>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone, to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We have two guests for this segment. We have Charlie Emer. He is the senior director data management and governance strategy at Honeywell. Thanks for joining us. >> Thank you. >> And Randy Mickey, senior vice president global professional services at Informatica. Thanks for coming on theCUBE. >> Thank you. >> Charlie, I want to start with you. Honeywell is a household name, but tell us a little bit about the business now and about your role at Honeywell. >> Think about it this way. When I joined Honeywell, even before I knew Honeywell, all I thought was thermostats. That's what people would think about Honeywell. >> That's what I thought. >> But Honeywell's much bigger than that. Look, if you go back to the Industrial Revolution, back in, I think, '20s, we talked about new things. Honeywell was involved from the beginning making things. But we think this year and moving forward in this age, Honeywell is looking at it as the new Industrial Revolution. What is that? Because Honeywell makes things. We make aircraft engines, we make aircraft parts. We make everything, household goods, sensors, all types of sensors. We make things. So when we say the new Industrial Revolution is about the Internet of Things, who best to participate because we make those things. So what we are doing now is what we call IIOT, Industrial Internet of Things. Now, that is what Honeywell is about, and that's the direction we are heading, connecting those things that we make and making them more advancing, sort of making life easier for people, including people's quality of life by making those things that we make more usable for them and durable. >> Now, you're a broad platform customer of Informatica. I'd love to hear a little bit from both of you about the relationship and how it's evolved over the years. >> Look, we look at Informatica as supporting our fundamentals, our data fundamentals. For us to be successful in what we do, we need to have good quality data, well governed, well managed, and secure. Not only that, and also accessible. And we using Informatica almost end to end. We are using Informatica for our data movement ETL platform. We're using Informatica for our data quality. We're using Informatica for our master data management. And we have Informatica beginning now to explore and to use Informatica big data management capabilities. And more to that, we also utilize Informatica professional services to help us realize those values from the platforms that we are deploying. IIoT, Industrial IoT has really been a hot trend. Industrial implies factories building big things, planes, wind farms, we've heard that before. But what's interesting is these are pre-existing physical things, these plants and all this manufacturing. When you add digital connectivity to it and power, it's going to change what they were used to be doing to new things. So how do you see Industrial IoT changing or creating a builder culture of new things? Because this connect first, got to have power and connectivity. 5G's coming around, Wi-Fi 6 is around the corner. This is going to light up all these devices that might have had battery power or older databases. What's the modernization of these industrial environments going to look like in your view? First of all, let me give you an example of the value that is coming with this connectivity. Think of it, if you are an aircraft engineer. Back in the day, a plane landed in Las Vegas. You went and inspected it, physically, and checked in your manual when to replace a part. But now Honeywell is telling you, we're connecting directly to the mechanic who is going to inspect the plane, and there will be sort of in their palms they can see and say wait a minute. This part, one more flight and I should replace this part. Now, we are advising you now, doing some predictive analytics, and telling you when this part could even fail. We're telling you when to replace it. So we're saying okay, the plane is going to fly from here to California. Prepare the mechanics in California when it lands with the part so they can replace it. That's already safety 101. So guaranteeing safety, sort of improving the equity or the viability of the products that we produce. When we're moving away from continue to build things because people still need those things built, safety products, but we're just making them more. We've heard supply chain's a real low-hanging fruit on this, managing the efficiency so there's no waste. Having someone ready at the plane is efficient. That's kind of low-hanging fruit. Any ideas on some of the creativity of new applications that's going to come from the data? Because now you start getting historical data from the connections, that's where I think the thing can get interesting here. Maybe new jobs, new types of planes, new passenger types. >> We are not only using the data to improve on the products and help us improve customer needs, design new products, create new products, but we also monitorizing that data, allowing our partners to also get some insights from that data to develop their own products. So creating sort of an environment where there is a partnership between those who use our products. And guess what, most of the people who use our products, our products actually input into their products. So we are a lot more business-to-business company than a B2C. So I see a lot of value in us being able to share that intelligence, that insight, in our data at a level of scientific discovery for our partners. >> Randy, I want to bring you into the conversation a little bit here (laughs). >> Thanks. >> So you lead Informatica's professional services. I'm interested to hear your work with Honeywell, and then how it translates to the other companies that you engage with. Honeywell is such a unique company, 130 years of innovation, inventor of so many important things that we use in our everyday lives. That's not your average company, but talk a little bit about their journey and how it translates to other clients. >> Sure, well, you could tell, listening to Charlie, how strategic data is, as well as our relationship. And it's not just about evolution from their perspective, but also you mentioned the historicals and taking advantage of where you've been and where you need to go. So Charlie's made it very clear that we need to be more than just a partner with products. We need to be a partner with outcomes for their business. So hence, a professional services relationship with Honeywell and Charlie and the organization started off more straightforward. You mentioned ETL, and we started off 2000, I believe, so 19 years ago. So it's been a journey already, and a lot more to go. But over the years you can kind of tell, using data in different ways within the organization, delivering business outcomes has been at the forefront, and we're viewed strategically, not just with the products, but professional services as well, to make sure that we can continue to be there, both in an advisory capacity, but also in driving the right outcomes. And something that Charlie even said this morning was that we were kind of in the fabric. We have a couple of team members that are just like Honeywell team members. We're in the fabric of the organization. I think that's really critically important for us to really derive the outcomes that Charlie and the business need. >> And data is so critical to their business. You have to be, not only from professional services, but as a platform. Yes. This is kind of where the value comes from. Now, I can't help but just conjure up images of space because I watch my kids that watch, space is now hot. People love space. You see SpaceX landing their rocket boosters to the finest precision. You got Blue Origin out there with Amazon. And they are Honeywell sensors either. Honeywell's in every manned NASA mission. You have a renaissance of activity going on in a modern way. This is exciting, this is critical. Without data, you can't do it. >> Absolutely, I mean, also sometimes we take a break. I'm a fundamentalist. I tell everybody that excitement is great, but let's take a break. Let's make sure the fundamentals are in place. And we actually know what is it, what are those critical data that we need to be tracking and managing? Because you don't just have to manage a whole world of data. There's so much of it, and believe me, there's not all value in everything. You have to be critical about it and strategic about it. What are the critical data that we need to manage, govern, and actually, because it's expensive to manage the critical data. So we look at a value tree as well, and say, okay, if we, as Honeywell, want to be able to be also an efficient business enabler, we have to be efficient inside. So there's looking out, and there's also looking inside to make sure that we are in the right place, we are understanding our data, our people understand data. Talking about our relationship with IPS, Informatica Professional Services, one of the things that we're looking at is getting the right people, the engineers, the people to actually realize that okay, we have the platform, we've heard of Clare, We heard of all those stuff. But where are the people to actually go and do the real stuff, like actually programming, writing the code, connecting things and making it work? It's not easy because the technology's going faster than the capabilities in terms of people, skills. So the partnership we're building with Informatica professional services, and we're beginning to nurture, inside that, we want to be in a position were Honeywell doesn't have to worry so much about