>>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.

(upbeat instrumental music) >> Narrator: Live from San Francisco, Celebrating 10 years of high tech coverage, it's theCUBE. Covering VMworld 2019. (upbeat instrumental music) Brought to you by VMware and its ecosystem partners. Hello and welcome back everyone. theCUBE's Live coverage here in San Francisco, California. I'm John Furrier, Dave Vellante, VMworld 2019 coverage. Dave, 10 years of Cube coverage, Yip! we started out 10 years ago. VMworld is the last show standing. Our next guest is Todd Forsythe, CMO of Veritas. Great to see you, first time on theCUBE. Thanks Todd. It is inaugural. (John laughs) Aafter 25 years in the industry, it's crazy. With your talent, I think we're going to have a good segment here. I'm sure we will. Very entertaining. No, seriously we've known each other, we were on an advisory board together. You're a prolific marketer, you do a lot of great things. You're progressive, you try new things with startups, but also you got to run a big operation. >> Todd: That's right. MarTech stacks, you like to look at platforms. This is a re-platforming of the internet we're seeing with Cloud 2.0, and I want to get your thoughts on this, because you got a unique perspective at Veritas, you know, an older brand modernized in real time. That's right. New products refresh in a massively changing growth, still growth market. It's a data business. Absolutely. That's right, a 100%. So what's your take on this? As you look at the landscape, you've got the modern brand, you got to take it out there, new products. You know it's interesting, I had a really fascinating conversation yesterday with a customer, and the customer said, "You know, I was walking through the expo hall, "and I saw Amazon, I saw Microsoft, "I saw IBM, I saw Dell Technologies, "I saw Kubernetes, I saw Pure, I saw Nutanix, "I thought I was in my own data center." And it's interesting, I think about our business, and in our business, data doesn't care. Like you know data doesn't care if you're running a modern architecture. Data doesn't care if you're running Legacy. So what we're really focused on is helping companies manage data in highly complex, and extremely demanding environments regardless of their infrastructure. And Cloud 2.0 speaks to the complexity of that, because you know, these, and we were talking earlier with VMware about these categories that used to exist, these Gartner Magic Quadrants. You know, you can't put something that's not a silo in a silo, you're horizontally disrupting. And data does that, data has to move around and it's got to move everywhere. So there's no more silo boxes of categories. A 100% agree, you know it's interesting, we launched Enterprise Data Services earlier this year, and that was the precise reason why, because we've relooked at what data protection is. Data protection is no longer backing up your data from a cloud to a cloud from your on Prime, it's a much broader category. It covers how your data becomes available, how resilient you are, understanding where your data is, how it's categorized so you can respond to ransomware attacks, manage regulations around the world. So our view of data protection is a platform that is horizontal and cuts across. Well you guys, I mean the heritage of Veritas is the original data management company, right? Yeah. With no hardware agenda, and so my question for you, Todd, is what attracted you to Veritas? Softball question, so the most amazing customers any company could possibly imagine, Global 2000, the top telecommunications companies, the top banks, top stock exchanges. Secondly a product strategy that's really zoned in, back to your point about this, a platform that cuts across all of these diverse technologies and solves problems for customers that abstracts them from the complex environments that they're in so they could focus on outcomes, and Greg has done an amazing job recruiting a top notch leadership team. So it was really great product, good leaders. Okay now, follow up is you guys, you know, number one, top anyway, right and with Gartner Magic Quadrant, everybody wants a piece of your hide, (chuckles) the whole industry is coming at you. So, what's the sort of messaging strategy to keep top spot from both outward facing and also product development? Sure, sure. So we look at two types of competitors. Competitors that are offering point solutions, predominately playing in the mid-market, and when you're a large financial institution, and you have a highly complex environment, you're in a multi-cloud world, and you can't afford to have a siloed backup data. So you need to understand how your data is classified, where it's stored. So if you're responding to ransomware, and that ransomware attack is targeted to a specific server, you need to know if you have PII there, or if you have cat pictures there. If you have cat pictures there, then, (chuckles). >> John: Let 'em have it. Let 'em have it, exactly. (John laughs) So our platform cuts across protection, availability, and insights, which categorizes your data. So the data gets categorized in NetBackup, extends to the analytics platform, so you know where your data is, and you could take action on your data. The hard question of the day, instead of a softball I'll give you a hard one, you got to refresh the brand of Veritas has got a lot of pros and cons. The pros are, you know, well-known, a lot of customers, I got a customer question later, but the brand is important, because you have the new modern platform products, platform and