>> Welcome back to the program. My name is Dave Valante 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. (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. >> Thanks Dave. >> Thank you. Great to be here. >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, Smart Factories, IIoT. Can you briefly explain, 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 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-of, two-of 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 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. 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, it'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 or decreasing the maintenance cycle of the equipments or improving the quality of products, right? So, I think these are 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 first started researching IoT and Industrial IoT 4.0, et cetera, I felt, well, 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 and a great opportunity. Of course, then I saw on TV somebody now they're building homes with 3D printers. It 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 in 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 the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security because now you are connecting the factories to each other. 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. 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 you did brought up security, because if you think about the operations technology folks, historically they air-gaped the systems, that's how they created security. That's changed. The business came in and said, 'Hey, we got to connect. We got to make it intelligence.' So, that's got to be a big challenge as well. >> It absolutely is, Dave. 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 kind of that hybrid right ones 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 bridge that gap between, like, Dave, you talked about IT and OT and also help what Aditi talked about is the Greenfield Plants versus the Brownfield Plants that they call it, that are the legacy ones and modernizing those. 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 plant, 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 it needs to be done. 'Cause that's the only way that 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, its.. so yeah. How long does a typical project take? I know it varies, but what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're- you know, like I said at the beginning, 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 enterprises and the IT folks are having a much bigger say and they 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 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 enterprise all the time. 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 Mac 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 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 make that across all the factories including the factory that we're in, then across the globe. That makes it much, much easier. You really do the hard work once and then repeat. Almost like 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. Lot different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is important. But the digital skillsets like IoT, having a skillset in in different Protocols for connecting the machines, right? That experience that comes with it. Data and Analytics, Security, Augmented Virtual Reality Programming. Again, looking at Robotics and the Digital Twin. So, the... 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 recruiting these types of resources with these skill sets to accelerate their Smart Factory implementation, as well as consulting firms like DXC Technology and others. We recruit, we train our talent 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 bring these to market? >> Yeah, Dell and DXC have a very strong partnership and we work very closely together to create solutions, to create strategies and how 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. 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 approaches that 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? Where are you confident that you're going to be to deliver the best value 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 that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously Bespoke Solutions upfront, together. The power of our scalable enterprise grade structured 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 advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, 30,000 people across the globe that are really, really good 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, 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 guys both on the, I think you were talking about converged infrastructure and I had a customer on, and it was actually the manufacturing customer. It was quite interesting. And back then it was how do we kind of replicate what's coming in the Cloud? 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. It was a pleasure speaking with you. I agree. >> All right, keep it right there for more discussions that educate and inspire on "The Cube."

>> 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. 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 laughs) 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. >> Thanks Dave. >> Thank you. Great to be here. >> Well- >> Nice to be here. >> 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. It's 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 'em 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 really to 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 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, one, if I could stay with you and maybe this is a bit esoteric, but when I first started researching IoT 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 and a great opportunity. Of course, then I saw on TV, somebody now, they're building homes with 3D printers. It 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 there's 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 in 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. Industry 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, right. 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 connect. We got to make it intelligent." So that's got to be a big challenge as well. >> It absolutely is Dave. 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, kind of, that hybrid, you know, write once, run anywhere on the factory floor down to the edge. And one of things we're seeing too is to help distinguish between what is the edge and that. 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 plant. 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. Is... Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is needs to be done because that's the only way 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 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 in 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 digital equivalent of building the Hoover Dam. I mean, 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 we're, you know, like I said at the beginning, 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 PC in a closet somewhere running a 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 enterprise all the time. 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 Mac 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. Different types of skillsets are needed from a traditional manufacturing skillset, right. Of course, the basic knowledge of manufacturing is important. But the digital skillsets, 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 resources with these skillsets to, you know, accelerate their smart factory implementation as well as consulting firms like DXC technology and others. We recruit. We train our talent 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 bring these to market? >> Yeah, I... Dell and DXC have a very strong partnership, you know, and we work very closely together to create solutions, to create strategies, and how 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 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 are you confident that, you know, you're going to deliver the best value 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 that's where it ends. What Dell and DXC Technology together bring to the table is we do 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 advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, what, 30,000 people across the globe that are really, really good 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, 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 guys both on the queue... I think we were talking about converged infrastructure and I had a customer on, and it was actually manufacturing customer. Was quite interesting. And back then it was how do we kind of replicate what's coming in the cloud? 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.

>>from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>And we're back with the data automation. Siri's. In this episode, we're gonna learn more about what I owe Tahoe is doing in the field of adaptive data governance how it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin, and I'm joined by a J. Bihar on the CEO of Iot Tahoe and Lester Waters, the CEO of Bio Tahoe. Gentlemen, it's great to have you on the program. >>Thank you. Lisa is good to be back. >>Great. Staley's >>likewise very socially distant. Of course as we are. Listen, we're gonna start with you. What's going on? And I am Tahoe. What's name? Well, >>I've been with Iot Tahoe for a little over the year, and one thing I've learned is every customer needs air just a bit different. So we've been working on our next major release of the I O. Tahoe product. But to really try to address these customer concerns because, you know, we wanna we wanna be flexible enough in order to come in and not just profile the date and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could, uh, extend the product without building a new version of the product. We wanted to be able to have plausible modules. We also focused a lot on performance. That's very important with the bulk of data that we deal with that we're able to pass through that data in a single pass and do the analytics that are needed, whether it's, uh, lineage, data quality or just identifying the underlying data. And we're incorporating all that we've learned. We're tuning up our machine learning we're analyzing on MAWR dimensions than we've ever done before. We're able to do data quality without doing a Nen initial rejects for, for example, just out of the box. So I think it's all of these things were coming together to form our next version of our product. We're really excited by it, >>So it's exciting a J from the CEO's level. What's going on? >>Wow, I think just building on that. But let's still just mentioned there. It's were growing pretty quickly with our partners. And today, here with Oracle are excited. Thio explain how that shaping up lots of collaboration already with Oracle in government, in insurance, on in banking and we're excited because we get to have an impact. It's real satisfying to see how we're able. Thio. Help businesses transform, Redefine what's possible with their data on bond. Having I recall there is a partner, uh, to lean in with is definitely helping. >>Excellent. We're gonna dig into that a little bit later. Let's let's go back over to you. Explain adaptive data governance. Help us understand that >>really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data driven culture and pushing what's traditionally managed in I t out to the business. And to do that, you've got to you've got Thio. You've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concerns itself. But they need to understand what kind of data they have, what shape it's in what's dependent on it upstream and downstream, and so that they could make their educated decisions on on what they need to do to achieve those business outcomes. >>Ah, >>lot of a lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just >>say, you >>know, the start date of alone must always be before