>> Hello everyone. Welcome to theCUBEs coverage of Compute Engineered for your Hybrid World sponsored by HPE and Intel. Today we're going to dive into advanced performance of VDI with the fourth gen Intel Zion scalable processors. Hello I'm John Furrier, the host of theCUBE. My guests today are Alan Chu, Director of Data Center Performance and Competition for Intel as well as Denis Kondakov who's the VDI product manager at HPE, and also joining us is Cynthia Sustiva, CAD/CAM product manager at HPE. Thanks for coming on, really appreciate you guys taking the time. >> Thank you. >> So accelerating VDI to the Edge. That's the topic of this topic here today. Let's get into it, Dennis, tell us about the new HPE ProLiant DL321 Gen 11 server. >> Okay, absolutely. Hello everybody. So HP ProLiant DL320 Gen 11 server is the new age center CCO and density optimized compact server, compact form factor server. It enables to modernize and power at the next generation of workloads in the diverse rec environment at the Edge in an industry standard designed with flexible scale for advanced graphics and compute. So it is one unit, one processor rec optimized server that can be deployed in the enterprise data center as well as at the remote office at end age. >> Cynthia HPE has announced another server, the ProLiant ML350. What can you tell us about that? >> Yeah, so the HPE ProLiant ML350 Gen 11 server is a powerful tower solution for a wide range of workloads. It is ideal for remote office compute with NextGen performance and expandability with two processors in tower form factor. This enables the server to be used not only in the data center environment, but also in the open office space as a powerful workstation use case. >> Dennis mentioned both servers are empowered by the fourth gen Intel Zion scale of process. Can you talk about the relationship between Intel HPE to get this done? How do you guys come together, what's behind the scenes? Share as much as you can. >> Yeah, thanks a lot John. So without a doubt it takes a lot to put all this together and I think the partnership that HPE and Intel bring together is a little bit of a critical point for us to be able to deliver to our customers. And I'm really thrilled to say that these leading Edge solutions that Dennis and Cynthia just talked about, they're built on the foundation of our fourth Gen Z on scalable platform that's trying to meet a wide variety of deployments for today and into the future. So I think the key point of it is we're together trying to drive leading performance with built-in acceleration and in order to deliver a lot of the business values to our customers, both HP and Intels, look to scale, drive down costs and deliver new services. >> You got the fourth Gen Z on, you got the Gen 11 and multiple ProLiants, a lot of action going on. Again, I love when these next gens come out. Can each of you guys comment and share what are the use cases for each of the systems? Because I think what we're looking at here is the next level innovation. What are some of the use cases on the systems? >> Yeah, so for the ML350, in the modern world where more and more data are generated at the Edge, we need to deploy computer infrastructure where the data is generated. So smaller form factor service will satisfy the requirements of S&B customers or remote and branch offices to deliver required performance redundancy where we're needed. This type of locations can be lacking dedicated facilities with strict humidity, temperature and noise isolation control. The server, the ML350 Gen 11 can be used as a powerful workstation sitting under a desk in the office or open space as well as the server for visualized workloads. It is a productivity workhorse with the ability to scale and adapt to any environment. One of the use cases can be for hosting digital workplace for manufacturing CAD/CAM engineering or oil and gas customers industry. So this server can be used as a high end bare metal workstation for local end users or it can be virtualized desktop solution environments for local and remote users. And talk about the DL320 Gen 11, I will pass it on to Dennis. >> Okay. >> Sure. So when we are talking about age of location we are talking about very specific requirements. So we need to provide solution building blocks that will empower and performance efficient, secure available for scaling up and down in a smaller increments than compared to the enterprise data center and of course redundant. So DL 320 Gen 11 server is the perfect server to satisfy all of those requirements. So for example, S&B customers can build a video solution, for example starting with just two HP ProLiant TL320 Gen 11 servers that will provide sufficient performance for high density video solution and at the same time be redundant and enable it for scaling up as required. So for VGI use cases it can be used for high density general VDI without GP acceleration or for a high performance VDI with virtual VGPU. So thanks to the modern modular architecture that is used on the server, it can be tailored for GPU or high density storage deployment with software defined compute and storage environment and to provide greater details on your Intel view I'm going to pass to Alan. >> Thanks a lot Dennis and I loved how you're both seeing the importance of how we scale and the applicability of the use cases of both the ML350 and DL320 solutions. So scalability is certainly a key tenant towards how we're delivering Intel's Zion scalable platform. It is called Zion scalable after all. And we know that deployments are happening in all different sorts of environments. And I think Cynthia you talked a little bit about kind of a environmental factors that go into how we're designing and I think a lot of people think of a traditional data center with all the bells and whistles and cooling technology where it sometimes might just be a dusty closet in the Edge. So we're defining fortunes you see on scalable to kind of tackle all those different environments and keep that in mind. Our SKUs range from low to high power, general purpose to segment optimize. We're supporting long life use cases so that all goes into account in delivering value to our customers. A lot of the latency sensitive nature of these Edge deployments also benefit greatly from monolithic architectures. And with our latest CPUs we do maintain quite a bit of that with many of our SKUs and delivering higher frequencies along with those SKUs optimized for those specific workloads in networking. So in the end we're looking to drive scalability. We're looking to drive value in a lot of our end users most important KPIs, whether it's latency throughput or efficiency and 4th Gen Z on scalable is looking to deliver that with 60 cores up to 60 cores, the most builtin accelerators of any CPUs in the market. And really the true technology transitions of the platform with DDR5, PCIE, Gen five and CXL. >> Love the scalability story, love the performance. We're going to take a break. Thanks Cynthia, Dennis. Now we're going to come back on our next segment after a quick break to discuss the performance and the benefits of the fourth Gen Intel Zion Scalable. You're watching theCUBE, the leader in high tech coverage, be right back. Welcome back around. We're continuing theCUBE's coverage of compute engineer for your hybrid world. I'm John Furrier, I'm joined by Alan Chu from Intel and Denis Konikoff and Cynthia Sistia from HPE. Welcome back. Cynthia, let's start with you. Can you tell us the benefits of the fourth Gen Intel Zion scale process for the HP Gen 11 server? >> Yeah, so HP ProLiant Gen 11 servers support DDR five memory which delivers increased bandwidth and lower power consumption. There are 32 DDR five dim slots with up to eight terabyte total on ML350 and 16 DDR five dim slots with up to two terabytes total on DL320. So we deliver more memory at a greater bandwidth. Also PCIE 5.0 delivers an increased bandwidth and greater number of lanes. So when we say increased number of lanes we need to remember that each lane delivers more bandwidth than lanes of the previous generation plus. Also a flexible storage configuration on HPDO 320 Gen 11 makes it an ideal server for establishing software defined compute and storage solution at the Edge. When we consider a server for VDI workloads, we need to keep the right balance between the number of cords and CPU frequency in order to deliver the desire environment density and noncompromised user experience. So the new server generation supports a greater number of single wide and global wide GPU use to deliver more graphic accelerated virtual desktops per server unit than ever before. HPE ProLiant ML 350 Gen 11 server supports up to four double wide GPUs or up to eight single wide GPUs. When the signing GPU accelerated solutions the number of GPUs available in the system and consistently the number of BGPUs that can be provisioned for VMs in the binding factor rather than CPU course or memory. So HPE ProLiant Gen 11 servers with Intel fourth generation science scalable processors enable us to deliver more virtual desktops per server than ever before. And with that I will pass it on to Alan to provide more details on the new Gen CPU performance. >> Thanks Cynthia. So you brought up I think a really great point earlier about the importance of achieving the right balance. So between the both of us, Intel and HPE, I'm sure we've heard countless feedback about how we should be optimizing efficiency for our customers and with four Gen Z and scalable in HP ProLiant Gen 11 servers I think we achieved just that with our built-in accelerator. So built-in acceleration delivers not only the revolutionary performance, but enables significant offload from valuable core execution. That offload unlocks a lot of previously unrealized execution efficiency. So for example, with quick assist technology built in, running engine X, TLS encryption to drive 65,000 connections per second we can offload up to 47% of the course that do other work. Accelerating AI inferences with AMX, that's 10X higher performance and we're now unlocking realtime inferencing. It's becoming an element in every workload from the data center to the Edge. And lastly, so with faster and more efficient database performance with RocksDB, we're executing with Intel in-memory analytics accelerator we're able to deliver 2X the performance per watt than prior gen. So I'll say it's that kind of offload that is really going to enable more and more virtualized desktops or users for any given deployment. >> Thanks everyone. We still got a lot more to discuss with Cynthia, Dennis and Allen, but we're going to take a break. Quick break before wrapping things up. You're watching theCUBE, the leader in tech coverage. We'll be right back. Okay, welcome back everyone to theCUBEs coverage of Compute Engineered for your Hybrid World. I'm John Furrier. We'll be wrapping up our discussion on advanced performance of VDI with the fourth gen Intel Zion scalable processers. Welcome back everyone. Dennis, we'll start with you. Let's continue our conversation and turn our attention to security. Obviously security is baked in from day zero as they say. What are some of the new security features or the key security features for the HP ProLiant Gen 11 server? >> Sure, I would like to start with the balance, right? We were talking about performance, we were talking about density, but Alan mentioned about the balance. So what about the security? The security is really important aspect especially if we're talking about solutions deployed at the H. When the security is not active but other aspects of the environment become non-important. And HP is uniquely positioned to deliver the best in class security solution on the market starting with the trusted supply chain and factories and silicon route of trust implemented from the factory. So the new ISO6 supports added protection leveraging SPDM for component authorization and not only enabled for the embedded server management, but also it is integrated with HP GreenLake compute ops manager that enables environment for secure and optimized configuration deployment and even lifecycle management starting from the single server deployed on the Edge and all the way up to the full scale distributed data center. So it brings uncompromised and trusted solution to customers fully protected at all tiers, hardware, firmware, hypervisor, operational system application and data. And the new intel CPUs play an important role in the securing of the platform. So Alan- >> Yeah, thanks. So Intel, I think our zero trust strategy toward security is a really great and a really strong parallel to all the focus that HPE is also bringing to that segment and market. We have even invested in a lot of hardware enabled security technologies like SGX designed to enhance data protection at rest in motion and in use. SGX'S application isolation is the most deployed, researched and battle tested confidential computing technology for the data center market and with the smallest trust boundary of any solution in market. So as we've talked about a little bit about virtualized use cases a lot of virtualized applications rely also on encryption whether bulk or specific ciphers. And this is again an area where we've seen the opportunity for offload to Intel's quick assist technology to encrypt within a single data flow. I think Intel and HP together, we are really providing security at all facets of execution today. >> I love that Software Guard Extension, SGX, also silicon root of trust. We've heard a lot about great stuff. Congratulations, security's very critical as we see more and more. Got to be embedded, got to be completely zero trust. Final question for you guys. Can you share any messages you'd like to share with the audience each of you, what should they walk away from this? What's in it for them? What does all this mean? >> Yeah, so I'll start. Yes, so to wrap it up, HPR Proliant Gen 11 servers are built on four generation science scalable processors to enable high density and extreme performance with high performance CDR five memory and PCI 5.0 plus HP engine engineered and validated workload solutions provide better ROI in any consumption model and prefer by a customer from Edge to Cloud. >> Dennis? >> And yeah, so you are talking about all of the great features that the new generation servers are bringing to our customers, but at the same time, customer IT organization should be ready to enable, configure, support, and fine tune all of these great features for the new server generation. And this is not an obvious task. It requires investments, skills, knowledge and experience. And HP is ready to step up and help customers at any desired skill with the HP Greenlake H2 cloud platform that enables customers for cloud like experience and convenience and the flexibility with the security of the infrastructure deployed in the private data center or in the Edge. So while consuming all of the HP solutions, customer have flexibility to choose the right level of the service delivered from HP GreenLake, starting from hardwares as a service and scale up or down is required to consume the full stack of the hardwares and software as a service with an option to paper use. >> Awesome. Alan, final word. >> Yeah. What should we walk away with? >> Yeah, thanks. So I'd say that we've talked a lot about the systems here in question with HP ProLiant Gen 11 and they're delivering on a lot of the business outcomes that our customers require in order to optimize for operational efficiency or to optimize for just to, well maybe just to enable what they want to do in, with their customers enabling new features, enabling new capabilities. Underpinning all of that is our fourth Gen Zion scalable platform. Whether it's the technology transitions that we're driving with DDR5 PCIA Gen 5 or the raw performance efficiency and scalability of the platform in CPU, I think we're here for our customers in delivering to it. >> That's great stuff. Alan, Dennis, Cynthia, thank you so much for taking the time to do a deep dive in the advanced performance of VDI with the fourth Gen Intel Zion scalable process. And congratulations on Gen 11 ProLiant. You get some great servers there and again next Gen's here. Thanks for taking the time. >> Thank you so much for having us here. >> Okay, this is theCUBEs keeps coverage of Compute Engineered for your Hybrid World sponsored by HP and Intel. I'm John Furrier for theCUBE. Accelerate VDI at the Edge. Thanks for watching.

(bright upbeat music) >> Welcome back to theCube's coverage of HPE's Green Lake announcement. We've been following the caves of Green Lake's announcement for several quarters now, and even years. And we're going to look at cloud adoption and frameworks to help facilitate cloud adoptions. You know, in 2020, the world was on a forced march to digital and there was a lot that they didn't know. Big part of that was how to automate, how to reduce your reliance on physically, manually and plugging things in. And so, customers need an adoption framework to better understand and how to de-risk that journey to the cloud. And with me to talk about that are Alexia Clements, who's the Vice President at Worldwide go to market for GreenLake cloud services at HPE and Alexei Gerasimov who's the vice president of Hybrid Cloud Delivery advisory and professional services at Hewlett Packard Enterprise. Folks, welcome to theCube. >> Alexia: Thanks so much for having us. >> You're very welcome. So, Alexei, what is a cloud adoption framework? How does that all work? >> Gerasimov: Yeah, thanks Dave. So the framework is a structured approach to elevate the conversation, to help our customers get outcomes. So we've been helping customers adopt the benefits in the most of IT for a decade. And we've noticed that they basically focus on eight key areas as they transform to cloud-like capabilities. It's a strategy and governance, it's innovation, people, a dev ops applications, operations security, and data. So we've structured our framework around those core components to help our customers get value. Because end of the day, it's all about changing the way they operate. To get the advantage of all of it. >> Yes. So you can't just pave the cow path and kind of plug your existing process. There's a lot that's unknown, as I said up front. So, so Alexia, maybe you could talk a little bit more about some of the real problems that you're solving with customers that you see in the field. >> Alexey: Yeah, absolutely. So most customers are going through some form of digital transformation and these transformations are difficult and they need a structured approach to help them through that journey. I kind of like to think of it as a recipe to make a meal. So you need to know what ingredients to buy and what are the steps to perform to make that meal. >> Okay. So when you talk to customers, what do you, what do you tell them? That's in it for them after the, after you've actually successfully helped them deploy? What are they telling you? >> Yeah, well, they're telling they now have reached their business outcomes and they're, you know, they're a more agile organization. >> What's the experience look like when you, when you go through one of these journeys and you, you apply the adoption framework, can you sort of paint a picture for us? >> Yeah, absolutely. So every customer is in some sort of transformation, like Alexia said, that transformation implies you've got to know where you start and again, know where you're going. So the experience traditionally is customers need to understand what are my current hybrid cloud capabilities? What do I have, what am I missing? What's lacking and then determine where do you want to go? And in order to get from point A to point B, they have to get a prescriptive approach. So the framework sort of breaks down their path from where they are to their desired maturity. And it takes them in the very prescriptive path to get there. >> So you start with an assessment, you do a gap analysis based on their skill sets. I presume you identify what's possible, help them understand, you know, best practice, which they may not achieve, but this is kind of their north star. Right? And then do you help? How do you help them fill those gaps? Because are skills gaps. Everybody talks about that today. You guys presumably can provide additional services to do that, but so can you add a little bit color to that scope? >> Yeah, yeah, absolutely. And so to your point, the first is a maturity level. So once you figure out the maturity level, you understand what needs to be done. So if you look at our domain, the eight domains that I mentioned and the framework, people is a big one, right? Most of the folks are struggling with people's skills and organizational capabilities. And it's so because it's an operating model change, right? And people are the key component to this operating model change. So we help our customers figure out how do we achieve that optimal operating level and operating a model maturity. And that could be on-prem that could be on public cloud. That could be hybrid. That could be at the edge. And yeah, we, if we can HP, the framework, by the way is pretty, pretty open and pretty objective. If we can help our customers address and achieve their sales gaps great. If we can not directly, then we can have a partner that can help them, you know, plug in something that we don't have. >> Are you finding that, that in terms of the maturity that most people have some kind of experience with, with cloud, but they're struggling to bring that cloud experience to their on-premise state. They don't want to just shove everything into the cloud. Right. So, what does that kind of typical journey look like for folks? I know there's--it's a wide spectrum, or you've got people that are maybe more mature. Maybe some of the folks in financial services got more resources, but can you sort of give us a sense as to what the typical, the average. >> Oh yeah yeah yeah, absolutely. By the way. So that give you a customer example, perfect example of a large North American integrated energy company. They decided to go cloud fresh, like a lot of companies. that wants to do cloud first. And why? The reason was agility. So they started going to the cloud and they realized in order to get agility, you can't just go to you, pick your public CSP, you got to change the way to operate. So they brought us in and they asked, could you help me figure out how we can change the organization? So we actually operate on the proper level of maturity. So we brought our team in. We help them figure out what do we need to look at? We need to look at operations. We need to look at people. We need to look at applications, and we need to figure out what gives you the best value. So when all said and done, they realized that their initial desire of, you know, public first or cloud first, wasn't really public cloud first. It's a way to operate. So now the customer is in three different public CSPs. They're on-prem, there are at edge and everywhere. So that's the focus. Yeah. >> Is the scope predominantly the technical organization. How deep does it go into the, to the business? Is it obviously the application development team is involved, but how deep into the business does this go? The framework. >> Right, and it's absolutely not a technology focused, the whole concept areas, it's outcomes based, and it's a results based. So if you look at the framework, there's really not a single element of the framework that says tech, like storage or compute. No, it's its people, its data, it's business value, strategy and governance, because the goal for us is being objective is we're just trying to help them address the outcomes. Not necessarily to give them more tech. >> So Alexia, I like that answer because it's a wider scope as, I mean, if we just focused on the tech and that's the swim lane, it'd be a lot easier. But as we all know, it's the people in the process that are really the hard part. So that, that makes the challenge for customers greater. You're hurting more cats. So what are the, some of the obstacles that potentially you help customers before they dive in understand. >> Yeah. So we're giving them a roadmap on where they need to go. So we're like I mentioned that recipe, so we're really trying to identify what is their strategy and where do they, what are the outcomes that they're trying to drive and help them on a street, you know, with that path to meet those outcomes. So some of those, I mean, every customer's a little bit different. I mean, we had one customer, which was a, one of the largest hospitals in north America and they, they would needed to, they wanted to go to the cloud, but they realized they couldn't put all of their patient data on the cloud. So what we did was we helped them in changing their operating model and really look to see how does that, how do they need to what's that end game for them, and actually help redo their operating model to have some in the cloud and some on-prem and, and really identify, you know, where they needed to go for their roadmap. So that was an obstacle that they had, hey, we can't put all this stuff out there. How does that now need to work in this new world? >> I would think the data model is a big deal here. I mean, you just gave an example where there's a, there's a, there's a governance and compliance aspect to it. So thinking about that example, did they have to change the way in which they provided federated governance was that presumably identify whose whose responsibility that was to adjudicate, but also had to get the, the implementers to follow that's the, how does that all work? Is it just the deep conversations? And then you figure out how to codify it or. >> No. So what so we have, so through those eight domains that Alexia mentioned, we go through, step-by-step how they need to think about it. And within mind, what are their business outcomes and goals that they're trying to achieve? So really identifying how they need to change that operating model to meet those business outcomes. >> So what's the output, it's a plan, right. That's tailored to the customer. Is that, is that correct? And, and then sort of assistance in implementing downstream or what do they get? >> Yeah, yeah, absolutely. Just to piggyback to what Alexia said, the alignment, the early alignment, the strategy and governance, as you mentioned, this is probably the most important thing, because everybody says we want to be cloud first, but what does that mean? Cloud first means different things to everyone. So we said, give him a plan. The first we'll help with figure out is what does that mean for you? Because at the end of the day, you're not going to the cloud for the sake of cloud, or anywhere you go into the cloud to get some sort of value. So what's that alignment. So the plan is supposed to help you on your road to that value, right? So we'll help them figure out what I want to do, why, for what purpose, what's going to actually address my business value. So yes, they will get a plan as part of it. But more importantly, they get, they get a set of activities, communication plans, which by the way, another block that you got to address. >> Dave: Huge. >> Yeah. >> Yeah. I mean, a lot of executives tell me, look, if you don't change your operating model and go to the cloud, yeah. You're talking, you know, nickels and dimes. If you want to get telephone numbers, you know, big companies, you want to get into bees with billions, you have to change the operating model. And the problem that they tell me is a lot of times the corner offices, okay, we're doing this, but everybody in the fat middle says, what are we doing? >> Right. And now more than ever, I mean, customers need to look at that model like a more modern operating model to realize the benefits of cloud capabilities, whether that be at the edge, their data centers, their colos cloud. So they really need to look at that. And what we've seen is with our framework, we're really helping customers accelerate their business outcomes. De-risk their transformation, and really optimize that cloud operating model. >> It's that alignment you reducing friction within the organization, confusing confusion. When people don't know which direction they're going, they'll just going to go wherever they're pointed. Right. Right. >> And you back to the alignment. So you've got alignment and you mentioned communication. You have to communicate up and down and left and right across the organization because that's one of the most probably ignored elements of any transformation lots of people don't know. So you got to communicate. And then you have to actually measure and report on how they, you know, how the transformation is happening. So we can help in all three of those. >> Especially when everybody's remote. Yeah. Right. And then I said, hey, these digital transformations, there's so much, that's unknown. >> Alexia: Right. It's difficult. >> It's a lot of new. And so you also have to, I presume part of the plan is, Hey, you're not, it's not going to be a hundred percent perfect. So you have to have. >> Alexia: Right. And you're constantly iterating on that plan. >> What does this have to do with GreenLake? >> Alexia: Yeah. So, I mean, GreenLake is HPE's you know, cloud everywhere. And what we're really doing is this framework is helping customers with that path to get that cloud-like experience and as a service model. And so the framework is really helping clients understand where do they need to go and what GreenLake solutions can help them get there. >> So the fundamental assumption of not every cloud player necessarily bad, I would say most hyperscalers is, hey, ultimately, all the data and the workloads are going to go to the cloud, that's their operating premise. So they all have an operating framework to facilitate that. >> Alexia: Right. >> It's, it's tongue in cheek, but it's true. So, but everybody has one of these. How was yours different? >> Yeah. So like, like you said, there's lots of different, you know, frameworks out there, but what we're really focused on is meeting those business goals and outcomes for clients. So we didn't focus on the technology. Like we mentioned what we were really focusing around. I mean, we kind of learned early on that every customer has technical capabilities, applications, data in multiple clouds, on-prem in colos and at the edge. So we didn't focus on like just the technology. So it's really driving business outcomes and their goals and, and the tech, all those frameworks that we just mentioned, they're really specifically driving a particular technology tool or vendor implementing a particular technology or vendor. >> So we've talked about outcomes a lot, but I wonder if we could peel the onion on that. So, you know, the highest level outcome is I want to increase revenue, cut costs, drop to the bottom line, increase shareholder value, improve employee experiences and retention, make customers happier, grow my business. I mean, those are, I mean, I, I don't know a lot of businesses that don't... >> Alexia: Right. >> want to do that, So. Okay. That's cool. But then I'm imagining you really start to peel the layers and say, okay, this is how we're going to get there. And you get down to specific objectives as to the, how is that sort of how this works? >> Right, and that's due to echo at Alexia. So that's exactly why ours is different. We're not focusing on how to adopt Microsoft or AWS or Alibaba with focusing on how we can deliver the customer experience or a better revenue, you know, or, you know, increase the value for the consumer for whatever the company will help him. So the framework we'll look at that and figure out how do we actually address it, whether it's on public cloud, whether it's on prem, whether it's at the edge. >> You mentioned Alexia, that something, hey, if we don't have the skills, we can get a partner who does, a big company. You got a huge partner network. So for example, if you might not have necessarily a deep industry expertise, that's where you might lean on a partner or is that, is that a good example or is there a better one? >> Yes and we know. We're not going to just like you mentioned AWS or Microsoft, Alibaba thing that everything will go to public cloud. I don't believe so, but at the same time we know not everything will stay on-prem. So the combination of on-prem, the edge, you know, private cloud and public cloud is what the customers are after. So our partners could be either third party, system integrator that can help us implement something or even the public CSPs, because we know our customers have capabilities everywhere. So the question becomes, how can we holistically address their needs, whether it's on-prem, whether it's in public cloud. >> Great. Guys, thanks so much. >> Alexia: Thank you. Thanks for having us. Appreciate it. >> My pleasure and thank you for watching everybody's as theCube's continuous coverage of HPE's GreenLake announcement, keep it right there for more great content. (bright upbeat music)