the churn in terms of getting people and retraining and retraining and retraining. We want to have a reliable partner who is also moving with the certain development and the progress around the products that we bought so we can have that success. So the partnership with IPS is for the-- >> The skill gaps we've been talking about, I know she's going to ask next, but I'll just jump in because I know there's two threads here. One is there's a new generation coming into the workforce, okay, and they're all data-full. They've been experiencing the digital lifestyle, the engineering programs. To data, it's all changing. What are some of the new expertise that really stand out when evaluating candidates, both from the Informatica side and also Honeywell? What's the ideal candidate look like, because there's no real four-year degree anymore? Well, Berkeley just had their first class of data analytics. That's new two-generation. But what are some of those skills? There's no degree out there. You can't really get a degree in data yet. >> Do you want to talk about that? >> Sure, I can just kick off with what we're looking at and how we're evolving. First of all, the new graduates are extremely innovative and exciting to bring on. We've been in business for 26 years, so we have a lot of folks that have done some great work. Our retention is through the roof, so it's fun to meld the folks that have been doing things for over 10, 15 years, to see what the folks have new ideas about how to leverage data. The thing I can underscore is it's business and technology, and I think the new grads get that really, really well in terms of data. To them, data's not something that's stored somewhere in the cloud or in a box. It's something that's practically applied for business outcomes, and I think they get that right out of school, and I think they're getting that message loud and clear. Lot of hybrid programs. We do hire direct from college, but we also hire experienced hires. And we look for people that have had degrees that are balanced. So the traditional just CS-only degrees, still very relevant, but we're seeing a lot of people do hybrids because they know they want to understand supply chain along with CS and data. And there are programs around just data, how organizations can really capitalize on that. >> And also we're hearing, too, that having domain expertise is actually just as important as having the coding skills because you got to know what an outcome looks like before you collect the data. You got to know what checkmate is if you're going to play chess. That's the old expression, right? >> I think people with the domain, both the hybrid experience or expertise, are more valuable to the company because maybe from the product perspective, from building products, you could be just a scientist, code the code. But when you come to Honeywell, for example, we want you to be able to understand, think about materials. Want you to be able to understand what are the products, what are the materials that we use. What are the inputs that we have to put into these products? Now a simple thing like a data scientist deciding what the right correct value of what an attribute should be, that's not something that because you know code you can determine. You have to understand the domain, the domain you're dealing with. You have to understand the context. So that comes, the question of context management, understanding the context and bringing it together. That is a big challenge, and I can tell you that's a big gap there. >> Big gap indeed, and understand the business and the data too. >> Yes. >> Charles, Randy, thank you both so much for coming on theCUBE. It's been a great conversation. >> Thank you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (funky techno music)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering VMworld 2017. Brought to you by VMware and its ecosystem partner. >> Hi, I'm Stu Miniman with my co-host Keith Townsend. Happy to welcome to the program first-time guest Paul Hodge, who's the global marketing manager of Honeywell Process Solutions, thanks so much for joining us. >> Thank you Stu. >> Alright, so Paul, you have to tell us, so Honeywell's the company, I think many people are aware. The process solutions; maybe you can tell us a little about that part of the organization and what your role there is. >> Sure, sure. So yeah, Honeywell, multi-national conglomerate, hundred thirty thousand people, forty billion dollars. Honeywell Process Solutions is then a subdivision within Honeywell that serves the manufacturing industry. So we go through and provide goods and services that allow people to go through and automate their plants for those pharmaceuticals or refining, and those types of things. >> And your role here coming to the show, you're actually a partner of Honeywell. >> Yes we are. >> Sometimes we've got tons of practitioners here, so tell us a little bit, you know, manufacturing, I think I know a few places where that makes a lot of sense for VMware, but tell us a little bit about the history of the partnership and your role there. >> Sure, so we've been partners with VMware since 2010. So it's been a long, long time partnership. And we've been bringing virtualization into the manufacturing industry, because we're typically quite conservative as a company, in terms of adopting technology, so it really takes an automation leader like Honeywell to go through and drive a new technology into the industry. So we've been doing that since, yeah, 2010. And yeah, this week we've been going through and talking about our new HDI hyperconversion infrastructure sort of solution that we've been doing, sort of, with VMware, and along with Dell EMC, that goes through and takes that a step further into our industry. >> Wow, so that's pretty interesting, I've worked in pharmaceuticals, manufacturing organization, and automation of IT is pretty difficult because of regulatory issues, et cetera, safety. What are some of the challenges that Honeywell is addressing in automation and specifically around VMware products? >> Sure, sure. I think the number one thing for our industry is purely simplicity, the people in our industry, they're not IT geeks, they don't have all of this knowledge, they don't have a storage administrator out there, so we have to go through and do all of that for them and take all of the complexity sort of out of the product. So it needs to be simple, but it just needs to be reliable, as well. I mean we're dealing with your refineries and pharmaceutical plants and things like that so the things just cannot stop. So you need simplicity with the reliability and availability and have both of them in sort of a package that's ready to go. And the other complexity is that we need to be able to deliver this anywhere around the world, and that's the other reason why it needs to be simple because it's not just going to North America, it's going to Europe, it's going to the Middle East, it's going to all different places. >> All right, well you say simplicity, and any time we've been talking about hyperconversion infrastructure, simplicity's usually at the top of the list. >> Paul: Absolutely, it's one of the big benefits. >> It seems like a natural fit there. Maybe, what is the solution, what made up with it, you said Dell EMC is part of it, of course VMware is part of it, how's it different from, say, the VxRail that Dell's been offering, you know, vSAN to hit ten thousand customers. What differentiates this compared to everything else that's available? >> Sure, so we're taking the vSAN, which is absolutely as you were saying, ten thousand customers out there very mature, very reliable, and we're taking it and sort of marrying it with the Dell EMC FX2 solution there which is an extremely powerful platform and flexible platform for going through and writing sort of, vSAN on top of it. So we've taken those two best in breed products there and we've gone through and built a reference configuration that's customized and optimized for the manufacturing industry. >> Yeah it's interesting. Keith, I remember when the FX2 launched, everybody was like, wait is this an HCI solution? Will it be there, will this be a platform for it? I don't know, is there anybody else leveraging that for this type of solution yet? >> vSAN is a very very popular sort of platform for, >> Stu: On the FX2. >> Yes, sorry, FX2, thank you. >> Yeah, I know vSAN is, but the marrying of those two together, is that a standard offering that was out there, or is that something that you've optimized? >> Certainly, I think there might be a vSAN ready version of that as well, but the reason why it's quite popular is because I can go through and have four vSAN nodes in the one FX2. So I can have a vSAN in a box with the FX2 solution, which makes it quite a nice fit. But it's really, the hardware platform aside, the value that Honeywell's providing is just really, the integration of those products. Building a reference design that's optimized for our industry and testing out all of the stack and delivering that for a market. >> So talking about building up a stack specifically for manufacturing, can you talk about, who's the end customer? Who's actually buying the solution? You say you don't, may not have a storage administrator. Are you guys selling to IT, or manufacturing operations? >> Mainly to the manufacturing part of the business, which is why it needs to be so simple is because those IT resources that you would normally have on the IT side of the business, they're just not there. And so we go through and sell to our customers there, refiners, pharmaceutical plants and things like that. And typically Honeywell is the one that's