>> Narrator: Live from Las Vegas, it's theCUBE. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to theCUBE's live coverage of ServiceNow Knowledge18. We're coming at you from Las Vegas, I'm your host, Rebecca Knight, along with my cohost Dave Vellante, we are theCUBE, we are the leader in live tech coverage. We are joined by Sebastiaan Laurijsse, he is the global senior director, IT, cyber security, digital transformations at NXP, thanks so much for coming on theCUBE Sebastiaan. >> Thank you for having me. >> Good to see you. >> Thank you. >> So I want to start out by asking you a little bit about NXP, what you do and then what your company does and then also what you do there. >> NXP is the leading semiconductors in providing products for automotive and our company vision is providing a sure connections and infrastructures for a smart world. And that's what we are trying to achieve by implementing new ways of working with making the world more autonomous, like autonomous driving et cetera, so that's really what we're trying to do. >> Dave: Cool company. >> We are really building the future of tomorrow. >> Yeah. >> Big, large company too right? >> Yeah. Roughly about 36 thousand employees currently. >> Wow, okay, yeah. >> So you said you're really building the future of tomorrow, unpack that a bit, tell our viewers exactly what you're doing there. >> So today what you have experienced also on this event is a lot about artificial intelligence and machine learning. NXP has been elected as the number three in the world as the provider of solutions for artificial intelligence. So if you really think what we are developing today, it's already started and will become available in five or three years from now. So it's, you only can imagine what the future brings us and what we will shape. >> When do you think owning your own car and driving your own car will become and exception? >> Driving your own car, you won't own a car anymore. It will be some kind of help that comes to your home on demand when you need it and it even predicts when you like to travel and then it comes by automatically. >> How far away is that, you think it's two decades? >> Nah I think here it's not about technology, I think we have the technology to even enable it today. >> Dave: It's policy. >> It's policy, regulation, compliancy that doesn't allow to lets go harvest all data to make the right decisions there. >> We had the insurance company on the other day and they were like, no we're going to figure this out. >> Out of necessity. >> We always figure this stuff out. >> Yeah it's really not about technology anymore, it's really about legal, what prevents us access the data to make the right decisions, right. >> It's amazing though just to watch the progression of automotive, I mean they're basically software defined vehicles now I mean how many semiconductors are in a car now? >> Yeah but also you can clearly see within that experience, we are transforming our business to more software because developing a product as hardware that needs to stay in for 15 years or longer if you look to a car. Then you would like to have the ability to be dynamic more on top of the product by using software so also our products are becoming software defined. >> So you're a very R and D centric culture. >> Sebastiaan: Yes. >> Maybe talk about that ethos and the cultural aspects, and maybe what the process looks like, share with our viewers. >> I think it's the most awesome part of the company. Of course we also manufacture our products but mainly R and D is so dynamic, we have so, tech savvy people and we have so much issues as IT and you think what are they consuming so much bandwidth on Netflix and then they tell me hey we are developing a product for 4K entertainment into the car. So I have an issue on my wider network, you're providing all kinds of services but you're building for entertainment into the car for the future. >> That car better be autonomous. >> Exactly. >> Yes. >> That's for the kids in the back seat I think. >> Yes. >> You once described ServiceNow as the platform of platforms can you talk a little bit about that from your R and D process? >> So what you clearly see and also I think that all companies will eventually become an IT company, yeah? Also the banking companies tell us now today they are an IT company with a banking license. What I truly believe in is that we need to close the gap between IT and the business so I think the future model is that IT will dissolve for a certain part into the business. But you don't want to have, of course you still have you shared services, you still have a hybrid model where you have the countries where you're providing support from, so you're not always as close to the business. You have 24 seven economy and you need to provide those services and what you don't want to build is human interfaces. So what you try to achieve by building the platform of platforms, the fabric is that you try to connect the business acumen, the business dynamics, the project management tools that requires management into the IT systems and since you can detect the phase where they are in if they are facing issues with their products the projects are slipping or delaying, you would like to increase automatically the severity of the incidents. So that they can automatically solve and you have a better understanding of the business priorities. >> NXP is really interesting because you're at the intersection of a lot of big trends. I'm mean you're a hardware-- >> Sebastiaan: IOT. >> You're hardware manufacturers, you're a software developer, security, AI, IOT and underlying all this is data. >> Yeah, the new money. >> Yeah, right so I'm just envisioning this pretty complicated matrix, I'm wondering if you could describe that in your terms. >> If you look from an IT infrastructure perspective the growth on data is enormous. To cope with that growth because the data allows us to make better products. Data could be a requirement but could be also the affect of the results. What we tried to prevent, the project in bringing to the real life that you feel your requirement of quality is increasing. We had consumer great, automotive great, and we had for the flying industry, also the same great. But however your norm is increasing, so what you clearly see by increasing the norm, we call that the total quality culture, you also would like to have a total quality product, you don't want to replace your phone one year from now and I think if you look four years back, a phone, one and a half years, two years and then you had a new one. But as products become more expensive, they become more part of your daily life, part of your personal brand even and it generates that data, we need, if you try to work on proper quality that will generate an enormous amount of data. But a data can use, you optimize your processes upfront in the future as such it becomes more cost efficient to develop new products. So it's really about the conditioning for more data is also conditionally need to optimize your processes. >> Where does ServiceNow fit in to all this? How do you use ServiceNow? >> So for me what you really see in ServiceNow today is the best work flow engine you can imagine. It really orchestrates all IT and connecting business processes. And I think the potential and I think if you look into the portfolio where they have HR, it's going beyond IT and now they often, as already said by John Donahue, they come in via the IT angle, ITSM but as the process become more and more part of your culture rather than inhabit a forced way of working then the platform starts supporting the culture of your organization because by machine learning a proper UI, visualization capabilities it becomes really part about metering, showing what you're doing and really helps you to orchestrate your daily work and that's also I think of the new company, it's a little too difficult to pronounce, have you ever, it's about orchestrating the future way of working. >> So we're hearing so much about this, making the world of work work better for people, you describe it as a work flow engine, really helping employees organize their work days, orchestrate their work days, improve them, can you describe the culture at NXP and sort of how ServiceNow is improving employees everyday lives. >> What we really try to do and it's also what we see it's easy to show the cost efficiency savings you have from a platform as ServiceNow. If you improve your onboarding by optimizing the process by three days, because that's your first point of engagement when you bring some people on board and if it goes fluently, work integration with ServiceNow providing the services, everything is ready at day one. Day one you're there, your laptop is ready, your provisions, your desk is ready, and you have orchestrated a process that's a flawless end user experience. And that's what we would like to provide with ServiceNow, orchestrate with ServiceNow, because that's what the uses is. If it's a need of any of the help of services, we would like them to go, shift left to ServiceNow and with help of knowledge help themselves. We are all doctor Google and we would like to have access to that information ourselves and not be dependent by the expert, we all become that expert. >> Are employees happier? I mean I think that's a question too. Because we know that from research that happier employees make more productive >> Are more productive. Workplaces. They're more likely to stay, recommend it to their friends and the network gets bigger, I mean what's your-- >> If you have a company that shapes the future, we have very happy employees. (laughing) >> Self fulfilling prophecy there. >> Yes. >> When did you go live with? >> So we are one of the first adopters in 2007 in Europe. So we really started then, I don't know the name because they talk about days, months and now they talk about locations. (laughing) But I think we did a big overhaul during some of our big integrations that we have done so we are really one of the first customers in Europe providing the product. >> And how far, where, what version you in now? >> We are ready to upgrade, we will skip one release if we go to-- >> It's coming to London. >> Yep, London. >> Oh okay. And you started with ITSM like most? >> ITSM, ITOM, so IT operation management and now we have the IT business management app like demand management, IT financial management, really orchestrating from demand to fulfilling. >> A lot of our guys have written that they feel like machine intelligence and ITOM go together very well. >> Yes. >> You agree with that and how do you see that affecting your business? >> So what we clearly see is that the mean time to detect, the mean time to repair, we would like to detect algae before they hit the end user. So you really would like to make sure that before they notice it's already been solved. Or when it goes wrong, they already say we're on top of it, we know, we know the impact, we know that the whole chain of events, a single network port or power outage somewhere in a room could cause a big effect on the whole IT service and therefore research now helps us to make sure that we are on top of the things. >> Sebastiaan you mentioned off camera that you are very intimately involved with ServiceNow and helping them with their roadmap, providing feedback so can you share with us some of the things that you talk about with them and what would you like to see, where's their white space, what's on their to do list from your perspective? >> So what, but of course, if you look to our portfolio, what we are doing as NXP. So a member of the product advisory council for IT operation management and I'm closely working also on the Lighthouse program with ServiceNow and all kind of new releases, what I really think if you see what you are investing, of course they are now coming forward with the chatbots, awesome but if I see how my children consume information, using YouTube and I think also John touched upon it, what we are building as NXP is in the flawless end user experience and everything as being you don't have a UI. If you look to your car, today you have a speedometer, an RPM meter, why do you have RPM on your dashboard, why? What's the value of you know? In the past you needed it to shift gears and why is it still there? Does it really add value? >> Cause it's cool. (laughing) We love dials, come on. >> So it's about the end user experience, it's about your lifestyle, your brand identity it's not as more about requirements so, of course UI is important, I believe it, what's more important I think to invest in that engine behind it machine learning, artificial intelligence and how to ingest data. So because what is really required to make smart decisions is a lot of data and still I think the platform has potential, but there's some room for improvement to get proper integration by onboarding more data making the right decisions and orchestrate the actions out of it and I think the learn think act, we have the same strategy as sense, think, act at NXP I think that's how robotics and AI will work in the future. >> Data is the fuel for your innovation. >> Yes. >> So it's a great point you're making. >> I wonder if you could talk a little bit about the feelings in Europe, you're based in the Netherlands, about automation and the future of jobs because in the United States there is a significant anxiety about the machines coming for our jobs and at least the media portray it that way and I'm curious from your perspective, what is the feeling in Europe? >> Of course I think I see the opportunity but automation will change of course, automation, machine learning, it will essentially change the whole way of working. Because what we say it's about helping the business by decision automation, making decisions so we try to reduce the human effort, we have a total equality culture but we still need more and more people to help them