(bright upbeat music) >> Okay, we're back with theCUBE's continuous coverage of HPE's latest GreenLake announcement, the continuous cadence that we're seeing here. You know, when you're trying to figure out how to optimize workloads, it's getting more and more complex. Data-driven workloads are coming in to the scene, and so how do you know, with confidence, how to configure your systems, keep your costs down, and get the best performance and value for that? So we're going to talk about that. With me are Chris Shin, who is the founder of CloudPhysics and the senior director of HPE CloudPhysics, and Sandeep Singh, who's the vice-president of Storage Marketing. Gents, great to see you. Welcome. >> Dave, it's a pleasure to be here. >> So let's talk about the problem first, Sandeep, if we could. what are you guys trying to solve? What are you hearing from customers when they talk to you about their workloads and optimizing their workloads? >> Yeah, Dave, that's a great question. Overall, what customers are asking for is just to simplify their world. They want to be able to go faster. A lot of business is asking IT, let's go faster. One of the things that cloud got right is that overall cloud operational experience, that's bringing agility to organizations. We've been on this journey of bringing this cloud operational agility to customers for their data states, especially with HPE GreenLake Edge-to-Cloud platform. >> Dave: Right. >> And we're doing that with, you know, powering that with data-driven intelligence. Across the board, we've been transforming that operational support experience with HPE InfoSight. And what's incredibly exciting is now we're talking about how we can transform that experience in that upfront IT procurement portion of the process. You asked me what are customers asking about in terms of how to optimize those workloads. And when you think about when customers are purchasing infrastructure to support their app workloads, today it's still in the dark ages. They're operating on heuristics, or a gut feel. The data-driven insights are just missing. And with this incredible complexity across the full stack, how do you figure out where should I be placing my apps, whether on Prim or in the public cloud, and/or what's the right size infrastructure built upon what's actually being consumed in terms of resource utilization across the board. That's where we see a tremendous opportunity to continue to transform the experience for customers now with data-driven insights for smarter IT decisions. >> You know, Chris, Sandeep's right. It's like, it's like tribal knowledge. Well, Kenny would know how to do that, but Kenny doesn't work here anymore. So you've announced CloudPhysics. Tell us more about what that is, what impact it's going to have for customers. >> Sure. So just as Sandeep said, basically the problem that exists in IT today is you've got a bunch of customers that are getting overwhelmed with more and more options to solve their business problems. They're looking at cloud options, they're looking at new technologies, they're looking at new sub-technologies and the level at which people are competing for infrastructure sales is down at the very, very, you know, splitting hairs level in terms of features. And they don't know how much of these they need to acquire. Then on the other side, you've got partners and vendors who are trying to package up solutions and products to serve these people's needs. And while the IT industry has, for decades, done a good job of automating problems out of other technology spaces, hasn't done a good job of automating their own problems in terms of what does this customer need? How do I best service them? So you've got an unsatisfied customer and an inadequately equipped partner. CloudPhysics brings those two together in a common data platform, so that both those customers and their partners can look at the same set of data that came out of their data center and pick the solutions that will solve their problems most efficiently. >> So talk more about the partner angle, because it sounds like, you know, if they don't have a Kenny, they really need some help, and it's got to be repeatable. It's got to be consistent. So how have partners reacting to this? >> Very, very strongly. Over the course of the four or five years that that CloudPhysics has been doing this in market, we've had thousands and thousands of VARs, SIs and others, as well as many of the biggest technology providers in the market today, use CloudPhysics to help speed up the sales process, but also create better and more satisfied customers. >> So you guys made... Oh, go ahead, please. >> Well, I was just going to chime into that. When you think about partners that with HPE CloudPhysics, where it supports heterogeneous data center environments, partners all of a sudden get this opportunity to be much more strategic to their customers. They're operating on real world insights that are specific to that customer's environment. So now they can really have a tailored conversation as well as offer tailored solutions designed specifically for the areas, you know, where help is needed. >> Well, I think it builds an affinity with the customer as well, because if the partners that trust advisor, if you give a customer some advice and it's kind of the wrong advice, "Hey, we got to go back and reconfigure that workload. We won't charge you that much for it". You're now paying twice. Like when an accountant makes a mistake on your tax return, you got to pay for that again. But so, you guys acquired CloudPhysics in February of this year. What can you tell us about what's transpired since then? How many engagements that you've done? What kind of metrics can you share? >> Yeah. Chris, do you want to weigh in for that? >> Sure, sure. The start of it really has been to create a bunch of customized analytics on the CloudPhysics platform to target specific sales motions that are relevant to HPE partners. So what do I mean by that? You'll remember that in May, we announced the Alletra Series 6,000 and 9,000. In tandem with that, CloudPhysics released a new set of analytics that help someone who's interested in those technologies figure out what model might be best for them and how much firepower they would need from one or the other of those solutions. Similarly, we have a bunch solutions and a market strength in the HCI world, hyper converged, and that's both SimpliVity and dHCI. And we've set up some analytics that specifically help someone who's interested in that form factor to accelerate, and again, pick the right solutions that will serve their exact applications needs. >> When you talk to customers, are they able to give you a sense as to the cost impacts? I mean, even if it's subjective, "Hey, we think we, you know, we save 10% versus the way we used to do it", or more or less. I mean, just even gut feel metrics. >> So I'll start that one, Sandeep. So there's sort of two ways to look at it. One thing is, because we know everything that's currently running in the data center - we discovered that - we have a pretty good cost of what it is costing them today to run their workloads. So anything that we compare that to, whether it's a transition to public cloud or a transition to a hosted VMware solution, or a set of new infrastructure, we can compare their current costs to the specific solutions that are available to them. But on the more practical side of things, oftentimes customers know intuitively this is a set of servers I bought four years ago, or this is an old array that I know is loose. It's not keeping up anymore. So they typically have some fairly specific places to start, which gives that partner a quick win, solving a specific customer problem. And then it can often boil out into the rest of the data center, and continual optimization can occur. >> How unique is this? I mean, is it, you know, can you give us a little glimpse of the secret sauce behind it? Is this kind of table stakes for the industry? >> Yeah. I mean, look, it's unique in the sense that CloudPhysics brings along over 200 metrics across the spectrum of virtual machines and guest OSs, as well as the overall CPU and RAM utilization, overall infrastructure analysis, and built in cloud simulators. So what customers are able to do is basically, in real time, be able to: A - be aware of exactly what their environment looks like; B - be able to simulate if they were going to move and give an application workload to the cloud; C - they're able to just right-size the underlying infrastructure across the board. Chris? >> Well, I was going to say, yeah, along the same lines, there have been similar technology approaches to different problems. Most notably in the current HPE portfolio, InfoSight. Best in class, data lake driven, very highly analytical machine learning, geared predominantly toward an optimization model, right? CloudPhysics is earlier in the talk track with the customer. We're going to analyze your environment where HPE may not even have a footprint today. And then we're going to give you ideas of what products might help you based on very similar techniques, but approaching a very different problem. >> So you've got data, you've got experience, you know what best practice looks like. You get a sense as to the envelope as to what's achievable, right? And that is just going to get better and better and better over time. One of the things that that I've said, and we've said on theCUBE, is that the definition of cloud is changing. It's expanding, it's not just public cloud anymore. It's a remote set of services, it's coming on Prim, there's a hybrid connection. We're going across clouds, we're going out to the edge. So can CloudPhysics help with that complexity? >> Yeah, absolutely. So we have a set of analytics in the cloud world that range from we're going to price your on-premise IT. We also have the ability to simulate a transition, a set of workloads to AWS, Azure, or Google Cloud. We also have the ability to translate to VMware based solutions on many of those public clouds. And we're increasingly spreading our umbrella over GreenLake as well, and showing the optimization opportunities for a GreenLake solution when contrasted with some of those other clouds. So there's not a lot of... >> So it's not static. >> It's not static at all. And Dave, you were mentioning earlier in terms such as proven. CloudPhysics now has operated on trillions of data points over millions of virtual machines across thousands of overall data assessments. So there's a lot of proven learnings through that as well as actual optimizations that customers have benefited from. >> Yes. I mean, there's benchmarks, but it's more than that because benchmarks tend to be static, okay. We consider rules of thumb. We're living in an age with a lot more data, a lot more machine intelligence. And so this is organic, it'll evolve. >> Sandeep: Absolutely. >> And the partners who work with their customers on a regular basis over at CloudPhysics, and then build up a history over time of what's changing in their data center can even provide better service. They can look back over a year, if we've been collecting, and they can see what the operating system landscape has changed, how different workloads have lost popularity, how other ones have gained. And they really can become a much better solution provider to that customer the longer CloudPhysics is used. >> Yeah, it gives your partners a competitive advantage, it's a much stickier model because the customer is going to trust your partner more if they get it right. So we're not going to change horses in the middle of the street. We're going to go back to the partner that set us up, and they keep getting better and better and better each time, we've got a good cadence going. All right. Sandeep, bring us home. What's your sort of summary? How should we think about this going forward? >> Well, I'll bring us right back to the way I started is, and to end, we're looking at how we continue to deliver best in class cloud operational experience for customers across the board with HPE GreenLake. And earlier this year, we unveiled this cloud operation experience for data, and for customers, that experience starts with a cloud consult where they can essentially discover services, consume services, that overall operational and support experience is transformed with HPE InfoSight. And now we're transforming this experience where any organization out there that's looking to get data-driven insights into what should they do next? Where should they place their workloads? How to right-size the infrastructure? And in the process, be able to transform how they are working and collaborating with their partners. They're able to do that now with HPE CloudPhysics, bringing these data driven insights for smarter IT decision-making. >> I like this a lot, because a lot of the cloud is trial and error. And when you try and you make a mistake, you're paying each time. So this is a great innovation to really help clients focus on the things that matter, you know, helping them apply technology to solve their business problems. Guys, thanks so much for coming to theCUBE. Appreciate it. >> Dave, always a pleasure. >> Thanks very much for having us. >> And keep it right there. We got more content from HPE's GreenLake announcements. Look for the cadence. One of the hallmarks of cloud is the cadence of announcements. We're seeing HPE on a regular basis, push out new innovations. Keep it right there for more. (bright upbeat music begins) (bright upbeat music ends)

>>Edge computing is predict, projected to be a multi-trillion dollar business. You know, it's hard to really pinpoint the size of this market. Let alone fathom the potential of bringing software, compute, storage, AI, and automation to the edge and connecting all that to clouds and on-prem systems. But what, you know, what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data for decades? We've talked about the data explosion. I mean, it's mind boggling, but guess what, we're gonna look back in 10 years and laugh. What we thought was a lot of data in 2020, perhaps the best way to think about edge is not as a place, but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies that that defines the edge. And so by locating compute as close as possible to the sources of data, to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone. And welcome to this cube conversation. My name is Dave Vellante and with me to noodle on these topics is Omar Assad, VP, and GM of primary storage and data management services at HPE. Hello, Omer. Welcome to the program. >>Hey Steve. Thank you so much. Pleasure to be here. >>Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >>Uh, David? I think that's a super important, important question. I think your ideas are quite aligned with how we think about it. Uh, I personally think, you know, as enterprises are accelerating their sort of digitization and asset collection and data collection, uh, they're typically, especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing, which is distributed factories all over the place, they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is being now being generated within their factories. A lot of robot automation is going on that requires a lot of compute power to go out to those particular factories, which is going to generate their data out there. We've got insurance companies, banks that are creating and interviewing and gathering more customers out at the edge for that. >>They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is that more than 50% of an enterprise is data, especially if they operate globally around the world is going to be generated out at the edge. What does that mean? More data is new data is generated at the edge, but needs to be stored. It needs to be processed data. What is not required needs to be thrown away or classified as not important. And then it needs to be moved for Dr. Purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, uh, you know, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >>Yeah. We're definitely aligned on that. There's some great points. And so now, okay. You think about all this diversity, what's the right architecture for these deploying multi-site deployments, robo edge. How do you look at that? >>Oh, excellent question. So now it's sort of, you know, obviously you want every customer that we talk to wants SimpliVity, uh, in, in, and, and, and, and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? So because let's, let's take a few examples. You've got large global retailers, uh, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise, how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with these customers? You don't want to send a lot of ID staff out there because that adds costs. You don't want to have islands of data and islands of storage and promote sites, because that adds a lot of States outside of the data center that needs to be protected. >>And then last but not the least, how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy better. And how do you protect all this data at the edge? So the right architecture in my opinion, needs to be extremely simple to deploy. So storage, compute and networking, uh, out towards the edge in a hyperconverged environment. So that's, we agree upon that. It's a very simple to deploy model, but then comes, how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HBS believe that it needs to be extremely simple. Just give me two cables, a network cable, a power cable, tied it up, connected to the network, push it state from the data center and back up at state from the ed back into the data center. Extremely simple. >>It's gotta be simple because you've got so many challenges. You've got physics that you have to deal your latency to deal with. You got RPO and RTO. What happens if something goes wrong, you've gotta be able to recover quickly. So, so that's great. Thank you for that. Now you guys have hard news. W what is new from HPE in this space >>From a, from a, from a, from a deployment perspective, you know, HPE SimpliVity is just gaining like it's exploding, like crazy, especially as distributed enterprises adopt it as it's standardized edge architecture, right? It's an HCI box has got stories, computer networking, all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface, from a data center, what have you have now added is the ability to backup to the cloud, right? From the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the, in the distributed file system. That is the heart and soul of, of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software into backup is fully integrated in the architecture and it's van efficient. >>In addition to that, now you can backup straight to the client. You can backup to a central, uh, high-end backup repository, which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only do we previously were, were one-on-one them leaving VMware deployments out at the edge sites. Now revolver also added both stateful and stateless container orchestration, as well as data protection capabilities for containerized applications out at the edge. So we have a lot, we have a lot of customers that are now deploying containers, rapid manufacturing containers to process data out at remote sites. And that allows us to not only protect those stateful applications, but back them up, back into the central data center. >>I saw in that chart, it was a light on no egress fees. That's a pain point for a lot of CEOs that I talked to. They grit their teeth at those entities. So, so you can't comment on that or >>Excellent, excellent question. I'm so glad you brought that up and sort of at that point, uh, uh, pick that up. So, uh, along with SimpliVity, you know, we have the whole green Lake as a service offering as well. Right? So what that means, Dave, is that we can literally provide our customers edge as a service. And when you compliment that with, with Aruba wired wireless infrastructure, that goes at the edge, the hyperconverged infrastructure, as part of SimpliVity, that goes at the edge, you know, one of the things that was missing with cloud backups is the every time you backup to the cloud, which is a great thing, by the way, anytime you restore from the cloud, there is that breastfeed, right? So as a result of that, as part of the GreenLake offering, we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HBS data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >>Nice. Hey, uh, can you, Omar, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for, particularly at the edge, and maybe talk about why they're choosing HPE? >>What are the major use cases that we see? Dave is obviously, uh, easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the us with hundreds of stores across us. Right now you cannot send service staff to each of these stores. These data centers are their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center, which you can literally push out and you can connect a network cable and a power cable, and you're up and running, and then automated backup elimination of backup and state and BR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration, both from a hardware and a software perspective, and the ability to backup and recover that instantly. >>That's one large use case. The second use case that we see actually refers to a comment that you made in your opener. Dave was where a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going to up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. Uh, at the same time, there is disaster recovery use cases where you have, uh, you know, campsites and local, uh, you know, uh, agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those, those are just a few cases where we were deployed. There was a lot of data collection, and there's a lot of mobility involved in these environments. So you need to be quick to set up quick, to up quick, to recover, and essentially you're up to your next, next move. >>You seem pretty pumped up about this, uh, this new innovation and why not. >>It is, it is, uh, you know, especially because, you know, it is, it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as, you know, we have lived through this pandemic, which, which I hope we see the tail end of it in at least 2021, or at least 2022. They, you know, one of the most common use cases that we saw, and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because, you know, mobility is limited in these, in these strange times that we live in. So from a central center, you're able to deploy applications, you're able to recover applications. And, and a lot of our customers said, Hey, I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service, which is offered on a consumption basis to the customers, and they can recover that anywhere they want. >>Fantastic Omer, thanks so much for coming on the program today. >>It's a pleasure, Dave. Thank you. >>All right. Awesome to see you. Now, let's hear from red bull racing and HPE customer, that's actually using SimpliVity at the edge. Countdown really begins when the checkered flag drops on a Sunday. It's always about this race to manufacture >>The next designs to make it more adapt to the next circuit to run those. Of course, if we can't manufacture the next component in time, all that will be wasted. >>Okay. We're back with Matt kudu, who is the CIO of red bull racing? Matt, it's good to see you again. >>Great to say, >>Hey, we're going to dig into a real-world example of using data at the edge and in near real time to gain insights that really lead to competitive advantage. But, but first Matt, tell us a little bit about red bull racing and your role there. >>Sure. So I'm the CIO at red bull racing and that red bull race. And we're based in Milton Keynes in the UK. And the main job job for us is to design a race car, to manufacture the race car, and then to race it around the world. So as CIO, we need to develop the ITT group needs to develop the applications is the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment. That's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 20 a movable deadlines, um, this big evolving prototype to manage with our car. Um, but we're also improving all of our tools and methods and software that we use to design and make and race the car. >>So we have a big can do attitude of the company around continuous improvement. And the expectations are that we continuously make the car faster. That we're, that we're winning races, that we improve our methods in the factory and our tools. And, um, so for, I take it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility, agility, and needs. So my job is, is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations >>That tear down and rebuild for 23 races. Is that because each track has its own unique signature that you have to tune to, or are there other factors involved there? >>Yeah, exactly. Every track has a different shape. Some have lots of strengths. Some have lots of curves and lots are in between. Um, the track surface is very different and the impact that has some tires, um, the temperature and the climate is very different. Some are hilly, some, a big curves that affect the dynamics of the power. So all that in order to win, you need to micromanage everything and optimize it for any given race track. >>Talk about some of the key drivers in your business and some of the key apps that give you a competitive advantage to help you win races. >>Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast, but also all of our business operations needed to be fast. We need to be able to design a car and it's all done in the virtual world, but the, the virtual simulations and designs need to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulation is the algorithms and have all the underlying infrastructure that runs it quickly and reliably. Um, in manufacturing, um, we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race, we have split seconds, literally seconds to alter our race strategy if an event happens. So if there's an accident, um, and the safety car comes out, or the weather changes, we revise our tactics and we're running Monte Carlo for example. And he is an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors, all of that needs it. Um, so work at a very high level. >>It's interesting. I mean, as a lay person, historically we know when I think about technology and car racing, of course, I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light, but not necessarily the data, but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge, if somebody who knows the track and where the Hills are and experience and gut feel, but today you're digitizing it and you're, you're processing it and close to real time. >>It's amazing. I think exactly right. Yeah. The car's instrumented with sensors, we post-process at Virgin, um, video, um, image analysis, and we're looking at our car, our competitor's car. So there's a huge amount of, um, very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that can leverage it are really key. Um, and that's a critical success factor for us. >>So let's talk about your data center at the track, if you will. I mean, if I can call it that paint a picture for us, what does that look like? >>So we have to send, um, a lot of equipment to the track at the edge. Um, and even though we have really a great wide area network linked back to the factory and there's cloud resources, a lot of the trucks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example, and you could lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions, all that needs to be at the edge where the car operates. So historically we had three racks of equipment, like a safe infrastructure, um, and it was really hard to manage, um, to make changes. It was too flexible. Um, there were multiple panes of glass, um, and, um, and it was too slow. It didn't run her applications quickly. Um, it was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. >>So we, um, we'd, we'd introduced hyperconvergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible, expensive legacy and introduce hyperconvergence. And we saw really excellent benefits for doing that. Um, we saw a three X speed up for a lot of our applications. So I'm here where we're post-processing data, and we have to make decisions about race strategy. Time is of the essence in a three X reduction in processing time really matters. Um, we also, um, were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a hundred thousand dollars a year in freight costs by shipping less equipment, um, things like backup, um, mistakes happen. >>Sometimes the user makes a mistake. So for example, a race engineer could load the wrong data map into one of our simulations. And we could restore that VDI through SimpliVity backup at 90 seconds. And this makes sure it enables engineers to focus on the car to make better decisions without having downtime. And we sent them to, I take guys to every race they're managing 60 users, a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >>Yeah. So you had the nice Petri dish and the factory. So it sounds like your, your goals, obviously your number one KPI is speed to help shave seconds time, but also costs just the simplicity of setting up the infrastructure. >>Yeah. It's speed. Speed, speed. So we want applications absolutely fly, you know, get to actionable results quicker, um, get answers from our simulations quicker. The other area that speed's really critical is, um, our applications are also evolving prototypes, and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >>So did you consider any other options or was it because you had the factory knowledge? It was HCI was, you know, very clearly the option. What did you look at? >>Yeah, so, um, we have over five years of experience in the factory and we eliminated all of our legacy, um, um, infrastructure five years ago. And the benefits I've described, um, at the track, we saw that in the factory, um, at the track we have a three-year operational life cycle for our equipment. When into 2017 was the last year we had legacy as we were building for 2018. It was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized time has even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >>Why, why SimpliVity? What why'd you choose HPE SimpliVity? >>Yeah. So when we first heard about hyperconverged way back in the, in the factory, um, we had, um, a legacy infrastructure, overly complicated, too slow, too inflexible, too expensive. And we stepped back and said, there has to be a smarter way of operating. We went out and challenged our technology partners. We learned about hyperconvergence within enough, the hype, um, was real or not. So we underwent some PLCs and benchmarking and, and the, the PLCs were really impressive. And, and all these, you know, speed and agility benefits, we saw an HP for our use cases was the clear winner in the benchmarks. So based on that, we made an initial investment in the factory. Uh, we moved about 150 VMs in the 150 VDI into it. Um, and then as, as we've seen all the benefits we've successfully invested, and we now have, um, an estate to the factory of about 800 VMs and about 400 VDI. So it's been a great platform and it's allowed us to really push boundaries and, and give the business, um, the service that expects. >>So w was that with the time in which you were able to go from data to insight to recommendation or, or edict, uh, was that compressed, you kind of indicated that, but >>So we, we all telemetry from the car and we post-process it, and that reprocessing time really it's very time consuming. And, um, you know, we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time and all, ultimately that meant an engineer could understand what the car was during a practice session, recommend a tweak to the configuration or setup of it, and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >>Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >>Yeah, I think we're optimistic. Um, we w we, um, uh, we have a new driver >>Lineup. Uh, we have, um, max for stopping his carries on with the team and Sergio joins the team. So we're really excited about this year and, uh, we want to go and win races. Great, Matt, good luck this season and going forward and thanks so much for coming back in the cube. Really appreciate it. And it's my pleasure. Great talking to you again. Okay. Now we're going to bring back Omer for quick summary. So keep it real >>Without having solutions from HB, we can't drive those five senses, CFD aerodynamics that would undermine the simulations being software defined. We can bring new apps into play. If we can bring new them's storage, networking, all of that can be highly advises is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly stressed environment. That is no bigger challenge than the formula. >>Okay. We're back with Omar. Hey, what did you think about that interview with Matt? >>Great. Uh, I have to tell you I'm a big formula one fan, and they are one of my favorite customers. Uh, so, you know, obviously, uh, one of the biggest use cases as you saw for red bull racing is Trackside deployments. There are now 22 races in a season. These guys are jumping from one city to the next, they've got to pack up, move to the next city, set up, set up the infrastructure very, very quickly and average formula. One car is running the thousand plus sensors on that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly, and then sometimes believe it or not, snapshots of this data needs to be sent to the red bull back factory back at the data center. What does this all need? It needs reliability. >>It needs compute power in a very short form factor. And it needs agility quick to set up quick, to go quick, to recover. And then in post processing, they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing now. And we accomplished that for, for the red bull racing guys in basically two are you have two SimpliVity nodes that are running track side and moving with them from one, one race to the next race, to the next race. And every time those SimpliVity nodes connect up to the data center collector to a satellite, they're backing up back to their data center. They're sending snapshots of data back to the data center, essentially making their job a whole lot easier, where they can focus on racing and not on troubleshooting virtual machines, >>Red bull racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient, and it shows a real impact. Thank you very much. I really appreciate those summary comments. Thank you, Dave. Really appreciate it. All right. And thank you for watching. This is Dave Volante. >>You.

