(upbeat music) >> Welcome back to the program, and we're going to dig into the number one topic on the minds of every technology organization, that's cybersecurity. You know, survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me to discuss this critical topic area, are Jim Shook, who's the Global Director of Cybersecurity and Compliance Practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on cloud and infrastructure consulting at DXC Technology. Gents, welcome, good to have you. >> Thanks Dave, great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart, they adapt to everything that we do. So we're seeing more and more, kind of living off the land. They're not necessarily deploying malware, makes it harder to find what they're doing. And I think though, Dave, we've adapted and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's, let's protect as much as possible so we keep the bad actors out as much as we can, but then let's have the ability to adapt to, and recover to the extent that the bad actors are successful. So we're recognizing that we can't be perfect a hundred percent of the time against a hundred percent of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So Andrew, you know, I like what Jim was saying, about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. But, and as Jim was saying, you can't be perfect. But, so given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown jewel data looks like, what a good copy of a recoverable asset looks like when you look at an attack if it were to occur, right? How you get that copy of data back into production, and not only that but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe seem to be data or an active directory or DNS dump, right? Understanding what your data actually entails that you can protect it, and that you can build out your recovery plan for it. >> So, and, where's that live? Where's that gold copy? You put in a yellow sticky? No, it's got to be, you got to be somewhere safe, right? So you have to think about that chain as well, right? >> Absolutely, yeah. So, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy but actually identifying what that golden crown jewel data, let's call it, actually entails as one aspect of it and then where to put it, how to protect it, how to make it immutable and isolated? That's the other portion of it. >> You know, if I go back to sort of earlier part of last decade, you know, cybersecurity was kind of a checkoff item. And then as you got toward the middle part of the decade and I'd say clearly by 2016 it, security became a boardroom issue. It was on the agenda, you know, every quarter at the board meetings. So, compliance is no longer the driver, is my point. The driver is business risk, real loss of reputation or data, you know, or money, et cetera. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean the, you know, bad actors are good at what they do. These losses by organizations, tens, hundreds of millions into the billions sometimes, plus the reputational damage that's difficult to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly, if they're larger organizations, but that's also on the table too. So you can't just rely on, oh, we need to do, you know, A, B, and C because our regulators require it. You need to look at what the actual risk is to the business and then come up with the strategy from there. >> You know, Jim, staying with you, one of the most common targets we hear of attackers is to go after the backup corpus. So how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for a resilience capability, but that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities and they've adapted to that. In every sophisticated attack that we see the backup is a target, the bad actors want to take it out or corrupt it or do something else to that backup so that it's not available to you. That's not to say they're always successful and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So, the capabilities that we talk about with resilience, let's harden that backup infrastructure. You've already got it in place, let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then, as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure which commonly we might say a cyber vault, although there's a lot of different capabilities for cyber vaulting, some far better than others, and that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this is, prior to the pandemic, they, you know, had their, as you're pointing out, Jim, they had their DR strategy in place but they felt like they weren't business resilient and they realized that when we had the forced march to digital. So, Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So, I'm glad you brought up resiliency. We take a position that to be cyber resilient it includes operational resiliency, it includes understanding at the C-level what the implication of an attack means, as we stated, and then how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown jewel data but also how to recover it in real-time. So DXC offers a, I don't want to call it a turnkey solution, since we architect these specific each client needs, right? When we look at what client data entails, their recovery point objectives, recovery time objectives, what we call quality of the restoration. But when we architect these out we look at not only how to protect the data but how to alert and monitor for attacks in real-time. How to understand what we should do when a breaches in progress. Putting together with our security operations centers a forensic and recovery plan and a runbook for the client. And then being able to cleanse and remediate so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect and recover, and keep bad actors out. And if we can't keep 'em out, to ensure that we are back into production in short order. >> You know, this discussion we've been having about DR kind of versus resilience, and you were just talking about RPO and RTO, I mean, it used to be that a lot of firms wouldn't even test their recovery 'cause it was too risky or, you know, maybe they tested it on, you know, July 4th or something like that. But I'm inferring that's changed. I wonder if we could, you know, double click on recovery, how hard is it to test that recovery and how quickly are you seeing organizations recover from attacks? >> So it depends, right, on the industry vertical, what kind of data, again. Financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours. In some instances we have the grace period of a day to a couple days. We do offer the ability to run scenarios once a quarter where we can stand up in our systems the production data that we are protecting to ensure that we have a good recoverable copy, but it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about. It's not on the technology of how the data gets protected it's focused on the recovery, that's all that we want to do. And so the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is you have to think about different scenarios. So, there are scenarios where the attack might be small, it might be limited to a database or an application. It might be really broadly-based, like the NotPetya attacks from a few years ago. The regulatory environment, we call those attacks severe but plausible. So you can't necessarily test everything with the infrastructure but you can test some things with the infrastructure. Others, you might sit around on a tabletop exercise or walk through what that looks like to really get that recovery kind of muscle memory so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down what are those critical applications? What do we need? What's most important? What has to come back first? And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense, understanding the value of that data is going to inform you how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCUBE, especially lately is around, you know, IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, you know, traditionally operations technologies have been air-gapped often by design. But as businesses, increasingly they're driving initiatives like Industry 4.0 and they're connecting these OT systems to IT systems. They're, you know, driving efficiency, preventative maintenance, et cetera. So, a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure and how should customers think about addressing these issues? >> Yeah, so bad actors can come in many forms. We've seen instances of social engineering, we've seen, USB stick dropped in a warehouse. That data that is flowing through the IOT devices is as sensitive now as your core mainframe infrastructure data. So, when you look at it from a protection standpoint, conceptually it's not dissimilar from what we've been talking about, where you want to understand, again, what the most critical data is. Looking at IOT data and applications is no different than your core systems now, right? Depending on what your business is, right? So when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front-end protection but we're looking at it from a resiliency perspective. Putting that data, understanding what data entails to put in the vault from an IOT perspective is just as critical as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the key points there, everything is interconnected. So even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them or controlling theM, SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly but there was uncertainty around that and the IT systems hadn't been secured so that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked but the outcome was the same that the business couldn't operate. So, you really have to take all of those into account. And I think that does go back to exactly what Andrew's saying, understanding your critical business services and then the applications and data, and other components that support those and drive those and making sure those are protected, you understand them, you have the ability to recover them if necessary. >> So guys, I mean you made the point, I mean, you're right, the adversary is highly capable, they're motivated 'cause the ROI is so, it's so lucrative. It's like this never ending battle that cybersecurity pros, you know, go through. It really is kind of frontline, sort of technical heroes, if you will. And so, but sometimes it just feels daunting. Why are you optimistic about the future of cyber from the good guys' perspective? >> I think we're coming at the problem the right way, Dave, so that focus. I'm so pleased with the idea that we are planning that the systems aren't going to be a hundred percent capable every single time and let's figure that out, right? That's real world stuff. So, just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time. And of course, you've raised the question of some nation states and other activities but there's a lot more information sharing, there's a lot more focus from the business side of the house and not just the IT side of the house that we need to figure these things out. >> Yeah, to add to that, I think furthering education for the client base is important. You brought up a point earlier, it used to be a boardroom conversation due to compliance reasons. Now as we have been in the market for a while we continue to mature the offerings, it's further education for not only the business itself but for the IT systems and how they interconnect, and working together so that these systems can be protected and continue to be evolved and continue to be protected through multiple frameworks as opposed to seeing it as another check the box item that the board has to adhere to. >> All right guys, we got to go. Thank you so much. Great conversation on a really important topic. Keep up the good work, appreciate it. >> Thanks Dan. >> Thank you. >> All right, thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real world problems, how this partnership has evolved over time. Really meeting the changing enterprise landscape challenges. Keep it right there. (upbeat music)

>> If you really want to make an impact to your business, it takes more than just moving your workloads into the cloud. So-called lift and shift is fine to reduce data center footprints and associated costs, but to really drive change, you don't want to simply "pave the cow path," as the saying goes. Rather, you need to think about the operating model, and that requires more comprehensive systems thinking. In other words, how will changes in technology affect business productivity? Or, you know what? Even flip that. What changes in my business process could lower cost, cut elapse times, and accelerate time to market, increase user productivity, and lower operational risks? And what role can technology play in supporting these mandates through modernization, automation, machine intelligence, and business resilience? And that's what we're here to discuss today. Welcome to Driving Business Results with Cloud Transformation, made Possible by Dell and DXC. My name is Dave Vellante, and today we're going to zoom out and explore many aspects of cloud transformation that leading organizations are acting on today. Yeah, sure, we're going to look at optimizing infrastructure, but we'll also dig deeper into cloud considerations, governance, compliance, and security angles, as well as the impact of emerging opportunities around edge and Industry 4.0. Our focus will be on how to remove barriers and help you achieve business outcomes. And to do this, our program features the long-term partnership between Dell and DXC. And we bring to this program six experts in three separate sessions, who are working directly with top organizations in virtually every industry to achieve high impact results. We're going to start with a conversation about cloud, the cloud operating model, and transforming key aspects of your infrastructure. And then we'll look into governance, security, and business resilience. And in our third session, we'll discuss exciting transformations that are occurring in smart manufacturing and facilities innovations. So let's get right into it with our first session. Enjoy the program. (bright music) Hello, and welcome to what is sure to be an insightful conversation about getting business results with cloud transformation. My name is Dave Vellante, and I'm here with James Miller, Chief Technologist for Cloud and Infrastructure Services, and Jay Dowling, Americas Sales Lead for Cloud and Infrastructure Services, both with DXC Technology. Gentlemen, thanks for your time today. Welcome to theCube. >> Great. Thanks for having us. >> Thank you Dave. Appreciate it. >> So let's get right into it. You know, I've talked to a lot of practitioners who've said, "Look, if you really want to drop zeros, like a lot of zeros to the bottom line, you can't just lift and shift." You really got to think about modernizing, the application portfolio. You got to think about your business model, and really think about transforming your business, particularly the operating model. So my first question, Jim, is, What role does the cloud play in modernization? >> Well, there are really three aspects that the, the cloud plays in modernization. You mentioned multiple zeros. One is cost optimization, and that can be achieved through business operations, through environmental, social, and governance. Also being more efficient with your IT investments. But that's not the only aspect. There's also agility and innovation. And that can be achieved through automation and productivity, speed to market for new features and functions, improvements in the customer experience, and the capability to metabolize a great deal more data in your environment, which the end result is an improvement in releasing of new things to the field. And finally, there's resilience. And I'm not really talking about IT resilience, but more of business resilience, to be able, to be able to handle operational risk, improve your securities and controls, deal with some of the talent gap that's in the industry, and also protect your brand reputation. So modernization is really about balancing these three aspects, cost optimization, agility and innovation, and resilience. >> So, so thank you for that. So Jay, I got to ask you, in the current climate, everybody's, you know, concerned, and there's not great visibility on the macro. So, Jim mentioned cost optimization. That seems to be one of the top areas that customers are focused on. The two I hear a lot are consolidating redundant vendors and optimizing cloud costs. So that's, you know, top of mind today. I think everybody really, you know, understands the innovation and, and, and agility piece, at least at a high level, maybe realizing it is different. And then the business resilience piece is really interesting because, you know, prior to the pandemic people, you know, they had a DR strategy, but they realized, "Wow, my business might not be that resilient." So Jay, my question to you is, What are you hearing when you talk to customers? What's the priority today? >> Yeah, the priority is an often overused term of digital transformation. You know, people want to get ready for next generation environments, customer experience, making sure they're improving, you know, how they engage with their clients and what their branding is. And what we find is a lot of clients don't have the underlying infrastructure in place today to get to where they want to get to. So cloud becomes an important element of that. But, you know, with DXC's philosophy, not everything goes to, not everything necessarily needs to go to cloud to be cost optimized, for instance. In many cases, you can run applications, you know, in your own data center, or on-prem, or in other environments, in a hybrid environment, or multi-cloud environment, and, and still be very optimized from a cost spend standpoint and also put yourself in position for modernization and for be able to do the, bring the things to the business that the clients are, you know, that their clients are looking for, like the CMO and the CFO, et cetera. Trying to use IT as a lever to drive business and to drive, you know, business acceleration and drive profitability, frankly. So there's a lot of dependency on infrastructure, but there's a lot of elements to it. And, and we advocate for, you know, there's not a single answer to that. We like to evaluate clients' environments and work with them to get them to an optimal target operating model, you know, so that they can really deliver on what the promises are for their departments. >> So if, let's talk about some of the, the barriers to realizing value in, in a context of modernization. We talked about cost optimization, agility, and, and, and resilience. But there's a business angle, and there's a technical angle here. 'Cause we always talk about people, process, and technology. Technology, oftentimes, CIOs will tell us, "Well, that's the easy part. We'll figured that out," whether it's true or not. But I agree, people and process is sometimes the tough one. So Jay, why don't you start. What do you see as the barriers, particularly from a business standpoint? >> I think people need to let their guard down and be open to the ideas that are, that are out there in the market from, you know, the, the standards that are being built by, you know, best in class models. And, and there's many people that have gone on, you know, cloud journeys and been very successful with it. There's others that have set high expectations with their business leaders that haven't necessarily met the goals that they need to meet or maybe haven't met them as quickly as they promised. So there's a, you know, there's a change management aspect that you'd need to look at with the, you know, with the environments. There's a, you know, there's a skillset set environment that they need to be prepared for. Do they have the people, you know, to deliver with the, you know, with the tools and the skills and the, and the models that that they're putting themselves in place for in the future versus where they are now? There's just a lot of, you know, there's a lot of different elements. It's not just a, "This price is better," or, "This can operate better than one environment over the other." I think we like to try to look at things holistically and make sure that, you know, we're being, you know, as much of a consultative advocate for the client, for where they want to go, what their destiny is, and based on what we've learned with other clients. You know, and we can bring those best practices forward because we've worked, you know, across such a broad spectra of clients versus them being somewhat contained and sometimes can't see outside of their own, you know, their own challenges, if you would. So they need, they need advocacy to help, you know, bring them to the next level. And we like to translate that through, you know, technology advances, which, you know, Jim's really good at doing for us. >> Yeah, Jim, is, is it, is it a, is the big barrier a skills issue, you know, bench strength? Are there other considerations from your perspective? >> Well, we, we've identified a number of factors that inhibit success of, of customers. One is thinking it's only a technology change in moving to cloud when it's much broader than that. There are changes in governance, changes in process that need to take place. The other is evaluating the cloud providers on their current pricing structure and performance. And, and we see pricing and structure changing dramatically every few months between the various cloud providers. And you have to be flexible enough to, to determine which providers you want. And it may not be feasible to just have a single cloud provider in this world. The other thing is a big bang approach to transformation, "I want to move everything, and I want to move it all at once." That's not necessarily the best approach. A well thought out cloud journey and strategy and timing your investments are really important to get at maximizing your business return on the journey to the cloud. And finally, not engaging stakeholders early and continuously. You have to manage expectations in moving to cloud on what business factors will get affected, how you will achieve your cost savings, and, and how you will achieve the business impact over the journey and reporting out on that with very strict metrics to all of the stakeholders. >> You know, mentioned multi-cloud just then. We had, in January 17th, we had our Supercloud 2 event. And Supercloud is basically, it's really multi, what multi-cloud should have been, I, I like to say. So it's this creating a common experience across clouds. And you guys were talking about, you know, there's different governance, there's different security, there's different pricing. So, and, and one of the takeaways from this event in talking to customers and practitioners and technologists is, you can't go it alone. So I wonder if you could talk about your partnership strategy, what do partners bring to the table, and what is, what is DXC's, you know, unique value? >> I'd be happy to lead with that if you'd like. >> Great. >> I, you know, we've got a vast partner ecosystem at DXC, given the size and, and the history of the company. I could use several examples. One of the larger partners in my particular space is Dell Technology, right? They're a great, you know, partner for us across many different areas of the business. It's not just a storage and compute play anymore. They're, they're on the edge. They're, you know, they're, they've got intelligence in their networking devices now. And they've really brought, you know, a lot of value to us as a partner. And, you know, there, there's somebody, you could look at Dell technology as somebody that might, you know, have a victim, you know, effect because of all the hyperscale activity and all the cloud activity. But they've really taken an outstanding attitude with this and say, "Listen, not all things are destined for cloud, or not all things would operate better in a cloud environment." And they like to be part of those discussions to see how they can, you know, how we can bring a multi-cloud environment, you know, both private and public, you know, to clients. And let's look at the applications and the infrastructure and, and what's, you know, what's the best optimal running environment, you know, for us to be able to bring, you know, the greatest value to the business with speed, with security, with, you know. And, you know, the things that they want to keep closest to the business are often things that you want to kind of, you know, keep on your premise or keep in your own data center. So they're, they're an ideal model of somebody that's resourced us well, partners with us well in the market. And, and we continue to grow that relationship day in and day out with those guys. And we really appreciate, you know, their support of our strategy, and, and we like to also compliment their strategy and work, you know, work together hand in hand in front of our clients. >> Yeah, you know, Jim, Matt Baker, who's the head of strategic planning at Dell talks about, "It's not a zero sum game." And I think, you know, you're right, Jay. I think initially people felt like, "Oh wow, it's, it is a zero sum game." But it's clearly not, and this idea of of, whether you call it supercloud or ubercloud or multicloud, clearly Dell is headed in in that direction. And I, you know, look at some of their future projects. There's their narrative. I'm curious from a technology standpoint, Jim, what your role is. Is it to make it all work? Is it to, you know, end to end? I wonder if you could help, you know, us understand that. >> Help us figure this out, Jim, here. (group laughing) >> Glad to expand on that. One of my key roles is developing our product roadmap for DXC offerings. And we do that roadmap in conjunction with our partners where we can leverage the innovation that our partners bring to the table. And we often utilize engineering resources from our partners to help us jointly build those offerings that adapt to changes in the market and also adapt to many of our customers changing needs over time. So my primary role is to look at the market, talk to our customers, and work with our partners to develop a product roadmap for delivering DXC products and services to our clients so that they can get the return on investment on their technology journeys. >> You know, we've been working with these two firms for a while now. Even predates, you know, the, the name DXC and that, that transformation. I'm curious as to what's, how you would respond to, "What's unique?" You know, you hear a lot about partnerships. You guys got a lot of competition. Dell has a lot of competition. What's specifically unique about this combination? >> I think, go ahead, Jim. >> I would say our unique approach, we call it cloud right. And that, that approach is making the right investments, at the right time, and on the right platforms. And our partners play a, play a key role in that. So we, we encourage our customers to not necessarily have a cloud first approach, but a cloud right approach where they place the workloads in the environment that is best suited from a technology perspective, a business perspective, and even a security and governance perspective. And, and the right approach might include mainframe. It might include an on-premises infrastructure. It could include private cloud, public cloud, and SaaS components all integrated together to deliver that value. >> Yeah, Jay, please. >> If you were... >> That is a complicated situation for a lot of customers. Chime in here. (Jay chuckles) >> And now, if you were speaking specifically to Dell here, like they, they also walk the talk, right? They invest in DXC as a partnership. They put people on the ground that their only purpose in life is to help DXC succeed with Dell in, you know, arm in arm in front of clients. And it's not, you know, it's not a winner take all thing at all. It's really a true partnership. They, they, they've brought solution resources. We have an account CTO. We've got executive sponsorship. We do regular QBR meetings. We have regular executive touchpoint meetings. It's really important that you keep a high level of intimacy with the client, with the partners, you know, and, and the, and the GSI community. And I, I've been with several GSIs, and, and this is an exceptional example of true partnership and commitment to success with Dell technology. I'm really extremely impressed on, on the engagement level that we've had there and, you know, continue to show a lot of support, you know, both for them. You know, there's other OEM partners, of course, in the market. There's always going to be other technology solutions for certain clients, but this has been a particularly strong element for us in our partnership and in our go-to-market strategy. >> Well, I think too, just my observation, is a lot of it's about trust. You guys have both earned the trust, the kind of, over the, over the years taking your arrows, you know, of over decades. And, and you know, that just doesn't happen overnight. So guys, I appreciate it. Thanks for your time. It's all about getting cloud right, isn't it? >> That's right. (chuckles) (Dave chuckles) >> Thank you Dave. Appreciate it very much. >> Dave, thank you. >> Jay, Jim, great to have you on. Keep it right there for more action on theCube. Be right back. (upbeat guitar music) (keyboard clicks) Welcome back to the program. My name is Dave Vellante, and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the Global CTO of Smart Manufacturing Edge and Digital Twins at Dell Technologies. That is such a cool title. (Todd chuckles) I want to be you. And Dr. Aditi Banerjee who's the Vice President, General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thank you. >> Thanks, Dave. Great to be here. >> Nice to be here. So, Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain like what is Industry 4.0 all about, and why is it important for the manufacturing industry? >> Yeah, sure, Dave. You know, it's been around for quite a while. And it's got, it's gone by multiple different names, as you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory, but it all really means the same thing. Its really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient, implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time?" So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this not as a one-off, two-off, individual use case point of view. But instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this, not to just enable one or two use cases, but enable many, many use cases across the spectrum." I mean, there's tons of them out there. There's predictive maintenance, and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor. But it needs to be done in a little bit different way. And, and, and really, to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it, and truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future, or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right? And so really that's a key thing, and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah so, Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely. Though, I would really add that what really drives Industry 4.0 is the business transformation, what we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right? For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments, or improving the quality of products, right? So I think these are a lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, I wonder if I could stay with you. And maybe this is a bit esoteric. But when I first started researching IoT and, and, and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage in a great opportunity. Of course, then I saw on TV, somebody now they're building homes with 3D printers. Its like, blows your mind. So that's pretty disruptive, but, so, but they got to continue. The incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses, very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will, or this transformation that we're talking about. So my question is, How are your customers preparing for this new era? What are the key challenges that they're facing and the, the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right? That is where the investments are going directly into building the factories with the new technologies, with the new connectivities, right, for the machines. For example, industrial IoT, having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right? Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at, "How do I modernize these factories? How do I connect the existing machine?" And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right? So cybersecurity becomes top of mind, right? So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right? So perhaps they start with the innovation program, and then they look at the business case, and they scale it up, right? >> Todd, I'm glad Aditi brought up security. Because if you think about the operations technology, you know, folks, historically, they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to, we got to connect. We got to make it intelligent." So that's, that's got to be a big challenge as well. >> It, it, it absolutely is Dave. And, and you know, you can no longer just segment that because really, to get all of those efficiencies that we talk about, that IoT and Industrial IoT and Industry 4.0 promise, you have to get data out of the factory. But then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from a, kind of that, that hybrid, you know, "write once, run anywhere" on the factory floor down to the edge. And one of the things we're seeing, too, is to help distinguish between what is the edge, and that, and, and bridge that gap between, like Dave, you talked about IT and OT. And also help that, what Aditi talked about, is the greenfield plants versus the brownfield plants that they call it, that are the legacy ones and modernizing those. Is, it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about really two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plan. It consists of industrial hardened devices that do that connectivity. The hard stuff about, "How do I connect to this obsolete legacy protocol and what do I do with it?" And create that next generation of data that has context. And then we see another edge evolving above that, which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is, is, needs to be done because that's the only way that its going to, it's going to work because of security requirements, because of latency requirements, performance, and a lot of times cost. It's really helpful to build that multiple edge strategy because then you kind of, you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise and applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the, the digital equivalent of building the Hoover Dam. I mean, it, it, it's, (chuckles) it, it, so. Yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that, in that we're, you know, like I said at the beginning, we, this is not new. Smart factory and Industry 4.0 is not new. It's been, it's, people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch, or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here, and a gateway there, and a server there, I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a, or a PC in a closet somewhere running a critical production application. So we're seeing the enterprise have a much bigger say at the table, much louder voice at the table to say, "We've been doing this at enterprise all the time. We, we know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications, really accelerate the outcomes that are needed to really drive that smart factory, and start to bring that same capabilities down into the, on the factory floor." That way, if you do it once to make it easier to implement, you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers, yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that, think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that, what you've done in that one factory, and then set. Let's that, make that across all the factories, including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat, almost like a cookie cutter. >> Got it. Thank you. Aditi, what about the skillsets available to apply these, to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean definitely, a lot. Different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is, is important. But the, the digital skillset sets like, you know, IoT, having a skillset in different protocols for connecting the machines, right, that experience that comes with it, data and analytics, security, augmented virtual reality programming. You know, again, looking at robotics and the digital twin. So you know, it's a lot more connectivity software, data driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of skill, resources with these skillsets to, you know, accelerate their smart factory implementation, as well as consulting firms like DXC Technology and others. We, we, we recruit. We, we train our talent to, to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to Industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to, to bring these to market? >> Yeah, Dell and DXC have a very strong partnership. You know, and we work very closely together to, to create solutions, to create strategies, and how we, we are going to jointly help our clients, right? So areas that we have worked closely together is edge compute, right, how that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right? So we have several areas that we collaborate in. And our approach is that we, we want to bring solutions to our client, and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question, kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where do you, are you confident that, you know, you're going to be, deliver the best value to, to customers? >> Absolutely. Great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things. And just, and, and, and that's, that's where it ends. What Dell and DXC Technology together bring to the table is, we do the optimization, the optimization of the engineering of those previously bespoke solutions upfront, together, right? The power of our scalables, enterprise-grade, structured, you know, industry standard infrastructure, as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global, trusted, trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And, and Dell's infrastructure, and our, what, 30,000 people across the globe that are really, really good at that, at that scalable infrastructure, to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's again, not just one individual solutions, it's all of the solutions that not just drive use cases, but drive outcomes with those solutions. >> Yeah, the, you're right, the partnership has gone, I mean, I first encountered it back in, I think it was 2010, May of 2010, we had you, you guys both on theCube. I think you were talking about converged infrastructure. And I had a customer on, and it was, actually a manufacturing customer, was quite interesting. And back then it was, "How do we kind of replicate what's coming in the cloud?" And, and you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation, and love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCube. (bright music) Welcome back to the program and we're going to dig into the number one topic on the minds of every technology organization. That's cybersecurity. You know, survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me to discuss this critical topic area are Jim Shook, who's the Global Director of Cybersecurity and Compliance Practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on Cloud and Infrastructure consulting at DXC Technology. Gents, welcome. Good to have you. >> Thanks Dave. Great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface, and, and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart. They adapt to everything that we do. So we're seeing more and more kind of living off the land. They're not necessarily deploying malware. Makes it harder to find what they're doing. And I think though, Dave, we've, we've adapted, and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's, "Let's protect as much as possible, so we keep the bad actors out as much as we can. But then, let's have the ability to adapt to and recover to the extent that the bad actors are successful." So we're recognizing that we can't be perfect a hundred percent of the time against a hundred percent of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So Andrew, you know, I like what Jim was saying about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. But, and, and as Jim is saying, you, you can't be perfect. But, so given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown jewel data looks like, what a good copy of a recoverable asset looks like. When you look at an attack, if it were to occur, right, how you get that copy of data back into production. And not only that, but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe CMBD data, or an active directory, or DNS dump, right? Understanding what your data actually entails so that you can protect it and that you can build out your recovery plan for it. >> So, and where's that live? Where's that gold copy? You put on a yellow sticky? No, it's got to be, (chuckles) you got to be somewhere safe, right? So you have to think about that chain as well, right? >> Absolutely. Yeah. You, so, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy, but actually identifying what that golden crown jewel data, let's call it, actually entails is one aspect of it. And then where to put it, how to protect it, how to make it immutable and isolated, that's the other portion of it. >> You know, if I go back to sort of earlier part of last decade, you know, cybersecurity was kind of a checkoff item. And as you got toward the middle part of the decade, and I'd say clearly by 2016, it, security became a boardroom issue. It was on the agenda, you know, every quarter at the board meetings. So compliance is no longer the driver, is, is my point. The driver is business risk, real loss of reputation or data, you know, it's, or money, et cetera. