the things that we need to make happen. This is, I think, the challenge. >> And they wouldn't happen in Western Europe because, what? The maturity of the infrastructure, the space limitations, the appetite? >> Johannes: I think, to give the example of Smart City. >> Dave: Yeah. >> So I think we have a lot of, in my remit we have lots of discussion on Smart City. But it's usually you have to find the city that is willing to pay a POC. >> Pete: 12 layers of bureaucracy. >> And exactly. And you need to talk to each and every city individually, whereas here, if you have a decision maker to say, "Yes, we do this." >> Pete: Yep. >> Dave: Right. >> And then we do it. >> Dave: You cut the line. >> And the answer is about readiness. When you go to a large enterprise that's very successful, you meet the CEO and you quickly conclude whether they're ready for digital transformation or not, are they gonna make this top of mind for them? Are they gonna give you time? Are they gonna talk about productivity? Or is this going to be an IT discussion, and they're gonna treat you as a supplier? Those that are ready, we roll up our sleeves, and we put in our dedicated resources to help them look at the transformation. When the government official is pushing and mandating for calls out, then obviously everyone wants to copy and talk about it. And this makes it easier for us to execute. >> You're talking, again, big numbers. Not one percent, ten percent, so that's the nirvana. How confident are you that you can actually have that type of impact? >> So we've got data points, right? If you look at healthcare in the Saudi Ministry of Health, we've been collaborating on looking at operating room optimization or emergency room optimization, without touching digitization. Looking at the process and utilization of appointments, no-show, and the way the clinical governance is taking place, we're showing 40 percent improvement. If you look at, the factory of the future with Obeikan in Saudi Arabia, we've got asset performance improvement project, and they already yielded a 12 percent improvement, and the entitlement is up to 20, that we're working on. When you transform something, it's iterative, right? When you transform something that you have not pushed for efficiency before it's easy for the first iteration to show an incremental change. >> Pete: Yup. >> That challenge will be for the change to last. And this is where digitization makes it last and makes it impactful. And when we look at the HPE relationship with the MOH on the electronic medical record, we've got right now two active projects with two hospitals, and it's all powered by Predix, and HP peripherals are being deployed to the site. And if we go to the Saudi Electricity Company, we've got a project now on asset performance management across all their assets and again HPE peripherals are also deployed and it's all about GE ecosystem of Predix-enabled solutions. >> So I've had the pleasure and honor of speaking in front of a relatively large group of CIOs a couple times in Africa in the past few years. And I always was surprised by the degree to which they suggested that the necessity of change in this region, and the fact that a little bit of technology can have an enormous impact, the degree to which we might actually see some leadership technology come out of this region. What do you think, are the types of issues, the types of problems that could be solved with this technology in Africa, the types of problems that solving them there could actually start driving the industry in different directions? Solving new classes, whole new classes of problems. Do you think that this type of technology can have a transformative effect, not only in Africa but more broadly? >> Absolutely, this is a way for systems to leapfrog. If you look at Kenya right now, they've got a transformation project for 98 hospitals. And they've got massive shortage of radiologists. So right now, we're replacing equipment in 98 hospitals but tele-radiology is the answer for the shortage of radiologists. If you look to South African Discovery, what Discovery is doing is best in class, and I haven't seen any other insurance company looking at the ecosystem the way they do it. So, absolutely, we're seeing pockets of excellence in Africa, and this can be a way to leapfrog. >> You said you started the conversations around security. >> Johannes: Cybersecurity. >> What was that conversation like? Why was that the starting point? I mean, obviously it's important, but why? >> To be honest, I would have to leave this to you, but I think it was because mainly there was we saw the customer interest. >> Yeah. >> And I would say, probably a year or two years ago, you would have not seen this as a very typical HPE alliance. We were technology people. We were software or hardware people. What you see in, and I mentioned in the beginning, in the world of IoT, things are blurring a bit. What is happening on the edge is very much in the business of General Electric. So I think this builds automatically the new ecosystem. When you look here at Discovery, the alliance has become more and more industrial companies. It's linked to industrial 4.0, car industries and all that. Everywhere where data is being created, we need to have the partnership because and that is because the data that is being created at the edge also needs to be computed at the edge. If we want to be successful, we gotta say, "We cannot limit ourselves to the "data centers the rest is the others." And this is where I think we find the very good connection point because now we have software that actually can operate at the edge. I think you have good examples on that. >> Yeah absolutely, if you look at the pain point in Middle East, Africa, majority of our partners and customers are government entities and for them top of mind securing their large industrial assets is important. And in the operations space there hasn't been much done on security, where you can go into a hospital and simulate light flickering with a voltmeter. And you can take over the temperature and play with it. Today there's a lot of smart sensors out there, but we're securing the IT firewall, but within the hospital, or within the plant, we can do a lot of crazy stuff. And we owe it to our partners to show our capability. That's what we do within our factories, and our platform is designed around security for operations. So the easier interlock with HPE is our ability to get closer to the edge and peripherals, and ensure the operation is secure and that's the first experiment but then, obviously, we're expanding beyond that to other opportunities. >> Dave: Excellent. Alright, gentlemen, we have to leave it there. Thanks so much for coming to theCube. >> Johannes: Thank you very much. >> Sharing your story, good luck with the partnership. >> Thank you. >> Dave: Hope you can come back, maybe in Las Vegas or maybe next year at this event, give us the update. >> For sure. Thank you very much. >> Thank you. Appreciate it. >> Dave: Okay. Keep it right there, everybody. We'll be back with our next guest. Dave Vellante, Peter Burris, this is theCube live from Madrid HPE Discover 2017. (upbeat music)

>> Hey, welcome back, everybody. Jefe Rick here with the Cube. We're in this war subscribed conference 2017 downtown San Francisco. 1,000 2 1,000 people talking about the subscription economy, I think is like the sixth year they've been doing this show. First time we've been here. We're excited to be here, but we're joined by a company that we spend a lot of time talking about. I ot in the industrial Internet, and that's G, but a new gas guidance bar. Ducasse. He's the head of predicts product management for Jean Digital. Welcome. >> Thanks, Jeff. Thanks for having me here. >> So you guys I mean, we were there in 2013 when Beth and Bill lost the industrial Internet initiative at the juiciest museum just across the street. So you guys have been in this space for a while. The G predicts cloud industrial cloud. You guys have been doing a lot of stuff there, so give us kind of an update. Where are you? Obviously picked a highlight. One of the key stories here. People don't think of G as necessarily a subscription economy type of play, but that >> so why are we here >> yet? So why are you here? >> We're here because we are subscription economy. I mean, what we're really focusing on with predicts is building a platform that allows third parties and first party applications to be built around the industrial space. And so a lot of what we're hearing from our customers is that they want to subscribe to those services, right. They want to subscribe to either the production of the services, but more importantly, maybe the different elements that bring the other solution. So the thing about the hospital like a digital twin, a virtual representation with physical asset A lot of times when people want to do is they want to build twins specific to specific asset. But they want to bring together the analytics and the data associated with that. Maybe some environmental factors that they subscribe to from 1/3 party, right, bring those all together doing analysis, right? And then basically give that stuff back. So they want to subscribe to things like analytics they want subscribe to data and the imports. So that's why we're here. We've been using Zamora a CZ part of our subscription service since the we kicked off G predicts last year way Wendy and February, and it's it's going to be a very flexible solution for us. >> So the parts and I don't think it's enough talk, and it really wasn't a lot of talking. The keynote is how a subscription relationship changes the way that you engage with a customer, because if you just sell him something, thank here's the transaction. You know, go off, you run your jet engine, go run your turbine. But if you have a subscription and it's an ongoing value delivery to pay for that ongoing money that they're giving you, it's a much kind of deeper relationship in kind of a single transact, well, >> it can develop. Do you have much deeper relationship? I think the thing that allows you to do is that allows you to experiment a little bit. Try a couple things, figure out what works best for you as a customer, right, and then invest in those areas. You don't have to make a big purchase order, right? Right. You don't have to go off and spend a lot of money on a bunch of software that may eventually go away, right? You can. You can. You can almost try before you buy or try as you buy, right? Probably better way of putting it right. And so what we're saying is you give people the ability to experiment. I think, you know, we talk within G about productivity, right? And the impact we could make him our own productivity to meet predicts, is much about innovation, right? Right. It's giving people the ability to try different things. Teo, Try on DH. See what happens when you bring in environmental factors. Right? Or usage data, right or operational data? Or, you know, we talked about jet engines lot looking at the different pilots. How do they operate the engines? Right, you know. So there's there's there's all these scenarios you can sort of experiment with on a subscription model. Find out what works and then go deep is necessary. >> It's interesting. 10. And the Kino talked about. What's different now is that you can buy. You can upgrade, you can cancel, you can downgrade. So again, this this interaction, as you just described, allows for a bunch of different types of engagement, not just the Big Bang and the other thing that's consistent with we hear over and over. I is a democratization, democratization of the data, democratization of the tools so that somebody does have a hypothesis that, you know, we've been looking at Obviously, a plane operating in the southwest United States is goingto have different characteristics. Is one operating in Alaska. But as you just said, maybe we should look att, pilot characteristics. Maybe we should look at, you know, back in. So when you open up that innovation platform now, you have so many more people coming up with hypothesis, testing hypothesis and you you open up the resource into your company to do so much better >> Well, and you have little innovation like so we have a partner based on Israel Plantain who's doing some stuff in the manufacturing space with G is we start thing about additive manufacturing. You want something about the composites and the materials that actually go into the engine, right, and sort of how of those and held up over time so you could build a much more longitudinal view of that. And again, it could be a subscription service where you start experimenting, you start understanding, especially with additive being sort of ah, mechanism to decentralize a lot of the manufacturing. You don't need to make a huge investment too. Doing that at those analytics. You put some software alongside the additive systems, and you've got the ability to innovate and understand better. Like what? Composites work better. I mean, you talk about the operation of the engine. But how about the manufacturing, the gym? Are there optimal environments right where you want to build those engines? And I think we've done great work. Is an industrial company to understand how to optimize systems and probably even like what the environmental factors are to build an engine effectively. But when you start distributing that, you really want to gauge that real time to understand what the impact would be, >> All right, So we were on short time leash here, actually, but I want to give you the last word. Give a plug for the critics. Transform show Coming up is part of minds of machines. We live for the first year. Last year that was 2,000 developers. Right? Ready? Great turnout for for really a development platform for an industrial Internet cloud. >> Yeah, s. So what we've done this year was a rain together transform, which is the event for our developer community with minds of machines, which is more targeted towards the business leaders were some of the leaders in the organization, and bringing them all under one roof will be here in San Francisco mid October. I don't have the exact dates, but I probably should. But it's like a roundabout on the >> Internet looking over there. >> But we're bringing those together, right? So we can have a dialogue that spans the complete spectrum. Yeah, right. It's the people that are building will have hackathons will have places where people can actually work on that will judge those those different solutions that are being hacked together on. Then we'll be presenting sort of the business value and the impact we're seeing with a lot of the industrial customers again. Many of them are, jeez, existing customers. We've got customers in different, you know, the auto industry elevator escalator industry, you know, fixtures manufacturing spaces that we haven't traditionally played. So we'll be talking about all the benefits were bringing this customer's Blessem new product introduction talk about >> All right, great event. I team meets ot. We went last year. Jeff was there. Beth was there, Bill was there all that? All the players that great show. Well, congratulations on your successful Zorro. And we look forward to seeing your minds machine. Okay, Thanks. Alright, He's Geeta Som Jeffrey, you're watching the cube crumbs or subscribe 2017. We'll be right back after this short break. Thanks for watching.