>>Yeah. >>Okay. Now we're going to look deeper into the intersection of technology and money and actually a force for good mobile. And the infrastructure around it has made sending money as easy as sending a text. But the capabilities that enable this to happen are quite amazing, especially because as users, we don't see the underlying complexity of the transactions. We just enjoy the benefits. And there's many parts of the world that historically have not been able to enjoy the benefits. And the ecosystems that are developing around these new platforms are truly transformative. And with me to explain, the business impacts of these innovations is all a person who is the head of mobile financial services at Ericsson Ola. Welcome to the program. Thanks for coming on. >>Thank you. Dave, Thank you for having me here in the program and really excited to tell me. Tell us about the product that we have within Ericsson. >>Okay, well, let's get right into it. I mean, your firm has developed the Ericsson wallet platform. What is that? Yes, >>so? So the wallet platform is one of the product, but, I mean, you can say offer here by Ericsson and the platform is built on enabled financial services not for only the bank segment, but also for the unbanked. And we have, you know, the function that we are providing as such Here is, uh, both transfer the service provided payment. You have the cash in the cash out. You have a lot of other feature that we kind of a neighbor through the ecosystem as such. And, uh, I would really like you say, to emphasize on on the use, and they really I'll say, uh, connectivity that we have in this platform here because, uh, looking at you can say the pandemic as such here. Now, we really have made you can say tremendous Shane here through all the functions etcetera feature that we have here. >>Yeah, so, I mean, I'm surrounded by banks in Massachusetts, right? No problem. I'm Boston, right? So But there's a lot of places in the world that that aren't I take for granted some of the capabilities that are there, but so part of this is to enable people who don't have access to those types of services. So maybe you could talk about that and talk about some of the things that you're enabling with the platform, >>right? So So you just think of their You can say unbanked people here, But we have across the emerging market. I think we have one point, you know, seven billion unbanked people here, but we actually can, through wallet platform enabled through getting a bank account, etcetera, and so on here and what we're actually providing you can say in this, uh, this feature is here is that you can pay your electricity bill, for example, Here, you can pay your your bill and you you can go through merchants. You can do the cashed out. You can do multiple thing here, just like I mean to to enable the the question that financial inclusion as well. So I mean I mean from from my point of view, where we're sitting, as I said, we also sitting in Sweden, we have bank account. We have something called swish where we send you can say money back, back and forward between the family, etcetera. So, on this type of transaction, we can and have enabled for all you can say, the user that I come across the the platform here and the kind of growth that we have within this usage here and and we're seeing also. I mean, we leverage here to get with a speed today on a fantastic scale that we actually have here with our our both you can say feature performers going, I will say, Really in in in in a in the direction that we couldn't imagine here you can say a few years back here. So it is fantastic transformation that we undergo here through through the platform of the technology that we have. >>You know, it reminds me of sort of the early days of mobile people talked about being able to connect, you know, remote users in places like Africa or other parts of the world that that haven't been able to enjoy things like a landline. Uh, and so I presume you're seeing a lot of interest in in those types of regions. Maybe you could talk about that a little bit. >>Yeah. Yeah, correct. I mean, I mean, we we see all of this region here, but for for example, Uh uh. Now, we we, uh we were not only entering, you can say the the, uh, specifically the African region, but also you can say the Middle East and the the the A C a specific and also actually Latin America. I mean, a lot of this country here are looking into you can say the expansion, how they can evolve. You can say the financial inclusion from what they have today, when they are, and you can say firm telecom provider, they would like to have an asset of different use case here, and we're seeing that transformation. But we have right now from just voice, you can say SMS and five year etcetera so on. This is the platform that we have to sort of enable the transaction for for a mobile financial system. But we would like also to see that the kind of operator or evolving the business with much more feature here. And this is another. You can say I was attraction to attract the user with the mobile transfer system. So we we we see this kind of expanding very heavily in this this kind of market. >>I think this is really transformative. I mean, in terms of people's lives. I mean, first of all, you're talking about the convenience of being able to move money as bits as opposed to paper, but as well I would think supporting entrepreneurship and business is getting started. I mean, there's a whole set of cultural and societal impacts that that you're having. How do you see that >>we we also providing you say I mean the world to such is also supporting, say microloans and need as an entrepreneur is to sort of start you can say any kind of company, but you need to kind of business around here. So we have seen that we have sort of enterprise services across function and the whole asset that we are that we are into today >>talking a little bit >>about >>partnerships and the ecosystems. I know you've got big partnerships with HPD. We're going to get to that. They're kind of a technology operator, but But what about, you know, other partnerships, like, I'm imagining that if I'm gonna pay my my my bill with this, you've got other providers that got to connect into your platform. So So how are those ecosystem partnerships evolving? >>Well, we are kind of the enabler, but we are providing to the operator the partnerships is then going through the operator. It could be any kind of you can say external instrument that we have today and the kind of you can go directly to the bank. You can go directly to any court provider. You have these amongst the court, etcetera and so on. But these are all partners of the and you can stay connected through there. You can say operator assault today. So what we're doing actually, with our platform is to kind of make the enable them to kind of provide the food ecosystem as partnership to to operate as us today. Here, So that that's kind of the baseline that we see how you can say we are sort of supporting of building the full ecosystem around the platform in order to connect here has come to both the like, the card. As I said here, the merchant, the bank, any kind of type of you can say I will say service provider here, but that we can see could enable the ecosystem >>okay. And so I mean, I don't want to geek out here, but it sounds like it's an open system that my developers can plug into through a p i s They're not gonna throw cold water on that. They're going to embrace it. So yeah, this is actually easy for me to integrate with, Is that correct? >>Correct. Correct. And they open API that we're actually providing today. I think that you can say there are five thousands of you can say developer, just you can say connecting to our system. And actually, we're also providing both sandbox and and other application in order to support this developers in order to to kind of create this ecosystem here. So it's a multiple things that we we see through you can say, hear, hear the both the partners partnership the open API or you can say the development that is doing through through the channels. So I mean, it's a fascinating, amazing development that we see up front here right now. >>Now, what's H. P s role in all this? What are they providing? How are you partnering with them? >>So it's very good question, I would say. And we we look back, you can say and we we have evaluated a lot of you say that the provider fruit year here, And, uh, you can just imagine the the kind of, uh, stability that we need to provide when it comes to the financial inclusion system here because what we need to have a very strong uptake of, uh, making sure that we don't both go with the performance and the stability and what we have seen in our lab is that hypocrisy today is we have domestically evolved how you can say our stability assessed on the system. And right now we are leveraging the the dog is with the microservices here, together with HV on the platform that you're providing. So I will say that the transformation we have done in the stability that we have get through the food. You can say HP system is really fantastic at the moment. >>Well, and you know, I'm no security expert, but I talked to a lot of security experts and what I what I do know is they tell me that that you can't just bolt security on. It's got to be designed in from the start. I would imagine that that's part of the HPD partnership. But what about security? Can I fully trust this platform >>now? It's It's very, very valid question. I would say we we have one of the most you can say secure system here were also running multiple external. You can say, uh, system validation there is called The PNDs s certification is a certification, But we we have external auditor, you can say trying to breach the system. Look at the process that we are developing making sure that we have You can say all of you can say the documentation really in shape and seeing that we follow the procedure when we are both developing the code and and also when we're looking into all the a p I s that were actually exposed to to to our end users. So I would say that we haven't had any bridge on our system and we we really working tightly. I'll say both together with I'll say, H b and and of course, the the customer, such and? And every time we do a Lawrence, we also make you can say final security validation on the system here in order to sort of see that we have and and two and because the application that is completely secure, So so that that that that's a very, very important topic. For from our point of view, >>Yeah, because it's the usual. I don't even want to think about that. Like I set up front. It's It's got to be hidden from me, all that complexity. But there's sort of the same question around compliance and privacy. I mean, often security, privacy. There's sort of two sides of the same coin, but compliance privacy You've got to worry about K. Y. C Know your customer? Uh, there's a lot of complexity around that, and and so that's another key piece. >>Mhm Now. Like you said, the K Y C is an important part that we have fully support in our system and we validate. You can say all the uses We we also are running, You can say with our credit scoring companies that the you can say our operator or are partnering with. So this combined, you can say, with both the K Y C and then and the credit scoring. But there were performing that. Let's make us a very you can say unique, stable platform as such. >>Okay, last question is, is what about going forward? What's the road map look like? What can you share? What should we expect going forward in terms of the impact that this will have on society and how the technology will evolve. >>Well, what is he going forward? And that's a very interesting question, because what we what we see right now is how we we we kind of have changed the life for for so many. You can say unbanked people here and we would like to have You can say, uh, any kind of assets that going forward here, any kind of you can see that the digital currency is a bouldering through both government. You can see over top players like Google. You can say, What's up all of these things. Here we want to be the one, but also connecting. You can say this type of platform together and see that we could be the heart of the ecosystem going forward here, independent in what kind of you can say customer we're aiming for. So I would say this This is kind of the role that we will play in the future here, depending on what kind of currency it would be. So it's very interesting future we see. With this, you can say abroad digital currency in the market and the trends that we are now right now, evolving on >>very exciting when we're talking about elevating, you know, potentially billions of people all, uh, thanks very much for sharing this innovation with the audience. And best of luck with this incredible platform. Congratulations. >>Thank you so much, Dave. And once again, thank you for having me here, and I'll talk to you soon again. Thank you. >>Thank you. It's been our pleasure. And thank you for watching. This is Dave Valenti. >>Yeah. Mhm. Yeah. Mhm. Okay.

from New York City it's the cube covering welcome to the new edge brought to you by pensando systems hey welcome back here ready Jeff Rick here with the cube we are in downtown Manhattan at the top of goldman sachs it was a beautiful day now the clouds coming in but that's appropriate because we're talking about cloud we're talking about edge and the launch of a brand new company is pensando and their event it's called welcome to the new edge and we're happy to have since we're goldman the guys who have the money we're barry Eggers a founding partner of Lightspeed ventures and randy pond the CFO a pensando gentlemen welcome thank you thank you so Barry let's start with you you think you were involved at this early on why did you get involved what what kind of sparked your interest we got involved in this round and the reason we got involved were mainly because we've worked with this team before at Cisco we know they're fantastic they're probably the most prolific team and the enterprise and they're going after a big opportunity so we were pleased when the company said hey you guys want to work with us on this as a financial investor and we did some diligence and dug in and found you know everything to our liking and jump right in didn't anybody tell them this startup is a young man's game they mixed up the twenty-something I think yeah they sort of turned the startup on its head if you will no pun intended that's going right yeah yeah and Randy you've joined him a CFO you've known them for a while I mean what is it about this group of people that execute kind of forward-looking transformation transformational technologies time and time again that's not a very common trait it's a it's a great question so you know the key for these guys have been well they've been together since the 80s so Mario look and primitive this is the 80s I work with them at their previous startup before Christian two ladies and they're the combination of their skills are phenomenal together so you know one of them has some of the vision of where they want to go the second guy is a substantive sort of engineer takes it from concept first drawing and then the Prem takes over the execution perspective and then drives this thing and they've really been incredible together and then we added Sony at crescendo as a as a product marketing person and she's really stepped up and become integral part on the team so they work together so well it just makes a huge difference yeah it's it's it's amazing that that a that they keep doing it and B that they want to keep doing it right because they've got a few bucks in the bank and they don't really need to do it but still to take on a big challenge and then to keep it under wraps for two and a half years that's pretty pretty amazing so curious Barry from your point of view venture investing you guys kind of see the future you get pitched by smart people all day when you looked at John Chambers kind of conversation of these ten-year kind of big cycles you know what did you think of that how do you guys kind of slice and dice your opportunities and looking at these big Nick's yeah going back going back to the team a little bit they've been pretty good at identifying a lot of these cycles they brought us land switching a long time ago with crescendo they sort of redefined the data center several times and so there's another opportunity what's driving this opportunity really is the fact that explosion of applications in the network and of course east-west traffic in the network so networks were more designed north-south and they're slowly becoming more east-west but because the applications are closer to the edge and networks today mostly provide services in the core the idea for pensando is well why don't we bring the service deliver the services closer to the applications improve performance better security and better monitoring yeah and then just the just the hyper acceleration of you know the amount of data the amount of applications and then this age-old it's we're going to use the data to the computer do you move the compute to the data now the answer is yes all the above so you got some money to work with we do you got a round that he could be around you guys are closing the C round so I think 180 people approximately I think somebody told me close enough so as you put some of this capital to work what are some of your priorities going forward so we will continue to hire both in the engineering side but more importantly now we're hiring in sales and service we've been waiting for the product quite frankly so we've just got our first few sales guys hired we've got a pretty aggressive ramp especially with the HP relationship to put people out into the field we've hired a couple guys in New York will continue to hire at the sales team we're ramping the supply chain and we've got a relative complicated supply chain model but that has to react now that we're going to market all that might be pretty used to do that we're changing facilities we need to grow we're sort of cramped in a one-story building open up one floor of a building right now so the money is going to be used sort of critically to really scale the business down they can go to market okay but a pretty impressive list of both partners and customers on launch day you don't see Goldman HPE Equinix I think it was quite a slide some of that is the uniqueness of the way we went to market and did the original due diligence on the product and bringing customers in early and then converting them to investors you end up with a customer investment model so they stayed with us Goldman's been through all three rounds we've been about HP and last model we had NetApp has been um two rounds now so we've we've continued to develop as a business with this small core group of customers and investors that we could try to expand every time we move to the next round and as Barry said earlier this is the first time we had a traditional financial investor in our rounds the rest of them have all been customers they've been friends and family for the most part did you join the board too right I did yeah so what are you what are you excited about what what do you see is I mean just clearly your side you invested but is there something just extra special here you know react chambers put in a 10-year 10-year cycle yeah we've talked about it I mean I'm excited to work with the team right there best-in-class working closely with John again is a lot of fun a chance to not exhaust yeah yeah you know a chance to read redefine the data center and be part of the next way even as a VC you love waves and build my Connick company right and I think we have a real opportunity in front of us it it takes a lot of money to do this and do it right and I think we have the team that proven they can execute on this kind of opportunities from I'm excited to see what the next five years hold for this company good well it was funny John teased him a little bit about you know all the M&A stuff that he was famous for at Cisco he's like I don't do that anymore now I'm an investor I want IPOs all the way what's all 18 thinks it is 18 companies in his portfolio their routes they're going to IPO all the way yeah that's that's a good point actually this team has been prolific and they've delivered products that have generated fifty billion dollars and any walk into any data center in the world you're gonna see a product this team has built however this team has not taken a company public so that's really the opportunity I think that's what excites them Randy's here it's why Jon's here that's why I'm here we want to build a company that can be an independent company be a lasting leader in a new category yeah so last word Randy for you for people that aren't familiar with the team that aren't familiar with with with what they've done what would you tell them about why you came to this opportunity and why you're excited about it well this there is no higher quality engineering team in the world didn't these people so it's to get re-engaged with them again with an entirely new concept that's catching a transition and the market was just too good an opportunity to pass I mean I had retired for 15 months and I came out of retirement to join this team much to the chagrin of my wife but I just couldn't pass up the opportunity high caliber talent it's um every day is is interesting I have to say well thanks for for sharing the story with us and and congratulations on a great day and in a terrific event thank you thank you very much all right he's berry he's Randy I'm Jeff you're watching the cube from the top of goldman sachs in Manhattan thanks for watching we'll see you next time

>>From New York city. It's the cube covering. Welcome to the new edge brought to you by systems. >>Okay, we'll come back. You're ready. Jeff Frick here with the cube. We're in downtown Manhattan at the top of Goldman Sachs, like 43 stories above the Hudson. It was a really beautiful view a couple hours ago, but the cloud has moved in and that's only appropriate cause it's cloud is a big theme of why we're here today. We're here for the Penn Zando event. It's called welcome to the new edge. They just come out of stealth mode after two and a half years, almost three years, raised a ton of money, got a really rockstar team and we're excited to have the CEO with us today to tell us a little bit about more what's going on. And that's prem Jane and again, the CEO of Penn Sandow prem. Great to see you. Nice to see you too. So everything we did running up to this event before we could get any of the news, we, we, we tried to figure out what was going on and all it kept coming up was NPLS, NPLS, NPLS, which I thought was a technology, which it is, but it's really about the team. Tell us a little bit about the team in which you guys have built prior and, and why you're such a, a well functioning and kind of forward thinking group of people. >>So I think the team is working together. Mario Luca, myself and Sony were working together since 1983 except for Sony. Sony joined us after the first company, which has crescendo, got acquired by Cisco in 1993 and since then four of us are working together. Uh, we have done many, uh, spinnings inside the Cisco and demo was the first one. Then we did, uh, uh, Nova systems, which was the second, then we did recently in CMA. Uh, and then after we left we thought we are going to retire, but we talked about it and we says, you know, there is still transitions happening in the industry and maybe we have few more years to go back to the, you know, industry and, and do something which is very challenging and, and uh, impacting. I think everything which we have done in the past is to create a impact in the industry and make that transition which is occurring very successful, >>which is really hard to do. And, and John Chambers who, who's on the board and spoke earlier today, you know, kind of talked about these 10 year cycles of significant change in our industry and you know, Clayton Christianson innovator's dilemma, it's really easy when you are successful at one of those to kind of sit on your laurels. In fact, it's really, really hard to kill yourself and go on to the next thing you guys have done this time and time and time again. Is there a unique chemistry in the way you guys look forward or you just, you just get bored with what you built and you want to build something new. I mean, what is some of the magic, because even John said, as soon as he heard that you were the team behind it, he was like, sign me up. I don't know what they're building but I don't really care cause I know these people can deliver. >>I think it's very good the, whenever you look at any startup, the most important thing which comes up as the team and you're seeing a lot of startup fails because the team didn't work together or they got their egos into this one. Since we are working for so long, they compliment each other. That's the one thing which is very important. Mario, Luca, myself, they come from engineering backgrounds. Sony comes from marketing, sales, uh, type of background and we all lady in terms of the brain, if you think about is the Mario behind the scene, Luca is really the execution machine and I'm, you can think like as a heart, okay. Putting this thing together. Uh, as a team, we work very complimentary with each other. It does not mean that we agree on everything, right? We disagree. We argue. We basically challenge each other. But one thing good about this particular team is that once we come to a conclusion, we just focus and execute. And team is also known to work with customers all the time. I mean, even when we started Penn Sando, we talked to many customers in the very beginning. They shape up our ideas, they shape up the directions, which is we are going and what transitions are occurring in the industries and all that. That's another thing which is we take customer very seriously in our thought process of building a product. >>So when you were thinking around sitting around the table, deciding whether you guys wanted to do it again, what were the challenges that you saw? What was the kind of the feedback loop that came in that, that started this? The, uh, the gym of the idea >>thing is also is that, uh, we had, we had developed so many different products as you saw today in the launch, eight or nine, uh, billion dollar product line and stuff like that. So we all have a very good system experience what is really needed, what transitions are occurring and stuff like that. When we started this one, we were not really sure what we wanted to do it, but in the last one when we did the, uh, NCMA, we realize that the enterprise thing, which we deliver the ACI solution for the enterprise, the realize that these services was the most complex way of incorporating into that particular architectures. So right from the beginning of interview realized that the, this particular thing is nobody has touched it, nobody thought about it out of the box thinking that how can you make it into a distributed fashion, which has also realized that cloud is going, everything distributed. >>They got away from the centralized appliances. So as the enterprise is now thinking of doing it cloud-like architectures and stuff like that. And the third thing which was really triggered us also, there was a company which is a new Poona which got acquired by Amazon in 2016 and we were looking at it what kinds of things they are doing and we said we can do much better architecturally and next generation, uh, architecture, which can really enable all the other cloud vendors. Some of them are our partners to make sure they can leverage that particular technologies and build the next generation cloud. And that's where this idea of new edge came in because we also saw that the new applications like IOT is five G's and artificial intelligence, machine learning, robotics or drones, you just name it intelligent devices, which is going to get connected. What is the best place to process them is at the edge or also at the backend with the application where the server is running these and that is another edge compute edge, right? >>In that particular sense. So our idea was to develop a product so that it can cover wide segment of the market, enterprise cloud providers, service borders, but focus very narrowly delivering these services into existing architectures. Also people who are building, building the next generation architectures. Right, so it's the distributed services platform or the distributed services architecture. So at its core for people that didn't make it today, what is it? It's basically is a distributed service platforms. The foundation of that is really our custom processor, which is we have designed is highly programmable. It's software defined so that all the protocols, which is typically people hardwired in our case is programmable. It's all programs which is we are writing the language which you selected as before and before extensions. The software stack is the major differentiated thing which is running on the top of this particular processor, which is we have designed in such a way that is hardware agnostics. >>The the, the capabilities which we have built is easily integrated into the existing environment. So if people already have cloud and they want to leverage our technologies, they can really deploy it in the enterprise. We are basically replacing lot of appliances, simplifying the architectures, making sure they can enable the service as they grow model, which is really amazing because right now they had to say firewall goes here, load balancer goes here, these a VPN devices goes there. In our case it's very simple. You put in every server of our technologies and our software stack and our Venice, which is our policy manager, which is sitting outside and it's based upon Kubernete X a architectures is basically a microservices, which is we are running and managing the life cycle of this particular product family and also providing the visibility and uh, uh, accountability in terms of exactly what is going on in that particular network. >>And it's all driven by intent-based architecture, which is policy driven, right? So software defined sitting on software defined Silicon. So you get the benefits of the Silicon, but it's also programmable Silicon, but it's still, you're sitting, you've got a software stack on top of that that manages that cloud and then the form factors as small as a Nick. Yes. So he can stick it in the HP HP server. Yeah. It specifically goes into any PCI slot in any server, uh, in the industry. Yes. It's amazing. Well, first incarnation, but, but, but, but, but that's a really simple implementation, right? Just to get radiation and easy to deploy. Right. And you guys are, you're yourself where involved in security that's involved in managing the storage. It's simple low power, which I thought was a pretty interesting attribute that you defined early on. Clearly thinking about edge and these distributed, uh, things all over the place. >>They're metal programmable. And then the other thing that was talked about a lot today was the observability. Yes. Um, why observability why was that so important? What were you hearing from customers that were really leading you down that path? Yeah, it's important. Uh, you know, surprisingly enough, uh, the visibility is one of the biggest challenge. Most of the data center faces today. A lot of people tried to do multiple different things, but they're never able to do it, uh, in, in the way we are doing it. One is that we don't run anything on the host. Some people have done it right on the train running the agent on the host. Some people have tried to run virtual machines on the those particular environment. In our case there's nothing which is running on the host site. It runs on our card and having end to end that visibility we can provide latency, very accurate latency to the, to the applications which is very important for these customers. >>Also, what is really going on there is the problem in the network. Isolation is another big thing. When something get lost they don't know where it got lost. We can provide that thing. Another important thing that you're doing, which is not being done in the industries. Everything which is we are doing is flow based means if I'm talking to you, there is a flow being set up between you and me and we are monitoring every flow and one of the advantages of our processor is we have four to eight gigabytes of memory, so we can keep these States, have these flows inside, and that gives a tremendous advantage for us to do lots of things, which as you can imagine going forward, we will be delivering it such as, for example, behavior of these flows and things from this point of view, once you understand the behavior of the flow, you can also provide lot of security features because if I'm not talking to you and suddenly I start talking to you and I know that there's something went wrong, right, right. >>And they should be able to look at the behavior analysis and should be able to tell exactly what's going on. You mean we want a real time snapshot of what's really happening instead of a instead of a sample of something that happened a little. No, absolutely. You're absolutely connected. Yeah. Yeah. Um, that's terrific. So you put together to accompany and you immediately went out and talked to a whole bunch of customers. I was amazed at the number of customers and partners that you had here at the launch. Um, was that for validation? Were you testing hypotheses or, or were there some things that the customers were telling you about that maybe you weren't aware of or maybe didn't get the right priority? I think it's all of the above. What you mentioned our, it's in our DNA by the way. You know, we don't design products, we don't design things without talking to customers. >>Validation is very important that we are on the right track because you may try to solve the customer problem, which is not today's problem. Maybe future's problem. Our idea was that then you can develop the product it was set on the shelf. We don't want to do that. We wanted to make sure that, that this is the hard problem customer is facing today. At the same time looking at it, what futuristic in their architecture is understanding the customers, how, what are they doing today, how they're deploying it. The use cases are understanding those very well and making sure that we are designing. Because when we design a seeker, when your designer processor, you know, you cannot design for one year, it has to be a longterm, right? And you need to make sure that we understand the current problems, we understand the future problems and design that in pretty much your spark and you've been in this space forever. >>You're at Cisco before. And so just love to get your take on exponential growth. You know, such an interesting concept that people have a really hard time grasping exponential growth and we're seeing it clearly with data and data flows and ultimately everything's got to go through the network. I mean, when you, when you think back with a little bit of perspective at the incredible increase in the data flow and the amount of data is being stored and the distribution of these, um, applications now out to the edge and store and compute and take action at the edge, you know, what do you think about, how do you, how do you kind of stay on top of that as somebody who kind of sees the feature relatively effectively, how do you try to stay on top of exponential curves? As you know, very valuable data is very important for anybody in any business. >>Whether it's financial, whether it's healthcare, whether it's, and it's becoming even more and more important because of machine learning, artificial intelligence, which is coming in to really process this particular data and predict certain things which is going to happen, right? We wanted to be close to the data and the closest place to be data is where the application is running. That's one place clears closest to the data at the edge is where data is coming in from the IOT devices, from the 5g devices, from the, you know, you know all kinds of appliances which is being classified under IOT devices. We wanted to be, make sure that we are close to the data, doesn't matter where you deploy and we want to be agnostic. Actually our technologies and architectures designed that this boundary is between North, South, East, West is going to go away in future cloud. >>A lot of things which is being done in the backend will be become at the edge like we talked about before. So we are really a journey which is just starting in this particular detectors and you're going to see a lot more innovations coming from us continuously in this particular directions. And again, based upon the feedback which you're going to get from cloud customers with enterprise customers, but they were partners and other system ecosystem partners, which is going to give us a lot of feedback. Great. Well again, thanks for uh, for having us out and congratulations to uh, to you and the team. It must be really fun to pull the covers off. absolutely. It is very historical day for us. This is something we were waiting for two years and nine months to see this particular date, to have our customers come on the stage and talk about our technologies and why they think it's very important. Thank you very much for giving me this opportunity to talk to you. Thank you. Alright, thanks prem. Thanks. He's prem. I'm Jeff. You're watching the cube where it depends. Sandow launch at the top of Goldman Sachs in downtown Manhattan. Thanks for watching. We'll see you next time.

