>> Announcer: You are CUBE Alumni. Live from Munich, Germany, it's theCUBE. Covering, DataWorks Summit Europe 2017. Brought to you by Hortonworks. >> Hello, everyone, welcome back to live coverage at DataWorks 2017, I'm John Furrier with my cohost, Dave Vellante. Two days of coverage here in Munich, Germany, covering Hortonworks and Yahoo, presenting Hadoop Summit, now called DataWorks 2017. Our next guest is Carlo Vaiti, who's the HPE chief technology strategist, EMEA Digital Solutions, Europe, Middle East, and Africa. Welcome to theCUBE. >> Thank you, John. >> So we were just chatting before we came on, of your historic background at IBM, Oracle, and now HPE, and now back into the saddle there. >> Don't forget Sun Microsystems. >> Sun Microsystems, sorry, Sun, yeah. I mean, great, great run. >> It was a long run. >> You've seen the computer revolution happen. I worked at HP for nine years, from '88 to '97. Again, Dave was a premier analyst during that run of client-server. We've seen the computer revolution happen. Now we're seeing the digital revolution where the iPhone is now 10 years old, Cloud is booming, data's at the center of the value proposition, so a completely new disruptive capability. >> Carlo: Sure, yes. >> So what are you doing as the CTO, chief technologist for HPE, how are you guys bringing this story together? 'Cause there's so much going on at HPE. You got the services spit, you got the software split, and HP's focusing on the new style of IT, as Meg Whitman calls it. >> So, yeah. My role in EMEA is actually all about having basically a visionary kind of strategy role for what's going to be HP in the future, in terms of IT. And one of the things that we are looking at is, is specifically to have, we split our strategy in three different aspects, so three transformation areas. The first one which we usually talk is what I call hybrid IT, right, which is basically making services around either On-Premise or on Cloud for our customer base. The second one is actually power the Intelligent Edge, so is actually looking after our collaboration and when we acquire Aruba components. And the third one, which is in the middle, and that's why I'm here at the DataWorks Summit, is actually the data-analytics aspects. And we have a couple of solution in there. One is the Enterprise great Hadoop, which is part of this. This is actually how we generalize all the figure and the strategy for HP. >> It's interesting, Dave and I were talking yesterday, being in Europe, it's obviously a different sideshow, it's smaller than the DataWorks or Hadoop Summit in North America in San Jose, but there's a ton of Internet of things, IoT or IIoT, 'cause here in Germany, obviously, a lot of industrial nations, but in Europe in general, a lot of smart cities initiatives, a lot of mobility, a ton of Internet of things opportunity, more than in the US. >> Absolutely. >> Can you comment on how you guys are tackling the IoT? Because it's an Intelligent Edge, certainly, but it's also data, it's in your wheelhouse. >> Yes, sure. So I'm actually working, it's a good question, because I'm actually working a couple of projects in Eastern Europe, where it's all about Industrial IoT Analytics, IIoTA. That's the new terminology we use. So what we do is actually, we analyze from a business perspective, what are the business pain points, in an oil and gas company for example. And we understand for example, what kind of things that they need and must have. And what I'm saying here is, one of the aspects for example, is the drilling opportunity. So how much oil you can extract from a specific rig in the middle of the North Sea, for example. This is one of the key question, because the customer want to understand, in the future, how much oil they can extract. The other one is for example, the upstream business. So doing on the retail side and having, say, when my customer is stopping in a gas station, I want go in the shop, immediately giving, I dunno, my daughter, a kind of campaign for the Barbie, because they like the Barbie. So IoT, Industrial IoT help us in actually making a much better customer experience, and that's the case of the upstream business, but is also helping us in actually much faster business outcomes. And that's what the customer wants, right? 'Cause, and was talking with your colleague before, I'm talking to the business guy. I'm not talking to the IT anymore in these kind of place, and that's how IoT allow us a chance to change the conversation at the industry level. >> These are first-time conversations too. You're getting at the kinds of business conversations that weren't possible five years ago. >> Carlo: Yes, sure. >> I mean and 10 years ago, they would have seemed fantasy. Now they're reality. >> The role of analytics in my opinion, is becoming extremely key, and I said this morning, for me my best center is that the detail, is the stone foundation of the digital economy. I continue to repeat this terminology, because it's actually where everything is starting from. So what I mean is, let's take a look at the analytic aspect. So if I'm able to analyze the data close to the shop floor, okay, close to the shop manufacturing floor, if I'm able to analyze my data on the rig, in the oil and gas industry, if I'm able to analyze doing preprocessing analytics, with Kafka, Druid, these kind of open-source software, where close to the Intelligent Edge, then my customers going to be happy, because I