the end date of alone and having that generic rule, regardless of the underlying database and applying it even when a new database comes online and having those rules applied. That's what adaptive data governance about I like to think of. It is the intersection of three circles, Really. It's the technical metadata coming together with policies and rules and coming together with the business ontology ease that are that are unique to that particular business. And this all of this. Bringing this all together allows you to enable rapid change in your environment. So it's a mouthful, adaptive data governance. But that's what it kind of comes down to. >>So, Angie, help me understand this. Is this book enterprise companies are doing now? Are they not quite there yet. >>Well, you know, Lisa, I think every organization is is going at its pace. But, you know, markets are changing the economy and the speed at which, um, some of the changes in the economy happening is is compelling more businesses to look at being more digital in how they serve their own customers. Eh? So what we're seeing is a number of trends here from heads of data Chief Data Officers, CEO, stepping back from, ah, one size fits all approach because they've tried that before, and it it just hasn't worked. They've spent millions of dollars on I T programs China Dr Value from that data on Bennett. And they've ended up with large teams of manual processing around data to try and hardwire these policies to fit with the context and each line of business and on that hasn't worked. So the trends that we're seeing emerge really relate. Thio, How do I There's a chief data officer as a CEO. Inject more automation into a lot of these common tax. Andi, you know, we've been able toc that impact. I think the news here is you know, if you're trying to create a knowledge graph a data catalog or Ah, business glossary. And you're trying to do that manually will stop you. You don't have to do that manually anymore. I think best example I can give is Lester and I We we like Chinese food and Japanese food on. If you were sitting there with your chopsticks, you wouldn't eat the bowl of rice with the chopsticks, one grain at a time. What you'd want to do is to find a more productive way to to enjoy that meal before it gets cold. Andi, that's similar to how we're able to help the organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >>And if it was me eating that food with you guys, I would be not using chopsticks. I would be using a fork and probably a spoon. So eso Lester, how then does iota who go about doing this and enabling customers to achieve this? >>Let me, uh, let me show you a little story have here. So if you take a look at the challenges the most customers have, they're very similar, but every customers on a different data journey, so but it all starts with what data do I have? What questions or what shape is that data in? Uh, how is it structured? What's dependent on it? Upstream and downstream. Um, what insights can I derive from that data? And how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud. Maybe they're doing a migration oracle. Maybe they're doing some data governance changes on bits about enabling this. So if you look at these challenges and I'm gonna take you through a >>story here, E, >>I want to introduce Amanda. Man does not live like, uh, anyone in any large organization. She's looking around and she just sees stacks of data. I mean, different databases, the one she knows about, the one she doesn't know about what should know about various different kinds of databases. And a man is just tasking with understanding all of this so that they can embark on her data journey program. So So a man who goes through and she's great. I've got some handy tools. I can start looking at these databases and getting an idea of what we've got. Well, as she digs into the databases, she starts to see that not everything is as clear as she might have hoped it would be. You know, property names or column names, or have ambiguous names like Attribute one and attribute to or maybe date one and date to s Oh, man is starting to struggle, even though she's get tools to visualize. And look what look at these databases. She still No, she's got a long road ahead. And with 2000 databases in her large enterprise, yes, it's gonna be a long turkey but Amanda Smart. So she pulls out her trusty spreadsheet to track all of her findings on what she doesn't know about. She raises a ticket or maybe tries to track down the owner to find what the data means. And she's tracking all this information. Clearly, this doesn't scale that well for Amanda, you know? So maybe organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well because they're still ambiguities in the data with Iota ho. What we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that attribute. One looks very much like a U. S. Social Security number and attribute to looks like a I c D 10 medical code. And we do this by using anthologies and dictionaries and algorithms to help identify the underlying data and then tag it. Key Thio Doing, uh, this automation is really being able to normalize things across different databases, so that where there's differences in column names, I know that in fact, they contain contain the same data. And by going through this exercise with a Tahoe, not only can we identify the data, but we also could gain insights about the data. So, for example, we can see that 97% of that time that column named Attribute one that's got us Social Security numbers has something that looks like a Social Security number. But 3% of the time, it doesn't quite look right. Maybe there's a dash missing. Maybe there's a digit dropped. Or maybe there's even characters embedded in it. So there may be that may be indicative of a data quality issues, so we try to find those kind of things going a step further. We also try to identify data quality relationships. So, for example, we have two columns, one date, one date to through Ah, observation. We can see that date 1 99% of the time is less than date, too. 1% of the time. It's not probably indicative of a data quality issue, but going a step further, we can also build a business rule that says Day one is less than date to. And so then when it pops up again, we can quickly identify and re mediate that problem. So these are the kinds of things that we could do with with iota going even a step further. You could take your your favorite data science solution production ISAT and incorporated into our next version a zey what we call a worker process to do your own bespoke analytics. >>We spoke analytics. Excellent, Lester. Thank you. So a J talk us through some examples of where you're putting this to use. And also what is some of the feedback from >>some customers? But I think it helped do this Bring it to life a little bit. Lisa is just to talk through a case study way. Pull something together. I know it's available for download, but in ah, well known telecommunications media company, they had a lot of the issues that lasted. You spoke about lots of teams of Amanda's, um, super bright data practitioners, um, on baby looking to to get more productivity out of their day on, deliver a good result for their own customers for cell phone subscribers, Um, on broadband users. So you know that some of the examples that we can see here is how we went about auto generating a lot of that understanding off that data within hours. So Amanda had her data catalog populated automatically. A business class three built up on it. Really? Then start to see. Okay, where do I want Thio? Apply some policies to the data to to set in place some controls where they want to adapt, how different lines of business, maybe tax versus customer operations have different access or permissions to that data on What we've been able to do there is, is to build up that picture to see how does data move across the entire organization across the state. Andi on monitor that overtime for improvement, so have taken it from being a reactive. Let's do something Thio. Fix something. Thio, Now more proactive. We can see what's happening with our data. Who's using it? Who's accessing it, how it's being used, how it's being combined. Um, on from there. Taking a proactive approach is a real smart use of of the talents in in that telco organization Onda folks that worked there with data. >>Okay, Jason, dig into that a little bit deeper. And one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is our ally. How do customers measure are? Why? What are they seeing with iota host >>solution? Yeah, right now that the big ticket item is time to value on. And I think in data, a lot of the upfront investment cause quite expensive. They have been today with a lot of the larger vendors and technologies. So what a CEO and economic bio really needs to be certain of is how quickly can I get that are away. I think we've got something we can show. Just pull up a before and after, and it really comes down to hours, days and weeks. Um, where we've been able Thio have that impact on in this playbook that we pulled together before and after picture really shows. You know, those savings that committed a bit through providing data into some actionable form within hours and days to to drive agility, but at the same time being out and forced the controls to protect the use of that data who has access to it. So these are the number one thing I'd have to say. It's time on. We can see that on the the graphic that we've just pulled up here. >>We talk about achieving adaptive data governance. Lester, you guys talk about automation. You talk about machine learning. How are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? Well, >>Azaz, we see Mitt Emmanuel day. The days of manual effort are so I think you know this >>is a >>multi step process. But the very first step is understanding what you have in normalizing that across your data estate. So you couple this with the ontology, that air unique to your business. There is no algorithms, and you basically go across and you identify and tag tag that data that allows for the next steps toe happen. So now I can write business rules not in terms of columns named columns, but I could write him in terms of the tags being able to automate. That is a huge time saver and the fact that we can suggest that as a rule, rather than waiting for a person to come along and say, Oh, wow. Okay, I need this rule. I need this will thes air steps that increased that are, I should say, decrease that time to value that A. J talked about and then, lastly, a couple of machine learning because even with even with great automation and being able to profile all of your data and getting a good understanding, that brings you to a certain point. But there's still ambiguities in the data. So, for example, I might have to columns date one and date to. I may have even observed the date. One should be less than day two, but I don't really know what date one and date to our other than a date. So this is where it comes in, and I might ask the user said, >>Can >>you help me identify what date? One and date You are in this in this table. Turns out they're a start date and an end date for alone That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one day to elsewhere, I'm going to say, Is it start dating and date? And these Bringing all these things together with this all this automation is really what's key to enabling this This'll data governance. Yeah, >>great. Thanks. Lester and a j wanna wrap things up with something that you mentioned in the beginning about what you guys were doing with Oracle. Take us out by telling us what you're doing there. How are you guys working together? >>Yeah, I think those of us who worked in i t for many years we've We've learned Thio trust articles technology that they're shifting now to ah, hybrid on Prohm Cloud Generation to platform, which is exciting. Andi on their existing customers and new customers moving to article on a journey. So? So Oracle came to us and said, you know, we can see how quickly you're able to help us change mindsets Ondas mindsets are locked in a way of thinking around operating models of I t. That there may be no agile and what siloed