then engineering the overall solution to solve the manufacturing problems, so we deliver it to our own engineers, and our own engineers then customize that to go through and solve a manufacturing problem. But in our industry there's typically quite a big separation between the IT part of the organization and the OT, if you will, part, which is why the simplicity is such a big part of what we do. >> Paul, can you expand on that at all? Something we, from the research side have been looking at, kind of the IT, OT, what you're hearing from customers. >> Sure. So I think the main reason, traditionally, why that separation has been there is just on the OT side, there's a very, very different need in terms of reliability and availability and criticality and what happens if certain things just go wrong. And traditionally, those skills have been in a separate part of the organization to the IT part of the organization. So in some companies, those two worlds are absolutely converging and IoT is certainly a big thing that is driving that convergence. But in other organizations, they are still remaining separate, it's just the cultural way that a company has gone through and run itself. So I think, whether they are merging, those worlds, or whether they're staying separate is really, changes on a corporation by corporation basis. >> Paul, let's talk a little bit more about that OT customer. One of the things that's been my experience, you walk into a manufacturing floor, you see a system there that's 15 years old easy. This is a tool and that tool is just there to do a function within the manufacturing process, but with all of the malware, and the encrypted, and shutting down an entire operation's perspective, how are you helping OT get to a point where they accept, I guess the flexibility that this needed in an operation to support, something like a FX2 on their data center floor, with running vSAN? >> Sure, well, first of all, I think it is that simplicity. I mean if it's too complicated, then it just will not be accepted for somebody like that. So that simplicity and the reliability as where I've already spoken about there, but I think Honeywell there is there as well, helping that OT side of the business to be able to go through and deploy a system of that level of complexity, because as you're saying there, it is very different in terms of the 15 year old thing that they might be upgrading from. But it delivers just so many benefits from them. I mean going from 100 servers, which is what, say, some plants typically might go through and have, and you're just going to maybe two FX2 base clusters of our systems there, it's just a massive reduction in terms of hardware, and with each piece of hardware we remove, that's space and power and cooling and maintenance and everything like that goes away with it. >> Alright Paul, talking about different architectures, you mentioned IoT, so I have to imagine that's having significant impact on your industry. >> It is. >> Walk us through that. What are you seeing, what is Honeywell's role there, what are your customers doing? >> It's actually an interesting area for Honeywell Process Solutions because first of all, we've been doing IoT for 30 years, in terms of really, from Honeywell Process Solutions. >> You were the hipster IoT company. >> Really, oh that's good, that's a compliment. >> Is that what you were saying, you were IoT before it was cool, is what I understand. (laughter) >> We've been out there doing it, I mean, our job in Honeywell Process Solutions is to take field data, marry it with an inch device, create value there and sometimes just make that available on the internet, but getting back to your question, though, is the IoT way of life is changing how we go through and do things. First of all, there's data that's out there that previously, people wouldn't have considered valuable, okay, so that data, they're trying to extract that data out there, so we're, I guess there's a wave if you will of people trying to get that previously non-valuable data out of the field, so that's one part there. Sometimes as well, projects are very, very geographically dispersed. Traditionally you would have had like a plant infrastructure and it would've been in a self-contained area but now it can be in over a very wide geographical area. So you've got to have a controller, which potentially is on the internet, and have that be highly secure all the way back to then, the sources that need to go through and consume that. So that's a difference in how it's going through and impacting us. But I think as well, there's I guess, building an awareness out there in the market of trying to go through and extract more information and more intelligence out of the data that people are already getting, and driving new waves there as well. >> What are some of the lessons you're helping customers, especially OT, understand when they move from this isolated manufacturing network to this distributed network, that they're extracting value from, but they're also exposing security risks, and just you know, control risks. They're not used to operating at this multi, manufacturing facility perspective, from an IT perspective. They're in essence becoming IT. What are some of the pitfalls you're helping them to avoid? >> Sure, well I think security is a great one that you've just gone through and mentioned there. Anything that we're, data that's running the plant, first and foremost, needs to be secure in terms of going through and doing that. So I think that's one of the first things is how you go through and design that system and make it secure. And so I think that's one of the areas there. But also, to extract data and value out of it requires infrastructure to be able to store the data, to be able to go through and allow third parties to do analytics and other types of things; on top of that, infrastructure. So Honeywell's doing a lot to provide that back-end infrastructure that people can go through and do data mining and do analytics, and solving those new problems on that infrastructure. >> So, this power of FX2, the Dell EMC reference architecture, the VMare vSAN, gives an awful lot of computer. I think competitors like AWS will come in and say, you know what, AWS Snowball Edge is designed for this big data use case where we can ingest IoT data at the edge, do some light processing on it. What are you running from a practical perspective that you're seeing users say you know that just isn't enough, we need this power of the FX2, this Dell EMC reference architecture and vSAN. >> Sure, sure. So I think it's serving a different market segment. So absolutely there's a market segment out there that says, I'm prepared to take my data and put it into an Edge device and send it to a cloud, into AWS, or wherever, okay and that's absolutely a market segment that's out there. But there's another segment of market and it is quite large, for manufacturing, that says, no, the data that I'm ingesting needs to stay within my corporate control, within the boundaries of the corporation, okay. And it's those types of customers there that need that on-premise compute capacity to be able to ingest that data, to be able to display it to operators, to be able to go through and solve other problems with that data; it needs to be local. And that could just be because they don't trust it, because remember, we lag in terms of our, our adoption. We're lagging as an industry in general. So I think it's a lot of those types of reasons, yeah. >> So I'm kind of curious about a practical self process. Again, these OT folks don't look at their traditional 100 racks and say, we need to do something with this. We need to change it. If it works, why change it? Especially in manufacturing. What's the catalyst for change? >> Sure, absolutely, well I think in a lot of these industries there, they're losing people in terms of the people that run those types of plants there. So I think the first catalyst for change is, I had all of this equipment that was taking me all of these people to go through and maintain. I just don't have those people anymore. I need to do more with less. So by removing those pieces of equipment there, I make myself sort of more efficient. Not only in terms of the maintenance of those pieces of equipment there, but there's always on-going changes that need to be made to these environments as well, so you need to be able to go through and deploy new virtual machines, you know, far more agile environment. And when you're dealing with, say, physical pieces of equipment, if I wanted to deploy a new node, I would need to order that node from a supplier. I would then need to go through and commission it, install the software, and rack it, and then do all that, I mean that's months and months and months of work and effort. With a virtual machine, I just go through and deploy it, and I'm done. Yeah. >> Paul, just want to get your final take. VMworld, the show itself, kind of the experience as a partner, what's your takeaway from that? >> It's been fantastic, for me, VMworld is always about the relationships and the conversations that go through and take place, whether that be with partners like VMware, or whether it be with other supplies that I go through and do business with, and everyone's here. It's just one of the events where it's just, you're only limited by your ability to get your calendar organized and see all of the people that you want to do. That's the only limit of what you can achieve here. But it's just been a fantastic event this year and Honeywell's been glad to be here. >> Paul Hodge, Honeywell Process Solutions. Really appreciate you joining us, and absolutely agree a thousand percent, I'm sure Keith would attest to this also, if you're not at this show, you need to be here next year. If you're in the European one, you should go over there. So many conversations, we're happy to bring you a number of them, give you just a taste or a flavor of what's been happening at VMworld 2017. Thank you for joining us for three days of program. We're going to be wrapping up shortly, but all of it goes on the website and check it all out. Thank you so much for watching theCUBE. (electronic music)