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean the, you know, the bad actors are good at what they do. These losses by organizations, tens, hundreds of millions into the billions sometimes, plus the reputational damage that's difficult to, to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly on, if they're larger organizations. But that's also on the table, too. So you can't just rely on, "Oh we need to do, you know, A, B and C because our regulators require it." You need to look at what the actual risk is to the business, and then come up with a strategy from there. >> You know, Jim, staying with you, one of the most common targets we hear of attackers is to go after the backup corpus. So how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for resilience capability. But that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities, and, and they've adapted to that. In every sophisticated attack that we see, the backup is a target. The bad actors want to take it out, or corrupt it, or do something else to that backup so that it's not available to you. That's not to say they're always successful, and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So the capabilities that we talk about with resilience, let's harden that backup infrastructure. You've already got it in place. Let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure, which commonly we, we might say a cyber vault. Although, there's a lot of different capabilities for cyber vaulting, some far better than others, and that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this, is prior to the pandemic, they, you know, had their, as you're pointing out, Jim, they had their DR strategy in place, but they felt like they weren't business resilient. And they realized that when we had the forced march to digital. So Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So I'm glad you brought up resiliency. We, we take a position that to be cyber resilient, it includes operational resiliency. It includes understanding at the C level what the implication of an attack means, as we stated, and then, how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown jewel data, but also how to recover it in real time. So DXC offers a, I don't want to call it a turnkey solution since we architect these specific to each client needs, right, when we look at what client data entails, their recovery point, objectives, recovery time objectives, what we call quality of the restoration. But when we architect these out, we look at not only how to protect the data, but how to alert and monitor for attacks in real time, how to understand what we should do when a breach is in progress, putting together with our security operations centers, a forensic and recovery plan and a runbook for the client, and then being able to cleanse and remediate so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect, and recover, and keep bad actors out. And if we can't keep them out to ensure that we are back into production in short order. >> You know, this, this discussion we've been having about DR kind of versus resilience, and, and you were just talking about RPO and RTO. I mean, it used to be that a lot of firms wouldn't even test their recovery 'cause it was too risky. Or, you know, maybe they tested it on, you know, July 4th or something like that. But, but it, I'm inferring that's changed. I wonder if we could, you know, double click on recovery? How hard is it to, to, to test that recovery, and, and how quickly are you seeing organizations recover from attacks? >> So it depends, right, on the industry vertical, what kind of data. Again, a financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours. In some instances we have the grace period of a day to a couple of days. We do offer the ability to run scenarios once a quarter where we can stand up in our systems the production data that we are protecting to ensure that we have a good recoverable copy. But it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about. It's not on the technology of how the data gets protected. It's focused on the recovery. That's all that we want to do. And so the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is, you have to think about different scenarios. So there are scenarios where the attack might be small. It might be limited to a database or an application. It might be really broadly based like the NotPetya attacks from a few years ago. The regulatory environment, we call those attacks severe but plausible. So you can't necessarily test everything with the infrastructure, but you can test some things with the infrastructure. Others, you might sit around on a tabletop exercise or walk through what that looks like to really get that, that recovery kind of muscle, muscle memory so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down, "What are those critical applications? What do we need, what's most important? What has to come back first?" And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense. Understanding the value of that data is going to inform you how to, how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCube, especially lately, is around, you know, IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, you know, traditionally, operations technologies have been air gapped, often by design. But as businesses, increasingly they're driving initiatives like Industry 4.0, and they're connecting these OT systems to IT systems. They're, you know, driving efficiency, preventative maintenance, et cetera. So a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure and how should customers think about addressing these issues? >> Yeah, so bad actors, you know, can come in many forms. We've seen instances of social engineering. We've seen, you know, a USB stick dropped in a warehouse. That data that is flowing through the IoT device is as sensitive now as your core mainframe infrastructure data. So when you look at it from a protection standpoint, conceptually, it's not dissimilar from what we've been been talking about where you want to understand, again, what the most critical data is. Looking at IoT data and applications is no different than your core systems now, right? Depending on what your, your business is, right? So when, when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front end protection, but we're looking at it from a resiliency perspective. Putting that data, understanding what what data entails to put in the vault from an IoT perspective is just as critical as as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the, the key points there. Everything is interconnected. So even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them, or controlling them, SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly. But there was uncertainty around that, and the IT systems hadn't been secured. So that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked, but the outcome was the same that the business couldn't operate. So you really have to take all of those into account. And I think that does go back to exactly what Andrew's saying, understanding your critical business services, and then the applications and data and other components that support those and drive those, and making sure those are protected. You understand them, you have the ability to recover them if necessary. >> So guys, I mean, you made the point. I mean, you're right. The adversary is highly capable. They're motivated 'cause the ROI is so, it's so lucrative. It's like this never ending battle that cybersecurity pros, you know, go through. It really is kind of frontline sort of technical heroes, if you will. And so, but sometimes it just feels daunting. Why are you optimistic about the future of, of cyber from the good guy's perspective? >> I think we're coming at the problem the right way, Dave. So that, that focus, I'm so pleased with the idea that we are planning that the systems aren't going to be hundred percent capable every single time, and let's figure that out, right? That's, that's real world stuff. So just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time. And of course, you've raised the question of some nation states and other activities. But there's a lot more information sharing. There's a lot more focus from the business side of the house and not just the IT side of the house that we need to figure these things out. >> Yeah, to, to add to that, I think furthering education for the client base is important. You, you brought up a point earlier. It used to be a boardroom conversation due to compliance reasons. Now, as we have been in the market for a while, we continue to mature the offerings. It's further education for not only the business itself, but for the IT systems and how they interconnect, and working together so that these systems can be protected and continue to be evolved and continue to be protected through multiple frameworks as opposed to seeing it as another check the box item that the board has to adhere to. >> All right, guys, we got to go. Thank you so much. Great conversation on a, on a really important topic. Keep up the good work. Appreciate it. >> Thanks Dan. >> Thank you. >> All right, and thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real world problems, how this partnership has evolved over time, really meeting the changing enterprise landscape challenges. Keep it right there. (bright music) Okay, we hope you enjoyed the program and learned some things about cloud transformation and modernizing your business that will inspire you to action. Now if you want to learn more, go to the Dell DXC partner page shown here, or click on the URL in the description. Thanks for watching everybody and on behalf of our supporters, Dell and DXC, good luck. And as always, get in touch if we can be of any assistance. (bright music)

>> Welcome back to the program, and we're going to dig into the number one topic on the minds of every technology organization. That's cybersecurity. Survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me, to discuss this critical topic area, are Jim Shook, who's the global director of cybersecurity and compliance practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on cloud and infrastructure consulting at DXC Technology. Gents, welcome, good to have you. >> Thanks, Dave. Great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart, they adapt to everything that we do, so, we're seeing more and more kind of living off the land, they're not necessarily deploying malware, makes it harder to find what they're doing. And I think, though, Dave, we've adapted and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's let's protect as much as possible, so we keep the bad actors out as much as we can, but then let's have the ability to adapt to and recover to the extent that the bad actors are successful. So, we're recognizing that we can't be perfect 100% of the time against 100% of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So, Andrew, I like what Jim was saying about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. And as Jim was saying, you can't be perfect, but so, given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown-jewel data looks like, what a good copy of a recoverable asset looks like when you look at an attack, if it were to occur, right? How you get that copy of data back into production. And not only that, but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe SIM2B data, or an Active Directory, or DNS dump, right? Understanding what your data actually entails, so that you can protect it, and that you can build out your recovery plan for it. >> So, and where's that live? Where's that gold copy? You put in a yellow sticky? You know, it's got to be somewhere safe, right? So, you have to think about that chain as well, right? >> Absolutely. Yeah. So, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy, but actually identifying what that golden crown-jewel data, let's call it, actually entails is one aspect of it, and then where to put it, how to protect it, how to make it immutable and isolated, that's the other portion of it. >> If I go back to sort of earlier part of last decade, cybersecurity was kind of a check-off item, and then as you got toward the middle part of the decade, and I'd say clearly by 2016, security became a boardroom issue, it was on the agenda every quarter at the board meetings. So, compliance is no longer the driver is my point. The driver is business risk, real loss of reputation, or data, or money, etc. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean, the bad actors are good at what they do, these losses by organizations tens, hundreds of millions into the billions, sometimes, plus the reputational damage that's difficult to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly, if they're larger organizations. But that's also on the table too. So, you can't just rely on, oh, we need to do A, B and C because our regulators require it. You need to look at what the actual risk is to the business, and then come up with the strategy from there. >> Jim, staying with you. One of the most common targets we hear of attackers is to go after the backup corpus. So, how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for a resilience capability. But that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities, and they've adapted to that. In every sophisticated attack that we see, the backup is a target, the bad actors want to take it out, or corrupt it, or do something else to that backup so that it's not available to you. That's not to say they're always successful, and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So, the capabilities that we talk about with resilience, let's harden that backup infrastructure, you've already got it in place, let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then, as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure, which commonly we might say a cyber vault, or there's a lot of different capabilities for cyber vaulting, some far better than others. And that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this prior to the pandemic, they had their, as you're pointing out, Jim, they had their DR strategy in place, but they felt like they weren't business-resilient, and they realized that when we had the forced march to digital. So, Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So, I'm glad you brought up resiliency. We take a position that to be cyber resilient, it includes operational resiliency, it includes understanding at the C level what the implication of an attack means, as we stated, and then how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown-jewel data, but also how to recover it in real time. So, DXC offers a, I don't want to call it a turnkey solution, since we architect these specific to each client needs, right? When we look at what client data entails, their recovery point, objectives, recovery time objectives, what we call quality of the restoration, but, when we architect these out, we look at not only how to protect the data, but how to alert and monitor for attacks in realtime. How to understand what we should do when a breach is in progress. Putting together with our security operations centers a forensic and recovery plan and a runbook for the client. And then being able to cleanse and remediate, so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect and recover and keep bad actors out. And if we can't keep 'em out, to ensure that we are back into production in short order. >> This discussion we've been having about DR kind of versus resilience, and you were just talking about RPO and RTO, I mean, it used to be that a lot of firms wouldn't even test their recovery, 'cause it was too risky, or maybe they tested it on July 4th or something like that, but I'm inferring that's changed. I wonder if we could double-click on recovery, how hard is it to test that recovery, and how quickly are you seeing organizations recover from attacks? >> So, it depends, right? On the industry vertical, what kind of data, again, financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours, in some instances we have the grace period of a day to a couple days, we do offer the ability to run scenarios once a quarter where we can stand up in our systems, the production data that we are protecting to ensure that we have a good recoverable copy. But it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about, it's not on the technology of how the data gets protected, it's focused on the recovery. That's all that we want to do. And so, the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is you have to think about different scenarios. So, there are scenarios where the attack might be small, it might be limited to a database or an application. It might be really broadly based, like the NotPetya attacks from a few years ago. In the regulatory environment we call those attacks severe but plausible. So, you can't necessarily test everything with the infrastructure, but you can test some things with the infrastructure, others, you might sit around on a tabletop exercise, or walk through what that looks like to really get that recovery kind of muscle memory, so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down what are those critical applications. What do we need? What's most important? What has to come back first? And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense. Understanding the value of that data is going to inform you how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCUBE, especially lately, is around IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, traditionally, operations technologies have been air gapped, often by design, but as businesses increasingly they're driving initiatives like Industry 4.0, and they're connecting these OT systems to IT systems. They're driving efficiency, preventative maintenance, etc. So, a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure, and how should customers think about addressing these issues? >> Yeah. So, bad actors can come in many forms, we've seen instances of social engineering, we've seen USB stick dropped in a warehouse. That data that is flowing through the IOT device is as sensitive now as your core mainframe infrastructure data. So, when you look at it from a protection standpoint, conceptually, it's not dissimilar from what we've been talking about, where you want to understand, again, what the most critical data is. Looking at IOT data and applications is no different than your core systems now, right? Depending on what your business is, right? So, when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front end protection, but we're looking at it from a resiliency perspective. Putting that data, understanding what data entails to put in the vault from an IOT perspective is just as critical as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the key points there. Everything is interconnected. So, even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them, or controlling them SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly, but there was uncertainty around that, and the IT systems hadn't been secured. So, that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked, but the outcome was the same, that the business couldn't operate. So, you really have to take all of those into account, and I think that does go back to exactly what Andrew's saying, understanding your critical business services, and then the applications and data, and other components that support those and drive those, and making sure those are protected, you understand them, you have the ability to recover them if necessary. >> So guys, I mean, you made the point, I mean, you're right. The adversary is highly capable, they're motivated, 'cause the ROI is so lucrative. It's like this never-ending battle that cybersecurity pros go through, it really is kind of frontline sort of technical heroes, if you will. But sometimes it just feels daunting. Why are you optimistic about the future of cyber from the good guys' perspective? >> I think we're coming at the problem the right way, Dave, so that focus, I'm so pleased with the idea that we are planning that the systems aren't going to be 100% capable every single time and let's figure that out, right? That's real-world stuff. So, just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time, and, of course, you've raised the question of some nation states and other activities, but there's a lot more information sharing, there's a lot more focus from the business side of the house, and not just the IT side of the house that we need to figure these things out. >> Yeah. To add to that, I think furthering education for the client base is important. You brought up a point earlier, it used to be a boardroom conversation due to compliance reasons. Now, as we have been in the market for a while, we continue to mature the offerings, it's further education for not only the business itself, but for the IT systems and how they interconnect, and working together so that these systems can be protected, and continue to be evolved, and continue to be protected through multiple frameworks as opposed to seeing it as another check-the-box item that the board has to adhere to. >> All right, guys. We got to go. Thank you so much. Great conversation on a really important topic. Keep keep up the good work. Appreciate it. >> Thanks, Dave. >> Thank you. >> All right. And thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real-world problems, how this partnership has evolved over time, really meeting the changing enterprise landscape challenges. Keep it right there.

(techno music) >> Welcome back to the Cube. I'm John Walls. We continue our coverage here at AWS reInvent 22. We're in the Venetian in Las Vegas, wrapping up our day one coverage here in the executive summit sponsored by Accenture and with me to talk about Accenture, couple of guys who are no strangers at all to the Cube. In fact, I think we got to give you like alumni passes or something. (Chris and Merim laugh) We got to come up with something like that. Um, Merim Becirovic is with us. Uh, Merim's a global IT at Accenture. And Chris Wegmann, who's already been on once today, as a matter of fact. >> Yeah (indistinct) >> So we're going to start charging you rent, Chris. (Chris and Merim laugh) Uh, global technology and practice lead with the AWS business group at Accenture. Good, glad to have you both back and, um, you're welcome to the Cube any time, by the way. >> So don't be scared. >> Thanks, great to be back. Let's talk about >> Sure. >> What, what you folks have been up to. So, um, you are, as we were talking earlier, you are where a lot of your clients would like to be. You, you've begun this transformation. You have fully migrated to the cloud, you've learned, right? >> Yes. You've hit all the bumps along the way. So talk about your journey. >> Yeah. >> And then how you think that experience could be translated to what your clients are going through. >> Yeah, so I'll, I'll hit it from the lessons learned and working together with our business group partners. We, so Accenture's journey to the cloud is complete. We have finished that journey, and as part of that journey, we have migrated all of the services it takes to run Accenture to the public cloud. So now that's done. That was complete. But now we are this, now it is this cloud continuum living in the cloud. And the, now, the thing we talk about, and I'd love to have Chris, you know, shine a little bit more, is we have built our digital core in a cloud, now. We're no longer dependent on data centers. And that has given us tremendous flexibility around how to enable the business as it has grown significantly since we started this journey a few years back. >> Yeah, you know, Merim, like you talk about, right? We talk about our client, we've talked to our clients about building this digital core, right? And, and we've been through that as Accenture, as a global IT organization, you know. Supporting well over 720,000 people. >> Yeah. >> Right? That growth over the last year has been tremendous. Right? So, without the strong digital core built on cloud, right? We couldn't do that, right? We couldn't add that number of people, right? We couldn't make the, the, the changes were needed during, uh, Covid to bring people home, working from home. You know, whether it being uh, the way we changed our business model or things like that, um, you know that was all enabled by cloud. It couldn't be done without that. And, you know, also the variable in our business, right? Is very tied now to our cloud consumption, right? So, you know, it goes up, it goes down, right? We've, you know, Merim and his team have completely built their, their their core with those, with those concepts uh, in mind. >> Yeah, I mean, you're talking about, you know, 700, 800,000 employees and how many countries did you say? >> 130 different countries, at least. >> 130 different countries. So, I mean, no small task, obviously, uh, to get everything done. When did you start? >> So our cloud journey, effectively, we started in 2015. And we were done, kind of right before Covid around 2019. We took a pause for a couple of different things but we could have probably done that faster. And if we were, if I was to do it again now, today we could probably do it in two to three years, flat. With everything that we've learned so far. >> So what's the application, then, to your clients' experiences that, I mean, been there, done that, right? >> You can, exactly right. I mean, you know, we always say that we want to be our best credential, right? And Merim and his team are our best credential in this space. Um, so, you know, a lot of our customers, you know, struggle making that commitment. A lot of 'em are past that struggle, now. They're committed, they're going. Uh, but I talk to a lot of my customers about, you know, do I, do I migrate? Do I modernize? You know, how do I do it? And, and it was interesting with Accenture, right? It, it started out very much as a migration program. >> Yeah. >> Right, so, we made the decision, Merim and his team made the decision to do a migration and now a modernization, right? And, and that's proven very effective. Uh, it, it's, it's, it's proven, you know, uh, we got that core in place, right? We were able to build off of that versus, you know, spending- it would've taken a lot more time just to start with a modernization approach. >> Yeah. Where, where do you draw the line between the two, between migration and modernization, then? Because just by migrating alone, you are modernizing, you know, some of your operations, so you're getting up to speed. But, but how do you draw that line and then how do you get people to jump over it? >> So I, I'll hit it from how our lessons learned. So, when we first started and we did the migrations it was literally lift and shift. And it was a lot of argument about lift and shift isn't worth it. But we found out it was, because it wasn't just about moving the work loads and keeping it like a data center. It was moving the work loads and then optimizing because everything in the cloud was significantly faster. So then I didn't have to consume all the services the same way I did in the data center. I can actually consume them smaller. But also as time went by, what we learned is, hey, now these services are working here. Which ones are actually costing us more money to run? And not that they were costing more than the data center, but it's relative to the cloud which ones cost more in the cloud? Then we looked at that and said, okay how do we want to modernize those? And then we modernized as container capabilities started the evolving, got much more mature. We shifted a lot of workloads to containers. But otherwise, the other principle we push very hard is big consumption of Lambda and uh, serverless capabilities on Amazon. So we have refactored multiple applications to give us that capability to say we no longer need the IAS capabilities, those servers, those VM's, and we run on, on serverless capability. And what's great about that is, now I don't have a server to patch, to scan, to remediate, to upgrade. I've moved away from that capability. And the teams can focus more on building the business capabilities the business wants. Um, like we did to our pricing team. I don't know if you knew this one, Chris, but all the pricing capability has been redone to be cloud native on, on AWS. >> And how, how do you deal with the folks that, that still kind of have a foot in the on-prem world that, um, that they're just not ready to give it up? You know, they, they like the control, they like the self-management. >> Yeah. >> They, they want to be in charge. >> Well, yeah. I mean, a lot of, a lot of our customers, it's, there's a reason why they need on-prem still. And there is on-prem, let's be clear. I mean, it, it is a hybrid cloud world for most of our, our customers, right? Whether they got manufacturing, whether they've got, you know, datas that are, you know, SCADA systems or, or operational IT systems that have to be close to their, their execution or to their, to their factories and things like that. So that's going to happen. I think everyone, and I shouldn't say everyone, but you know, most of our customers know they need to get there, right? And are somewhere on their journey, right? Very few have not started at all. Uh, but it's about acceleration, right? And I, I do think, um, we're going to see more and more acceleration. We saw it with Covid, right? >> Mm-hm. >> And then, you know, obviously I think we're going to see it again, right? With you know, kind of what's going on with the economy and stuff like that. It, it's, you know, it's a great way to push that change through. >> Right. >> And I, I'm really excited, to be honest what I'm really excited about, if I look at what Merim and his team's doing, is they're just leveraging that digital core and truly taking the investments that the hyper scaler's are making, the AWS's are making, and leveraging 'em. So we're not making that investment, right? We're a capital white company, right? So we don't like making good capital investments, right? And we're taking advantage of the capital investments. And we couldn't do that of the, of the hyper scales. We couldn't do that without being there. Right? >> Right. >> We just couldn't do it. >> And maybe, John, if I can build on that. >> Sure. >> Like, one of, one of the things for me when I think about the cloud is, I'm not alone. You know, because when you're in a data center when you're running a data center, you're kind of on an island. And on that island, if you've got security issues, if you got stuff you're dealing with with attackers, you know, you're, you're kind of on an island and you're alone. Whereas in this world, I am where all the investment is, where all the security capabilities are being built, and I have partners that are there with us that help us when these situations come up. So for me, I'm very uh, grateful that we pushed very hard in the beginning to get here. But I wouldn't have it any other way. For us. >> So like, do you- do you want to live outside the fort? >> Yeah. >> No >> No. (laughs) >> You're exactly right. >> Yeah. >> I don't want to live outside the fort. >> Right. >> There are a lot of bad guys out there right now. >> Yeah. >> All right, so, the journey is over. >> Right. You can unpack your bags and get comfortable, right? (Merim laughs) >> No. >> Hardly. >> No. >> So, so what is the, what has this done in terms of setting you up for your future plans? And, and >> So I'll talk about a couple different things and maybe you can build on it, Chris, from what you're seeing, like for us, we, we got very good at, I hate the concept of just FinOps but it's the way of being in the cloud. It's different than running a data center and uh, the way we think about building services, consuming services, allocating services, provisioning services. There's just so much more flexibility there that we can completely fine tune the service that we want to provide. That helps us from when we think about 360 degree value, as we talk to our clients, for ourselves to say it also helps just simply on the sustainability agenda, right, because now, as Amazon builds their capabilities to be more sustainable, those SKUs are available to us, we can naturally consume those SKUs much more effectively. Um, and then uh, the next thing to me, what I'm, what I'm especially excited about is all the stuff we're doing around network. So, you know, pre-Covid, 95% of our traffic was just straight to the internet because we had already finished the journey. So now what do you need a wide area network for anymore? >> Right. >> If you're not routing traffic between data centers what do you need it for? So, we have been working with, with AWS especially, like building these cloud land type capabilities and consuming it. So think of consuming, uh, network same way as you do the cloud. So I'm excited about that one. >> Yeah. That, that, I'm super excited about that, right? Because you know, network's at the core of everything you do, right? And there's always a lot of concern, hey, when I go to the cloud, my network costs are going to go up, right? Um, but I think we've proven, right? >> Yes. >> Being able, that those costs can come down, right? And we can have a better experience, uh, deal with the ebbs and flows of our business whether it's people working from home, people working in the office, you know, or at the client sites. We, we've, you know, we've got that cloud-based backbone that we support. You know, I, I mean Merim, I agree a hundred percent. I think you and your team have done a great job of cost management, cloud cost management, optimization, right? You didn't stop, right? >> No. >> You didn't lo- you didn't just live after the migration on VMs. Right? You know, you went serverless, you went, you know, containerization. >> Yep. >> Uh, and that's kept our cloud bill going down. >> Yes. >> Right. Versus going up, right? >> Yes. >> And I hear from a lot of customers concerned about cloud costs and that type of stuff, but you've proven right, >> Yes. >> That you can keep it flat, if not going down because you're using those last minutes. Sustainability is the other thing that I truly am, I, I love, right? Is, you know, we're all trying to become a more sustainable, sustainable organization. We're trying to help our clients become more sustainable organizations. And you know, you know, your ability to take on Gravitant processors, right? Which use less power. >> Yes. >> Right? Overnight, right? >> Yes. >> Or, hey, I'm using a, you know a, uh, serverless lambda, whatever, right? And I'm not running that server. >> Right. >> You know, so, you're able to show that sustainability gains, um, you know, very quickly. Which you could not do, right? You know, in just doing cloud basic migrations. >> Well, I tell you what I think is impressive, is that you put your money where your mouth is, right? >> Yep. (laughs) >> Is that, that it's, and, and if I'm going to be a client, not to, you know, give you guys a pat on the back, you don't need it. You're doing great without me. But I'd say you've been there, you've done that. And, and so I can learn from you. You understand my pain. >> Yes. >> You understand my reservations, my challenges and uh, you could be my, my headlights here. (Merim laughs) >> So, I think great approach. Kudos to you and certainly wish you both success and to your fourth and fifth appearances on the Cube. (Merim and Chris laugh) Um, we have slots tomorrow if you're arou- available. So, maybe we'll fill it up >> There you go. >> and bring it back again. >> Awesome. >> Guys, thanks for being here. >> Sure. >> It was very nice. >> Appreciate the time. >> All right. >> That's great. >> I've been talking, uh, about Accenture. This is the, of course, executive summit being sponsored by Accenture here at AWS reInvent 22. I'm John Walls. You're watching the Cube, the leader in tech coverage.