on day wanting to break free of that and adopt a more agile A p I at driven approach. A lot of the work that we're doing with our recall no is around, uh, accelerating what customers conduce with understanding their data and to build digital APS by identifying the the underlying data that has value. Onda at the time were able to do that in in in hours, days and weeks. Rather many months. Is opening up the eyes to Chief Data Officers CEO to say, Well, maybe we can do this whole digital transformation this year. Maybe we can bring that forward and and transform who we are as a company on that's driving innovation, which we're excited about it. I know Oracle, a keen Thio to drive through and >>helping businesses transformed digitally is so incredibly important in this time as we look Thio things changing in 2021 a. J. Lester thank you so much for joining me on this segment explaining adaptive data governance, how organizations can use it benefit from it and achieve our Oi. Thanks so much, guys. >>Thank you. Thanks again, Lisa. >>In a moment, we'll look a adaptive data governance in banking. This is the Cube, your global leader in high tech coverage. >>Innovation, impact influence. Welcome to the Cube. Disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader in high tech digital coverage. >>Our next segment here is an interesting panel you're gonna hear from three gentlemen about adaptive data. Governments want to talk a lot about that. Please welcome Yusuf Khan, the global director of data services for Iot Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and good John Vander Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this in this panel. Great >>to be >>tried for me. >>Alright, Santiago, we're going to start with you. Can you talk to the audience a little bit about the first Bank of Nigeria and its scale? This is beyond Nigeria. Talk to us about that. >>Yes, eso First Bank of Nigeria was created 125 years ago. One of the oldest ignored the old in Africa because of the history he grew everywhere in the region on beyond the region. I am calling based in London, where it's kind of the headquarters and it really promotes trade, finance, institutional banking, corporate banking, private banking around the world in particular, in relationship to Africa. We are also in Asia in in the Middle East. >>So, Sanjay, go talk to me about what adaptive data governance means to you. And how does it help the first Bank of Nigeria to be able to innovate faster with the data that you have? >>Yes, I like that concept off adaptive data governor, because it's kind of Ah, I would say an approach that can really happen today with the new technologies before it was much more difficult to implement. So just to give you a little bit of context, I I used to work in consulting for 16, 17 years before joining the president of Nigeria, and I saw many organizations trying to apply different type of approaches in the governance on by the beginning early days was really kind of a year. A Chicago A. A top down approach where data governance was seeing as implement a set of rules, policies and procedures. But really, from the top down on is important. It's important to have the battle off your sea level of your of your director. Whatever I saw, just the way it fails, you really need to have a complimentary approach. You can say bottom are actually as a CEO are really trying to decentralize the governor's. Really, Instead of imposing a framework that some people in the business don't understand or don't care about it, it really needs to come from them. So what I'm trying to say is that data basically support business objectives on what you need to do is every business area needs information on the detector decisions toe actually be able to be more efficient or create value etcetera. Now, depending on the business questions they have to solve, they will need certain data set. So they need actually to be ableto have data quality for their own. For us now, when they understand that they become the stores naturally on their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also empower and be actually, in a way flexible to adapt the different questions that they have in orderto be able to respond to the business needs. Now I cannot impose at the finish for everyone. I need them to adapt and to bring their answers toe their own business questions. That is adaptive data governor and all That is possible because we have. And I was saying at the very beginning just to finalize the point, we have new technologies that allow you to do this method data classifications, uh, in a very sophisticated way that you can actually create analitico of your metadata. You can understand your different data sources in order to be able to create those classifications like nationalities, a way of classifying your customers, your products, etcetera. >>So one of the things that you just said Santa kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the users so that they can be adaptive. >>More and more business users wants autonomy, and they want to basically be ableto grab the data and answer their own question. Now when you have, that is great, because then you have demand of businesses asking for data. They're asking for the insight. Eso How do you actually support that? I would say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, off course, technology is one of the biggest winners without technology. We couldn't have been working remotely without these technologies where people can actually looking from their homes and still have a market data marketplaces where they self serve their their information. But even beyond that data is a big winner. Data because the pandemic has shown us that crisis happened, that we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore that we need to adapt and we need to be flexible. How do we do that with data. Every single company either saw the revenue going down or the revenue going very up For those companies that are very digital already. Now it changed the reality, so they needed to adapt. But for that they needed information. In order to think on innovate, try toe, create responses So that type of, uh, self service off data Haider for data in order to be able to understand what's happening when the prospect is changing is something that is becoming more, uh, the topic today because off the condemning because of the new abilities, the technologies that allow that and then you then are allowed to basically help your data. Citizens that call them in the organization people that no other business and can actually start playing and an answer their own questions. Eso so these technologies that gives more accessibility to the data that is some cataloging so they can understand where to go or what to find lineage and relationships. All this is is basically the new type of platforms and tools that allow you to create what are called a data marketplace. I think these new tools are really strong because they are now allowing for people that are not technology or I t people to be able to play with data because it comes in the digital world There. Used to a given example without your who You have a very interesting search functionality. Where if you want to find your data you want to sell, Sir, you go there in that search and you actually go on book for your data. Everybody knows how to search in Google, everybody's searching Internet. So this is part of the data culture, the digital culture. They know how to use those schools. Now, similarly, that data marketplace is, uh, in you can, for example, see which data sources they're mostly used >>and enabling that speed that we're all demanding today during these unprecedented times. Goodwin, I wanted to go to you as we talk about in the spirit of evolution, technology is changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >>Yeah, Thank you. Um, well, Oracle Digital is a business unit that Oracle EMEA on. We focus on emerging countries as well as low and enterprises in the mid market, in more developed countries and four years ago. This started with the idea to engage digital with our customers. Fear Central helps across EMEA. That means engaging with video, having conference calls, having a wall, a green wall where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today, and this helps us to engage with our customers in the way we were already doing and then about my team. The focus of my team is to have early stage conversations with our with our customers on digital transformation and innovation. And we also have a team off industry experts who engaged with our customers and share expertise across EMEA, and we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important so we can help the customer and for the customer means that we will help them with our technology and our resource is to achieve their goals. >>It's all about outcomes, right? Good Ron. So in terms of automation, what are some of the things Oracle's doing there to help your clients leverage automation to improve agility? So that they can innovate faster, which in these interesting times it's demanded. >>Yeah, thank you. Well, traditionally, Oracle is known for their databases, which have bean innovated year over year. So here's the first lunch on the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multi medal converts, database and machine learning based automation for full life cycle management. Our databases self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security, and it's self repairing the automates failure, detection fail over and repair. And then the question is for our customers, What does it mean? It means they can focus on their on their business instead off maintaining their infrastructure and their operations. >>That's absolutely critical use if I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that. But we know that whether we're talking about beta management or digital transformation, a one size fits all approach doesn't work to address the challenges that the business has, um that the i t folks have, as you're looking through the industry with what Santiago told us about first Bank of Nigeria. What are some of the changes that you're seeing that I owe Tahoe seeing throughout the industry? >>Uh, well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data where you have almost a data Policeman who tells you what you can and can't do, just doesn't work anymore. It's too slow. It's too resource intensive. Uh, data management data, governments, digital transformation itself. It has to be collaborative on. There has to be in a personalization to data users. Um, in the environment we find ourselves in. Now, it has to be about enabling self service as well. Um, a one size fits all model when it comes to those things around. Data doesn't work. As Santiago was saying, it needs to be adapted toe how the data is used. Andi, who is using it on in order to do this cos enterprises organizations really need to know their data. They need to understand what data they hold, where it is on what the sensitivity of it is they can then any more agile way apply appropriate controls on access so that people themselves are and groups within businesses are our job and could innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >>Yeah, that one size fits all term just doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Used to talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do >>before it's it's automatically being able to classify terabytes, terabytes of data or even petabytes of data across different sources to find duplicates, which you can then re mediate on. Deletes now, with the capabilities that iota offers on the Oracle offers, you can do things not just where the five times or 10 times improvement, but it actually enables you to do projects for Stop that otherwise would fail or you would just not be able to dio I mean, uh, classifying multi terrible and multi petabytes states across different sources, formats very large volumes of data in many scenarios. You just can't do that manually. I mean, we've worked with government departments on the issues there is expect are the result of fragmented data. There's a lot of different