that ask the right questions. Because the innovation of course come from a lot of data But still have people who connect the dots of never existing connections before. If you have a lot of data and you don't know which questions to ask, would you build a new solution? So it's still about smart people and creativity and of course we know patterns, we know what people are doing. But still the real breakthroughs is being done by people and therefore we need those people still in the future. So the anxiety is there yes, automation is there but I think it's about building a joint incentive between your outsource provider, your source provider between your workforce is what's the incentive for them on automation because otherwise you get a culture of fear and anxiety and a lot of doubt and that will be counterproductive for your company value. >> What do you think as a journalist. I mean you're right, the mainstream media talks about this a lot and they're actually accurate, the data is there to suggest that machines are replacing humans and cognitive functions and that's a concern but there's not a lot written in the media about the opportunity, there is some about the opportunities but more importantly what to do about it, in other words, public policy, education, I mean maybe I'm just missing it but. >> No, I agree with you, I completely agree and also this idea that Sebastiaan is bringing up is showing, proving that this can work for you, I mean this is actually going to improve your work life by taking Carol out of the drudge work or show opportunities for humans and robots to work alongside of each other. >> Yes. >> Rebecca: So there you go. >> Well in tech you better be an optimist you know. >> It's true. >> Although it seems like Musk and Stephen Hawking weren't optimists but maybe they're thinking you know hundreds of years-- >> Light years ahead. >> Right, right, right, right. You report directly to the CIO, at this conference, we're hearing so much about the changing role of the CIO and how the CIO has to be thinking so much more broadly about the business than ever before I mean how do you see it? >> So that's an interesting question because that's exactly where we are in today so we have had the classic way of the CIO, financial risk control et cetera then we have the transforminal CIO, then we have the CDO, or we have the future COO who takes care of operations because today IT is often being seen in the enterprise companies as a shared service center, something you do with the lights off but clearly bank accounts, what I already told you before was we are now IT companies with a banking license as IT becomes more dominant, it becomes part of operations and yes, we need a transformational CIO, CDO or a new type of COO that sees IT as part of the operations and the way of working. And of course you can give the new title, but at the end it's just a smart guy who helps the company succeed and brings IT as one together to make success. It's not about the role or responsibility, I think there's still the name of a chief information, chief data officer it's still the right title because he makes sure he gets the right data towards the business to make the right decisions faster. >> Right, great. >> It's not about running only the lights on. When the lights doesn't go on, it's IT's fault, right? >> Rebecca: Always, always. >> Always. >> Yeah that need doesn't go away but it's table stakes now. >> Exactly, Sebastiaan, thanks so much for coming on theCUBE, it was a pleasure having you here. >> Thank you. >> I'm Rebecca Knight, for Dave Vallante we will have more from theCUBE's live coverage of ServiceNow Knowledge18 coming up just after this. (upbeat music)

(upbeat music) >> Welcome to our industry drill-down. This is from manufacturing. I'm here with Michael Ger who is the managing director for automotive and manufacturing solutions at Cloudera, and in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data. Connected trucks are fundamental to optimizing fleet performance, costs, and delivering new services to fleet operators, and what's going to happen here is Michael's going to present some data and information, and we're going to come back and have a little conversation about what we just heard. Michael, great to see you. Over to you. >> Oh, thank you, Dave, and I appreciate having this conversation today. Hey, this is actually an area, connected trucks. This is an area that we have seen a lot of action here at Cloudera, and I think the reason is kind of important because first of all, you can see that this change is happening very, very quickly. 150% growth is forecast by 2022, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits. We're talking about a B2B type of situation here. So this is truck makers providing benefits to fleet operators, and if you look at the top benefits that fleet operators expect, you see this in the graph over here. Almost 80% of them expect improved productivity, things like improved routing, so route efficiencies and improved customer service, decrease in fuel consumption, but better be, this isn't technology for technology's sake. These connected trucks are coming onto the marketplace because hey, they can provide tremendous value to the business, and in this case, we're talking about fleet operators and fleet efficiencies. So, one of the things that's really important to be able to enable this, trucks are becoming connected because at the end of the day, we want to be able to provide fleet efficiencies through connected truck analytics and machine learning. Let me explain to you a little bit about what we mean by that because how this happens is by creating a connected vehicle analytics machine learning lifecycle, and to do that, you need to do a few different things. You start off, of course, with connected trucks in the field, and you could have many of these trucks 'cause typically, you're dealing at a truck level and at a fleet level. We want to be able to do analytics and machine learning to improve performance. So you start off with these trucks, and the first you need to be able to do is connect to those trucks. You have to have an intelligent edge where you can collect that information from the trucks, and by the way, once you've conducted this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now, what I'm going to show you, the ability to take this real-time action is actually the result of a machine learning life cycle. Let me explain to you what I mean by that. So we have this truck, so we start to collect data from it. At the end of the day, what we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics, and we start with being able to ingest that data into the cloud, into the enterprise data lake. We store that data. We want to enrich it with other data sources, so for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've collected from those trucks, and you want to augment that with your dealership service information. Now, you have sensor data and the resulting repair orders. You're now equipped to do things like predict when maintenance will occur. You've got all the data sets that you need to be able to do that. So what do you do here? Like I said, you ingest it, you're storing it, you're enriching it with data. You're processing that data. You're aligning, say, the sensor data to that transactional system data from your repair maintenance systems. You're bringing it together so that you can do two things. First of all, you could do self-service BI on that data. You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to create machine learning models. So if you have the sensor values and the need, for example, for a dealership repair order so you could start to correlate which sensor values predicted the need for maintenance, and you could build out those machine learning models, and then, as I mentioned to you, you could push those machine learning models back out to the edge which is how you would then take those real-time actions I mentioned earlier. As that data that then comes through in real-time, you're running it against that model, and you can take some real-time actions. This analytics and machine learning model, machine learning life cycle, is exactly what Cloudera enables. This end-to-end ability to ingest data, store it, put a query lay over it, create machine learning models, and then run those machine learning models in real-time. Now, that's what we do as a business. Now, one such customer, and I just wanted to give you one example of a customer that we have worked with to provide these types of results is Navistar, and Navistar was kind of an early adopter of connected-truck analytics, and they provided these capabilities to their fleet operators. And they started off by connecting 475,000 trucks, up to well over a million now, and the point here is that they were centralizing data from their telematics service providers, from their trucks' telematics service providers. They're bringing in things like weather data and all those types of things, and what they started to do was to build out machine learning models aimed at predictive maintenance, and what's really interesting is that you see that Navistar made tremendous strides in reducing the expense associated with maintenance. So rather than waiting for a truck to break, and then fixing it, they would predict when that truck needs service, condition-based monitoring, and service it before it broke down so that you can do that in a much more cost-effective manner. And if you see the benefits, they reduced maintenance costs 3 cents a mile down from the industry average of 15 cents a mile down to 12 cents cents a mile. So this was a tremendous success for Navistar, and we're seeing this across many of our truck manufacturers. We're working with many of the truck OEMs, and they are all working to achieve very, very similar types of benefits to their customers. So just a little bit about Navistar. Now, we're going to turn to Q&A. Dave's got some questions for me in a second, but before we do that, if you want to learn more about how we work with connected vehicles and autonomous vehicles, please go to our website, what you see up on the screen. There's the URLs, cloudera.com/solutions/manufacturing, and you'll see a whole slew of lateral and information in much more detail in terms of how we connect trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >> Thank you, Michael. That's a great example. I love the lifecycle. We can visualize that very well. You've got an edge-use case. You're doing both real time inference, really, at the edge, and then you're blending that sensor data with other data sources to enrich your models, and you can push that back to the edge. That's that life cycle, so really appreciate that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into? >> Yeah, that's a great question, Dave 'cause everybody always thinks about machine learning, like this is the first thing you go to. Well, actually it's not. The first thing you really want to be able to do, and many of our customers are doing, is look, let's simply connect our trucks or our vehicles or whatever our IOT asset is, and then you can do very simple things like just performance monitoring of the piece of equipment. In the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how is the driver performing? Is there a lot of idle time spent? What's route efficiencies looking like? By connecting the vehicles, you get insights, as I said, into the truck and into the driver, and that's not machine learning any more, but that monitoring piece is really, really important. So the first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, what I call, the machine learning and AI models where you're using inference on the edge, and there you start to see things like predictive maintenance happening, kind of real-time route optimization and things like that, and you start to see that evolution again to those smarter, more intelligent, dynamic types of decision-making. But let's not minimize the value of good old-fashioned monitoring to give you that kind of visibility first, then moving to smarter use cases as you go forward. >> You know, it's interesting. I'm envisioning, when you talked about the monitoring, I'm envisioning you see the bumper sticker how am I driving? The only time somebody ever probably calls is when they get cut off, and many people might think, oh, it's about Big Brother, but it's not. I mean, that's yeah, okay, fine, but it's really about improvement and training and continuous improvement, and then of course the route optimization. I mean, that's bottom-line business value. I love those examples. >> Great. >> What are the big hurdles that people should think about when they want to jump into those use cases that you just talked about? What are they going to run into, the blind spots they're going to get hit with? >> There's a few different things. So first of all, a lot of times, your IT folks aren't familiar with kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set. There's very specialized hardware in the car and things like that and protocols. That's number one. That's the classic IT-OT kind of conundrum that many of our customers struggle with, but then more fundamentally is if you look at the way these types of connected truck or IOT solutions started, oftentimes the first generation were very custom built, so they were brittle. They were kind of hardwired. Then as you move towards more commercial solutions, you had what I call the silo problem. You had fragmentation in terms of this capability from this vendor, this capability from another vendor. You get the idea. One of the things that we really think that needs to be brought to the table is first of all, having an end-to-end data management platform that's kind of integrated, it's all tested together. You have a data lineage across the entire stack, but then also importantly, to be realistic, you have to