(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)

(upbeat music) (upbeat music) >> Hello, and welcome back to theCUBE's coverage of Red Hat Summit 2021 virtual. I'm John furrier, host of theCUBE We've got a great segment with a customer Roberto Calandrini, Head of Architecture, Digital and AI services for Snam customer need to leak oil and gas and AI services for Snam customer need to leak oil and gas great industrial IOT and digital transformation. Roberto, thank you for coming on the cube and spending the time. >> Hi, John. Good to see you. Thank you for inviting me. >> That's awesome. Before we get started, I love the story and again I think security edge and in, in in disease industry for disruptions is huge story here. But before we get started, talk about Snam. Give me a quick overview of Snam, who you guys are. What's your focus customers you have and your role there. >> Of course. So it was not is one of the major global energy infrastructure company and is managing a international and a national asset specifically and a national asset specifically in the natural gas utility segment. There's what the story Kelly Snam did. And it recently positioned itself as a leader of the energy transition, investing a lot in startups of the energy transition, investing a lot in startups mostly focused on, for example, H2 so hydrogen, these the very recent topic, bio Nathan with numb for environment sustainable mobility, energy efficiency, and reforestation. So we kind of So we kind of expanded our core businesses in terms of positioning ourselves much more within the energy transition segments and still developing a lot, what we used to do in the natural gas, in the natural gas industry. And my role there is, as you said, Head of Architecture And my role there is, as you said, Head of Architecture Digital and AI Services. So I'm basically responsible for managing the entire technology stack of Snam and focusing a lot on developing artificial intelligence services for our business lines. >> That's awesome. Well, thanks for sharing that. Let's talk about the digital transmission you've been rearchitecting. You guys redesign your applications map impacting your architecture from the data center to the edge recently, even the center of that your responsibility for the business. What were the business drivers and objectives for you to reach that transformation goal and target? >> Yeah, thanks for, for the question. So they basically, we were mainly interested in exploiting three main three main objectives with our transformation. The first was very much related to our business strategy. So having a more agile So having a more agile and flexible digital architecture that will still on one one end provide us with the reliability that we need in order to sustain our business critical application. And on the other end, provide the agility And on the other end, provide the agility and flexibility the speed in some sense that our new business line will lead in order to succeed. So let's say speed and agility. The second one was a focus on platformization and servitization of our industry specific application. So what we used to develop, as So what we used to develop, as let's say, very focused full stack application now, thanks to the modern architectures can be developed on top of platforms or using microservices. on top of platforms or using microservices. And that will apart from providing us agility And that will apart from providing us agility and flexibility will give us more alignment will give us more alignment between what we invest. So the cost of our software development efforts So the cost of our software development efforts and the business value we derive and the business value we derive from the software we produce basically. >> John: Can I... >> So I focus on value. >> Can I ask you real quick on the business drivers? Can you talk about the impact of domain expertise? One of the trends we're seeing is you want to scale of cloud and having an architecture that's going to enable value creation and customer value for your customers but in these vertical disruptions these new opportunities in these industries like you're a very specialized industry get natural gas and you still need that domain expertise if you want to tap in and advantage of the AI. >> Absolutely. >> Can you share your vision on how you're doing that and how that relates to the business driver? >> Yeah. So let's say that this is very, very aligned with >> Yeah. So let's say that this is very, very aligned with with our strategy that focuses with our strategy that focuses on platformization servitization. So if you think So if you think about how we can explore the best, the value of our people so our industry specific expertise, there are two main ways. The first is to build from scratch as we used to do The first is to build from scratch as we used to do in the past full stack applications that are really focused on a specific, this specific need of a business line. And so focused on the business side of the industry or we can leverage modern architecture and develop services that serve that specific need. and develop services that serve that specific need. So this will let us basically being able to So this will let us basically being able to So this will let us basically being able to satisfy our internal customer. So our internal clients and the business need and at the same time, being able to use that software so that service for an external customer or potential potentially for, for our peers. So in order to provide value exploiting our business expertise, in order to, for example you cited AI using what we developed as an AI system, for example, for two in order to solve demand for customer problems and provide that same business value for, for for other companies that are are they share our same business need. >> Yeah. It's a data workload. I mean, it's at the end of the day you need the data >> Exactly. >> and that's going to come back. I want to unpack the data workload when we talk about the edge, but real quick, I want to talk about the role red hat played in your journey to execute your architecture and transformation. Can you share how Red Hat helped you in this? >> Sure. So let's say that, you know, >> Sure. So let's say that, you know, it all began in two, 2018. it all began in two, 2018. When we started to set up our cloud readiness map When we started to set up our cloud readiness map in order to assess what we will, we'll be able to transform. in order to assess what we will, we'll be able to transform. So scale lift and shift or refactor of of our application map into a modern architecture application. into a modern architecture application. So this cloud readiness journey started So this cloud readiness journey started with assessing the level of modularity with assessing the level of modularity with assessing the level of modularity in some way of some of our main applications. And what we started to do is to develop the first blueprints in order to start to develop new system in order to start to develop new system and new application on a cloud native framework and new application on a cloud native framework and Red Hat really Apple with this but providing a container orchestration platform OpenShift on which we started to build up our new, our new application, that up our new, our new application, that so the cloud native application by application map so the cloud native application by application map then in 2019, we started to accelerate this then in 2019, we started to accelerate this let's say moving to a CNA environment journey. let's say moving to a CNA environment journey. let's say moving to a CNA environment journey. And we started to move the first 10 to 20% And we started to move the first 10 to 20% of our workload on the platform as a service environment. of our workload on the platform as a service environment. So an OpenShift and this is something that we are still doing while at the same time, developing different project at the same time, developing different project that tries to turn what we used to have developed that tries to turn what we used to have developed as custom application toward platforms. as custom application toward platforms. So we are basically transforming our application map leveraging the power for what regards to the customer application of modern architectures. So microservices bays So microservices bays and the container orchestration platform provided by Red Hat OpenShift. And at the same time the other main technological driver is platform migration. the other main technological driver is platform migration. So with basically trying to leverage, especially for the processes that are already very standardized. for the processes that are already very standardized. So usually corporate processes. So staff SEF function processes what we're doing there is to build on top of very what we're doing there is to build on top of very let's say industry standard platform. I don't want to, to provide you with names but you can imagine most but you can imagine most of them are software as a service platforms. And this is really happiness because we are as a target. And this is really happiness because we are as a target. We are, we have as, as a target for 2022 to basically have the number for 2022 to basically have the number of application with respect to the number of application our application map of 2018. our application map of 2018. >> So big, big step increase in applications. >> Yeah, yeah, yeah >> That's great. That's cool. And then the ecosystem of energy efficiency and aiming for lower carbon emissions that's a goal you guys are helping with. How is Red Hat helping in the ecosystem in your ecosystem? Do you see them going above and beyond? >> You know, the, for what regards to new business lines? I think that the container orchestration platform I think that the container orchestration platform so OpenShift would provide us with the right level so OpenShift would provide us with the right level of flexibility and agility to move of flexibility and agility to move at the speed of those businesses. That is quite different with respect to our classical ones and frequently needs a much higher speed of development. and frequently needs a much higher speed of development. >> Yeah. Awesome. Well, that's great. Great to see that success with Red Hat let's let's shift gears to the topic of the edge. >> Yeah We've been reporting on Silicon angle industrial edge for many years now. And we were calling out the security potential there as risky, obviously it's, it's it's industrial there's you also got generic edge which is consumer edge and everything in between the edge is just part of the network. And you think about this, this is important for you are what are you doing for you are what are you doing with the edge and IOT from a use case standpoint? What have you already done? And what are you planning to deploy soon? Take us through your, your edge IOT use case how it is today and how you see it tomorrow. >> So let's say that Snam has long OT history that basically started that Snam has long OT history that basically started at the very beginning of our SCADA system. So what we have right now is quite complex Brown So what we have right fields situation for what regards edges and gateways fields situation for what regards edges and gateways fields situation for what regards edges and gateways and technical component that resides on, on the field. and technical component that resides on, on the field. So you can, you, you, you must consider that the Italian network is for the modern that the Italian network is for the modern modern 34,000 kilometers and modern 34,000 kilometers and as many different plants, small, medium, and as many different plants, small, medium, and and large plants spread across the country. and large plants spread across the country. And what we are trying to do leveraging also Red Hat technologies among with Red Hat technologies among with with others is trying to get the benefit with others is trying to get the benefit of containers and microservice development. So the benefit coming from cloud native application and getting those to the edge. from cloud native application and getting those to the edge. So the usual problem So the usual problem with OT as historically been a standardization with OT as historically been a standardization so a very heterogeneous number of components Virginia's protocols of components Virginia's protocols in order for them to communicate with the charters and relatively low level of security. with the charters and relatively low level of security. This is, this was mainly due to the segregation principle This is, this was mainly due to the segregation principle physical segregation principle that used to physical segregation principle that used to dominate the OT field with IOT. Of course, as you were saying we are terrifically expanding the attack surface we are terrifically expanding the attack surface from the cybersecurity standpoint, but at the same time that is mainly why we are approaching that is mainly why we are approaching in a very structural way. Our technology stack implementation including security by design in all our architectural blueprints and implementation. And we strongly believe that pushing the capability And we strongly believe that pushing the capability of container orchestration and containerization to the edge and being able to orchestrate that from the cloud or from our data centers will provide us with a very high level of high-quality and flexibility and the capability to exploited best the geographical distribution of the data. to exploited best the geographical distribution of the data. You know, you were saying a center point will be You know, you were saying a center point will be was soaked around data, and it is correct, but it in our specific case, our data basically came from points in our specific case, our data basically came from points in our specific case, our data basically came from points as I was saying, spread it all across the country. So having different data, gravity points enabled So having different data, gravity points enabled by container rise and centrally orchestrated by container rise and centrally orchestrated by container rise and centrally orchestrated environments will enable us to get the best also environments will enable us to get the best also in terms of, from the cybersecurity perspective because what will be acquired on the centralized environment is only exclusively on the centralized environment is only exclusively what is needed at the centralized environment. what is needed at the centralized environment. All the rest on our target architecture will be entirely elaborated on the field, very close to where the data physically on the field, very close to where the data physically and this will be excludable exclusively enabled by by a containerized approach. >> That's awesome. Great, great. A use case there, Roberto, what's next A use case there, Roberto, what's next for your future plans and your technology journey? Obviously AI is going to be very important and data and leveraging that you've got the core cloud data center edge perspective. >> Yeah, of course. Yeah. What, what, what's next? >> What's your future? Let's say, let's say that what we currently implemented is Let's say, let's say that what we currently implemented is and in average cloud environment so we basically have two data center and one cloud tenant, our infrastructure due to, again and one cloud tenant, our infrastructure due to, again and one cloud tenant, our infrastructure due to, again the use of OpenShifts will be easily extensible the use of OpenShifts will be easily extensible the use of OpenShifts will be easily extensible to other potentially to other cloud providers. So we will move, we're evaluating the move to a multicloud So we will move, we're evaluating the move to a multicloud a hybrid multicloud environment. At the same time our main focus right now is to close our IOT foundation. our main focus right now is to close our IOT foundation. And within the IOT foundation I think the main focus right now is on gateways and edges. I think the main focus right now is on gateways and edges. As you were saying, these are quite complex components As you were saying, these are quite complex components and must be greatly evaluated, especially from the cybersecurity standpoint and last from the cybersecurity standpoint and last but not least the data we need to. but not least the data we need to we started our data platform journey and we currently are acquiring data from legacy systems and we currently are acquiring data from legacy systems different kinds of legacy system and SCADA system. What we would like to reach is a complete IOT What we would like to reach is a complete IOT What we would like to reach is a complete IOT acquisition system that will be directly connected to our components, acquiring data on the field. Right now we are in, let's say Right now we are in, let's say in the middle of this digital transformation and we are hemming to close our and we are hemming to close our our journey in the next couple of years. >> That's great, Roberto, great story. Love the conversation. First of all, I love your title Head of Architecture, Digital AI Services. I mean, that speaks to this modern error of, of, of cloud distributed computing. You hit all the hit, all the key things, right? It's an architectural system distributed system. It's a digital business. Now, even though there's physical assets offline, online coming together in a modern way and AI really speaks to the underlying data which is combination of many, many things, you know you're you get all the action there. >> Roberto: Yeah! >> How do you feel? What's your advice to other people in the same boat you're in? >> No, I, I think that, that the interesting part of what we do that the interesting part of what we do at least in, in my specific area, and this is what digital at least in, in my specific area, and this is what digital or sustained for is digital service design. This is something new that is quite uncommon within the utility sector. And it is basically a group of people that apart And it is basically a group of people that apart from being technologists focus a lot on the interaction from being technologists focus a lot on the interaction design of what we are or what we are trying to build design of what we are or what we are trying to build in terms of the technology stack. So these are people that basically try to make the very So these are people that basically try to make the very complex technology stack we talk about in our interview much more simple the, to the final user and think about the level of interaction, complexity about the level of interaction, complexity that all our user will have with our technology stack. Especially when we talk about IOT now, and you start to interact, not just with digital systems, but also with digital or physical systems. with digital or physical systems. So yes, we, we, we have a lot on our plate >> It reminds me of the late eighties, early nineties when open standards really hit the scene and then incubated and then accelerated was seeing that same dynamic happening now with cloud. And you're a pioneer and really appreciate you taking the time to come on The Cube and speak with me about this and share your story. And more importantly than Red Hat success there. 'cause it's Red Hat summit, a story here, Roberto. Thank you very much for sharing your insights and experiences. >> Thank you for your time, John. This has been a pleasure. >> Really appreciate it. Okay. That's Red Hat CUBE coverage here with theCUBE. I'm John furrier. Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hi, I'm Stu Miniman and welcome to this Cube Conversation. I'm coming to you from our Boston area studio, and we always love when we get to talk to practitioners, and not just any practitioner. CIOs, obviously under huge pressures in general, but in today's day and age, lots of pressures on the CIO. So, I'm happy to welcome to the program Rich Gagnon. He is the CIO from the city of Amarillo in Texas. Rich, thank you so much for joining us. >> Glad to be here. Thanks for inviting me. >> All right, so, you know, CIO in a city in Texas, why don't you give us a little bit of what your role entails, a little bit of your background, and looking forward to the conversation. >> So, my background is actually more from the private sector side of the house. Previous to coming to the city of Amarillo, I was the Vice President of Systems Engineering for Palo Alto Networks, for the Americas. Before that, the Global Vice President of Systems Engineering for F5 Networks, and before that, the Director of Global Infrastructure for GameStop. So I stepped into government with a very private-sector, profit-centered mindset, if you will, coming from very high-growth companies. My role with the city is really to be an enabler for local government, to drive not only IT direction, but as a smaller community, I also have to wear the CSO hat, and the Data Privacy Officer hat. Pretty much anything when it comes to leadership of IT and technology, as an enabler to the government, that role falls on me. >> Wow, so a pretty broad mandate that you have there. Rich, give us a little bit, how does that span? How many constituents do you have in your infrastructure, your IT? Maybe you can sketch that out a little bit for us, too. >> Sure, so, I've had peers from the private sector ask me, "What's it like to actually lead in local government?" And the best comparison I can come up with is someone like GE. I have 49 different subsidiaries, different departments that operate as individual business units, only I don't have GE's money or their staff. We have 200,000 people and the departments we support span everything, from the obvious, like public safety, police, fire. We have an airport, a public clinic, water treatment plants, public health. There are streets, all the infrastructure departments. It's very diverse. >> Wow. And with all of those constituents that you have, why don't you give us the pre-COVID-19 discussion first, which is, what are some of those pressures there, from a budgeting standpoint? Are there specific initiatives you've been driving? And how are you responding to all those variables? >> Sure. Well, coming in, it was a little jarring. City leadership was very transparent that the city had sort of stood still for about a decade. I come from a high-growth environment where money was not the precious resource, really. It was always time. It was about speed to market. How do we get competitive advantage and move fast enough to maintain it? That was not the case here. I stepped into an environment where the limitations were Cat 3 cable and switches that still ran CatOS. The year before I came in, the big IT accomplishment was finally completing the migration to Windows 7 and Office 2007. That's where we started. So, for the past three years, I guess I'm starting my fourth year, we have undergone massive transformation. I think my staff thinks I'm a bit of a maniac, because we've run like we were being chased by a rabid dog. We have updated, obviously, the Layer 1 infrastructure, replaced the entire network. We've rolled out a new data center that's all hyper-converged. That enabled us to move our security model from the traditional Layer 3 firewall at the edge to a contextually-based data center with regulation on east-west traffic and segregation. We have rolled out VDI and Office 2016 and Windows 10. It's been a lot. >> Yeah, it really sounds like you went through multiple generations of change there. It's almost like going a decade forward, not just one step forward. Bring us through a little bit, that transformation. Obviously, there should be some clear efficiencies you had, but give us kind of the before and after as you started to deploy some of these technologies. Was there some reskilling? Did you hire some new people? How did that all go? >> Very much so. And like everything, it starts with financials, right? All of the resources at the city within IT were focused on operations, so there was literally no capital budget. As where typically you would update as you go, and update infrastructure, what happened was, as the infrastructure aged, the approach was to hire more staff to try to keep aging infrastructure up and running. That's a failing strategy. So, by moving to HCI, we've actually recovered about 26% of our operating budget, which allowed us to move that money into innovation and infrastructure updating. It took a tremendous amount of reskilling. Fortunately, the one thing that's been, I think, most surprising to me coming to local government, is the creativity of the staff. They were hungry for change. They were excited by the opportunity to move things forward. So, we spent an entire year doing nothing but training. We had a massive amount of budget poured into, "Let's bring the staff up to speed. "Let's get as many vendors in front of them as possible. "Let's get them educated on where the trends are going. "What is hyper-converged architecture "and why does it matter? "What is DevOps and why is the industry heading that way?" So as I said, we started, really, Layer 2-3, established that, built out the new data center, and now our focus is now, we built that platform, and our focus is starting to shift onto business relationship management. We've met with all 49 departments. We do that every six months. We're building 49 different roadmaps for every department, on "What applications are you using? "How do we help you modernize? "How do we help you serve the citizens better?" Because that's how IT serves the community. We serve the community by serving the departments that serve them directly, and being an innovation engine, if you will, for local government, to drive through new applications and ways to serve. So the transition has really started to happen is we've gotten that base platform out of the way and the things that were blocking us from saying, "Yes, and we can do more." >> Wow, so Rich, it's been an interesting discussion as the global pandemic has hit, so many people have talked about, "Boy, when I think about working from home "or managing in this environment, if I was using "10- or 15-year-old technology, "I don't know how, "or if I'd be able to do any of what I had." So, I know Dell brought you over, you're talking HCIs, so I believe you're talking about VxRail as your HCI platform. Talk to us about what HCI enabled as you needed to shift to remote workforce and support, that overall urgent need. >> It's been massive. And it's been interesting to see the IT team absorb it. As we matured, I think they embraced the ability to be innovative and to work with our departments, but this instance really justified why I was driving progress so fervently, why it was so urgent to me. Three years ago, the answer would have been no. We wouldn't have been in a place where we could adapt. With VxRail in place, in a week, we spun up hundreds of instant clones. We spun up a 75-person call center in a day and a half for our public health. We rolled out multiple applications for public health so they could do remote clinics. It's given us the flexibility to be able to roll out new solutions very quickly and be very adaptive. And it's not only been apparent to my team, but it's really made an impact on the business, and now what I'm seeing is those of my customers that were a little lagging or a little conservative are understanding the impact of modernizing the way they do business because it makes them adaptable as well. >> All right, so, Rich, you talked a bunch about the efficiencies that HCI put in place. How about that overall management? You talked about how fast you spun up these new VDI instances. You need to be able to do things much simpler. How does the overall lifecycle management fit into this discussion? >> It makes it so much easier. In the old environment, one, it took a lot of man hours to make change. It was very disruptive when we did make change. It overburdened, I guess that's the word I'm looking for. It really overburdened our staff to cause disruption to business. It wasn't cost-efficient. And then, simple things, like, I've worked for multi-billion dollar companies where we had massive QA environments that replicated production. You simply can't afford that at local government. Having this sort of environment lets me do a scaled-down QA environment, and still get the benefit of rolling out non-disruptive change. As I said earlier, it's allowed us to take all of those cycles that we were spending on lifecycle management, because it's greatly simplified, and move those resources and reskill them in other areas where we can actually have more impact on the business. It's hard to be innovative when 100% of your cycles are just keeping the ship afloat. >> Well, it's definitely a great proof point. So often, you deploy a solution, and when push comes to shove, will it deliver on that value that we're hoping for? HCI has been around for quite a while, but a crisis like this, how can you move past, how can your team respond? Congratulations to your team on that. The Dell team has recently done a number of updates on the VxRail platform. I'm curious, as someone who's been using the platform, what particularly is interesting to you, and what pieces of that have the most relevance to your organization? >> There are a few. So we're starting to look at our SCADA environments, industrial controls. And we're looking at some processing at the edge in those environments. So the new organized D series are interesting. There's some plant environments where that might really make sense to us. We've also partnered with our local counties and we have a DR site where being able to extend the network out to that DR site is going to be very powerful for us. And then there's just some improvements in vSphere that will allow us to do a little QA-ing, if you will, on new code before we roll it out, that I think will have a pretty huge impact for us as well. >> Excellent. So, Rich, when you think about the services that you need to deliver to all of your constituencies, walk us through how the pandemic has affected the team, how you're making sure that your employees are taken care of, but that you can still deliver all of those services. >> So from an internal perspective, not running a legacy architecture has made that a whole lot easier. We've remoted most of the IT team. Our entire development team is at home. Most of our support team is at home. Most of the city is still at home. So being able to do that, one, just having the capability has been huge for us. But also, from a business perspective, it's allowed most of our city functions just to keep running. So, modified services, for sure, but we're still functioning, and I just don't think that would have been capable, we wouldn't have been capable of supporting that, even two and a half years ago. >> So, Rich, we've talked a bit about your infrastructure. I'm curious, is the city, are you leveraging any public cloud environments, or any specific SaaS solutions that are enabling some of what you're doing today also? >> Yes, and we could probably have a 30-minute discussion on what is hybrid cloud and what is multicloud. In our instance, we are leveraging quite a bit of SaaS. We've migrated a lot of our services to SaaS offerings. We have spun up several applications in the cloud. I wouldn't call them truly hybrid. In my mind, hybrid is, I am able to take the workload and very seamlessly move it between my private infrastructure and one or more clouds. This is more, workloads specifically assigned to a public cloud. But yes, we've leveraged that. Simple things like Office365 and Outlook, but just as powerful for us has been VDI and being able to offer Horizon to our employees at home. And, with my other hat on, still maintain the contextual-based security, right? So I didn't have to open up the kingdom. I can still maintain the control that I need to to be able to sleep at night. >> Yeah, it's interesting. One of the questions I love to ask someone in your position is the role of data, how you think of security, how you think of the technology and put those together. Does it help that you wear both the CSO hat and the CIO hat? How do you think about leveraging data? Is there anything that you're sharing with other municipalities, without giving up, of course, personal information? >> Sure. It causes a lot of internal arguments, right? Because there's the two halves of my brain: the CIO half that wants to roll out as much service as I can and be innovative, and the CSO half of my brain that thinks about the exposure of the service that I'm about to roll out. That's part of where we're migrating now as we start to look into our whole approach to data. We've got the platform in place. We're now really migrating our thinking into revamping the way we look at data. I have seven sources for the same data. How do I consolidate and have one source of truth, and where does that reside? My development team is really starting to migrate out of classic development and more into the automation side of the house. How are we interfacing with all of our vendors? That's in review now. And how are we tying to third-party apps? Yeah, that's really the point we're at in our maturity that, now that the infrastructure is in place, we're now migrating to, "what is our data plan?" >> Excellent. Final question I have for you, Rich. I'd love your thoughts on the changing role of CIO. I loved the discussion you had at the beginning going from, really, the private sector to the public sector. Obviously, unique pressures on all businesses right now dealing with the global pandemic, but how do you see the role of the CIO today and how has it been changing? >> I think there's an expectation that you bring value to the business, whether that's local government, or retail, or banking. I think the expectation is that you're not just managing an infrastructure or managing a team, and providing service, but how do you bring actual value to the organization that you serve? And that means that you have to understand the business and all aspects of the business. I think you have to, at least I do as a CIO, I have to spend a tremendous amount of time understanding my internal customer and what are they trying to accomplish, and often, to show them a new way that they just may not be aware of. So I think there's a little more expectation as a CIO that you're going to drive value to whatever business that you're serving. >> Well, Rich, thank you so much. Really enjoyed the conversation. Congratulations on being able to react fast. So glad that you were able to get the transformation project done ahead of this hitting, because otherwise, it would have been a very different conversation. Thanks so much for joining us. >> Thank you. >> All right, I'm Stu Miniman. Stay safe and thank you for watching theCUBE.