>> Hey welcome back here everybody. Jeff Frick here with theCUBE. We're at the Zuora Subscribe Conference 2017, downtown on San Francisco. 1,000, 2,000 people talking about the subscription economy. I think it's like the sixth year they've been doing the show. First time we've been here. We're excited to be here. But we're joined by a company that we spent a lot of time talking about IOT and the industrial internet, and that's GE, but a new guest, Gytis Barzdukas, he is the head of Predix Product Management for GE Digital. Welcome. >> Thanks Jeff, thanks for having me here. >> So you guys, I mean, we were there in 2013 when Beth and Bill launched the industrial internet initiative at the Jewish History Museum just across the street. So you guys have been in this space for a while, the GE Predix Cloud, industrial internet cloud, you guys have been doing a lot of stuff there. So give us a kind of update, where are you? Obviously picked to highlight one of the key stories here. People probably don't think of GE as necessarily a subscription economy type of play, but, >> So why are we here? >> Jeff: Yeah, so why are you here? >> Well we're here because we are a subscription economy. What we're really focusing on with Predix is building a platform that allows third-parties and first-party applications to be built around the industrial space, and so a lot of what we're hearing from our customers is that they want to subscribe to those services. They want to subscribe to either the production of the services, but more importantly maybe the different elements that bring together a solution. So think about the concept like a digital twin, a virtual representation of a physical asset. A lot of times what people want to do is they want to build twins specific to a specific asset. But they want to bring together the analytics, and the data associated with that, and maybe some environmental factors that they subscribe to from a third-party, bring those all together, do an analysis, And then basically give that stuff back. So they want to subscribe to things like analytics. They want to subscribe to data and the inputs, so that's why we're here. We've been using Zuora as part of our subscription service since we kicked off GE Predix last year. We went GA in February, and it's proven to be a very flexible solution for us. >> So the part that I don't think gets enough talk, and there really wasn't a lot of talk in the keynote, is how a subscription relationship changes the way that you engage with the customer. 'Cause if you just sell 'em something, here's the transaction, you know, go off, go run your jet engine, run your turbine, but if you have a subscription, and it's an ongoing value delivery to pay for that ongoing money that they're giving you, it's a much kind of deeper relationship than kind of a single transaction relationship. >> It can develop to be a much deeper relationship. I think the thing that it allows you to do is, it allows you to experiment a little bit, try a couple things, figure out what works best for you as a customer and then invest in those areas. You don't have to make a big purchase order. You don't have to go off and spend a lot of money on a bunch of software that may eventually go away. You can almost try, before you buy, or try as you buy is probably a better way of putting it. And so what we're seeing is we give people the ability to experiment. I think, we talk within GE about productivity and the impact we can make in our own productivity. To me Predix is as much about innovation. It's giving people the ability to try different things, to try and see what happens when you bring in environmental factors or usage data, or operational data, or we talk about jet engines a lot. Looking at the different pilots, how do they operate the engines? So there's all these scenarios you can sort of experiment with on a subscription model, find out what works and then go deep as necessary. >> And it's interesting, Tien in the keynote talked about how what's different now is that you can buy, you can upgrade, you can cancel, you can downgrade, so again this interaction as you just described, allows for a bunch of different types of engagement, not just the big bang. >> Yep, yeah. >> And the other thing that's consistent with who you're over and overwrite is the democratization. Democratization of the data, democratization of the tools so that if somebody does have a hypothesis, we've been looking at obviously a plane operating in the southwest United States is going to have different characteristics as one operating in Alaska. But as you just said maybe we should look at pilot characteristics. Maybe we should look at back ends, so when you open up that innovation platform, now you have so many more people coming up with hypothesis, testing hypothesis, and you open up the resources to your company to do so much better. >> Well, and you have little innovation, so we have a partner based in Israel, Plataine, who's doing some stuff in the manufacturing space with GE as we start thinking about additive manufacturing. You want to start thinking about the composites and the materials that actually go into the engine, and sort of how have those held up over time? So you can build a much more longitudinal view of that, and again, it can be a subscription service where you start experimenting, you start understanding, especially with additive being sort of a mechanism to decentralize a lot of the manufacturing. You don't need to make a huge investment to doing those analytics. You put some software alongside the additive systems, and you've got the ability to innovate and understand better what composites work better. You talk about the operation of the engine, but how about the manufacturing of the engine? Are there optimal environments where you want to build those engines? And I think we've done great work as an industrial company and understand how to optimize systems and probably even like what the environmental factors are to build an engine effectively, but when you start distributing that, you really want to gauge that real time to understand what the impact could be. >> All right, so we're on short time leash here, unfortunately, but I want to give you the last word, give a plug for the Predix Transform Show coming up as part of Minds and Machines. We went for the first year last year. It was 2,000 developers, pretty great turnout for really a development platform for an industrial internet cloud. >> Yeah, so what we've done this year is we're bringing together Transform, which is the event for our developer community with Minds and Machines which is more targeted towards the business leaders or some of the IT leaders in their organization and bringing them all under one roof. It'll be here in San Francisco mid-October. I don't have the exact dates. I probably should, but I think it's like-- >> I can look it up on the internet. That's why we have the internet. >> But we're bringing those together. So we can have a dialogue that spans the complete spectrum. It's the people that are building, and we'll have hackathons, we'll have places where people can actually work on that. We'll judge those different solutions that are being hacked together. And then we'll be presenting sort of the business value and the impact we're seeing with a lot of the industrial customers. Again, many of them are GE's existing customers. But we've got customers in the auto industry, elevator, escalator industry, fixtures, manufacturing, spaces that we haven't traditionally played, and so we'll be talking about all of the benefits. We're bringing in those customers plus some new product introductions which I can't talk about now. >> All right great event, IT meets OT. We went last year. Jeff was there, Beth was there, >> They will be there. >> Jeff: Bill was there, all the players. A great show. >> okay, Jeff. >> Jeff: Congratulations on your success with Zuora and we look forward to seeing you at Minds and Machines. >> Okay, thanks Jeff. >> All right, he's Gytis, I'm Jeff Frick. You're watching theCUBE from Zuora Subscribe 2017. We'll be right back after this short break. Thanks for watching.

>> Narrator: Live from Las Vegas, it's theCUBE covering HPE Discover 2017 brought to you by Hewlett Packard Enterprise. >> Welcome back, everyone. We are here live in Las Vegas for HPE Discover 2017 exclusives look at angle cube coverage, our seventh year. I'm John Furrier with my co-host Dave Vellante. Our next guest is Tripp Partain, HPE CTO for GE General Electric and Anthony Rokis, VP of Software Engineering at Predix with GE Digital. Guys, welcome back to theCUBE. Good to see you. >> Thanks guys. >> Thanks for coming on. Obviously, GE has really been on the front end of IOT. You guys have been doing extremely well and changing over, bringing digital to analog, kind of connecting those worlds. What's your take on this intelligent Edge? You got to love the messaging. You got to love the messaging with HP. >> God, it's great. I think this is really starting to take off. If you look at our positioning, we really are going after the Edge, right. And with Predix being our forefront in the Predix system, we really believe in the opportunity here. I think, as you heard Meg speak yesterday, the engagement between GE Digital and HPE is getting stronger, we're finding more and more synergies over time. And both our strategy and their strategy are really starting to line up very nicely, both Edge and computing in general. >> I had a chance a couple years ago to host a panel with your CEO Jeff Amels, and United Airlines, Hospital in Chicago and at that time it was really hardcore, tangible dollars on the line. I mean, we're talking highly instrumented devices and machinery that you guys are in and there were some significant dollars involved. Just getting the data is a very low-hanging fruit, but big numbers, this is now going mainstream where everyone's kind of having this awakening moment, Tripp, where it's kind of like, "Hey, we're just going mainstream." So what's next for you guys, as the world starts getting up to speed on IOT, what's next for GE? What are you guys doing now to go onto the next level? What's that next tier of digital IOT for you guys? >> Yeah, honestly in my view and GE's view if you look at what we've done in the past, it's really the foundations getting in place. It's censor-enabled devices getting assets. The censor is more progressive, and that's kind of been the first sort of step, right. Then we get into how do we collect that data? Where you think GE is headed now, are the smart analytics. It's the outcomes that are going to drive those big dollars in productivity. It's really getting into the digital industrial revolution area. To date, it's been a lot of the foundation getting in place, and I think that's where you're going to see tremendous growth over time is. When you unleash data scientists on wealth of information, the outcomes in the productivity, the world and the economy is going to see is going to be great. >> I love that quote, with Jeff Immelt. We refer to it all the time. I went to bed an industrial giant, and woke up, you know a software company. And so it clearly underscores the transformation. We were talking off-camera about the study that we did many, many years ago. I mean, the numbers are staggering. It's in the trillions. But one of the things that we found was this notion of, and we talk about it all the time and I'd love to get your take on it is the IT and OT. They're not talking to each other. Typically, they're not birds of a feather. What are you seeing, Tripp, in your experience with customers in terms of those organizational, let's start with the IT side and we can talk about the OT side. >> Yeah, and as we've had our partners show up with GE continue to develop, the one thing we've found is we have a lot of similar customers. And in these same customers are extremely large customers, but what's interesting we don't talk to any of the same people. Right, on the HPE side we tended to talk to the IT teams and data center and GE would be out in the factory floor or out in the field and more industrial. But in order to really fulfill the IOT promise, the two groups are really having to come together and I think it's taking time for messaging to really sort out there. And one of the things that we're really doing, taking advantage of our partnership to help solve the problem is when we have IT teams come to visit HPE, we bring along GE operational experts to actually talk about the business side of the outcome so it's not just an IT conversation. And really intentionally crossing those paths and leveraging our partner in GE to bring that capability to us so we can have a holistic conversation to the customer. >> So who's in charge here, who's driving the bus? Is it the OT guys, or the IT guys, or somebody above? >> It's both, there's two drivers. >> Uh oh. >> Two hands, four hands on the wheel almost. If you look at the OT side, there's a lot of challenges we're facing where HPE and the IT community is coming to help. For instance, data sovereign team, right. So one of the challenges we have is a lot of our companies, our customers, want data sovereignty and this is where IT has solved that problem for us and on the OT side, we need to figure out how do we store, maintain, analyze that data within a country. And again, that's why we're bringing the IT companies with us and partner to help us. >> So when a plane flies from Spain, crosses France, Germany, and ends up in Ireland, where is the data? (laughs) >> Very good question. >> Well it infringes the data, because there's sort of a data love triangle going on. You've obviously got devices installed, HPE brings equipment, and the customer. So, talk about the conversations that you're having with your customers. I mean, who owns the data. The factory says, "Hey, wait a minute it's a system. That's my data." GE obviously has to do predictive maintenance and same with HPE. There's all this data flowing. What about data, I don't know, ownership or IP, what are those conversations like? >> Yeah, I can say certainly from the GE side it's always been our stance that the customer owns their data, right. We are running a multi-tenency environment and a platform. And they own that data. How that data is stored, we can help facilitate, right. We offer Cloud store and a couple other technologies that allow that. But at the end of the day in a multi-tenent environment, the customer owns that data. And we will facilitate with HPE where that data needs to reside based on the customer's need. >> So you're not trying in any way to monetize that data? I mean, I'm astounded, why not? >> I think the monetization really comes in with how you empower the customer to get the value out of the data. And in a former life, I worked through the data monetization world and there is certain amounts of value in the data itself. There's also value in helping the