(upbeat music) >> From New York City, it's theCUBE. Covering "Welcome To The New Edge." Brought to you by Pensando Systems. >> Hey, welcome back here ready. Jeff Frick here with theCUBE. We are high atop Goldman Sachs in downtown Manhattan, I think it's 43 floors, for a really special event. It's the Pensando launch. It's really called welcome to the new edge and we talked about technology. We had some of the founders on but, these type of opportunities are really special to talk to some really senior leaders and we're excited to have John Chambers back on, who as you know, historic CEO of Cisco for many, many years. Has left that, is doing his own ventures he's writing books, he's investing and he's, happens to be chairman of the board of Pensando. So John, thanks for taking a few minutes with us. >> Well, more than a few minutes, I think what we talked about today is a major industry change and so to focus on that and focus about the implications will be a lot of fun. >> So let's jump into it. So, one of the things you led with earlier today was kind of these 10 year cycles and they're not exactly 10 years, but you outlined a series of them from mainframe, mini client server everybody knows kind of the sequence. What do you think it is about the 10 year kind of cycle besides the fact that it's easy and convenient for us to remember, that, kind of paces these big disruptions? >> Well, I think it has to do with once a company takes off they tend to, dominate that segment of the industry for so long that even if a creative idea came up they were just overpowering. And then toward the end of a 10 year cycle they quit reinventing themselves. And we talked earlier about the innovator's dilemma and the implications for it. Or an architecture that was designed that suddenly can't go to the next level. So I think it's probably a combination of three or four different factors, including the original incumbent who broke the glass, disrupted others, not disrupting themselves. >> Right, but you also talked about a story where you had to shift focus based on some customer feedback and you ran Cisco for a lot longer than 10 years. So how do you as a leader kind of keep your ears open to something that's a disruptive change that's not your regular best customer and your regular best salesman asking for a little bit faster, a little bit cheaper, a little bit of more the same versus the significant disruptive transformational shift? >> Well this goes back to one of my most basic views in life is I think we learn more from our setbacks or setbacks we were part of, or even the missteps or mistakes than you ever do your successes. Everybody loves to talk about successes and I'm no different there. But when you watched a great state like West Virginia that was the chemical center of the world and the coal mining center of the world, the 125,000 coal mines, six miners very well paid, 6,000 of the top engineers in the world, it was the Silicon Valley of the chemical industry and those just disappear. And because our state did not reinvent itself, because the education system didn't change, because we didn't distract attract a new set of businesses in we just kept doing the right thing too long, we got left behind. Then I went to Boston, it was the Silicon Valley of the world. And Route 128 around Boston was symbolic with the Silicon Valley and I-101 and 280 around it. And we had the top university at that time. Much like Stanford today, but MIT generating new companies. We had great companies, DEC, Wang, Data General. Probably a million jobs in the area and because we got stuck in a segment of the market, quit listening to our customers and missed the transitions, not only did we lose probably 1.2 million jobs on it, 100,000 out of DEC, 32,000 out of Wang, etc, we did not catch the next generation of technology changes. So I understand the implications if you don't disrupt yourself. But I also learned, that if you're not regularly reinventing yourself, you get left behind as a leader. And one of my toughest competitors came up to me and said, "John, I love the way you're reinventing Cisco "and how you've done that multiple times." And then I turned and I said "That's why a CEO has got to be in the job "for more than four or five years" and he said, "Now we disagree again." Which we usually did and he said, "Most people can't reinvent themselves." And he said "I'm an example." "I'm a pretty good CEO" he's actually a very good CEO, but he said, "After I've been there three or four years "I've made the changes, that I know "I've got to go somewhere else." And he could see I didn't buy-in and then he said, "How many of your top 100 people "you've been happy with once they've been "in the job for more than five years?" I hesitated and I said "Only one." And he's right, you've got to move people around, you've got to get people comfortable with disruption on it and, the hardest one to disrupt are the companies or the leaders who've been most successful and yet, that's when you got to think about disruption. >> Right, so to pivot on that a little bit in terms of kind of the government's role in jobs, specifically. >> Yes. >> We're in this really strange period of time. We have record low unemployment, right, tiny, tiny unemployment, and yet, we see automation coming in aggressively with autonomous vehicles and this and that and just to pick truck drivers as a category, everyone can clearly see that autonomous vehicles are going to knock them out in the not too distant future. That said, there's more demand for truck drivers today than there's every been and they can't fill the positions So, with this weird thing where we're going to have a bunch of new jobs that are created by technology, we're going to have a bunch of old jobs that get displaced by technology, but those people aren't necessarily the same people that can leave the one and go to the other. So as you look at that challenge, and I know you work with a lot of government leaders, how should they be thinking about taking on this challenge? >> Well, I think you've got to take it on very squarely and let's use the U.S. as an example and then I'll parallel what France is doing and what India is doing that is actually much more creative that what we are, from countries you wouldn't have anticipated. In the U.S. we know that 50% of the Fortune 500 will probably not exist in 10 years, 12 at the most. We know that the large companies will not incrementally hire people over this next decade and they've often been one of the best sources of hiring because of AI and automation will change that. So, it's not just a question of being schooled in one area and move to another, those jobs will disappear within the companies. If we don't have new jobs in startups and if we don't have the startups running at about three to four times the current volumes, we've got a real problem looking out five to 10 years. And the startups where everyone thinks we're doing a good job, the app user, third to a half of what they were two decades ago. And so if you need 25 million jobs over this next decade and your startups are at a level more like they were in the 90s, that's going to be a challenge. And so I think we've got to think from the government perspective of how we become a startup nation again, how we think about long-term job creation, how we think about job creation not taking money out of one pocket and give it to another. People want a real job, they want to have a meaningful job. We got to change our K through 12 education system which is broken, we've got to change our university system to generate the jobs for where people are going and then we've got to retrain people. That is very doable, if you got at it with a total plan and approach it from a scale perspective. That was lacking. And one of the disappointing things in the debate last night, and while I'm a republican I really want who's going to really lead us well both at the presidential level, but also within the senate, the house. Is, there was a complete lack of any vision on what the country should look like 10 years from now, and how we're going to create 25 million jobs and how we're going to create 10 million more that are going to be displaced and how we're going to re-educate people for it. It was a lot of finger pointing and transactional, but no overall plan. Modi did the reverse in India, and actually Macron, in all places, in France. Where they looked at GDP growth, job creation, startups, education changes, etc, and they executed to an overall approach. So, I'm looking for our government really to change the approach and to really say how are we going to generate jobs and how are we going to deal with the issues that are coming at us. It's a combination of all the the above. >> Yep. Let's shift gears a little bit about the education system and you're very involved and you talked about MIT. Obviously, I think Stanford and Cal are such big drivers of innovation in the Bay area because smart people go there and they don't leave. And then there's a lot of good buzz now happening in Atlanta as an investment really piggy-backing on Georgia Tech, which also creates a lot of great engineers. As you look at education, I don't want to go through K through 12, but more higher education, how do you see that evolving in today's world? It's super expensive, there's tremendous debt for the kids coming out, it doesn't necessarily train them for the new jobs. >> Where the jobs are. >> How do you see, kind of the role of higher education and that evolving into kind of this new world in which we're headed? >> Well, the good news and bad news about when I look at successful startups around the world, they're always centered around a innovative university and it isn't just about the raw horse power of the kids, It starts with the CEO of the university, the president of the university, their curriculum, their entrepreneurial approach, do they knock down the barriers across the various groups from engineering to business to law, etc? And are they thinking out of box? And if you watch, there is a huge missing piece between, Georgia Tech more of an exception, but still not running at the level they need to. And the Northeast around Boston and New York and Silicon Valley, The rest of the country's being left behind. So I'm looking for universities to completely redo their curriculum. I'm looking for it really breaking down the silos within the groups and focus on the outcomes. And much like Steve Case has done a very good job on focusing, about the Rust Belt and how do you do startups? I'm going to learn from what I saw in France at Polytechnique and the ITs in India, and what occurred in Stanford and MIT used to occur is, you've got to get the universities to be the core and that's where they kids want to stay close to, and we've got to generate a whole different curriculum, if you will, in the universities, including, continuous learning for their graduates, to be able to come back virtually and say how do I learn about re-skilling myself? >> Yeah. >> The current model is just not >> the right model >> It's broken. >> For the, for going forward. >> K through 12 is >> hopelessly broken >> Yeah. >> and the universities, while were still better than anywhere else in the world, we're still teaching, and some of the teachers and some of the books are what I could have used in college. >> Right, right >> So, we got to rethink the whole curriculum >> darn papers on the inside >> disrupt, disrupt >> So, shifting gears a little bit, you, played with lots of companies in your CEO role you guys did a ton of M&A, you're very famous for the successful M&A that you did over a number of years, but in an investor role, J2 now, you're looking at a more early stage. And you said you made a number of investments which is exciting. So, as you evaluate opportunities A. In teams that come to pitch to you >> Yeah. >> B. What are the key things you look for? >> In the sequence you've raised them, first in my prior world, I was really happy to do 180 acquisitions, in my current world, I'm reversed, I want them to go IPO. Because you add 76% of your headcount after an IPO, or after you've become a unicorn. When companies are bought, including what I bought in my prior role, their headcount growth is pretty well done. We'd add engineers after that, but would blow them through our sales channel, services, finance, etc. So, I want to see many more of these companies go public, and this goes back to national agenda about getting IPO's, not back to where they were during the 90's when it was almost two to three times, what you've seen over the last decade. But probably double, even that number the 90's, to generate the jobs we want. So, I'm very interested now about companies going public in direction. To the second part of your question, on what do I look for in startups and why, if I can bridge it, to am I so faired up about Pensando? If I look for my startups and, it's like I do acquisitions, I develop a playbook, I run that playbook faster and faster, it's how I do digitization of countries, etc. And so for a area I'm going to invest in and bet on, first thing I look at, is their market, technology transition, and business model transition occurring at the same time. That was Amazon of 15 years ago as an example. The second thing I look at, is the CEO and ideally, the whole founding team but it's usually just the CEO. The third thing I look for, is what are the customers really say about them? There's only one Steve Jobs, and it took him seven years. So, I go to the customers and say "What do you really think of this company?" Fourth thing I look for, is how close to an inflection point are they. The fifth thing I look for, is what they have in their ecosystem. Are they partnering? Things of that type. So, if I were to look at Pensando, Which is really the topic about can they bring to the market the new edge in a way that will be a market leading force for a whole decade? Through a ecosystem of partners that will change business dramatically and perhaps become the next major tech icon. It's how well you do that. Their vision in terms of market transitions, and business transitions 100% right. We've talked about it, 5G, IOT, internet of things, going from 15 billion devices to 500 billion devices in probably seven years. And, with the movement to the edge the business models will also change. And this is where, democratization, the cloud, and people able to share that power, where every technology company becomes a business becomes a, every business company becomes a technology company. >> Right. >> The other thing I look at is, the team. This is a team of six people, myself being a part of it, that thinks like one. That is so unusual, If you're lucky, you get a CEO and maybe a founder, a co-founder. This team, you've got six people who've worked together for over 20 years who think alike. The customers, you heard the discussions today. >> Right. >> And we've not talked to a single cloud player, a single enterprise company, a single insurance provider, or major technology company who doesn't say "This is very unique, let's talk about "how we work together on it." The inflection point, it's now you saw that today. >> Nobody told them it's young mans game obviously, they got the twenty-something mixed up >> No, actually were redefining (laughs) twenty-something, (laughs) but it does say, age is more perspective on how you think. >> Right, right. >> And Shimone Peres, who, passed away unfortunately, two years ago, was a very good friend. He basically said "You've got all your life "to think like a teenager, "and to really think and dream out of box." And he did it remarkably well. So, I think leaders, whether their twenty-something, or twenty-some years of experience working you've got to think that way. >> Right. So I'm curious, your take on how this has evolved, because, there was data and there was compute. And networking brought those two thing together, and you were at the heart of that. >> Mm-hmm. Now, it's getting so much more complex with edge, to get your take on edge. But, also more importantly exponential growth. You've talked about going from, how ever many millions the devices that were connected, to the billions of devices that are connected now. How do you stay? How do you help yourself think along exponential curves? Because that is not easy, and it's not human. But you have to, if you're going to try to get ahead of that next wave. >> Completely agree. And this is not just for me, how do I do it? I'm sharing it more that other people can learn and think about it perhaps the same way. The first thing is, it's always good to think of the positive, You can change the world here, the positive things, But I've also seen the negatives we talked about earlier. If you don't think that way, if you don't think that way as a leader of your company, leader of your country, or the leader of a venture group you're going to get left behind. The implications for it are really bad. The second is, you've got to say how do you catch and get a replicable playbook? The neat thing about what were talking about, whether it's by country in France, or India or the U.S., we've got replicable playbooks we know what to run. The third element is, you've got to have the courage to get outside your comfort zone. And I love change when it happens to you, I don't like it when it happens to me And I know that, So, I've got to get people around me who push me outside my comfort zone on that. And then, you've got to be able to dream and think like that teenager we talked about before. But that's what we were just with a group of customers, who were at this event. And they were asking "How do we get "this innovation into our company?" "How do we get the ability to innovate, through not just strategic partnerships with other large companies or partnerships with startups?" But "How do we build that internally?" It's comes down to the leader has to create that image and that approach. Modi's done it for 1.3 billion people in India. A vision, of the future on GDP growth. A digital country, startups, etc. If they can do it for 1.3 billion, tell me why the U.S. can not do it? (laughs) And why even small states here, can't do it. >> Yeah. Shifting gears a little bit, >> All right. >> A lot of black eyes in Silicon Valley right now, a lot of negativity going on, a lot of problems with privacy and trading data for currency and, it's been a rough road. You're way into tech for good and as you said, you can use technology for good you can use technology for bad. What are some things you're doing on the tech for good side? Because I don't think it gets the spotlight that it probably should, because it doesn't sell papers. >> Well, actually the press has been pretty good we just need to do it more on scale. Going back to Cisco days, we never had any major issues with governments. Even though there was a Snowden issue, there were a lot of implications about the power of the internet. Because we work with governments and citizens to say "What are the legitimate needs so that everybody benefits from this?" And where the things that we might have considered doing that, governments felt strongly about or the citizens wouldn't prosper from we just didn't do it. And we work with democrats and republicans alike and 90% of our nation believed tech was for good. But we worked hard on that. And today, I think you got to have more companies doing this and then, what, were doing uniquely in JC2, is were literally partnering with France on tech is for good and I'm Macron's, global tech ambassador and we focus about job creation and inclusion. Not just in Paris, or around Station F but throughout all the various regions in the country. Same thing within India, across 26 different states with Modi on how do you drive it through? And then if we can do it in France or India why can't we do it in each state in the U.S.? Partnering with West Virginia, with a very creative, president of the university there West Virginia University. With the democrats and republicans in their national senate, but also within the governor and speaker of the house and the president and senate within West Virginia, and really saying were going to change it together. And getting a model that you can then cookie cut across the U.S. if you change the curriculum, to your earlier comments. If you begin to focus on outcomes, not being an expert in one area, which is liable not to have a job >> Right. >> Ten years later. So, I'm a dreamer within that, but I think you owe an obligation to giving back, and I think they're all within our grasps >> Right >> And I think you can do, the both together. I think at JC2 we can create a billion dollar company with less than 10 people. I think you can change the world and also make a very good profit. And I think technology companies have to get back to that, you got to create more jobs than you destroy. And you can't be destroying jobs, then telling other people how to live their lives and what their politics should be. >> Yeah. >> That just doesn't work in terms of the environment. >> Well John, again, thanks for your time. Give you the last word on >> Sure >> Account of what happened here today, I mean you're here, and Tony O'Neary was here or at the headquarters of Goldman. A flagship launch customer, for the people that weren't here today why should they be paying attention? >> Well, if we've got this market transition right, the technology and business model, the next transition will be everything goes to the edge. And as every company or every government, or every person has to be both good in their "Area of expertise." or their vertical their in, they've got to also be good in technology. What happened today was a leveling of the playing field as it relates to cloud. In terms of everyone should have choice, democratization there, but also in architecture that allows people to really change their business models, as everything moves to the edge where 75% of all transactions, all data will be had and it might even be higher than that. Secondly, you saw a historic first never has anybody ever emerged from stealth after only two and a half years of existing as a company, with this type of powerhouse behind them. And you saw the players where you have a customer, Goldman Sachs, in one of the most leading edge areas, of industry change which is obviously finance leading as the customer who's driven our direction from the very beginning. And a company like NetApp, that understood the implication on storage, from two and a half years ago and drove our direction from the very beginning. A company like HP Enterprise's, who understood this could go across their whole company in terms of the implications, and the unique opportunity to really change and focus on, how do they evolve their company to provide their customer experience in a very unique way? How do you really begin to think about Equinix in terms of how they changed entirely from a source matter prospective, what they have to do in terms of the direction and capabilities? And then Lightspeed, one of the most creative intra capital that really understands this transition saying "I want to be a part of this." Including being on the board and changing the world one more time. So, what happened today? If we're right, I think this was the beginning of a major inflection point as everything moves to the edge. And how ecosystem players, with Pensando at the heart of that ecosystem, can take on the giants but also really use this technology to give everybody choice, and how they really make a difference in the future. As well as, perhaps give back to society. >> Love it. Thank you John >> My pleasure, that was fun. >> Appreciate it. You're John, I'm Jeff you're watching theCUBE. Thanks for watching, we'll see you next time. (upbeat music)

>>From New York city. It's the cube covering. Welcome to the new edge brought to you by systems. >>Hey, welcome back everybody. Jeff, Rick here with the cube. We are in Manhattan at the top of Goldman Sachs. It is a great view if you ever get an opportunity to come up here, I think 43 floors over the Hudson you could see forever. But this is the cloud events. So the clouds are here and we're excited to be here is the Penn Penn Sandow launch in the name of the event is welcome to the new edge, which is a pretty interesting play. We hear a lot about edge but we haven't really heard of that company really focusing on the edge as their primary go to market activity and really thinking about the edge first. So we're excited to have the cofounder cube Olam and many time guests a Sony Gian Deni. She's the co founder and chief business officer. So many great to see you. Good to see you too. >>And our hosts here at Goldman Sachs is uh, Josh Matthews. He's a managing director of technology at Goldman. Josh. Great to see you. You too. And thank you and thanks for hosting us. Nice. A nice place to come to work every day. So great conversation today. Congratulations on the launch of the company over two years in stealth mode. Talk a little bit about that. What is it like to be in stealth mode for so long and you guys raised big money, you've got a big team, you're doing heavy duty technology. What's it been like to finally open up the curtains and tell everybody what you've been? >>It's clearly very interesting and exciting. Normally it's taken me nine months to deliver a baby this time it's been two and a half years of being instilled while we have been getting ready for this baby to come out. So it's phenomenally exciting that too to be sharing the stage with our customers and our investors and our strategic partners. >>Yeah, I thought it was pretty interesting that you're launching with customers and when you really told the story on stage of how early you engaged with Josh and his team, um, first I want to get your kinda your perspective. Why were you doing that so early and what did that ultimately do with some of the design decisions that you guys made? And then we'll come back to Josh as to, you know, his participation. >>So I think whenever you conduct technology transitions, having a sense from customers that have the ability to look out two to three years is very important because when you're capturing market transitions, doing it with customer inputs is far more relevant than going about it alone. Uh, the other key thing about this architectural shift is that it allows the flexibility for every customer to go take pieces of how they want to bring the cloud architectures and bring it into their environment. So understanding that use case and understanding the compelling reasons of what problems both technological and business can be solving and having that perspective into the product definition and the design and the influence that customers like Josh you've had is why we are sitting here and talking about them in production. Uh, as opposed to, yeah, we're thinking about where we are. We are looking at it from a proof of concept perspective. Right. >>And Josh, your, your perspective, you said earlier today that, you know, as long as a sign is involved, you're, you're, uh, you're happy to jump in and see what she's been working on. So how, >>you know, how did you get involved, how did they reach out to you and, and what is it like working on, you know, technology so early in its development that you get to actually have some serious influence? Well, it's an amazing opportunity, um, to get exactly what you want, um, exactly what you know is going to solve problems for the business here. Um, you know, and the other thing is, you know, we've worked with this team, uh, through almost every spinning. Uh, I think it was a little young for the, maybe the first one. Um, but, uh, otherwise this team has worked with them through at least 15 years or more. So we knew the track record for execution and then for us on this product, I mean, it was an opportunity because it's truly a startup. Um, you know, Sony and the team brought us in. >>Uh, we kind of just put out problems on the table that we were trying to solve and then, you know, they came up with the product and the idea and we were able to put together, you know, yeah, these are our priority one, two, three that we want to go for. And you know, we've just been developing alongside them. So both software and, you know, driving what the feature set is. Right. So what were some of those problems guys? Price seemed like forever ago when you started this conversation, but as you kind of looked forward a couple of years back that you could see that were coming, that you needed addressed. You know, it's funny, we started with kind of like, well we think containerization is going to be explosive and, and you know, really everything's on virtual machines or bare metal, mostly virtual machines. So one, you know, as containers come out, how do we track them, secure them, um, how do we even secure, uh, you know, the virtual machines and our environment cause they're, you know, over almost a quarter million of them. >>The idea of being able to put, um, network policy, that's I would say incorruptible, not actually on the server, but at, you know, that's why we use firewalls, right? So solving that security problem was number one. The other one was being able to have the telemetry to see what's happening, what's changing, um, and troubleshoot at, you know, at the network layer from every single server. Again, it's all about scale. Like things were just scaling and the throughput's going up, traditional methods of being able to see what's on your network. You can't look in the middle, it just can't keep up. It's just speeds and feeds. So being able to push those things to the edge. And then lastly, it really happened more, um, through the process here. But about a year and a half ago, um, we began segmenting our network the same way a 5g provider does with a technology called segment routing. >>And we just said, that's kind of our follow on technologies to, you know, put the network in the server and put this segment routing capability all the way out at the edge. So, you know, some things we foresaw and other things we've just developed. You know, it's been, it's been two and a half years. So, um, it's been a great partnership and you know, I think more, more features will come. Well Sony, you and the team, but it's been talked about all day long, have have a history of multiple times that you've kind of brought these big transformational technologies. Um, head what, what did you guys see a couple of years back and kind of this progression, you saw this opportunity >>to do something a little bit different than you've done in the past, which is actually go out, raise, raise around and uh, and do a real startup. What was the opportunity that you saw this? >>So we saw a number of challenges and opportunities. At the same time, we, we clearly saw that, uh, the cloud architectures that have been built by the leaders, like the incumbents like AWS today have a lot of the intelligence that is being pushed into their, their respective compute platforms. Uh, and we also noticed that at the same time, while that was what was needed to build the first generation of the cloud, the new age applications, and even as gardener has predicted that 75% of all enterprise data and applications will be processed