be able to integrate to industry kind of best practices as well in terms of solution components in the car, the hardware, and all those types of things. So I think there's, it's just stepping back for a second, I think that there has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of offerings. Our job as a software maker is to make that easier and connect those dots so customers don't have to do it all and all on their own. >> And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about new types of hardware coming in. You guys are optimizing for that. We see the IT and the OT worlds blending together, no question, and then that end-to-end management piece. This is different from, you're right, from IT. Normally everything's controlled, you're the data center, and this is a metadata rethinking, kind of how you manage metadata. So in the spirit of what we talked about earlier today, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >> Yeah, I'm really glad you're asking that Dave because we actually embarked on a project called Project Fusion which really was about integrating with, when you look at that connected vehicle lifecycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Now, Cloudera's piece of this was ingesting data and all the things I talked about being storing and the machine learning. So we provide that end-to-end, but what we wanted to do is we wanted to partner with some key partners, and the partners that we did integrate with were NXP. NXP provides the service-oriented gateways in the cars. That's the hardware in the car. Wind River provides an in-car operating system that's Linux, that's hardened and tested. We then ran our Apache MiNiFi which is part of Cloudera Dataflow in the vehicle, on that operating system, on that hardware. We pumped the data over into the cloud where we did all the data analytics and machine learning and built out these very specialized models, and then we used a company called Airbiquity once we built those models to do. They specialize in automotive over-the-air updates, so they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's an established ecosystem, if you will, of leaders in this space. What we wanted to do is make sure that Cloudera was part and parcel of this ecosystem, and by the way, you mentioned Nvidia as well. We're working close with Nvidia now so when we're doing the machine learning, we can leverage some of their hardware to get some still further acceleration in the machine learning side of things. So yeah, one of the things I always say about these types of use cases, it does take a village, and what we've really tried to do is build out an ecosystem that provides that village so that we can speed that analytics and machine learning life cycle just as fast as it can be. >> This is, again, another great example of data-intensive workloads. It's not your grandfather's ERP that's running on traditional systems. These are really purpose built. Maybe they're customizable for certain edge-use cases. They're low cost, low power. They can't be bloated, and you're right, it does take an ecosystem. You've got to have APIs that connect, and that takes a lot of work and a lot of thought. So that leads me to the technologies that are sort of underpinning this. We talked a lot on theCUBE about semiconductor technology, and now that's changing, and the advancements we're seeing there. What do you see as some of the key technology areas that are advancing this connected vehicle machine learning? >> You know, it's interesting. I'm seeing it in a few places, just a few notable ones. I think, first of all, we see that the vehicle itself is getting smarter. So when you look at that NXP-type of gateway that we talked about, that used to be kind of a dumb gateway that was really, all it was doing was pushing data up and down and provided isolation as a gateway down from the lower level subsystems, so it was really security and just basic communication. That gateway now is becoming what they call a service oriented gateway, so it can run. It's got discs, it's got memory, it's got all this stuff. So now you could run serious compute in the car. So now, all of these things like running machine learning inference models, you have a lot more power in the car. At the same time, 5G is making it so that you can push data fast enough making low-latency computing available even on the cloud. So now you've got incredible compute both at the edge in the vehicle and on the cloud. And then on the cloud, you've got partners like Nvidia who are accelerating it still further through better GPU-based compute. So I mean the whole stack, if you look at that machine learning life cycle we talked about, no Dave, it seems like there's improvements in every step along the way. We're starting to see technology optimization just pervasive throughout the cycle. >> And then, real quick. It's not a quick topic, but you mentioned security. I mean, we've seen a whole new security model emerge. There is no perimeter anymore in a use case like this, is there? >> No, there isn't, and one of the things that we're- Remember, we're the data management platform, and the thing we have to provide is provide end-to-end lineage of where that data came from, who can see it, how it changed, and that's something that we have integrated into from the beginning of when that data is ingested through when it's stored through when it's kind of processed, and people are doing machine learning. We will provide that lineage so that security and governance is assured throughout the data learning life cycle. >> And federated, in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it. >> Dave, thank you, and thanks for the audience for listening in today. >> Yes, thank you for watching. Keep it right there. (upbeat music)

>>Welcome to our industry. Drill-downs from manufacturing. I'm here with Michael Gerber, who is the managing director for automotive and manufacturing solutions at cloud era. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data connected trucks are fundamental to optimizing fleet performance costs and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're gonna come back and have a little conversation about what we just heard. Michael, great to see you over to you. >>Oh, thank you, Dave. And I appreciate having this conversation today. Hey, um, you know, this is actually an area connected trucks. You know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because, you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022. Um, and the reasons, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made truck makers providing benefits to fleet operators. And if you look at the F the top fleet operator, uh, the top benefits that fleet operators expect, you see this in the graph over here. >>Now almost 80% of them expect improved productivity, things like improved routing rates. So route efficiencies and improve customer service decrease in fuel consumption, but better technology. This isn't technology for technology sake, these connected trucks are coming onto the marketplace because Hey, it can provide for Mendez value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable this right, um, trucks are becoming connected because at the end of the day, um, we want to be able to provide fleet deficiencies through connected truck, um, analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle analytics machine learning life cycle, and to do that, you need to do a few different things, right? >>You start off of course, with connected trucks in the field. And, you know, you can have many of these trucks cause typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first you need to be able to do is connect to those products, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you the ability to take this real-time action is actually the result of your machine learning license. Let me explain to you what I mean by that. >>So we have this trucks, we start to collect data from it right at the end of the day. Well we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected collected from those trucks. And you want to augment that with your dealership, say service information. Now you have, you know, you have sensor data and there was salting repair orders. You're now equipped to do things like predict one day maintenance will work correctly for all the data sets that you need to be able to do that. >>So what do you do here? Like I said, you adjusted your storage, you're enriching it with data, right? You're processing that data. You're aligning say the sensor data to that transactional system data from your, uh, from your, your pair maintenance systems, you know, you're bringing it together so that you can do two things you can do. First of all, you could do self-service BI on that date, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor right values and the need, for example, for, for a dealership repair, or as you could start to correlate, which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time action. >>I mentioned earlier as that data that then comes through in real-time, you're running it against that model, and you can take some real time actions. This is what we are, this, this, this, this analytics and machine learning model, um, machine learning life cycle is exactly what Cloudera enables this end-to-end ability to ingest, um, stroke, you know, store it, um, put a query, lay over it, um, machine learning models, and then run those machine learning models. Real-time now that's what we, that's what we do as a business. Now when such customer, and I just wanted to give you one example, um, a customer that we have worked with to provide these types of results is Navistar and Navistar was kind of an early, early adopter of connected truck analytics. And they provided these capabilities to their fleet operators, right? And they started off, uh, by, um, by, you know, connecting 475,000 trucks to up to well over a million now. >>And you know, the point here is with that, they were centralizing data from their telematics service providers, from their trucks, from telematics service providers. They're bringing in things like weather data and all those types of things. Um, and what they started to do was to build out machine learning models, aimed at predictive maintenance. And what's really interesting is that you see that Navistar, um, made tremendous strides in reducing the need or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring and service it before it broke down so that you could do that in a much more cost-effective manner. And if you see the benefits, right, they, they reduced maintenance costs 3 cents a mile, um, from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. >>So this was a tremendous success for Navistar. And we're seeing this across many of our, um, um, you know, um, uh, truck manufacturers. We were working with many of the truck OEMs and they are all working to achieve, um, you know, very, very similar types of, um, benefits to their customers. So just a little bit about Navistar. Um, now we're gonna turn to Q and a, Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our lives or to our website, what you see up, uh, up on the screen, there's the URLs cloudera.com for slash solutions for slash manufacturing. And you'll see a whole slew of, um, um, lateral and information, uh, in much more detail in terms of how we connect, um, trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >>Thank you. Uh, Michael, that's a great example. You've got, I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that lifecycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into. >>Yeah, that's really, that's a great question. They, you know, cause you know, everybody always thinks about machine learning. Like this is the first thing you go, well, actually it's not right for the first thing you really want to be able to go around. Many of our customers are doing slow. Let's simply connect our trucks or our vehicles or whatever our IOT asset is. And then you can do very simple things like just performance monitoring of the, of the piece of equipment in the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how has the, how has the driver performing? Is there a lot of idle time spent, um, you know, what's, what's route efficiencies looking like, you know, by connecting the vehicles, right? You get insights, as I said into the truck and into the driver and that's not machine learning. >>Right. But that, that, that monitoring piece is really, really important. The first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, uh, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like, uh, predictive maintenance happening, um, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making, but let's not, let's not minimize the value of good old fashioned monitoring that site to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >>You know, it's interesting. I'm, I'm envisioning when you talked about the monitoring, I'm envisioning a, you see the bumper sticker, you know, how am I driving this all the time? If somebody ever probably causes when they get cut off it's snow and you know, many people might think, oh, it's about big brother, but it's not. I mean, that's yeah. Okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization, I mean, that's, that's bottom line business value. So, so that's, I love those, uh, those examples. Um, I wonder, I mean, one of the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into, uh, you know, the blind spots they're, they're going to, they're going to get hit with, >>There's a few different things, right? So first of all, a lot of times your it folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? That's very specialized hardware in the car and things like that. And protocols that's number one, that that's the classic, it OT kind of conundrum that, um, you know, uh, many of our customers struggle with, but then