>>Hello. And welcome back to the cubes coverage here. Live in Las Vegas for 80 bucks. Re-invent 2021. We're in person been two years since the cube has been on the ground here at a live event, it's a hybrid event. Check them out online. AWS has got to reinvent site as well as cube online. I'm Jennifer, your host got a great guest here from Latin America. Honeywell is VP of Latin America for AWS, a lot of global change, but the regions, a lot of great stuff, cultural integration. If you will, a skills people all around the world using cloud compute. Jaime's great, but coming on the cube. Thanks for coming on. >>Thank you, John. Thank you. It's a pleasure to be >>Here with you. Um, I wish I could speak in the native tongue, but I can't. I ended it, but I know online there's some special rooms that people have on the cube sites. So a lot of tech, a lot of cloud native in the world, I'm seeing Latin America and all the events we've done had great participation in the cloud ecosystem in Latin America, a lot of young talent, a lot of things happening. What's what's going on. >>Well, as you can see around us today, a lot of things are happening in the cloud. We are in this inflection point in the industry of technology that is accelerating innovation, accelerating transformation all over the world. And obviously Latin America is not an exception. We're seeing this momentum. We're seeing large enterprise companies leveraging the cloud to transform their customer experiences, to drive innovation. We're seeing startups to drive competitiveness and try to compete with the world. And that's also enabling a lot of younger generations to move faster, to innovate, to dream big and drive new ideas. So you're seeing that same momentum in Latin America, all across the region. But this is the one John and we, and we are seeing this happening for many years ahead. >>You know, I love inflection points and I've been saying this and just wrote a blog post about it on siliconangle.com that we are now at another inflection point where cloud is going next gen, where in any kind of revolution, every inflection point, this cultural revolution starts with the young people. And I've never seen an impact with Kubernetes and microservices and the modern approach of the younger generation. It's like if I was 20, that'd be a kid in the candy store. What I don't have to build land is there for me. I got to don't have to provision any servers. So the I'm seeing an impact for the younger generation around cloud and it's global phenomenon. What's what's going on in the younger talent in Latin America. Well, >>Let's just say, I mean, generations see inflection as opportunity, opportunity to make new things happen to, as I said to dream big and actually enabled their ideas to become a reality. And that's where you're seeing all across the region. You see this in Brazil, you see these in Argentina, you see this Columbia, Mexico, largest startup communities that are competing with the world. And you have, you know, we have an example like Newman that was here this morning, like started seven years ago, 2014 with a view transforming the financial services experience. That's where we're seeing all across Latin America, because >>The young kids slinging APIs around with containers. Now you've got the container movement. We had a great showing from Brazil and our DockerCon event. Um, net, very notable, um, intelligence coming out of that area. Amazing young talent. I'm just blown away by the, by the work, but in the region itself is still transformation. So I know you're, you're well known for doing really big deals at AWS. Uh, I can say that big banks, multimillion dollar deals. So there's growth there there's existing business transforming while new entrepreneurs are coming in. It's kind of a best of both worlds. What's the, what's the growth look like. >>Uh, as you mentioned, very large enterprises understand that the cloud and a transformation of culture is going to allow them to innovate them, to have loyal customers, every large enterprise customer. We're thinking about different ways to contact their customers, transforming the experience you're seeing customers like like Bancolombia that are migrating their legacy systems into the cloud in order to make faster decisions, to increase agility, to increase innovation and lead their people. Because at the end journey is all about the people that their people build on behalf of their customers and transform their experiences. >>You know, one of the things I noticed during the pandemic, and I'd love to get your reaction to this because I know you're living that as well every day, even before the pandemic, but since everything went virtual now hybrid, you're seeing a very low friction point to get in and collaborate. There's almost a new social construct, connective tissue between no boundaries. So you can have an event like here at reinvent, we're in person, but yet there's an online community digitally engaging. So we're starting to see cell formation where people around the world are getting together. How has it impacted how you manage and how you engage with your customers in your region? >>Well, as I said, it's a combination of many things. Our customers are still like people in person. That's why we have our business in Brazil. We have obviously in Argentina, Colombia, Mexico, Costa Rica, Peru, we still have presses. There we are where we work very close with our customers. We understand they need and what they want to do. But now, for example, during the last two years, I've had the opportunity to leverage in technology, be present in what we call virtual trips in most of our countries full day experiences. And I have to tell you at the beginning, I was concerned. I didn't have the opportunity to meet some of these people before today. When I see them in person here in re-invent this like, as if we had met from four. So as you say is the hybrid experience that allows us to be in-person with our customers, with our partners across the region, but also in a remote base, having the opportunity to build the same relations. And that's what technology is enabling better experience, faster innovation and moral agility and growth all across Latin America. >>So it's one of the things I talked to Adams Leschi about before reinvent a week ago, um, on a bank exclusive interview with him was he was very adamant about the clouds expanding everywhere. Honestly, you've got the edge in manufacturing, ADP percent everywhere, but he mentioned the regions, the continued growth of regions. It's been 10 years since Latin America. How's that impacted what you got going on there. And what's next from a region perspective. And how has that changed the landscape >>While you're touching? John is probably the most important thing we're seeing. You're absolutely right. We started 10 years ago, December 14 in Brazil with an office and a region there Caesar will launch offices in most of our countries. Now the important thing here is how our technology is enabling our companies, Latin American companies. We have 17 million companies in Latin America be more competitive. You know, some examples, I just mentioned Nubank, but we have that is competing with very large companies. You have Bancolombia you have GBM in Mexico. So what we're seeing is our companies be able by leveraging the latest and best technology to compete with the world and to drive that competitiveness that we need. The other thing about talent. If we enable and empower our Latin American talent or builders to build these new experiences, that's, what's going to allow the region to accelerate their growth, their competitiveness, and their social benefits. >>It was interesting too, is that you can see from the trends to do that. You can do it really fast now instantly. So it's, it's an amazing opportunity. Um, I gotta ask you while you're here, cause I'm really curious. I'm sure the viewers will be as well. What's what's going on in Latin America from a trend stamp. What's the vibe like? What's the, what's the environment like what's the, what's the mindset like there in those regions, from an entrepreneurship perspective, from a cloud enablement perspective, a cultural perspective, what's your report? How would you report on that? >>First of all, we're seeing the cloud accelerate all across Latin America. And I, and as I said, it's really day one for all of us. The other thing is that our customers are understanding that digital transformation is not a technology transformation. It's a cultural transformation leverage by leveraging technology for that to happen. It's about people. It's about mechanisms in the company. It's about the way companies make decisions. And that's why, why the power of the cloud is so important in impact to empower these people, to make things happen. In fact, what we're seeing in Latin America is CEOs of some of these companies like Bancolombia CEO is very engaged in this transformation where he's reviewing technology, he's understanding the cloud because that's how they realize or how they understand the importance of, you know, changing their companies, focused on their customers. The other thing is Latin American companies