sources. There's lot of different formats and without these newer technologies to address it with automation on machine learning, the project isn't durable. But now it is on that that could lead to a revolution in some of these businesses organizations >>to enable that revolution that there's got to be the right cultural mindset. And one of the when Santiago was talking about folks really kind of adapted that. The thing I always call that getting comfortably uncomfortable. But that's hard for organizations to. The technology is here to enable that. But well, you're talking with customers use. How do you help them build the trust in the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive the kind of a tech in the culture? >>It's really good question is because it can be quite scary. I think the first thing we'd start with is to say, Look, the technology is here with businesses like I Tahoe. Unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting being agile around it, Andi trying new ways of doing things. Uh, if you don't wanna get less behind that Santiago on the team that fbn are a great example off embracing it, testing it on a small scale on, then scaling up a Toyota, we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with a customer. Picky use case, install the application, uh, analyzed data. Drive out Cem Cem quick winds. So we worked in the last few weeks of a large entity energy supplier, and in about 20 days, we were able to give them an accurate understanding of their critical data. Elements apply. Helping apply data protection policies. Minimize copies of the data on work out what data they needed to delete to reduce their infrastructure. Spend eso. It's about experimenting on that small scale, being agile on, then scaling up in a kind of very modern way. >>Great advice. Uh, Santiago, I'd like to go back to Is we kind of look at again that that topic of culture and the need to get that mindset there to facilitate these rapid changes, I want to understand kind of last question for you about how you're doing that from a digital transformation perspective. We know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges and changes that we're all going through? >>The new technologies allowed us to discover the dating anyway. Toe flawed and see very quickly Information toe. Have new models off over in the data on giving autonomy to our different data units. Now, from that autonomy, they can then compose an innovator own ways. So for me now, we're talking about resilience because in a way, autonomy and flexibility in a organization in a data structure with platform gives you resilience. The organizations and the business units that I have experienced in the pandemic are working well. Are those that actually because they're not physically present during more in the office, you need to give them their autonomy and let them actually engaged on their own side that do their own job and trust them in a way on as you give them, that they start innovating and they start having a really interesting ideas. So autonomy and flexibility. I think this is a key component off the new infrastructure. But even the new reality that on then it show us that, yes, we used to be very kind off structure, policies, procedures as very important. But now we learn flexibility and adaptability of the same side. Now, when you have that a key, other components of resiliency speed, because people want, you know, to access the data and access it fast and on the site fast, especially changes are changing so quickly nowadays that you need to be ableto do you know, interact. Reiterate with your information to answer your questions. Pretty, um, so technology that allows you toe be flexible iterating on in a very fast job way continue will allow you toe actually be resilient in that way, because you are flexible, you adapt your job and you continue answering questions as they come without having everything, setting a structure that is too hard. We also are a partner off Oracle and Oracle. Embodies is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happened. What happened there is that when our customers engaged with algorithms and again without your powers, well, the machine learning that is there for for speeding the automation of how you find your data allows you to create a new alliance with the machine. The machine is their toe, actually, in a way to your best friend to actually have more volume of data calculated faster. In a way, it's cover more variety. I mean, we couldn't hope without being connected to this algorithm on >>that engagement is absolutely critical. Santiago. Thank you for sharing that. I do wanna rap really quickly. Good On one last question for you, Santiago talked about Oracle. You've talked about a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle. What you guys were doing there to help your customers get the resilience that they have toe have to be not just survive but thrive. >>Yeah. Oracle has a cloud offering for infrastructure, database, platform service and a complete solutions offered a South on Daz. As Santiago also mentioned, We are using AI across our entire portfolio and by this will help our customers to focus on their business innovation and capitalize on data by enabling new business models. Um, and Oracle has a global conference with our cloud regions. It's massively investing and innovating and expanding their clouds. And by offering clouds as public cloud in our data centers and also as private cloud with clouded customer, we can meet every sovereignty and security requirements. And in this way we help people to see data in new ways. We discover insights and unlock endless possibilities. And and maybe 11 of my takeaways is if I If I speak with customers, I always tell them you better start collecting your data. Now we enable this partners like Iota help us as well. If you collect your data now, you are ready for tomorrow. You can never collect your data backwards, So that is my take away for today. >>You can't collect your data backwards. Excellently, John. Gentlemen, thank you for sharing all of your insights. Very informative conversation in a moment, we'll address the question. Do you know your data? >>Are you interested in test driving the iota Ho platform kick Start the benefits of data automation for your business through the Iota Ho Data Health check program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iota ho. Look time with a data engineer to learn more and see Io Tahoe in action from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs to be to Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you could get to know your data and quickly on. We also want to encourage you to engage with the media and Patrick, use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Savita, take it away. Alright. >>Thankfully saw great to be here as Lisa mentioned guys, I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. I said my name is Patrick Samit. I'm the enterprise data engineer here in Ohio Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>So, guys, what patent I got? I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off to being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted to walk you guys through some of those client questions, slash pain points that we're hearing from different industries and different rules and demo how our platform here, like Tahoe, is used for automating Dozier related tasks. So with that said are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one. So I'm a data practitioner. I just started my job. ABC Bank. I have, like, over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I saw who help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of excel and something similar to video which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing, you can see here visualization of the data relationships created by the I. O. Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um so is the data catalog automatically populated? Correct. Okay, so So what I'm using Iota hope at what I'm getting is this complete, unified automated platform without the added cost? Of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore tents. They're always such a joy to deal with. Now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Uh, personally, I'm a big fan of this view, as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought, Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. >>Now you >>notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships on. You can leverage to try and make new and powerful relationships within your data. >>Eso So this is really cool, and I can see how this could be leverage quickly now. What if I added new data sources or your multiple data sources and need toe identify what data sensitive can iota who detect that? >>Yeah, definitely. Within the hotel platform. There, already over 300 pre defined policies such as hip for C, C, P. A and the like one can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And it's something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface. Someone can work these into the action items within your project management systems, and I think this lends to the collaboration as a team can work through the discovery simultaneously, and as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota hope, you can be sure you're in compliance. >>So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely the collaboration and documentation that the Iot Tahoe interface lends itself to really help you build that confidence that your compliance is sound. >>So we're planning a migration. Andi, I have a set of reports I need to migrate. But what I need to know is, uh well, what what data sources? Those report those reports are dependent on. And what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>Eso So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>Let's say I add another data source. I can still see that unified 3 60 view. >>Yeah, One future that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished alone for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for things. Visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh, can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. This also provides tabular output in case you needed for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust >>it? So >>what I'm interested in knowing, really is is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that I owe Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, eso >>you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. >>So I get now the value of IATA who brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions. Allowing insight into the strength of these business rules is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, >>so those custom rules can I apply that across data sources? >>Yeah, you could bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us. we are Website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Your Honor, >>if I could ask you one quick question, how do you advise customers? You just walk in this great example this banking example that you instantly to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules. A swell is identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>I'm no, I think actually passed something that pretty well, I mean, just just by automating all those manual task. I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation >>started. Excellent. So, Pete and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality, something you can maximize the value of it. Thanks >>for watching. Thanks again, Lisa, for that very insightful and useful deep dive into the world of adaptive data governance with Iota Ho Oracle First Bank of Nigeria This is Dave a lot You won't wanna mess Iota, whose fifth episode in the data automation Siri's in that we'll talk to experts from Red Hat and Happiest Minds about their best practices for managing data across hybrid cloud Inter Cloud multi Cloud I T environment So market calendar for Wednesday, January 27th That's Episode five. You're watching the Cube Global Leader digital event technique

>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.