customer determine what their data can offer to them and the business cases that we're able to jointly present to the customer and the value that that generates still allows for us to monetize the process by which we help enable the customer to really bring these data assets together. Really understand areas that they may have seen silos of the data before, but they weren't looking holistically at it and being able to, in a very timely fashion correlate between that and then actually see a different answer to a problem where yes, this meter may be reading 80 and it should be 60, but if I throttle it to 70 and I get 10% more output, it's worth running at 70 because of the benefit on the revenue. So you actually can make trade offs across certain areas where you weren't able to do that. >> But Predix is informing models, is it not, I mean. >> Yeah, I mean at the end of the day, we're taking that data and for the customer created an outcome. Right, the analytic, the information that we can derive out of it to make a more productive or a more efficient outcome of running operation, that's where we get the monetization from. >> If data's a new oil then you need to refine it, was your point about the monetization question. That's interesting because we see the same thing where if you make the data freely available or you treat it as an asset to the customer, it's how it's monetized in its effect. Or there's a tacticle, let's monetize our data. So depending on how you look at it, there's different approaches, right? I mean this is kind of the key thing. >> Right, and even though this is not the way now, if you follow the history of how other industries have dealt with the data. So I came out of credit services long ago, and it's very common now, in the credit services industry for data to be monetized and leveraged like for credit reports and for that whole banking financial process to take place, but it didn't start that way. So my guess is, as we continue to show value to the customer of their own data as they then start to think about, "Wow but if I could do comparables between my data and industry data that would help me even more." I expect that the customers that today that are worried about who owns the data, will eventually start asking players like HPE and GE Digital to help them solve that problem. And they'll evolve to that sort of data monetization like a lot of the other industries have. >> A whole new digital just creates a whole new way to look at things, it's not a linear supply chain anymore whether relative to data or what not, so super cool. Final question for you guys and I appreciate you coming on theCUBE and sharing your insights. What's next for the partnership with HPE and GE Digital? Obviously, the digital transformation's in full swing impacting business transformation, impacting the Dev Ops aspect of Cloud. All this cool stuff's happening, true private Cloud's on fire, hybrid's the doorway to Multi Cloud. A lot of cool stuff happening, what's next for you guys? >> Yeah I think from our side we're really excited about the partnership on the Edge, right. When we start looking at the computing requirements and needs at the Edge, close to the asset, low latency that's where HPE and GE are really going to start to partner very heavily and you're going to see a lot more engagement at that level. So I think the Edge is going to be our focal point. >> Oh absolutely, and I think the uniqueness we bring to the market with our Edge line converged systems, we're able to do things at the Edge, leveraging GE Predix and then also bringing in other third party partners in conjunction and now you have enough computer power in the right form factor that can all sit and reside at the Edge, process at the Edge and solve the problems there locally. Doesn't take away from the Cloud aspect, doesn't take away from being able to have a macro view across multiple scenarios. But if I'm on an oil rig in the middle of the North Sea, you know it's going to be very important for me to have everything I need in the right form factor at the lowest power utilization possible and still solve my problems. >> And can process all the data right there. Guys, we are pushing it to the Edge here theCUBE goes out to the events, that's the Edge of the action. We'll bring you all the great videos. Thanks for coming on, this is theCUBE live coverage from the Edge at HPE Discover 2017. I'm John Furrier, Dave Vellante. Be right back with more, stay with us. (digital music)

>> Live from Bellevue, Washington it's theCUBE. Covering Smartsheet Engage '18. Brought to you by Smartsheet. >> Welcome back to theCUBE. We are live at Smartsheet Engage 2018 in Bellevue, Washington. I am Lisa Martin with Jeff Frick, and we've had a great day talking with Smartsheet executives, analysts, users, and we're excited to welcome to theCube for the first time, Gurmeet Mangat, the site manager Wind Power Generation at GE Renewable Energy. Gurmeet, great to have you on the program. >> Thank you Lisa, thank you Jeff. I'm really happy to be here. >> So you're a user of Smartsheet, but you're also a renegade. So before we get into your renegade status, tell us a little bit about GE Renewable Energy and your role. You got a big role as site manager. What, 75 turbines across multiple locations? So let's talk about GE Renewable Energy and your role as site manager. >> Sure, no problem. So GE Renewable Energy. One of our missions statements is to unleash limitless energy. How we do that, we harness the power of the sun, the water, and the wind. So try to produce clean efficient energy to power countries, homes, businesses, whatever needs that powered energy. As a manager I manage, like you said three wind farms, three different customers. A very complex role to have. I'm coming right from not just operations, human resources, financials. So everything's required of someone like me to manage that business end to end. It's a challenge, at the same time I seek opportunity in a lot of what's going on and leveraging Smartsheet as one of the tools. It's something I've been using over the past year to optimize the business and run those turbines. >> So it's so funny because I would say GE turbine farms and GE engines are the most quoted, often referenced IOT devices in this next gen conversation about IOT and data and how much data they throw off of any other kind of product out there, and you're sitting right in the middle of it actually managing the real machines and managing the real data. >> Yeah, exactly. So I mean the, the machines themselves are highly automated. They're spinning out a lot of data and we've got great systems in place to manage that information. Make it transferrable, viewable to a lot of the people that need it. The opportunity is not necessarily in the equipment that GE manufactures but the back-end business that drives that manufacturing, that drives those services. That's where, again we leveraged Smartsheet over the last year to close a lot of data quality issues. We're ruling out and canceling a lot of the human error of the process steps that we're seeing in a lot of businesses today. We're really taking the initiative of managing our data, bringing us, making us actually competitive in the fourth industrial revolution. I mean I've had a strong believe that if you're not managing your data correctly today, you'll market yourself out of the business, you won't stay ahead of the game. So I think, like I was saying before the biggest opportunity right now is the back-end of the business. Smart GE does a great job at manufacturing and producing high quality products. I think there's huge opportunity in saving the back end and optimizing the process that runs that. >> When you say the back end, there's always a lot of conversation about you know going from reactive to predictive to prescriptive. Analytics, again everybody likes to talk about keeping the turbine up. Are you talking about those types of processes or is it more, you know how that energy is fed into the grid and more kind of the connection to the broader ecosystem, when you say back end? >> Let's talk about the proactive and reactive situation, 'cause that's really what we're trying to drive. >> Okay. >> There can be particular cases where a turbine could fail in the middle of winter, a high-wind season and the visibility's not great. So what we've done is we've taken Smartsheet. We've given our technicians a mobile application tool to collect data as they visit turbines. We're taking information within Smartsheet, we aggregate it, we quantify it, and now we're able to predict turbine behavior based on this information. A little bit faster than some of the tools that GE provides today. A perfect example is about a month ago we determined that a turbine needed a quarter of a million dollar repair before any GE tool told us that. That was simply because of giving our technicians a tool, which is a Smartsheet webform and telling us what happens everyday you visit that turbine. That goes into the background. We take the information, aggregate it into a dashboard viewings. That gives us a great visual control and visual aid of our business. >> That visibility-- >> I was going to say, is he collecting different data, or are you processing it in a different way with the tooling that you set up with Smartsheet that gives you that visibility? >> They are, so we are collecting different data. So GE gives us a lot of data on our turbine health efficiency, how it's operating. It might quantify the number of faults per megawatt hour and per (mumbles) it for us for example. But what we're creating with Smartsheet is we're creating our own organic KPIs I'll call them, some metrics that we are creating ourselves to try to drive different behavior. So when the techs go in, we talk about parts consumptions, for example. So if this part's been consumed 20 times over the last month, you've got to ask why. You know, why do you keep visiting this turbine to do that. So that visibility drives a different discussion now, so now we can engage with engineers with different type, different information. They might be able to say, "Okay, "you know what, you guys got some good data here. "We think you're right. "We should execute this repair." >> So, that example that you gave and give me the number again that working with Smartsheets your team was able to find a, what did you say, a $250 million? >> $250,000 repair. >> Thousand dollar repair. >> That's the cost of the repair, but it's a proactive repair versus reactive so now we're not facing a long wait time, finding a crane, bringing a crane on site, getting the paperwork in place to get the job done 'cause it's not an easy repair. >> But there's a very impressive snowball effect of the benefits back to the business. You've found it faster. You were able to get, you know the parts needed faster, repair it faster. Clearly that goes all the way back up the chain from a revenue perspective. >> Absolutely. >> But you, when I alluded to you earlier, this renegade status, you brought Smartsheet in from your previous job and you've said, "This has enabled us "to find something faster than "our brand of technology's product would have been "able to do." Talk to us about this conviction that you brought in and is it kind of becoming viral within GE Renewable Energy yet? >> Good question. It's becoming viral, a lot of people are listening now. So we've talked to GE digital VPs. I've talked to the ERP providers in Europe, what they're doing with GE. So we've essentially, I call it a success story. They're not going to adopt Smartsheet. They want to build their own enterprise solution but, the reason why I call it a success story is because I've changed the way that they are thinking today. >> That's huge. Cultural change? >> I've presented a solution to them. I've essentially told them, you need to give us something that works for us faster. If you do this, it gives managers capacity to improve your business, really develop people that are working underneath you, engage them, empower them, and move the business forward not on a typical five year plan that most businesses have in place. But it's a step change. >> Right, right. >> It takes you year over year and you're stepping every year to something new, and I think in today's day and age with how fast things are moving, you need that. >> And I'm curious to unpack a little bit on this example where you said you know, it's this failing part that was giving you a leading indicator that there was a bigger problem. So that was just kind of a different way to look at the solution, right? You're identifying kind of a stupid consumption pattern on a spare part that shouldn't happen as opposed to the core data that's coming off that machine and that's what gave you kind of the unique insights. Does that come from you? Does that come techs who are in the field and have kind of a sense of, "Maybe we should be looking at this, "maybe we should be looking at that." How do you start to empower people or where do some of these different kind of points of view that then can be backed up with data in the Smartsheet process come from? >> So, it's all techs. (clears throat) Coming into the job last year, I asked one of the techs, I said, "Why are you going to this turbine?" And the question why is such a powerful question to ask. They said, "We're going to fix this." So what happened last time? They had no idea. So I said, "There's no "information to support your visit today? "And you don't understand why you're going today." They said, "As a result of something that was not "done correctly before." So we fixed that part first. We started giving them the information upfront. We gave them a tool to collect the data. So now they are empowered to provide very direct feedback to myself as a manager and even to an engineering team, like in New York for example. Something technicians never felt empowered to do before. They are the driving factors for those data collection, the decision making. I definitely appreciate that by giving them feedback on a daily basis, that what you guys are doing is changing the way that we manage the business. It's a very driven culture change by the front line. It's not something that I'm pushing down. I'm asking them to help me push it upwards to the senior level. >> And they've got to love it. They've got to love thinking that they've actually got input as opposed to just being called to go out and fix things when it breaks. >> Exactly. They're driving their day. They can go to work in the morning. They can look at the whole personality of a turbine, what's outstanding, what was done last time and the conversations are very quick in the morning. It used to be a 7 o'clock startup. They're not driving out 'til eight, 8:30, nine o'clock by the time they get their stuff together. I mean we're averaging a seven am to about 7:30 departure now. >> So each person is saving 60 to 90 minutes everyday. >> Every