at the edge by 2025. If that happens, then you need that intelligence at the edge. You need the ability to go do it where the action is, which is at the edge. And very consistently we found that the architectures, including scale out storage, we're also driving the need for this intelligence to be on in a scale-out manner. >>So if you're going to scale out computing, you need the services to be going hand in hand with that scale. Our computer architecture for the enterprises so they can simplify their architectures and bring the cloud models that have only existed in the cloud world, into their own data centers and their own private clouds. So there were these technology transitions we saw were coming down the pike. It's easier said now in 2019 it wasn't so simple in 2017 because we had to look at these multiple technology transitions. And surprisingly, when we call those things out, as we were shaping the company's strategy, getting validation of the use cases from customers like Josh was pivotally important because it was for the validating that this would be the direction that the enterprises and the cloud customers would be taking. So the reason you start with a vision, you start with looking at where the technology transitions are going to be occurring and getting the customers that are looking farther out validated plays a very important role so that you can go and focus on the biggest problems that you need to go and solve. Right, right. >>It just seems like the, the, the big problem, um, for most layman's is, is the old one, which, why networking exists in the first place, which is do you bring the data to the compute or do you bring the compute to the data? And now as you said, in kind of this hyper distributed world, um, that's not really a viable answer either one, right? Because the two are blended and have to be together so that you don't necessarily have to move one to the other or the other back the other direction. So, and then the second piece that you talked about over and over in your, in your presentation with security and you know, everybody talks about security all the time. Everybody gets hacked every day. Um, and there's this constant theme that security has to be baked in, you know, kind of throughout the process as opposed to kind of bolted on at the end. You guys took that approach from day, just speak >>it into the architecture. Yes. That was crucially important because when you are trying to address the needs of the enterprise, particularly in regulated markets like financial services, you want to be in a position where you have thought about it and baked it into the platform ground up. Uh, and so when we are building the program of a process, so we had the opportunity to go put the right elements on it. In order to make it tamper proof, we had to go think about encrypting all the traffic and communication between our policy manager and the distributed services platforms at the edge. We also then took it a step further to say, now if there were to be a bad actor that were to attack from an operating system vulnerability perspective, how do we ensure that we can contain that bad actor as opposed to being propagated over the infrastructure? So those elements are things you cannot bolt on at design time, or when you need to go put those into the design day one, right. Only on top of that foundation, then can you build a very secure set of services, whether it's encryption, whether it's distributed via services, so on and so forth. >>Uh, and Josh, I'm curious on your take as we've seen kind of software defined everything, uh, slowly take over as opposed to, you know, kind of single purpose machines or single purpose appliances, et cetera. Yep. Really a different opportunity for you to control. Um, but also to see a lot of talk today about, about policy management. A lot of talk about, um, observability and as you said now even segmentation of the networks, like you segment the nodes and you segment everything else. You know, how, how do you see this kind of software defined everything continuing to evolve and what does it enable you to do that you can't do with just a static device? I mean, the approach we took, um, we started like, you know, years ago, about six years ago was saying we can get computers, uh, deployed for our applications. No problem. Uh, and you know, at, at on demand and in our internal cloud, now we can do it as a hybrid cloud solution. >>One of the biggest problems we had in software defined was how do you put security policy, firewall policy, um, with that compute and in, you know, our industry, there's lots of segmentation for material nonpublic information. Um, compliance, you know, it could be internet facing, B2B facing. Uh, we do that today. We program various firewall vendors automatically. Uh, we allow our application developers to create, um, these policies and push them through as code and then program the firewall. What we were really looking to do here is distribute that. So we F day one in getting pen Sandow into production was to use our uh, our firewall system. It's called pinnacle. We, um, we programmed from pinnacle directly into the Penn Songdo Venice manager via API and then it, you know, uses its inventory systems to push those things out. So for us, software defined has been around, I like to call it the store front, but for the developer it's network policy, it's load balancing. >>Um, and, and that's really what they see. Those are the big products on the net. Everything else is just packet forwarding to them. So we wanted with pen Sandow at least starting with security to have that bar set day one and then get, you know, all the benefits of scale, throughput and having the policies close to the, on the edge. You know, we're back to talking about the edge. We want to right there with the, with the deployment, with the workload or the application. And that's, that's what we're doing right off the bat. Yeah. What are the things you mentioned in your talk was w is, you know, kind of in the theme of atomic computing, right? You want to get smaller and smaller units so that you can apply and redeploy based on wherever the workload is and in the change. And you said you've now been able to, you know, basically take things out of dedicated, you know, kind of a dedicated space, dedicated line and dedicated job so that you can now put them in a more virtualized situation. >>Exactly. Grab more resources as you need them. Well, you'd think the architecture, I mean even just theater of the mind is just, you're saying, I'm going to put this specific thing that I have to secure behind these firewalls. So it's one cabinet of computers or a hundred it's still behind a set of firewalls. It's a very North, South, you know, get in and get out here. You're talking about having that same level of security and I think that's novel, right? There hasn't been, if you look at virtual firewalls or you know, IP tables on Linux, I mean it's corruptible. It's, it's, it can be attacked on the computer. And once it's, you know, once you've been attacked in that, that that attack vector has been, you know, hit your, your compromised. This is a separate management plane. Um, you know, separate control plane. The server doesn't see it. >>That security is provided. It's at scale, it's East, West. The more computers that have the pen Sandow, you know, architecture inside of them, the, you know, the wider you can go, right. And then the North South goes away. I'm just curious to get your perspective. Um, as you know, everyone is a technology company. At the same time, technology budgets are going down, people are hard to hire. Uh, your data is growing exponentially and everything's a security threat. Yes. So as you get up in the morning, get ready to drive to work and you're drinking your coffee, I mean, how do you, you know, kind of communicate to make sure to senior management knows kind of what your objectives are in this, this kind of ongoing challenge to do more with less. And it, even though it's an increasingly strategic place or is it actually is what the company does now, it just happens to wrap it around your plane services or financial services or travel or whatever. >>Uh, I think your eye, and I had said it to John before, um, it has to come from that budget has to come from somewhere. So I think a combination of, of one that's less, well, I'll say the one that's easier to quantify is you're going to take budget from say appliance manufacturer and move it to a distributed edge and you're going to hopefully save some money while you do it. Um, you're going to do it at scale. You're gonna do it at, you know, high throughput and the security is the same or better. So that's, that's one, that's one place to take capital from. The other one is to say, can I use the next computer? Yes. Because I don't have to deploy these other new computers behind this stack of firewalls. Is there agility there? Is there efficiency, um, on my buying less servers and using, you know, more of what I have and doing it, you know, able to deploy faster. >>And it's harder to quantify. I think if you could, you know, over time, see I bought 20% less server, uh, capacity or, you know, x86 capacity, that's a savings. And the other one that's very hard to quantify, but it's always nice to have the development community. And we've had it recently where they say, Hey, this took me a month to deploy instead of a year. Um, and you know, the purchase cycles, uh, you know, for procurement and deployment, they're long, you know, in enterprise you want them to be quick, but they're really not. So all of those things add up. And that's the story. You know, I would tell, you know, any manager, right? Yeah, >>yeah. I think, you know, the old historic way that utilization rates were just so, so, so, so low between CPU and memory, everything else. Cause if nothing else, because to get another box, you know, could take a long time. Yeah. Well, final, final question for you, Tony. You talked about architectures and being locked into architectures and you and you talked about you guys are already looking forward, you know, to kind of your next rev, your next release, kind of your next step forwards. What, where do you see kind of the direction, don't give away any secrets, but um, you know, kind of where you guys going. What are your priorities now that you've launched? You got a little bit more money in the bank. >>Well, our biggest priorities will be to focus on customer success is to make sure that the customer journey is indeed replicable at scale, is to enable the partner's success. Uh, so in addition to Goldman Sachs, the ability to go and replicate it across the federated markets, whether it's global financial services, healthcare, federal, and partnering with each B enterprise so that they can on their platform, amplify the value of this architecture, not just on the compute platforms but on, in other areas. And the third one clearly is for our cloud customers is to make sure that they are in a position to build a world class cloud architecture on top of which then they can build their own, deliver their own services, their own secret sauces, uh, so that they can Excel at whatever that cloud is. Whether it's to become the leading edge platform as a service customer, whether it is to be the leading edge of software's a service platform customer. So it's all about the execution as a, as you heard in that room. And that's fundamentally what we're going to strive to be, is to be a great execution machine and keep our heads down and focused on making our customers and our partners very successful. >>Well, certainly, congratulations again to you and the team on the launch today. And Josh, thank you for hosting this terrific event and being an early customer. Yeah. Yeah. Happy to be. Alright. I'm Jetta. Sone. Josh, we're the topic. Goldman Sachs at the Penn Sandow the new welcome to the new edge. Thanks for watching. We'll see you next time.

>> From New York City, it's theCUBE, covering Welcome to the New Edge. Brought to you by Pensando Systems. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're on top of Goldman Sachs in downtown Manhattan. It was a really beautiful day a couple of hours ago, but the rain is moving in, but it's appropriate 'cause we're talking about cloud. And we're here for a very special event. It's the Pensando launch, I'll get the pronunciation right, Pensando launch, and it's really about Welcome to the New Edge. And to start off, I mean, I couldn't come up with two better tech executives who've been around the block, seen it all, and they're both here for this launch event which is pretty special. On my left, Antonio Neri, CEO and president of HP. Antonio, great to see you. >> Thanks for having me. >> And John Chambers, of course we know him from his many years at Cisco, but now he's the chairman of Pensando, and of course J2 Ventures, and an author, and John, you're keeping yourself busy. >> I am, tryin' to change the world one more time. >> All right, so let's talk about that changing the world, 'cause you are two very high, powerful people. You run big companies, and you talked about, in your opening remarks, the next wave. You talked about these kind of 10-year waves. And we're starting a new one, which is why you got involved. Why did you see that coming, what do you see in Pensando, and how are we going to address this opportunity? >> Well, when you think about it, every 10 years there's a new leader in the marketplace, and nobody has stayed on top longer than 10 years and has led in the next market transition. We think about mainframes, IBM clearly the leader there, the mini computers, I'm biased toward Wang, but DEC was there. Then the client server and obviously Microsoft and Intel playing a very key role, followed by the internet where Cisco was very, very successful. And then followed, literally by that, by social media and then the cloud and then what I think will be bigger than any of the prior ones, it's about what happens as the cloud moves to the edge. And we may end up having a different term every time, but that really is what we saw today. And how we came together with a common vision as the cloud moves to the edge, what could an ecosystem of partners do, with a foundation, with Pensando at the core of that, to really take advantage from how do you deliver services to our joint customers in a way that no one else can. And have the courage, really, to go challenge Amazon in terms of their market dominance, but provide choice and say it's a multi cloud world. How do you provide that choice and then how do you differentiate it together with each partner? >> Antonio, you guys have been talking about edge for a long, long time. You've been on this for a while. HP's such a great company. Used to be, I think, one of the great validators if anyone could do a deal with HP. It was really a technology validation and a business validation, and I think that still holds true. So you must have, knocking on your door all day long. What did you see in this opportunity with Pensando? >> Well, first of all, John and I see the world from the same lens. We see a world where the enterprise of the future will be essentially cloud enabled and data-driven. And therefore we have to remove these barriers, call it the cloud in one place or the other one. We are going to live what are calling a edge-to-cloud world where, is a cloudless. Where the cloud experience is distributed everywhere. And where action happens is where we live and work right now, right here. We're having a conversation, we're producing data, and we are transmitting this real time. So, the point is, we believe the edge is a new frontier and that's where the vast majority is being created, 75%. of it created the edge. And this is where it starts by having a common vision and ultimately a same DNA, same culture. John and I share the same values for passion for customers, passion for driving a customer-driven innovation, and ultimately change the world like we have done for decades. And I think Hewlett-Packard Enterprise is uniquely positioned to be the edge-to-cloud platform delivered as a service. And together with Pensando and the great technology I bring about from the silicon side and on the softer side, together with our own knowhow and engineering capabilities, we can change the world again. >> And the fun part is, we can almost finish each other's sentences. (all laughing) We have a little bit different accent. The stability to have a common vision, having never really talked about it, and then a view of the common culture. Because strategic partnerships are really hard. And you said it on stage, but I cannot agree with it more. If you're cultures aren't similar, if you don't think how does your partner win first and how do you win second, this is very hard to do. And we can finish each other's sentences. >> And I think there is another point here that John and I truly believe, because it's part of our values. It's to use technology for good. So, one thing is accelerating the business innovation and what our enterprise customers are going through, but then how apply that technology to deliver some good. And we as a company have a clear purpose in life, which is to advance the way people live and work. So, I think as we go through this massive inflection point, both from the business side and the technology side, not only we can create a better world, but also give back somewhat to the communities as well. >> There are massive changes, and it's a sea-change in infrastructure in the way things are done, but you hit on three really key, simple words in your remarks earlier. Trust, engineering-driven, which is HP's culture from the earliest garage days, and customer-centric. So, we hear about data-driven but in engineering, you don't necessarily want to lead with that. Customer-centric you do have to lead and it's pretty interesting at Pensando, you talk to all these customers, and you're just launching the company today, you've been in stealth for over two years. But all these customers have been engaged with you since the very, very beginning. Pretty interesting approach. >> It is, and we do share a common passion on that. Every company says they're customer-driven, but just ask how the CEO spends his or her time. I just asked their customers, do they replace them first on every issue? We share that common value completely. >> Yeah, I spend 50% of my time on the road talking to customers. That's my goal, because I believe the truth is in the cold face. When you talk to customers, you get the truth, what the challenges and opportunities are. And we need to bring that succinct feedback back into our problem management engineering team to try to solve there's a problem. So take advantage of those opportunities by delivering a better experience. It starts with experience first and technology comes second. >> The other piece you talked about is your team, and diversity and really the power of diversity. And, I think it was, the Lincoln cabinet, band of people that didn't get along with each other and had a bunch of different points of view. But because of that, it surfaces issues and it lets you see multi sides. You said you handpicked that team. What are some of the things you thought about when you handpicked your team when you took the reins a couple years ago from the-- >> Well, it starts by, thought leadership and what, how they see the world, ultimately what the strengths are and how we bring those strengths for the power of one. I agree with John, I believe a team comes first, individual comes second. And if you can bring the best of each individual in a concerted way where you create an environment for debate and ultimately for getting alignment and moving forward with execution. That's what that is all about, leadership. So, I handpicked those people because each of them had that unique quality. Whether it's, you know, being very self-centric in the way you deliver the value proposition or very technology-centric, or very services oriented. So, we have picked those people for a reason and it's not easy to manage a very opinionated team. (all laughing) But once you can get them aligned, is actually incredible fun to watch. >> You know, I would make one tweak to what you just asked the question on. I had a chance to watch his team for the first time in our garage startup at my house. And they are very diverse with different opinions, they are very comfortable with disagreeing with each other. But they have a common set of values and a common end goal. I'm not sure the Lincoln cabinet had that. And that's so important to realize, because what we're about to do together and what each of us are trying to do in our own endeavors, it's so important to have a team that has that type of culture and the ability to move for that. >> The other team that mentioned, that kept coming up throughout the day, was the team that you're working with on Pensando. And how this team has been together for, I think you said the new 20, right? 25 plus years, and have built multiple projects, multiple products over many, many years. And now have this cohesion as you keep saying, they can finish their own sentences. You know, a really specific approach to get this group together that you know is not going to be strategy, it's going to be delivery. >> It is going to be the combination, if I may. And it is very unique that a team works together for over 25 years. It's a team that is a family and we are about as diverse as it gets in our backgrounds, our accents, our countries that our families came from. But it's a team that competes purely on getting market transitions right, that is always driven by our customers and what we need to do and build and put 'em always first in everything we do. And then it's fearless. We outline audacious goals at being number one in everything we do, and out of the eight products that we built together, we are number one in all eight. All of 'em with over 50% market share, and there was no number two. And so the ability to execute with that type of precision, customer-driven and the courage to do it and understand what we know and what we don't know. Coming together one more time, I mean it's really exciting, it will be a new definition of 20 somethings in a startup. >> So, getting you the last word Antonio, as you looked at John's chart with those 10-year blocks and the garage has been around Palo Alto for a long time. >> 82 years. >> You guys have seen a lot, 82 years, you've been through a few of these and you're still here and still doing a great job and still winning. So, as you look at that from your current position as CEO, what goes through your head? How are you making sure you're keeping ahead? How are you avoiding the Clayton Christensen Innovator's Dilemma, to make sure you're killing your own business before somebody else kills kind of the old stuff and making sure you're out in front. >> When I became a CEO, in the transition from Meg to me, I established three key priorities for myself. One is our customers and partners. Keep them at the center of everything we do. That's one of our core values. Second is innovation, innovation, innovation. Innovation from a customer-driven approach. And third is the culture of the company. And what a great example here with John, you know, leading an iconic company for decades. And so to me, I have been working very aggressive on the three of those aspects. And I'm very pleased with the progress we have made. But, now is about writing the next chapter of this company. And in order to write that next chapter company, you need to have a strong alignment at the top, all the way down, what I call ropes to the ground. So, fun enough, John is going to be in my event here in a couple of weeks. We'll bring the leadership team, the top 400 leaders, talking about how to disrupt yourself and how you pay for the company into the future. And the future, as I said, is we see an enterprise that's edge-centric, cloud-enabled, and data-driven, delivered as a service. So we are going to be the, as a service company with an edge-to-cloud platform that accelerates business from the data. And the combination of Pensando technologies and engineering capabilities, with our vision and our own intellectual property, we think we can deliver those unique experience for the customers in a more agile, cost-effective way and democratize the cloud, as John say, for the world. So, I'm incredibly excited about doing this. And who thought that John Chambers and Antonio Neri would be here, you know. And the reality is it takes leadership, so I value leadership, I value trust, and this partnership is built on trust. And we both have the same values. >> I appreciate you taking the time. I mean, we're going to talk about the products a little bit later. We've got some of the deeper product people. But, you know, I think the leadership thing is so important and I think it's harder. I think it's hard to be a great leader, it's hard to lead through transitions, and the pace of change is only accelerating, so the challenge is only going to increase. But I think communication and trust is such a big piece. I saw Dave Pottruck speak many, many times and he's very, very good. And I asked him, 'cuz we had a thing at school. I said, "Dave, why are you so good?" And he said, "Very simple. "As a CEO, my job is to communicate. "I have three constituents. "I have my customers, I have the street, "and I have my employees. "And so I treat it as a skill, I practice, I got a coach, "and I treat it like any other skill." And it's so hard and so important to provide that leadership, provide that direction, so everybody can pull the rope in the same direction. Nothing but the best to both of you and thanks for taking a few minutes. >> Thank you. >> It was a lot of fun. >> All right. >> It's a pleasure. >> Thank you. >> He's Antonio, he's John, I'm Jeff. You're watching theCUBE, from the top of Goldman Sachs in Manhattan. Thanks for watching, we'll see you next time. (upbeat music)

>> From the Regency Center in San Francisco, it's theCUBE covering Serverlessconf San Francisco 2018 brought to you by SiliconANGLE Media. >> I'm Stu Miniman and you're watching theCUBE's coverage of Serverlessconf 2018 here in San Francisco at the Regency ballroom. I'm happy to welcome back to the program Simon Wardley, who's a researcher with the Leading Edge Forum, I spoke with you last year at Serverless in New York City, and thanks for joining me again here in San Francisco. >> Absolute pleasure, nice to be back. >> Alright, so many things have changed, Simon, we talked off camera and we're not going into it, your wardrobe stays consistent >> Always. >> But, you know, technology tends to change pretty fast these days. >> Mhmm. >> You do a lot of predictions and I'm curious starting out when you think about timelines and predictions, how do you deal with the pace of change, and put things out, I have my CTOs, like well, if I put a 10 year forecast down there, I can be off on some of the twists and curves, and kind of hit closer to the mark. Give us some of your thoughts as to how you look out and think about things when we know it's changing really fast. >> Okay, okay, so there are a number of different comments in there, one about how do you do predictions, one about the speed of change, okay? So I'm going to start off with the fact that one of the things I use is maps. And maps are based on a couple of characteristics. Any map needs an anchor, in the case of the maps of business that I do, that's the user, and often the business, and often regulators. You also need movement and position in a map. So position's relative to the anchor, so a geographical map, if you've got a compass then this piece is north, south, east or west of that. In the sort of maps that I do, it's the value chain which gives you position relative to the user or the business at the top. Movement, in a geographical map you have consistency of movement, so if I go, I don't know, north from England I end up in Scotland, so you have the same thing with a business map, but that evolution is described, sorry, that movement is described by evolution. So what you have is the genesis of novel and new activities custom-build examples, products and rental services, commodity and utility services, and that's driven by supply and demand competition. Now, that evolution axis, in order to create it, you have to abolish time. So one of the problems when you look at a map is there is no easy use of time in a map. You can have a general direction and then you have to use weak signals to get an idea of when something is likely to happen. So for example if I take nuts and bolts, they took 2,000 years to go from genesis to commodity, electricity was 1,400 years from genesis to commodity, utility, computing 80 years. So, there are weak signals that you can use to identify roughly when something is going to transition, particularly between stages like product to a commodity. Product-product substitution very unpredictable, genesis of novel acts, you can usually say when stuff might appear, but not what is going to appear because in that space it's actually what we call the uncharted, the unexplored space. So, one of the problems is time is an extremely difficult thing to predict without the use of weak signals. The second thing is the pace of change. Because what happens is components evolve, and when we see them shift from product to more commodity and utility, we often see a big change in the value chains that that impacts. And you can get multiple components evolving, and they overlap, and so we feel that the pace is very very fast, despite the fact that it actually takes about 30 to 50 years to go from genesis to the point of industrialization, becoming a commodity, and then about 10 to 15 years for that to actually happen. So if you look at something like machine learning, we can start with it back in the '70s, 3D printing 1968, the Battelle Institute, all of this stuff, virtual reality back in the 1960s as well. So the problem is, one, time's very difficult. The only way to effectively manage time is to use weak signals, it's probability. The second thing is the pace of change is confusing because what we're seeing is overlapping points of industrialization like for example cloud, and what's going here with Serverless. That doesn't actually imply that things are rapidly changing because you've actually got this overlapping pattern. Does that make sense? >> Yes, it does actually. >> Perfect. >> Because you think, we have in hindsight we always think that things happen a lot faster but-- >> Yeah. >> it's funny, infrastructure space when I talk to some of the people that I came up with, they were like oh yeah, come on, we did this in mainframe decades ago. and now we're trying again, we're trying again. Things like-- >> Containers, for example, you've got LXE before that, and we had Solaris Zones before that, so it's all sort of like, interconnected together. >> Okay, so tie this into Serverless for us. >> Okay. >> You were a rather big proponent of Platform as a Service, is this a continuation of us trying to get that abstraction of the application or is it something else? What is the map we are on, and, you know, help us connect things like PaaS and Serverless and that