give them very fast response, and the decision-maker can get to decision in a faster time. Today, it takes a long time to take these type of decision. So that's why we want to move into the power Intelligent Edge. >> So you're saying, data's foundational, but if you get to the Intelligent Edge, it's dynamic. So you have a dynamic reactive, realtime time series, or presences of data, but you need the foundational pre-data. >> Perfect. >> Is that kind of what you're getting at? >> Yes, that's the first step. Preprocessing analytics is what we do. In the next generation of, we think is going to be Industrial IoT Analytics, we're going to actually put massive amount of compute close to the shop manufacturing floor. We call internally or actually externally, convergent planned infrastructure. And that's the key point, right? >> John: Convergent plan? >> Convergent planned infrastructure, CPI. If you look at in Google, you will find. It's a solution we bring in the market a few months ago. We announce it in December last year. >> Yeah, Antonio's smart. He also had a converged systems as well. One of the first ones. >> Yeah, so that's converge compute at the edge basically. >> Correct, converge compute-- >> Very powerful. >> Very powerful, and we run analytics on the edge. That's the key point. >> Which we love, because that means you don't have to send everything back to the Cloud because it's too expensive, it's going to take too long, it's not going to work. >> Carlo: The bandwidth on the network is much less. >> There's no way that's going to be successful, unless you go to the edge and-- >> It takes time. >> With a cost. >> Now the other thing is, of course, you've got the Aruba asset, to be able to, I always say, joke, connect the windmill. But, Carlo, can we go back to the IoTA example? >> Carlo: Correct, yeah. >> I want to help, help our audience understand, sort of, the new HP, post these spin merges. So perviously you would say, okay, we have Vertica. You still have partnership, or you still own Vertica, but after September 1st-- >> Absolutely, absolutely. It's part of the columnar side-- >> Right, yes, absolutely, but, so. But the new strategy is to be more of a platform for a variety of technology. So how for instance would you solve, or did you solve, that problem that you described? What did you actually deliver? >> So again, as I said, we're, especially in the Industrial IoT, we are an ecosystem, okay? So we're one element of the ecosystem solution. For the oil and gas specifically, we're working with other system integrator. We're working with oil and the industry gas expertise, like DXC company, right, the company that we just split a few days ago, and we're working with them. They're providing the industry expertise. We are a infrastructure provided around that, and the services around that for the infrastructure element. But for the industry expertise, we try to have a kind of little bit of knowledge, to start the conversation with the customer. But again, my role in the strategy is actually to be a ecosystem digital integrator. That's the new terminology we like to bring in the market, because we really believe that's the way HP role is going to be. And the relevance of HP is totally depending if we are going to be successful in these type of things. >> Okay, now a couple other things you talked about in your keynote. I'm just going to list them, and then we can go wherever we want. There was Data Link 3.0, Storage Disaggregation, which is kind of interesting, 'cause it's been a problem. Hadoop as a service, Realtime Everywhere, and then Analytics at the Edge, which we kind of just talked about. Let's pick one. Let's start with Data Link 3.0. What is that? John doesn't like the term data link. He likes data ocean. >> I like data ocean. >> Is Data Link 3.0 becoming an ocean? >> It's becoming an ocean. So, Data Link 3.0 for us is actually following what is going to be the future for HDFS 3.0. So we have three elements. The erasure coding feature, which is coming on HDFS. The second element is around having HDFS data tier, multi-data tier. So we're going to have faster SSD drives. We're going to have big memory nodes. We're going to have GPU nodes. And the reason why I say disaggregation is because some of the workload will be only compute, and some of the workload will be only storage, okay? So we're going to bring, and the customer require this, because it's getting more data, and they need to have for example, YARN application running on compute nodes, and the same level, they want to have storage compute block, sorry, storage components, running on the storage model, like HBase for example, like HDFS 3.0 with the multi-tier option. So that's why the data disaggregation, or disaggregation between compute and storage, is the key point. We call this asymmetric, right? Hadoop is becoming asymmetric. That's what it mean. >> And the problem you're solving there, is when I add a node to a cluster, I don't have to add compute and storage together, I can disaggregate and choose whatever I need, >> Everyone that we did. >> based on the workload. >> They are all multitenancy kind of workload, and they are independent and they scale out. Of course, it's much more complex, but we have actually proved that this is the way to go, because that's what the customer is demanding. >> So, 3.0 is actually functional. It's erasure coding, you said. There's a data tier. You've got different memory