day now departure. >> That's a big roll up. In fact, I was looking at some of the productivity stats that Smartsheet talks about on their website and they say an average per, individual user of Smartsheet will save about 300 hours a year. An organization can save up to 60,000 hours a year. >> I believe that. That's believable. I mean there's, just a technical aspect of managing a turbine. If we even talk about you know issuing a purchase order. Managing contractor labor, invoices. The tool that we're using today is a complete end to end P & L management tool. So it takes invoicing from subcontractors, labor. We are inventory tracking, we are tracking any health and safety issues. Everything from end to end, so it's really done a great job for us. >> That's all built within your Smartsheet? >> Correct. >> Wow. >> And it's all mobile, so. I mean I'm not at my site this week, but on a daily basis I have visibility to my business. You're talking about 70, 80 plus machines, that's over you know about a hundred million dollars in assets that have to be managed effectively, efficiently, and correctly. >> You have visibility into everyday from wherever you are? >> Exactly, yes. >> That's a huge transformation. So we talked about you being a renegade and other groups within GE on divisions that are curious about this. I'm curious, have you heard anything today that they have announced that excites you, or maybe was any of this part of a feedback that you provided, as we've heard all day Jeff that they're very responsive to customer feedback in terms of product innovation. Anything you're going to go back to the office and be excited, like the next generation or what's coming available soon? Is it going to enable me to do X-Y-Z now? >> That's a good question. So GE is a very tough company to change. There will be a lot of takeaways from this trip and when I go head back. After the last conversation I had with GE digital and the team, they are going to hire a new resource and set budget aside to help close the gaps that we've identified. So I think after this visit and some of the things I've learned throughout the conference and when I head back I'll only be able to identify a few more gaps that they need to fill, and I'll push that up to them probably in the next week when I get back there and hopefully they can appreciate that candid feedback and take that and run with it. >> But you were able to fund your existing project just out of your own discretionary funds? >> Exactly. I mean that's one of the benefits of Smartsheet. It costs really nothing to create something, and my job is to manage wind farms, so I've taken initiative to create, I call it a mini-ERP system using Smartsheet with an associate of mine, and it's an organic creation. It didn't take us, I mean to run three wind farms, I started last April, it probably took us less than six months to create a working system. That's awesome feedback for Smartsheet, their tools are very user-friendly. It's lightweight, it takes away the fear of coding that Excel gives to some people. If you're a new user of any application you can kind of walk into it and run with it. That's one of the reasons why we took it from nothing to something in such a short period of time. >> Wow. >> That's a ground swell in action that has some significant results. But you'd better be careful. I'm imagining your success is going to go so viral, you're going to have way more than 75 turbines and three wind farms >> That's possible. >> to manage. (laughs) >> There's been a recent acquisition and there's other sites around me that my boss is, or my directors said, "Hey, what are you doing next week?" >> Oh! (laughs) >> "Let's go visit this site for a few minutes." Okay, I know what you're getting at. >> Kind of a good problem to have, but thanks so much for stopping by and sharing with us what you're doing as a renegade. It seems pretty contagious. >> Appreciate it, thank you for having me. >> Thanks. >> Thanks. >> For Jeff Frick I'm Lisa Martin and you're watching theCUBE live from Smartsheet Engage 2018. Stick around, Jeff and I will be back to wrap up the show in just a minute. (digital music)

(upbeat orchestral music) >> Welcome back everybody. Jeff Frick here with the CUBE. We're at our Palo Alto studios having a CUBE conversation about digital transformation, industrial internet, AI, ML, all things great, and we're really excited to have a representative of GE, one of our favorite companies to work with because they're at the cutting edge of old industrial stuff and new digital transformation and building a big software organization out in San Ramon. So we're so happy to have here first time Jeff Erhardt. He is the VP Intelligent Systems from GE Digital. Jeff, great to see you. >> Pleasure to be here. Thanks for having me. >> Absolutely, so how did you get into GE? You actually, a creature of the valley, you've been here a little while. How did you end up at GE? >> I have. I'm a new guy, so I've been here about a year and a half, I came in via the acquisition of a company called Wise IO where I was the CEO, so I've spent the last 10 years or so of my life building two different analytic startups. One was based around a very popular and powerful open source language called R and spent a lot of time working with much of the Fortune 500. Think the really data driven companies now that you would think of, the Facebooks, the Goldman Sachs, the Mercks, the Pfeizers helping them go through this data driven journey. Anyway, that company was acquired by Microsoft and is embedded into their products now. But the biggest thing I learned out about that was that even if you have really good data science teams, it's incredibly hard to go from white board into production. How do you take concepts and make them work reliably repeatably, scalably over time? And so, Wise IO was a machine learning company that was a spin out from Berkeley, and we spent time building what I now refer to as intelligent systems for the purposes of customer support automation within things like the sales force and Zendesk ecosystem, and it was really that capability that drew us to GE or drew GE to approach us, to think about how do we build that gap not just from algorithms, but into building true intelligent applications? >> Right, so GE is such a great company. They've been around for a hundred years, original DOW component, Jeff Immelt's not there now, but he was the CEO I think for 16 years. A long period of time. Beth Comstock, fantastic leader. Bill Ruth building this great organization. But it's all built around these industrial assets. But they've started, they did the industrial internet launch. We helped cover it in 2013. They have the Pridix Cloud, their own kind of industrial internet cloud, had a big developer conference. But I'm curious coming from kind of a small Silicon Valley startup situation. When you went into GE, what's kind of the state of their adoption, you know, kind of how had Bill's group penetrated the rest of GE and were they making process? We're people kinda getting it, or were you still doing some evangelical work out in the field? Absolutely both, meaning people understand it are implementing yet I think there was maybe misunderstandings about how to think about software data in particular analytics and AI machine learning. And so a big part of my first year at the company was to spend the time coming in really from the top down, from sort of the CEO and CDO levels across the different business understanding what was the state of data and data driven processes within their businesses. And what I learned really quickly was that the core of this business, and this is all public information been well publicized, is in things like GE Aviation. It's not necessarily the sale of the engine that is incredible profitable, but rather it's maintaining and servicing that over time. >> Right. >> And what organizations like them, like our oil and gas divisions, with things like their inspection capabilities like our power division had really done is they had created as a service businesses where they we're taking data across the customer base, running it through a data driven process, and then driving outcomes for our customers. And all of a sudden the aha moment was wow, wait a minute. This is the business model that every startup in the valley is getting funded to take down the traditional software players for. It's just not yet modern, scalable, repeatable, with AI machine learning built in, but that's the purpose and the value of building these common platforms with these applications on top that you can then make intelligent. >> Right. >> So, once we figure that out it was very easy to know where to focus and start building from that. >> So it's just, it's kinda weird I'm sure for people on the outside looking in to say data driven company. We all want to drive data driven companies. But then you say, well wait a minute, now GE builds jet engines. There's no greater example that's used at conferences as to the number of terabytes of data an engine throws off on a transcontinental flight. Or you think of a power plant or locomotion and you think of the control room with all this information so it probably seems counterintuitive to most that, didn't they have data, weren't they a data driven organization? How has the onset of machine learning and some of the modern architectures actually turned them into a data driven company, where before I think they were but really not to the level that we're specifying here. >> Yah, I-- >> What would be your objective, what are you trying to take on this? >> Absolutely, machine learning, AI, whatever buzz words you want to use is a fascinating topic. It's certainly come into vogue. like many things that are hyped, gets confused, gets misused, and gets overplayed. But, it has the potential to be both an incredibly simple technology as well as an incredibly powerful technology. So, one of the things I've most often seen cause people to go awry in this space is to try to think about what is the new things that I can do with machine learning? What is the green field opportunity? And whenever I'm talking to somebody at whatever level, but particularly at the higher levels of the company is I like to take a step back and I like to say, "What are the value producing, data driven workflows within your business?" And I say define for me the data that you have, how decisions are made upon it, and what outcome that you are driving for. And if you can do that, then what we can do is we can overlay machine learning as a technology to intelligently automate or augment those processes. And in turn what that's gonna do is it's gonna force you to standardize your infrastructure, standardize those workflows, quantify what you're trying to optimize for your customers. And if you do that in a standardized and incremental way, you can look backward having accomplished some very big things. >> Right, and those are such big foundational pieces that most people I think discount again, just the simple question of where is your data. >> That's right. >> What form is it in? So another interesting concept that we cover all the time with all the shows we go to is democratization, right? So it seems to me pretty simple, actually. How do you drive innovation, democratize the data, democratize the tool to manipulate the data, and democratize the ability to actually do something about it. That said, it's not that easy. And this kind of concept that we see evolving from citizen developer to citizen integrator to citizen data scientist is kinda where we all want to go to, but as you've experienced first hand it's not quite as easy as maybe it appears. >> Yah, I think that's a very fair statement and you know, one of the things, again I spend a lot of time talking about, is I like to think about getting the right people in the right roles, using the right tools. And the term data scientist has evolved over the past five plus years going from to give Drew Conway some credit of his Venn diagram of a program or a math kinda domain expert, into meaning anybody that's looking at data. And there's nothing wrong with that, but the concept of taking anybody that has ability to look at data within something like a BI or a Tableau tool, that is something that should absolutely be democratized and you can think about creating citizens for those people. On the flip side, though, how do you structure a true intelligent system that is running reliably, robustly, and particular in our field in mission critical, high risk, high stakes applications? There are bigger challenges than simply are the tools easy enough to use. It's very much more a software engineering problem than it is a data access or algorithmic problem. >> Right. >> And, so we need to build those bridges and think about where do we apply the citizens to for that understanding, and how do we build robust, reliable software over time? >> Right, so many places we can go, and we're gonna go a lot of them. But one of the things you touched on which also is now coming in vogue is kind of ML that you can, somebody else's ML, right? >> Mhmm. >> As you would buy an application at an app store, now there's all kinds of algorithmic equations out there that you can purchase and participate in. And that really begs an interesting question of kinda the classic buy versus build, or as you said before we turned on the cameras buy versus consume because with API economy with all these connected applications, it really opens up an opportunity that you can use a lot more than was produced inside your own four walls. >> Absolutely. >> For those applications. >> Yep. >> And are you seeing that? How's that kinda playing out? >> So we can parse that in a couple of different ways. So the first thing that I would say is there's a Google paper from a few years back that we love and it's required reading for every new employee that we bring on board. And the title of it was machine Learning is the High Interest Credit Card of Technical Debt. And one of the key points within that paper is that the algorithm piece is something like five percent of an overall production machine learning implementation. And so it gets back to the citizen piece. About it's not just making algorithms easier to use, but it's also about where do you consume things from an API economy? So that's the first thing I would think about. The second thing I would think about is there's different ways to use algorithms or APIs or pieces of information within an overall intelligent system. So you might think of speech to text or translation as capabilities. That's something where it probably absolutely makes sense to call an API from an Amazon or a Microsoft or a Google to do that, but then knowing how to integrate that reliably, robustly into the particular application or business problem that you have, is an important next step. >> Right. >> The third thing that I would think about is, it