space. >> So back in 2005, the company I ran, we mapped out our value chain, and we realized that compute was shifting from product to utility. Now that had a number of impacts. A, that shift from product to utility tends to be exponential, people have inertia due to past practice, you see a co-evolution of practice, around the changing characteristic. It's normally to do with something called MTTR, mean time to recovery changes. And so you see rapid efficiency, rapid speed of development, being able to build new sources, new areas of value. So that happened with infrastructure, and we also knew it was going to happen with platform, which is why we built something called Zymkey, which was a code execution environment, totally stateless, event-driven, utility billing, and billing to the function, and that was basically a shift of the code execution platform from a product, lamp.net stack, to a much more utility form. Now we were way too early, way too early, because the educational barriers to get people into this idea of building with functions, functional program, much more declarative environment, was really different, I mean when Amazon launched EC2 in 2006, that was a big enough shock for everybody else, and now of course, now we're in 2014, Lambda represents that shift, and the timing's much much better. Now the impact of the shift is not only efficiency and speed of development of new things, and being able to explore new sources of value, but also a change of practice, and in the past, change of practice created DevOps, this is likely to create a new type of practice. For us, we've also got inertia to change because of pre-existing systems and governance and ways of working, sunk capital, physical capital, social capital. So it's all perfectly normal. So in terms of being able to predict and far-predict these types of future, well for me, actually, Lambda's my past, because that's where we were. It's just the timing was wrong, and so when it came out, it was like for me, it was like, this is really powerful stuff and the timing is much, and we're seeing it here, it's now really starting to grow. >> Alright, you've poked a little bit at some of the container discussions going on in the industry, you know, I look at the ecosystem here, and of course AWS is the big player, but there's lots of other Serverless out there. There's discussion of Multicloud. >> Yeah. >> How does things like Kubernetes, and there was this new term canative, or cane-native project, that was just announced, and we're all, don't expect that you've dug in too deeply, but, if you look at containers and Kubernetes, and Serverless, do these combine, intersect, fight? How do you see this playing out? >> So when I look at the map, you know, you've got the code execution layer, the framework which has now become more of a utility, and that's what we call platform. The problem is, is people will application to containers, and therefore describe their environments as application-container platforms, and the platform term became really messy, basically meant everything, okay. But if we break it down into code execution, this is what we call frameworks, this is becoming utility, this is where things like Lambda is, underneath that, are all these components like operating systems, and containers, and container management, Kubernetes type systems. So if you now look at the value chain, the focus is on building applications, and those applications need functions, and then lower down the stack are all these other components. And that will tend to become less visible over time. It's a bit like your toaster. I mean, your toaster contains nuts and bolts and all sorts of things, do you care? Have you ever noticed? Have you ever broken one open and had a look? >> Only if something's not working right. >> (laughs) Only if something, maybe, a lot of people these days wouldn't even go that far, they'd just go and buy themselves a new toaster. The point is, what happens is, as layers industrialize, the lower-order systems become much less visible. So, containers, I'm a big fan of containers. I know Solomon and the stuff in Docker, and I take the view that they are an important but invisible subsystem, and the same with container management and things like containers. The focus has got to be on the code execution. Now when you talk about canative, I've go to say I was really excited with Google Next last week, with their announcements like functions going GA, I thought that was really good. >> We've been hoping that it would have happened last year. >> Yeah exactly, I wanted this before, but I'm really pleased they've got functions coming out GA. There was some really interesting stuff around SDO, and there was the GRPC stuff which is, sort of, I think a hidden gem. In terms of the canative stuff, really interesting stuff there in terms of demos, not something I've played with, I'm sort of waiting for them to come out with canative as a service, rather than, you know, having to build your own. I think there was a lot of good and interesting stuff. The only criticism I would have was the emphasis wasn't so much on basically, serverless code execution building, it was too much focused on the lower end systems, but the announcements are good. Have I played with canative? No, I've just gone along and seen it. >> So Simon, the last question I have for you is, we spoke a year ago today, what are you excited about that's matured? What are you still looking for in this space, to really make the kind of vision you've been seeing for a while become reality, and allow serverless to dominate? >> So, when you get a shift from, say, product to utility, you get this co-evolution of practice, this practice is always novel and new. It starts to emerge, and gets better over time. The area that I think we're going to see that practice is the combining of finance and development, and so when you're running your application, and your application consists of many different functions, it's being able to look at the capital flow through your application, because that gives you hints on things like what should I refactor? Refactoring's never really had financial value. By exposing the cost per function and looking at capital flow, it's suddenly does. So, what I'm really interested in is the new management practices, the new tooling around observing capital flow, monitoring, managing capital flow, refactoring around that space and building new business models. And so there's a couple of companies here with a couple of interesting tools, it's not quite there yet, but it's emerging. >> Well, Simon Wardley, really appreciate you. >> Oh, it's a delight! >> Mapping out the space a little bit, to understand where things have been going. >> Absolute pleasure! >> And thank you so much, for watching as always, theCUBE. (upbeat music)

(electronic music) >> Narrator: From the SiliconANGLE Media office in Boston Massachusetts, it's the CUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Cloud is dead, it's all going to the edge. Or is it? Hi everybody, this is Dave Vellante and I'm here with Stu Miniman. Stu, where does this come from, this narrative that the cloud is over? >> Well Dave, you know, clouds had a good run, right? It's been over a decade. You know, Amazon's dominance in the marketplace but Peter Levine from Andreessen Horowitz did an article where he said, cloud is dead, the edge is killing the dead. The Edge is killing the cloud and really we're talking about IoT and IoT's huge opportunity. Wikibon, Dave we've been tracking for many years. We did you know the original forecast for the Industrial Internet and obviously there's going to be lots more devices at the edge so huge opportunity, huge growth, intelligence all over the place. But in our viewpoint Dave, it doesn't mean that cloud goes away. You know, we've been talking about distributed architectures now for a long time. The cloud is really at the core of this building services that surround the globe, live in just hundreds of places for all these companies so it's nuanced. And just as the cloud didn't overnight kill the data center and lots of discussion as to what lives in the data center, the edge does not kill the cloud and it's really, we're seeing some major transitions pull and push from some of these technologies. A lot of challenges and lots to dig into. >> So I've read Peter Levine's piece, I thought was very thought-provoking and quite well done. And of course, he's coming at that from the standpoint of a venture capitalist, all right. Do I want to start you know, do I want to pour money into the trend that is now the mainstream? Or do I want to get ahead of it? So I think that's what that was all about but here's my question Stu is, in your opinion will the activity that occurs at the edge, will it actually drive more demand from the cloud? So today we're seeing the infrastructure, the service business is growing at what? Thirty five percent? Forty percent? >> Sure, sure. Amazon's growing at the you know, 35 to 40 percent. Google, Microsoft are growing double that right now but overall you're right. >> Yeah, okay and so, and then of course the enterprise players are flat if they're lucky. So my question is will the edge actually be a tailwind for the cloud, in your opinion? >> Yeah, so first on your comment there from an investment standpoint, totally can understand why edge is greenfield opportunity. Lots of different places that I can place bets and probably can win as opposed to if I think that today I'm going to compete against the hyperscale cloud guys. You know, they're pouring 10 billion dollars a year into their infrastructure. They have huge massive employment so the bar to entry is a lot higher. I'm sorry, the second piece was? >> So will the edge drive more demand for the cloud? >> Yeah, absolutely. I think it does Dave because you know, let's take something like autonomous vehicles. Something that we talk about. I need intelligence of the edge. I can't wait for some instruction to go back to the cloud before my Tesla plows into an individual. I need to know that it's there but the models themselves, really I've got all the compute in the cloud. This is where I'm going to train all of my models but I need to be able to update and push those to the edge. If I think about a lot of the industrial applications. Flying a plane is, you know, things need to happen locally but all the anomalies and new things that we run into there's certain pieces that need to be updated to the cloud. So you know, it's kind of a multi-layer. If we look at how much data will there be at the edge, well there's probably going to be more data at the edge than there will be in the central cloud. But how much activity, how much compute do I need, how much things do I need to actually work on. The cloud is probably going to be that central computer still and it's not just a computer, as I said, a distributed architecture. That's where, you know. When we've looked at big data in the early days Dave, when we can put those data lines in the cloud. I've got thousands or millions of compute cycles that I can throw at this at such a lower price and use that there as opposed to at the edge especially. What kind of connectivity do I have? Am i isolated from those other pieces? If you go back to my premise of we're building distributed architectures, the edge is still very early. How do I make sure I secure that? Do I have the network? There's lots of things that I'm going to build in a tiny little component and have that be there. And there's lots of hardware innovation going on at that edge too. >> Okay, so let's talk about how this plays out a little bit and you're talking about a distributed model and it's really to me a distributed data model. The research analysts at Wikibon have envisioned this three-tier data model where you've got data at the edge, which you may or may not persist. You've got some kind of consolidation or aggregation layer where it's you know, it's kind of between the edge and the deep data center and then you've got the cloud. Now that cloud can be an on-prem cloud or it could be the public cloud. So that data model, how do you see that playing out with regard to the adoption of cloud, the morphing of cloud and the edge and the traditional data center? >> Yeah we've been talking about intelligent devices at the edge for a couple decades now. I mean, I remember I built a house in like 1999 and the smart home was already something that people were talking about then. Today, great, I've got you know. I've got my Nest if I have, I probably have smart assistants. There's a lot of things I love-- >> Alexa. >> Saw on Twitter today, somebody's talking like I'm waiting for my light bulbs to update their firmware from the latest push so, some of its coming but it's just this slow gradual adoption. So there's the consumer piece and then there's the business aspect. So, you know, we are still really really early in some of these exciting edge uses. Talk about the enterprise. They're all working on their strategy for how devices and how they're going to work through IoT but you know this is not something that's going to happen overnight. It's they're figuring out their partnerships, they're figuring out where they work, and that three-tiered model that you talked about. My cloud provider, absolutely hugely important for how I do that and I really see it Dave, not as an or but it's an and. So I need to understand where I collect my data, where it's at certain aspects are going to live, and the public cloud players are spending a lot of time working on on that intelligence, the intelligence layer. >> And Stu, I should mention, so far we're talking about really, the infrastructure as a service layer comprises database and middleware. We haven't really addressed the the SAS space and we're not going to go deep into that but just to say. I mean look, packaged software as we knew it is dead, right? SAS is where all the action is. It's the highest growth area, it's the highest value area, so we'll cover that in another segment. So we're really talking about that, the stack up to the middleware, the database, and obviously the infrastructures as a service. So when you think about the players here, let's start with AWS. You've been to I think, every AWS re:Invent maybe, with the exception of one. You've seen the evolution. I was just down in D.C. the other day and they have this chart on the wall, which is their releases, their functional releases by year. It's just, it's overwhelming what they've done. So they're obviously the leader. I saw a recent Gartner Magic Quadrant. It looked like, I tweeted it, it looked like Ronnie Turcotte looking back on Secretariat from the Belmont and whatever it was. 1978, I think it was. (laughs) 31 lengths. I mean, massive domination in the infrastructure as a service space. What do you see going on? >> Yeah so, Dave, absolutely. Today the cloud is, it's Amazon's market out there. Interestingly if you say, okay what's some of the biggest threats in the infrastructure as a service? Well, maybe China, Dave. You know, Alibaba was one that you look at there. But huge opportunity for what's happened at the edge. If you talk about intelligence, you talk about AI, talk about machine learning. Google is actually the company that most people will talk about it, can kind of have a leadership. Heck, I've even seen discussion that maybe we need antitrust to look at Google because they're going to lock things up. You know, they have Android, they have Google Home, they have all these various pieces. But we know Dave, they are far behind Amazon in the public cloud market and Amazon has done a lot, especially over the last two years. You're right, I've been to every Amazon re:Invent except for the first one and the last two years, really seen a maturation of that growth. Not just you know, devices and partnerships there but how do they bring their intelligence and push that out to the edge so things like their serverless technology, which is Lambda. They have Lambda Greengrass that can put to the edge. The serverless is pervading all of their solutions. They've got like the Aurora database-- >> And serverless is profound, not just that from the standpoint of application development but just an entire new business model is emerging on top of serverless and Lambda really started all that but but carry on. >> Yeah and when you look in and you say okay, what better use case than IoT for, well I need infrastructure but I only need it when I need it and I want to call it for when it's there. So that kind of model where I should be able to build by the microsecond and only use what I need. That's something that Amazon is at the forefront, clear leadership position there and they should be able to plug in and if they can extend that out to the edge, starting new partnerships. Like the VMware partnerships, interesting. Red Hat's another partnership they have with OpenShift to be able to get that out to more environments and Amazon has a tremendous ecosystem out there and absolutely is on their radar as to how their-- >> They're crushing it So we were at Google Next last year. Big push, verbally anyway, to the enterprise. They've been making some progress, they're hiring a lot of people out of formerly Cisco, EMC, folks that understand the enterprise but beyond sort of the AI and sort of data analytics, what kind of progress has Google made relative to the leader? >> So in general, enterprise infrastructure service, they haven't made as much progress as most of us watching would expect them to make. But Dave, you mentioned something, data. I mean, at the center of everything we're talking about is the data. So in some ways is Google you know, come on Google, they're smarter than the rest of us. They're skating to where the puck is Dave and infrastructure services, last decades argument if it's the data and the intelligence, Google's got just brilliant people. They're working at the some of these amazing environments. You look at things like Google's Spanner. This is distributed architecture. Say how do I plug in all of these devices and help the work in a distributed gradual work well. You know, heck, I'd be reading the whitepapers that Google's doing in understanding that they might be really well positioned in this 3D chess match that were playing. >> Your eyes might bleed. (laughs) I've read the Google Spanner, I was very excited about it. Understood, you know, a little bit of it. Okay, let's talk about Microsoft. They're really of the big cloud guys. They're really the one that has a partnership strategy to do both on-prem and public cloud. What are your thoughts on that now that sort of Azure stack is starting to roll out with some key partners? >> Yeah absolutely, it's the one that you know. Dave, if you use your analogy looking back, it's like well the next one, it's gaining a little bit, gaining a little bit but still far back. There is Microsoft. Where Microsoft has done best of course is their portfolio of business applications that they have. That they've really turned the green light on for enterprises to adopt SAS with Office 365. Azure stack, it's early days still but companies that use Microsoft, they trust Microsoft. Microsoft's done phenomenal working with developers over the last couple of years. Very prominent like the Kubernetes shows that I've been attending recently. They've absolutely got a play for serverless that we were talking about. I'm not as up to speed as to where Microsoft sits for kind of the IoT edge discussions. >> But you know they're playing there. >> Yeah, absolutely. I mean, Microsoft does identity better than anyone. Active Directory is still the standard in enterprises today. So you know, I worry that Microsoft could be caught in the middle. If Google's making the play for what's next, Microsoft is still chasing a little bit what Amazon's already winning. >> Okay and then we don't have enough time to really talk about China, you mentioned it before. Alibaba's you know, legit. Tencent, Baidu obviously with their captive market in China, they're going to do a lot of business and they're going to move a lot of compute and storage and networking but maybe address that in another segment. I want to talk about the traditional enterprise players. Dell EMC, IBM, HPE, Cisco, where do they stand? We talk a lot at Wikibond about true private cloud. The notion that you can't just stick all your data into the public cloud. Andy Jassy may disagree with that but there are practical realities and certainly when you talk to CIOs they they underscore that. But that notion of true private cloud hasn't allowed these companies to really grow. Now of course IBM and Oracle, I didn't mention Oracle, have a different strategy and Oracle's strategy is even more different. So let's sort of run through them. Let's take the arms dealers. Dell EMC, HPE, Cisco, maybe you put Lenovo in there. What's their cloud strategy? >> Well first of all Dave I think most of them, they went through a number of bumps along the road trying to figure out what their cloud strategy is. Most of them, especially let's take, if you take the compute or server side of the business, they are suppliers to all the service providers trying to get into the hyperscalers. Most of them have, they all have some partnership with Microsoft. There's a Assure stack and they're saying, okay hey, if I want an HPE server in my own data center and in Azure, Microsoft's going to be happy to provide that for you. But David, it's not really competing against infrastructure as a service and the bigger question is as that market has kind of flattened out and we kind of understand it, where is the opportunity for them in IoT. We saw, you know Dave. Last five years or so, can I have a consumer business and an enterprise business in the same? HPE tore those two apart. Michael Dell has kept them together. IBM spun off to Lenovo everything that was on the more consumer side of the business. Where will they play or will companies like Google, like Apple, the ones that you know, Dave. They are spending huge amounts of money in chips. Look at Google and what they're doing with TP use. Look at Apple, I believe it was, there was an Israeli company that they bought and they're making chips there. There's a different need at the edge and sure, company like Dell can create that but will they have the margin, will they have the software, will they have the ecosystem to be able to compete there? Cisco, I haven't seen on the compute side, them going down that path but I was at Cisco Live and a big talk there. I really like the opening keynote and we had a sit down on the CUBE with the executive, it said really if I look out to like 2030. If Cisco still successful and we're thinking about them, we don't think of them as a network company anymore. They are a software company and therefore, things like collaboration, things like how it's kind of a new version of networking that's not on ports and boxes. But really as I think about my data, think about my privacy and security, Cisco absolutely has a play there. They've done some very large acquisitions in that space and they've got some deep expertise there. >> But again, Dell, HPE, Cisco, predominantly arms dealers. Obviously don't have, HPE at one point had a public cloud, they've pulled back. HP's cloud play really is cloud technology partners that they acquire. That at least gives them a revenue stream into the cloud. Now maybe-- >> But it's a consultancy. >> It's a consultancy, maybe it's a one-way trip to the cloud but I will say this about CTP. What it does is it gives HPE a footprint in that business and to the extent that they're a trusted service provider for companies trying to move into the cloud. They can maybe be in the catbird seat for the on-prem business but again, largely an arms dealer. it's going to be a lower margin business certainly than IBM and Oracle, which have applications. They own their own public cloud with the Oracle public cloud and IBM cloud, formerly SoftLayer, which was a two billion dollar acquisition several years ago. So those companies from a participation standpoint, even a tiny market share is compared to Amazon, Google, and Microsoft. They're at least in that cloud game and they're somewhat insulated from that disruption because of their software business and their large install base. Okay, I want to sort of end with, sort of where we started. You know, the Peter Levine comment, cloud is dead, it's all going to the edge. I actually think the cloud era, it's kind of, it's here, we're kind of. It's kind of playing out as many of us had expected over the last five years. You know what blew me away? Is Alexa, who would have thought that Amazon would be a leader in this sort of natural language processing marketplace, right? You would have thought it would come from, certainly Google with all the the search capability. You would have thought Apple with Siri, you know compared to Alexa. So my point is Amazon is able to do that because it's got a data model. It's a data company, all these companies, including Apple, Google, Microsoft, Amazon, Facebook. The largest market cap companies in the world, they have data at the core. Data is foundational for those companies and that's why they are in such a good position to disrupt. So cloud, SAS, mobile, social, big data, to me still these are kind of the last 10 years. The next 10 years are going to be about AI, machine intelligence, deep learning, machine learning, cognitive. We're trying to even get the names right but it starts with the data. So let me put forth the premise and get your commentary. and tie it back in the cloud. So the innovation, in the next 10 years is going to come from data and to the extent that your data is not in silos, you're going to be in a much better position than if it is. Number two is your application of artificial intelligence, you know whatever term you want to use, machine intelligence, etc. Data plus AI, plus I'll bring it back to cloud, cloud economics. If you don't have those cloud economics then you're going to be at a disadvantage of innovation. So let's talk about what we mean by cloud economics. You're talking about the API economy, talking about global scale, always on. Very importantly something we've talked about for years, virtually zero marginal costs at volume, which you're never going to get on-prem because this creates a network effect. And the other thing it does from an innovation context, it attracts startups. Or startups saying, hey I want to build on-prem. No, they don't want to build in the cloud. So it's data plus artificial intelligence plus cloud economics that's going to drive innovation in the next ten years. What are your thoughts? >> Yeah Dave, absolutely. Something I've been saying for the last couple of years, we watched kind of the the customer flywheel that the public clouds have. Data is that next flywheel so companies that can capture that. You mentioned Amazon and Alexa, one of the reasons that Amazon can basically sell that as a loss is lots of those people, they're all Amazon Prime customers and they're ordering more things from Amazon and they're getting so much data that drive all of those other services. Where is Amazon going to threaten in the future? Everywhere. It is basically what they see. The thing we didn't discuss there Dave, you know I love your premise there, is it's technology plus people. What's going to happen with jobs? You and I did the sessions with Andy McAfee and Eril Brynjolfsson, it's racing with the machine. Where is, we know that people plus machines always beat so we spent the last five years talking about data scientist, the growth of developers and developers and the new king makers. So you know what are those new jobs, what are those new roles that are going to help build the solutions where people plus machine will win and what does that kind of next generation of workforce going to look like? >> Well I want to add to that Stu, I'm glad you brought that up. So a friend of mine David Michelle is just about to publish a new book called Seeing Digital. And in that book, I got an advance copy, in there he talks about companies that have data at their core and with human expertise around the data but if you think about the vast majority of companies, it's human expertise and the data is kind of bolted on. And the data lives in silos. Those companies are in a much more vulnerable position in terms of being disrupted, than the ones that have a data model that everybody has access to with human expertise around it. And so when you think about digital disruption, no industry is safe in my opinion, and every industry has kind of its unique attributes. You know, obviously publishing and books and music have disrupted very quickly. Insurance hasn't been disrupted, banking hasn't been disrupted, although blockchain it's probably going to affect that. So again, coming back to this tail-end premise is the next 10 years is going to be about that digital disruption. And it's real, it's not just a bunch of buzzwords, a cloud is obviously a key component, if not the key component of the underlying infrastructure with a lot of activity in terms of business models being built on top. All right Stu, thank you for your perspectives. Thanks for covering this. We will be looking for this video, the outputs, the clips from that. Thanks for watching everybody. This is Dave Vellante with Stu Miniman, we'll see you next time. (electronic music)