levels. >> And I forgot to mention, the containerization of the application. Having dockerized the application for example. Using mesosphere for example, right? So having the containerization of the application is what all of that means, because what we do in Hadoop, we actually build the different clusters, they need to talk to each other, and change data in a faster way. And a solution like, a product like SQL Manager, from Hortonworks, is actually helping us to get this connection between the cluster faster and faster. And that's what the customer wants. >> And then Hadoop as a service, is that an on-premise solution, is that a hybrid solution, is it a Cloud solution, all three? >> I can offer all of them. Hadoop is a service could be run on-premise, could be run on a public Cloud, could be run on Azure, or could be mix of them, partially on-premise, and partially on public. >> And what are you seeing with regard to customer adoption of Cloud, and specifically around Hadoop and big data? >> I think the way I see that option is all the customer want to start very small. The maturity is actually better from a technology standpoint. If you're asking me the same question maybe a year ago, I would say, it's difficult. Now I think they've got the point. Every large customer, they want to build this big data ocean, note the delay, ocean, whatever you want to call it. >> John: Love that. (laughs) >> All right. They want to build this data ocean, and the point I want to make is, they want to start small, but they want to think very high. Very big, right, from their perspective. And the way they approach us is, we have a kind of methodology. We establish the maturity assessment. We do a kind of capability maturity assessment, where we find that if the customer is actually a pioneer, or is actually a very traditional one, so it's very slow-going. Once we determine where is the stage of the customer is, we propose some specific proof of concept. And in three months usually, we're putting this in place. >> You also talked about realtime everywhere. We in our research, we talk about the, historically, you had batchy of interactive, and now you have what we call continuous, or realtime streaming workloads. How prevalent is that? Where do you see it going in the future? >> So I think is another train for the future, as I mentioned this morning in my presentation. So and Spark is actually doing the open-source memory engine process, is actually the core of this stuff. We see 60 to 70 time faster analytics, compared to not to use Spark. So many customer implemented Spark because of this. The requirement are that the customer needs an immediate response time, okay, for a specific decision-making that they have to do, in order to improve their business, in order to improve their life. But this require a different architecture. >> I have a question, 'cause you, you've lived in the United States, you're obviously global, and spent a lot of time in Europe as well, and a lot of times, people want to discuss the differences between, let's make it specific here, the European continent and North America, and from a sophistication standpoint, same, we can agree on that, but there are still differences. Maybe, more greater privacy concerns. The whole thing with the Cloud and the NSA in the United States, created some concerns. What do you see as the differences today between North America and Europe? >> From my perspective, I think we are much more for example take IoT, Industrial IoT. I think in Europe we are much more advanced. I think in the manufacturing and the automotive space, the connected car kind of things, autonomous driving, this is something that we know already how to manage, how to do it. I mean, Tesla in the US is a good example that what I'm saying is not true, but if I look at for example, large German manufacturing car, they always implemented these type of things already today. >> Dave: For years, yeah. >> That's the difference, right? I think the second step is about the faster analytic approach. So what I mentioned before. The Power the Intelligent Edge, in my opinion at the moment, is much more advanced in the US compared to Europe. But I think Europe is starting to run back, and going on the same route. Because we believe that putting compute capacity on the edge is what actually the customer wants. But that's the two big differences I see. >> The other two big external factors that we like to look at, are Brexit and Trump. So (laughs) how 'about Brexit? Now that it's starting to sort of actually become, begin the process, how should we think about it? Is it overblown? It is critical? What's your take? >> Well, I think it's too early to say. UK just split a few days ago, right, officially. It's going to take another 18 months before it's going to be completed. From a commercial standpoint, we don't see any difference so far. We're actually working the same way. For me it's too early to say if there's going to be any implication on that. >> And we don't know about Trump. We don't have to talk about it, but the, but I saw some data recently that's, European sentiment, business sentiment is trending stronger than the US, which is different than it's been for the last many years. What do you see in terms of just sentiment, business conditions in Europe? Do you see a pick up? >> It's getting better, it is getting better. I mean, if I look at the major countries, the P&L is going positive, 1.5%. So I