more fundamentally is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. And as you move towards, um, more commercial solutions, you had what I call the silo, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor, you get the idea, you know, one of the things that we really think that we need with that, that needs to be brought to the table is first of all, having an end to end data management platform, that's kind of integrated, it's all tested together. >>You have the data lineage across the entire stack, but then also importantly, to be realistic, we have to be able to integrate to, um, industry kind of best practices as well in terms of, um, solution components in the car, how the hardware and all those types things. So I think there's, you know, it's just stepping back for a second. I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of art, um, offerings. Um, our job as a software maker is to make that easier and connect those dots. So customers don't have to do it all on all on their own. >>And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about, you know, new types of hardware coming in, you guys are optimizing for that. We see the it and the OT worlds blending together, no question. And then that end to end management piece, you know, this is different from your right, from it, normally everything's controlled or the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. Um, so in the spirit of, of what we talked about earlier today, uh, uh, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >>Yeah, I'm really glad you're asking that because we actually embarked on a product on a project called project fusion, which really was about integrating with, you know, when you look at that connected vehicle life cycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Cloudera is Peter piece of this was ingesting data and all the things I talked about being storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners and the partners that we did with, um, integrate with or NXP NXP provides the service oriented gateways in the car. So that's a hardware in the car when river provides an in-car operating system, that's Linux, right? >>That's hardened and tested. We then ran ours, our, uh, Apache magnify, which is part of flood era data flow in the vehicle, right on that operating system. On that hardware, we pump the data over into the cloud where we did them, all the data analytics and machine learning and, and builds out these very specialized models. And then we used a company called Arabic equity. Once we both those models to do, you know, they specialize in automotive over the air updates, right? So they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, um, you know, uh, ecosystem, if you will, of leaders in this space, what we wanted to do is make sure that our, there was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working closely with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some further acceleration in the machine learning side of things. So, uh, yeah, you know, one of the things I always say about this types of use cases, it does take a village. And what we've really tried to do is build out that, that, uh, an ecosystem that provides that village so that we can speed that analytics and machine learning, um, lifecycle just as fast as it can be. This >>Is again another great example of, of data intensive workloads. It's not your, it's not your grandfather's ERP. That's running on, you know, traditional, you know, systems it's, these are really purpose-built, maybe they're customizable for certain edge use cases. They're low cost, low, low power. They can't be bloated, uh, ended you're right. It does take an ecosystem. You've got to have, you know, API APIs that connect and, and that's that, that takes a lot of work and a lot of thoughts. So that, that leads me to the technologies that are sort of underpinning this we've talked we've we talked a lot in the cube about semiconductor technology, and now that's changing and the advancements we're seeing there, what do you see as the, some of the key technical technology areas that are advancing this connected vehicle machine learning? >>You know, it's interesting, I'm seeing it in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about that used to be kind of a, a dumb gateway. That was really all it was doing was pushing data up and down and provided isolation, um, as a gateway down to the, uh, down from the lower level subsistence. So it was really security and just basic, um, you know, basic communication that gateway now is becoming what they call a service oriented gate. So it can run. It's not that it's bad desk. It's got memories that always, so now you could run serious compute in the car, right? So now all of these things like running machine learning, inference models, you have a lot more power in the corner at the same time. >>5g is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now you now you've got credible compute both at the edge in the vehicle and on the cloud. Right. And, um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, it's still further through better GPU based compute. So I mean the whole stack, if you look at it, that that machine learning life cycle we talked about, no, David seems like there's improvements and EV every step along the way, we're starting to see technology, um, optimum optimization, um, just pervasive throughout the cycle. >>And then real quick, it's not a quick topic, but you mentioned security. If it was seeing a whole new security model emerge, there is no perimeter anymore in this use case like this is there. >>No there isn't. And one of the things that we're, you know, remember where the data management platform platform and the thing we have to provide is provide end-to-end link, you know, end end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into from the beginning of when that data is ingested through, when it's stored through, when it's kind of processed and people are doing machine learning, we provide, we will provide that lineage so that, um, you know, that security and governance is a short throughout the, throughout the data learning life cycle, it >>Federated across in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it, >>Dave. Thank you. And thank you. Thanks for the audience for listening in today. Yes. Thank you for watching. >>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces loss opportunities. Michael. Great to see you >>Take it away. All right. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right. And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, massive assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done, essentially programmed a robot to do something. It did the same thing over and over and over irrespective about it, of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfast. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adaptive right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives and very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, of, uh, companies, um, and manufacturers moving to improve while its quality promise still accounted to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. >>Plant downtime, cost companies, $50 billion a year. So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just merely to implement technology. We're doing it to move these from drivers, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle, what like, right, because this is actually the business that cloud era is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI, this, this analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors have connected over the internet. So suddenly we can collect all this data from your, um, ma manufacturing plants. What do we want to be able to do? >>You know, we want to be able to collect it. We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking the time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You've got, you're going to ingest that data. >>You're going to store it. You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. >>But as I mentioned, you, and what's really important here is the fact that once you've stored long histories that say that you can build out those machine learning models I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need, a correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for Maples. Once you understand that you can actually then build out those models for deploy the models out the edge, where they will then work in that inference mode that we talked about, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that PR that predicted the need for maintenance? If so, let's take real-time action, right? >>Let's schedule a work order or an equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connecting connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is bought for Russia, for SIA, for ACA is the, um, is the, was, is the, um, the, uh, a supplier associated with Peugeot central line out of France. They are huge, right? This is a multi-national automotive parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. >>Um, and then once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor data firms coming in, you know, monitor the process. That was the first step, right. Uh, and, you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models or compute. And what they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad Bali outcome. Then you teach the machine to make that decision on its own. >>So now, now the machine, the camera is doing the inspections. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you can start with monitoring, moved to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing, a lot more detail, and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the cost, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit, >>You know, there's, there's, there, there's a few of the, but I think, you know, one of the ones, uh, w one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant, are running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietorial pro protocols. That information can be very, very difficult to get to. Right. So, and it's, it's a much more unstructured than from your OT. So th the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world. And for a long time, the silos, um, uh, the silos a, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge, >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So, Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right. And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, but just talking about simple monitoring next level down, and we're seeing is something we would call quality event forensic analysis. >>And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims kick up. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots. What about warranty issues? What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning, we're simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole slew of machine learning, use dates, you know, and that ranges from things like Wally or say yield optimization. >>We start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. And you're certain start to say, which, um, you know, which on a sensor values or factors drove good or bad yield outcomes, and you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with, with monitoring, get a lot of value, start then bringing together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases, there's this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and the new player would come in and he'd be perfectly white uniform, and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so I question it relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. And it kind of goes back to one of the things I alluded to alluded upon earlier. We've had some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of, um, industry for porno, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and, and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to li lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry, manufacturing. Yeah. >>Yeah. I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can, we've finally been able to get to the OT data, right? That's that's number one, you know, numb number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, the super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed a book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to, to your equipment. All of those things are making this, um, there's, you know, the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, uh, very much more quickly. Yeah, we got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined us. Thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>> Welcome to our industry drill down. This is for manufacturing. I'm here with Michael Ger, who is the managing director for automotive and manufacturing solutions at Cloudera. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data. Connected trucks are fundamental to optimizing fleet performance, costs, and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're going to come back and have a little conversation about what we just heard. Michael, great to see you! Over to you. >> Oh, thank you, Dave. And I appreciate having this conversation today. Hey, you know, this is actually an area, connected trucks, you know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022 and the reasons, and I think this is why we're seeing a lot of action and a lot of growth, is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made, truck makers providing benefits to fleet operators. And if you look at the, the top fleet operator, the top benefits that fleet operators expect, you see this in, in the, in the graph over here, now almost 80% of them expect improved productivity, things like improved routing, right? So route efficiencies, improved customer service, decrease in fuel consumption, better better technology. This isn't technology for technology's sake, these connected trucks are coming onto the marketplace because, hey, it can provide tremendous value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable us, right, trucks are becoming connected because at the end of the day, we want to be able to provide fleet efficiencies through connected truck analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle, analytics, machine-learning life cycle, and to do that, you need to do a few different things, right? You start off, of course, with connected trucks in the field. And, you know, you could have many of these trucks because typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first thing you need to be able to do is connect to those trucks, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you collect the, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you, the ability to take this real-time action, is actually the result of your machine-learning lifecycle. Let me explain to you what I mean by that. So we have these trucks, we start to collect data from it, right? At the end of the day what we'd like to be able to do is pull that data into either your data center or into the cloud, where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected, collected from those trucks. And you want to augment that with your dealership, say, service information. Now you have, you know, you have sensor data and the resulting repair orders. You're now equipped to do things like predict when maintenance will work, all right. You've got all the data sets that you need to be able to do that. So what do you do? Like I said, you're ingested, you're storing, you're enriching it with data, right? You're processing that data. You're aligning, say, the sensor data to that transactional system data from your, from your your repair maintenance systems; you're, you're bringing it together so that you can do two things. You can do, first of all, you could do self-service BI on that data, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor values and the need, for example, for, for a dealership repair, or is, you could start to correlate which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time actions I mentioned earlier. As that data that then comes through in real-time, you're running it again against that model. And you can take some real-time actions. This is what we, this is this, this, this analytics and machine learning model, machine learning life cycle is exactly what Cloudera enables. This end-to-end ability to ingest data; store, you know, store it, put a query lay over it, create machine learning models, and then run those machine learning models in real time. Now that's what we, that's what we do as a business. Now one such customer, and I just want to give you one example of a customer that we have worked with to provide these types of results is Navistar. And Navistar was kind of an early, early adopter of connected truck analytics, and they provided these capabilities to their fleet operators, right? And they started off by, by, you know, connecting 475,000 trucks to up to well over a million now. And you know, the point here is that they were centralizing data from their telematics service providers, from their trucks' telematics service providers. They're bringing in things like weather data and all those types of things. And what they started to do was to build out machine learning models aimed at predictive maintenance. And what's really interesting is that you see that Navistar made tremendous strides in reducing the need, or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring, and service it before it broke down, so that you can do that in a much more cost-effective manner. And if you see the benefits, right, they reduce maintenance costs 3 cents a mile from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. So this was a tremendous success for Navistar. And we're seeing this across many of our, you know, truck manufacturers. We're, we're working with many of the truck OEMs, and they are all working to achieve very, very similar types of benefits to their customers. So just a little bit about Navistar. Now, we're going to turn to Q and A. Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our web, to our website. What you see up, up on the screen. There's the URL. It's cloudera.com forward slash solutions, forward slash manufacturing. And you'll see a whole slew of collateral and information in much more detail in terms of how we connect trucks to fleet operators who provide analytics. Use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >> Thank you, Michael. That's a great example you've got. I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really, at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that life cycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into? >> Yeah, that's really, that's a great question, Dave, you know, cause, you know, everybody always thinks about machine learning like this is the first thing you go to. Well, actually it's not, right? For the first thing you really want to be able to go down, many of our customers are doing, is look, let's simply connect our trucks or our vehicles or whatever our IOT asset is, and then you can do very simple things like just performance monitoring of the, of the piece of equipment. In the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how is the, how is the driver performing? Is there a lot of idle time spent? You know, what's, what's route efficiency looking like? You know, by connecting the vehicles, right? You get insights, as I said, into the truck and into the driver and that's not machine learning even, right? But, but that, that monitoring piece is really, really important. So the first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like predictive maintenance happening, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making. But let's not, let's not minimize the value of good old fashioned monitoring, that's to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >> You know, it's interesting, I'm I'm envisioning, when you talked about the monitoring, I'm envisioning, you see the bumper sticker, you know, "How am I driving?" The only time somebody ever probably calls is when they get cut off it's and you know, I mean, people might think, "Oh, it's about big brother," but it's not. I mean, that's yeah okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization. I mean, that's, that's bottom line business value. So, so that's, I love those, those examples. >> Great! >> I wonder, I mean, what are the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into? You know, the blind spots they're, they're going to, they're going to to get hit with. >> There's a few different things, right? So first of all, a lot of times your IT folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? There's very specialized hardware in the car and things like, like that and protocols. That's number one. That's the classic IT OT kind of conundrum that, you know, many of our customers struggle with. But then, more fundamentally, is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. Then as you move towards more commercial solutions, you had what I call the silo problem, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor. You get the idea. You know, one of the things that we really think that we need that we, that needs to be brought to the table, is, first of all, having an end to end data management platform. It's kind of an integrated, it's all tested together, you have a data lineage across the entire stack. But then also importantly, to be realistic, we have to be able to integrate to industry kind of best practices as well in terms of solution components in the car, the hardware and all those types of things. So I think there's, you know, it's just stepping back for a second, I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of offerings. Our job as a software maker is to make that easier and connect those dots, so customers don't have to do it all on all on their own. >> And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about new types of hardware coming in. You guys are optimizing for that. We see the IT and the OT worlds blending together, no question. And then that end-to-end management piece, you know, this is different from, your right, from IT, normally everything's controlled, you're in the data center. And this is a metadata, you know, rethinking kind of how you manage metadata. So in the spirit of, of what we talked about earlier today, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >> Yeah, I'm really glad you're asking that, Dave, because we actually embarked on a product on a project called Project Fusion, which really was about integrating with, you know, when you look at that connected vehicle lifecycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Now Cloudera's piece of this was ingesting data and all the things I talked about in storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners. And the partners that we did integrate with were NXP. NXP provides the service-oriented gateways in the car, so that's the hardware in the car. Wind River provides an in-car operating system. That's Linux, right? That's hardened and tested. We then ran ours, our, our Apache MiNiFi, which is part of Cloudera data flow, in the vehicle, right on that operating system, on that hardware. We pumped the data over into the cloud where we did the, all the data analytics and machine learning, and built out these very specialized models. And then we used a company called Airbiquity, once we built those models, to do, you know, they specialize in automotive over-the-air updates, right? So they can then take those models, and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, you know, ecosystem, if you will, of leaders in this space. What we wanted to do is make sure that Cloudera was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working close with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some still further acceleration in the machine learning side of things. So yeah, you know, one of the things I, I, I always say about these types of use cases, it does take a village. And what we've really tried to do is build out that, that, that an ecosystem that provides that village so that we can speed that analytics and machine learning lifecycle just as fast as it can be. >> This is, again, another great example of data intensive workloads. It's not your, it's not your grandfather's ERP that's running on, you know, traditional, you know, systems, it's, these are really purpose built, maybe they're customizable for certain edge-use cases. They're low cost, low, low power. They can't be bloated. And you're right, it does take an ecosystem. You've got to have, you know, APIs that connect and, and that's that, that takes a lot of work and a lot of thought. So that, that leads me to the technologies that are sort of underpinning this. We've talked, we've talked a lot on The Cube about semiconductor technology, and now that's changing, and the advancements we're seeing there. What, what do you see as some of the key technology areas that are advancing this connected vehicle machine learning? >> You know, it's interesting, I'm seeing it in, in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about. That used to be kind of a, a dumb gateway that was, really all it was doing was pushing data up and down, and provided isolation as a gateway down to the, down from the lower level subsystems. So it was really security and just basic, you know, basic communication. That gateway now is becoming what they call a service oriented gateway. So it can run. It's not, it's got disc, it's got memory, it's got all this. So now you could run serious compute in the car, right? So now all of these things like running machine-learning inference models, you have a lot more power in the car. At the same time, 5G is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now, now you've got incredible compute both at the edge in the vehicle and on the cloud, right? And, you know, and then on the, you know, on the cloud, you've got partners like Nvidia, who are accelerating it still further through better GPU-based computing. So, I mean the whole stack, if you look at that, that machine learning life cycle we talked about, you know, Dave, it seems like there's improvements in every, in every step along the way, we're starting to see technology optim, optimization just pervasive throughout the cycle. >> And then, you know, real quick, it's not a quick topic, but you mentioned security. I mean, we've seen a whole new security model emerge. There is no perimeter anymore in this, in a use case like this is there? >> No, there isn't. And one of the things that we're, you know, remember we're the data management plat, platform, and the thing we have to provide is provide end-to-end, you know, end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into, from the beginning of when that data is ingested, through when it's stored, through when it's kind of processed and people are doing machine learning; we provide, we will provide that lineage so that, you know, that security and governance is assured throughout the, throughout that data learning life's level. >> And federated across, in this example, across the fleet, so. All right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it. >> Dave, thank you. And thanks for the audience for listening in today. >> Yes, thank you for watching. Keep it right there.