>> Narrator: From Hell's Kitchen in New York City, it's theCUBE. On the ground at Serverlessconf. Brought to you by SiliconANGLE Media. >> Hi I'm Stu Miniman, here with theCUBE at Serverlessconf in New York City, really excited to have on the program one of the keynote speakers and a first time guest on theCUBE, it's someone I've know through the interwebs and have read his stuff for many years, Simon Wardley who's a researcher with a leading edge firm, Simon, great to see you. Thanks so much for joining us. >> Thank you ever so much for inviting me. It's a delight to be here. >> Alright, so my understanding is thanks to this event, you've reached a lifelong career goal. You're now a Sith Lord? (laughing) >> Well, somebody basically took a quote of mine and put it on a Star Wars poster with The Empire at the bottom, so yes, it is absolutely there you are, I am a Sith Lord, so delightful. >> The quote was that Serverless will just fundamentally change the architecture of how we build things. Something along those lines, I believe. >> Absolutely, yes. >> Alright, so let's start there. There are so many, come on, we all got really excited when containers came out. We're going to talk to John Willis >> You did. (laughing) >> We're going to talk about unikernels. The industry as a whole, there's frothiness and buzz >> Okay. >> So Serverless, you know, how's it different? How's it the same? Why's it so important from your standpoint? >> So, really good questions. So, to explain that question, we have to start off with a subject that is dear to my heart which is mapping. So when we look at the value chain of any organization, the components in that value chain are evolving and they evolve from the genesis, the novel and new to custom built examples and eventually products and rental services and then commodity and utility services. And that process is driven by supply and demand competition. It happens not only to activities, but to practice and data, but we give them different terms. They have all of the same characteristics as when they evolve. Now, when you look at that evolving environment, what you discover is there are two basic forms of disruption. There is the highly unpredictable form, which either occurs due to the appearance of something novel and new, which we don't know what it's going to impact or to product substitution. So that's the Nokia versus Apple, sort of battle, you don't know which way it's going to go until after the battle. And there is a second form of disruption, which is much more anticipatable or predictable and that is the product to utility change. So we know that when things evolve from product to utility we're going to see a rapid period of change and then there's a punctuated equilibrium. Explotion of higher order systems. We're going to see co-evolution of practice, disruption of past companies stuck behind inertia barriers. Yes it's going to be a bad efficiency, no we're not going to save any money 'cause we're just going to do more stuff with it and we're going to have all these new things as well. And we can anticipate that in advance. So when you start looking at value chains of organization, it's always the shift from product to commodity and utility which makes the big transformation in industry. And so one of them was compute. Shifting from products, as in servers, to utility as in cloud. Unfortunately dreadful term, cloud, an awful word, you know it's not a wispy thing up in the sky, it is something very specific, the shift from compute to utility. >> Would you put virtualization along that continuum? >> Okay, so virtualization was one of the underlying components, which actually helped with that happen. >> Yes. >> And so you've also got the explosion of practices around that co-evolution of practice, things like DevOps. Well, the same transition is now happening in the platform space. So, we're moving away from a product stack, things like, LAMP and .NET, to much more utility-based code execution environments. And that's what we're getting with Lambda. And we're going to see an explosion of new things built on top, inertia barriers, companies stuck behind, they'll die off, It'll be a rapid change punctuated equilibrium. You'll get all sorts of new things built. So we're going through that big transformation. Now, these transformations have been going on for about 300 years, some of them impact micro scale economics, some macro, the biggest we call ages. And that all depends upon how widespread that component is in other value chains, so when we're talking about software, we're talking about a component which is in almost all other value chains, it's shifting from product to utility, massive change, highly predictable. This is what Serverless is about. So, will it change everything? Absolutely it will. >> Alright, so Simon, I'm wondering, if you've mapped out for Serverless, where's the land of economic expection, the land of happiness and the land of despair? (laughing) >> Well, okay, happiness, despair and expectation? >> Yes. >> Okay interesting one. So the land of despair will be getting stuck behind the inertia barriers, dismissing it, saying it's not going to impact, it's not going to impact, no, no, because there's a punctuated equilibrium, it'll surprise you because it's an exponential growth, so you'll think you've got loads and loads of time and 10 years from now, you're like, be panicking, oh my gosh, it's impacting, I can't get the skills for people to help me do the transformation. My entire industry and business model is starting to disappear, so that is the land of despair that's coming to people, that's easy to defend against because most people can't see the environment. They're going to just walk straight into that one. The land of happiness. Well, obviously other than being the utility providers who'll be extremely happy about the growth of their industry, another area of happiness will be some of the novel and new things built on top. So, we're bound to see the, sort, of, one person, two person company who builds a fuction which is sold through something like the marketplace and everybody uses and they sell it for a billion. So, we'll get the two person billion dollar company and I'm sure that will make them delightfully happy. So, that's despair, happiness, also inflated expectations. So one of the big lies will be, Serverless is going to save me money in terms of reducing my IT budget. I'm afraid not. This is Jevons Paradox, this is being going on since 1865. All that's going to happen is yes, it becomes more efficient but we'll do more stuff because we're in competition so we'll spend exactly the same as we've always done, but just doing vastly more. But none the less, loads of consultants will write reports about how it will save you money and lots of people will be disappointed. >> I want to poke at that for a second. (laughing) I don't disagree with Javons Paradox when it comes to power, but example, say you know, our host for this event, A Cloud Guru. >> Yeah. >> They're priced to deliver per user is way lower than if they'd have done this the traditional way and I've heard many examples here at the show already where they've said, oh if I had built it this way, you know, it's now an order of magnitude less dollars, so. >> Let's forget order of mag, let's go many orders of magnitude. So from now to say the 1980s, for a thousand dollars, I can get a million times more compute resource than I could back then. Has my IT budget reduced a million fold during that time? And the answer is >> Yeah. >> What, my IT budget has reduced a million fold? >> No, no, no my IT budget has not reduced a million fold. >> Not at all, because we've just ended up doing vastly more stuff. >> Yeah, yeah. >> So the point is, yes. >> Budgets are always flat, yes. >> So the point is yes, we will be able to do the same things but more efficiently, but your IT budget doesn't reduce because we end up doing more things. So we're in competition, say, you and me and say you evolve, you use these environments you don't reduce your IT spending, you do more things, I'm now having to spend more and more just to try and keep up with you. So eventually I'm forced to adopt to that new world. So what happens is the individual acts become more efficient, but because we do more, we don't save anything. >> You know, want to look at kind of, maps versus strategy. >> Okay. >> I guess one of the things, if I'm talking to the typical Enterprise CIO or Board and they say, oh, well, a year ago I heard about Serverless, or today I heard about Serverless, you know, the strategy is going to change greatly because this is changing so rapidly, how do you help companies understand when things are changing so fast, how do I set a strategy for today? How long do I keep it? How often do I revisit it? >> So, if you map an environment, like all maps, they're dynamic, so you're constantly adapting and changing them as the environment is changing. So, when you look at, you have the purpose of your company, you have the landscape you're operating in, there are a number of climatic patents, about 30 of them, which impact that environment, will change it, so you need to understand those. Then there's sort of university useful patents known as doctrine, then there's game play. Now, for most organizations, because they cannot see the environment, they cannot distinguish, or may just be completely oblivious to any of this, so when they were talking about change, if I look at how things evolve from genesis, custom built product commodity, most organizations will go genesis, that's an innovation, every custom built feature differentiation of a product's an innovation, every shift in product to utility is an innovation, so all they see is innovation, innovation, innovation. And therefore, it's very easy to get sucked in to one size fits all methods work. One size innovation programs, where in fact, the genesis you would be using something like a lightweight XP, the product development, much more lean enterprise, so SCRUM and MVP and the utility is much more outsourcing or Six Sigma. So you should be using multiple techniques and multiple methods and most organizations aren't in that position. And if they're not in that position, of being able to see the environment, it's difficult to see where to attack, it's difficult to understand why here over there, it's difficult to manipulate the market. So, what happens is most organizations work on gut feel, whatever's popular in HPR and just act. And you can call that strategy if you wish. >> Alright, so I wish we could talk for another couple of hours, but want to give you the final take away >> Yes. >> Serverless today, how should people be thinking about it and what should they be looking for over the next six to 12 months in this space? >> So, the key thing about Serverless is we're seeing a shift from platform from product to utility, so you should be developing skills in that space. And we're seeing co-evolution of practice. By that, we mean there is a new set of practices combining finance and development together. What those practices are, we don't know yet. You have to experiment and explore. That's why attending events and being involved in building stuff will help you discover those practices. So today if your company, well it depends on your position, so if you're a company which is behind the game, you, say, haven't gone into infractructure as a service, you're not doing DevOps, you're own people are resistant to this change cause the other vendors say you're going to lose their jobs and blah, then rather then embarking on a five to seven year program, 'cause that's how long it will take to do that, you should move up the stack and start with Serverless and learning those practices. 'Cause no one knows them well, so you can take your people who've got inertia and re-train them in that space overcoming that inertia and give yourself a path forward. So, depends on your position, but I think most companies should be experimenting in this space. >> Alright, well Simon Wardley, it's a pleasure to catch up with you today. >> Delight. >> Hope to have you back on theCUBE at another event soon. Thank you so much for watching theCUBE.

>> Hi I'm Peter Burris and welcome once again to Wikibons's weekly research meeting on theCUBE. (funky electronic music) This week we're going to discuss something that we actually believe is extremely important. And if you listen to the recent press announcements this week from Deli MC, the industry increasingly is starting to believe is important. And that is, how are we going to build systems that are dependent upon what happens at the edge? The past 10 years have been dominated about the cloud. How are we going to build things in the cloud? How are we going to get data to the cloud? How are we going to integrate things in the cloud? While all those questions remain very relevant, increasingly, the technology's becoming available, the systems and the design elements are becoming available, and the expertise is now more easily bought together so that we can start attacking some extremely complex problems at the edge. A great example of that is the popular notion of what's happening with automated driving. That is a clear example of huge design requirements at the edge. Now to understand these issues, we have to be able to generalize certain attributes of the differences in the resources, whether they be hardware or software, but increasingly, especially from a digital business transformation standpoint, the differences in the characteristics of the data. And that's what we're going to talk about this week. How do different types of data, data that's generated at the edge, data that's generated elsewhere, going to inform decisions about the classes of infrastructure that we're going to have to build and support as we move forward with this transformation that's taking place in the industry. So to kick it off, Neil Raden I want to turn to you. What are some of those key data differences and what taxonomically do we regard as what we call primary, secondary, and tertiary data? Neil. >> Well, primary data come in from sensors. It's a little bit different than anything we've ever seen in terms of doing analytics. Now I know that operational systems do pick up primary data, credit card transactions, something like that. But, scanner data, not scanner data, I mean sensor data is really designed for analysis. It's not designed for record keeping. And because it's designed for analysis, we have to have a different way of treating it than we do other things. If you think about a data lake, everything that falls into that data lake has come from somewhere else, it's been used for something else. But this data is fresh, and that requires that we really have to treat it carefully. Now, the retention and stewardship of that requires a lot of thought. And I don't think industry has really thought of that through a great deal. But look, sensor data is not new, it's been around for a long time. But what's different now is the volume and the lack of latency in it. But any organization that wants to get involved in it really needs to be thinking about what's the business purpose of it. If you're just going into, IOT as we call it generically, to save a few bucks you might as well not bother. It really is something that will change your organization. Now, what do we do with this data is a real problem because for the most part, these senses are going to be remote, and there's going to be a lot of, that means they're going to generate a lot of data. So what do we do with it? Do we reduce it at the sight? That's been one suggestion. There's an issue that any model for reduction could conceivably lose data that may be important somewhere down the line. Can the data be reconstituted through metadata or some sort of reverse algorithms? You know, perhaps. Those are the things we really need to think about. My humble opinion is the software and the devices need to be a single unit. And for the most part, they need to be designed by vendors, not by individual ITs. >> So David Floyer, let's pick up on that. Software and devices as single unit, designed more by vendors who have specific demand expertise, turn into solutions and present it to business. What do you think? >> Absolutely, I completely concur with that. The initial attempts to using the sensors and connecting to the sensors were very simple things like for example, the nest, the thermostats. And that's worked very well. But if you look at it over time, the processing for that has gone into the home, into your Apple TV device or your Alexa or whatever it is. So, that's coming down and now it's getting even closer to the edge. In the future, our proposition is that it will get even closer and then those will put together solutions, all types of solutions that are appropriate to the edge that will be taking not just one sensor but multiple sensors, collecting that data together, just like in the autonomous car for example where you take the lidars and the radars and the cameras etcetera. We'll be taking that data, we'll be analyzing it, and we'll be making decisions based on that data at the edge. And vendors are going to play a crucial role in providing these solutions to IT and to the OT and to many other parts. And a large value will be in their expertise that they will develop in this area. >> So as a rule of thumb, when I was growing up and learned to drive, I was told always keep five car lengths between you and whatever's in front of you at whatever speed you're traveling. What you just described David is that there will be sensors and there will be processing that takes place in that automated car that isn't using that type of rule of thumb but know something about tire temperature, and therefore the coefficient of friction on the tires, know something about the brakes, knows what the stopping power needs to be at the speed and therefore what buffer needs to be between it and whatever else is around it. >> Absolutely. >> This is no longer a rule of thumb, this is physics and deep understanding of what it's going to require to stop that car. >> And on top of that, what you'll also want to know, outside from your car is, what type of car is in front of you? Is that an autonomous car, or is that somebody being driven bye Peter? In which case, you have 10 lengths behind you. >> But that's not going to be primary data. Is that what we mean by secondary data? >> No, that's still primary because you're going to set up a connection between you and that other car. That car is going to tell you I'm primary to you, that's primary data. >> Here's what I mean, correct use primary data but, from a standpoint of that the car in that case is submitting a signal, right? So even though to your car it's primary data, but one of the things from a design standpoint that's interesting, is that car is now transmitting a digital signal about it's state that's relevant to you so that you can combine that >> Correct. inside effectively, a gateway inside your car. >> Yes. >> So there's external information that is in fact digital coming in, combining with the sensors about what's happening in your car. Have I got that right? >> Absolutely. That to me is a sort of sengrey one, then you've got the tertiary data which is the big picture about the traffic conditions >> Routes. and the weather and the routes and that sort of thing which is at that much higher cloud level, yes. So David Vellante, we always have to make sure as we have these conversations. We've talked a bit about this data, we've talked a little bit about the classes of work that's going to be performed at the different levels. How do we ensure that we sustain the business problem in this conversation? >> So, I mean I think Wikibon's done some really good work on describing what this sort of data model looks like from edge devices where you have primary data, the gateways where you're doing aggregated data in the cloud where maybe the serious modeling occurs. And my assertion would be is that the technology to support that elongating and increasingly distributed data model has been maturing for a decade and the real customer challenge is not just technical, it's really understanding a number of factors and I'll name some. Where in the distributed data value chain are you going to differentiate? And how does the data that you're capturing in that data pipeline contribute to monetization? What are the data sources, who has access to that data, how do you trust that data, and interpret it, and act on it with confidence? There are significant IP ownership in data protection issues. Who owns the data? Is it the device manufacturer, is it the factory, etcetera. What's the business model that's going to allow you to succeed? What skill sets are required to win? And really importantly, what's the shape of the ecosystem that needs to form to go to market and succeed? These are the things that I think customers are really struggling with that I talk to. >> Now, the one thing I'd add to that and I want to come back to it is the idea that, and who is ultimately bonding the solution because this is going to end up in a court of law. But let's come to this IP issue, George. Let's talk about how local data is going to be, is going to enter into the flow of analytics, and that question of who owns data, because that's important and then have the question about some of the ramifications and liabilities associated with this. >> Okay well, just on the IP protection and the idea that a vendor has to take sort of whole product responsibility for the solution. That vendor is probably going to be dealing with multiple competitors when they're sort of enabling say, self-driving car or other, you know edge, or smaller devices. The key thing is that, a vendor will say, you know, the customer keeps their data and the customer gets the insights from that data. But that data is informing in the middle a black box, an analytic black box. It's flowing through it, that's where the insights come out, on the other side. But the data changes that black box as it flows through it. So, that is something where, you know, when the vendor provides a whole solution to Mercedes, that solution will be better when they come around to BMW. And the customers should make sure that what BMW gets the benefit of, goes back to Mercedes. That's on the IP thing. I want to add one more thing on the tertiary side which is, when you're close to the edge, it's much more data intensive. When we've talked about the reduction in data and the real-time analytics, at the tertiary level it's going to be more where time is a bigger factor and you're essentially running a simulation, it's more compute intensive. And so you're doing optimizations of the model and those flow back as context to inform both the gateway and the edge. >> David Floyer I want to turn it to you. So we've talked a little bit about the characteristics of the data, great list of Dave Vellante about some of the business considerations, we will get very quickly in a second to some of the liability issues cause that's going to be important. But take us through how, which George just said about the tertiary elements. Now we've got all the data laid out, how is that going to map to the classes of devices? And we'll then talk a bit about some of the impacts on the industry. What's it going to look like? >> So if we take the primary edge first, and you take that as a unit, you'll have a number of senses within that. >> So just released, this is data about the real world that's coming into the system to be processed? >> Yes. So it'll have, for example, cameras. If we take a simple example of making sure that bad people don't get into your site. You'll have a camera there which will be facial recognition. They'll have a badge of some sort, so you'll read that badge, you may want to take their weight, you may want to have a infrared sensor on them so that you can tell their exact distance. So, a whole set of sensors that the vendor will put together for the job of insuring you don't get bad guys in there. And what you're insuring is that bad guys don't get in there, that's obviously one, very important, and also, that you don't go and- >> Stop good guys from going in. stop good guys from going in there. So those are the two characteristics >> The false-positive problem. the false-positives. Those are the two things you're trying to design that- >> At the primary edge. at the primary edge. And there's a mass amount of data going into that, which is only going to be reduced to very, very little data coming up to the next level which is this guy came here, this was his characteristics, he didn't look well today, maybe you should see a nurse, or whatever other information you can gather from that will go up to that secondary level, and then that'll also be a record of to HR maybe, about who has arrived there or what time they arrived, to the manufacturing systems about who is there and who has those skills to do a particular job. There are multiple uses of that data which can then be used for differentiation for whatever else from that secondary layer into local systems and then equally they can be pushed up to the higher level which is, how much power should be generating today, what are the higher levels. >> We now have 4,000 people in the building, air condition therefore is going to look like this, or, it could be combined with other types of data like over time we're going to need new capacity, or payroll, or whatever else it might be. >> And each level will have its own type of AI. So you've got AI at the edge, which is to produce a specific result, and then there's AI to optimize at the secondary level and then the AI optimize bigger things at the tertiary level. >> So we're going to talk more about some of the AI next week, but for right now we're talking about classes of devices that are high performance, high bandwidth, cheap, constrained, proximate to the event. >> Yep. >> Gateways that are capable of taking that information and start to synthesize it for the business, for other business types of things, and then tertiary systems, true private cloud for example, although we may have very sizable things at the gateway as well, >> There will be true private clouds. that are capable of integrating data in a more broad way. What's the impact in the industry? Are we going to see IT firms roll in and control this sweeping, (man chuckles) as Neil said, trillions of new devices. Is this all going to be intel? Is it all going to be, you know, looking like clients and PCs? >> My strong advice is, that the devices themselves will be done by extreme specialists in those areas that they will need a set of very deep technology understanding of the devices themselves, the senses themselves, the AI software relevant to that. Those are the people that are going to make money in that area. And you're much better off partnering with those people and letting them solve the problems, and you solve, as Dave said earlier, the ones that can differentiate you within your processes, within your business. So yes, leave that to other people is my strong advice. And from an IT's point of view, just don't do it yourself. >> Well the gateway's, sound like you're suggesting, the gateway is where that boundary's going to be. >> Yes. That's where the boundary is. >> And the IT technologies may increasingly go down to the edge, but it's not clear that the IT vendor expertise goes down to the edge >> Correct. at the same degree. >> Correct. >> So, Neil let's come back to you. When we think about this arrangement of data, you know, how the use cases are going to play out, and where the vendors are, we still have to address this fundamental challenge that Dave Vellante bought up. Who's going to end up being responsible for this? Now you've worked in insurance, what does that mean from an overall business standpoint? What kinds of failure weights are we going to accommodate? How is this going to play out? What do you think? >> Well, I'd like to point out that I worked in insurance 30 years ago. (men chuckling) >> Male Voice: I didn't want to date ya Neil. (men chuckling) >> Yeah the old reliable life insurance company. Anyway, one of the things David was just discussing sounded a lot to me like complex event processing. And I'm wondering where the logical location event needs to be, because it needs some prior data to do CEP, you have to have something to compare it against. But if you're pushing it all back to the tertiary level, there's going to be a lot of latency. And the whole idea was CEP was, you know, right now. So, that I'm a little curious about. But I'm sorry, what was your question? >> Well no, let's address that. So CEP David, I agree. But I don't want to turn this into a general discussion and CEP. It's got its own set of issues. >> It's clear there have got to be complex models created. And those are going to be created in a large environment, almost certainly in a tertiary type environment. And those are going to be created by the vendors of those particular problem solvers at the primary edge. To a large extent, they're going to provide solutions in that area. And they're going to have to update those. And so, they are going to have to have lots and lots of test data for themselves and maybe some companies will provide test data if it's convenient for those, for a fee or whatever it is, to those vendors. But the primary model itself is going to be in the tertiary level, and that's going to be pushed down to the primary level itself. >> I'm going to make an assertion here that the, the way I think about this Neil is that the data coming off at the primary level is going to be the sensor data, the sensor said it was good. Then that is recorded as an event, we let somebody in the building. And that's going to be a key feature of what happens at the secondary level. I think a lot of complex processing is likely to end up at that secondary level. >> Absolutely. >> Then the data gets pushed up to the tertiary level and it becomes part of an overall social understanding of the business, it's behavior data. So increasingly, what did we do as a consequence of letting this person in the building? Oh we tried to stop him. That's going to be more of the behavioral data that ends up at the tertiary level, will still do complex event processing there. It's going to be interesting to see whether or not we end up with CEP directly in the sensor tower. Might under certain circumstances, that's a cost question though. So let me now turn it in the last few minutes here Neil back to you. At the end of the day, we've seen for years the question of how much security is enough security? And businesses said, "Oh I want to be 100% secure." And sometimes see-so said "We got that. You gave me the money, we've now made you 100% secure." But we know it's not true. Same thing is going to exist here. How much fidelity is enough fidelity down at the edge? How do we ensure that business decisions can be translated into design decisions that lead to an appropriate and optimized overall approach to the way the system operates? From a business standpoint back, what types of conversations are going to take place in the boardroom that the rest of the organization's going to have to translate into design decisions? >> You know, boy, bad actors are going to be bad actors. I don't think you can do anything to eliminate it. The best you can do is use the best processes and the best techniques to keep it from happening and hope for the best. I'm sorry, that's all I can really say about it. >> There's quite a lot of work going on at the moment from Arm, in particular. They've got a security device image ability. So, there's a lot of work going on in that very space. It's obviously interesting from an IT perspective is how do you link the different security systems, both from an Arm point of view and then from a X86 as you go further up the chain. How are they going to be controlled and how's that going to be managed? That's going to be a big IT issue. >> Yeah, I think the transmission is the weak point. >> Male Voice: What do you mean by that Neil? >> Well the data has to flow across networks, that would be the easiest place for someone to intercept it and, you know, and do something nefarious. >> Right yeah, so that's purely in a security thing. I was trying to use that as an analogy. So, at the end of the day, the business is going to have to decide how much data do we have to capture off the edge to ensure that we have the kinds of models we want, so that we can realize the specificity of actions and behaviors that we want in our business? That's partly a technology question, partly a cost question. Different sensors are able to operate at different speeds for example. But ultimately, we have to be able to bring those, that list of decisions or business issues that Dave Vellante raised, down to some of the design questions. But it's not going to be throw a $300 micro processor everything. There's going to be very, very concrete decisions that have to take place. So, George do you agree with that? >> Yes, two issues though. One, there's