(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 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.

>>from around the globe. It's the Cube with digital coverage of workplace next made possible by Hewlett Packard Enterprise. >>Thanks very much. Welcome back to the Cube. 3 65. Coverage of workplace next HP. I'm your host, Rebecca. Night. There was some great discussion there in the past panel, and we now are coming to you for some reaction. We have a panel of three people. Harold Senate in Miami. He is the prominent workplace futurist and influencer. Thanks so much for joining us, Harold. >>My pleasure. My pleasure. Way having me, >>we have Herbert loaning Ger. He is a digital workplace expert. And currently see Iot of University of Salzburg. Thanks so much for coming on the show. >>Thank you very much for the invitation. >>And last but not least, Chip McCullough. He is the executive director of partner Ecosystems and one hey is coming to us from Tampa, Florida. >>Thank you, Rebecca. Great to be here. >>Right. Well, I'm really looking forward to this. We're talking today about the future of work and co vid. The pandemic has certainly transformed so much about the way we live and the way we were is changed the way we communicate the way we collaborate, the way we accomplish what we want to accomplish. I want to start with you. Harold, can you give us, um, broad brush thoughts about how this pandemic has changed the future of >>work? Well, this is quite interesting because we were talking about the future of work as something that was going to come in the future. But the future waas very, very long, far away from where we are right now. Now, suddenly, we brought the future of work to our current reality covered, transformed or accelerated the digital transformation that was already happening. So digital transformation was something that we were pushing somehow or influencing a lot because it's a need because everything is common digital. All our life has transformed because of the digital implementation, off new technologies in all areas. But for companies, what was quite interesting is the fact that they were looking for or thinking about when toe implement or starting implementing nuisance in terms of technology. On suddenly the decision Waas, where now we are in this emergency emergency mode that the Covic that the pandemic created in our organizations on this prompted and push a lot of this decision that we were thinking maybe in the future to start doing to put it right now. But this gay also brought a lot of issues in terms off how we deal with customers. Because this is continuity is our priority. How we deal with employees, how we make sure that employees, customers on we and the management this in relation are all connected in the street and work together to provide our president services to our customers. >>So you're talking about Kobe is really a forcing mechanism that has has really accelerated the digital transformation that so many companies in the U. S. And also around the world. Um, we heard from the previous panel that there was this Yes. We can attitude this idea that we can make this happen, um, things that were ordinarily maybe too challenging or something that we push a little bit further down the road. Do you think that that is how pervasive is that attitude and is that yes, we can. And yes, we have Thio. >>Absolutely, absolutely. You know, here in Miami, in Florida, we are used to have the hurricanes. When we have a hurricane is something that Everybody gets an alarm mode emergency mode and everybody started running. But we think or we work on business continuity implementing the product culture policies. But at the same time we think, Okay, people before a couple of which no more than that. Now, when we have those situations we have really see, we really see this positive attitude. Everybody wants to work together. Everybody wants to push to make things happen. Everybody works in a very collaborative mode. Everybody really wants to team and bring ideas and bring the energy that is necessary so we can make it happen. So I would say that now that is something that the pandemic product to the new situation where we don't know how long this mist ake this will take maybe a couple of months more, maybe a year. Maybe more than that, we still don't know. But we really know is that digital transformation on the future of work that we were thinking was going to be on the wrong way Now is something that we're not going back with this >>chip. I want to bring you in here. We're hearing that the future of work is now and this shift toward the new normal. I want to hear you talk a little bit about what you're seeing in terms of increased agility and adaptability and flexibility. How is that playing out, particularly with regard to technology? >>Yeah, I think the the yes, we can attitude. We see that all over the place and many instances it's like heroic efforts. And we heard that from the panel, right? Literally heroic efforts happening and people are doing that. It reminds me of an example with the UK National Health System, where we rolled out 1.2 million teams, Microsoft teams users in seven days. I mean, those are the kinds of things we're seeing all over the place, and and now that yes, we can approach is kind of sinking in. And I think Harold was kind of talking about that, right? It's sinking in tow, how we're looking at technology every day. We're seeing things like, you know, the the acceleration of the move to cloud, for example, a substantial acceleration to the movement, the cloud, a substantial acceleration to be more agile, and we're just seeing that kind of in in all of our work now and and That's the focus for organizations they want to know now. How do we capture this amazing innovation that happened as a result of this event and take it forward in their organizations going forward? >>And so they're thinking about how they captured this. But Herbert, at this time of tremendous uncertainty and at a time when the economic recovery, the global economic recovery, is stop and start, how are you thinking about prioritizing? What kinds of criteria are you using and how are you evaluating what needs to happen? >>I think that's very simple, and I use my standard procedure here in the most e think it must be possible for the users and therefore, for the companies to work and be productive. That's that's, I think, the most important thing technology should be provided the best possible support here, for example, of the state off the our digital workplace. But in this uncertain times, we have some new demands At the moment. That means we have new priorities, for example, conducting teamwork ships online. Normally, we have conducted such events in special conference rooms or in a hotel for the will of the world, for example, we now have the requirement create all off our workshops and also the documentation off it we had to Allah instead of using, for example, physical pain, port to group topics and so on. So we saw here a change that larger events to We need the factions for breakout rooms and so on. And honestly, at the moment, big events in the with