products. Yeah, yeah. You got to get the name, Veritas has old meaning. You have a lot of older customers, you have legacy customers. How are you going to go out there and refresh? Is there any new plans there? We have a ton of plans. You know, we have the product, we have the customers. The product, the platform product is amazing. We are a quiet company, so we need to be noisier in the marketplace, and we need to insert ourselves into relevant conversations that are top of mind with CEOs and CIOs, whether it's ransomware. If you look at all of the ransomware attacks, It's a huge opportunity I read like two weeks ago in the State of Texas there were 10, 15 municipalities that were attacked. At the same time. At the same time! and we have a solution that can help customers recover from ransomware, so we have to insert ourselves in those dialogues because we have a very, very specific point of view that can help customers. Well but to John's point, right? Everybody that you compete with will say, we have a solution that, you know, helps solve ransomware. So, how do you separate from the pack? Like I said, everybody's trying to take pick you off. You know, we want a piece of that install base, right? 'Cause you got to keep the install base, and you got to keep growing, right? I'll lay down the gauntlet. I would love any competitor to showcase how they can support 500 workloads, a 150 storage targets, 60 cloud providers, at enterprise scale with a high degree of reliability. Our differentiator is we can cut across these very complex, very demanding IT environments, at scale. I want to get your thoughts on the customer journey question, because I think, you know, you mentioned the customer base, we've been following what you guys do. I mean I ran, I was in Bahrain for a regional Amazon event I was covering in the Middle East, and in the exhibit was this Veritas, and they recognized, hey, there's theCUBE guy. I was like, hey, thanks for watching. But seriously, you guys are everywhere. You got huge customers, but you probably have a lot of customers that are trying to go from here to there, VMware was talking almost specifically around, you know, you got the enterprise scale world, and they want that cloud Nirvana, and then there's that missing middle in between. So, you probably have a lot of transformational stories. What is the patterns of customer profile that you see? Some of them making the journey? Some of them are having a hard time? What's the state of the mind of the customer that you guys have? Well, we are definitely seeing a hybrid, multi-cloud world as you've heard here this week. 52% Of our customers are running in a hybrid cloud environment, and we have a core relationship with their legacy infrastructure, and our customers are asking our help to extend their data protection, and their NetBackup environment into the cloud, to backup the cloud, and across new modern workloads. So our customers are pulling us into their environments to do more. And that's cloud and hybrid basically. Cloud and hybrid. Public cloud and on premises. And our customers are also realizing that they're responsible for backing up their own data in the cloud. There's this misperception in the industry that if I move to a cloud provider, the cloud provider will manage my data, when in actuality you are still responsible for your data. You know, Amazon with security has a shared responsibility model. They say, okay, we protect the EC2, the infrastructure for S3, et cetera. You're responsible for pretty much everything else. And I think that you could draft off that message. Yeah, yeah! You guys too, a couple of years ago, had a great event call Veritas Vision where everybody came in, and then you changed that. Now you sort of go to where the customers are, and I'm wondering, how's that working out? It predated you. Yeah! Yeah, yeah, yeah, yeah, yeah! So I won't ask you why that decision was made, but you know, how's it working out? I mean, a company like yours, there was like four, 5,000 people there, it was a really good event. So, a great question, and a highly relevant question, because we're just about to launch our series. So, you know, having run large, large, large user conferences, and you look at distribution of your customers and, you know, you typically find that 80% of your customers are coming from the US. You look at our customer base, global international customers. We have a high percentage of customers that are outside of the U.S. So, our strategy is let's take our user conference, let's take our message, let's take our value proposition to the customer. So, we are kicking off next month an entire series around the world, Germany, Paris, Rome, Seoul, Bali, Singapore, Melbourne, our vision series, where it's our anti-user conference. We're taking content directly to our customers. Is this regional or are those cities based? How is that segment? City based. So it's like an Amazon Summit kind of thing you go to? Yeah, yep. Okay so as a follow-up. So, as a seasoned pro in this space, why either, or, why not do both? I mean there's a budget obviously is one thing, it's expensive to run these events, I get it, but. I would prefer to put more money to where the customer is at. The field, kind of. Yeah, into the fields, you know, one of the life lessons of being a marketer is go to where the customer is. Don't try to get the customer to come to you. Well, your head of sales will love that message, you're going well. (Todd laughs) So our strategy is to go where the customer is. Yeah, and that does help sales