understand that they need to understand their customer needs work backwards from that and leverage that their technology, the cloud in order to improve the experiences of the >>Costumes. So I had to put you on the spot on a question. I gotta ask you, you know, if this is 10 years of re-invent, we've been here for nine. And I remember the first one we went with the second one. Wasn't many people here were like getting guests from the hallway. Hey, come on up on the cube. And now we can't, there's no open spots. Um, 15 years is how old Amazon is Amazon web services. So, so as Adam takes over and you have Amazon going in the next 15 years, what's your vision on how that evolves? Because you know, you're looking at the pandemic ending and pandemic has proven to a lot of people that digital works here, but as exposed what doesn't work, you can't hide the ball anymore if your business, but you're exposed. If you're in the cloud or you've got modern software, if no one's using it, it's not working change it. You can do it fast. So the whole hiding behind, you know, I bought this project, what this software, old guard, new guard, I mean, you can't hide the volume where, so that changes things, but also the creativity of refactoring business is also there. So you got, you got fear. I don't can't hide the ball and you're exposed to opportunity. >>What's your reaction to that? In fact, what I was going to say is where we see some opportunity. I mean, if you see 15 years side where you see, first of all, is all customers in Latin America or everywhere else leveraging the cloud. That's the most important thing. Number two, people leveraging technology to make things happen. It's about building. It's about me. And we talk about this before is when you realize that people are looking for better ways to improve their experience, launching the startups. And this is in finance, in the financial services. This is in manufacturing. This is in all the different industries across Latin America. We see opportunity. The other one, John is a region like Latin America understands that with people you need to enable them. It's about talent. And in order to enable talent, you need to educate them. So in AWS, we're actually investing a lot of time and effort to what to give them the best training content in their local language to launch programs that allow them to innovate like activate that enables to start off to launch. So what we're doing is giving Vilders younger generation tools to be more successful and again, dream big and make things happen now. So the next 15 years, Saba opportunity transforming faster decision-making agility in the way companies move and also driving competitiveness in Latin America to be able to compete in a globalized environment because everything is interconnected and it's about global reach today. And that's why we need our talent to invest, educate, to drive the transformation of the region. >>The global connectedness is a real point there. Great insight. I think the cultural revolutions here, the younger generations engaged existing businesses transforming, which means if they don't do it right, they're going to lose it to the other guy, other people. So I have, okay. Final question for you. Thanks for coming on. Appreciate your time. I know you're busy looking at the pandemic ending. What's the major patterns that you're seeing in Latin America, around companies strategies to transform out of the pandemic, a growth strategy, because everyone I talked to was like, we're going to come out with a tailwind and we're going to be on the upward slope. Obviously they're using cloud of course, but is there a pattern of that coming out of the pandemic with an upward growth? So >>We're seeing all across Latin America companies looking for better ways to reach our customers. That is the fact traditional touch points are not enough. Now they are building on top of that. So we are seeing Latin American companies invested, transform their legacy systems in order to look for different ways to approach the customers. Number two, we're seeing Latin American companies to leverage data in order to make better, more informed and faster decisions and to scale their business and accelerate and empower their teams. We're seeing companies in Latin America, investing in tools to let their people make things happen. As I said before, cultural transformation, digital transformation is about people. It's about fast decision making and it's about leveraging the technology to make it happen. We're seeing a lot of startup communities across our countries, new ideas, taking place. And as you know, AWS has always been focused on let known supporting startups and those new ideas. So we're seeing a lot of things happen in the region. A lot of momentum, a lot of growth. And what we're seeing is the cloud enabling that growth, that opportunity that you were talking about with our view that 15 years out, a lot of new business models are going to be late making hat. They can have >>Great point. I think just to highlight that one key thing, talent, you just add talent to the cloud capabilities. You can get there faster, you do it with a team, even better. Um, collaborations changing. Just the ability to capture opportunities are now faster than when we were growing up. They have a better don't think literally that you wish you were 20. Again, I do with all this code out there. >>And that's where we say it's about the people. And I can tell you from one of our biggest investments, my biggest investments is given the talent that opportunity, given our best training content in local language so they can learn new and better ways of making things happen. So again, as I said, leveraging supporting startups to grow. So all the problems around talent for Latin American cities, for our customers and our partners, because at the end, we understand that our partners expand our solutions to the market. And these are partners that allow us to be present in the many countries that are part of Latin America. >>Well, we'd love your vision, love your, love, your, your insight. And we will have a cube region in your area, and we're going to contact you. The cube will open their doors for the Latin America community. So look for that this year. Thanks for coming on. Now, >>joining you and hosting you in our countries. You're going to see a lot of enthusiasm, passion, and growth and opportunity Latina, >>A lot of great action. The younger generations engaged the older generations transforming the business models. The cloud is going next, gen. This is the cube bringing all the live action. You're watching the queue, the leader in global tech coverage. I'm John Farrow, your host. Thanks for watching.

>>mhm Yes. >>Welcome back to the Cube coverage of Red Hat summit 21 2021. I'm john for host of the Cubans virtual this year as we start preparing to come out of Covid a lot of great conversations here happening around technology. This is the emerging technology with Red hat segment. We've got three great guests steve watt manager, distinguished engineer at Red Hat hurl saying senior software engineer Red Hat and luke Hines, who's the senior software engineer as well. We got the engineering team steve, you're the the team leader, emerging tech within red hat. Always something to talk about. You guys have great tech chops that's well known in the industry and I'll see now part of IBM you've got a deep bench um what's your, how do you view emerging tech um how do you apply it? How do you prioritize, give us a quick overview of the emerging tech scene at Redhead? >>Yeah, sure. It's quite a conflated term. The way we define emerging technologies is that it's a technology that's typically 18 months plus out from commercialization and this can sometimes go six months either way. Another thing about it is it's typically not something on any of our product roadmaps within the portfolio. So in some sense, it's often a bit of a surprise that we have to react to. >>So no real agenda. And I mean you have some business unit kind of probably uh but you have to have first principles within red hat, but for this you're looking at kind of the moon shot, so to speak, the big game changing shifts. Quantum, you know, you got now supply chain from everything from new economics, new technology because that kind of getting it right. >>Yeah, I think we we definitely use a couple of different techniques to prioritize and filter what we're doing. And the first is something will pop up and it will be like, is it in our addressable market? So our addressable market is that we're a platform software company that builds enterprise software and so, you know, it's got to be sort of fit into that is a great example if somebody came up came to us with an idea for like a drone command center, which is a military application, it is an emerging technology, but it's something that we would pass on. >>Yeah, I mean I didn't make sense, but he also, what's interesting is that you guys have an open source D N A. So it's you have also a huge commercial impact and again, open sources of one of the 4th, 5th generation of awesomeness. So, you know, the good news is open source is well proven. But as you start getting into this more disruption, you've got the confluence of, you know, core cloud, cloud Native, industrial and IOT edge and data. All this is interesting, right. This is where the action is. How do you guys bring that open source community participation? You got more stakeholders emerging there before the break down, how that you guys manage all that complexity? >>Yeah, sure. So I think that the way I would start is that, you know, we like to act on good ideas, but I don't think good ideas