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of H. P. S. Big customer event. Patrick Moorehead is here of moor insights and strategy is the number one analyst in the research analyst. Business. Patrick. Always a pleasure. Great to see you, >>David. Great to see you too. And I know you're you're up there fighting for that number one spot to. It's great to see you and it's great to see you in the meetings that were in. But it's even more fun to be here on the cube. I love to be on the cube and every once in a while you'll even call me a friend of the cube, >>unquestionably my friend and so and I can't wait second half. I mean you're traveling right now. We're headed to Barcelona to mobile World Congress later on this month. So so we're gonna we're gonna see each other face to face this year. 100%. So looking forward to that. So you know, let's get into it. Um you know, before we get into H. P. E. Let's talk a little bit about what you're seeing in the market. We've got, you know, we we finally, it feels like the on prem guys are finally getting their cloud act together. Um it's maybe taken a while, but we're seeing as a service models emerge. I think it's resonating with customers. The clearly not everything is moving to the cloud. There's this hybrid model emerging. Multi cloud is real despite what, you know, >>some some >>cloud players want to say. And then there's this edges like jump ball, what are you seeing in the marketplace? >>Yeah. Davis, as exciting as ever in. Just to put in perspective, I mean the public cloud has been around for about 10 years and still only 20% around 20% of the data in 20% of the applications are there now will be a very important ones and I'm certainly not a public cloud denier, I never have been, but there are some missing pieces that need to come together. And you know, even five years ago we were debating dave the hybrid cloud. And I feel like when amazon brought out outposts, the conversation was over right now, what you have is cloud native folks building out hybrid and on prem capabilities, you have a classic on, on prem folks building out hybrid and as a service capabilities. And I really think it boils down 22 things. I mean it's, it's wanting to have more flexibility and you know, I hate to use it because it sounds like a marketing word, but agility, the ability to spin up things and spin down things in a very, a quick way. And uh you know what they've learned, The veterans also know, hey, let's do this in a way that doesn't lock us in too much into a certain vendor. And I've been around for a long time. David and I'm a realist too. Well, you have to lock yourself into something. Uh it just depends on what do you want to lock yourself into, but super exciting and what H. P. E. You know, when they further acts in the sea with Green Lake, I think it was four years ago, uh I think really started to stir the pot. >>You know, you mentioned the term cloud denial, but you know, and I feel like the narrative from, I like to determine as I think you should use the term veteran. You know, it's very, they're ours is the only industry patrick where legacy is a pejorative, but so, but the point I want to make is I feel like there's been a lot of sort of fear from the veteran players, but, but I look at it differently, I wonder what your take is. I, I think, I think I calculated that the Capex spending by the big four public clouds including Alibaba last year was $100 billion. That's like a gift to the world. Here we're gonna spend $100 billion like the internet. Here you go build. And so I, and I feel like companies like HP are finally saying, yeah, we're gonna build, we're gonna build a layer and we're gonna hide the complexity and we're gonna add value on top. What do you think about that? >>Yeah. So I think it's now, I wish, I wish the on prem folks like HP, you would have done it 10 years ago, but I don't think anybody expected the cloud to be as big as it's become over the last 10 years. I think we saw companies like salesforce with sas taking off, but I think it is the right direction because there are advantages to having workloads on prem and if you add an as a service capability on top of the top of that, and let's say even do a Coehlo or a managed service, it's pretty close to being similar to the public cloud with the exception, that you can't necessarily swipe a credit card for a bespoke workload if you're a developer and it is a little harder to scale out. But that is the next step in the equation day, which is having, having these folks make capital expenditures, make them in a Polo facility and then put a layer to swipe a credit card and you literally have the public cloud. >>Yeah. So that's, that's a great point. And that's where it's headed, isn't it? Um, so let's, let's talk about the horses on the track. Hp as you mentioned, I didn't realize it was four years ago. I thought it was, wow, That's amazing. So everybody's followed suit. You see, Dallas announced, Cisco has announced, uh, Lenovo was announced, I think IBM as well. So we, so everybody's sort of following suit there. The reality is, is it's taken some time to get this stuff standardized. What are you seeing from, from HP? They've made some additional announcements, discover what's your take on all this. >>Yeah. So HPD was definitely the rabbit here and they were first in the market. It was good to see. First off some of their, Um, announcements on, on how it's going and they talked about $428 billion 1200 customers over 900 partners and 95% retention. And I think that's important. Anybody who's in the lead and remember what aws I used to do with the slide with the amount of customers would just get bigger and bigger and bigger and that's a good way to show momentum. I like the retention part two which is 95%. And I think that that says a lot uh probably the more important announcements that they made is they talked about the G. A. Of some of their solutions on Green Lake and whether it is A. S. A. P. Hana. Ml apps HPC with Francis, VD. I was Citrus and video but they also brought more of what I would call a vertical layer and I'm sure you've seen the vertical ization of all of these cloud and as a service workloads. But what they're doing with Epic, with EMR and looseness, with financial payments and Splunk and intel with data and risk analysis and finally, a full stack for telco five G. One of the biggest secrets and I covered this about five years ago is HPV actually has a full stack that Western european carriers use and they're now extending that to five G. And um, so more horizontal, uh, and, and more vertical. That was the one of the big swipes, uh, that I saw that there was a second though, but maybe we can talk about these. >>Yeah. Okay. Okay. So, so the other piece of that of course is standardization right there there because there was a, there was a, there was a lot of customization leading up to this and everybody sort of, everybody always had some kind of financial game they can play and say, hey, there's an adversary as a service model, but this is definitely more of a standardized scalable move that H P E. Is making with what they call Lighthouse. Right? >>Yeah, that's exactly right. And I've talked to some Green Lake customers and they obviously gave it kudos or they wouldn't have HP wouldn't have served them up and they wouldn't have been buying it. But they did say, um, it took, it took a while, took some paperwork to get it going. It's not 100% of push button, but that's partially because hp allows you to customize the hardware. You want a one off network adapter. Hp says yes, right. You want to integrate a different type of storage? They said yes. But with Green Lake Lighthouse, it's more of a, what you see is what you get, which by the way, is very much like the public cloud or you go to a public cloud product sheet or order sheet. You're picking from a list and you really don't know everything that's underneath the covers, aside from, let's say, the speed of the network, the type of the storage and the amount of the storage you get. You do get to pick between, let's say, an intel processor, Graviton two or an M. D processor. You get to pick your own GPU. But that's pretty much it. And HP Lighthouse, sorry, Green Lake Lighthouse uh is bringing, I think a simplification to Green Lake that it needs to truly scale beyond, let's say the White House customers that HP Yeah, >>Well done. So, you know, and I hear your point about we're 10 years in plus. And to me this is like a mandate. I mean, this is okay, good, good job guys about time. But if I had a, you know, sort of look at the big player, it's like we have an oligopoly here in this, in this business. It's HP, Cisco, you got Dell Lenovo, you've got, you know, IBM, they're all doing this and they all have a different little difference, you know, waste of skin of catch. And your point about simplicity, it seems like HP HP is all in antony's like, okay, here's what we're going to announce that, you know, a while ago. So, and they seem to have done a good job with Wall Street and they got a simple model, you know, Dell is obviously bigger portfolio, much more complicated. IBM is even more complicated than that. I don't know so much about Lenovo and in Cisco of course, has acquired a ton of SAAS companies and sort of they've got a lot of bespoke products that they're trying to put together. So they've got, but they do have SAS models. So each of them is coming at it from a different perspective. How do you think? And so and the other point we got lighthouse, which is sort of Phase one, get product market fit. Phase two now is scale, codify standardized and then phase three is the moat build your unique advantage that protects your business. What do you see as HP ES sort of unique value proposition and moat that they can build longer term. >>That's a great, great question. And let me rattle off kind of what I'm seeing that some of these players here, So Cisco, ironically has sells the most software of any of those players that you mentioned, uh with the exception of IBM um and yeah, C I >>CSDB two. Yeah, >>yeah, they're the they're the number two security player, uh Microsoft, number one, So and I think the evaluation on the street uh indicate that shows that I feel like Dell tech is a very broad play because not only do they have servers, storage, networking insecurity, but they also have Pcs and devices. So it's a it's a scale and end play with a focus on VM ware solutions, not exclusively of course. Uh And um then you've got Lenovo who is just getting into the as a service game and are gosh, they're doing great in hyper scale, they've got scale there vertically integrated. I don't know if if too many people talk about that, but Lenovo does a lot of their own manufacturing and they actually manufacture Netapp storage solutions as well. So yeah, each of these folks brings a different game to the table. I think with h P e, what you're bringing the table is nimble. When HP and HP split, the number one thing that I said was that ah, h P E is going to have to be so much faster than it offsets the scale that Dell technology has and the HBs credit, although there, I don't think we're getting credit for this in the stock market yet. Um and I know you and I are both industry folks, not financial folks, but I think their biggest thing is speed and the ability to move faster. And that is what I've seen as it relates to the moat, which is a unique uh competitive advantage. Quite frankly, I'm still looking for that day uh in in in what that is. And I think in this industry it's nearly impossible. And I would posit that that any, even the cloud folks, if you say, is there something that AWS can do that as your can't if it put it put its mind to it or G C P. I don't think so. I think it's more of a kind of land and expand and I think for H P E. When it comes to high performance computing and I'm not just talking about government installations, I'm talking about product development, drug development. I think that is a landing place where H P. E already does pretty well can come in and expand its footprint. >>You know, that's really interesting um, observations. So, and I would agree with you. It's kind of like, this is a copycat industry. It's like the west coast offense like the NFL, >>so, >>so the moat comes from, you know, brand execution and your other point about when HP and HP split, that was a game changer because all of a sudden you saw companies like them, you always had a long term relationship with H P E, but or HP, but then they came out of the woodworks and started to explode. And so it really opened up opportunities. So it really is a execution, isn't it? But go ahead please. >>Dave if I had to pick something that I think HP HPV needs to always be ahead in as a service and listen you and I both know announcements don't mean delivery, but there is correlation between if you start four years ahead of somebody that other company is going to have to put just, I mean they're going to have to turn that ship and many of its competitors really big ships to be able to get there. So I think what Antonio needs to do is run like hell, right? Because it, it I think it is in the lead and as a service holistically doesn't mean they're going to be there forever, but they have to stay ahead. They have to add more horizontal solutions. They have to add more vertical solutions. And I believe that at some point it does need to invest in some Capex at somebody like Anna Quinn X play credit card swiper on top of that. And Dave, you have the public, you have the public cloud, you don't have all the availability zones, but you have a public cloud. >>Yeah, that's going to happen. I think you're right on. So we see this notion of cloud expanding. It's no longer just remote set of services. Somewhere out in the cloud. It's like you said, outpost was the sort of signal. Okay, We're coming on prem. Clearly the on prem uh, guys are connecting to the cloud. Multi cloud exists, we know this and then there's the edge but but but that brings me to that sort of vision and everybody's laying out of this this this seamless integration hiding the complexity log into my cloud and then life will be good. But the edge is different. Right? It's not just, you know, retail store or a race track. I