very much matters what your space is. And there's a difference between doing things like image classification on things like Imagenet which is publicly available images which are well documented. Is it a dog versus a cat? Is it a hot dog versus not? Versus some of the things that we face with an industrial context, which aren't really publicly available. So we deal with things like within our oil and gas business we have a very large pipeline inspection integrity business where the purpose of that is to send the equivalent of an MRI machine through the pipes and collect spectral images that collect across 14 different sensors. The ability to think that you're gonna take a pre trained algorithm based on deep learning and publicly available images to something that is noisy, dirty, has 14 different types of sensors on it and get a good answer-- >> Right. >> Is ridiculous. >> And there's not that many, right? >> And there's not that many. >> That's the other thing I think people underestimate the advantage that Google has we're all taking pictures of dogs and blueberries-- >> Correct. >> So that it's got so much more data to work with. >> That's right. >> As opposed to these industrial applications which are much smaller. >> That's right. >> Lets shift gears again, in terms of digital transformation one of the other often often said examples is when will the day come that GE doesn't sell just engines but actually sells propulsion miles? >> Yep. >> To really convert to a service. >> Yah. >> And that's ultimately where it needs to go cause it's kinda the next step beyond maintenance. >> Yep. >> How are you seeing that digital transformation play out? Do people kinda get it? Do the old line guys that run the jet engine see that this is really a better opportunity? >> Mhmm. >> Cause you guys have, and this is the broader theme, very uniques data and very unique expertise that you've aggregated across in the jet engines base all of your customers in all of the flying conditions and all of the types of airplanes where one individual mechanic or one individual airline just doesn't have an expertise. >> Yep. >> Huge opportunity. >> That's exactly right, and you can say the ame thing in our power space, in our power generation space. You can say the same thing in the one we we're just talking about, you know things like our inspection technology spaces. That's what makes the opportunity so powerful at GE and it's exactly the reason why I'm there because we can't get that any place else. It's both that history, it's that knowledge tied to the data, and very importantly it's what you hinted at that bares repeating is the customer relationships and the customer base upon which you can work together to aggregate all that data together. And if you look at what things are being done, they're already doing it. They are selling effectively, efficiency within a power plant. They are selling safety within certain systems, and again, coming back to why create a platform. Why create standardized applications? Why put these on top? Is if you standardize that, it gives you the ability to create derivative and adjacent products very easily, very efficiently, in ways that nobody else can match. >> Right, right. And I love the whole, for people who aren't familiar with the digital twin concept, but really leveraging this concept of a digital twin not to mimic kinda the macro level, but to mimic the micro level of a particular part unit engine in a particular ecosystem where you can now run simulations, you can run tests, you can do all kinds of stuff without actually having that second big piece of capital gear out there. >> That's right, and it's really hard to mimic those if you didn't start from the first phase of how did you design, build, and put it in to the field? >> Right, right. So, I want to shift gears a little bit just on to philosophical things that you've talked about and doing some research. One of them is that tech is the means to an end, and I know people talk about that all the time, but we're in the tech business. We're here in Silicon Valley. People get so enamored with the technology that they forget that it is a means to an end. It is now the end and to stay focused. >> That's right. >> How are you seeing that kind of play out in GE Digital? Obviously Bill built this humongous organization. I'm super impressed he was able to hire that many people within the last like four years in San Ramon. >> Yah. >> Originally I think just to build the internal software workings within the GE business units, but now really to go much further in terms of industrial internet connectivity, etc. So how do you see that really kinda playing out? >> Yah, I think one of my favorite quotes that I forget who it came from but I'll borrow it is, "Customers don't want to buy a one inch drill bit, they want to buy a one inch hole." >> Right. >> And I think there is both an art and a science and a degree of understanding that needs to go into what is the real customer problem that they are trying to solve for, and how do you peel the onion to understanding that versus just giving what they ask for? >> Right. >> And I think there's an organizational design to how do you get that right. So we had a visitor from Europe, the chairman of one of our large customers, who is going through this data driven journey, and they were at the stage of simply just collecting data off of their equipment. In this case it was elevators and escalators. And then understanding how was it being used? What does it mean for field maintenance, etcetera? But his guys wanted to move right to the end stage and they wanted to come in and say, "Hey, we want to build AI machine learning systems." And we spent some time talking through them about how this is a journey, how you step through it. And you could see the light bulb go off. That yes, I shouldn't try to jump right to that end state. There's a process of going through it, number one, and then the second thing we spent some time talking about was how he can think about structuring his company to create that bridge between the new technology people who are building and doing things in a certain way, and the people who have the legacy knowledge of how things are built, run, and operated? >> Right. >> And it's many times those organizational aspects that are as challenging or as big of barriers to getting it right as a specific technology. >> Oh, for sure, I mean people process and tech it's always the people that are the hard part. It's funny you bring up the elevator or escalator story, We did a show at Spunk many moons ago and we had a person on from an elevator company and the amazing insight they connected Spunk to it. They could actually tell the health of a building by the elevator traffic. >> Yah. >> Not the health of it's industrial systems and it's HVAC, but whether some of the tenants were in trouble. >> Yep. >> By watching the patterns that were coming off the elevator. While different kinda data driven value proposition than they had before. >> Yep. So again, if you could share some best practices really from your experiences with R and now kinda what you're doing at GE about how people should start those first couple of steps in being data driven beyond kinda the simple terms of getting your house in order, getting your data in order, where is it. >> Yah. >> Can you connect to it? Is it clean? >> Yah. >> How should they kinda think about prioritizing? Ho do they look for those easy wins cause at the end of the day it's always about the easiest wins to get the support to move to the next level. >> Yah, so I've sorta got a very simple Hilo play book and you know the first step is you have to know your business. And you have to really understand and prioritize. Again, sometimes I think about not the build, buy decision per say, but maybe the build consume decision. And again, where does it take the effort to go through hiring the people, understanding building those solutions, versus where is it just best to say, "I'm best to consume this product or service from somebody else." So that's number one, and you have to understand your business to do that, really well. The second one is, and we touched on this before, which is getting the right people in the right seats of the bus. Understanding who those citizen data scientists are versus who your developers are, who your analytics people are, who your machine learning people are, and making sure you've got the right people doing the right thing. >> Right. >> And then the last thing is to make sure, to understand that it is a journey. And we like to think about the journey that we go through in sort of three phases, right? Or sort of three swim lanes that could happen, both in parallel, but also as a journey. And we think about those as sort of basic BI and exploratory analytics. How do I learn is there any there there? And fundamentally you're saying, I want to ask and answer a question one time. Think about traditional business reporting. But once you've done that, your goal is always to put something into production. You say, "I've asked and answered once, now I want to ask and answer hundreds, millions, billions of times-- >> Right, right. >> In a row." And the goal is to codify that knowledge into a statistic, an analytic, a business role. And then, how do you start running those within a consistent system? And it's gonna do and force exactly what you just said. Do I have my data in one place? Is it scalable? Is it robust? Is it queryable? Where is it being consumed? How do I capture what's good or bad? And once I start to then define those, I can then start to standardize that within an application workflow and then move into, again, these complex, adaptive, intelligent systems powered by AI machine learning. And so, that's the way we think about it. Know your business, get the people right, understand that it's a systematic journey. >> Right, and then really bake it into the application. >> That's right. >> That's the thing, we don't want to make the same mistake that we do with big data, right? >> Yep. >> Just put it into the application. It's not this stand alone-- >> Correct. >> You know, kinda funny thing. >> Exactly. >> Alright, Jeff, I'll give you the last work before we wrap for the day. So you've been with GE now for about a year and a half, about halfway through 2018. What are your priorities for the next 12 months? If we sit down here, you know June one next year, what are you working on, what's kinda top of mind for you going forward? >> Yah, so top of the line for me, so as I mentioned sort of our first year here was really surveying the landscape, understanding how this company does business, where the opportunities are. Again, where those data driven work flows are. And we have an idea of of that with the core industrial. And so what we've been doing is getting that infrastructure right, getting those people right, getting the V ones of some very powerful systems set up. And so, what I'm gonna be doing over the next year or so is really working with them to scale those out within those core parts of the business, understand how we can create derivative and adjacent products over those, and then how we can take them to market more broadly based upon that, exactly as you said earlier, large scale data that we have available, that customer insight, and that knowledge of how we've been building the stuff, so. >> Alright, I look forward to it. >> I look forward to being back in a year. >> All right, Jeff Erhardt. Thanks for watching. I'm Jeff Frick. You're watching the CUBE from our Palo Alto studios. See you next time. (upbeat orchestra music)

>> Announcer: Live, from Orlando, Florida, it's The Cube. Covering PentahoWorld 2017. Brought to you by, Hitachi Vantara. >> Welcome back to Orlando, everybody. This is PentahoWorld, #pworld17 and this is The Cube, The leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Jim Kobielus Donna Prlich is here, she's the Chief Product Officer of Pentaho and a many-time Cube guest. Great to see you again. >> Thanks for coming on. >> No problem, happy to be here. >> So, I'm thrilled that you guys decided to re-initiate this event. You took a year off, but we were here in 2015 and learned a lot about Pentaho and especially about your customers and how they're applying this, sort of, end-to-end data pipeline platform that you guys have developed over a decade plus, but it was right after the acquisition by Hitachi. Let's start there, how has that gone? So they brought you in, kind of left you alone for awhile, but what's going on, bring us up to date. >> Yeah, so it's funny because it was 2015, it was PentahoWorld, second one, and we were like, wow, we're part of this new company, which is great, so for the first year we were really just driving against our core. Big-Data Integration, analytics business, and capturing a lot of that early big-data market. Then, probably in the last six months, with the initiation of Hitachi Ventara which really is less about Pentaho being merged into a company, and I think Brian covered it in a keynote, we're going to become a brand new entity, which Hitachi Vantara is now a new company, focused around software. So, obviously, they acquired us for all that big-data orchestration and analytics capability and so now, as part of that bigger organization, we're really at the center of that in terms of moving from edge to outcome, as Brian talked about, and how we focus on data, digital transformation and then achieving the outcome. So that's where we're at right now, which is exciting. So now we're part of this bigger portfolio of products that we have access to in some ways. >> Jim: And I should point out that Dave called you The CPO of Pentaho, but in fact you're the CPO of Hitachi Vantara, is that correct? >> No, so I am not. I am the CPO for the Pentaho product line, so it's a good point, though, because Pentaho brand, the product brand, stays the same. Because obviously we have 1,800 customers and a whole bunch of them are all around here. So I cover that product line for Hitachi Vantara. >> David: And there's a diverse set of products in the portfolios >> Yes. >> So I'm actually not sure if it makes sense to have a Chief Products officer for Hitachi Vantara, right? Maybe for different divisions it makes sense, right? But I've got to ask you, before the acquisition, how much were you guys thinking about IOT and Industrial IOT? It must have been on your mind, at about 2015 it certainly was a discussion point and GE was pushing all this stuff out there with the ads and things like that, but, how much was Pentaho thinking about it and how has that accelerated since the acquisition? >> At that time in my role, I had product marketing I think I had just taken Product Management and what we were seeing was all of these customers that were starting to leverage machine-generated data and were were thinking, well, this is IOT. And I remember going to a couple of our friendly analyst