(dramatic music) >> Hey everyone, welcome to the special CUBEConversations here in Palo Alto, CA theCUBE Studios. I'm John Furrier, host of theCUBE, founder of SiliconANGLE Media. We are here for some exclusive news around security audits, blockchain smart contracts, and a hot new startup Amber Data we have the Chief Operating Officer Tongtong Gong who's here, Chief Operating Officer of Amber Data, great to see you! You guys, I've interviewed Shawn Douglass, the CEO, founder, before and he was really getting the technology going. Amazing progress, we have some exclusive discoveries here, welcome to theCUBE! >> Thank you, thank you, thanks for having me here. It's awesome, we've done so much in the past couple weeks, and really excited to announce that we have taken security audits, automated that to be able to provide automated at scale security audits for all the smart contracts, Ethereum, through our platform. >> This has been a huge problem, we've been covering it for the past year, with video but also in the blogs, Ethereum specifically has been the developer chain of choice, people are using Ethereum, programming on it, and that's where a lot of the DApps, decentralized apps, which we think there's going to be a tsunami of, we're a bit bullish on it, but the problem is that everyone went in and rushed with these ICO's, and they didn't think about, "Hey we better make sure our token generating event works" because they've got to do a smart contract on that, and then ultimately these marketplaces that will be emerging from these apps through the communities will be a lot of smart contracts, as the transaction of choice. This is what is the benefit of token economics. The problem is, security. The security audits have been a pain in the butt, they've been expensive, and there's been a time lag in getting it done. So you've got a time factor, too slow, too expensive, and it was last minute. >> Right. >> This has been a huge problem. Are you saying that you solved that problem? >> Yeah, kind of! So give you some stats. There are about 7.8 million to 8 million smart contracts on the chain today. On average, there's about 500-600 smart contracts get deployed every day into Mainnet Ethereum. What we've done, we talked to a lot of security teams that's in this space, and at the end of the day everybody use the same tools, set of tools, to preform security audits. What we have done, is we have programatically did that so we can run security audits on every smart contract on the chain. So we launched this feature last Friday, what we did is we picked the top 2000 smart contracts, based on transaction values-- >> On the Mainnet? >> On the Mainnet. And we preformed the audits on those, and last night, yeah three days later, we preformed all 8000 smart contracts that's been created and deployed in the past 90 days. So the top 2000 active ones, and the 8000 recently deployed ones, we preformed security audits on those. >> So this is pretty incredible, so I want to make sure I get this right. If this is the case, this is the first ever automation, or devOPS like approach to smart contract audits and security. So let me just kind of slow down if you don't mind. Today, most people will go in and manually look at code reviews or use some tooling to do that, and then they get a report. Businesses have been doing that, OSHO does that, many more do it, and they're bringing tools to the market, they are too, but I don't think anyone's actually automated at volume. So you're saying, you're automating, ingesting data from the chain-- >> Mhm. We analyze the bytecode as well as the source code to identify vulnerabilities and issues, things like integer overflow into the code, and we actually assign custom, we have our own scoring system to score basically the vulnerability exposure of the smart contract. >> Okay so I want to kind of push back on that because I'm skeptical. So, you do byte review-- >> Bytecode >> Bytecode and source code review, and then it's a black box and you type up a report, or you actually flag the code itself? Do you service it automatically? Does that happen automatically? Take me through what you do manually, and what happens with the computers. What is automated? >> Everything's automated. So we integrated the tools that every expert uses in this space today, to run the security audits on the smart contract and the bytecode and then we flag the particular source code and function calls that's flagged with the issue-- >> That's in the code itself? >> That's in the code itself, in that service, through our website, through our console, and you can actually see it. You can search on any smart contract and the console dashboard will show you the real time live streaming events of your smart contract function calls, as well as the vulnerability-- >> This is amazing. So this means that you can save a lot of time, love this feature, this is exciting. This is actually the first news I've ever hear of this, so I want to make sure I get it right. How many contracts can you do? How fast does it take? So you mentioned you've ingested last week, stuff off the chain, how many contracts was that? >> We did last week, 2000 and then up to last night, we finished 8000 smart contract scans. We're continuing to do that for every smart contract on the chain. >> How fast is this, because I remember back when I was learning how to code for the first time, back in the old days, you had to press a button, you'd have a compiler, and you'd get a bug in the line, syntax error, there it is. That's the normal kind of old school computer science. Syntax, compiler, interpreter, whatever you want to call it. It sounds like you're doing something similar, the same kind of speed. It's code review, analysis to the contracts, security through the tools... How fast is it? I mean, how long does it take to do a review of one smart contract, for instance? >> Actually, I don't know that. I would say minutes. >> Not days? >> Not days. No. Minutes. >> So it's not like it goes out, hourglass... Check your email it'll be there? It happens pretty much on the fly? >> It happens pretty much on the fly, real time. >> So how many contracts can you guys do in a day? >> We've done 8000 in three days, so... A lot! (both laughing) And we have ten machines running right now as we're speaking-- >> So you throw some clout at it, scale up-- >> Exactly, scale up. >> Scaling out is easy to do, you just go... >> Our goal is to basically make it very easy for developers to understand the state and health of their smart contract and then they can go find consultants, experts to fix those vulnerabilities and issues. >> Yeah, this is going to be a rising tide. I think, rising tide floats all boats when you have these emerging markets. You move to the next problem, and you do. Jeff Frick always says that in theCUBE and he's right! You take away security, you're now enabling this tool for these consultants to actually add more value. >> Exactly. >> Is that the focus? Do you guys even know who's going to use this tool yet? Obviously, this is a game changer. I mean, if I'm a data scientist I love this. Also I'm a trader, I might want the data, I'm a risk management, audit compliance person? I mean...you guys-- >> Yeah! At Amber Data our mission has always been providing, enabling infrastructure, enabling tool sets to allow developers, to allow operators, to allow the industry, to allow businesses to adopt blockchain, that's always been our mission and we have built the splunk, you know like search, a feature for blockchain, we have built APM, we have built dissimilar Mixpanel... It's all about providing access to data and to information, to allow everyone to have a better understanding, better transparency into the state and health of the blockchain, the state and health of their smart contracts. So that's you know, in line with-- >> So talk about the scoring thing, because okay, I love this automation I think that that's a game changer. So congratulations, this is the first I've heard of it, and I think this might be the first news in the industry out there. How does it work beyond that? What else do you guys do? Are you ingesting, are you adding overlays to it? What is the focus next? I mean, you're ingesting it, you're doing some security audits... Where does it go from there? >> So, we're actually working with the Web3 Data Foundation. So the Web3 Data Foundation is building a decentralized data marketplace to allow everyone in the ecosystem to list, subscribe, consume, distribute, monetize data assets that's generated by the blockchain and data that's on blockchain. >> So what's the URL for that? Web3... >> Web3data.org >> Three the number or three... >> Three the number, yeah. >> So web3 number data dot org? >> Yeah. >> Okay and is that an open community? Is it a foundation? >> Yes it's