come from any one place. And so we typically organize our teams around sort of horizontal technology sectors. So you've got, you know, luke who's heading up security, but I have an edge team, cloud networking team, a cloud storage team. Cloud application platforms team. So we've got these sort of different areas that we sort of attack work and opportunities, but you know, the good ideas can come from a variety of different places. So we try and leverage co creation with our customers and our partners. So as a good example of something we had to react to a few years ago, it was K Native right? So the sort of a new way of doing service um and eventing on top of kubernetes that was originated from google. Whereas if you look at Quantum right, ibms, the actual driver on quantum science and uh that originated from IBM were parole. We'll talk about exactly how we chose to respond to that. Some things are originated organically within the team. So uh luke talking about six law is a great example of that, but we do have a we sort of use the addressable market as a way to sort of focus what we're doing and then we try and land it within our different emerging technologies teams to go tackle it. Now. You asked about open source communities, which are quite interesting. Um so typically when you look at an open source project, it's it's there to tackle a particular problem or opportunity. Sometimes what you actually need commercial vendors to do is when there's a problem or opportunity that's not tackled by anyone open source project, we have to put them together to create a solution to go tackle that thing. That's also what we do. And so we sort of create this bridge between red hat and our customers and multiple different open source projects. And this is something we have to do because sometimes just that one open source project doesn't really care that much about that particular problem. They're motivated elsewhere. And so we sort of create that bridge. >>We got two great uh cohorts here and colleagues parole on the on the Quantum side and you got luke on the security side. Pro I'll start with you. Quantum is also a huge mentioned IBM great leadership there. Um Quantum on open shift. I mean come on. Just that's not coming together for me in my mind, it's not the first thing I think of. But it really that sounds compelling. Take us through, you know, um how this changes the computing landscape because heterogeneous systems is what we want and that's the world we live in. But now with distributed systems and all kinds of new computing modules out there, how does this makes sense? Take us through this? >>Um yeah john's but before I think I want to explain something which is called Quantum supremacy because it plays very important role in the road map that's been working on. So uh content computers, they are evolving and they have been around. But right now you see that they are going to be the next thing. And we define quantum supremacy as let's say you have any program that you run or any problems that you solve on a classical computer. Quantum computer would be giving you the results faster. So that is uh, that is how we define content supremacy when the same workload are doing better on content computer than they do in a classical computer. So the whole the whole drive is all the applications are all the companies, they're trying to find avenues where Quantum supremacy are going to change how they solve problems or how they run their applications. And even though quantum computers they are there. But uh, it is not as easily accessible for everyone to consume because it's it's a very new area that's being formed. So what, what we were thinking, how we can provide a mechanism that you can you don't connect this deal was you have a classical world, you have a country world and that's where a lot of thought process been. And we said okay, so with open shift we have the best of the classical components. You can take open shift, you can develop, deploy around your application in a country raised platform. What about you provide a mechanism that the world clothes that are running on open shift. They are also consuming quantum resources or they are able to run the competition and content computers take the results and integrate them in their normal classical work clothes. So that is the whole uh that was the whole inception that we have and that's what brought us here. So we took an operator based approach and what we are trying to do is establish the best practices that you can have these heterogeneous applications that can have classical components. Talking to our interacting the results are exchanging data with the quantum components. >>So I gotta ask with the rise of containers now, kubernetes at the center of the cloud native value proposition, what work clothes do you see benefiting from the quantum systems the most? Is there uh you guys have any visibility on some of those workloads? >>Uh So again, it's it's a very new, it's very it's really very early in the time and uh we talk with our customers and every customers, they are trying to identify themselves first where uh these contacts supremacy will be playing the role. What we are trying to do is when they reach their we should have a solution that they that they could uh use the existing in front that they have on open shift and use it to consume the content computers that may or may not be uh, inside their own uh, cloud. >>Well I want to come back and ask you some of the impact on the landscape. I want to get the look real quick because you know, I think security quantum break security, potentially some people have been saying, but you guys are also looking at a bunch of projects around supply chain, which is a huge issue when it comes to the landscape, whether its components on a machine in space to actually handling, you know, data on a corporate database. You guys have sig store. What's this about? >>Sure. Yes. So sick store a good way to frame six store is to think of let's encrypt and what let's encrypt did for website encryption is what we plan to do for software signing and transparency. So six Door itself is an umbrella organization that contains various different open source projects that are developed by the Six door community. Now, six door will be brought forth as a public good nonprofit service. So again, we're very much basing this on the successful model of let's Encrypt Six door will will enable developers to sign software artifacts, building materials, containers, binaries, all of these different artifacts that are part of the software supply chain. These can be signed with six door and then these signing events are recorded into a technology that we call a transparency log, which means that anybody can monitor signing events and a transparency log has this nature of being read only and immutable. It's very similar to a Blockchain allows you to have cryptographic proof auditing of our software supply chain and we've made six stores so that it's easy to adopt because traditional cryptographic signing tools are a challenge for a lot of developers to implement in their open source projects. They have to think about how to store the private keys. Do they need specialist hardware? If they were to lose a key then cleaning up afterwards the blast radius. So the key compromise can be incredibly difficult. So six doors role and purpose essentially is to make signing easy easy to adopt my projects. And then they have the protections around there being a public transparency law that could be monitored. >>See this is all about open. Being more open. Makes it more secure. Is the >>thief? Very much yes. Yes. It's that security principle of the more eyes on the code the better. >>So let me just back up, is this an open, you said it's gonna be a nonprofit? >>That's correct. Yes. Yes. So >>all of the code is developed by the community. It's all open source. anybody can look at this code. And then we plan alongside the Linux Foundation to launch a public good service. So this will make it available for anybody to use if your nonprofit free to use service. >>So luke maybe steve if you can way into on this. I mean, this goes back. If you look back at some of the early cloud days, people were really trashing cloud as there's no security. And cloud turns out it's a more security now with cloud uh, given the complexity and scale of it, does that apply the same here? Because I feel this is a similar kind of concept where it's open, but yet the more open it is, the more secure it is. And then and then might have to be a better fit for saying I. T. Security solution because right now everyone is scrambling on the I. T. Side. Um whether it's zero Trust or Endpoint Protection, everyone's kind of trying everything in sight. This is kind of changing the paradigm a little bit on software security. Could you comment on how you see this playing out in traditional enterprises? Because if this plays out like the cloud, open winds, >>so luke, why don't you take that? And then I'll follow up with another lens on it which is the operate first piece. >>Sure. Yes. So I think in a lot of ways this has to be open this technology because this way we have we have transparency. The code can be audited openly. Okay. Our operational procedures can be audit openly and the community can help to develop not only are code but our operational mechanisms so we look to use technology such as cuba netease, open ship operators and so forth. Uh Six store itself runs completely in a cloud. It is it is cloud native. Okay, so it's very much in the paradigm of cloud and yeah, essentially security, always it operates better when it's open, you know, I found that from looking at all aspects of