mean there's the far edge, there's the Tesla car, there's gonna be compute everywhere and that sort of ties into the data. The data flows, you know the real time influencing at the edge ai new semiconductor models. You you came out of the semiconductor industry, you know it inside and out arm is exploding, dominating in the edge with apple and amazon Alexa and things like that. That's really where the action is. So this is a really interesting cocktail and soup that we have going on. How do >>you say? Well, you know, Dave if the data most data, I think one thing most everybody agrees on is that most of the data will be created on the edge, whether that's a moving edge a car, a smartphone or what I call an edge data center without tile flooring. Like that server that's bolted to the wall of Mcdonald's. When you drive through, you can see it versus the walmart. Every walmart has a raised tile floor. It's the edge to economically and performance wise, it doesn't make any sense to send all that data to the mother ships. Okay. And whether that's unproven data center or the giant public cloud, more efficient way is to do the compute at the closest way possible. But what it does, it does bring up challenges. The first challenge is security. If I wanted to, I could walk in and I could take that server off the Mcdonald's or the Shell gas station wall. So I can't do that in a big data center. Okay, so security, physical security is a challenge. The second is you don't have the people to go in there and fix stuff that are qualified. If you have a networking problem that goes wrong in Mcdonald's, there's nobody there that can help uh they can they can help you fix that. So this notion of autonomy and management and not keeping hyper critical data sitting out there and it becomes it becomes a security issue becomes a management issue. Let me talk about the benefits though. The benefits are lower latency. You want you want answers more quickly when that car is driving down the road And it has a 5GV 2 x communication cameras can't see around corners. But that car communicating ahead, that ran into the stop sign can, through Vita X talked to the car behind it and say, hey, something is going on there, you can't go to, you can't go to the big data center in the sky, let's make that happen, that is to be in near real time and that computer has to happen on the edge. So I think this is a tremendous opportunity and ironically the classic on prem guys, they own this, they own this space aside from smartphones of course, but if you look at compute on a light pole, companies like Intel have built complete architecture is to do that, putting compute into five G base stations, heck, I just, there was an announcement this week of google cloud and its gaming solution putting compute in a carrier edge to give lower latency to deliver a better experience. >>Yeah, so there, of course there is no one edge, it's highly fragmented, but I'm interested in your thoughts on kinda whose stack actually can play at the edge. And I've been sort of poking uh H P E about this. And the one thing that comes back consistently is Aruba, we we could take a room but not only to the, to the near edge, but to the far edge. And and that, do you see that as a competitive advantage? >>Oh gosh, yes. I mean, I would say the best acquisition That hp has made in 10 years has been aruba, it's fantastic and they also managed it in the right way. I mean it was part of HB but it was, it was managed a lot more loosely then, you know, a company that might get sucked into the board and I think that paid off tremendously. They're giving Cisco on the edge a absolute run for their money, their first with new technologies, but it's about the solution. What I love about what a ruble looks at is it's looking at entertainment solutions inside of a stadium, a information solution inside of an airport as opposed to just pushing the technology forward. And then when you integrate compute with with with Aruba, I think that's where the real magic happens. Most of the data on a permanent basis is actually video data. And a lot of it's for security, uh for surveillance. And quite frankly, people taking videos off, they're off their smartphones and downloaded video. I I just interviewed the chief network officer of T mobile and their number one bit of data is video, video uploaded, video download. But that's where the magic happens when you put that connectivity and the compute together and you can manage it in a, in an orderly and secure fashion. >>Well, I have you we have a ton of time here, but I I don't pick your brain about intel the future of intel. I know you've been following it quite closely, you always have Intel's fighting a forefront war, you got there battling a. M. D. There, battling your arm slash and video. They're they're taking on TSMC now and in foundry and, and I'll add china for the looming threat there. So what's your prognosis for for intel? >>Yeah, I liked bob the previous Ceo and I think he was doing a lot of of the right things, but I really think that customers and investors and even their ecosystem wanted somebody leading the company with a high degree of technical aptitude and Pat coming, I mean, Pat had a great job at VM or, I mean he had a great run there and I think it is a very positive move. I've never seen the energy at Intel. Probably in the last 10 years that I've seen today. I actually got a chance to talk with Pat. I visited Pat uhh last month and and talk to him about pretty much everything and where he wanted to take the company the way you looked at technology, what was important, what's not important. But I think first off in the world of semiconductors, there are no quick fixes. Okay. Intel has a another two years Before we see what the results are. And I think 2023 for them is gonna be a huge year. But even with all this competition though, Dave they still have close to 85% market share in servers and revenue share for client computing around 90%. Okay. So and they built out there networking business, they build out a storage business um with obtain they have the leading Aid as provider with Mobileye. And and listen I was I was one of Intel's biggest, I was into one of Intel's biggest, I was Intel's biggest customer when I was a compact. I was their biggest competitor at A. M. B. So um I'm not obviously not overly pushing or there's just got to wait and see. They're doing the right things. They have the right strategy. They need to execute. One of the most important things That Intel did is extend their alliance with TSMC. So in 2023 we're going to see Intel compute units these tiles they integrate into the larger chips called S. O. C. S. B. Manufactured by TSMC. Not exclusively, but we could see that. So literally we could have AMG three nanometer on TSMC CPU blocks, competing with intel chips with TSMC three nanometer CPU blocks and it's on with regard to video. I mean in video is one of these companies that just keeps going charging, charging hard and I'm actually meeting with Jensen wang this week and Arm Ceo Simon Segers to talk about this opportunity and that's a company that keeps on moving interestingly enough in video. If the Arm deal does go through will be the largest chip license, see CPU licensee and have the largest CPU footprint on on the planet. So here we have A and D. Who's CPU and Gpu and buying an F. P. G. A company called Xilinx, you have Intel, Cpus, Gpus machine learning accelerators and F. P. G. S and then you've got arms slashing video bit with everything as well. We have three massive ecosystems. They're gonna be colliding here and I think it's gonna be great for competition date. Competition is great. You know, when there's not competition in Cpus and Gpus, we know what happens, right. Uh, the B just does not go on and we start to stagnate. And I did, I do feel like the industry on CPU started to stagnate when intel had no competition. So bring it on. This is gonna be great for for enterprises then customers to, and then, oh, by the way, the custom Chip providers, WS has created no less than 15 custom semiconductors started with networking uh, and, and nitro and building out an edge that surrounded the general compute and then it moved to Inferential to for inference trainee um, is about to come out for training Graviton and gravitas to for general purpose CPU and then you've got Apple. So innovation is huge and you know, I love to always make fun of the software is eating the world. I always say yeah but has to run on something. And so I think the combination of semiconductors, software and cloud is just really a magical combination. >>Real quick handicap the video arm acquisition. What what are the odds that that they will be successful? They say it's on track. You've got to 2 to 13 to 1 10 to 1. >>I say 75%. Yes 25%. No China is always the has been the odd odd man out for the last three years. They scuttled the qualcomm NXP deal. You just don't know what china is going to do. I think the Eu with some conditions is gonna let this fly. I think the U. S. Is absolutely going to let this fly. And even though the I. P. Will still stay over in the UK, I think the U. S. Wants to see, wants to see this happen. Japan and Korea. I think we'll allow this china is the odd man out. >>In a word, the future of H. P. E. Is blank >>as a service >>patrick Moorehead. Always a pleasure my friend. Great to see you. Thanks so much for coming back in the cube. >>Yeah, Thanks for having me on. I appreciate that. >>Everybody stay tuned for more great coverage from HP discover 21 this is day Volonte for the cube. The leader and enterprise tech coverage. We'll be right back. >>Mm.

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018. Brought to you by Google Cloud and it's ecosystem partners. >> Welcome back everyone. This is theCUBE live coverage of Google Cloud Next '18 in San Francisco. I'm John Furrier with Jeff Frick. We're at day three of three days of wall-to-wall coverage. Go to SiliconANGLE dot com on theCUBE dot net. Check out the on demand videos and the Cloud series special journalism report that we have out there, tons of articles, tons of coverage of Google Next with the news, analysis and opinion, of course, SiliconANGLE. Our next guest is Indranil Chakraborty, Project Manager for IoT Google Cloud. Certainly IoT part of the network part of the Cloud, one of the hottest areas in Cloud is IoT. We've been seeing that. Welcome to theCUBE. >> Thank you. >> Thanks for joining us. IoT is certainly the intersection of a lot of things: Cloud, data center, A.I., soon to be, you know, cryptocurrency and blockchain coming down, not for you guys, but in general those are the big hottest areas. >> IOT is not like, you can't say it's an IoT category, so IoT has to kind of sit in the intersection of a lot of different markets that are kind of pure playing. >> So I first want you to explain to the folks out there watching, what is the Google IoT philosophy? What is the products trying to do? And what are guys announcing here? >> Absolutely. Thanks for having me here, it's really great to be here. And if you think about IoT, and if you think about what we have on Google Cloud, we already have a great set of service for data storage, processing, and machine intelligence. Right, so we have Cloud Machine Learning Engine, we have an on start ML. So most of those data processing and intelligence services are already there. What we announced last year was Cloud IoT Core, which is our fully-managed service for our customers and partners who easily and securely connect their IoT devices to Google Cloud, so they can start transmitting data and then ingest and store in the user downstream services for analysis and machine intelligence. >> I mean, IoT is a great use case of Cloud because one, Cloud shows that you can be incented to collect data. >> Right. >> Cuz now you have the lower cost storage, You've got machine learning, all these things are going on. It's great. >> Exactly. >> But Iot is now the Edge of the network. You've got sensors. You've got cars, like Teslas, people can relate to. So everything's coming online has, not just an IP connection, anything that's a sensor. The IoT's been just evolving. What is the Edge to you guys? What does that mean when I say IoT Edge? What is Google view of the Edge? >> Yeah absolutely, it's a great question. You know, we identified early on the emergent trend of moving compute and intelligence to the edge and close to the device itself. So this week, as you already know, we've announced two products for Edge. One is Cloud IoT Edge, which is a software stack which can run on your gateway device, cameras, or any connected device that has some compute capabilities, which extends that powerful AI and machine learning capabilities of Google Cloud to your Edge device. And we also announced Edge TPU, which is a Google designed high performing chip for to run machine learning inference on the Edge device itself. And so with the combination of Cloud IoT Edge as a software stack and with our Edge TPU, we think we have an integrated machine learning solution for on Google Cloud platform. >> How does that get rolled out? So the chip, I'm assuming, you're doing OEM or deals with manufacturers. Same with the software stack. Is the software stack portable? Explain how you roll those out. >> Yeah, you know we are big into working with our ecosystem and we really want to build a robust part of ecosystem. So we are working with semiconductor companies, such as NXP and Arm, who will build a system-on-module using our Google Edge TPU, which can then be used by gateway device makers. So we have partnership with Harting, Nokia, NEXCOM. We're going to take those SOM, add it to their gateway devices, so to take it to the market. We're also working with a lot of computing companies, such as ADLINK, Acton, and a couple of others, Olya. So they can build an analytic solution using our Cloud IoT Edge software and