a nonprofit foundation, and I believe they're launching a token, Web3 Data token, and Amber Data's working with the Web3 Data foundation as a launch partner to utilize the data ingestion pipeline we have built and to serve up all the data for everyone to have access to it. >> Okay so what's your business model at Amber Data? Are you going to have your own token? Are you going to use the foundation as the token holding place? Can you just take us through the relationship of Amber Data with the foundation? I mean, I get the foundation but what you're doing here is essentially you're building IT operations into the blockchain and scaling things with automation, which certainly is only going to get better with more compute and A.I and other cool things, so I love that. How do you make money? Is it a token model? Is it just, classic, you get paid? What's the relationship? Is the foundation issuing tokens, do you have your own token? Take us through that. >> So the Web3 Data Foundation is the one issuing the token. We are the launch partner, so we are using the bulk diagnostic data ingestion pipeline that we can ingest all the data, and we're building together, building the data marketplace using smart contracts, to enable everyone to list, curate, consume, distribute, monetize the data. You think about it, right? Data on blockchain is just a fraction of the data out there. And as staff development, going on, as a trading application going on, there's a lot of data that's going to be generated by blockchain as well, and those datas aren't getting captured, analyzed, and utilized today. I think today, as a trader, investor, or as a developer, people don't have access to this data, to have data driven decisions, to help them continuously improve. Whether it's application or investment decisions. So the data marketplace will enable everyone to be able to have that access. >> And also it might enable more faster solution of decentralized applications-- >> Exactly. >> Which, Fred Kruger and I were talking on Twitter, I mean Facebook, about this, that we think that's the killer app, it's going to be the tsunami of apps coming. But all these chain problems are out there, so it's a little bit of a resetting going on in the industry. Obviously you see that with some of the pricing and funding and everything, but for the most part we see a big market coming. So I've got to ask you, the obvious question from there is, which chains are you supporting? You mentioned Mainnet which is great for Ethereum-- >> Yeah today we're supporting Ethereum Mainnet, and Rinkeby, the test net. We also support Aion's Mainnet and test net. We also support Stellar, we're working on EOS and TRON as well, so we have open sourced our data collector to allow community to contribute to that and we'll use Web3 Data Token to incentivize the community to contribute, to verify, to enrich the data. >> So I've got to ask you the security question, maybe this might be for more the nerds and the geeks, delving down in the product level, but maybe you can get it. Security is huge, so I'm skeptical. You're doing scoring, can you be hacked? What's the security answer to that? Like, whoa if she's controlling the score, I might want to spoof the code and take over and say it's okay, ya know? >> The code we get is actually on the chain, it's the code that you put on the chain, so good luck spoofing the data on the blockchain. >> That's the whole point of block chain, that's already answered. That's a dumb question, I got that. I always ask dumb questions. Alright, so what's next for you guys? How big are you guys, what's the story? I've been following you guys on Twitter and Telegram, you've been traveling a lot. What's the update on the company, what's the status? >> So we are, as a launch partner for Web3 Data Foundation, right now there's a token sale, we're in the middle of closing our presale. It's a soft offering, and we're building and expanding the team as we're speaking. >> How much are you raising on the staff, can you talk about that? >> No. >> No? Okay you don't have to say the number. Just be careful, it gets hard to raise too much. So the foundation, and you guys. Okay, I want to ask you a personal question, we love women in blockchain, I wear the "Satoshi is female" shirt as much as I can... How did you get into this? Because there's a lot of women coming into blockchain, more than people are advertising. I'm seeing a lot more women in tech, certainly a lot more women in crypto. Blockchain and crypto, you guys are doing almost a cloud devOPs serious venture here. How did you get into this, what's your story? >> I've always been a cloud girl. I started my career building Yatuzi computing, enterprise grid computing. I was 23 years old and working for Axiom in a data center in Arkansas, and I'm the only one that wears high heel6s in data center, and get stuck in a vent you know? That's my background, so it's not a far stretch to understand blockchain and the usefulness of it if you talk about distribute computing, distribute storage. So it's very natural for me, from a technology perspective, get into this space. On a personal note, I really believe in decentralization, and I believe the change it's going to make to our lives and to our offspring's lives in the future. >> It's real, you think? >> It's real. It's here to stay. >> So what's your vision of blockchain? What are people not getting? Obviously there's a lot of scams out there that have kind of tainted on the ICO side, but what are people missing? When you talk to people, you have kind of like, "Oh I get it" and people kind of of like "I don't really see that" ? What's the main thing that they're missing, what's missing? >> I think it's missing that killer Dapp to get people to realize "Oh it's actually easy to use". I don't have to think about the inner workings, and it just works. My mom still lives in Beijing, I talk to her on Skype all the time, she's not worrying about TCPIP, she's not worrying about, how is this phone call getting encrypted or not encrypted? What's my network bandwidth? She just use the phone and call me, like I'm right next to her. I think as we develop building the apps, people don't think about that they're using blockchain, they just use it. >> It's like explaining it to a parent or someone who's not technical.. "Hey how does the internet work? Can't I just "type a keyword in to the browser or a search engine?" Instead today, it's more like "Hey, you know how BGP works?" and "You know how packets move around?" It's so hard to explain, so it's got to be easier. >> Way easier, yeah. >> Totally agree, totally agree. Well Tongtong, thank you for coming on theCUBE, appreciate it, great update, exclusive news. Automation, bringing cloud computing and utility computing, real geeky stuff to the table here. This is theCUBE Conversation and I'm John Furrier. Amber Data COO, Tongtong Gong here, inside theCUBE. Thanks for watching. (dramatic music)

>> Voiceover: Live from San Francisco, it's theCUBE, covering Samsung Developer Conference 2017 brought to you by Samsung. >> Welcome back, we're live here in San Francisco. We're here at the SDC, the Samsung Developer Conference. I'm John Furrier, the co-founder of SiliconANGLE and co-host of theCUBE. My next guest, Lisa Fetterman, who is of Nomiku and she's a three-time, triple-star winner, Forbes Under 30- >> Inc 30 Under 30, and Zagat 30 under 30. That's a weird one. >> That's a great one. You're likely to get the Michelin Star soon. Tell us about your company. It's a really super story here. You have this new device you guys started. Tell the story. >> Well, speaking of Michelin Stars, I used to work under the best chefs in the nation. I worked under my Mario Batali, Jean-Georges at the three Michelin Star restaurants and I saw this huge, hulking piece of laboratory equipment. We would cook so many of our components in it and I'd lusted after one for myself, but they were $2000 and up, so that was like you know what, I'm going to save up money and then I went on a date with a plasma physicist and he said, "Hey, you know what, "we could just make it on our own." We run to the hardware store, we make a prototype. We travel all across the United States and teach people how to make their own DIY open-source sous vide kits to the point where we amassed so much attention that Obama invited us to the White House. And then we put it on Kickstarter and it becomes the #1 most-funded project in our category, and we are here today with our connected home sous vide immersion circulator that interacts with Samsung's Smart Fridge. >> That's a fantastic story of all in a very short time. Well done, so let me just back up. You guys have the consumer device that all the top chef's have. >> That's right. >> That's the key thing, right? >> It's consumable, low-priced, what's