actually. So, while you're on that point, you're a very progressive marketer, for the folks that don't know you, I'll share with them that, you know, you like to try things, and you love start-ups, and you love to promote new things. The marketing stack, I've said on theCUBE, and we'd love to have you challenge us if you want, love to debate it, I said, the MarTech Stack just didn't pan out. I mean, it worked? No, no, it didn't, no! Did it work? Is it evolving? Is it siloed? Is the cloud changing the MarTech Stack? So again, pretty aggressive statement, but my point is, email marketing was great for that generation, still is. There's new organic flows, maybe I'm biased, but I'd love to get your thoughts. How is the marketing tech world evolving with cloud computing? So, I'm going to say something provocative. >> John: Okay, all right, here we go. I think the CRM industry has gotten B2B marketing wrong. What I mean by that is you look at most CRM capabilities in B2B and they're focused on an individual. They're focused on a lead, they're focused on nurturing an individual, but if you look at our customers and enterprise, individuals don't buy, buying groups, committees, and accounts buy. So where we're focused is looking at accounts, and understanding account company based behavior that shows buying intent and triggers, which then initiate our marketing. So it's not built around a lead, it's built around-- >> John: So account based marketing? Account based marketing, but account based insight and intelligence around, is there a project or buying opportunity? And you know our good friends at Manigo, that's what they do, which is AI driven, trigger-based marketing. And that's where I think the industry is going. And what's your thoughts on organic marketing, because one of the things that's hot, is we live this world with theCUBE, and we've been kind of pioneering this model where co-creating content together and pushing it out into these digital streams is an organic process. It's technically earned media and PR parlance, but we're seeing the evolution of the CMO-like action around storytelling, right? And so, like community based storytelling, it's an organic function, it's hard to control. You can't just buy it, it's got to be kind of nurtured or enabled. That's right. What's your view on that? Because this is an emerging trend we're seeing, VM were just reorganizing a whole storytelling integrated group of PR pros, that are acting like the marketing, in their marketing. Well you know, one of the most active, customer segments we have is our VOX Community, and if you think to your point about co-creation of content in collaboration, our VOX Community collaborates on solving problems that customers have, they call that-- >> John: Can you take a minute to explain, what is VOX Community to us? VOX is a community of our technical users, where they help each other share best practices and solve problems. >> Dave: A lot of how-to? A lot of how-to-- Not Vox Media. Not Vox Media, correct. Just need to make sure to get that out there. Forums, there's videos. Is this your community, or is it third-party? Veritas. Okay, Veritas. It's a Veritas community, yeah. And then to your other point, John, the marketing world has changed. We've quickly moved into a world where we now have an anonymous relationship with our customer, with email, with direct mail. Yeah, we're always driving to registration to capture a name, that world is long gone. A Facebook show that's been weaponized, so you know. Yeah, that's right. It's the data business, at the end of the day. The user experience is horrible, right? Everybody hates that, and so yeah, there are other ways now you can use data, you can infer. Yeah, that's right, exactly. You can read the tea leaves, and probably make a pretty high prediction, or highly accurate prediction. What is the most under reported trend that you think marketers should look at in terms of capabilities that are working out in the field for you? I would say the ability to leverage predicative analytics, call it AI or machine learning, understand what's happening at an account, and whether there's a buying trigger. I think accessing that information, learning from that information in terms of how should you initiate a selling motion, and then enabling the sales force with that intelligence, I think is a wide open territory. All right, we got a-- So a couple of other things. If I can? Yeah! Just to get it in. So you guys made a big platform enhancements a couple of years ago, and then a big eight dot, whatever it was, eight dot something, two, three, five. I think it was 8.2. Customer momentum, can you update us on that? Maybe even customer examples, and then I've got a partner question for you. Yeah, so I talked about the value of the platform, and we'll take Renault as an example. So Renault, a NetBackup customer, Renault wanted to make their virtualized SAP environment highly available, and they looked at a variety of different solutions, and they looked at some solutions that were homegrown and others, and they realized just extending the Veritas platform was faster time to market, 60% cost savings. So, there's a perfect example of a customer leveraging our platform play. And a couple partner questions. So, you know, we're here at VMworld, so your VMware partnership obviously pretty important, and then we're at Pure Accelerate next month, you guys are there, you got a big presence there, I know you got a tight partnership with them. That's right. Give us the partner update. Partner update, so we have very solid relationships with Amazon, Microsoft, VMware, Google, Nutanix, Pure, and that's where we're really doubling down in terms of technology integration, joint go-to-market. >> Dave: Great. And the community site is vox.veritas.com, I just was checking it out. Thank you for the plug. There's a church one, there's a religious one, not to be confused with Vox Media, so just want to make sure everyone got that URL. We think community is super important. Thanks for coming on theCUBE Excellent! and sharing your insights. Thank you gentlemen. Thank you. Todd Forsythe, CMO of Veritas. More live coverage of VMworld after this short break. (upbeat dance music)