folks and they were like, yeah, that's IOT, so it was interesting, it was right before we were acquired. So, we'd always focus on these blueprints of we've got to find the repeatable patterns, whether it's Customer 360 in big data and we said, well they're is some kind of emerging pattern here of people leveraging sensor data to get a 360 of something. Whether it's a customer or a ship at sea. So, we started looking at that and going, we should start going after this opportunity and, in fact, some of the customers we've had for a long time, like IMS, who spoke today all around the connected cars. They were one of the early ones and then in the last year we've probably seen more than 100% growth in customers, purely from a Pentaho perspective, leveraging Machine-generated data with some other type of data for context to see the outcome. So, we were seeing it then, and then when we were acquired it was kind of like, oh this is cool now we're part of this bigger company that's going after IOT. So, absolutely, we were looking at it and starting to see those early use cases. >> Jim: A decade or more ago, Pentaho, at that time, became very much a pioneer in open-source analytics, you incorporated WECA, the open-source code base for machine-learning, data mining of sorts. Into the core of you're platform, today, here, at the conference you've announced Pentaho 8.0, which from what I can see is an interesting release because it brings stronger integration with the way the open-source analytic stack has evolved, there's some Spark Streaming integration, there's some Kafaka, some Hadoop and so forth. Can you give us a sense of what are the main points of 8.0, the differentiators for that release, and how it relates to where Pentaho has been and where you're going as a product group within Hiatachi Vantara. >> So, starting with where we've been and where we're going, as you said, Anthony DeShazor, Head of Customer Success, said today, 13 years, on Friday, that Pentaho started with a bunch of guys who were like, hey, we can figure out this BI thing and solve all the data problems and deliver the analytics in an open-source environment. So that's absolutely where we came form. Obviously over the years with big data emerging, we focused heavily on the big data integration and delivering the analytics. So, with 8.0, it's a perfect spot for us to be in because we look at IOT and the amount of data that's being generated and then need to address streaming data, data that's moving faster. This is a great way for us to pull in a lot of the capabilities needed to go after those types of opportunities and solve those types of challenges. The first one is really all about how can we connect better to streaming data. And as you mentioned, it's Spark Streaming, it's connecting to Kafka streams, it's connecting to the Knox gateway, all things that are about streaming data and then in the scale-up, scale-out kind of, how do we better maximize the processing resources, we announced in 7.1, I think we talked to you guys about it, the Adaptive Execution Layers, the idea that you could choose execution engine you want based on the processing you need. So you can choose the PDI engine, you can choose Spark. Hopefully over time we're going to see other engines emerge. So we made that easier, we added Horton Work Support to that and then this concept of, so that's to scale up, but then when you think about the scale-out, sometimes you want to be able to distribute the processing across your nodes and maybe you run out of capacity in a Pentaho server, you can add nodes now and then you can kind-of get rid of that capacity. So this concept of worker-nodes, and to your point earlier about the Hitachi Portfolio, we use some of the services in the foundry layer that Hitachi's been building as a platform. >> David: As a low balancer, right? >> As part of that, yes. So we could leverage what they had done which if you think about Hitachi, they're really good at storage, and a lot of things Pentaho doesn't have experience in, and infrastructure. So we said, well why are we trying to do this, why don't we see what these guys are doing and we leverage that as part of the Pentaho platform. So that's the first time we brought some of their technology into the mix with the Pentaho platform and I think we're going to see more of that and then, lastly, around the visual data prep, so how can we keep building on that experience to make data prep faster and easier. >> So can I ask you a really Columbo question on that sort-of load-balancing capabilities that you just described. >> That's a nice looking trench coat you're wearing. >> (laughter) gimme a little cigar. So, is that the equivalent of a resource negotiator? Do I think of that as sort of your own yarn? >> Donna: I knew you were going to ask me about that (laughter) >> Is that unfair to position it that way? >> It's a little bit different, conceptually, right, it's going to help you to better manage resources, but, if you think about Mesos and some of the capabilities that are out there that folks are using to do that, that's what we're leveraging, so it's really more about sometimes I just need more capacity for the Pentaho server, but I don't need it all the time. Not every customer is going to get to the scale that they need that so it's a really easy way to just keep bringing in as much capacity as you need and have it available. >> David: I see, so really efficient, sort of low-level kind of stuff. >> Yes. >> So, when you talk about distributed load execution, you're pushing more and more of the processing to the edge and, of course, Brian gave a great talk about edge to outcome. You and I were on a panel with Mark Hall and Ella Hilal about the, so called, "power of three" and you did a really good blog post on that the power of the IOT, and big data, and the third is either predictive analytics or machine learning, can you give us a quick sense for our viewers about what you mean by the power of three and how it relates to pushing more workloads to the edge and where Hitachi Vantara is going in terms of your roadmap in that direction for customers. >> Well, its interesting because one of the things we, maybe we have a recording of it, but kind of shrink down that conversation because it was a great conversation but we covered a lot of ground. Essentially that power of three is. We started with big data, so as we could capture more data we could store it, that gave us the ability to train and tune models much easier than we could before because it was always a challenge of, how do I have that much data to get my model more accurate. Then, over time everybody's become a data scientist with the emergence of R and it's kind of becoming a little bit easier for people to take advantage of those kinds of tools, so we saw more of that, and then you think about IOT, IOT is now generating even more data, so, as you said, you're not going to be able to process all of that, bring all that in and store it, it's not really efficient. So that's kind of creating this, we might need the machine learning there, at the edge. We definitely need it in that data store to keep it training and tuning those models, and so what it does is, though, is if you think about IMS, is they've captured all that data, they can use the predictive algorithms to do some of the associations between customer information and the censor data about driving habits, bring that together and so it's sort of this perfect storm of the amount of data that's coming in from IOT, the availability of the machine learning, and the data is really what's driving all of that, and I think that Mark Hall, on our panel, who's a really well-known data-mining expert was like, yeah, it all started because we had enough data to be able to do it. >> So I want to ask you, again, a product and maybe philosophy question. We've talked on the Cube a lot about the cornucopia of tooling that's out there and people who try to roll their own and. The big internet companies and the big banks, they get the resources to do it but they need companies like you. When we talk to your customers, they love the fact that there's an integrated data pipeline and you've made their lives simple. I think in 8.0 I saw spark, you're probably replacing MapReduce and making life simpler so you've curated a lot of these tools, but at the same time, you don't own you're own cloud, you're own database, et cetera. So, what's the philosophy of how you future-proof your platform when you know that there are new projects in Apache and new tooling coming out there. What's the secret sauce behind that? >> Well the first one is the open-source core because that just gave us the ability to have APIs, to extend, to build plugins, all of that in a community that does quite a bit of that, in fact, Kafka started with a customer that built a step, initially, we've now brought that into a product and created it as part of the platform but those are the things that in early market, a customer can do at first. We can see what emerges around that and then go. We will offer it to our customers as a step but we can also say, okay, now we're ready to productize this. So that's the first thing, and then I think the second one is really around when you see something like Spark emerge and we were all so focused on MapReduce and how are we going to make it easier and let's create tools to do that and we did that but then it was like MapReduce is going to go away, well there's still a lot of MapReduce out there, we know that. So we can see then, that MapReduce is going to be here and, I think the numbers are around 50/50, you probably know better than I do where Spark is versus MapReduce. I might be off but. >> Jim: If we had George Gilbert, he'd know. >> (laughs) Maybe ask George, yeah it's about 50/50. So you can't just abandon that, 'cause there's MapReduce out there, so it was, what are we going to do? Well, what we did in the Hadoop Distro days is we created a adaptive, big data layer that said, let's abstract a layer so that when we have to support a new distribution of Hadoop, we don't have to go back to the drawing board. So, it was the same thing with the execution engines. Okay, let's build this adaptive execution layer so that we're prepared to deal with other types of engines. I can build the transformation once, execute it anywhere, so that kind of philosophy of stepping back if you have that open platform, you can do those kinds of things, You can create those layers to remove all of that complexity because if you try to one-off and take on each one of those technologies, whether it's Spark or Flink or whatever's coming, as a product, and a product management organization, and a company, that's really difficult. So the community helps a ton on that, too. >> Donna, when you talk to customers about. You gave a great talk on the roadmap today to give a glimpse of where you guys are headed, your basic philosophy, your architecture, what are they pushing you for? Where are they trying to take you or where are you trying to take them? (laughs) >> (laughs) Hopefully, a little bit of both, right? I think it's being able to take advantage of the kinds of technologies, like you mentioned, that are emerging when they need them, but they also want us to make sure that all of that is really enterprise-ready, you're making it solid. Because we know from history and big data, a lot of those technologies are early, somebody has to get their knees skinned and all that with the first one. So they're really counting on us to really make it solid and quality and take care of all of those intricacies of delivering it in a non-open-source way where you're making it a real commercial product, so I think that's one thing. Then the second piece that we're seeing a lot more of as part of Hitachi we've moved up into the enterprise we also need to think a lot more about monitoring, administration, security, all of the things that go at the base of a pipeline. So, that scenario where they want us to focus. The great thing is, as part of Hitachi Vantara now, those aren't areas that we always had a lot of expertise in but Hitachi does 'cause those are kind of infrastructure-type technologies, so I think the push to do that is really strong and now we'll actually be able to do more of it because we've got that access to the portfolio. >> I don't know if this is a fair question for you, but I'm going to ask it anyway, because you just talked about some of the things Hitachi brings and that you can leverage and it's obvious that a lot of the things that Pentaho brings to Hitachi, the family but one of the things that's not talked about a lot is go-to-market, Hitachi data systems, traditionally don't have a lot of expertise at going to market with developers as the first step, where in your world you start. Has Pentaho been able to bring that cultural aspect to the new entity. >> For us, even though we have the open-source world, that's less of the developer and more of an architect or a CIO or somebody who's looking at that. >> David: Early adopter or. >> More and more it's the Chief Data Officer and that type of a persona. I think that, now that we are a entity, a brand new entity, that's a software-oriented company, we're absolutely going to play a way bigger role in that, because we brought software to market for 13 years. I think we've had early wins, we've had places where we're able to help. In an account, for instance, if you're in the data center, if that's where Hitachi is, if you start to get that partnership and we can start to draw the lines from, okay, who are the people that are now looking at, what's the big data strategy, what's the IOT strategy, where's the CDO. That's where we've had a much better opportunity to get to bigger sales in the enterprise in those global accounts, so I think we'll see more of that. Also there's the whole transformation of Hitachi as well, so I think there'll be a need to have much more of that software experience and also, Hitachi's hired two new executives, one on the sales side from SAP, and one who's now my boss, Brad Surak from GE Digital, so I think there's a lot of good, strong leadership around the software side and, obviously, all of the expertise that the folks at Pentaho have. >> That's interesting, that Chief Data Officer role is emerging as a target for you, we were at an event on Tuesday in Boston, there were about 200 Chief Data Officers there and I think about 25% had a Robotic Process Automation Initiative going on, they didn't ask about IOT just this little piece of IOT and then, Jim, Data Scientists and that whole world is now your world, okay great. Donna Prlich, thanks very much for coming to the Cube. Always a pleasure to see you. >> Donna: Yeah, thank you. >> Okay, Dave Velonte for Jim Kobielus. Keep it right there everybody, this is the Cube. We're live from PentahoWorld 2017 hashtag P-World 17. Brought to you by Hitachi Vantara, we'll be right back. (upbeat techno)