>> Announcer: Live from Barcelona Spain, it's theCUBE! Covering Cisco Live 2020, brought to you by Cisco and its ecosystem partners. >> Welcome back, this is theCUBE's live coverage of Cisco Live 2020 here in Barcelona, Spain. I'm Stu Miniman, my cohost for this segment is Dave Vellante, John Furrier is also in the house. We're doing about three and a half days, wall-to-wall coverage. The surface area that we are covering here is rather broad and I use that term, my guest is laughing, Vikas Butaney, who is the Vice President of IoT, of course. Extending the network to the edge, to the devices, and beyond with Cisco. Thank you so much for joining us. >> It's great to be here. >> All right, the IoT thing. I've worked with Cisco my entire career, I've watched through the fog computing era for a couple of years. Edge of course, one of the hottest conversations, something that I bought up in many of the conversations, the across the portfolio but Liz Centoni was up on the main stage for the day one keynote talking a lot about IoT and IT and OT and your customers of the like. So let's start there, what's new, and how does IoT fit into the overall Cisco Story? >> Absolutely. So as Liz was on the main stage and David talked about the cross domain and multi-domain architecture; Now, IoT and our operational environment is one of the key domains within that environment. And what Liz announce yesterday are two pieces of news that we are releasing at Cisco Live. First of them is an IoT security architecture which ties together the capabilities with cyber vision and then integrates it within the rest of our IT security portfolio and the second part that I'm also excited to talk about is Edge Intelligence. It's about how we are helping our customers extract the data at the edge, then deploy and move it to wherever the applications are in the multicloud environment. >> You know, we definitely want to dig into those pieces, but IoT is such a diverse solution set so it's often helpful to talk about specific industries, any customer examples so what can you share with us there to help illuminate where Cisco's helping the customers love the security angles and edge? >> That's right. Just a level set, when we think about industrial IoT we're really talking about the heavier industries, plant environments for a manufacturing company. We're thinking about roadways for a public sector customer. We're thinking the grid for utility environments. We're thinking refineries and oil extraction upstream environments, right. So this is the kind of spectrum in which we are working in, where customers have real businesses, real assets where the operations is the heart of the enterprise that they are running. And the technology can really be a revolutionary change for them to help them connect and then extract the data and then make sense of the data to improve their business practice so industrial IoT, whether you're a roadway in Austria like Asfinag, you're a utility in Germany like NRG, or EDF in France as an example. Enel in turn in Italy, all of these industries and all of these customers are using industrial IoT technologies in running their businesses better today. >> Where are we in terms of that critical infrastructure being both connected and instrumented? Where are we on the adoption curve? >> Sure, look and many of these industries we have talked about SCADA systems, right, that have been here for thirty plus years for our customers and most of those is really a one-way flow of information, right. And typically customers stood up separate side load networks which weren't really connected to the rest of the enterprise so, Rockwell has a saying from the shop floor to the top floor, right like how the digital enterprise where all of these environments are coming together is where customers are. Critical infrastructure, as you said, in this day and age with security and other kind of threats, customers are a little hesitant about how they connect it all together. But Cisco is working with these customers and helping them think through the benefits they can get but also make sure, from a cyber security point of view, that you're helping protect assets, manage these environments because you can't just arbitrarily connect them because IT tool sets just are not ready to manage these environments. >> I love that all the examples you gave were European, of course, being here in Europe. I'm curious, there's some technologies where North America might take the lead or Asia might take the lead. Is IoT relatively distributed? Is Europe kind of on-par or with the rest of the world when it comes to general adoption? >> What we have found in Europe, because of many countries like Germany leading in the renewable energy effort, and the climate is a big focus here. Data privacy and concerns around data sharing are much more top-of-mind in Europe, so we find those kind of use cases getting adopted much much faster. In Germany, as an example, NRG which is one of our customers, and they were here with us last year at Cisco live and we launched a capability with them. They are trying to manage the real time flow of energy in their grid environment, such that make sure there are no outages, no brownouts in these environments. So utilities and customers like that across Europe are adopting technology faster. Manufacturing, as always, is a leading use case. There we see some of the automotives in US are leading a little bit more in getting environments connected to their environment but overall, IoT is a global market. We work, we have over 70,000 enterprise IoT customers today at Cisco so we are fortunate to be able to serve these customers on a global basis across the range of industries I talked about earlier. >> In a lot of respects too, I would say the US is behind, right, when you look at public policy from a federal standpoint, the US doesn't really have a digital strategy from an overall perspective whereas certainly India does and countries in Europe. You look at the railway systems in Europe. >> Vikas: Much more advanced, yeah. >> Beautiful and shiny and advanced. So I would say the US has a little bit of work to do here, in my perspective. >> That's right, in India Prime Minister Modi started the effort around One Hundred Smart Cities, right, and Cisco is working with many of those smart cities with our Cisco Kinetic for Cities to kind of create, connect all of the sensor networks. Video surveillance, safety, environmental sensors, managing the flow of that data and digitizing those environments, right, and in Europe we've been working in France, Germany, Italy, UK. I think we are seeing much more adoption in these specific industries but it's a global market and again, like I said, 70,000 customers, we get to see quite a bit of the landscape around the globe. >> What should we know about the architecture? Can you give us kind of a high-level summary? What are the basics? >> Sure, so in the comprehensive IoT security architecture we released this week, it really starts with, you have to be able to identify the devices, right. In IT environments, you know, to your laptop and to your PC, they have been managed by MDM technologies for years but in the industrial environment I might have a programmable logic controller that I deployed 15 years ago. It's not ready for modern capabilities so what you really have to start with is identifying all of these assets in the communication baselines that are happening there, that's step one. Step number two is really, now that I know that this is a PLC or that's a controller, I need to come up with a policy, a security policy which says this cell in a plant environment can only talk to the other cell but doesn't need to talk to a paint zone. So I'll give you an example in automotive, if I'm welding a car, I'm building a car, the welding robots need to be communicating with each other. There's no real reason that the welding robot needs to talk to the paint shop, as an example. So you can come up with a set of policies like that to keep these environments separate because if you don't, then if there is one infection, one malware, one security, then it just traverses your whole factory. And we know customers in Europe that their networks have gone down and they've impacted 150 to 200 million dollars of downtime impact. >> Well we had a real world use case 10 years ago or so with Stuxnet with Siemens PLC and boom it went all over the world, I mean it was amazing. >> Exactly right, so again back to identification then I create the policy, then I implement the policy within our switching or a firewall network but you're never done so you have to keep monitoring on a real time basis as the landscape changes. What's happening, how do I keep up with it? And that's where things like anomaly detection are super important, right, so those are the four steps off the architecture that I want to talk about. >> So it sounds like something like cyber security is both a threat and an opportunity of bringing together IT and OT. Bring us inside a little bit those dynamics, we know it's one of the bigger challenges in the IoT space. >> Yeah, I mean I think, look, both parties whether I'm an operational person or an IT person, both of us, both audiences have their own care-abouts. If I'm a plant manager, I'm measured on number of units I'm producing, the quality, the reliability of my products. If I'm in IT I really am measured on downtime of the network or the cyber security threat. There aren't really common measurable capabilities but cyber and security, it kind of brings both the parties together. So when we use our cyber vision product, we're able to provide to that plant manager visibility to what's happening, how are their PLC's performing, did anybody change my program, is my recipe for my given product I'm making secure and safe? So you have to appeal to the operational user with what they care about. IT really cares about to manage the threat surface, don't let that threat kind of propigate. Now at the board level because the board sees both sides of it, they're asking these teams to work together because they have a complimentary skill set. >> Well I think that's critical because, rhetorical question, who's bigger control freaks? Network engineers or operation technology engineers? They both, you know, keep that operation going and are very protective of their infrastructure. So it's got to come from top down and it is a board level discussion, right? >> Yeah that's right, we have customers where, you know, the board, the CEO has mandated to say listen, whether it's for the national threat actors or other corporate espionage, I need to protect the corporate intellectual property. Because it's not just a process, it's also about safety of employees and safety of their assets that comes into play, right. So when some of the customers we're working with, where the CEO has kind of dictated that the IT teams help the operational environments, but it is a two-way street, like, there has to be value for both parties to come together to solve these challenges. >> Okay so we talked a little bit about the threat, also when we're talking IoT, there's all that data involved. What's the opportunity there for customers with data, how's Cisco involved? >> Absolutely, look, I think one of the reasons customers are doing digitization projects is because they're trying to use the data to make better business decisions. It has to improve, yield, and meet their KPI's of their industry. So far what we have seen is that all of the data is really trapped in all of these distributed environments. Gartner tells you that 75% of the data will be produced at the IoT edge. But our customers to date have not had the tool set to be able to get access to the data, cleanse the data at the edge of the network, bring the right data that they can create insights with, and improve their businesses so it's been a heterogeneous environment, lots of protocols, lots of legacy, so that's kind of what our customers are struggling with today. >> Yeah, absolutely and most of that data is going to stay at the edge so I need to be able to process the edge. Heck I even went to a conference last year, talked about satellites that are collecting all of the data, I need to be able to have the storage, the processing, the compute there because I can't send all of the data back, as fast as it is. So it's a changing architecture as to where I collect data, where I process data. We think it is very much additive to traditional cloud and data center environments today, it's just yet another challenge that enterprises need to deal with. >> That's right, so the work that Cisco is doing in the IoT edge environment is we are enabling these customers to connect their remote terminal units, their machines, and their robots and providing them the tool set with four capabilities. First, extract the data. So we have a set of protocols like Modbus, like OPC UA where they can extract the data from their machine so that's step number one. Second is to transform the data, as you said, over an LTE circuit or over a connection, I'm not going to be able to send all of the data back so how do I transform the circuit, transform the data where I maybe take an average over the last five minutes or I kind of put some functions, and we are providing, as we are in the Devnet zone, we are providing developers the capability such that they can use visual studio, they can use Javascript to write logic that can run right at the edge of the network so now you have extracted the data, you have transformed the data. Governance is a key topic, who should have access to my data, especially here in Europe where we're concerned about privacy, we're concerned about data governance. We are enabling our customers to come up with the right logic by which if there's a machine data and you are the supplier, I'm only going to give you the data, the temperature, the vibration, the pressure that you need to support the machine, but I'm not going to give you the number of units I produce. I'm not going to give you the data about my intellectual property. And then you have to integrate to where the data is going, right. So what we're doing is we are working with the public cloud providers, we are working with software ISVs, and we are giving them the integration capability and the benefit of this for the customer is we have done pre-integration on the extraction part and we have done pre-integrations on the delivery part, which allows the projects to go faster and they can deliver their IoT efforts. >> So how do you envision the compute model at the edge, I mean, probably not going to throw a zillion cores so maybe lighter weight components, and I have some follow up on that as well. >> Sure, absolutely. Look, Moore's law is a friend of ours here, right, like with every cycle, every generation of CPU technology, you get more and more compute capabilities. So the IoT gateways that we provide to our customers today have four ARM cores in them. We are using a couple, two of those ARM cores for the networking function but those cores are available for our customers. We have designed an extra memory for them to be able to process these applications and we give them SSD and some storage at that so we can provide up to sixty gigs or one hundred gigs of storage so now that gateway, that communication device, a router, a switch that's at the edge of the network can kind of do a dual purpose. It can not only process and provide you security for the communications but is now an edge processing node so we call them IoT gateways and I can tell you, we are deploying these kind of products on buses. You know, in a mass transit bus, we all ride these buses, there are over six systems that are on that bus. A video surveillance system, I'm going to monitor the tire pressure, I want to monitor if the driver is going over the speed limit. We have now connected all of these systems and we are running logic at the edge such that the riders have a safer experience and then they can get real time visibility to where the bus is as well. >> Yeah and my follow up was on persisting, so you mentioned storage, you know, flash storage at the edge and then you also referred to earlier the challenges this data today is locked in silos or maybe it's not even persisted, it's analog data sometimes. So do you envision, if you think about successful digital companies, kind of born digital, data's at the core and traditionally big manufacturing firms, large infrastructure, the manufacturing plant is the center of the universe and data sort of sits around it. Do you envision a period where that data is somehow virtualized and we have access to it, we could really build digital businesses around that data, what are your thoughts? >> Absolutely. So we have been working with a customer, it's a steel manufacturer in Austria, the heartland of Europe as an example. And they make high quality steel, right, and when they're building the high quality steel, they have two hundred different machine types and like you're saying, the data is trapped in there. This customer is trying to digitize and trying to do that but they have been struggling for the last two years or so to be able to get the data because it's a variety of machines and they want to use our IoT services but they haven't been able to pipeline the data all the way to their cloud environments so that was one of our lighthouse customers and we worked with them like, you know, roll up your sleeves and kind of designed the system with them. And we worked to get that data such that now, they're not quite a born-digital company but they are a hard manufacturing company, they can get the best of the tool sets and analytics and all of the things that contemporary tech companies use and they can bridge them into this digital environment. >> Yeah and this is how the incumbents can compete with the sort of digital natives, right I mean it's an equilibrium that occurs. >> That's right, I mean look we love the digital companies but they're not really, they don't have physical assets there or out there working. They're working in a more physical or more of the real economy whether if you are an oil company and you're getting, extracting oil from a pumpjack, right, well you need to still have the capability to do that better. So that's what we're doing, whether you're a transportation, like the bus example I gave you, an oil and gas company whose trying to extract oil from the ground or you are a manufacturer or you're a utility, if we improve use of our digital technologies and operate, improve the efficiency of the business, a 0.1%, a 1%, that has got a much much bigger implication for us as a society and the world at large. But just making them better and more efficient. >> Huge productivity gains. >> Exactly right, that's right, right. >> Massive, yeah. >> So I think that technology and IoT technologies can benefit all of these industries and you know Cisco is kind of invested and kind of helping our 70,000 customers to get better with all of these capabilities. >> Awesome, congratulations. 70,000 customers, big number, rolling out IoT solutions. Look forward to keeping track of Cisco's IoT solutions. >> Super excited to be here, thanks again. >> For Dave Vellante, I'm Stu Miniman, back with lots more wall-to-wall coverage here at Cisco Live 2020 in Barcelona. Thanks for watching theCUBE. (upbeat music)

>> Live, from Mountain View, California, it's theCUBE! Covering DevNet Create 2019, brought to you by Cisco. >> Hey, welcome back to theCUBE, Lisa Martin with John Furrier covering day two of Cisco DevNet Create 2019, and guess who we're here with? Susie Wee SVP and CTO of Cisco DevNet. Susie thank you so much for having theCUBE here and for joining John and me today. >> Oh thank you for being here. >> So this event, there were so many bodies in here yesterday, it was pretty toasty, it's getting toasty now, this is the third DevNet Create, this community John and I have been hearing that and feeling it and seeing it, see it, learn it, code it, kind of all on your theme there the last day and a half. This is a really inspiring, really national sharing community that you guys have built here. >> It is, it's amazing, I mean just the energy here as you bring together folks. Everybody wants to learn, there's so many new technologies out there, but new technologies that can turn into business advantage, and the attendees here they all feel it, and it's a different mixture of people because there's app developers, there's infrastructure and networkers, and just bringing these folks together to see what they can achieve is amazing. So that's the energy that you can really feel here. >> And the thing that's interesting and that I'd like to perspective on where this all started from, is DevNet Create is interesting, you know Amazon's Andy Jassy, the CEO of Amazon Web Services, uses the term builders. So you hear builders, maker culture, create. But creation is a critical part of your ethos here, and with cloud computing, Microsoft's earnings came out they were a trillion dollar market cap now, Amazon crushes their earnings again, you're seeing what cloud is doing that's enabling these creators, a new class of developer, but it's not like a new breed, it's just a new kind of orientation. This is part of your vision to share the story. >> Well and kind of the whole thing is that, you know I'm all about innovation and creation. And I believe that people just want to create. My four year old, she just wants to create. It's just in people's blood, but to now get out there and to do it, you need a catalyst. You can't just sit in a room and then create, and sometimes it's about how you bring new fields together, how you bring new technologies together, how you bring non-technologies together, how you just bring different types of people and perspectives together, and that's really what DevNet Create is all about. So, we started DevNet five years ago, just with the idea that the network is going become programmable. The infrastructure is going to provide more resources, and it's going to be programmable and provide more power to applications, so from then to now, last summer we hit half a million developers, now we're at 590 thousand developser, and we're growing. >> Well we're lucky to be part of it and thank you for including theCUBE in DevNet Create, and bringing something to the DevNet community. It's been fun and inspirational, but to be practical in the industry, you need to have a wind at your back, you need to have a wave to ride on, and creation is also about momentum. And if you look at the marketplace today, there's some big waves happening. Cloud computing is obvious, one everyone looks at, that's already changed the nature of companies, Cisco's multi-cloud looking at a bigger vision there. But new waves are coming, I mean Wifi Six is a game changer, you've got 5G. So you talked about this in the keynote, I want you to take a minute to explain that the big waves that you outlined, because with big waves there's more fun, there's more creation. There's wealth creation, there's economic vitalizations, a new vibe. Share the waves. >> Inside of the whole thing is that we say there's the infrastructure. You get your networking, you get your compute, it evolves to cloud computing and all of that, but on top of that are these applications. And this amazing set of applications, and we know that those are creating entirely new and disruptive businesses and business models, and there's a lot of growth in all of that. Now traditionally what happens is that with every wave of infrastructure advancement, comes a new set of applications and businesses, so going back to our olden days but, there was a time where you started to get a converged IP network, or you put data and voice together on an IP network, and then came voice over IP. Then came cloud computing. And you can do internet search, and you know, we're old enough to remember before then. Then you got 3G, and instead of just having the cell phone, you could do mobile apps on cell phones, so you had mobile apps. And then with 4G, you could do mobile video, and now you just expect it. Now you could think, okay the infrastructure is done, but no there's more, so some of the things that are happening right now that's really exciting is that, is I kind of talked about it in three areas. In networking we a have a couple really big things going on, which is Wifi Six and 5G. And so there's a whole site and we'll talk more about that. In computing there's the fact that actually GPU's are everywhere, and with that you can do AIML everywhere. So AI and machine learning. And then the third one is just an advancement in architecture. We knew that we'd move to mobile, we knew that we moved to compute, but now what becomes real is the edge. Edge computing. And so when you bring these things together you have new capabilities in network with Wifi Six and 5G, you have new capabilities in computing because GPU's are everywhere so you can do AI and ML, and then you actually have a spot at the edge where you can do edge processing, and then all of a sudden there's this whole new world of applications just waiting to be built, and we want to let developers know that. Because you kind of develop and you build from what you know, like oh this is just how good I can do, but there's a whole new capability coming. >> Well first of all, let's unpack those talk chats, because one of the things that I, as an entrepreneur, you know we've always talked about this, the creativity that comes from entrepreneurial thinking, whether your a true entrepreneur starting a company, or within a company doing that inside a company, takes creative juices, you got to have that catalyst as you mentioned, but also you got to imagine new ideas, right. And so by enabling, say Wifi for instance, everyone knows what Wifi is, but when you think about the new advances of Wifi and having connectivity with wireless and wired networks, with new data access, it just opens up this creative outlet. This is going to be the tsunami or the renaissance of applications. And you've been talking about it. >> It is, it is. And so like if we kind of geek out, because I was working on HD TV before it really became HD TV and their doing things like OFDM, and you know, we're so excited, spread spectrum technologies, but right now with Wifi Six, we can really geek out again. So OFDM is moving to OFDMA, OFDM multiple access. That means like, an access point usually talks to one client at a time, but now it can split up and talk to multiple clients at a time. And with that you can actually get much higher capacity, right so you can actually really use your kind of, network more efficiently, and then you can actually now also do scheduling. And then you can actually guarantee that a client is going to be scheduled in and get transmissions. That changes what you can do with Wifi and the way you think about it. And then there's this power savings, because now we can tell a device the time to wake up, so you kind of sleep sleep sleep sleep sleep, here's your target wake up time. Sleep sleep sleep sleep sleep, here's your target wake up time, that extends battery life, so you can have sensors that'll be out there for one year, five years, ten years, doing its thing. And so that takes all those IoT applications you've always wanted to build, but makes them real. Because someone has to go up and install that sensor, and the battery life matters. >> And so the second wave is the GPU anywhere which I like, because when you think of GPUS, Nvidia, you're thinking of graphics, you're thinking of gaming, but it's actually a processor for machine learning, so what are your thoughts on this because if you put GPUs in devices everywhere, and the data that you're now accessing across the network brings more intelligence. What's the impact of this GPU anywhere? Is it just IoT, is it just applications, what's the net net? >> So kind of, the most important thing about it is that before, you kind of needed to have a PhD to do AI and machine learning, right? And we have friends who are experts at that and they're continuing to push the envelope in there. I was just back at MIT and just the advances in AL and ML is amazing. But the other thing that's happening is that this is just getting wrapped up so developers can just use it. So you can actually have a TensorFlow.js library that'll just sit on your mobile device. You can actually just using your browser, you can actually write a web app that uses that and then uses the GPU, which just means right there you can write a little web app, with like five lines of code, you can say, find all the people in this picture. Find the bottles in this picture. Right so just be like, doing that on the fly, and you don't have to have a PhD in machine learning, you can actually, developers can just use this capability. And so that's kind of what unlocks it, is just because it's accessible to everyone and now you'll get that mixed wave of innovation when people can just use it and find the right applications for it. >> So looking at these three big changes that you've talked about, network, compute, architectural, did you leverage these big waves to design this years Create? Because we're hearing all about the three technologies tracks. Tell us a little bit about that. >> It is, well so first of all we have Wifi Six here, live, and people know there's the idea of it, we've done some performance tests around it and we're like it screams. You know, it just, it really does scream, and you're used to not counting on that, right? And so it opened up peoples' eyes and they're thinking differently now about what they can do here. >> What sort of reactions of the geeks at Cisco when they look at the data of Wifi Six, what's some of the anecdotal reactions that they're saying? >> People are surprised, cause everybody's kind of cynical about it. Cause, quite honestly, even getting ready for it, it just like guys we're going to jump on Wifi Six. And they're like eh, yeah, well, whatever. And then one of my guys Oshitosha went off and did the speed test and he started working with it and he came back into my office, his eyes were popped out of his head, (gasp), that's fast. >> And you showed that yesterday, all the cameras came in like, whoa! >> Because you don't have that expectation, but once you know it, it's going to really unleash this whole new set of things. There's actually something else interesting we did with the edge processing with the GPUs which is the idea of edge computing, not a new idea, the reality of it, is still coming into play. Now what happens is Cisco just announced some new products. These industrial routers, it's an industrial gateway, it means that you can like put it up on the telephone pole, you can put it into a manufacturing plant, you know at high temperatures, and it's the gateway that will connect all of your devices and senors, and be the networking conduit to get everything back. So that's an awesome product, the mass product actually hosts applications. And what matters is the deployment of these infrastructures, right? So Cisco's partners will get out there, they're going to sell and kind of install this networking equipment in manufacturing companies, but now it can host applications so developers can actually reach it. And so now that's a place for developers, but we're doing something new here, which is that we have a prototype of taking that product, we have a prototype GPU, a Nvidia Jetson that we've put on top of it, and we're letting developers hack at it. And say, would you use this? Like, tell us some of your best ideas, try it out. Because we still need to figure out the market and what's there, and we're doing it with developers. >> And where do they go with the creativity there? Because obviously one's a gateway so they're used to gateways, and they understand edge devices. What are some of the ideas that are going to come out of hacking a GPU? Is it running data analytics on the edge? Is it hosting an application and managing edge devices themselves? What are some of the cool things? >> I mean things like video sensing. So now like at your edge you have lots of cameras and because you can do GPU processing, you can actually take these multi-camera inputs, do video sensing algorithms, you know things that you kind of dreamed about before, but now just doing that for real. You know, finding construction workers, finding the hard hats, in the images to make sure that you can actually have people be safe. One thing that we know about AI and machine learning, is like a lot of times people say, okay I'm going to hire a data scientist, a data scientist comes in, and they can't really get the data. Like they don't have anything to work on until there's a good data set to work on. Well actually as you connect up these environments, that's one data set coming in. So you connect up like transportation systems, like SCADA, like utilities protocols, you're actually talking to manufacturing equipment. >> Real time data from traffic, Teslas. >> Exactly. And so that stuff comes in, but then you need to kind of munch on that data to know, when should I be looking, how can I get it into a form that I can do some AI and machine learning on it. >> So new use cases, you expect new use cases to emerge? >> They are, and it's really cool because