the world will not Still the same leg in a physical world, for example Ah, big conferences, technology conferences and so on. >>No, Absolutely. And what you're describing is this this hybrid world in which some people are going into offices and and others of us are not, And we are we're doing what we need to dio in in digital formats. I wanna ask you chip about this hybrid workplace. This appears to be this construct that we're seeing more and more in the marketplace. We heard Gen. Brent of HP talking about this in the previous panel. How do you see this playing out in the next 12 to 24 months and beyond, even in our pandemic and and post pandemic lives? And what do you see as the primary advantages and drawbacks of having this hybrid workforce. >>Well, I I think it's very interesting, right? And I think it s century. We were very lucky because we are 500,000 employees that have been fully, you know, kind of hybrid work or remote enabled, even going into the pandemic. And many other companies and organizations did not have that in place, right? The key to me is you had this protective environment will call the office right where everybody went in tow work to they had their technology there. The security was in place around that office, and everything was kind of focused on that office and all sudden, that office, it didn't disappear, but it became distributed. And the key behind we are a big user of Aruba Technologies within Accenture. And it became very important, in my view, to be able to take >>ah, >>lot of the concepts that you brought into the office and distributed it out. So we're we have offerings where we're using technologies such as Aruba's remote access points in virtual desktop technologies, right that enable us to take all the rules >>and >>capability and functionality and security that you had in that nice controlled office environment and roll it out, thio the workers wherever they may be sitting now, whether it be at home, whether it be sitting on the road someplace, um, traveling whatever. And that's really important. And I did see a couple instances with organizations where they had security incidents because of the way they rolled out that office of the future. So it's really important as we go forward that not only do we look at the enablement, but we also make sure we're securing that to our principles and standards going >>forward. >>So the principles and standards I wanna I wanna talk to you a little bit about that. Harold. There are the security elements that we that we just heard about. But there's also the culture, the workplace culture, the mission, the values of the organization when employees air not co located. When we are talking about distributed teams, how do you make sure that those values are are consistent throughout the organization and that employees do feel that they are part of something bigger, even if they're not in the cubicle next door or just in the hallway? >>That that is a great question, because here what happens now is that we still need to find a balance in the way we work. Maybe some company says we need to fool the day with busier conferences so we can see each other so we can make sure what we're doing and we're connected. But also we need to get some balance because we need to make sure that we have time to do the job. Everybody needs to do their job but also need to communicate to each other on communication, in the whole group, in a video in several video conferences in the day. Maybe it's not enough or not with effective for that communication. So we need to find the right balance because we have a lot of tools, a lot of technology that can help us on by helping us in this moment to make sure that we are sharing our values, values that common set off values that makes or defines on how organizations need to be present in every interaction that we have with our employees on. We need to also make sure that we're taking care off the needs off employees because when we see from a former employee standpoint, what is going on we need to understand the context that we're working today instead of working on at the office. We're working from home at home. Always. We have also we have our partners wife, Children also that are in the same place. We're also connected with work or with distance learning so that there is a new environment, the home environment, that from a company perspective, also needs to be taken into consideration now how we share our values well, it's a time something that we need to understand. Also, that we all always try to understand is that every crisis bring on opportunity together. So we should see. This also is an opportunity toe. Refocus our strategies on culture not to emerge stronger on to put everybody with the yes attitude with really desire to make things happen every day in this time in this same symphony. Oh, but how we do that also, it's an opportunity for delivering training. Delivery is an opportunity to make sure that we identify those skills that are needed for the future of work in the digitals, because we have a lot of digital training that is needed on those skills that are not exactly a tech, but they are needed also, from the human perspective to make sure that we are creating a strong culture that even working in a hybrid or or remote work, we can be strong enough in the market. >>So I wanna let everyone here have the last word in picking up on on that last point that this is an exceedingly complex time for everyone, Unprecedented. There's so much uncertainty. What is your best advice for leaders as they navigate their employees through this hybrid remote work environment? Um, I want to start with you, Herbert. >>From my opinion, I think communication is very important. So communicate with your team and your employees much more than in the past and toe and be clear in your statements and in your answers. I think it's very important for the team >>chip. Best advice. >>So you know, it feels like we've jumped maybe two years ahead and innovation, and I think you know, from a non organization standpoint, except that, you know, embrace it, capture it. But then also at the same time, make sure you're applying your principles of security and those pieces to it, so do it in the right way, but embrace the change that's that's happened, >>Harold. Last last. Best advice for for managers during this time >>he communication are absolutely essential. Now let's look for new way of communicating that it's not only sending emails is not only sending text messages, we need to find ways to connect to each other in this remote working environment on may be coming again. Toe pick up the phone on, Have a chat conversation with our employees are working remotely. But doing that with kind off frequently, I would say that would be very effective toe. Improve the communication on to create this environment where everybody feels part off an organization >>everyone feels part of the team. Well, thank you so much. All of you. To Harold, Herbert and Chip. I really appreciate a great conversation here. >>My pleasure. My pleasure. Very much. >>They tuned for more of the Cube 3 65 coverage of HPV workplace Next