think from that perspective, we are getting better. Of course we are still suffering from the Chinese, and Japanese market sometimes. Especially in some of the big large deals. The inclusion of the Japanese market, I feel it, and the Chinese market, I feel that. But I think the economy is going to be okay, so it's going to be good. >> Carlo, I want to thank you for coming on and sharing your insight, final question for you. You're new to HPE, okay. We have a lot of history, obviously I was, spent a long part of my career there, early in my career. Dave and I have covered the transformation of HP for many, many years, with theCUBE certainly. What attracted you to HP and what would you say is going on at HP from your standpoint, that people should know about? >> So I think the number one thing is that for us the word is going to be hybrid. It means that some of the services that you can implement, either on-premise or on Cloud, could be done very well by the new Pointnext organization. I'm not part of Pointnext. I'm in the EG, Enterprise Group division. But I am fan for Pointnext because I believe this is the future of our company, is on the services side, that's where it's going. >> I would just point out, Dave and I, our commentary on the spin merge has been, create these highly cohesive entities, very focused. Antonio now running EG, big fans, of where it's actually an efficient business model. >> Carlo: Absolutely. >> And Chris Hsu is running the Micro Focus, CUBE Alumni. >> Carlo: It's a very efficient model, yes. >> Well, congratulations and thanks for coming on and sharing your insights here in Europe. And certainly it is an IoT world, IIoT. I love the analytics story, foundational services. It's going to be great, open source powering it, and this is theCUBE, opening up our content, and sharing that with you. I'm John Furrier, Dave Vellante. Stay with us for more great coverage, here from Munich after the short break.

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at Twitter's world headquarters at the Data Privacy Day, a full day event of sessions and breakout sessions really talking about privacy. Although privacy is dead in 1999 get over it, not really true and certainly a lot of people here beg to differ. We're excited to have our next guest Jules Polonetsky, excuse me, CEO of Future of Privacy Forum. Welcome. >> Thank you, great to be here. Exciting times for data, exciting times for privacy. >> Yeah, no shortage of opportunity, that's for sure. The job security and the privacy space is pretty high I'm gathering after a few of these interviews. >> There's a researcher coming up with some new way we can use data that is both exciting, curing diseases, studying genes, but also sometimes orwellian. Microphones are in my home, self-driving cars, and so, getting that right is hard. We don't have clear consensus over whether we want the government keeping us safe by being able to catch every criminal, or not getting into our stuff because we don't trust them >> Right. [Jules] - So challenging times. [Jeff] - So, before we jump into it, Future Privacy Forum, kind of a little bit about the organization, kind of your mission... [Jules] - We're eight years old at the Future Privacy Forum, we're a think tank in Washington, D.C. Many of our members are the chief privacy officers of companies around the world, so about 130 companies, ranging from many of the big tech companies. And as new sectors start becoming tech and data, they join us. So, the auto industries dealing with self-driving cars, connected cars, all those issues. Wearables, student data, so about 130 of those companies. But then the other half of our group are advocates and academics who are a little bit skeptical or worried. They want to engage, but they are worried about an Orwellian future. So we bring those folks together and we say, 'Listen, how can we have data that will make cars safer? How can we have wearables that'll help improve fitness? But also have reasonable, responsible rules in place so that, we don't end up with discrimination, or data breaches, and all the problems that can come along?' [Jeff] - Right, cause it's really two sides of the same coin and it's always two sides of the same coin. And typically on new technology, we kind of race ahead on the positive, cause everybody's really excited. And lag on kind of what the negative impacts are and/or the creation of rules and regulations about because this new technology, very hard to keep up. [Jules] - You know the stakes are high. Think about AdTech, right? We've got tons of adtech. It's fueling free content, but we've got problems of adware, and spyware, and fake news, and people being nervous about cookies and tracking. And every year, it seems to get more stressful and more complicated. We can't have that when it comes to microphones in my home. I don't want to be nervous that if I go into the bedroom, suddenly that's shared across the adtech ecosystem. Right? I don't know that we want how much we sweat or when it's somebody's time of the month, or other data like that being out there and available to data brokers. But, we did a study recently of some of the wearables, the more sensitive ones. Sleep trackers, apps that people use to track their periods, many of them, didn't even have a privacy policy, to say 'I don't do this, or I don't do that with your data.' So, stakes are high. This isn't just about, you know, are ads tracking me? And do I find