the price point? >> We do hardware, software, and goods. Right now the price of our machine is $49 on souschef.nomiku.com because it interacts with the food program. So there's food that comes with the machine. You weigh the food in front of the machine. It automatically recognizes the time and temperature. It interacts with different time and temperatures of different bags of food, and you just drop it in. In 30 minutes, you have a gourmet chef-prepared meal just the way that we would do it in Michelin Star restaurants. >> And now you're connecting it to Samsung, so they have this SmartThings Messaging. That's kind of the marketing, SmartCloud, SmartThings. What does that mean, like it's connected to the wifi, does it connect to an app? Take us through how it connects to the home. >> We're connected through Family Hub, which is the system inside of the Samsung Smart Fridge. Every single Samsung Smart Fridge ships with a Nomiku app pre-downloaded inside of it and the fridge and the Nomiku talk to each other so there's inventory management potential. There's learning consumer behaviors to help them. Let's say you cook a piece of chicken at 4:00 AM. You go to a subset of people who also do that, like wow, and then we recognize that those folks do CrossFit. They will eat again at 7:00 AM because they eat more little meals rather than full meals, and then we can recommend things for them as their day goes along, and help manage things for them, like a personal assistant. >> So it's like a supply chain of your personal refrigerator. So can you tell if the chicken's going to go bad so you cook the chicken now, kind of thing? That would be helpful. >> You can actually tell if the chicken's going to go bad. If the chicken, if there's a recall or the chicken's expired and you tap it with the machine, the machine will tell you to throw it out. >> So tell us about some of the travel's you've been under. You said you've traveled the world. You also have done a lot of writing, best-selling author. Tell us about your books and what you're writing about. >> I wrote the book called Sous Vide at Home. It's an international best-seller and it's sous vide recipes. Everybody has been lusting after sous vide since we invented the technology in 2012, so much actually that the market for it grows 2.5x every single year so the adoption rate is insane. The adoption rate for sous vide actually has surpassed that of the internet, the cell phone, and the personal computer. >> Why is the excitement on the Kickstarter, obviously, the record-breaking, and the sales, and the trend, why is it so popular? Is it 'cause it's a convenience? Is it the ease of use, all of the above? What's the main driver? >> All of the above. If you ever cooked in the kitchen and you've lost your confidence, it was mostly because you messed something up in the kitchen. This is foolproof cooking. So at 57 degrees Celsius, that's when the fat and the collagen melt into the muscle of steak, making each bite so juicy, tender, and delicious. We can set it at exactly that magic temperature, drop a steak in, and then put it in the water. When you cook it like that, there's no overcooking the muscle and it becomes effectively marbled by all that juicy, fat deliciousness. >> Aw, I'm kind of hungry already. >> Yeah. >> Lenny wants a steak. I can hear Leonard moaning over there. Okay, let's get down to the science here because a lot of people might not understand what temperatures to cook anything. Do you guys provide some best practices because this is a game-changer for my family of four. >> We want to meal cooked fast, but you want to have meals staged potentially and then recook them. How does someone use it? Is there a playbook? Is there a cookbook? >> Like we say in the industry, there's an app for that. The app is on the Smart Fridge and it's also on your smartphones. Moreover, so the machine acts as a stand-alone sous vide machine for you to cook your own recipes, and it also reads rfid tags from our meals. If you use our meals, then it's a no-brainer. You just tap and then put in the water. There's nothing more. Actually people get flustered that it's so easy. They're like, "That's it? "That was all that was?" But I hate smart devices that actually make people stupider. Being a stand-alone sous vide machine, you can create any of your recipes whether it's from my cookbook, the app, which is community-focused, so we have over 1000 recipes inside there from our community. People make it and they share it with the world. >> So with the Kickstarter, I'm just going to ask that next question. I'll say community layer. >> Sure. Kind of like is it a Reddit page? Do you have your own pages? What's going on with the community? Tell us about the community. >> Oh, the community. Everybody who has an OmniCube downloads our app called Tender and inside you can make your own-- >> Not to be confused with Tinder. >> Correct. >> Tender. >> Although I wouldn't mind if you confused it and instead of going out, I guess you're making dinner. >> Wife left for the steak and right for the chicken. >> (laughing) Exactly, exactly. We love the play on the word. >> That's great. >> When you make your own little profile, it encourages you to share. It's really fun because you can keep your recipes in there so you never have to look it up ever again. You can bing it and it goes directly to your machine. It's great for professional chefs, too 'cause you can share it with your entire team. >> So maybe we should start a Cube food channel. You can get a dedicated recipe channel. Exciting. >> That's great. Will you be my sous chef? >> (laughing) Course, I'm a great guest to have do that. If I can do it, anyone can do it. How do I get one? How do people buy? What's the deal? >> It's namiku.com for just our hardware, and in California, we've launched our food program on souschef.nomiku.com. Right now our machines for the food program are only $49. That is such a great value considering that souv vide machines are usually $200 and up right now. >> Awesome, well thank you so much for coming on. I really appreciate it. Lisa Fetterman is CEO, entrepreneur of Namiku, entrepreneur of great stuff here in the Cube. Of course, we're bringing the food, tech, and remember, farming tech is big, too, so as the culture gets connected, the food from the farm to the table is being changed with data and IT. More after this short break. (innovative tones)

(upbeat music) >> Man: From the Computer History Museum in the heart of Silicon Valley, it's theCUBE! Covering Food IT. Fork to farm. Brought to you by Western Digital. >> Welcome back everybody, Jeff Frick here with theCUBE. We're in Mountain View, California at the Computer History Museum at Food IT, a really interesting conference about 350 people talking about the impacts of IT and technology in the agricultural space. Everything from farming, through to how you shop, how you consume, and what happens to the waste that we all, unfortunately, throw away way too much. We're excited to have our next guest, Mike Wolf, he's the creator and curator of The Spoon and the Smart Kitchen Summit. Mike, welcome! >> Hey, thanks for having me, I'm excited! >> Absolutely! So first off, before we jump in, what do you think of the show here? >> It's great! It's very focused on agriculture and the food chain, which is crucial. I focus a lot on the kitchen, when food gets to our homes, what we do with it, but this is where it all starts, so it's really important. >> It's so much stuff going on-- >> Yeah. >> With the kitchen and food preparation with all these services that will-- >> Yeah. >> Either bring you your meal, or they'll bring you pre-portioned and uncooked meals. So let's talk about a little bit, what is the Smart Kitchen Summit, and what is The Spoon? >> So I focused on the smart home a lot over my career. I've written a book on how to network your home, but about four or five years ago I noticed no one's really talking about how we're going to recreate the kitchen. We've focused from a digital home perspective on the living room. We saw the Netflix revolution, over-the-top, we've seen huge market value creation in the living room. But the kitchen was kind of left behind. So I said, let's start a conversation, let's focus on how we can recreate cooking in the kitchen. And the Smart Kitchen Summit is entering it's third year, it's kind of become the premier event about how technology will reshape how we get food, bringing her home, how we cook it, and how we eat it. >> Well it's funny though, because people would always say, you know, "I have the iPad on the front of my fridge, "it'll tell me when it's time to go get milk." So clearly, that's a pretty-- >> Yeah. >> Pretty low... Not of real significant use in this case, I would imagine, there's a lot more to it than that. >> Yeah, I think