there was a time when there was all of this stuff you could do on the web, and in the cloud, and with our applications, but it's coming back to the physical world. >> And that's what you mean by the edge, is then this architectural thing, that's really the edge. The new architecture of having these kinds of capabilities is going to create sets of applications that we've never seen before. New startups, new applications. >> It is and really the kind of thing with DevNet Create is bringing in the community of people who do install infrastructure, knowing that this infrastructure is becoming programmable, and having that able to host the applications and the innovations that are coming from the developers, it's like, it just unlocks entirely new business models. And I think here these two communities are meeting and mixing, and I think that's the energy that we're seeing out here. Because they didn't expect to talk to each other. When we started DevNet Create, we knew that it was coming, we didn't know how the people would mix, and this has evolved to where people are mixing in entirely new ways and making connections, and someone who's written an app is like, oh, you're a partner, you can deploy this in all different countries, that's a new kind of deployment model for my app. >> We talked a little bit about that yesterday, with our guest as well as Mandy, and you've got these kind of different worlds colliding, but one of the things that John pointed out, is that this is not a marketing driven event, this is not for lead generation, this is a truly collaborative event, and you're getting clearly developers and infrastructure guys and girls from clearly, very probably, computing companies who are sharing. So I can imagine the cultural change that this can bring to, born in the cloud, traditional enterprise, maybe something that wasn't originally planned, but I can just imagine these worlds colliding and seeing how much better they can work together. >> And that is something that with DevNet, if you even go to the world of networking and IT and you know, just enterprises, there's a new model. So things become programmable, people's biggest problem is automation, doing things at scale, like how do I go ahead and deploy my networks across all these sites around the world? You can automate that. How do I take machinery and get business insights from that so I can actually use it for more, you know, you want to do that in software. And so you have to change your mindset cause then it is about collaboration, it's about sharing software and everyone knows that they can get there faster by sharing code and ending up with a code repository, we have code exchange, that we've created in DevNet, we've just opened it up last year, we now have over 400 repos, we just crossed over 400 on there. >> You guys are changing the way people are doing work within your own community, both DevNet and DevNet Create, bringing those worlds together. And it's working, it's magical so congratulations on all the success you've had. I got to ask you about your journey because we've talked years before you even joined Cisco and we've been following and talking to you since you've been here, and I was saying on our opening yesterday, Cisco as a company is like a big aircraft carrier, it's making the big move right, and you're seeing Chuck Robinson, the CEO, cloud, everything has APIs on, every portfolio project got APIs, so he's the pulling company into telemove, which is let's get cloudified, let's figure out our role in cloud computing and beyond, and you're mentioning some of those things, as you continue to show progress in the growth of DevNet and the community, it's changing Cisco. And we're seeing as we cover with theCUBE, and Chuck's called you out publicly and said Susie, great job, so this is a recognition that DevNet and the work that you and your team are doing is changing the face of Cisco internally and externally. How is that going, as the battleship starts to move, and by the way, data center is still more important than ever before with fibrated multicloud, things are lining up for Cisco, and you're a big part of it. What's going on in the company, and what's Chuck Robinson saying to you in your meetings with him, like hey, good job, or let's double down. >> Yeah, no Chuck is an amazing leader. And Chuck completely understands the vision, and that's why he's been supporting DevNet. So he's been supporting DevNet, not just because oh, he likes Susie or anything like that, it's because he understands the importance of programmability he understands what it means for starting new businesses and creating new business models. What it means for the ecosystem to grow into it, what that opportunity is. So he's always understood it, and I'm super lucky because he's been supporting these efforts. But now what's happening is of course he wants more. And I just presented to Chuck and his executive leadership team last week, about the plans that we have going forward. We've actually just kind of, what I would say is that, we've done the MVP of DevNet, so I know that you know, we've got the half a million members, actually almost 600 thousand >> Product market fit, it's all there >> We know have like, real assets, we have a real community, we have companies that are changing how they work, using our assets and really forming in this community, and now to get it to the next level, he's actually really kind of, sponsoring and working with us to develop it to the next level. And really the team is all coming together. The engineering team, the customer experience teams, sales and marketing, and then how we work externally with all of our communities. And so we're really growing into the next level. >> And you've got a great team, you know we've worked with all of you team, a lot of your team, but one of things that I like about what you've done here, is that, and you said it yesterday on stage at closing keynote, you feel like a star, you used the word MVP, minimum viable product. That's a startup word. So you have this startup culture, and you're in a big company so it's working. Is it contagious, are people, are there antibodies coming at you, are there people joining you, what's going on because how do you keep that startup vibe going. >> Yeah, I think that I'm just very fortunate because my team all has that attitude, they're very externally driven, so they're like, how do I help our developers, how do we help our community, how do bring them along, and we totally drive ourselves by that. And then we're constantly asking them how can we help you more, what do you want from us, and they say if we're doing something that's not useful to you, tell us now so we can stop, so we can build something else. And so we continue to evolve. And so we actually listen and then we really figure out how to go to that next level. Now what's really fun is that also though, we work with all of the other organizations, right, so you know I'm not going to replicate the sales force, we work with them, I'm not going to replicate the SEs that are out on the field. They're using DevNet, and they're running their own DevNet express events in their countries for their partners and customers. So we've really built out, really collaboratively and we've gotten so much support. And the first days, everyone was like, hey, guys you have a software strategy, you need to look at developers, you need APIs, and they're like nice job Susie, yes. Keep on going. >> You're bringing the Dev Ops ethos to the culture. DevNet's an API to all the other organizations. >> Well and now that we are where we are, it's just, it's the partnerships like our product teams are investing and improving their APIs. We advocate for the developers viewpoint into those, and it's a collaboration. Like so I don't make the products, our product teams make the products. I don't sell the products, our sales team sells the products. Right, so we've really brought together the forces and we're fortunate because everyone is joining in. >> Well it sounds to me like what DevNet is doing, is really driving this organic cultural evolution within Cisco. Is it, would you say, and maybe I'm making a leap here, it sounds to me, like what I've seen, and this is my first DevNet as well, is that DevNet seems to be an accelerator of Cisco's evolution. >> I would it's an accelerator, and you know, what I want to say is that we have great efforts going on across the company, and people are trying to figure it out. So I can't say I'm the one driving it, that would just be too much to say. But we are trying to accelerate each other's efforts and now that we've grown a community, we've provided a platform. Like, we do get more than a million eyeballs a month onto our site. And we use that as a channel, so we really working to accelerate and kind of catalyze each other's efforts. >> And if you step out and zoom out, you can see how it all hangs together. You've got APIs in all the products, so that's an enabler. You have developer onboarding of new kinds of customers and existing ones melting together, kind of in the same melting pot of developers, and you got the cloud wave behind you, and Ad Gen AI. And then you can see Cisco becoming multicloud, it's almost like it's feeding and turning in the right spot, where, I mean you don't have a cloud, but I mean you have connectivity, you have data, you have Dev Ops, Net Dev, so it seems like a nice positioning for the future. But you have all this other revenue and customers, so it's going to take some time. >> We have great products. Our products five years ago, we had handful of products with APIs. Now, our whole portfolio is programmable. So that's not my efforts, those are the product teams building great products, and entering this world of programmability. We're bringing in the community and giving them the tools so they can use them, right? So otherwise you can't just make a product and have it sit there, you need to help it come along. >> Okay, what was your presentation to Chuck? What's the vision? Where do you go next? You've got some great momentum, congratulations on the success, we love being a part of this, a lot of action. It's very inspiring and intoxicating at the same time, what's next, what's the vision? >> Yeah, so really if we, and I love the way that we've built up DevNet, is because we started with our developers, and the communities that needed to become developers, or power users of software. So, we've done the technical enablement, like we have documented APIs, we have learning labs, we have sandboxes so people can just code. So we've really been focusing on enabling them and providing all that technical enablement. And now what happens is people are asking us, how do I make this real, how do I spread this across my organization, how do I bring these solutions to my customers and then to the world? And in order to do that I need to change how I do manufacturing, in order to do this, I need to change how we build solutions, and so help us with that fuller solution, so we're really stepping up to go beyond the technical enablement, to just bringing it to reality, and to real solutions that are in operational environments, and so it's just really exciting to be working together on all that. And then we'll have a bunch more new stuff coming that we'll talk about at Cisco Live. >> And you have a great party at Cisco Live, you also have those social club event, you got to keep that going, right? >> Of course, we'll keep the social club going and we'll have a bunch of new things to announce at Cisco Live as well. >> It's starting in just a few weeks from now, so last question, your takeaway from this, some of the anecdotes that you've heard the last day and a half of DevNet Create 3. >> Yeah, so you know, kind of the vision that we had set forward. And it's one that we've been thinking about it, it's just that the infrastructure really enables a new set of applications and business models. And we had the idea of it, but again with these advances that we talked about, with Wifi Six and 5G, with GPUs enabling AI and machine learning, and with edge computing, is that people get it. And people know that it's not like some day you will have this, and some day you will have that, which I've been in research, I know that view. But it's actually like right here and right now. >> Making it real. >> Making it real, and it's available for people to use, like this next one to two years is going to be super exciting for the industry, cause it's not just theoretical, it's not just what it could do, but there's real goals that are right out there for people to develop exciting new things. >> I wish I was younger, I wish I was in my 20s, I mean like. >> It's okay, we take old people and young people all together, diversity, yes. >> More inclusion, young and old. It's so exciting because it's such an enablement, and knowing what's the megatrends that are the real waves, it's actually real, it's happening. >> And I actually want to, while we do talk about diversity and inclusion and enablement, what's really exciting is I just brought us that, we have some of our partners who are transforming themselves, and we actually have some women in tech initiatives that have started out. >> I love that, tell us about it. >> Okay so, Presidio, Verizon, they've invested in helping the women in their organizations, well they're helping everybody evolve to embrace programmability and automation to understand the application, you know the opportunities there. So they are fully, kind of, taking this paradigm and transforming their workforces to embrace it. But in addition we've partnered to also provide extra support, and call out for the women who are making the journey, and who have to, you know, face maybe some additional challenges, or just ensuring that they have the opportunity and they get the visibility, and they've both sponsored, so Presidio, Verizon, have both sponsored bringing some of their women to DevNet Create. >> I loved how you brought them on stage this morning, without telling them. They endeavor you, and you just had this genuinely enormous smile of pride. >> I'm so proud of them. >> And you should be. But that's amazing that Cisco and DevNet is also making that investment in women in technology. >> And we're doing it together with them and I'm just proud of what their doing, and this is the workforce. You saw the women up on stage if you guys watched the keynote, you'll see that it's out there. These are the people you want to hire, and why would you not use that workforce. >> Exactly, why would you not? >> And get them all young too, like you mentioned your daughter, when she starts putting the Meraki switch at home, you know you've made it. She's almost ready. >> Yes she's handling a computer for me already, she's like mommy you have two, how come I don't have one? >> She says mommy why are you using command line? >> That's next! Susie, you're an inspiration, an inspirational female in technology, we all often gravitate towards Sheryl Sandberg. I think we should start including Susie Wee in that. Thank you so much, >> No thank you very much. For having us at DevNet, it's been a pleasure to meet you, and have the chance to interview you, and we can't wait to see where do you go from here. >> We will continue to change the world together, thank you. >> I love it. Awesome. For John Furrier, I'm Lisa Martin. You're watching theCUBE live, from Cisco DevNet Create 2019. Thanks for watching. (upbeat music)

(uplifting orchestral music) >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the Palo Alto studios, having theCUBE Conversation, a little break in the action of the conference season before things heat up, before we kind of come to the close of 2018. It's been quite a year. But it's nice to be back in the studio. Things are a little bit less crazy, and we're excited to talk about one of the really hot topics right now, which is edge computing, fog computing, cloud computing. What do all these things mean, how do they all intersect, and we've got with us today David King. He's the CEO of FogHorn Systems. David, first off, welcome. >> Thank you, Jeff. >> So, FogHorn Systems, I guess by the fog, you guys are all about the fog, and for those that don't know, fog is kind of this intersection between cloud, and on prem, and... So first off, give us a little bit of the background of the company and then let's jump into what this fog thing is all about. >> Sure, actually, it all dovetails together. So yeah, you're right, FogHorn, the name itself, came from Cisco's invented term, called fog computing, from almost a decade ago, and it connoted this idea of computing at the edge, but didn't really have a lot of definition early on. And so, FogHorn was started actually by a Palo Alto Incubator, just nearby here, that had the idea that hey, we got to put some real meaning and some real meat on the bones here, with fog computing. And what we think FogHorn has become over the last three and a half years, since we took it out of the incubator, since I joined, was to put some real purpose, meaning, and value in that term. And so, it's more than just edge computing. Edge computing is a related term. In the industrial world, people would say, hey, I've had edge computing for three, 40, 50 years with my production line control and also my distributed control systems. I've got hard wired compute. I run, they call them, industrial PCs in the factory. That's edge compute. The IT roles come along and said, no, no, no, fog compute is a more advanced form of it. Well, the real purpose of fog computing and edge computing, in our view, in the modern world, is to apply what has traditionally been thought of as cloud computing functions, big, big data, but running in an industrial environment, or running on a machine. And so, we call it as really big data operating in the world's smallest footprint, okay, and the real point of this for industrial customers, which is our primary focus, industrial IoT, is to deliver as much analytic machine learning, deep learning AI capability on live-streaming sensor data, okay, and what that means is rather than persisting a lot of data either on prem, and then sending it to the cloud, or trying to stream all this to the cloud to make sense of terabytes or petabytes a day, per machine sometimes, right, think about a jet engine, a petabyte every flight. You want to do the compute as close to the source as possible, and if possible, on the live streaming data, not after you've persisted it on a big storage system. So that's the idea. >> So you touch on all kinds of stuff there. So we'll break it down. >> Unpack it, yeah. >> Unpack it. So first off, just kind of the OT/IT thing, and I think that's really important, and we talked before turning the cameras on about Dr. Tom from HP, he loves to make a big symbolic handshake of the operations technology, >> One of our partners. >> Right, and IT, and the marriage of these two things, where before, as you said, the OT guys, the guys that have been running factories, you know, they've been doing this for a long time, and now suddenly, the IT folks are butting in and want to get access to that data to provide more control. So, you know, as you see the marriage of those two things coming together, what are the biggest points of friction, and really, what's the biggest opportunity? >> Great set of questions. So, quite right, the OT folks are inherently suspicious of IT, right? I mean, if you don't know the history, 40 plus years ago, there was a fork in the road, where in factory operations, were they going to embrace things like ethernet, the internet, connected systems? In fact, they purposely air gapped an island of those systems 'cause they was all about machine control, real-time, for safety, productivity, and uptime of the machine. They don't want any, you can't use kind of standard ethernet, it has to be industrial ethernet, right? It has to have time bound and deterministic. It can't be a retry kind of a system, right? So different MAC layer for a reason, for example. What did the physical wiring look like? It's also different cabling, because you can't have cuts, jumps in the cable, right? So it's a different environment entirely that OT grew up in, and so, FogHorn is trying to really bring the value of what people are delivering for AI, essentially, into that environment in a way that's non-threatening to, it's supplemental to, and adds value in the OT world. So Dr. Tom is right, this idea of bringing IT and OT together is inherently challenging, because these were kind of fork in the road, island-ed in the networks, if you will, different systems, different nomenclature, different protocols, and so, there's a real education curve that IT companies are going through, and the idea of taking all this OT data that's already been produced in tremendous volumes already before you add new kinds of sensing, and sending it across a LAN which it's never talked to before, then across a WAN to go to a cloud, to get some insight doesn't make any sense, right? So you want to leverage the cloud, you want to leverage data centers, you want to leverage the LAN, you want to leverage 5G, you want to leverage all the new IT technologies, but you have to do it in a way that makes sense for it and adds value in the OT context. >> I'm just curious, you talked about the air gapping, the two systems, which means they are not connected, right? >> No, they're connected with a duct, they're connected to themselves, in the industrial-- >> Right, right, but before, the OT system was air gapped from the IT system, so thinking about security and those types of threats, now, if those things are connected, that security measure has gone away, so what is the excitement, adoption scare when now, suddenly, these things that were separate, especially in the age of breaches that we know happen all the time as you bring those things together? >> Well, in fact, there have been cyber breaches in the OT context. Think about Stuxnet, think about things that have happened, think about the utilities back keys that were found to have malwares implanted in them. And so, this idea of industrial IoT is very exciting, the ability to get real-time kind of game changing insights about your production. A huge amount of economic activity in the world could be dramatically improved. You can talk about trillions of dollars of value which the McKenzie, and BCG, and Bain talk about, right, by bringing kind of AI, ML into the plant environment. But the inherent problem is that by connecting the systems, you introduce security problems. You're talking about a huge amount of cost to move this data around, persist it then add value, and it's not real-time, right? So, it's not that cloud is not relevant, it's not that it's not used, it's that you want to do the compute where it makes sense, and for industrial, the more industrialized the environment, the more high frequency, high volume data, the closer to the system that you can do the compute, the better, and again, it's multi-layer of compute. You probably have something on the machine, something in the plant, and something in the cloud, right? But rather than send raw OT data to the cloud, you're going to send processed intelligent metadata insights that have already been derived at the edge, update what they call the fleet-wide digital twin, right? The digital twin for that whole fleet of assets should sit in the cloud, but the digital twin of the specific asset should probably be on the asset. >> So let's break that down a little bit. There's so much good stuff here. So, we talked about OT/IT and that marriage. Next, I just want to touch on cloud, 'cause a lot of people know cloud, it's very hot right now, and the ultimate promise of cloud, right, is you have infinite capacity >> Right, infinite compute. >> Available on demand, and you have infinite compute, and hopefully you have some big fat pipes to get your stuff in and out. But the OT challenge is, and as you said, the device challenge is very, very different. They've got proprietary operating systems, they've been running for a very, very long time. As you said, they put off boatloads, and boatloads, and boatloads of data that was never really designed to feed necessarily a machine learning algorithm, or an artificial intelligence algorithm when these things were designed. It wasn't really part of the equation. And we talk all the time about you know, do you move the compute to the data, you move the data to the compute, and really, what you're talking about in this fog computing world is kind of a hybrid, if you will, of trying to figure out which data you want to process locally, and then which data you have time, relevance, and other factors that just go ahead and pump it upstream. >> Right, that's a great way to describe it. Actually, we're trying to move as much of the compute as possible to the data. That's really the point of, that's why we say fog computing is a nebulous term about edge compute. It doesn't have any value until you actually decide what you're trying to do with it, and what we're trying to do is to take as much of the harder compute challenges, like analytics, machine learning, deep learning, AI, and bring it down to the source, as close to the source as you can, because you can essentially streamline or make more efficient every layer of the stack. Your models will get much better, right? You might have built them in the cloud initially, think about a deep learning model, but it may only be 60, 70% accurate. How do you do the improvement of the model to get it closer to perfect? I can't go send all the data up to keep trying to improve it. Well, typically, what happens is I down sample the data, I average it and I send it up, and I don't see any changes in the average data. Guess what? We should do is inference all the time and all the data, run it in our stack, and then send the metadata up, and then have the cloud look across all the assets of a similar type, and say, oh, the global fleet-wide model needs to be updated, and then to push it down. So, with Google just about a month ago, in Barcelona, at the IoT show, what we demonstrated was the world's first instance of AI for industrial, which is closed loop machine learning. We were taking a model, a TensorFlow model, trained in the cloud in the data center, brought into our stack and referring 100% inference-ing in all the live data, pushing the insights back up into Google Cloud, and then automatically updating the model without a human or data scientist having to look at it. Because essentially, it's ML on ML. And that to us, ML on ML is the foundation of AI for industrial. >> I just love that something comes up all the time, right? We used to make decisions based on the sampling of historical data after the fact. >> That's right, that's how we've all been doing it. >> Now, right, right now, the promise of streaming is you can make it based on all the data, >> All the time. >> All the time in real time. >> Permanently. >> This is a very different thing. So, but as you talked about, you know, running some complex models, and running ML, and retraining these things. You know, when you think of edge, you think of some little hockey puck that's out on the edge of a field, with limited power, limited connectivity, so you know, what's the reality of, how much power do you have at some of these more remote edges, or we always talk about the field of turbines, oil platforms, and how much power do you need, and how much compute that it actually starts to be meaningful in terms of the platform for the software? >> Right, there's definitely use cases, like you think about the smart meters, right, in the home. The older generation of those meters may have had very limited compute, right, like you know, talking about single megabyte of memory maybe, or less, right, kilobytes of memory. Very hard to run a stack on that kind of footprint. The latest generation of smart meters have about 250 megabytes of memory. A Raspberry Pi today is anywhere from a half a gig to a gig of memory, and we're fundamentally memory-bound, and obviously, CPU if it's trying to really fast compute, like vibration analysis, or acoustic, or video. But if you're just trying to take digital sensing data, like temperature, pressure, velocity, torque, we can take humidity, we can take all of that, believe it or not, run literally dozens and dozens of models, even train the models in something as small as a Raspberry Pi, or a low end x86. So our stack can run in any hardware, we're completely OS independent. It's a full up software layer. But the whole stack is about 100 megabytes of memory, with all the components, including Docker containerization, right, which compares to about 10 gigs of running a stream processing stack like Spark in the Cloud. So it's that order of magnitude of footprint reduction and speed of execution improvement. So as I said, world's smallest fastest compute engine. You need to do that if you're going to talk about, like a wind turbine, it's generating data, right, every millisecond, right. So you have high frequency data, like turbine pitch, and you have other conceptual data you're trying to bring in, like wind conditions, reference information about how the turbine is supposed to operate. You're bringing in a torrential amount of data to do this computation on the fly. And so, the challenge for a lot of the companies that have really started to move into the space, the cloud companies, like our partners, Google, and Amazon, and Microsoft, is they have great cloud capabilities for AI, ML. They're trying to move down to the edge by just transporting the whole stack to there. So in a plant environment, okay, that might work if you have massive data centers that can run it. Now I still got to stream all my assets, all the data from all of my assets to that central point. What we're trying to do is come out the opposite way, which is by having the world's smallest, fastest engine, we can run it in a small compute, very limited compute on the asset, or near the asset, or you can run this in a big compute and we can take on lots and lots of use cases for models simultaneously. >> I'm just curious on the small compute case, and again, you want all the data-- >> You want to inference another thing, right? >> Does it eventually go back, or is there a lot of cases where you can get the information you need off the stream and you don't necessarily have to save or send that upstream? >> So fundamentally today, in the OT world, the data usually gets, if the PLC, the production line controller, that has simple KPIs, if temperature goes to X or pressure goes to Y, do this. Those simple KPIs, if nothing is executed, it gets dumped into a local protocol server, and then about every 30, 60, 90 days, it gets written over. Nobody ever looks at it, right? That's why I say, 99% of the brown field data in OT has never really been-- >> Almost like a security-- >> Has never been mined for insight. Right, it just gets-- >> It runs, and runs, and runs, and every so often-- >> Exactly, and so, if you're doing inference-ing, and doing real time decision making, real time actual with our stack, what you would then persist is metadata insights, right? Here is an event, or here is an outcome, and oh, by the way, if you're doing deep learning or machine learning, and you're seeing deviation or drift from the model's prediction, you probably want to keep that and some of the raw data packets from that moment in time, and send that to the cloud or data center to say, oh, our fleet-wide model may not be accurate, or may be drifting, right? And so, what you want to do, again, different horses for different courses. Use our stack to do the lion's share of the heavy duty real time compute, produce metadata that you can send to either a data center or a cloud environment for further learning. >> Right, so your piece is really the gathering and the ML, and then if it needs to go back out for more heavy lifting, you'll send it back up, or do you have the cloud application as well that connects if you need? >> Yeah, so we build connectors to you know, Google Cloud Platform, Google IoT Core, to AWS S3, to Microsoft Azure, virtually any, Kafka, Hadoop. We can send the data wherever you want, either on plant, right back into the existing control systems, we can send it to OSIsoft PI, which is a great time series database that a lot of process industries use. You could of course send it to any public cloud or a Hadoop data lake private cloud. You can send the data wherever you want. Now, we also have, one of our components is a time series database. You can also persist it in memory in our stack, just for buffering, or if you have high value data that you want to take a measurement, a value from a previous calculation and bring it into another calculation during later, right, so, it's a very flexible system. >> Yeah, we were at OSIsoft PI World earlier this year. Some fascinating stories that came out of-- >> 30 year company. >> The building maintenance, and all kinds of stuff. So I'm just curious, some of the easy to understand applications that you've seen in the field, and maybe some of the ones that were a surprise on the OT side. I mean, obviously, preventative maintenance is always towards the top of the list. >> Yeah, I call it the layer cake, right? Especially when you get to remote assets that are either not monitored or lightly monitored. They call it drive-by monitoring. Somebody shows up and listens or looks at a valve or gauge and leaves. Condition-based monitoring, right? That is actually a big breakthrough for some, you know, think about fracking sites, or remote oil fields, or mining sites. The second layer is predictive maintenance, which the next generation is kind of predictive, prescriptive, even preventive maintenance, right? You're making predictions or you're helping to avoid downtime. The third layer, which is really where our stack is sort of unique today in delivering is asset performance optimization. How do I increase throughput, how do I reduce scrap, how do I improve worker safety, how do I get better processing of the data that my PLC can't give me, so I can actually improve the performance of the machine? Now, ultimately, what we're finding is a couple of things. One is, you can look at individual asset optimization, process optimization, but there's another layer. So often, we're deployed to two layers on premise. There's also the plant-wide optimization. We talked about wind farm before, off camera. So you've got the wind turbine. You can do a lot of things about turbine health, the blade pitch and condition of the blade, you can do things on the battery, all the systems on the turbine, but you also need a stack running, like ours, at that concentration point where there's 200 plus turbines that come together, 'cause the optimization of the whole farm, every turbine affects the other turbine, so a single turbine can't tell you speed, rotation, things that need to change, if you want to adjust the speed of one turbine, versus the one next to it. So there's also kind of a plant-wide optimization. Talking about time that's driving, there's going to be five layers of compute, right? You're going to have the, almost what I call the ECU level, the individual sub-system in the car that, the engine, how it's performing. You're going to have the gateway in the car to talk about things that are happening across systems in the car. You're going to have the peer to peer connection over 5G to talk about optimization right between vehicles. You're going to have the base station algorithms looking at a micro soil or macro soil within a geographic area, and of course, you'll have the ultimate cloud, 'cause you want to have the data on all the assets, right, but you don't want to send all that data to the cloud, you want to send the right metadata to the cloud. >> That's why there are big trucks full of compute now. >> By the way, you mentioned one thing that I should really touch on, which is, we've talked a lot about what I call traditional brown field automation and control type analytics and machine learning, and that's kind of where we started in discrete manufacturing a few years ago. What we found is that in that domain, and in oil and gas, and in mining, and in agriculture, transportation, in all those places, the most exciting new development this year is the movement towards video, 3D imaging and audio sensing, 'cause those sensors are now becoming very economical, and people have never thought about, well, if I put a camera and apply it to a certain application, what can I learn, what can I do that I never did before? And often, they even have cameras today, they haven't made use of any of the data. So there's a very large customer of ours who has literally video inspection data every product they produce everyday around the world, and this is in hundreds of plants. And that data never gets looked at, right, other than training operators like, hey, you missed the defects this day. The system, as you said, they just write over that data after 30 days. Well, guess what, you can apply deep learning tensor flow algorithms to build a convolutional neural network model and essentially do the human visioning, rather than an operator staring at a camera, or trying to look at training tapes. 