that intrusive? This is about if I'm driving my car, and it's helping me navigate better and it's giving me directions, and it's making sure I don't shift out of my lane, or it's self-parking, that that data doesn't automatically go to all sorts of places where it might be used to deny me benefits, or discriminate, or raise my insurance rates. [Jeff]: Right, right. Well, there's so many angles on this. One is, you know, since I got an Alexa Dot for Christmas, for the family, to try it out and you know, it's interesting to think that she's listening all the time. [Jules] - So she's not >> And you push the little >> Let's talk about this >> button, you know. >> Or is she not? >> This is a great topic to [Jules] -talk about because a sheriff recently, wanted to investigate a crime and realized that they had an Amazon Echo in the home. And said, 'Well maybe, Amazon will have data about what happened >> Right >> Maybe they'll be clues, people shouting,' you know. And Amazon's fighting because they don't want to hand it over. But what Amazon did, and what Google Home did, and the X-Box did, they don't want to have that data. And so they've designed these things, I think, with actually a lot of care. So... the Echo, is listening for it's name. It's listening for Alexa... >> Right. And it keeps deleting. It listens, right it hears background noise, and if it didn't hear Alexa, drops it, drops it, drops it. Nothing is said out of your home. When you say 'Alexa, what's the weather?' Blue light glows, opens up the connection to Amazon, and now it's just like you're typing in a search or going directly >> Right, right. [Jules] - And so that's done quiet carefully. Google Home works like that, Siri works like that, so I think the big tech companies, despite a lot of pain and suffering over the years of being criticized, and with the realization that government goes to them for data. They don't want that. They don't want to be fighting the government and people being nervous that the IRS is going to try find out information about what you're doing, which bedroom you're in, and what time you came home. >> Although the Fit Bit has all that information. >> Exactly >> Even though Alexa doesn't. [Jules] - So the wearables are another exciting, interesting challenge. We had a project that was funded by both Robert Johnson Foundation, which wants Wearables to be used for health and so forth. But also from a lot of major tech companies. Because everybody was aware that we needed some sort of rules in place. So if Fit Bit, or Jaw Bone, or one of the other Wearables can detect that maybe I'm coming down with Parkinson's or I'm about to fall, or other data, what's their responsibility to do something with that? On one hand, that would be a bit frightening. Right, you got a phone call or an email saying 'Hey, this is your friendly friends at your Wearable and we think >> showing up at your front door >> You should seek medical, you know, help. You would be like, whoa, wait a second, right? On the other hand, what do you do with the fact that maybe we can help you? Take student data, alright. Adtech is very exciting, there's such opportunities for personalized learning, colleges are getting in on the act. They're trying to do big data analytics to understand how to make sure you graduate. Well, what happens when a guidance counselor sits down and says, 'Look, based on the data we have, your grades, your family situation, whether you've been to the gym, your cafeteria usage, data we took off your social media profile, you're really never going to make it in physics. I mean, the data says, people with your particular attributes... Never, never... Rarely succeed in four years at graduating with a degree. You need to change your scholarship. You need to change your career path. Or, you can do what you want, but we're not going give you that scholarship. Or simply, we advise you.' Now, what did we just tell Einstein? Maybe not to take Physics, right. But on the other hand, don't I have some responsibility, if I'm a guidance counselor, who would be looking at your records today, and sort of shuffling some papers and saying, 'Well, maybe you want to consider something else?' So, either we talk about this as privacy, but increasingly, many of my members, again who are chief privacy officers if these companies, are facing what are really ethical issues. And there may be risks, there may be benefits, and they need to help decide, or help their companies decide, when does the benefit outweigh the risk? Consider self-driving cars, right? When does the self-driving car say 'I'm going to put this car in the ditch Because I don't want to run somebody over?' But now it knows that your kids are in the backseat, what sort of calculations do we want this machine making? Do we know the answers ourselves? If the microphone in my home hears child abuse, if 'Hello Barbie' hears a child screaming, or, 'Hey, I swallowed poison,' or 'My dad touched me inappropriately,' what should it do? Do we want dolls ratting out parents? And the police showing up saying, 'Barbie says your child's being abused.' I mean, my gosh, I can see times when my kids thought I was a big Grinch and if the doll was reporting 'Hey dad is being mean to me,' you know, who knows. So, these are challenges that we're going to have to figure out, collectively, with, stakeholders, advocates, civil libertarians, and companies. And if we can chart a path forward that let's us