security over the years that I've worked in this realm. >>Okay, so just just to add to that some some other context around Six Law, that's interesting, which is, you know, software secure supply chain, Sixth floor is a solution to help build more secure software secure supply chains, more secure software supply chain. And um so um there's there's a growing community around that and there's an ecosystem of sort of cloud native kubernetes centric approaches for building more secure software. I think we all caught the solar winds attack. It's sort of enterprise software industry is responding sort of as a whole to go and close out as many of those gaps as possible, reduce the attack surface. So that's one aspect about why 6th was so interesting. Another thing is how we're going about it. So we talked about um you mentioned some of the things that people like about open source, which is one is transparency, so sunlight is the best disinfectant, right? Everybody can see the code, we can kind of make it more secure. Um and then the other is agency where basically if you're waiting on a vendor to go do something, um if it's proprietary software, you you really don't have much agency to get that vendor to go do that thing. Where is the open source? If you don't, if you're tired of waiting around, you can just submit the patch. So, um what we've seen with package software is with open source, we've had all this transparency and agency, but we've lost it with software as a service, right? Where vendors or cloud service providers are taking package software and then they're making it available as a service but that operationalize ng that software that is proprietary and it doesn't get contributed back. And so what Lukes building here as long along with our partners down, Lawrence from google, very active contributor in it. Um, the, is the operational piece to actually run sixth or as a public service is part of the open source project so people can then go and take sixth or maybe run it as a smaller internal service. Maybe they discover a bug, they can fix that bug contributed back to the operational izing piece as well as the traditional package software to basically make it a much more robust and open service. So you bring that transparency and the agency back to the SAS model as well. >>Look if you don't mind before, before uh and this segment proportion of it. The importance of immune ability is huge in the world of data. Can you share more on that? Because you're seeing that as a key part of the Blockchain for instance, having this ability to have immune ability. Because you know, people worry about, you know, how things progress in this distributed world. You know, whether from a hacking standpoint or tracking changes, Mutability becomes super important and how it's going to be preserved in this uh new six doorway. >>Oh yeah, so um mutability essentially means cannot be changed. So the structure of something is set. If it is anyway tampered or changed, then it breaks the cryptographic structure that we have of our public transparency service. So this way anybody can effectively recreate the cryptographic structure that we have of this public transparency service. So this mutability provides trust that there is non repudiation of the data that you're getting. This data is data that you can trust because it's built upon a cryptographic foundation. So it has very much similar parallels to Blockchain. You can trust Blockchain because of the immutable nature of it. And there is some consensus as well. Anybody can effectively download the Blockchain and run it themselves and compute that the integrity of that system can be trusted because of this immutable nature. So that's why we made this an inherent part of Six door is so that anybody can publicly audit these events and data sets to establish that there tamper free. >>That is a huge point. I think one of the things beyond just the security aspect of being hacked and protecting assets um trust is a huge part of our society now, not just on data but everything, anything that's reputable, whether it's videos like this being deep faked or you know, or news or any information, all this ties to security again, fundamentally and amazing concepts. Um I really want to keep an eye on this great work. Um Pearl, I gotta get back to you on Quantum because again, you can't, I mean people love Quantum. It's just it feels like so sci fi and it's like almost right here, right, so close and it's happening. Um And then people get always, what does that mean for security? We go back to look and ask them well quantum, you know, crypto But before we get started I wanted, I'm curious about how that's gonna play out from the project because is it going to be more part of like a C. N. C. F. How do you bring the open source vibe to Quantum? >>Uh so that's a very good question because that was a plan, the whole work that we are going to do related to operators to enable Quantum is managed by the open source community and that project lies in the casket. So casket has their own open source community and all the modification by the way, I should first tell you what excuse did so cute skin is the dedicate that you use to develop circuits that are run on IBM or Honeywell back in. So there are certain Quantum computers back and that support uh, circuits that are created using uh Houston S ticket, which is an open source as well. So there is already a community around this which is the casket. Open source community and we have pushed the code and all the maintenance is taken care of by that community. Do answer your question about if we are going to integrate it with C and C. F. That is not in the picture right now. We are, it has a place in its own community and it is also very niche to people who are working on the Quantum. So right now you have like uh the contributors who who are from IBM as well as other uh communities that are specific specifically working on content. So right now I don't think so, we have the map to integrated the C. N. C. F. But open source is the way to go and we are on that tragic Torri >>you know, we joke here the cube that a cubit is coming around the corner can can help but we've that in you know different with a C. But um look, I want to ask you one of the things that while you're here your security guru. I wanted to ask you about Quantum because a lot of people are scared that Quantum is gonna crack all the keys on on encryption with his power and more hacking. You're just comment on that. What's your what's your reaction to >>that? Yes that's an incredibly good question. This will occur. Okay. And I think it's really about preparation more than anything now. One of the things that we there's a principle that we have within the security world when it comes to coding and designing of software and this aspect of future Cryptography being broken. As we've seen with the likes of MD five and Sha one and so forth. So we call this algorithm agility. So this means that when you write your code and you design your systems you make them conducive to being able to easily swap and pivot the algorithms that use. So the encryption algorithms that you have within your code, you do not become too fixed to those. So that if as computing gets more powerful and the current sets of algorithms are shown to have inherent security weaknesses, you can easily migrate and pivot to a stronger algorithms. So that's imperative. Lee is that when you build code, you practice this principle of algorithm agility so that when shot 256 or shot 5 12 becomes the shar one. You can swap out your systems. You can change the code in a very least disruptive way to allow you to address that floor within your within your code in your software projects. >>You know, luke. This is mind bender right there. Because you start thinking about what this means is when you think about algorithmic agility, you start thinking okay software countermeasures automation. You start thinking about these kinds of new trends where you need to have that kind of signature capability. You mentioned with this this project you're mentioning. So the ability to actually who signs off on these, this comes back down to the paradigm that you guys are talking about here. >>Yes, very much so. There's another analogy from the security world, they call it turtles all the way down, which is effectively you always have to get to the point that a human or a computer establishes that first point of trust to sign something off. And so so it is it's a it's a world that is ever increasing in complexity. So the best that you can do is to be prepared to be as open as you can to make that pivot as and when you need to. >>Pretty impressive, great insight steve. We can talk for hours on this panel, emerging tech with red hat. Just give us a quick summary of what's going on. Obviously you've got a serious brain trust going on over there. Real world impact. You talk about the future of trust, future of software, future of computing, all kind of going on real time right now. This is not so much R and D as it is the front range of tech. Give us a quick overview of >>Yeah, sure, yeah, sure. The first thing I would tell everyone is go check out next that red hat dot com, that's got all of our different projects, who to contact if you're interested in learning more about different areas that we're working on. And it also lists out the different areas that we're working on, but just as an overview. So we're working on software defined storage, cloud storage. Sage. Well, the creator of Cf is the person that leads that group. We've got a team focused on edge computing. They're doing some really cool projects around um very lightweight operating systems that