>> Announcer: Live from San Juan, Puerto Rico, it's theCUBE. Covering Blockchain Unbound, brought to you by Blockchain Industries. (upbeat samba music) >> Hey, everyone. Welcome back to our exclusive coverage in Puerto Rico for Blockchain Unbound Global Conference, where everyone from around the world is coming here. And the Blockchain cryptocurrency, a decentralized application market, changing the game, the future of work, future of government, the future of the world happening. The biggest wave in the tech generation we've seen in centuries. And I'm here in Puerto Rico at the Vanderbilt Hotel. Our next guest, Anthony Delgado, the CEO of Disrupt. We're got some real innovative projects around bringing his work and his vision to Puerto Rico. Anthony, thanks for spending the time. >> Thank you for having me. >> So, talk about your project. Tell me a bit about your project. For instance, you learn how to code. What's goin' on with that? You're doing it in New Jersey, in Newark schools there. Just take me in to explain what you're working on. >> Absolutely. So, back in January, I met a gentleman. His name was David, and he's from Puerto Rico, and he's lived in Puerto Rico for the last eight years, and he runs a tourism company. And when the hurricane happened, his for-profit company transformed into a non-profit. And the same trucks that he used to do tours, he start doing humanitarian work. And I met him at an app release party for a client of mine, and he looked me in my face and says, "Anthony, I'm doing to best work of my life." And I was like, "oh my God! "I'm not doing the best work of my life!" And so, we go to a diner, and I had the worst tuna fish sandwich that I've ever had in my life, but the best conversation. And we start brainstorming about how can we transform and help the people of Puerto Rico? So, the first problem is energy. Close to 50 percent of the island still does not have energy. In the capital, in the beautiful place we are now, power has been restored, but there are many cities that are still forgotten. So, me as the tech guy, I'm like, hey, we can do solar panels. Like, there's tons of sunshine in Puerto Rico, right? So, solar energy. And then the next thing he brought to my attention was that the entire economy is actually based on tourism. So, now, with the hurricane and all those things that are in the media, not only did people lose their jobs, ah, not only did people lose their homes, but they lost their job as well. So, we start brainstorming. We're like, okay, well, let's create a coding school to teach the digital skills that are needed, to the people in Puerto Rico. So, we're goin' back and forth, and he said, "Okay, that's a great idea, "but how are these kids going to pay for this school?" So, the concept that we've come up with is to combine education with vacation, and basically encourage people who are paying to go to school in New York City and encourage them to come to Puerto Rico, experience this beautiful island, learn how to code in the a.m. and have an amazing vacation in the p.m. And that's what we're building. So, we're building the Caribbean Institute of Technology, where we combine education with a vacation. >> So, Institute of Technology. We were talking before we came on camera that you were at the Institute of Technology, a school my two brothers went to. Great engineering school, renowned for it's program. You're doing work there there as well, so you're taking your mission of what you're doing there in New Jersey and bringing it to Puerto Rico. Sounds like you were really impacted by that conversation. As you're here in Puerto Rico, what's your assessment? Good call? Are you happy, and what's on your to-do list as you're down here? So, it's beautiful. I mean, I was here two weeks ago, and now I'm back for this global currency conference. I really feel like there's an unlimited amount of opportunity here in the island. It's the strongest internet, there's huge tax incentives if you start a new business here, and it's really a blank canvas. You know, the hurricane was a horrible atrocity that happened, but now we have this blank canvas to create a vision for Puerto Rico. So, we created a foundation. It's called Vision for PR. And the question that we're asking ourselves is: What would we do if we were creating a new city in America today? What would it look like? It would have solar energy. The power lines would be below ground instead of above ground, right? You know, the economy would be based on the digital economy and not tourism, right? So, we look at countries like Bali, we look at countries like India. We look at countries where they have this huge influx of currency that's getting generated from overseas. So, we really want to be part of the driving force that has Puerto Rico being the Hong Kong of the Caribbean. >> And it really is a clean sheet of paper, because certainly the hurricane puts a