the existing devices that can't get re-instrumented, that they already have their software, hardware stack. >> There's a legacy in place? >> Yes. But there's another thing which is, some of the most advanced research that's been going on that produced much of today's distributed computing and big data infrastructure, like the Berkeley Analytics lab, and say their contributions spark in related technologies. They're saying we have to throw everything out and start over for secure real-time systems. That you have to build from hardware all the way up. In other words, you're starting from the sand to re-think something that's secure and real-time that you can't layer it on. >> So very quickly David, that's a great point George. Building on what George has said very quickly, the primary responsibility for bonding the behavior or the attributes of these devices are going to be with the vendor. >> Of creating the solution? >> Correct. >> That's going to be the primary responsibility. But obviously from an IT point of view, you need to make sure that that device is doing the job that's important for your business, not too much, not too little, is doing that job, and that you are able to collect the necessary data from it that is going to be of value to you. So that's a question of qualification of the devices themselves. >> Alright so, David Vellante, Neil Raden, David Floyer, George Gilbert, action item round. I want one action item from you guys from this conversation. Keep it quick, keep it short, keep it to the point. David Floyer, what's your action item? >> So my action item is don't go into areas that you don't need to. You do not need to become experts, IT in general does not need to become experts at the edge itself. Rely on partners, rely on vendors to do that unless of course you're one of those vendors. In which case, you'll need very, very deep knowledge. >> Or you choose that that's where you're value stream your differentiations is going to be which means you just became one of those values. >> Yes, exactly. >> George Gilbert. >> I would build on that and I would say that if you look at the skills required to build these full stack solutions, there's data science, there's application development, there's the analytics. Very few of those solutions are going to have skills all in one company. So the go-to market model for building these is going to be something that, at least at this point in time, we're going to have to look to like combinations like IBM working with sort of supply chain masters. >> Good. Neil Raden, action item. >> The question is not necessarily one of technology because that's going to evolve. But I think as an organization, you need to look at it from this end which is, would employing this create a new business opportunity for us? Something we're not already doing. Or number two, change our operations in some significant way. Or number three, you know, the old red queen thing. We have to do it to keep up with the competition. >> Male Voice: David Vellante, action item. >> Okay well look, at the risk of sounding trite, you got to start the planning process from the customer on in, and so often people don't. You got to understand where you're going to add value for customers and constructing and external and internal ecosystem that can really juice that value creation. >> Alright, fantastic guys. So let me quickly summarize. This week on the Wikibon Friday research meeting in the cube, we discussed a new way of thinking about data characteristics that will inform system design and a business value that's created. We observe that data is not all the same when we think about these very complex, highly distributed, and decentralized systems that we're going to build. That there's a difference between primary data, secondary data, and tertiary data. Primary data is data that is generated from real world events or measurements and then turned into signals that can be acted upon very proximate to that real world set of conditions. A lot of sensors will be there, a lot of processing will be moved down there, and a lot of actuators and actions will take place without referencing other locations within the cloud. However, we will see circumstances where the events that are taken, or the decisions that are taken on those vents, will be captured in some sort of secondary tier that will then record something about the characteristics of the actions and events that were taken, and then summarized and then pushed up to a tertiary tier where that data can then be further integrated in other attributes and elements of the business. The technology to do this is broadly available but not universally successfully applied. We expect to see a lot of new combinations of edge-related device to work with primary data. That is going to be a combination of currently successful firms in the OT or operational technology world, most likely in partnership with a lot of other vendors that have demonstrated significant expertise and understanding the problems, especially the business problems, associated with the fidelity of what happens at the edge. The IT industry is going to approach very aggressively and very close to this at that secondary level, through gateways and other types of technologies. And even though we'll see IT technology continue to move down to the primary level, it's not clear exactly how vendors will be able to follow that. More likely, we'll see the adoption of IT approaches to doing things at the primary level by vendors that have the main expertise in how that level works. We will however see significantly interesting true private cloud and public cloud data end up from the tertiary level end up with a whole new sets of systems that are going to be very important from an administration and management standpoint because they have to work within the context of the fidelity of this overall system together. The final point we want to make is that these are not technology problems by themselves. While significant technology problems are on the horizon about how we think about handling this distribution of data, managing it appropriately, our ability, ultimately, to present the appropriate authority at different levels within that distributive fabric to ensure the proper working condition in a way that nonetheless we can recreate if we need to. But these are, at bottom, fundamentally business problems. They're business problems related to who owns the intellectual property that's being created, they're business problem related to what level in that stack do I want to show my differentiation to my customers and they're business problems from a liability and legal standpoint as well. The action item is, all firms will in one form or another be impacted by the emergence of the edge as a dominate design as consideration for their infrastructure but also for their business. Three ways, or a taxonomy that looks at three classes of data, primary, secondary, and tertiary, will help businesses sort out who's responsible, what partnerships I need to put in place, what technologies and I going to employ, and very importantly, what overall business exposure I'm going to accommodate as I think ultimately about the nature of the processing and business promises that I'm making to my marketplace. Once again, this has been the Wikibon Friday research meeting here on theCUBE. I want to thank all the analysts who were here today, but especially thank you for paying attention and working with us. And by all means, let's hear those comments back about how we're doing and what you think about this important question of different classes of data driven by different needs of the edge. (funky electronic music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> They are disruptive technologies. And if you think about the disruption that's happening in business, with IoT, with OT, and with big data, you can't get anything more disruptive to the whole of the business chain as this particular area. It's an area that I focused on myself, asking the question, should everything go to the cloud? Is that the new future? Is 90% of the computing going to go to the cloud with just little mobile devices right on the edge? Felt wrong when I did the math on it, I did some examples of real-world environments, wind farms, et cetera, it clearly was not the right answer, things need to be near the edge. And I think one of the areas to me that solidified it was when you looked at an area like video. Huge amounts of data, real important decisions being made on the content of that video, for example, recognizing a face, a white hat or a black hat. If you look at the technology, sending that data somewhere to do that recognition just does not make sense. Where is it going? It's going actually into the camera itself, right next to the data, because that's where you have the raw data, that's where you have the maximum granularity of data, that's where you need to do the processing of which faces are which, right close to the edge itself, and then you can send the other data back up to the cloud, for example, to improve those algorithms within that camera, to do all that sort of work on the batch basis over time, that's what I was looking at, and looking at the cost justification for doing that sort of work. So today, we've got a set people here on the panel, and we want to talk about coming down one level to where IoT and IT are going to have to connect together. So on the panel I've got, I'm going to get these names really wrong, Sanjeev Kumar? >> Yes, that's right. >> From FogHorn, could you introduce yourself and what you're doing where the data is meeting the people and the machines? >> Sure, sure, so my name is Sanjeev Kumar, I actually run engineering for a company called FogHorn Systems, we are actually bringing analytics and machine learning to the edge, and, so our goal and motto is to take computing to where the data is, than the other way around. So it's a two-year-old company that started, was incubated in the hive, and we are in the process of getting our second release of the product out shortly. >> Excellent, so let me start at the other end, Rohan, can you talk about your company and what contribution you're focusing on? >> Sure, I'm head product marketing for Maana, Maana is a startup, about three years old, what we're doing is we're offering an enterprise platform for large enterprises, we're helping the likes of Shell and Maersk and Chevron digitally transform, and that simply means putting the focus on subject matter experts, putting the focus on the people, and data's definitely an important part of it, but allowing them to bring their expertise into the decision flows, so that ultimately the key decisions that are driving the revenue for these behemoths, are made at a higher quality and faster. >> Excellent. Well, two software companies, we have a practitioner here who is actually doing fog computing, doing it for real, has been doing it for some time, so could you like, Janet George from Western Digital, can you introduce yourself, and say something from the trenches, of what's really going on? >> Okay, very good, thank you. I actually build infrastructure for the edge to deal with fog computing, and so for Western Digital, we're very lucky, because we are the largest storage manufacture, and we have what we call Internet of Things, and Internet of Test Equipment, and I process petabytes of data that comes out of the Internet of Things, which is basically our factories, and then I take these petabytes of data, I process them both on the cloud and then on the edge, but primarily, to be able to consume that data. And the way we consume that data is by building very high-profile models through artificial intelligence and machine learning, and I'll talk a lot more about that, but at the end of the day, it's all about consuming the data that you collect from anywhere, Internet of Things, computer equipment, data that's being produced through products, you have to figure out a way to compute that, and the cloud has many advantages and many trade-offs, and so we're going to talk about the trade-offs, that's where the gap for computing comes into play. >> Excellent, thanks very much. And last but not least, we have Val, and I can never pronounce your surname. >> Bercovici. >> Thank you. (chuckling) You are in the midst of a transition yourself, so talk about where you have been and where you're going. >> For the better part of this century, I've been with NetApp, working at various functions, obviously enterprise storage, and around 2008, my developer instinct kind of fired up, and this thing called cloud became very interesting to me. So I became a self-anointed cloud czar at NetApp, and I ended up initiating a lot of our projects which we know today as the NetApp Data Fabric, that culminated about 18 months ago, in acquisition of SolidFire, and I'm now the acting CTO of SolidFire, but I plan to retire from the storage industry at the end of our fiscal year, at the end of April, and I'm spending a lot of time with particularly the Cloud Native Compute Foundation, that is, the opensource home of Google's Kubernetes Technology and about seven other related projects, we keep adding some almost every month, and I'm starting to lose track, and spending a lot of time on the data gravity challenge. It's a challenge in the cloud, it's a particularly new and interesting challenge at the edge, and I look forward to talking about that. >> Okay, and data gravity is absolutely key, isn't it, it's extremely expensive and extremely heavy to move around. >> And the best analogy is workloads are like electricity, they move fairly easily and lightly, data's like water, it's really hard to move, particularly large bodies around. >> Great. I want to start with one question though, just in the problem, the core problem, particularly in established industries, of how do we get change to work? In an IT shop, we have enough problems dealing with operations and development. In the industrial world, we have the IT and the OT, who look at each other with less than pleasure, and mainly disdain. How do we solve the people problem in trying to put together solutions? You must be right in the middle of it, would you like to start with that question? >> Absolutely, so we are 26 years old, probably more than that, but we have very old and new mix of manufacturing equipment, it's a storage industry, and in our storage industry, we are used to doing things a certain way. We have existing data, we have historical data, we have trend data, you can't get rid of what you already have. The goal is to make connectors such that you can move from where you're at to where you're going, and so you have to be able to take care of the shift that is happening in the market, so at the end of the day, if you look at five years from now, it's all going to be machine learning and AI, right? Agent technology's already here, it's proven, we can see, Siri is out here, we can see Alexa, we can see these agent technologies out there, so machine learning is a getting a lot of momentum, deep learning and neural networks, things like that. So we got to be able to look at that data and tap into our data, near realistically, very different, and the way to do that is really making these connections happen, tapping into old versus new. Like for example, if you look at storage, you have file storage, you have block storage, and then you have object storage, right? We've not really tapped into the field of object storage, and the reason is because if you are going to process one trillion objects like Amazon is doing right now with S3, you can't do it with the file system level storage or with the blog system level storage, you have to go to objects. Think Internet of Things. How many trillions of objects are going to come out of these Internet of Things? So one, you have to be positioned from an infrastructure standpoint. Two, you have to be positioned from a use case prototyping perspective, and three, you got to be able to scale that very rapidly, very quickly, and that's how change happens, change does not happen because you ask somebody to change their behavior, change happens when you show value, and people are so eager to get that value out of what you've shown them in real life, that they are so quick to adapt. >> That's an excellent-- >> If I could comment on that as well, which is, we just got through training a bunch of OT guys on our software, and two analogies that actually work very well, one is sort of, the operational people are very familiar with circuit diagrams, and so, and sort of, flow of things through essentially black boxes, you can think of these as something that has a bunch of inputs and has a bunch of outputs. So that's one thing that worked very well. The second thing that works very well is the PLC model, and there are direct analogies between PLC's and analytics, which people on the floor can actually relate to. So if you have software that's basically based on data streams and time, as a first-class citizen, the PLC model again works very well in terms of explaining the new software to the OT people. >> Excellent, okay, would you want to come in on that as well? >> Sure, I think a couple of points to add to what Janet said, I couldn't agree more in terms of the result, I think Maana did a few projects, a few pilots to convince customers of their value, and we typically focus very heavily on operationalizing the output, so we are very focused on making sure that there is some measurable value that comes out of it, and it's not until the end user started seeing that value that they were willing and open to adopt the newer methodologies. A second point to that is, a lot of the more recent techniques available to solve certain challenges, there are deep learning neural nets there's all sorts of sophisticated AI and machine learning algorithms that are out there, a lot of these are very sophisticated in their ability to deliver results, but not necessarily in the transparency of how you got that, and I think that's another thing that Maana's learning, is yes, we have this arsenal of fantastic algorithms to throw at problems, but we try to start with the simplest approach first, we don't unnecessarily try to brute force, because I think an enterprise, they are more than willing to have that transparency in how they're solving something, so if they're able to see how they were able to get to us, how the software was able to get to a certain conclusion, then they are a lot happier with that approach. >> Could you maybe just give one example, a real-world example, make it a little bit real? >> Right, absolutely, so we did a project for a very large organization for collections, they have a lot of outstanding capital locked up and customers not paying, it's a standard problem, you're going to find it in pretty much any industry, and so for that outstanding invoice, what we did was we went ahead and we worked with the subject matter experts, we looked at all the historical accounts receivable data, we took data from a lot of other sources, and we were able to come up with models to predict when certain customers are likely to pay, and when they should be contacted. Ultimately, what we wanted to give the collection agent were a list of customers to call. It was fairly straightforward, of course, the solution was not very, very easy, but at least on a holistic level, it made a lot of sense to us. When we went to the collection agents, many of them actually refused to use that approach, and this is part of change management in some sense, they were so used to doing things their way, they were so used to trying to target the customers with the largest outstanding invoice, or the ones that hadn't paid for the longest amount of time, that it actually took us a while, because initially, what the feedback we got was that your approach is not working, we're not seeing the results. And when we dug into it, it was because it wasn't being used, so that would be one example. >> So again, proof points that you will actually get results from this. >> Absolutely, and the transparency, I think we actually sent some of our engineers to work with the collections agents to help them understand what approach is it that we're taking, and we showed them that this is not magic, we're actually, instead of looking at the final dollar value, we're looking, we're calculating time value lost, so we are coming up with a metric that allows us to incorporate not just the outstanding amount, or the time that they haven't paid for, but a lot of other factors as well. >> Excellent, Val. >> When you asked that question, I immediately went to more of a nontechnical business side of my brain to answer it, so my experience over the years has been particularly during major industry transitions, I'm old enough to remember the mainframe to client server transition, and now client server to virtualization and cloud, and really, sales reps have that well-earned reputation of being coin-operated, though it's remarkable how much you can adjust compensation plans for pretty much anyone, in a capitalist environment, and the IT/OT divide, if you will, is pretty easy to solve from a business perspective when you take someone with an IT supporting the business mentality, and you compensate them on new revenue streams, new business, all of a sudden, the world perspective changes sometimes overnight, or certainly when that contract is signed. That's probably the number one thing you can do from a people perspective, is incent them and motivate them to focus on these new things, the technology is, particularly nowadays is evolving to support them for these new initiatives, but nothing motivates like the right compensation plan. >> Excellent, a great series of different viewpoints. So the second question I have again coming down a bit to this level, is how do we architect a solution? We heard you got to architect it, and you've got less, like this, it seems to me that that's pretty difficult to do ahead of where you're going, that in general, you take smaller steps, one step at a time, you solve one problem, you go on to the next. Am I right in that? If I am, how would you suggest the people go about this decision-making of putting architectures together, and if you think I'm wrong and you have a great new way of doing it, I'd love to hear about it. >> I can take a shorter route. So we have a number of customers that are trying to adopt, are going through a phased way of adopting our technology and products, and so it begins with first gathering of the data, and replaying it back, to build the first level of confidence, in the sense that the product is actually doing what you're expecting it to do. So that's more from monitoring administration standpoint. The second stage is you should begin to capture analytical logic into the project, where it can start doing prediction for you, so you go into, so from operational, you go into a predictive maintenance, predictive maintenance, predictive models standpoint. The third part is prescriptive, where you actually help create a machine learning model, now, it's still in flux in terms of where the model gets created, whether it's on the cloud, in a central fashion, or some sort of a, the right place, the right context in a multi-level hierarchical fog layer, and then, you sort of operationalize that as close to the data again as possible, so you go through this operational to predictive to prescriptive adoption of the technology, and that's how people actually build confidence in terms of adopting something new into, let's say, a manufacturing environment, or things that are pretty expensive, so I give you another example where you have the case of capacitors being built on a assembly line, manufacturing, and so how do you, can you look at data across different stations and manufacturing on a assembly line? And can you predict on the second station that it's going to fail on the eighth one? By that, what you're doing is you are actually reducing the scrap that's coming off of the assembly line. So, that's the kind of usage that you're going to in the second and third stage. >> Host: Excellent. Janet, do you want to go on? >> Yeah, I agree and I have a slightly different point of view also. I think architecture's very difficult, it's like Thomas Edison, he spent a lot of time creating negative knowledge to get to that positive knowledge, and so that's kind of the way it is in the trenches, we spend a lot of time trying to think through, the keyword that comes to mind is abstraction layers, because where we came from, everything was tightly coupled, and tightly coupled, computer and storage are tightly coupled, structured and unstructured data are tightly coupled, they're tightly coupled with the database, schema is tightly coupled, so now we are going into this world of everything being decoupled. In that, multiple things, multiple operating systems should be able to use your storage. Multiple models should be able to use your data. You cannot structure your data in any kind of way that is customized to one particular model. Many models have to run on that data on the fly, retrain itself, and then run again, so when you think about that, you think about what suits best to stay in the cloud, maybe large amounts of training data, schema that's already processed can stay on the cloud. Schema that is very dynamic, schema that is on the fly, that you need to read, and data that's coming at you from the Internet of Things that's changing, I call it heteroscedastic data, which is very statistical in nature, and highly variable in nature, you don't have time to sit there and create rows and columns and structure this data and put it into some sort of a structured set, you need to have a data lake, you need to have a stack on top of that data lake that can then adapt, create metadata, process that data and make it available for your models, so, and then over time, like I totally believe that now we're running into near realtime compute bottleneck, processing all this pattern processing for the different models and training sets, so we need a stack that we can quickly replace with GPUs, which is where the future is going, with pattern processing and machine learning, so your architecture has to be extremely flexible, high layers of abstraction, ability to train and grow and iterate. >> Excellent. Do you want to go next? >> So I'll be a broken record, back to data gravity, I think in an edge context, you really got to look at the cost of processing data is orders of magnitude less than moving it or even storing it, and so I think that the real urgency, I don't know, there's 90% that think of data at the edge is kind of wasted, you can filter through it and find that signal through the noise, so processing data to make sure that you're dealing with really good data at the edge first, figuring out what's worth retaining for future steps, I love the manufacturing example, I have lots of customer examples ourselves where, for quality control in a high-moving assembly line, you want to take thousands of not millions of images and compare frame and frame exactly according to the schematics where the device is compared to where it should be, or where the components, and the device compared to where they should be, processing all of that data locally and making sure you extract the maximum value before you move data to a central data lake to correlate it against other anomalies or other similarities, that's really key, so really focus on that cost of moving and storing data, yeah. >> Yes, do you want the last word? >> Sure, Maana takes an interesting approach, I'm going to up-level a little bit. Whenever we are faced with a customer or a particular problem for a customer, we try to go over the question-answer approach, so we start with taking a very specific business question, we don't look at what data sources are available, we don't ask them whether they have a data lake, or we literally get their business leaders, their subject matter experts, we literally lock them up in a room and we say, "You have to define "a very specific problem statement "from which we start working backwards," each problem statement can be then broken down into questions, and what we believe is any question can be answered by a series of models, you talked about models, we go beyond just data models, we believe anything in the real world, in the case of, let's say, manufacturing, since we're talking about it, any smallest component of a machine should be represented in the form of a concept, relationships between people operating that machinery should be represented in the form of models, and even physics equations that are going into predicting behavior should be able to represent in the form of a model, so ultimately, what that allows us is that granularity, that abstraction that you were talking about, that it shouldn't matter what the data source is, any model should be able to plug into any data source, or any more sophisticated bigger model, I'll give you an example of that, we started solving a problem of predictive maintenance for a very large customer, and while we were solving that predictive maintenance problem, we came up with a number of models to go ahead and solve that problem. We soon realized that within that enterprise, there are several related problems, for example, replacement of part inventory management, so now that you figured out which machine is going to fail at roughly what instance of time from now, we can also figure out what parts are likely to fail, so now you don't have to go ahead and order a ton of replacement parts, because you know what parts are going to likely fail, and then you can take that a step further by figuring out which equipment engineer has the skillset to go ahead and solve that particular issue. Now, all of that, in today's world, is somewhat happening in some companies, but it is actually a series of point solutions that are not talking to each other, that's where our pattern technology graph is coming into play where each and every model is actually a note on the graph including computational models, so once you build 10 models to solve that first problem, you can reuse some of them to solve the second and third, so it's a time-to-value advantage. >> Well, you've been a fantastic panel, I think these guys would like to get to a drink at the bar, and there's an opportunity to talk to you people, I think this conversation could go on for a long, long time, there's so much to learn and so much to share in this particular information. So with that, over to you! >> I'll just wrap it up real quick, thanks everyone, give the panel a hand, great job. Thanks for coming out, we have drinks for the next hour or two here, so feel free to network and mingle, great questions to ask them privately one-on-one, or just have a great conversation, and thanks for coming, we really appreciate it, for our Big Data SV Event livestreamed out, it'll be on demand on YouTube.com/siliconangle, all the video, if you want to go back, look at the presentations, go to YouTube.com/siliconangle, and of course, siliconangle.com, and Wikibond.com for the research and content coverage, so thanks for coming, one more time, big round of applause for the panel, enjoy your evening, thanks so much.