30 days later, I'm doing inference-ing of the video image on the fly. >> So, do your systems close loop back to the control systems now, or is it more of a tuning mechanism for someone to go back and do it later? >> Great question, I just got asked that this morning by a large oil and gas super major that Intel just introduced us to. The short answer is, our stack can absolutely go right back into the control loop. In fact, one of our investors and partners, I should mention, our investors for series A was GE, Bosch, Yokogawa, Dell EMC, and our series debuted a year ago was Intel, Saudi Aramco, and Honeywell. So we have one foot in tech, one foot in industrial, and really, what we're really trying to bring is, you said, IT, OT together. The short answer is, you can do that, but typically in the industrial environment, there's a conservatism about, hey, I don't want to touch, you know, affect the machine until I've proven it out. So initially, people tend to start with alerting, so we send an automatic alert back into the control system to say, hey, the machine needs to be re-tuned. Very quickly, though, certainly for things that are not so time-sensitive, they will just have us, now, Yokogawa, one of our investors, I pointed out our investors, actually is putting us in PLCs. So rather than sending the data off the PLC to another gateway running our stack, like an x86 or ARM gateway, we're actually, those PLCs now have Raspberry Pi plus capabilities. A lot of them are-- >> To what types of mechanism? >> Well, right now, they're doing the IO and the control of the machine, but they have enough compute now that you can run us in a separate module, like the little brain sitting right next to the control room, and then do the AI on the fly, and there, you actually don't even need to send the data off the PLC. We just re-program the actuator. So that's where it's heading. It's eventually, and it could take years before people get comfortable doing this automatically, but what you'll see is that what AI represents in industrial is the self-healing machine, the self-improving process, and this is where it starts. >> Well, the other thing I think is so interesting is what are you optimizing for, and there is no right answer, right? It could be you're optimizing for, like you said, a machine. You could be optimizing for the field. You could be optimizing for maintenance, but if there is a spike in pricing, you may say, eh, we're not optimizing now for maintenance, we're actually optimizing for output, because we have this temporary condition and it's worth the trade-off. So I mean, there's so many ways that you can skin the cat when you have a lot more information and a lot more data. >> No, that's right, and I think what we typically like to do is start out with what's the business value, right? We don't want to go do a science project. Oh, I can make that machine work 50% better, but if it doesn't make any difference to your business operations, so what? So we always start the investigation with what is a high value business problem where you have sufficient data where applying this kind of AI and the edge concept will actually make a difference? And that's the kind of proof of concept we like to start with. >> So again, just to come full circle, what's the craziest thing an OT guy said, oh my goodness, you IT guys actually brought some value here that I didn't know. >> Well, I touched on video, right, so without going into the whole details of the story, one of our big investors, a very large oil and gas company, we said, look, you guys have done some great work with I call it software defined SCADA, which is a term, SCADA is the network environment for OT, right, and so, SCADA is what the PLCs and DCSes connect over these SCADA networks. That's the control automation role. And this investor said, look, you can come in, you've already shown us, that's why they invested, that you've gone into brown field SCADA environments, done deep mining of the existing data and shown value by reducing scrap and improving output, improving worker safety, all the great business outcomes for industrial. If you come into our operation, our plant people are going to say, no, you're not touching my PLC. You're not touching my SCADA network. So come in and do something that's non-invasive to that world, and so that's where we actually got started with video about 18 months ago. They said, hey, we've got all these video cameras, and we're not doing anything. We just have human operators writing down, oh, I had a bad event. It's a totally non-automated system. So we went in and did a video use case around, we call it, flare monitoring. You know, hundreds of stacks of burning of oil and gas in a production plant. 24 by seven team of operators just staring at it, writing down, oh, I think I had a bad flare. I mean, it's a very interesting old world process. So by automating that and giving them an AI dashboard essentially. Oh, I've got a permanent record of exactly how high the flare was, how smoky was it, what was the angle, and then you can then fuse that data back into plant data, what caused that, and also OSIsoft data, what was the gas composition? Was it in fact a safety violation? Was it in fact an environmental violation? So, by starting with video, and doing that use case, we've now got dozens of use cases all around video. Oh, I could put a camera on this. I could put a camera on a rig. I could've put a camera down the hole. I could put the camera on the pipeline, on a drone. There's just a million places that video can show up, or audio sensing, right, acoustic. So, video is great if you can see the event, like I'm flying over the pipe, I can see corrosion, right, but sometimes, like you know, a burner or an oven, I can't look inside the oven with a camera. There's no camera that could survive 600 degrees. So what do you do? Well, that's probably, you can do something like either vibration or acoustic. Like, inside the pipe, you got to go with sound. Outside the pipe, you go video. But these are the kind of things that people, traditionally, how did they inspect pipe? Drive by. >> Yes, fascinating story. Even again, I think at the end of the day, it's again, you can make real decisions based on all the data in real time, versus some of the data after the fact. All right, well, great conversation, and look forward to watching the continued success of FogHorn. >> Thank you very much. >> All right. >> Appreciate it. >> He's David King, I'm Jeff Frick, you're watching theCUBE. We're having a CUBE conversation at our Palo Alto studio. Thanks for watching, we'll see you next time. (uplifting symphonic music)

>> Narrator: Live from Orlando, Florida. It's theCUBE! Covering .conf18. Brought to you by Splunk. >> Welcome back to Orlando, everybody. Splunk .conf18. This is theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host, Stu Miniman. Carnival Cruise Lines is back. We heard from them yesterday, we heard them on the main stage of .conf. CEO is up there with Doug Merritt. Sheldon White is here. He's an enterprise architect at Carnival Cruise Line And Alex Taberras, who's the director of threat intelligence at Carnival. Gents, welcome to theCUBE. >> Thank you. >> Doing a lot of talk on security today. They've lined us up, which is great. We love the conversation. So much to learn. Alex, I'll start with you. When you think about security and threat intelligence, what are the big changes that you've seen over the last, whatever, pick a time. Half a decade? Decade? Couple of years even. >> Alex: So, it's just the amount of threats that are coming in now and how fast they're coming in, right? We can't seem to be keeping up with everything that's happening in the environment, everything that's happening outside, trying to get into our environment and cause all that damage, right? So, that's why Splunk is awesome, right? I get to see everything come in, real time. I'm able to quickly pinpoint any action I need to take, send it to my team and have them immediate right away. >> So, Sheldon, yesterday we had ship and shore from Carnival and he was talking about really different problems. You know, the folks on the ship, they got 250 thousand people on the ocean at any one point in time collecting data, trying to make a better experience, keep them connected. Folks on the shore, obviously, websites and things like that. Where do you fit into that mix of ship and shore? >> Sheldon: Right, so there's an entire value stream that we map out as enterprise architects. And so, what we do there is analyze all the customer touch points. And then we aggregate all of that information into a pipeline that we then address our audiences with those critical KPIs. Operational and infrastructure, the entire stack. >> Dave: You guys obviously have very strong relationship with Splunk. We heard from your CEO, Arnold Donald, right? >> Alex: Correct. >> Interesting name, I haven't messed that up yet so. (laughing) And so, where did that relationship start? Did it start in SecOps? Did it start in IT operations management? >> Alex: So, it really started in Devops, right? And they started... They purchased Splunk, I think back in like 2007, 2008. And they started looking at it, right? And I think I was talking to one of our other architects and it was one gig is what we started at, right? Now, we're upwards of 600 gigs. Just for security. So, it started there and it just kind of morphed into this huge relationship where we're partnering and touching all aspects of our business with Splunk. You know, and the Cloud and everything else. >> So, we heard, I don't know if you guys saw the key notes today, but we saw some announcements building on yesterday's Splunk next announcement. We heard some business workflow and some industrial IOT. I would think both of those are relevant for you guys. Not industrial IOT, but your IOT. Do you see Splunk permeating further into the organization? I guess, the answer's yes. You kind of already said that. But I'm interested in what role you guys play in facilitating that ? Are you kind of champions, evangelist, experts, consultants? How does that work? How do you see that (mumbles)? >> Sheldon: So, we see ourselves as internal consultants. We have our internal customers that depend on our guidance and our end-to-end view of the business processes. So, and now as enter our Cloud journey, into the second year of our Cloud journey, just we're able to accelerate our time to value for our internal customers to gain even greater insights into what's happening ship and shore. >> Dave: I wonder how, if you can talk about, how enterprise architecture has changed over the last decade even. You know, it used to be you were trying to harden the two tier or three tier architecture and harden top, don't touch it, it works. And then, of course, we all know, it created a lot of different stove pipes and a lot of data was locked into those stove pipes. That's changed, obviously. Cloud, now the Edge. Maybe because you guys were always sort of a distributed data company, you approached it differently. But I wondered if you could gives us (mumbles)? >> Sheldon: No, that's an interesting question. Because the evolution is not so much enterprise architect as it is eco system architect, right? So, now you have these massively distributed systems. So, you're really managing an eco system of internal and third party. And then all the relevant touch points, right? Like Alex mentioned, all that perimeters constantly shifting now. So, yeah, our focus is always aligning with the on-time business process and our internal customers. >> Yeah, wonder if we could dig into the Cloud a little. Alex, can we start with you? How does Cloud fit into your world of security? >> Alex: So, for me, the Cloud, as far as Splunk goes, it allows me to expand and contract as needed, right? So before, we used to have our on premise hardware, very finite RAM memory, I mean, disk space everything. So now, with the Cloud, I'm able to expand my environment as I move across all my North American brands, European brands, to be able to gather all that data, look at it and take action on it, right? >> Stu: And Sheldon, you're using AWS. We see they're, every software provider lives in AWS. It's often in the marketplace. We been seeing a lot this week that there's a deeper partnership. There's actually a lot of integration. Maybe give us your viewpoint on what you've seen on how Splunk and AWS work together to meet your requirements. >> Yeah. So, that's an interesting evolution as well of that partnership, right? So, you're starting to see things like the S3 API integration. So that you're removing storage from the critical path. And now that opens up different scale of possibilities, right? And internal opportunities. But yes, as you can see, leveraging the machine learning toolkit. I saw that one coming. It's going to be interesting to see how that keeps evolving, right? And also, like I was speaking to Alex, about the natural language capability. So, that also is well brought into the dimension of how our senior leadership with interact with these operational platforms. >> Yeah, I got to thank you. You're going to have your customer's natural language has to get into some of their rooms. It's definitely future. >> Sheldon: Oh, it's going to be apart of that value chain. Yeah, for sure. >> Dave: How does the S3 API integration affect you guys? Obviously, you got to put Syntax in an object store, which is going to scale. What does that mean for you guys? >> Sheldon: So, using the Splunk developer Cloud, we could develop all sorts of solutions to manage it intelligently how our storage, right? In near real time. So, we can completely automate and that end-to-end just integration with Splunk, how it ingest, how long that data stays relevant and how we offload it into things like Glacier. >> Dave: In the enablement, there is the S3 API. So, you're taking advantage of all the AWS automation tooling. >> Sheldon: Correct. >> Is that right? >> Sheldon: Correct. >> Alright. >> Sheldon: That's another example of that side integration. Not only with the S3 API. Lex, for the natural language. Obviously, TensorFlow and the machine learning toolkit. So, I think you're going to see that type of... those type of capabilities expanding as Splunk evolves. Next year, I'm sure they're going to have a ton of more, you know, announcements around how this evolution continues, right? >> Dave: So, you know, I was interested in the TensorFlow and Spark integration. And Stu and I were talking in an earlier segment. It's great, developers love that. We saw a lot of demos today that was like, looks so simple. Anybody could do it. Even I might be able to do it. But as practitioners of Splunk, is it really going to be that easy? Are business users actually going to be able to pick this stuff up and what are they going to have to do in order to take advantage of Splunk? Some training involved? >> Sheldon: Right, right. >> What's the learning curve going to be like? >> Sheldon: That's a great question, because there's a dual focus to this, right? First, is offloading from the developer. All that heavy lifting of creating this user interface and the dashboards, per say. Now, its all API driven. So, as you saw, maybe in the keynote this morning, that within the demo, was an API driven dashboard came together in several minutes. But one is offloading that and the second part is just enabling the business user with other capabilities, like natural language process. And they don't necessarily need to be on that screen. They can get acception reporting through emails and voice commands. So, training is also part of it, obviously. So, it's a multifaceted approach to leveraging these new capabilities. >> Dave: Are you guys responsible for the physical infrastructure of your ships? I mean, is that part of your purview? Okay. So, really there's is an industrial IOT component big time for you guys. >> Absolutely. >> Alex: And there's a huge push now for Maritime security, right? We saw what happened with Maersk and NotPetya virus, right? So, how it took them out of operation for about three weeks. So, this IOT is very, I think, awesome, right? I was speaking to some of the Splunk guys yesterday about it. How we could leverage that on our ships to gather that data, right, from our SCADA systems. And from our bridge and engine control systems to be able to view any kind of threat. Any kind of vulnerability that we might be seeing in the environment. How we can control that and how we can predict anything from happening, right? So, that's going to be very key to us. >> Dave: So, Splunk is going to take that data right off the machines. Which Stu and I were talking, that to us is a huge advantage. So many IT companies are coming and saying, "Hey! We're going to put a box at the edge". That's nice, but what about the data? So, Splunk's starting with the data, but it's the standards of that data. They're really driven by engineers and operations technology folks. Is Splunk sort of standard agnostic? Can they be able to ingest that data? What has to be done for you guys to take advantage of that? >> So, we'll have to ingest that data. And we'll have to, you know, look at it and see what we're seeing, right? This is all brand new to us as well. >> Dave: Right. >> Right. This whole Maritime thing has risen up in the past year, year and a half. So, we're going to have to look at the data and then kind of figure out what we want to see. Normalize it, you know, we'll probably get some PS services or something to assist us. Some experts. And then we just go from there, right? We build our dashboards and our reports. >> Dave: And predictive maintenance is a huge use case for you guys. >> Alex: Absolutely. >> I mean, to me, it's as important as the airlines. >> Alex: Absolutely, yes. >> So, I would think, anytime you... Well, first of all, real time during a journey. But anytime that journey is completed, you must bring in the inspectors and, I'm sure, very time consuming and precise. >> So, I know that some of our senior leadership, especially in the Maritime space, has now looking towards Splunk to do some of that predictive maintenance. To make sure that we have that right nuts and bolts, right? Per say, on the ship. To be able to fix any issue that might arise at sea while we're on there. >> Dave: Now, it's expect that the drive is going to be for human augmentation and of drive efficiency. >> Alex: Correct. >> You're not just going to trust the machines right out of the box. No way, right? >> Alex: No. But it's empowering those engineers, right? As we see with some of the dashboards that they're coming up with at the keynote. Empowering some of the those engineers that are in the engine room. That are in bridge. To be able to see those issues come up, right? And be able to track. >> Dave: Plus, I would imagine this is the kind of thing like an airline pilot. You're double checking, you're triple checking. So, you might catch misses earlier on in the cycle. >> Alex: Yeah. I could see it having huge impact. >> Stu: Yeah. Sheldon, I was just thinking through the other next announcement. I wonder if Splunk business flows sounds like something that might fit into your data pipeline? Get insights, understand satisfaction. Seems like it might be a fit. Is that of interest to you? >> Sheldon: Yeah, it sure is. Because we definitely want to, since we've evolved with kind of fragmented systems. We still have main frames, we still have whole call center environment that we need to ensure that it's parts of the end-to-end guest experience. So, for sure, we're getting into the whole early adopter program on the process flow. >> Yeah. Can you give us little insight? What kind of back and forth do you have with Splunk? What sort of things are you asking that would help make your jobs easier going forward? >> So, going forward, I know they're addressing a lot so the ingestion and data standardization. And now, with the decoupling of the storage, which is awesome, makes our lives a lot easier. But the evolution of the natural language and the integration with AWS natively is huge for us, as well as our Cloud program matures. And we start enabling Serverless architectures, for example. So, yeah. No, it's a very important part. >> Stu: Yeah. I mean, Serverless is actually something we're pretty interested. What are some of the early places that you're finding value there? >> Well, many people don't know this, but Carnival's also one of the largest travel agencies in the United States. So, we have the whole... Well, it's the whole global air travel platform that we're currently migrating to a Serverless architecture, integrates with Sabre. So, we're looking at things like open trace for that. And I know that our friends at Splunk are enabling capabilities for that type of management. >> Dave: And what's the business impact of Serverless there? You're just better utilization of resources? Faster time to value? Maybe you could describe. >> Yeah. Near real time processing. Scaling up and scaling down seasonally. Our key aspects of that. Removing the constraints of CPU and storage and-- >> Dave: Alex, has it changed the security paradigm at all? Serverless? How does it change it? >> Alex: So, it does. It let's me not have to worry so much about on premise stuff, right? As I did before. So, that helps a lot, right? And being able to scale up and down quickly as much data as we're ingesting is very key for us. >> Dave: You guys are heavy into Cloud, it's obvious. I wonder if you could share with us how you decide, kind of, what goes? If you're not all in on Cloud, right? It's not 100 percent Cloud? >> Sheldon: No, we could never be all in. >> No. >> Dave: And we've put forth that notion for years. We call it "true private cloud". That what you want to do is bring the Cloud experience to your data, wherever that data lives. There's certain data and workloads that you're not just going to put into the Cloud. >> Sheldon: That's correct. >> So, you would confirm that. That's the case. Like, you just said it. >> Correct. >> Dave: You're never going to put some of these workloads on Cloud. >> Well, we have floating data centers. So, we'll always be in a hybrid model. But there is a decision framework around how we create those application, migration pipelines. And the complexity and interdependencies between these platforms, some are easier to move than others. So, yeah. No, we're quite aware of-- >> Dave: And so, my follow up question is are you trying to bring that Cloud experience to those... to the floating data centers, wherever possible? And how is the industry doing? If you had a grade them in terms of their success. I mean, you certainly hear this from the big tech suppliers. "Oh, yes! We've got private Cloud" and "It's just like the public Cloud". And we know it's not and it doesn't have to be. >> Sheldon: Right. >> But if it can substantially mimic that public Cloud experience, it's a win for you guys. So, how is the industry doing in your view? >> So, I think it's a crawl, walk, run type of thing. Obviously, you have these floating cities and satellite bandwidth is a precious resource that we have to use wisely, right? So, we definitely are Edge computing strategy is evolving rapidly. What do we act upon at the Edge? What do we send to the Cloud? When do we send it? There also some business drivers behind this. For example, one of our early Cloud forays was in replicating a guest activity aboard the ship. So, we know if somebody buys a margarita off the coast of Australia, we know it five seconds later. And then, we could act upon that data. Casino or whatever data it may be in near real time. >> So, a lot of data stays at the floating data center, obviously. >> Correct. >> Much of it comes back to the Cloud. When it comes back to the Cloud is a decision, 'cause of the expense of the bandwidth. What do you do? You part the ship at the data center and put a big fire hose in there? (laughing) >> Alex: I wish it was that easy. >> You got a bunch of disc drives that you just take and load up? That's got to be a challenge. >> So, there business requirements, right? So, we have to figure out what application is more important, right? So, usually like our ship property management system, right. Where we have all our guests data, as far as their names, birth dates, all that stuff. That takes priority over a lot of other things, right. So, we have to use, like Sheldon said, that bandwidth wisely. 'Cause we don't really own a lot of the ports that we go into. So, we can't, just like you say, plug in a cable and move on, right? We still rely heavily on our satellites. So, bandwidth is our number on constraint and we have to, you know, we share it with our revenue generating guests as well. So, obviously, they take priority and a lot of factors go into that. >> Dave: And data's not shrinking. So, I'll give you guys the last word, if you could just sort of summarize, in your view, some of the big challenges that you're going to try to apply Splunk towards solving in the next near to mid term. >> Alex: Well, I'm more security focused. So, for me, its just making sure that I can get that data as fast as possible. I know that I saw yesterday at the keynote, the mobile app. That for me is going to be like one of the things I'm going to go like, research right away, right? 'Cause for me, its' getting that alert right away when something's going on, so that I can mitigate quickly, move fast and stop those threats from hitting our environment. >> Dave: Sheldon? >> Yes, I think the challenges are, like you mentioned earlier, about the stove pipes and how organizations evolve. Now, with this massive influx of data, that just making sense of it from a people, technology and processes standpoint. So that we could manage the chaos, so to speak, right? And make sure that we have an orderly end-to-end view of all the activity on the ships. >> Dave: Well, thank you guys. Stu and I are like kids in a candy shop, 'cause we getting to talk to so many customers this week. So, we really appreciate your time and your insights and the inspiration for your peers. So, thank you. >> Oh, thank you very much. >> Alex: Thank you for having us. >> Dave: You're welcome. Alright, keep it right there everybody. Stu and I will be back right after this short break. You're watching theCUBE Live from .conf18. Be right back. (techno music)