use these new technologies in ways that advances society, I think we'll succeed. If we don't think about it, we'll wake up and we'll learn that we've really constrained ourselves and narrowed our lives in ways that we may not be very happy with. [Jeff] - Fascinating topic. And like on the child abuse thing, you know there are very strict rules for people that are involved in occupations that are dealing with children. Whether it's a doctor, or whether it's a teacher, or even a school administrator, that if they have some evidence of say child abuse, they're obligated >> they're obligated. [Jeff] - Not only are they obligated morally, but they're obligated professionally, and legally, right, to report that in. I mean, do you see those laws will just get translated onto the machine? Clearly, God, you could even argue that the machine probably has got better data and evidence, based on time, and frequency, than the teacher has happening to see, maybe a bruise or a kid acting a little bit different on the school yard. [Jules] - You can see a number of areas where law is going to have to rethink how it fits. Today, I get into an accident, we want to know who's fault is it. What happens when my self-driving car gets into an accident? Right? I didn't do it, the car did it. So, do the manufacturers take responsibility? If I have automated systems in my home, robots and so forth, again, am I responsible for what goes wrong? Or, do these things have, or their companies have some sort of responsibility? So, thinking these things through, is where I think we are first. I don't think we're ready for legal changes. I think what we're ready for is an attitude change. And I think that's happened. When I was the chief privacy officer, at AOL, many years ago, we were so proud of our cooperation with the government. If somebody was kidnapped, we were going to help. If somebody was involved in a terrorism thing, we were going to help. And companies, I think, still recognize their responsibility to cooperate with, you know, criminal activity. But they also recognize that it is their responsibility to push back when government says, 'Give me data about that person.' 'Well, do you have a warrant? Do you have a basis? Can we tell them so they can object? Right? Is it encrypted? Well, sorry, we can't risk all of our users by cracking encryption for you because you're following up on one particular crime.' So, there's been a big sea change in understanding that if you're a company, and there's data you don't want to have to hand over, data about immigrants today, lots of companies, in the Valley, and around the country, are thinking, 'Wait a second, could I be forced to hand over some data that could lead to someone being deported? Or tortured? Or who knows what?' Given that these things seem to be back on the table. And, you know again, years ago, you were a good asterisk, you participated in law enforcement and now people participate, but they also recognize that they have a strong obligation to either not have the data, like Amazon, will not have data that this sheriff wants. Now, their Smart Meter and how much water they're using, and all kinds of other information, frankly about their activity at home, since many other things about our homes is now smarter, may indeed be available. How much water did you use at this particular time? Maybe you were washing blood stains away. That sort of information is >> Wild [Jules] - going to be out there. So, the machines will be providing clues that in some cases are going to incriminate us. And companies that don't want to be in the middle, need to think about designing, for privacy, so as to avoid, creating a world where, you know, whole data is available to be used against us. [Jeff] - Right and then there's the whole factor of the devices are in place, not necessarily the company is using it or not, but, you know, bad actors taking advantage of cameras, microphones, all over and hacking into these devices to do things. And, it's one thing take a look at me while I'm on my PC, it's another thing to take control of my car. Right? And this is where, you know, there's some really interesting challenges ahead. As IT continues to grow. Everything becomes connected. The security people always like to say, you know, the certainty attack area, it grows exponentially. [Jules] - Yeah. Well cars are going to be an exciting opportunity. We have released, today, a guide that the National Auto Dealers Association is providing to auto dealers around the country. Because, when you buy a car today, and you sell it or you lend it, there's information about you in that vehicle. Your location history, maybe your contacts, your music history, and we never would give our phone away without clearing it, or you wouldn't give your computer away, but you don't think about your car as a computer, and so, this has all kinds of advice to people. Listen, your car is a computer. There's things you want to do, to take advantage of, >> Right. [Jules]- New services, safety. But there are things you want to also do to manage your privacy, delete. Make sure you're not sharing your information in a way you don't want it. [Jeff] - Jules, we could go on all day, but I think I've got to let you go to get back to the sessions. So, thanks for taking a few minutes out of your busy day. [Jules] - Really good to be with you. [Jeff] - Absolutely. Jeff Frack, you're watching The Cube. See you next time. (closing music)