real awakening to the needs here. And now that you look at the infrastructure and how it needs to be revamped, this is an opportunity to lay down some fat pipes, high-speed internet, loop Blockchain, the Blockchain.edu chain project that they've got goin' on, http://educhain.io is interesting. The young people, they want more. I mean, that's my vibe here, I'd sense. Yet the old guard, they're scared. They want to preserve their culture, yet there's this huge incentive to move beyond tourism. This is an opportunity for Puerto Rico to be sovereign nation at a level that could go significantly higher-level than they are now. So, that's all great. What do you do? I mean, it seems like Brock Pierce is laying down his vision: come here, bring your cash, bring your community, do good. How is the playbook evolving? Because that's a question people want to know How do I come to Puerto Rico, do it right, not offend the culture, enable them, come together? What's your experience with the playbook? >> Absolutely. So, you know, technology and access to the internet, it democratizes the world. You know, now you're on a level playing field. If you have four G connectivity, and you're on an island, you can compete globally and be a part of the global economy. So, really the opportunity here - [Interviewer] Are you going to start a company here? >> Yeah, so we are starting the Caribbean Institute here in Puerto Rico. And um, yes, so we had this-- >> As a separate corporation? >> Separate corporation. So, we have a non-profit that runs in New Jersey called Newark Kids Code, where we teach kids to code, and we really want to take that model and teach people to code here in Puerto Rico as well. So we started a corporation, it's the Caribbean Institute of Technology-- [Interviewer] Is it going to be a virtual school? Is it going to put up a facility? >> No, no, it's in person. It's in person, so, we have the architect right now working on the renderings. I'd love to share those with you as well. >> Well, certainly, we'll publish them on our blog. But so you're going to put an actual location here. So this is your notion of having people take a vacation and work here. >> Yeah, so that's all well and good, but, like you mentioned, how does that help the people from Puerto Rico? So, what we've created is a scholarship program. So, for every single person from the United States or overseas that comes here to take our coding school, we sponsor someone from the island. >> It's like a fellowship. >> Yes. (Interviewer laughs) >> Alright, so what else are you working on? I see Disrupt is your company. Tell us a bit about you and what you do, and what's goin' on with Disrupt. >> Absolutely! So, Disrupt is a media agency based in New York City. And we focus on creating innovative products that change the world. So, we work with clients who have innovative products that are making a big impact. One of the products that we're working on is called True Connect. It's AI for sales people. And basically it syncs with your Google calendar and it gives you recommendations on ways to connect with your clients. So, it gives you a news feed of news stories, but it's not stories that you're personally interested in, it's stories that your clients would be interested in, so you have topics of conversation. >> It's kind of like a reversed Linked In. >> Yes. (Interviewer laughs) A reversed Linked In, absolutely. >> You also do some really important projects that matter to peoples' lives. Talk about the project that you're working on for the autism kids, that's really interesting. Take a moment to explain that. >> Absolutely. So, another one of our clients is Debbie Stone. She has a non-profit called Pop Earth. And it's basically a free school for kids with autism. So, based on that she's starting a IOT company called the Popu Lace. It's an IOT device, it's about the size of a quarter, and it has GPS, 4G connectivity, and it hooks into a student's shoelaces. There's a huge problem with kids with autism, if they wander off from school, they can get hit by a car, and they don't have the communication skills to get found again. So this device puts a geofence around their school-- >> Alzheimer's, there's a zillion use cases. So, geofencing a location, like Snapchat ads they do, but this is for a good reason, safety and impact to people's lives. >> Absolutely. >> Caregivers, too, they matter. >> Yeah, caregivers, people who go mountain climbing, hiking, all of these other use cases. Primarily focusing on children during the beginning, but yes, Alzeimer's, and hikers, and tons of uses for this. >> Great stuff. Congratulations, Anthony, great to have this conversation with you, really inspired. Good luck with the Puerto Rico opportunity, the Caribbean Institute of Technologies. Will it be on the Caribbean, Bahamas? We were just there for Poly Con. Other islands, start at Puerto Rico... >> Absolutely. So, we're actually open-sourcing the floor plan for the building that we're building. So, the building that we're building has solar energy. It's a green building. And we're open-sourcing that floor plan so that anyone in the Caribbeans, South America, anywhere in the world can adopt this model. >> It's the wee work for paying it forward. >> Absolutely. >> Well done, Anthony. Anthony Delgado, CEO of Disrupt, doing amazing work here, paying it forward, contributing here with the Caribbean Institute of Technology. I'm John Ferrier, in Puerto Rico for our on-the ground coverage of Blockchain Unbound. Be back with more. Thanks for watching. >> Thank you for having me.