>> Narrator: Live from Orlando, Florida, it's theCUBE covering .conf18. Brought to you by Splunk. >> Welcome back to Orlando, everybody, of course home of Disney World. I'm Dave Vellante with Stu Miniman. We're here covering Splunk's Conf18, #conf, sorry, #splunkconf18, I've been fumbling that all week, Stu. Maybe by day two I'll have it down. But this is theCUBE, the leader in live tech coverage. Phillip Adams is here, he's the CTO and lead architect for the National Ignition Facility. Thanks for coming on. >> Thanks for having me. >> Super-interesting off-camera conversation. You guys are basically responsible for keeping the country's nuclear arsenal functional and secure. Is that right? >> Phillip: And effective. >> And effective. So talk about your mission and your role. >> So the mission of the National Ignition Facility is to provide data to scientists of how matter behaves under high pressures and high temperatures. And so what we do is basically take 192 laser beams of the world's largest laser in a facility about the size of three football fields and run that through into a target the size of a B.B. that's filled with deuterium and tritium. And that implosion that we get, we have diagnostics around that facility that collect what's going on for that experiment and that data goes off to the scientists. >> Wow, okay. And what do they do with it? They model it? I mean that's real data, but then they use it to model real-world nuclear stores? >> Some time back if you actually look on Google Earth and you look over Nevada you'll see a lot of craters in the desert. And we aren't able to do underground nuclear testing anymore, so this replaces that. And it allows us to be able to capture, by having a small burning plasma in a lab you can either simulate what happens when you detonate a nuclear warhead, you can find out what happens, if you're an astrophysicist, understand what happens from the birth of a star to full supernova. You can understand what happens to materials as they get subjected to, you know, 100 million degrees. (laughs) >> Dave: For real? >> Phillip: For real. >> Well, so now some countries, North Korea in particular, up until recently were still doing underground testing. >> Correct. >> Are you able to, I don't know, in some way, shape or form, monitor that? Or maybe there's intelligence that you can't talk about, but do you learn from those? Or do you already know what's going on there because you've been through it decades ago? >> There are groups at the lab that know things about things but I'm not at liberty to talk about that. (laughs) >> Dave: (chuckles) I love that answer. >> Stu: Okay. >> Go ahead, Stu. >> Maybe you could talk a little bit about the importance of data. Your group's part of Lawrence Livermore Labs. I've loved geeking out in my career to talk to your team, really smart people, you know, some sizeable budgets and, you know, build, you know, supercomputers and the like. So, you know, how important is data and, you know, how's the role of data been changing the last few years? >> So, data's very critical to what we do. That whole facility is designed about getting data out. And there are two aspects of data for us. There's data that goes to the scientists and there's data about the facility itself. And it's just amazing the tremendous amount of information that we collect about the facility in trying to keep that facility running. And we have a whole just a line out the door and around the corner of scientists trying to get time on the laser. And so the last thing IT wants to be is the reason why they can't get their experiment off. Some of these experimentalists are waiting up to like three, four years to get their chance to run their experiment, which could be the basis of their scientific career that they're studying for that. And so, with a facility that large, 66 thousand control points, you can consider it 66 thousand IOT points, that's a lot of data. And it's amazing some days that it all works. So, you know, by being able to collect all that information into a central place we can figure out which devices are starting to misbehave, which need servicing and make sure that the environment is functional as well as reproducible for the next experiment. >> Yeah well you're a case-in-point. When you talk about 66 thousand devices, I can't have somebody going manually checking everything. Just the power of IOT, is there predictive things that let you know if something's going to break? How do you do things like break-fix? >> So we collect a lot of data about those end-point devices. We have been collecting them and looking at that data into Splunk and plotting that over time, all the way from, like, capacitors to motor movements and robot behavior that is going on in the facility. So you can then start getting trends for what average looks like and when things start deviating from norm and set a crew of technicians that'll go in there on our maintenance days to be able to replace components. >> Phillip what are you architecting? Is it the data model, kind of the ingest, the analyze, the dissemination, the infrastructure, the collaboration platform, all of the above? Maybe you could take us inside. >> I am the infrastructure architect, the lead infrastructure architect, so I have other architects that work with me, for database, network, sys admin, et cetera. >> Okay, and then so the data, presumably, informs what the infrastructure needs to looks like, right, i.e. where the data is, is it centralized, de-centralized, how much is it, et cetera. Is that a fair assertion? >> I would say the machine defines what the architecture needs to look like. The business processes change for that, you know, in terms of like, well how do you protect and secure a SCADA environment, for example. And then for the nuances of trying to keep a machine like that continually running and separated and segregated as need be. >> Is what? >> As need be. >> Yeah, what are the technical challenges of doing that? >> Definitely, you know, one challenge is that the Department of Energy never really shares data to the public. And for, you know, it's not like NASA where you take a picture and you say, here you go, right. And so when you get sensitive information it's a way of being able to dissect that out and say, okay well now we've got to use our community of folks that now want to come in remotely, take their data and go. So we want to make sure we do that in a secure manner and also that protects scientists that are working on a particular experiment from another scientist working on their experiment. You know, we want to be able to keep swim lanes, you know, very separated and segregated. Then you get into just, you know, all of these different components, IT, the general IT environment likes to age out things every five years. But our project is, you know, looking at things on a scale of 30 years. So, you know, the challenges we deal with on a regular basis for example are protocols getting decommissioned. And not all the time because, you know, the protocol change doesn't mean that you want to spend that money to redesign that IOT device anymore, especially when you might have a warehouse full of them and then back-up, yeah. >> So obviously you're trying to provide access to those who have the right to see it, like you say, swim lanes get data to the scientists. But you also have a lot of bad guys who would love to get their hands on that data. >> Phillip: That's right. >> So how do you use, I presume you use Splunk at least in part in a security context, is that right? >> Yeah, we have a pretty sharp cyber security team that's always looking at the perimeter and, you know, making sure that we're doing the right things because, you know, there are those of us that are builders and there are those that want to destroy that house of cards. So, you know, we're doing everything we can to make sure that we're keeping the nation's information safe and secure. >> So what's the culture like there? I mean, do you got to be like a PhD to work there? Do you have to have like 15 degrees, CS expert? I mean, what's it like? Is it a diverse environment? Describe it to us. >> It is a very diverse environment. You've got PhD's working with engineers, working with you know, IT people, working with software developers. I mean, it takes an army to making a machine like this work and, you know, it takes a rigid schedule, a lot of discipline but also, you know, I mean everybody's involved in making the mission happen. They believe in it strongly. You know, for myself I've been there 15 years. Some folks have been there working at the lab 35 years plus, so. >> All right, so you're a Splunk customer but what brings you to .conf? You know, what do you look to get out of this? Have you been to these before? >> Ah yes, you know, so at .conf, you know, I really enjoy the interactions with other folks that have similar issues and missions that we do. And learning what they have been doing in order to address those challenges. In addition staying very close with technology, figuring out how we can leverage the latest and greatest items in our environment is what's going to make us not only successful but a great payoff for the American taxpayer. >> So we heard from Doug Merritt this morning that data is messy and that what you want to be able to do is be able to organize the data when you need to. Is that how you guys are looking at this? Is your data messy? You know, this idea of schema on read. And what was life like, and you may or may not know this, kind of before Splunk and after Splunk? >> Before Splunk, you know, we spent a lot of time in traditional data warehousing. You know, we spent a lot of time trying to figure out what content we wanted to go after, ETL, and put that data sets into rows and tables, and that took a lot of time. If there was a change that needed to happen or data that wasn't on-boarded, you couldn't get the answer that you needed. And so it took a long time to actually deliver an answer about what's going on in the environment. And today, you know one of the things that resonated with me is that we are putting data in now, throwing it in, getting it into an index and, you know, almost at the speed of thought, then being able to say, okay, even though I didn't properly on-board that data item I can do that now, I can grab that, and now I can deliver the answer. >> Am I correct that, I mean we talk to a lot of practitioners, they'll tell you that when you go back a few years, their EDW they would say was like a snake swallowing a basketball. They were trying to get it to do things that it really just wasn't designed to do, so they would chase intel every time intel came up with a new chip, hey we need that because we're starved for horsepower. At the same time big data practitioners would tell you, we didn't throw out our EDW, you know, it has its uses. But it's the right tool for the right job, the horses for courses as they say. >> Phillip: Correct. >> Is that a fair assessment? >> That is exactly where we're in. We're in very much a hybrid mode to where we're doing both. One thing I wanted to bring up is that the message before was always that, you know, the log data was unstructured content. And I think, you know, Splunk turned that idea on its head and basically said there is structure in log data. There is no such thing as unstructured content. And because we're able to rise that information up from all these devices in our facility and take relational data and marry that together through like DB Connect for example, it really changed the game for us and really allowed us to gain a lot more information and insight from our systems. >> When they talked about the enhancements coming out in 7.2 they talked about scale, performance and manageability. You've got quite a bit of scale and, you know, I'm sure performance is pretty important. How's Splunk doing? What are you looking for them to enhance their environment down the road, maybe with some of the things they talked about in the Splunk Next that would make your job easier? >> One of the things I was really looking forward to that I see that the signs are there for is being able to roll off buckets into the cloud. So, you know, the concept of being able to use S3 is great, you know, great news for us. You know, another thing we'd like to be able to do is store longer-lived data sets in our environment in longer time series data sets. And also annotate a little bit more, so that, you know, a scientist that sees a certain feature in there can annotate what that feature meant, so that when you have to go through the process of actually doing a machine-learning, you know, algorithm or trying to train a data set you know what data set you're trying to look for or what that pattern looks like. >> Why the S3, because you need a simple object store, where the GET PUT kind of model and S3 is sort of a de facto standard, is that right? >> Pretty much, yeah, that and also, you know, if there was a path to, let's say, Glacier, so all the frozen buckets have a place to go. Because, again, you never know how deep, how long back you'll have to go for a data set to really start looking for a trend, and that would be key. >> So are you using Glacier? >> Phillip: Not very much right now. >> Yeah, okay. >> There are certain areas my counterparts are using AWS quite a bit. So Lawrence Livermore has a pretty big Splunk implementation out on AWS right now. >> Yeah, okay, cool. All right, well, Phillip thank you so much for coming on theCUBE and sharing your knowledge. And last thoughts on conf18, things you're learning, things you're excited about, anything you can talk about. >> (laughs) No, this is a great place to meet folks, to network, to also learn different techniques in order to do, you know, data analysis and, you know, it's been great to just be in this community. >> Dave: Great, well thanks again for coming on. I appreciate it. >> Thank you. >> All right, keep it right there, everybody. Stu and I will be right back with our next guest. We're in Orlando, day 1 of Splunk's conf18. You're watching theCUBE.

(uplifting music) >> Hi, I'm Peter Burris and welcome again to another CUBE Conversation from our wonderful studios here in Palo Alto, California. Another great topic to talk about, we've got Robert Schmid, who is the Chief IoT Technologist at Deloitte. Welcome to The Cube, Robert. >> Thanks for having me. >> You also have your own video cast, so why don't we get that out of the way. What is it? >> Yeah, every Friday at 9 AM Pacific I do a show called Coffee Chat with Mr. IoT and Miss Connected. I just actually added a co-host, I thought I needed someone to help me. And we talk about IoT. It's on YouTube, you can find it on the channel, and it's really odd for me, that you're going to ask me the questions and I'm going to have to answer. (laughing) So I'm going to try to eat my own, my own advice here and be short. >> Well you know maybe someday you can have one of the Wikibon folks in your podcast, or video cast, we'd love to do that. >> Yeah that'd be great. >> Alright let's start here though. Deloitte's a great name, been around for a long time, associated with customer value in very profound ways, complex applications. That certainly characterizes IoT. What's going on with IoT at Deloitte? >> For us, we started a whole practice around IoT, and I'm leading that practice, but the thing for us was, there were a lot of science experiments going on around IoT, technology based, but we really wanted to bring it to what's the value behind IoT? So we really focused on use cases, and today we see that most focuses are on industrial IoT, though we spend a lot of time around connected products as well. I personally actually today worked on a project in a factory in Chicago, on a shop floor, connecting machines and measuring data and providing value. I work with an airline at an airport, around their travel so really helping guide you throughout the day. Interesting fact, you know we swipe away a lot of notifications without actually doing anything with it but when airline tells you, "Please come in 10 minutes early, the TSA wait time is long." I know you and I got to be there. >> You pay attention. >> Yeah, we got to be there early. We actually react to those notifications so I work on that and I work with high tech companies around their platforms, how to make their platforms better. >> You've raised a lot of really, really important issues but let's start with this notion of use cases >> Sure. >> A factory floor with a lot of PLCs, spitting out information, mediated by individuals or users and the data, where's it end up? That's real different from an airport where a lot of the data's being generated by a human being as they move places or is intended to be consumed by a human being. What kind of common patterns are you seeing in these use cases that brings them all under this notion of IoT? >> I always think of IoT as taking sensor data and making decisions based on those and what's interesting to me is that it creates this real interesting dilemma that we thought we knew what goes on with users, how they work and what they do. We do surveys just to find out what they're saying, the survey's actually probably not what they do but now with sensors we know what they do all the way to machines where we have decades of people having experience about, "This sounds a little odd, the machine doesn't sound right" but then they don't know what to do with it and now we can measure that because really at the end of the day, vibration isn't anything else but sound, right? So for me this is all about, and what's common about this, is that we really take that, we think we know to we actually know because we can now measure with sensors what goes on in that area. >> So it's almost like taking a lot of that time motion analysis, operations research that we used to do periodically, episodically with human beings doing their best to record stuff and bringing a lot of that discipline continuously and in real time so that it can better inform overall decisions, right? >> Yeah, I mean almost near real time, many of these cases and that's a really interesting scenario for me, right? Because now can actually see what happens in the factory when I tune the mix or the blend of my raw materials, what happens to the product that gets made at the end of that. >> As we think about the challenges or the changes that we foresee going on, is there a difference in thinking about humans as users or humans as consumers of a lot of this data and machines? I know there is, but how is this, because kind of the machine side has always been associated with SCADA, OT and the disciplines and approaches for that side seem a little bit different than what's coming out of the mobile world which is still very, very closely associated with how we utilize or how we deploy these systems to inform decisions in either case, is that right? >> I don't really know if we do so much about decisions for machines. I think at the end of the day many of the decisions are still made by humans. I mean I think about this like, we have a heating element running over, at the end of the day it still is a human that goes and sort of like says, "Yeah, let's turn that off." >> But there's still automation that takes place? >> Absolutely there's automation but automation takes place today. >> Sure. >> None of this is particularly new. I mean OT has done automation forever, right? >> Right. >> I think the interesting part is now taking the learning and connecting the different data points together. I talked about the factory floor, I just showed, actually, at the show we created a virtual factory line, life size. You can download it, it's the virtual factory by Deloitte. If I get my phone going I can show you, but it's not. Right here. (laughing) I call it "the internet of rubber ducks". >> "The internet of rubber ducks"? >> The internet of rubber ducks. Yeah, it's kind of cute. You have these little yellow ducks and if you load the app you can see them being made. But it's actually really what goes on at the factory and it really shows how when you change the blend at the beginning of a production line, how it effects at the end of the factory line, the outcome, how much scrap you have. What's the scrap? What's the overall equipment efficiency? OEE and so forth. What happens is now we can connect data from the very beginning of the factory line with he very end of the factory line and then combine that with contextual data such for example as temperature or the vibration on the machine or the current which we haven't done before. This whole time series of data that we now correlate becomes really critical and I don't think that's something we've done really as much before. That has not driven automation in this zone. >> If we think about it, we're talking about sensors which as you said, SCADA's been around for a long time and it tends to automate very, very proximate to where that sensor tower might be but a lot of the information that went into decisions was actually then generated by a person, perhaps a shift supervisor or somebody else or a machine operator said, "I heard a rattle" but there's no time so it's difficult to correlate and now we're talking about up leveling a lot of that information so it becomes part of the natural flow out of the machine but still for human consumption to make decisions? >> Yeah, very much like that. As I said, I talked about the blend of the materials that go in and then now we can correlate that particular part of the sheet. We can look on video and see how it looked and check the quality and then see at the end how many pieces of product did we produce. Actually in that particular case, it's really fascinating, it wasn't so much about reducing cost, it was actually increasing output. For them each line costs about 10 million and with the findings we have and what we're doing with them, we can actually give them the ability not to build another line but actually produce more lines because they can sell more which is a great position to be in. >> Sure, absolutely. >> You actually impact the top line rather than just the bottom line. >> Well productivity fundamentally is a function of what work you can perform for what costs are required to perform that work and if you can improve the effectiveness of something, keep the cost the same but get more work out of it, that's a big, big plus on the bottom line. >> And they have the market to sell it in to, right? >> Absolutely. >> If you just make more and you can't sell it- >> Well there's that, too. >> Yeah, which is really the good thing about that particular example. >> But talk about how, for example, you noted that they can look at a video of how the plastic or the sheets coming off the machine or set of rollers perhaps but how does AI start to be incorporated in to this IoT discussion? And what kind of use cases are you seeing becoming appropriate or more appropriate or made more productive by some of these new technologies we bring, some of the analytics and some of the IoT elements together? >> We find that we do a variety of theories. We go in and we say, "Hm, how about this? How about that?" And then we have our data scientists go and look at models for that and see what goes on and then put machine learning in and then we take those machine learning models and feed it back into, we talked before a little bit about this, but age processing is really something where we now process some of those models on the edge. The algorithm development and all the analysis we send that to the Cloud, we do number crunching there and we really take advantage of the unlimited capacity. >> A lot of the training happens up at the Cloud? >> A lot of the training happens in the Cloud and then whatever models come down, we load those on the edge and we actually do make decisions right there on the edge or we give the operator the choices to make the decisions right there on the edge. >> Training up in the Cloud but the inferencing actually is proximate to the actual action so there's locality for the action based on what's in the model and there's a lot of training that can happen, quite frankly, where you don't have to underwrite the cost of the infrastructure to do it? >> Exactly. >> That suggests that there is going to be a fair amount of change in the industry over the next few years in this notion of moving from OT to IT or SCADA to IoT. This is not just a set of technology issues, there's some fundamental other questions that are going to be important. A lot of people just kind of assume, "Oh, well throw a bunch of general purpose stuff at these IoT related things and it's going to be the IT industry all over again." Or is really the expertise associated with the use case going to be more important? How is that use case going to be ultimately realized? Is it going to be a bunch of piece parts or is it going to be more of a holistic approach to really understanding the nature of the solution and making sure that the outcome is the first and focal point? >> I'm going to come back to your question in a second. I just always, I have to smile because, so I have a Masters in petroleum engineering. So when I studied, I built really fancy models, like differential models, indicial models and you know, I simulated fracturing and- >> Process control's built with that stuff. >> I lived a good part of my life in OT and then after I came out of university I really moved more and more into IT so I've spent most of my career in information technology, including being a CIO. I always thought that the most fancy math we'd ever do is percentage calculations and that was pretty fancy. (laughs) Now, I find myself in this awesome place where I can bring together some of that OT, some of that real deep data science work that I did early on in my life, now with some of the process and system implementation expertise and practice that have come out of IT. They really come together, I don't think one takes over the other. I think there's real sort of meeting each other and going like, "Wow, okay. I guess we really got to work together." So that's really fun. About your question around what solutions do we see today? I see a lot of very vertical, very one use case oriented solutions, that go all the way from the sensor to edge to Cloud to, hopefully, integration to the back office systems because without that you can't really take good action. But they're very narrow and so, like in the good old Cloud days when Cloud became really big, there were really good point solutions and the good Cloud providers sold to the business user right there and then and ran around IT. And I see the same in IoT happening right now. You get a very good solution for temperature control on a truck, for example, right? Which is a very narrow solution but the moment you want to start doing something with your warehouse where you have other sensors and you need a horizontal platform, those vertical solutions fall short. That's what I think is sort of like the interesting dilemma right now. You have these vertical pillars and you have the horizontal platforms that the big providers have and so it'll be interesting to see when we're going to see some consolidation in this space when some of the vertical solutions are going to get bought out by the horizontals to provide better use cases. It's a little bit like the ERPs who did every industry and then eventually they realized, "We need industry focused solutions." We'll see the same in the IT space. >> The IT industry has always supposed that we can transfer knowledge we gain in one domain into other customers, into other use cases. It almost sounds like what you're saying is we're going to have that vertical organization of expertise, which is absolutely essential to solve that complex, core business problem. High risk, high value, high uncertainty, often bespoke, never done before but over time we will see a degree of experience sharing and diffusion so that over time we might see better, more applicable platforms that are capable of providing that foundation for a broader set of use cases but that' going to be a natural process of accretion. Is that how you kind of see it? >> Yeah, I mean we're all going to need streaming capabilities. We're all going to need capabilities for machine learning, for cognitive, for video analytics. We'll all need that but I think it'll be specific to the individual use case in a sense of, I'll give you an example, I just had a data scientist show me how he started looking at 20 year old scientific research on gear boxes. What frequencies happen in gear boxes, specifically to certain scenarios. That's not replicable from a gearbox to a pump, you know? >> Right. >> You have different, so there is specific things and yes it might be the same gearbox in one factory that produces, I don't know, rubber ducks to another factory who makes metal sheets but it's still gearbox specific, right? I think this is the specificity we're going to see around models, around learning and around sensors to a certain extent. >> Excellent, Robert Schmid, Chief IoT Technologist at Deloitte, thanks very much for being on theCUBE. >> Thanks for having me, Peter. It was a pleasure, thank you. (uplifting music)

>> Announcer: From San Francisco, it's theCUBE! Covering OSIsoft PI World 2018. Brought to you by OSIsoft. >> Hey welcome back everybody Jeff Frick here with theCUBE. We're at OSIsoft's PI World 2018 in downtown San Francisco, they've been at it for decades and decades and decades talking really about OT and efficiency. And we're excited to be here it's our first time, and really want to talk to a customer, excited to have our next customer CJ Smith, She's a Project Manager for the city of Riverside CJ great to see you. >> Thank you, hi! >> So you represent a whole slew of mid-sized US cities, so how big is Riverside for people that aren't familiar? >> We serve 120,000 customers so we're not too small, but we're definitely not as big as some of the other cities. >> Right and then as we said before we turned on the cameras, you guys have a whole department for utilities, you have your own utility as well. >> Yes we do have a public utility division within the city, also an IT and public works, parks and recs like other cities as well. But we do have the utility, which is different than some of the stand along utilities, like LADWP for example. >> Right but it's good you were saying off camera that that gives you guys a nice revenue source, so it's a nice asset for the city to have. >> Yeah the utility is revenue generating department. >> Okay so what are you doing here at PI World, how are you guys using OSI software? >> So we started down PI back in August 2016, as an enterprise agreement customer, and at that time we really lacked visibility into our system so we needed something to help us gather the data and make sense of it, because we had data all over the place, and it was hard to answer simple questions it was hard to find simple data. And so we started down the PI journey at that time, and we basically used it like a data hub to aggregate data, turn that data into information, and then we disseminate it using dashboards. So PI Vision dashboards which used to be PI Coresight, as well as reports. >> So what were some of the early data sources that you leveraged, that you saw the biggest opportunity to get started, or yet even more importantly your earliest successes where'd your early success come from? >> So our very first work group that we worked with was our Water Operations and our Water SCADA team. >> Seems to be a pattern here a lot of water talk here at OSIsoft. >> Yeah I'll talk about electricity too. But we started on water and the first thing we did was implement their data, it was called a Water Operations dashboard, and they were doing it manually in Excel, and it would take a staff person over eight hours to do it. And they would do it the next day for the previous day data. So imagine how opposite of real time that is right? So we integrated that data with PI. >> And how many data elements? How big is the spreadsheet this poor person is working on? >> So the Water SCADA tags that we brought in were near 1500 tags, so you imagine that much data and calculations with over 1500 calculations behind it. So it was a ton of effort. >> Right. >> And a huge quick win for them! So it's saved staff time, they now have actual intelligence, real time data, the managers get alerts to their phones about the status of wells, and so it was really helpful to that work group. So that one was one of our first and earliest wins on PI. >> Was it a hard sell? To those people to use it? It wasn't because we did find a champion in that group, someone that would help us. Actually the manager he was very interested in technology and automation. And they understood that even though it would be a time investment up front, it would save them a ton of time in the long run, for the rest of the year. And so one of the things that helped us get buy-in early on is that we used an Agile approach. So we would tell the manager, I only need you for five weeks. I need you and your staff for five weeks, and then you don't have to talk to us anymore. We will deliver the product in five weeks, we will do all the work, but if you could give us five weeks of your time, then you could have all your time back the rest of the year. And that helped us get buy-in from the managers and a commitment, because they can identify with okay just five weeks. >> Right so those were probably the operational folks, what about on the IT folks how was getting buy-in from the IT folks? >> The funny thing is and the thing we did different is, we have a great relationship with IT, and we really forged a partnership with them early on, even from the very beginning when we were just reviewing the agreement. We got their buy-in early on to say okay, this is what we're thinking about doing, we want you to be part of the team, and we really built a partnership with this project so that it could be successful. So they work hand in hand with our PI implementation team every step of the way. They've been on this journey every step of the way with us. So we don't have some of the challenges that other companies that I hear are talking a lot about here with IT and it kind of being a bottleneck, we didn't have that same experience because we really worked hard up front to have the buy-in with them and really build a partnership with them, so that they're implementing PI with us. And another selling point with that is, we're using PI as a data hub or like a bus, a data bus essentially. So for them it's good because we're saying look we're only going to have this point to point system, instead of having all of these individual points we're only going to connect to one system, which will be easier for them to manage and maintain, and we'll instruct staff to go to PI to get the data. So that's a selling point for IT it's more secure, it's more manageable. >> And did you use an outside integrator, or did you guys do it all in house? >> Our implementation team is a combination of in house staff and a consulting firm as well. >> And then it's curious 'cause then you said once you add all the data it's kind of a data bus, how long did it take for somebody to figure out hmmm this is pretty cool maybe there's data set number two, data set number three, data set number four? >> So right after our first six week implementation, we rolled out a new implementation every four to six weeks. >> Every four to six weeks? >> Yeah so we did a sprint cycle the whole first year, and actually the whole second year we're currently in right now, and so we touched a different work group every single time, delivering a new solution to them. So we picked up a lot of traction so much that now, other departments in the city want it, public works is asking for it, the city manager's office so it's really picking up some good buzz, and we're kind of working our way down discussion of smart city talks, and seeing how PI can support smart city, big data advanced analytic initiatives at the city. >> So what are some of the favorite examples of efficiency gains, or savings that department A got that now department B sees and they want to get a piece of that what are some of your favorite success stories? >> I would say two of