>> Announcer: Live from Washington DC, it's the Cube, covering .NEXT Conference. Brought to you by Nutanix. (electronic music) >> Welcome back to .NEXT everybody. This is the Cube, the leader in live tech coverage. We go out to the events and extract the signal from the noise. My name is Dave Vellante, and I'm with my cohost, Stu Miniman. This is day two of .NEXT. Dr. John Bates is here. He's the CEO of TestPlant, and author of Thingalytics. Sir, welcome to the Cube. >> Thanks. >> Nice to have you on. >> Nice to be here. >> Thingalytics, everybody's talking about things. >> This thing, that thing, the refrigerator, the iode things. What's Thingalytics? >> Well, things, i.e. connected devices, sensors and so on. They're not very interesting unless you actually do something with them. So you search through all that data that's coming out for the opportunities and threats to your business, for example, and then you act on it, while you've got time and perhaps, beat your competitor. So, Thingalytics is about smart, big data analytics, and the internet of things coming together. >> Okay, and what's the premise of the book? >> Well the premise of the book is, you know, everybody thinks, I mean if it's one message from it, it's IoT is not so hard to get into. So get started. You know, start small, and here's some lessons of how you can do it. And here's some stories from different industries of how thought leaders, you know, like Coca Cola, or GE, or many different companies, Medtronic, in different industries have actually got started and really been extremely disruptive in what they've done. >> And is this getting started, is this all for companies, or are you seeing individuals that can also participate? >> You know, I do have a chapter in there about the Smarthome. So, obviously that's the aspect where the individual is going to come. But you know, I think it's really the real winner in this will be the industrial and the enterprise, Internet of Things. I guess that the key message is for business leaders. >> Do you think that given that there's, the internet of things requires things, and there's so many things that are installed by these big, industrial companies that the whole IoT thing will be maybe less of a disruption than it will be an evolution of companies like GE, and Siemens and Hitachi, and guys like that. Is that a reasonable premise, or will we see a whole new wave of companies? Certainly we'll see startups come in, but will they attack these big industrial giants, that have been around for a hundred years? >> You know, this is a really great question, and I think that, at the moment, the opportunity is in the hands of the big buyer. You know, keynoting at .NEXT, Bill McDermott coming in to do his presentation. I sold my IoT platform company to SAP. And why, for example has SAP got an amazing opportunity? Because they've got all these applications, they've done an amazing job of taking ERP and adding a whole load of applications: financial planning, supply chain, business networks. But those applications model the real world. But they're not connected to the real world. So what happens when you take a model of a financial model about the value of a factory or a mine, and connect it to the real world. Suddenly, it's not theoretical. It actually is calculating in real time, the value of those assets. The supply chain is really about that. So, SAP is an unbelievable opportunity. IBM has an unbelievable opportunity. GE has an unbelievable opportunity. But it's going to be how they execute, and is someone going to come in, and do something unbelievably disruptive we haven't even thought about. So, those guys need to make all the running right now to really protect themselves. >> I wonder if you could comment on this. I see some of the execution risks as what Jeffrey Immelt said, "I went to bed an industrial giant," "and woke up a software company." >> John: (laughs) Yes. >> Wow, it's hard to be a successful software company. So, is that one of the many execution risks? Are there others? >> I think you're absolutely right. I mean, if you take GE for example, my friend, Bill Ruh. He's the chief digital officer, the CDO of GE Digital. >> Dave: We know him, yeah, sure. >> Yeah, he's awesome. Completely new business, but it's really hard. I think that's taken longer than they expected to build up that Predix platform. And are they going to be the people, it depends what business you're in. If you're the business of buying aircraft engines, then rather than buying an aircraft engine, you want to buy engine as a service. So that's the kind of the thing that maybe you'll buy from GE, or maybe it's one of GE's partners and GE provides the infrastructure. But I think they've learned that's really much harder than they thought. And I think everybody's sort of discovering that. It's not so much the thingalytics, I've realized, it's the thingonomics, the economics of the internet things. That's the really important thing to get right. >> We actually worked with GE when they were coming out with the Industrial Internet, and we did a lot of interviews. There's some of these barriers that we're going to hit along the way. As a matter of fact, at Wikibon, our team that works on it, they call it the Internet of Things and People because there's so much that needs to happen to be able to move forward. Some of them are just old industrial things, some of them are regulations, some of them are the mindsets. How do you see some of these, what do you see as some of the major barriers, and how do we knock them down to be able to accelerate this even more? >> Absolutely. Well, first, you're absolutely right. One of the key barriers is a cultural barrier, or a, oh, that's just too hard, getting back to why did I write Thingalytics. And I think it's a question of people have just got to get started, not try and boil the ocean, and try and get some successful projects going. But definitely there's a cultural thing, and you just have to get those people together that think differently. And there's a reason why this new role of the Chief Digital Officer was created, but you can have many Chief Digital Officers throughout your company, just sort of get them together with that thought. One of the other things I can bring up that is really, really hard and why I went from being in the core of the IoT platform world into a company that's a software testing company, when you're going to launch this stuff, how do you, de-risk it, how do you make sure, in this world where there's all these sensors at the edge, all these strange mobile devices on the front end, and the cloud in the middle, how do you make sure you test that? It's a really complicated distributed architecture, that requires completely new technology. You don't even own the code, so how do you test that? So there's a whole load of issues there, but I think you have to put at the heart of it, think differently, think digitally. >> So what's the company you sold to SAP? Tell us about that. >> So the company's called Plat.One, and it was one of the leaders in platforms, software platforms, to enable Internet of Things application. So the idea is that you're going to build an Internet of Things application. You could start and hardwire, start writing some code and hardwire against all these devices and sensors, but then you start shipping your applications. What about if you made the wrong decisions? What about if you spent years just writing all the integrations to your factory floor, or your logistics networks? So, there's a whole load of common protocols out there, in machine to machine, and they call it a new Internet of Things protocols. Plat.One, new and could talk to all these protocols and make machines talk to each other. It could virtualize that, so that you disconnect those protocols from the application you write. So you're modeling things like, in a Smart city, truck and streetlamps, rather than bits and bytes. So then when you change the implementation from one city to another, you're future-proofed. And then graphical tools to model and plug them together, and a platform that manages microservices at the edge and the cloud. So you're managing an adaptive platform that you can place logic, depending on what it is. And that enabled SAP to rapidly roll out ITOs. >> And your company had customers? >> Yeah, a lot of customers, people like, you know, a great customer, Pirelli. Pirelli, obviously a tire manufacturer as you know them, but what they can do, if they plug sensors into their tires and have telematics boxes on tops of trucks or vehicles, suddenly they can go to the fleet management markets and sell them big data analytics because they know where the trucks are, they know how they're being driven, and what's more, rather than selling you a tire, they could lease you a tire as a service because they can track it, they know how much use you've got out of it. Unbelievable new thingonomic models. So, that's an example, flextronics, T-Systems, we had a whole lot of interesting smart cities using it, logistics, manufacturers. So yeah, it was a great, but early stage company, and you have to ask yourself the question, can you, as a small company, win, or would you be better off partnering with an SAP with that unbelievable reach? >> One of the things, I've got a networking background, we hear all these new protocols and the maturity there, there's the security risk there. I hear the fleet of trucks that was like, oh wait, I might turn off these sensors or do something malicious. The surface area has just grown by orders of magnitude. How do we address this as the industry? What is some of the advice you're giving for this? >> You're absolutely right, 'cause when we were talking about the issues earlier, that's a corker, isn't it, you know, the security of it. And as a Tesla owner, it was great when hackers tried to hack into the Tesla and they couldn't. All they could do was make the horn go beep. Which you can do from your app on your phone, anything that was publicly there, but couldn't take control of the car. That was great, that was nice. But with all this highly distributed model, you've got to be able to have end-to-end security. So in Plat.One for example, we had the ability to have role-based, end-to-end security right from the application to the device. And that was part of the platform, so you got that for free. But you've got to make sure that's the case in your applications. >> What's the opportunity for jobs in the growing IoT economy? >> You know, IoT giveth and IoT taketh away. (Dave laughs) We're all thinking let's bring more jobs back to America, which is a political thing at the moment. But are these jobs are going to be replaced by robots? I mean, is there a global issue, which is, are these jobs going to be replaced by robots, and by algorithims? The answer is yes, but on the other hand, are more jobs going to be created? Are people going to become much more productive? So I think humans are going to become more productive, for sure, for things like smart factories, smart cities, and life's going to get better in smart cities, but yeah, we're also going to lose jobs. I draw an analogy to trading, financial markets trading, where we used to have traders in the pits waving pieces of paper, then it went to Bloomburg terminals where people entered their trades automatically, then it went to algorithmic trading and high frequency trading where algorithms run it. Still humans involved, but less and less. But the humans are more productive and more coordinated. >> Hey, what if we put a 30% tax on all IoT-related initiatives, that would help preserve jobs. (John laughs) So tell-- >> Wouldn't slow down innovation or corporate profit or anything like that. >> Hey, here's an idea for you, Since we're in Washington I thought I'd throw out some good ideas. >> (laughs) Yes, exactly, very topical. >> So, tell us about your software testing company, TestPlant. >> So, the reason I was really excited to join TestPlant is there's this new world, you put IoT together with the mobile world and the cloud world, and you have the world of digital. How do you make sure that in this new digital enterprise that everybody's going to compete in, that you're, how do you make sure you're doing well, and how do you make sure your stuff works, and how do you make sure you're beating your competitors? So, TestPlant's all about end-to-end testing of the digital experience. It's taking testing to a new level, 'cause if you think about testing, it used to be about, does your code work? Now, it's about, are you offering up an unbelievable, delightful digital experience to your customers, because testing now has become a profit center. It's the differentiator between you doing an amazing job of launching an app and getting five stars in the app store, or crashing and burning because something's gone down, or there's a usability issue or there's a problem. So that's what we do, we test applications using artificial intelligence through the eye of the user, we actually, our algorithms actually use the applications and connect to the APIs and can take control and automate the testing process and discover these business metrics and show customers what good really is. >> So John, you were the founder of Plat.One, is that right? >> So I was an early joiner of Plat.One, I was the CEO, I wasn't the founder, we have two amazing founders. >> Okay, but you helped do the initial raise? >> Yes, exactly, and I took it from an early interesting technology to the company that got bought by SAP >> Made it viable, and sellable, you're an investor, I heard you say. >> John: Yes. Okay, now you're an author, you're CEO now of an more established company, right? >> John: Yes. >> Jack-of-all-trades here, well, maybe that's not a fair term, but you do a lot of different things. What are your thoughts on which things you enjoy the most, where do you see all of this headed? >> Well-- >> Polymath is the word I was looking for. (John laughs) >> Well, I started off actually as a professor, a university professor, and I took some of my research and started my first company. I loved building a start-up from scratch, and taking that as a first streaming analytics or real-time analytics company, and I then spent over a decade as a C-level executive in public software companies. But I haven't had so much fun as what I'm doing right now. It's beautiful, it's sort of mid-sized, really great private equity, backers, the Carlyle group, so I love what I'm doing right now, it's definitely my favorite gig, so far, I think that's the nice sweet spot for me. >> That's great, well, John, we love having big brains in the Cube, Stu and I, and it rubs off a little bit, at least we think it does, so thanks very much for coming on. >> John: Thank you gentlemen. >> You're welcome, alright, keep it right there, buddy. We'll be back with our next guest. We're live from Nutanix NEXTconf, this is the Cube.