Edge TPU to combine with the rest of Cloud IoT platform. So we're pretty excited about the partners. >> But every coin has two sides, right? So the kind of knock on the Edge is, now you're attack surface on the security side is growing exponentially. So clearly, security is an important part of what you guys do. And now this is kind of a different challenge when you're now, your point to presence is not like our point to presence, but are going to expand exponentially to all these connected autonomous devices. >> Yep, that's a great point. And you know, we take security very seriously. In fact, last year when we announced Cloud IoT Core, we reject any connection that doesn't use TLS, number one, right? And number two, we individually authenticate each and every device using an asymmetry keypad. In addition to that, we've also announced partnership with Microchip. So Microchip has built this microcontroller crypto, which can have the private key inside the crypto, and we use JWT token that was signed by inside the chip itself. So your private key never leaves the chip at all. So that's one additional reinforcement for security. So we have end to end security. We make sure that the devices are connecting over TLS, but we also have hardware root of trust on the Edge device as well. >> The token model is interesting. Talk about blockchain because you know, David Floy on our analyst team, he and I are constantly riffing on that. IoT actually is interesting use case for blockchain and potentially token economics. How do you guys view that? I know that you just mentioned that this is kind of a thing there. Does it fit in your vision at all? What's your position on how that would work out? >> You know, we are closely looking at the blockchain technology. As of today, we don't have anything specific to announce in terms of a product perspective, but we do have, we do use JSON web token, which is standard on the web, use to sign those using our private keys. So that works beautifully, but we're closely monitoring and looking at it. We don't have anything to announce today. >> Not yet, but they're going to share that. Their research is working on it, interesting scenario. So in general, benefits to customers who're working with IoT, your team, cuz you have the core, you have the chip, you have the software stack. There's always an architectural discussion depending upon the environment. Do you move the compute to the data? Do you move the data to the Cloud? What's the role of data in all this cuz certainly you got the processing power. What's the architectural framework and benefits to the customers who are working with Google. >> Yeah, so let's make a specific example, LG CNS. They want to improve their productivity in the factory, and what they've done is they've built a machine learning model to detect defects on their assembly line using Cloud machine learning engine. And they've used this one engineer a couple of weeks and they would train the model on Cloud. Now with Cloud IoT Edge and the Edge TPU, they can run that train model locally on the camera itself, so they can do realtime defect analysis at a pretty fast moving assembly line. So that's the model which we are working on where you use Cloud for high compute for training, but you use the Edge TPU and the Cloud IoT Edge for local inference for real time detection as well. >> How do you guys look at the IoT market because depending on how you're looking at it, you can look at smart cities, you can look at self-driving cars? There's a huge aperture of different use cases. It could be humans with devices, also you guys have Android, so it's kind of a broad scope. You guys got to kind of have that core tech, which it sounds like you're putting in the center of all this. How do you guys look at that? How do you guys organize around that? I think Ann Green mentioned verticals, for instance, is there different verticals? I mean, how do you guys go at that mark with the product? >> IoT is a nation market. And what we offer as Google Cloud, is a horizontal platform, what we call it is Cloud IoT platform, which has got Cloud IoT core on the Cloud side, Cloud IoT Edge, the Edge TPU. And we really want to work with our partners our solution integrators and ISVs, to help build those vertical applications. And so we're working with partners on the healthcare side, manufacturing. We have Odin Technology as one of the partner to really build this vertical up. >> You guys are not going to be dogmatic, this is how our IoT sleeve. You're going to let a thousand flowers bloom kind of philosophy. Put it out there, connect, and let the innovation happen with the ecosystem. >> Yeah, we really believe in driving, moving the, having robust ecosystem. So we want to provide a horizontal platform, which really makes it easy for partners and customers to build vertical solutions. >> Another kind of unique IoT challenge, which you didn't have in the past, we've all seen great pictures of the inside of Google Data Centers. They're beautiful and tight and lots of pretty pictures, very different than out in a minefield or a lot of these challenging IT environments where power could be a challenge. The weather could be a challenge. Connectivity to the internet could be a challenge. Obviously, and then you need to power them. When you talk about how much store do you have locally, how much compute do you have locally. So as you look at that landscape, how has that shaped your guys' views? What are some of the unique challenges that you guys have faced? And how are you overcoming some of those? >> Yeah, that's a great question and this is one of the primary reasons why we announced Cloud IoT Edge, which is software stack, and Edge TPU. So that for use cases where you have limited connectivity, oil wells or farm field, windmills. Connectivity is limited, and you cannot rely on connectivity for reliable operations. But you can use Cloud IoT Edge with our partner device ecosystem to run some of the compute locally. You can store data locally. You can analyze locally, and then push some of the incremental data to the Cloud to further update your model in the Cloud. So that's how we were thinking about this. We have to have some compute locally for those reasons. >> Release the hard coupling, if you will. So it's really got to be a dynamic coupling based on the situation, based on the timing, maybe. >> Exactly. >> Schedule updates, and these type of things. So it's not just connected. >> Exactly. It doesn't need to be continuously connected, right? As long as there's enough connectivity to download some of the updated model, to download the latest firmware and the software. You can run local compute and local machine learning inference on the Edge itself. That's the model we're looking at. So you can train in Cloud, push down the updates to the Edge device, and you can run local compute and intelligence on the device itself. >> A lot of conscious we've been having lately has been about, how do you manage the Edge, has been an area of discussion. Why I want to have a multi-threaded computer, basically, on a device that could be attacked with malware, putting bounds around certain things. You need the IP there. You want to have as much compute, obviously, we'd agree. But there's going to be policies you're starting to think about. This is where I think it gets interesting when you look at what's going on at the abstractions up the stack that you guys are doing. How does that kind of thinking impact some rollouts of IoT because I'm looking to imagine that you won't have policies. Some might trickle data back. It might not be data intensive. Some might want more security. Containers, all this kind of tying in. Is that right? Am I getting that right? How do you see that happening? >> So when you think about Edge, there are different layers. There are different tiers. There are the gateway class devices, which has high compute, and all the way to sensors. Our focus really is on the Edge devices, which has some decent compute capabilities and you can scale up to high-end devices as well. And when you think about policies, on the Cloud side, we have IM policies, so you can define roles, and you can define policies, based on which you can decide which devices should get what software or which user should get access to particular data types as well. So we have the infrastructure already, and we're leveraging that for the IoT platform. >> Yeah, and automate a lot of those kind of activities as well. >> Exactly. >> Alright, so I got to ask you about the show. What's some of the cool things you're seeing, for the folks that couldn't make it that are watching this video live and on demand. What's happening here at Google? What's the phenomenon Google Cloud? What are some of the hot stories? What's the vibe? What are the cool things that you are seeing? >> Absolutely. So I'm biased, so I'm going to start with IoT. You know, we have an IoT showcase where we have a pedestal where we're showing the Edge TPU and the Edge TPU board as well. And there is a lot of work which is happening there. There's a maintenance team there as well, so I would highly encourage attendees to go check it out. >> What are people saying about that? The demos and the sessions, what are some of the feedback? Share some color commentary around reactions. >> Yeah, we've been getting a lot of positive reactions. In fact, we just had a couple of breakout sessions, and a lot of interest from partners across the board to engage with us. So we are pretty excited with our announcement on the Edge side. The whole orchestration of training model in the Cloud and then pushing it down and then sending updates, that's where it really makes it easy for a lot of the partners. So they're excited about it as well. >> They're going to make some good money with it too. You guys are making the mark, and not trying to go too far. Laying the foundational work, the horizontal scale. >> Yes, exactly. And we really focused, for the Edge TPU, we really focused on performance per dollar and performance per watt. And so that has been what we are striving to really have high performance for lower cost. So that's what we're targeting. And a couple of other things, the whole server-less capabilities, and the fact that Cloud functions have become GA, is pretty exciting. And Cloud IoT Core is also a fully managed server-less architecture in a machine. The AI and auto ML which we announced with NLP and text and speech is pretty exciting as well. And that works very well with some of our IoT use cases as well. So I think those are a couple of announcements, which I'm pretty excited about. >> Yeah, I think the automation theme too, really resonated well on all that. Cuz what comes out of that is, humans still got to be more proficient in doing the new stuff, but also they got to run this. And you've got developers enough to build apps that drives value, so you got the value development with the applications, and then also the operational side, which is, I don't want to say becoming generic, but it's not specialized as used to be. Network operator, this guys does this, this gal does that. I mean, it used to be very stove piped. Now it's much more of a how do you run the environment? >> Exactly, and to your point, even on the IoT space, it's also very relevant. I mean there are a lot of overlaps between what used to be just devops and OTE and IT. There are a lot of overlaps there. And so we're looking at it closely as well to make sure that we can really simplify the overall requirement and the tooling which is needed for building an IoT solution. >> For the people that are not following Google as closely as say we are, for instance, they're not inside the ropes, inside the baseball, if you will, in the industry. See Google Cloud, they know Google as Gmail, search, et cetera. They look a couple years ago, Google Cloud had app engine, the OG of Google Cloud, as it's called. What would you say to the folks now that are watching? What's different about Google Cloud now, and what should they know about Google Cloud that they may not know about. What would you say to that person? >> Absolutely, and the first thing is we are very serious about enterprise. You can see here the number of attendees who have come here and how we have multiple buildings where we organized the conference. We're very serious over enterprise. Second, back in the days, two years back, we were really focused on building products, which works for specific use cases. We didn't think about end to end solution, but now the focus has changed. And we're really thinking about, we always had the technology with packaging the products, and now we're thinking about providing end to end solutions, the framework where for a business user, enterprise user, they can just take the solution, and they know it will work. Alright, so there's been a lot of focus on that. And our key differentiator is about machine intelligence and AI, right? That's where Google thrives. We've been spending a lot of time on it, and now we're focused on democratizing AI. Not just on the Cloud, but also on the Edge with the announcement of HTPU. >> And I really think you guys have done a good job with the mindset of making it consumable. In an end to end framework with the option. We've got Kubernetes, and Container's been around for a while, but it's working with multiple environments. I think that is a real mindset shift. >> Exactly. >> So congratulations. >> Thank you. >> Thanks for coming on, appreciate it. >> Absolutely, was great having you guys. >> Google IoT, just plug into the Google Cloud. It'll suck all your data in. Give you some compute at the Edge. Open it up to partners, really focusing on the ecosystem and enabling new types of functionality. It's theCUBE, bringing you the data here on day three at Google Cloud Next '18. We'll be right back with more coverage. Stay with us after this short break. (modern music)