mine, I shared one on the big stage yesterday about the superpower I talked about our operations manager, who started receiving actionable intelligence overnight. And he got an alert around midnight, and he called his operator and said hey, what's going on with that well? And the operator said very puzzled, how do you know that there's something going on with this well? And he replied and said because I have superpowers. And so his superpower was PI, and that's one of my favorite stories because it's just simple and it resonates with people, because he is receiving alerts and push notifications that he never had before to his mobile device at home. So that's a huge win. >> Was the operator tied in to that same notification, or did that person know before the operator? >> The manager knew before the operator. So the operator didn't know about PI at the time and we had just rolled it out. And so the manager was just kind of testing it and adopting it, and so it was kind of like he had a leg up a little bit and they were confused like how do you know you're at home? >> Man: Right. >> He's like I have superpowers. (laughing) It's probably my funniest and best story, and one that I always tell because it helps everyone, no matter if it's an executive to a field person, really understand the power behind PI. I think another one if I had to pick another example of a win that I think was powerful is, our work order and field map. So we have our field crews right now that have a map, that's powered from our work order and asset management system pushing data to PI, which then pushes it to Esri through the PI integrator, and they're out using it in the field and it helps them route their work, they can see where their workers are, they can see customer information. And that map is really changing the way the field crews work. So imagine a day before this system where, they would go in and have to print every work order from the system. And not all asset management systems are really user friendly. They're kind of archaic a little clunky, so I won't say the name of our system. >> And doesn't work well if there's a change right? >> Yeah and they're not really mobile friendly. So that's part of the challenge, but because of that now public works wants that map, parks and rec every department that has field forces, they want something similar so that they can get all the data from all the other systems in one app in one location on their device. >> And do you find that's kind of a system pattern, where often department A needs very similar to what department B needed with just a slight twist? So it's pretty easy to make minor modifications to leverage work across a bunch of different departments? >> Absolutely a lot of work groups are similar, maybe a little different like you said, but especially those that have field forces. Sometimes it makes it easy to sell it to the next group, it's like look this is what we've done, is this something that you kind of need? Or what would you need differently? Like we've developed field collection tools. That's easy to replicate. Once you see it it's easy to say you know what that works but I need it to say this and I need it to say this. If you just show them a white paper, it's hard for them to say this is what I need. Most people just don't know, but it's easy once you see a suit to say oh I don't like that tie I don't like that shirt, I don't like those pants. >> But something close. >> Yeah but something like that right? So that's the benefit once you start having a solution to easily modify and reproduce. And then the good thing about Agile, you're running sprints so you're learning every sprint. You're kind of learning as you go, and you're able to refine it and refine it and make the process that much better. >> Right. On the superpower thing employee retention is a challenge, getting good people is a challenge, I'm just curious how that impacts the folks working for you, that now suddenly they do have this new tool that does allow them to do their job better, and it's not just talk it's actually real and gave that person a head up on the actual operation person sitting on the monitor devices. So as it proliferates what is the impact on morale, and are more people rising up to say hey, I want to use it for this I want to use it for that. >> Yeah we are getting a lot of interest, and I think the challenge is, and I talked about this a little bit during my session, is change management and culture. Some people see automation and technology as sometimes a threat because of job security, or the I've always done it this way type of mentality. >> Man: Never a good answer. >> Right but once you kind of get them to see that we're just automating your process to make it better so that you can do cooler and better things, so that you can actually analyze the data instead of inputting data. So you can actually solve problems versus spending all your time trying to identify the data and collect information. So staff are starting to see the value, and after the first year and a half, we've gotten a lot of traction. I don't really have to sell it as much, it's now such a huge part of our culture that the first question when we want to implement a new system is does that integrate with PI? I don't even have to ask them. Everyone else is asking well have you thought about using PI for that? So we always kind of look to PI first to say, can we create this solution in PI? And then if not we look at other solutions and if we're looking at other solutions we say, does that solution integrate with PI? So that's become part of our norm to make sure that it plays nice with what we're calling our foundational technology which is PI. >> Right so you talked a lot about departments. Is there kind of a cross-department city level play that you're rolling data and or dashboards into something that's a higher level than just the department level? >> Yeah so far the only thing that we have done that's kind of cross divisional not just in one division, is our overtime dashboards. So we recently created overtime dashboards throughout the entire city so that executive level department heads have visibility into overtime, which just gives them trends so that they can know what departments are receiving the most overtime? Is that overtime associated with what type of cause? Was it something outside of our control? Was it a planned overtime? And then most importantly where we're trending. Where are we on track to be by the end of the year, given our current rate so that they can be proactive in making changes. Do we need to do something different? Do we need to hire more people in this department? Do we have too many people in this department? Can we make shifts? So it's giving that level of visibility, and that's a new rollout that we just have completed, but it's something that we're already seeing a lot of interest in doing more of. Cross divisional things so that the city manager's office and that level has more view into the whole city. >> Right well CJ it sounds like you're doing a lot of fun stuff down at Riverside. >> Woman: We are we are! >> And you can never save enough water in California, so that's very valuable work. >> Woman: That's true! >> Well thanks for taking a minute and sharing your story, I really enjoyed it. >> Thank you for having me. >> Absolutely she's CJ Smith I'm Jeff Frick, you're watching theCUBE from OSIsoft PI World 2018 in San Francisco, thanks for watching. (upbeat music)

>> Announcer: From downtown San Francisco, it's theCUBE, covering RSA North America 2018. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the RSA Conference in downtown San Francisco. Forty thousand plus security experts really trying to help us all out. Protect our borders not so much, but protects access to these machines, which is harder and harder and harder everyday with bring your own devices and all these devices. So really, it's a different strategy. And we're really excited to have ExtraHop back, we had ExtraHop on last year for the first year, he's Matt Cauthorn, the VP of security at ExtraHop. So Matt, what do you think of the show? >> Oh, amazing. Absolutely amazing. Super packed, been walking like crazy. Got all my steps in, its fantastic. >> Alright, so you guys have been in network security for a long time? >> Yeah so we've been, so we live in the East-West corridor, inside the enterprise, inside the perimeter doing wire data analytics, and network security analytics. Our source of data is the network itself. >> Okay. And the network is increasing exponentially with all the traffic that's going through, the data sources are increasing exponentially with all the traffic going through. >> That's right. >> So how are you guys keeping up with the scale, and what's really the security solution that you guys are implementing? >> So the point you make is really interesting. Yes, it is increasing exponentially, and as a data source the network is the only sort of observational point of truth in the entirety of IT. Everything else is sort of self-reported. Logs, end points, those are very valuable data sources, but as an empirical source of truth, of evidence, the network wins. That assumes you can scale. And that assumes you're fluent with the protocols that are traversing the network, and you're able to actually handle the traffic in the first place. And so for us just this week, we announced a 100gb per second capable appliance, which you know is an unprecedented amount of analytics from the network's perspective. So we're very proud about that. >> So what are you looking for? What are some of the telltale signs that you guys are sniffing for? >> So generally, we auto-classify and auto-discover all of the behaviors on the wire. From the devices themselves, to the services that those devices expose, as well as the transactions that those devices exchange. And so from a context perspective, we're able to go far deeper than almost anyone else in the space, that we know of at least. Far deeper and far more comprehensive sort of analysis as it relates to the network itself. >> And the context is really the key, right? Tag testing what, why, how. System behavior, that's what you're looking for? >> A great example is a user logging into a database, that might be part of a cluster of databases, and understanding what the user's behavior is with the database, which queries are being exchanged, what the database response is in the first place. Is it an error, is it an access denied? And does this behavior look like a denial of service, for example. And we can do all of that in real time, and we have a machine learning layer that sits over top and sort of does a lot of the analytics, and the sort of insights preemptively on your behalf. >> And it's only going to get crazier, right? With IOT and 5g. Just putting that much more data, that many more devices, that much more information on the network. Yeah, so IOT in particular is interesting, because IOT is challenging to instrument in traditional ways, and so you really do have to fall back to the network at some point for your analysis. And so that's where we're very, very strong in the IOT world and industrial controls, SCADA and beyond. Healthcare, HL7 for example. So we're able to actually give you a level of insight that's really, really difficult to get otherwise. >> And we've been hearing a lot of the keynotes and stuff, that those machines, those end points are often the easiest path in for the bad guys. >> Yes they are. >> An enormous security camera or whatever, because they don't have the same OS, they don't have all the ability to configure the protections that you would with say a laptop or a server. >> That's right. There's a surprising number of IOT devices out there that are running very, very old. And vulnerable operating systems are easy to exploit. >> Alright, so Matt I guess we're into Q2 already, hard to believe the years passing by. What's priorities for 2018 for you and ExtraHop? >> So we've announced a first class, purpose-built security solution this year, and really the plan is to continue the sort of momentum that we've accrued. Which is very encouraging, the amount of interest that we've had. It's hard to keep up, frankly. Which is fantastic. We want to continue to build on that, grow out the use cases, grow out the customer base and continue our success. >> Alright Matt, well we'll keep an eye on the story, and thanks for stopping by. >> Great, thank you. Appreciate it. >> Alrighties Matt, I'm Jeff, you're watching theCUBE from RSA Conference, San Francisco. Thanks for watching.

>> Transactions, totally on track with the original schedule, we're getting all the regulatory approvals, everything is kind of lined up. Financing 100%, fully committed. You know, we're going to only accelerate that. >> Announcer: Cube coverage of the EMC World 2016 continues in a moment. (techno beat sounds) Live from Silicon Valley, it's theCube, covering Mobile World Congress 2017. Brought to you by Intel. >> Hello and welcome to theCube here live in Palo Alto studios for a special two days of coverage of Mobile World Congress 2017. The hashtag is MWC17. Get on Twitter, tweet us at theCube. We'll be answering questions. I'm John Furrier, with Peter Burris, the next two days breaking down Mobile World Congress. We've got a great bunch of guests coming in. We'll be covering all the action here in Palo Alto. 8:00 a.m. through the whole day. As the day winds down in Barcelona, we'll be covering all the top news, all the analysis here on theCube, so stay with us, multiple days. Go to thecube365.net/mwc17. If you're watching this, that's where the live broadcast will be. Also we'll be on Twitter. Peter, good to see you, two days, getting geared up. Mobile World Congress is changing as a show from phone to IOT, AI, autonomous vehicles. Certainly a lot of action to talk about. Saturday and Sunday. The pre show releases is all phone, it's all the time. They're kind of getting the phone stuff out of the way earlier and now they're in the throws of the show and it should be exciting. >> Well yeah, because the usecases that the industry is following right now are, require or presume that significant amounts of processing can happen virtually anywhere. The Internet of things and people, which kind of brings together the idea of what can you do on your phone if you're a human being, and what can you do with a device or a machine somewhere with a bunch of censors demands that we have very high speed, secure low latency networks. And that's what 5G is promising. >> Well we're super excited. For the folks watching, we are now going to be having our new studio here in Palo Alto. We just moved in in January, 4500 square feet. Now we can cover events, we don't have to be there with theCube. We will not be there, there's not enough room in Barcelona, a it's a long flight, but we do have people on the ground, and we'll be covering it here in the studio, and we'll be calling folks on the ground this morning and tomorrow morning to get the lay of the land. They'll be coming back from their dinners, from their parties, and find out what the vibe. But certainly we have all the action at theCube365.net/mwc17, so check it out there. And again, the top news, again this is all sponsored by Intel, want to give a shot out to Intel. This would not be possible without Intel's sponsorship. They're certainly on the ground, as well as support from SAP Cloud with their news that they're being renamed HANA Cloud. So I want to give a shout out and thank Intel and thank SAP, check them out. They've got huge transformational demos. Intel really leading the charge out there, so I want to make sure that we give a thanks to Intel. Peter, the big story, I want to get your thoughts on this. Just jump right in. Saturday and Sunday, you saw a combination of the tone setting up leading into the weekend, and through the weekend. One was 5G, the 5G is the key enabler for wireless, bringing in gigabits of speed to the phone. Are the apps ready? That's the questions we're going to find out, and we're going to dig into. Is 5G ready for prime time? And certainly all the glam and sizzle was the new phones. LG had a good announcement. Samsung had a big announcement, although they're not going to be at the show, but surprisingly Nokia and Blackberry, two old guard phone guys, kind of rebooting. Blackberry trying to put out their keynote product, and also with Nokia, they rolled out the three, the six, three, five, and six products for new phones to try to get into the Apple game. And now the 3310, which is the old school phone. So you saw the phones. And then the other player that announced a phone and watch was Huawei, and they're also in the infrastructure game. So 5G wireless connectivity and phones, and then in the middle we have yet to hear some of the things, so as you look at the market and your research that you're covering, digital business, the business value of technology, what's your take on this? >> Well, John, the industry for the past probably 15, 20 years has been driven by what you do in the consumer markets. That's where you get the volumes that drive down or generate economies, that drive down costs, that make new volumes possible. And so 5G is going to be, the Mobile World Congress is a representation of that symbiotic relationship between the consumer and the enterprise world. So that on the one hand you have the consumer markets with the phones driving a lot of the volumes that are going to dictate the rate at which a lot of this stuff happens. On the other hand, you have enterprises which are aggressively considering those new use cases about IOT and as we say IOT and P. And other considerations that are in many respects really worth where some of those first adoptions are going to be, so it's an interesting dance between consumer and enterprise now where one fuels the growth in the other. Even if the actual applications are not linked. By that I mean we do say IOT and P, internet of things and people, which presumes that there's going be a lot of sensors on your phone. There's going to be a lot of sensors on your body that are tied to your phone, et cetera. But that's not necessarily the thing that's going to dictate the new application architectures that happen within the enterprise around some of these other things. That's going to be driven by what we call the edge. >> I love this IOT and P, p for people, but things are people, so Internet of things is the big trend. And for the mainstream people IOT is kind of a nuance, it's kind of industry discussion. But AI seems to encapsulate that people see the autonomous vehicles. They see things like smart cities. That kind of gives folks a touch point, or mental model for some of the real meat on the bone, the real change that's happening. Talk about the IOT piece in particular because when you talk about the people aspect of it, the edge of the network used to be an IT or technology concept, a device at the edge of the network. You talk to it, data gets sent to it, but now you've got watches, you have more of an Apple-esque like environment, mention the consumer. But there's still a lot of stuff in between, under the hood around IOT that's going to come out. It's called network transformation and industry parlance. Where's the action there, what's your take on that? You guys do a lot of research on this. >> Well the action is that data has real costs. And data is a real thing. Just very quickly, on the distinction between IOT and IOT and P, the only reason why we draw that distinction, and this is important, I think about what happens in that middle, is that building thing for people and building things for machines is two very, very different set of objectives. So the whole notion of operational technology and SCADA which is driven what's been happening a lot in IOT over the last 20 years. There's a legacy there that we have to accommodate. Has been very focused on building for machines. The building for people I think is going to be different, and that's what the middle is going to have to accommodate. That middle is going to have to accommodate both the industrial implications, or the industrial use cases, as well as the more consumer or employee or human use cases. And that's a nontrivial challenge because both of those can be very, very different. One you're focusing a little bit more on brutal efficiency. The other one more on experience and usability. I don't know the last time that anybody really worried about the experience that a machine had, you know the machine experience of an application. But we have to worry about that all the time with people. So when we think about the edge, John, there's a number of things that we've got to worry about. We have to worry about physical realities, it takes time to move something from point A to point B, even information. The speed of light is a reality. And that pushes things out more to the edge. You have to worry about bandwidth. One of the things that's interesting about IOT, or about 5G as it relates to IOT, while we may get higher bandwidth speeds sometimes, for the most part 5G is going to provide a greater density of devices and things, that's probably where the bandwidth is going to go. And so the idea is we can put a lot more sensors onto a machine or into a phone or into some use case and drive a lot more sources of data, that then have to get processed somewhere, and increasingly that's going to be processed at the edge. >> So Peter, I want to get your thoughts, and one of the things for the folks watching, is I spent a lot of time this week with you talking about the show and looking at the outcome of what we wanted to do and understand the analysis of what is happening at Mobile World Congress. Yes, it's a device show, it's always been about the phones, 4G, and there's been this you know inch by inch move the ball, first and ten, move the chains, and use the football analogy, but now it seems to be a whole new shift. You go back 10 years, iPhone was announced in 2007, we seem to be at a moment with we need to step up function to move the industry. So I want to get your thoughts for the folks that you're talking to, IT folks, or even CXOs or architects on the service provider side. There's a collision between IT, traditional business, and service providers who have been under the gun, the telecoms who have been trying to figure out a business model for competing against over the top and moving from the phone business model to a digital business model. So your business value of technology work that Wikibon has been doing, is very relevant. I want to get your thoughts on what does it take, is the market ready for this business value of technology because 5G gives that step up function. Are the apps ready for prime time? Are the people who are putting solutions in place for the consumers, whether it's for business or consumers themselves, service providers, telecoms or businesses with IT in the enterprise, is the market ready? Is this a paradigm shift? What's your thoughts and how do you tease that out for the folks that are trying to implement this stuff? >> Well is it a paradigm shift? Well yeah, as the word should be properly used, but the paradigm shift is, there is a lot of things that go into that. So what we like to say, John, when we talk to our users about what's happening, we like to say that the demarkation point, we're in the middle of right now. Now is a period of maximum turbulence, and before this it was I had known processes, accounting, HR, even supply chains, somewhat falls into that category, but the technology was unknown. So do I use a mainframe, do I use a mini computer? What kind of network do I use? What software base do I use? What stack do I use? All of these are questions, and it took 50 years for us to work out, and we've got a pretty good idea what that technology set's going to look like right now. There's always things at the margin, so we know it's going to be Cloud. We know it's going to be very fast networks like 5G. We know there's going to be a range of different devices that we're using, but the real question is before was known process, unknown technology, now it's unknown technology, or unknown process and known technology, because we do know what that base is going to look like. What those stacks are broadly going to look like. But the question is how are we going to apply this? What does it mean to follow a consumer? What does it mean from a privacy standpoint to collect individual's information? What does it mean to process something in a location and not be able to move data or the consequences of that processing somewhere else? These are huge questions that the industry is going to have to address. So when we think about the adoption of some of this stuff, it's going to be a real combination of what can the technology do, but also what can we do from a physical, legal, economic, and other standpoint. And this is not something that the computing industry has spent a lot of time worrying about. Computing has always focused not on what should do, but what can we do. And the question of what should we do with this stuff is going to become increasingly important. >> And the turbulence point is even compounded by the fact that even the devices themselves and the networks are becoming more powerful. If you look at what Cloud is doing with compute. If you look at some of the devices, even just the chip wars between Intel and say Qualcomm for instance. Intel had a big announcement about their new radio chip. Qualcomm has the Snapdragon, we know Qualcomm is in the Apple iPhone. Now Intel has an opportunity to get that kind of business. You got Huawei trying. >> I think they're both in the Apple iPhone right now, but I think your point is. >> Huawei is trying to be on Apple. In their announcements, they're going very Apple like, and they have network gear, so we know them from the infrastructure standpoint, but everyone wants to be, Apple seems to be the theme. But again the devices also have power, so you have process change, new value chains are developing and the device will be more popular. So again this is a big turbulent time, and I want to get your thoughts on the four areas that are popping out of Mobile World Congress. One, autonomous vehicles, two, entertainment and media. Smart cities and smart homes seem to be the four areas that have this notion of combining the technologies and the power that are going to generate these new expectations by consumers and users, and create new value opportunities for businesses and telcom's around the world, your thoughts? >> Those are four great use cases, John. But they all come back to a single notion, and the single notion, this is something that you know. We've been focused on it at Wikibon for quite some time. What is digital business? Digital business is the application of data to differentially sustain and create customers. So what you just described, those four use cases, are all how are we going to digitize, whether it be the city, the home, the car, or increasingly entertainment, and what will that mean from a business model, from a consumer standpoint, from a loyalty standpoint, et cetera? As well as a privacy and legal obligation standpoint. So, but all of them have different characteristics, right. So the car is going to have an enormous impact because it is a self contained unit that either does or does not work. It's pretty binary. Either you do have an autonomous car that works, or you don't, you don't want to see your 'yes it works' in a ditch somewhere. Entertainment is a little bit more subtle because entertainment is already so much digital content out there, and there's only going to be more, but what does that mean? Virtual reality, augmented reality, when we start talking about... >> Just by the way, a big theme of the Samsung announcement is all this teasing out the VR, virtual reality and augmented reality. >> Absolutely, and that's going to, look, because it's not just about getting data in, you also have to enact the results of the AI and the analysis. We call it systems of enactment. You have to then have technologies that allow you to, like a transducer, move from the digital world back into the analog world where human beings actually spend our time. We don't have digital transducers. >> Well that's a great point. The virtual reality use case that Samsung pointed out, and the hanging fruit is in hospitals. >> Peter: Yeah. >> Doctors can look at VR and say, hey I want to have, we've heard that football players like Tom Brady, used VR to look at defenses and offenses to get a scheming kind of thing. >> And there's no question we're going to see VR and AR, augmented reality, in entertainment as well, and media as well, but a lot of the more interesting use cases, at least from my perspective, are going to be how does that apply in the world of business. When we think about connected cities, now we're starting to talk about the relationship between all three. What does it mean, where is the edge in autonomous car? Is it in the car, or is in some metropolitan area? Or some cell like technology. And the connected city in part is going to be about how does a city provide a set of services to a citizenry, so that the citizen can do more autonomous things while still under control. >> It changes the relationship between the person, consumer, and the analog metaphor. So for instance, whether it's a car or the city, a town or city has to provide services to residents. And in an analog world, that's garbage, that's street cleaning, et cetera, having good roads. Now it's going to be, paths for autonomous vehicles, and autonomous vehicles is interesting, I just shared a post on the 365, theCube365.net/MWC17, where Autoblog ran a post that said, Silicon Valley is failing in the car business. But they looked at it too narrowly. They looked at it from the car manufacturing standpoint, not from the digital services that is impacting transportation, and this is the new normal. >> Look, you and I talked about this in theCube a year ago, was the car going to be a, was the car going to be a peripheral or is a car going to be a computer? And it's become pretty clear that the car is going to be a computer. And anybody who argues that Silicon Valley has lost that, has absolutely no idea what they're talking about. Let's be honest. >> John: Yeah, it's true. >> You're going to put more processing in a car, love Detroit, love what's going on in Japan, love elsewhere in the world, but the computers and the chips are going to come from a Silicon Valley company. >> Yeah, and I would agree with that. >> And software. >> Yeah, transportation doesn't change, but the device does. So final thought I want to get before we end the segment is as we say in theCube, and as Dave Vellante used to say, just squint through the noise or all the action at Mobile World Congress, how do you advise folks and how you looking through all this action, how would you advise doers out there, people who are trying to make sense of this, what should they be squinting through? What should they be looking for for reading the tea leaves of Mobile World Congress? >> I'd say the first and most important thing is there's so much turbulence that IT professionals have built their careers on trying to have the sober, be the ones who have the sober outlook on what technology can do. When we look at the amazing things that you can do with technology, it almost looks like magic. But it's not, these are still computers that fail if you give them the wrong instructions, and that's because you build the wrong software and et cetera. And I think the real important thing that we're telling our clients is focus on the sober reality of what it means to create value out of all this technology. You have to say what's the business want to do, what's the business use case? How am I going to architect it, how am I going to build it, what's the physical realities? What's the legal realities, et cetera? So it's try to get a little bit more sober and pragmatic about this stuff even as we get wowed by what all this technology can do and ultimately will mean. >> And the sober reality comes down to putting the value equation together, synthesizing what's ready, what's prime time, and again, it's an Apple world right now. I think this show is interestingly turning into an app show for business IT enterprise and telcom service providers, so we're going to bring all the action. We've got some great guests, we've got entrepreneurs with Ruth Cohen, who is a founder of Virtustream. We got SAP coming on, we got a call in to Lynn Comp who is at Intel, she's going to be on the phone with us giving us some commentary and what's going on at Mobile World Congress. From under the hood, in the network, all the action, we have more analysis with Peter. We have the global vice-president of the Cloud platform and SAP coming in, Tom Joyce, a technology executive. Willie Lou is the chairman of the 6G, talking about the impact of the wireless and that transformation. Ensargo Li, who is former HPE executive who built out their NFE function for the communications group, commentating on what's real and what's not. Stay tuned, more Cube coverage for two days from Mobile World Congress. Here in Palo Alto, bringing you all the action and analysis. Be right back with more after this short break. (techno beat sounds)