>> Announcer: Live from Las Vegas. It's the CUBE. Covering HPE Discover 2017. Brought to you by Hewitt Packard Enterprise. >> Welcome back everyone. We are live in Las Vegas for HPE Discover 2017. This is the CUBE's exclusive three days of coverage. Day three here on the floor in Las Vegas. I'm John Furrier. My co-host Dave Vellante with Silicon Angle the CUBE. Our next guest is Denzil Samuels. Who's the Global Chief Channel Officer for HPE. Welcome to the CUBE. >> John, thank you. Good to be here, thank you. >> So we... Dave and I like to talk about the channels. I have a history with HP. Everyone knows I've worked there for a good almost nine years. And a lot of that time in the channel business. A lot of people also know that HP has always been channel friendly. >> It has. >> Really for a long time. >> Absolutely, we were born in the channel. >> Now more that ever with the cloud here. And the cloud really being multi cloud and pervasive. The channel part is going to be a huge opportunity. And they're close to the customers. They're on the front lines. What is the mix of business? What is the strategy? How are you guys taking the transformation message? The digital transformation to the channel and what are they doing with it? >> Wow, there's a lot of questions thrown in there. (laughing) Let me start with the channel business. The channel business is 70% of Hewitt Packard's revenue. We have 87,000 channel partners around the world. Every region of the world. Many, many countries. And you're right John. We were born in the channel. Right, so we're channel centric. We're channel friendly. We've got the best partner program in the world. And it's not us saying that. It's the partners telling us and industry analysts telling us that as well. In terms of the opportunity right now. You know, the cloud wars are still going on between the cloud players. But I think that what that's done is it's changed the model with respect to the fact that we're now looking more to congeal model. Right, where customers want to buy on a consume basis. Pay as you go. Pay as you use it. And that represents an incredible opportunity for the channel. And the channel has really embraced that. They realize that they've got to be more than just valueated resellers. They've realized they've got to morph and evolve into being service providers as well. And we're seeing that transition occur at a fairly accelerated pace. >> I'm assuming the channel guys would... And you see the global systemic race. Clearly lining up. They see that opportunity and a lot of transformation conversations. But as you go into the long tail of the channel. You said a ton of partners. They've always been hungry for services because that's where their gross profits are. Right, so you know, they take in hardware. The solutions. Very solution centric. But as the business model shifts to the customers renting versus buying per say. They're kind of teed up for the services. How has that specific transition taken place? I'm sure that they've probably been eager for being more of a service provider. >> Yeah. >> With the sense of actually maybe having their own cloud service or a variety of other services. What's the makeup and how is that going? >> Well, what they love about our message is the fact that we know that the customers are not only going to go to pure cloud. We also know the customers want a choice. They want to know if they can actually have their processing. Their compute done. Not just in their data centers. Or maybe even on Prime but even at the edge. And so because we have really the only simple hybrid IT strategy out there. That's why the channel partners love us. Because they're giving their customers a choice. They're saying it doesn't matter where your processing this. Whether it's on a consumption basis. Whether it's on the edge. In the cloud. On Prime, off Prime. We can do that for you and we can do that with HP. So they're embracing that. They love the consumption model. They love our flex capacity offerings. And our HP financial services offerings as well. So it's exciting for them. >> So my simple mental model of the channel. You talked about transformation before. As you've got sort of box sellers. I know it's kind of a pejorative. But it's still probably the largest component of the business. You've got solution providers and then I guess cloud service providers. Maybe ISV's are sort of a separate channel. You could make that argument. Maybe the hoodies are becoming a new channel as well. But, thinking about box sellers and solution providers. The box sellers have to transform. We all know that. They got big boats. Big houses. A lot of them are happy. They're going to retire. But the up and comers, they better transform. Solution providers, it used to be okay was it SAP or Oracle. Now it's IoT Solutions and different solutions. My specific question is. What's happening with that transformation from box seller to solution provider? And how is HP sort of helping it's channel get there? >> Great question Dave. I think one of the things that I love about it is that it's been going on for a few years. So it's not new. I talked, for example, to a partner yesterday in New Zealand. And I said, hey how's your transition to service provider been going? Cause they were a traditional evaluated reseller or box seller as you say. And they said, "Oh yeah, we became a service provider nine years ago." (laughing) And I talked to a distributor in Germany yesterday. And they said, I said. How many resellers are you actually converting to or helping transform their business into becoming service providers. And they said, "About 20 a week." So the shift has been going on for a few years. And there's a lot of information out there. There's white papers, there's training. There's sales plans, compensation plans have been modified. A lot of that material is there. We've helped through a lot of it. We have a playbook for making that transition. We have a service offering that we offer them. To help them if they don't know. But for the most part, the distributors. And the big evaluated resellers, they know it. It's the smaller guys that are making that change. >> And if we're starting a business. The three of us starting a business tomorrow. We'd say okay, let's build a subscription business. And we'll have a monthly revenue steam. Okay which is great. But if you're already used to the heroin of the big heap up front. Transitioning that model. The same is true for Hewitt Packard Enterprise I would imagine. As you're customers shift to a ratable model. You've got to change your financial model. You see a number of cases on Wall Street where companies are trying to make that transformation. Particularly software companies. >> Yeah. >> What kind of discussions are you having with the channel with regard to that pay as you go model. And how that affects their cash flow and income statements. >> Yeah, I think that what we're doing is, to help that. In terms of leasing options with the HP financial solutions. But I'm also helping with our flex capacity offerings. Those are two great triggers for the channel. They love that. They also like the fact that we're trying to be one step ahead of them, right. So I think the power of this is really around what the customer's buying. And they know that if they don't sell the way the customer's buying. They're going to be irrelevant. >> Denzil, can you explain how a flex capacity is not just renting. >> Yeah, I think one of the big, see... When you're rent you're buying. You're buying a size. Right, and you may use to the full size of your needs. Or you may not. But you're buying that. Whatever your capacity is that you're buying. You're buying that up front. If you don't use it, you loose it. Flex capacity is the opposite. You're actually paying for what you use. And some months you may use more. And some months you may use less. But you're paying for what you need. That's a huge advantage to the end customer. >> And HPE has figured out how to make a profitable business out of accommodating that. Cause you have to put the capacity there. Whether it's used or not. If it doesn't get used. You take the margin hit. Right? But you've figured out how to sort of maximize your profitability in that model. >> Yeah, we haven't just figured out how to do it. We've actually got a consulting practice that allows other to forget how to do it. So we actually help our. We actually help our partners morph to that as well. >> Is that just experience or is it some kind of magic analytics. >> I think it's a combination of a bunch of things. But let me tell you one thing that's really important in this transition to service providers. We used to do when we were dealing with the box sellers. The traditional certifications, right. You get a certification on a product. A product type. And you measure it on that. As part of the partner program. We've changed and evolved that partner program to say. Hey, listen. It's not just certifications on product. It's competencies. Right, data central analytics would be a competency. Understanding SAP HANA. How to implement that is a competency. So, as you move a program to embrace service providers away from traditional resellers. Having those competencies is huge. Understanding verticals is huge. And that all plays in to the usage question that you asked. >> This is a really great opportunity I think for partners. And this is what we've been kind of talking about on the CUBE. And a variety of different events we go to is. The cloud is really a amazing enabler because it's horizontally scalable but you need specialism in those unique domains. >> That's exactly right. >> Whether it's SAP. And big data highlights this. So you got to have that scale. And then you can also be specialized. So this opens the door up for a huge opportunity for your partners. So I totally get that. So I want to ask you guys how HP's changed? How you engage with your partners with digital? Because now you have to then be more efficient. >> Yes. >> With these guys. It's going to be there's 1,000 flowers are blooming and all these ridicules and these industries. >> Right. >> How are you guys using digital specifically to make yourself more efficient to move the market forward? >> That's a great question. So first, let me answer that by saying in the world of digital transformation. There isn't a single partner category. We're not just talking about service providers. We're not just talking about valued resellers or distributors. We're talking about independent software vendors and developers. We're talking about systems integrators. Sentiment manufacturers, device manufacturers. >> IoT opens a huge door. >> IoT. And let me tell you something. There may be partner categories we don't even know about today. But they'll be partner categories tomorrow. So the program we've built encompasses all of that. It encompasses all of these partner categories. Every region, every vertical, everywhere around the world. In terms of the digital piece of it specifically. The transformation. There's some interesting things going on with companies like GE Digital. You know, they've got jet engines now that they can actually transmit information on the wear and tear of every single blade within that jet engine. We can connect. We can connect that and collect and connect that data. We can analyze it. We can run inserts on it. And we can feedback powerful information that makes then drive outcomes to the customer, right. And all of that is linked through the IRT technology. Our edge technology and our hybrid IT approach. >> So you guys have the ABB cloud like in the sense of. Standing up programs with automation so we see Tesla self driving cars. Are you going to have a self driving channel? I mean. (laughing) At the end of the day you need to have these agile capabilities. This is kind of what you have to kind of get on right? >> You know, I think everyone has to do that. And I think there's no company right now in my mind that's more positioned to do that better than we are. And let me tell you why. We have billions of dollars of cash in the bank. We have no debt. Meg, over the last few years, has trimmed us down to be nimble to take advantage of this. The last thing you want to be in a market that's moving at this speed is be slow. And what we're seeing with some of our competitors is that they're getting very, very big. Very, very fat. And a lot in their burden with debt. That is not where you want to be in a market that's moving at the pace that this is moving in. You want to have the ability and the cash to invest. And as you say, do things real time. Do things that are just at speed of light. >> Well, if you guys can. I mean, talk about Dell and MC obviously. They think bigger is better. But I think your point is if you can be nimble. And by the way, decentralize the way the organizational structure is. You can ride many waves. >> You can ride many waves. And we don't have the 50 billion dollars of debt that Dell and MC have. >> Right. >> Right. >> Denzil, thanks so much for coming on the CUBE. I really appreciate it. And you know the strategic nature of the channel. Again 70% of the business probably will grow. And again, that's always been the good mix. And a lot of leverage there. Cost of sales is lower. Everyone is making money. That's the key thing too, right? >> That is the key thing. That's the key thing. >> And the channel part is profitability. >> Yeah. I agree. It's 70% of our business that will continue to grow. What will change though, is the mix. Right, we'll move. We'll move from the 80% box sellers. And we'll move more and more. So that will be probably 60% of our business over the next two to three years. And we're going to see 40% of the channel businesses it's going to be the value at. >> We'll be watching you guys. And of course as Meg Whitman says the right mix. Is her message here. >> Yes. >> HPE Discover. We're going to check in and see how that evolves. Thanks for coming and sharing your insights here at the CUBE. >> Awesome. >> Really appreciate it. Live coverage here from HPE Discover. I'm John Furrier with David Vellante. You're watching the CUBE. Stay with us for day three as it continues. Our three days of wall to wall coverage. Thanks for watching. We'll be right back. (tech music)