>>Hi. Welcome to the Cube. I'm Lisa Martin on the ground at the Computer History Museum with the Association for Corporate Go Silicon Valley. Tonight is their 13th annual grow worth, and we're very excited to be with one of their pick sponsors. Deloitte Christie. Simon's from Deloitte. Welcome. Thank you. Great to have you here. So you are a veteran and technology. You've been in the tech industry over 25 years. You've probably seen incredible transformation. Tell us about what you're doing with Deloitte and the advisory service is not you. Offer way. Offer a number of service is advisory audit tax too in Silicon Valley to a lot of these emerging growth companies. So it's been very exciting >>in my >>career to see the evolution of what I call old technology right where we kind of got the traditional software semiconductor box companies to what is now digitally what I call a new technology and what is propelling the economy in the throat that we're seeing. Not only in Bali. Exactly. So right now you are working, leading hurt and development of Deloitte's technology practice up in San Francisco. You're working with clients and you mentioned digital and clown Internet media sectors tell us about that, especially as you mentioned new technology. So a lot of them are startup companies, which is really sweet spotted, A C G. And that's why we're so involved with a G. But a lot of these new technology companies that you mentioned, you know, cloud software service, Internet media, data security, those types of companies, eyes really propelling the digital economy. So we see a lot of growth in that sector, primarily in San Francisco but also in the broader Bay Area. Silicon being checked better and as you are you mentioned out of what's going on domestically but also internationally. How do you see the influence of Silicon Valley here in Silicon Valley as well as across the globe? You know, there's a lot of factors weigh serve companies all over the globe. So primarily, Silicon Valley is propelling a lot of those. And to the extent that companies here are international, most of a lot of multinational companies and do sell their products lovely there, developed here with products are actually sold. Are you seeing kind of the inverse where companies may be headquartered in in Europe or Asia? are influencing and bringing technology over to the Silicon Valley. Next thing, let us here. Yeah, some of that, especially as we think about, uh, engineers and the aspects and some of that development that happens there, obviously sourcing that from around interest of an industry perspective in 2017. It's like every company's tech way. Look at tests around the street. Look at Walmart Labs and what they're doing there. How are you seeing some of the clients you advise for? What are some of the industries that you're seeing are now technology? There's definitely a convergence says you mentioned Too many industries, actually, all industries. So when we think about financial service is no fintech. When you think about life sciences, health, when you think about retail, right, you got Internet. So definitely saying convergence and technology is impacting our daily lives and almost everything that we d'oh and in almost every product and service that we buy, there's some form or elements of technology. Exactly. It's really remarkable. Speaking of remarkable, tonight we're here with a C G to recognize two Fantastic Cos Twilio is the emerging growth winner, 2017 and video the Outstanding Growth Award winner. If you look at and video, for example, inventor of the GPS, which is really catalyzed a tremendous amount of technology across industries. If we were just talking about you, look at their market kind of what you see them over the next couple of years. The market drivers you think they're gonna impact mentioned and video write graphic way historically have been known for games and films and virtual reality kind of thing. Now they're actually moving more into artificial intelligence. Artificial intelligence? A. I knew Buzz Word, right? So there's probably a lot of opportunities for a video that technology evolves and develops over the next. Several questions for Twilio. Who's winning the emerging world? What would you do for them? So they're, you know, cloud platform company for software developers. So you think that part of the new technology is and a cloud, so providing an opportunity for engineers to develop software and software is involved in almost everything that we do as well in our daily lives. So you know that convergence of all the industries that's happening, a lot of that is a result of software and the developers who are creating that software Twilio is providing a platform for that communicated a tremendous opportunity. Companies in this new technology. Christy, thank you so much for joining us on the Cuban. Sharing your insight. Have a great evening tonight. Yes, it's, uh, it's a great turn out Isn't a lot of fun. It is. I want to thank you for watching way around the museum with a c D E f G. I'm Lisa Martin. Thanks.