tablets and screens, and connecting to things with apps is like five percent of what's interesting. If you look at the refrigerator, the internet refrigerator, I was just talking to an LG guy, they created the first internet refrigerator in 2000, and it was $20,000, and no one bought it, 'cause everyone said "Why would I want to "connect my refrigerator "to the internet?" >> Right, right. >> Well, I kind of think we're at this point where now it becomes interesting. We can maybe have the fridge understand what our food is. The fridge itself is kind of a... The family bulletin board, so why not put a big screen on there if it's only a couple extra hundred dollars? >> Right. >> And so I think there's all sorts of ways in which we're getting food, like you said, new ways like Blue Apron, Cooking By Numbers services, new ways to cook food that are coming from the professional kitchen, like sous vide, high-precision cooking technology that's democratized for technology, and things like automated beer brewing appliances. I've always wanted a beer, brew beer, but my wife said "No way, you're going to have "the smelly..." >> Right. >> "Beer coming in my house." But I can use technology to make this automated and easy? I'm one of those guys that say "Let's do that." Then I can brag to my friends that I've actually made beer at home. >> Right, right. >> So. >> Well, it's funny 'cause we saw this other thing in the kitchen not that long ago, right? Where everybody had to have a Wolf, and it was kind of this, you know, kind of professionalize your kitchen with all these really heavy-duty, you know... >> Yeah. >> Appliances, that really, most people probably don't need a Wolf so they can keep their flambe at the perfect temperature-- >> Yeah. >> For extended periods of time. >> Yeah. >> So what are some of these things that are coming down the line that people haven't really thought of that you see as you study this phase? >> Well, so our research shows that everyone, almost every age group is using more digital technology in the kitchen, and that's iPhones, smart phones, and tablets, because what they're doing is looking for what they're going to have for dinner. So that starts the process of digitization in the kitchen, and so you've seen almost for 15, into 17, years now services like Allrecipes and Yummly creating kind of this digital recipe services. Now, we've also seen, really one of the most popular videos on the internet, BuzzFeed Tasty was the biggest video publisher for many months this year, doing a couple billion views a year, per month of these simple cooking videos. So... >> Right. >> A lot of it is very much generational. So millennials are grabbing on to these how-to-cook, you know, how-to-cook videos. They're very interested in cooking, but the definition of cooking is changing, so what they're seeing is the worrying about cooking through online, but also maybe applying cooking technology in a new way. Whether that's a very simple cooking appliance, like a sous vide circulator, or maybe an air fryer, or if you want to go high-end something, like a June Oven. So if you look forward, starting to add artificial intelligence, image recognition, and these type of technologies to the cooking process could make things a lot easier and make things faster, and kind of give you cooking super powers that you may otherwise not have. >> Right. It's so interesting! It continues to be a trend over and over, that it's kind of the hollowing of the middle, right? You are either you don't ever cook, right? >> Yeah. >> Everything is DoorDash, or however you get your... The meal. Or you kind of get to these specialty items where you're way into it as a hobby and, I mean, those videos, the cooking videos-- >> Yeah. >> Are fascinating to me, the popularity of those things. >> Yeah. >> But if you're kind of stuck in the middle, in the no-man's-land of what we think of maybe as a traditional kitchen, that's probably not a great place to be. >> Yeah, I think, you know, I'm that... I'm a different archetype depending on the day of the week, right? I may be in the middle of the week, and I'm tired, I have kids, I don't want to cook. Maybe something that automates my cooking maybe makes it easy with food delivery, it's fully cooked. That would be a great idea! But maybe on the weekend, I want to become, like, a maker, and really, like I say, the only maker space in the home, right now, besides the garage, is the kitchen. It's where I'm actually using my hands to make stuff. And I think that's great nowadays when we're all spending so much time in front of screens, moving around ones and zeros with our mouses, I think... Our research shows that people want to cook, but the definition of cooking is changing. So they may be assembling salads, or, and they're buying something from Costco and they're calling that cooking. But I think if we can have technology that allows us to actually make stuff in the home, where it's fresh and tastes good, it's healthy, and we feel like we're rewarding a craft, I think there's a lot of people who would want that. >> That's so interesting, that it's makers and craftsmanship, and you think back to kind of the traditional, beautiful cookbooks, right? That people would buy, maybe to actually use, maybe just 'cause they want to be associated with that type of activity and those types of photographs and stuff. So it's a very different way to think about it, as a maker versus, you know, just got to get the food out for the kids, I'm tired on a Thursday night at 6 p.m. >> Yeah, sometimes it's just sustenance, right? That's why packaged food is great. We like these protein bars. They're expensive, but they provide everything in one in, like, a flat piece of food. But at the same time, there's a whole food movement. Ever since John Mackey founded Whole Foods back in the early 80's, until the time that Amazon acquired it, the customer base has been growing. What I think is interesting is we can potentially see the democratization of better quality food. As you see, the decentralization of processed food, right? So over the past 100 to 200 years, all the technology around food has been towards centralized processing, and putting it into cans, making it... But what happens is you take all the nutritional value out of it. >> Right. >> But if you can start to think about bringing fresher food in the home, at a lower cost through optimized value chains, like what maybe Amazon can do with Whole Foods. Maybe that brings fresher food to the home at a lower cost, or it gets beyond the five to ten percent of the consumer, which is buying from Whole Foods. >> Right. >> It's a high-end type of retail channel, right? But I think everyone wants better food, so I think that's where I think technology could play a process. >> Well, just specifically, what are you thoughts on the Amazon acquisition of Whole Foods, and the impact of that? Not only for those two companies, specifically, but as a broader impact within the industry? >> I am excited for what Amazon could do with this technology. I live in Seattle, so I've been watching they're, what I would call lab experiments with Amazon Go, which is this recreation of the grocery store, this idea of walk in, walk out, don't ever talk to the cashier, that's really fascinating. Then you get Whole Foods, which is a pretty traditional retailer, even though it's kind of created the organic food movement in a lot of ways. I think bringing Amazon technology into theirs is really exciting, but I also think it validates the need for physical store fronts. I think Amazon's been trying to do online delivery, rolling trucks at your home for ten years. They've been working on Amazon for us for ten years, and they haven't been really... They haven't really reached massive scale. So I think this validates the idea of you need physical store fronts. Those physical store fronts may look very different in ten years, but the fact that Amazon is going to need that as a distribution point, as a point of presence in different neighborhoods, I think is fascinating. >> Alright, well, Mike we're almost out of time. I'll give you the last word. Where should people go to get more information about what you're up to? >> Yeah, go to TheSpoon.tech if you want to see our writing, podcast, and the future of food and cooking. And if you want to come to our event, go to SmartKitchenSummit.com. >> Alright, he's Mike Wolf, I'm Jeff Frick, you're watching theCUBE from Food IT. A lot of really interesting stuff. Again, it's all the way from the farm, the germination of the seeds, all the way through to what you eat, how you eat, and what you do with the stuff you don't. So thanks a lot Mike. >> Yeah, thanks! >> Alright, I'm Jeff Frick, you're watching theCUBE. We'll be right back after this short break. Thanks for watching. (upbeat music)