and kubernetes, you know, open shift based deployments that can run on, you know, devices that you screw into the sheet rock, you know, for that's that's really interesting. Um We have a cloud networking team that's looking at over yin and just intersection of E B P F and networking and kubernetes. Um and then uh you know, we've got an application platforms team that's looking at Quantum, but also sort of how to advance kubernetes itself. So that's that's the team where you got the persistent volume framework from in kubernetes and that added block storage and object storage to kubernetes. So there's a lot of really exciting things going on. Our charter is to inform red hats long term technology strategy. We work the way my personal philosophy about how we do that is that Red hat has product engineering focuses on their product roadmap, which is by nature, you know, the 6 to 9 months. And then the longer term strategy is set by both of us. And it's just that they're not focused on it. We're focused on it and we spend a lot of time doing disambiguate nation of the future and that's kind of what we do. We love doing it. I get to work with all these really super smart people. It's a fun job. >>Well, great insights is super exciting, emerging tack within red hat. I'll see the industry. You guys are agile, your open source and now more than ever open sources, uh, product Ization of open source is happening at such an accelerated rate steve. Thanks for coming on parole. Thanks for coming on luke. Great insight all around. Thanks for sharing. Uh, the content here. Thank you. >>Our pleasure. >>Thank you. >>Okay. We were more, more redhead coverage after this. This video. Obviously, emerging tech is huge. Watch some of the game changing action here at Redhead Summit. I'm john ferrier. Thanks for watching. Yeah.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back everybody Jeff Frick here with the cube. This is a special week it's Grace Hopper week, and Grace Hopper is the best name in tech conferences. The celebration of women in computing, and we've been going there for years we're not there this year, but one of the themes that comes up over and over at Grace Hopper is women and girls need to see women in positions that they can envision themselves being in someday. That is a really important piece of the whole diversity conversation is can I see people that I can role model after and I just want to bring up something from a couple years back from 2016 when we were there, we were there with Mimi Valdez, Christina Deoja and Dr. Jeanette Epps, Dr. Jeanette Epps is the astronaut on the right. They were there talking about "The Hidden Figures" movie. If you remember it came out 2016, it was about Katherine Johnson and all the black women working at NASA. They got no credit for doing all the math that basically keep all the astronauts safe and they made a terrific movie about it. And Janet is going up on the very first Blue Origin Space Mission Next year. This was announced a couple of months ago, so again, phenomenal leadership, black lady astronaut, going to go into space and really provide a face for a lot of young girls that want to get into that and its clearly a great STEM opportunity. So we're excited to have four terrific women today that well also are the leaders that the younger women can look up to and follow their career. So we're excited to have them so we're just going to go around. We got four terrific guests, our first one is Annabel Chang, She is the Head of State Policy and Government Regulations at Waymo. Annabel great to see you, where are you coming in from today? >> from San Francisco >> Jeff: Awesome. Next up is Inamarie Johnson. She is the Chief People and Diversity Officer for Zendesk Inamarie, great to see you. Where are you calling in from today? >> Great to be here. I am calling in from Palos Verdes the state >> Jeff: awesome >> in Southern California. >> Jeff: Some of the benefits of a virtual sometimes we can, we couldn't do that without the power of the internet. And next up is Jennifer Cabalquinto she is the Chief Financial Officer of the Golden State Warriors. Jennifer, great to see you Where are you coming in from today? >> Well, I wish I was coming in from the Chase Center in San Francisco but I'm actually calling in from Santa Cruz California today. >> Jeff: Right, It's good to see you and you can surf a lot better down there. So that's probably not all bad. And finally to round out our panelists, Kate Hogan, she is the COO of North America for Accenture. Kate, great to see you as well. Where are you coming in from today? >> Well, it's good to see you too. I am coming in from the office actually in San Jose. >> Jeff: From the office in San Jose. All right, So let's get into it . You guys are all very senior, you've been doing this for a long time. We're in a kind of a crazy period of time in terms of diversity with all the kind of social unrest that's happening. So let's talk about some of your first your journeys and I want to start with you Annabel. You're a lawyer you got into lawyering. You did lawyering with Diane Feinstein, kind of some politics, and also the city of San Francisco. And then you made this move over to tech. Talk about that decision and what went into that decision and how did you get into tech? 'cause we know part of the problem with diversity is a pipeline problem. You came over from the law side of the house. >> Yes, and to be honest politics and the law are pretty homogenous. So when I made the move to tech, it was still a lot of the same, but what I knew is that I could be an attorney anywhere from Omaha Nebraska to Miami Florida. But what I couldn't do was work for a disruptive company, potentially a unicorn. And I seized that opportunity and (indistinct) Lyft early on before Ride Hailing and Ride Sharing was even a thing. So it was an exciting opportunity. And I joined right at the exact moment that made myself really meaningful in the organization. And I'm hoping that I'm doing the same thing right now at Waymo. >> Great, Inamarie you've come from one of my favorite stories I like to talk about from the old school Clorox great product management. I always like to joke that Silicon Valley needs a pipeline back to Cincinnati and Proctor and Gamble to get good product managers out here. You were in the classic, right? You were there, you were at Honeywell Plantronics, and then you jumped over to tech. Tell us a little bit about that move. Cause I'm sure selling Clorox is a lot different than selling the terrific service that you guys provide at Zendesk. I'm always happy when I see Zendesk in my customer service return email, I know I'm going to get taken care of. >> Oh wow, that's great. We love customers like you., so thank you for that. My journey is you're right from a fortune 50 sort of more portfolio type company into tech. And I think one of the reasons is because when tech is starting out and that's what Zendesk was a few five years back or so very much an early stage growth company, two things are top of mind, one, how do we become more global? And how do we make sure that we can go up market and attract enterprise grade customers? And so my experience having only been in those types of companies was very interesting for a startup. And what was interesting for me is I got to live in a world where there were great growth targets and numbers, things I had never seen. And the agility, the speed, the head plus heart really resonated with my background. So super glad to be in tech, but you're right. It's a little different than a consumer products. >> Right, and then Jennifer, you're in a completely different world, right? So you worked for the Golden State Warriors, which everybody knows is an NBA team, but I don't know that everyone knows really how progressive the Warriors are beyond just basketball in terms of the new Chase Center, all the different events that you guys put on it. And really the leadership there has decided we really want to be an entertainment company of which the Golden State Warrior basketball team has a very, very important piece, you've come from the entertainment industry. So that's probably how they found you, but you're in the financial role. You've always been in the financial role, not traditionally thought about as a lot of women in terms of a proportion of total people in that. So tell us a little bit about your experience being in finance, in entertainment, and then making this kind of hop over to, I guess Uber entertainment. I don't know even how you would classify the warriors. >> Sports entertainment, live entertainment. Yeah, it's interesting when the Warriors opportunity came up, I naturally said well no, I don't have any sports background. And it's something that we women tend to do, right? We self edit and we want to check every box before we think that we're qualified. And the reality is my background is in entertainment and the Warriors were looking to build their own venue, which has been a very large construction project. I was the CFO at Universal Studios Hollywood. And what do we do there? We build large attractions, which are just large construction projects and we're in the entertainment business. And so that sort of B to C was a natural sort of transition for me going from where I was with Universal Studios over to the Warriors. I think a finance career is such a great career for women. And I think we're finding more and more women entering it. It is one that you sort of understand your hills and valleys, you know when you're going to be busy and so you can kind of schedule around that. I think it's really... it provides that you have a seat at the table. And so I think it's a career choice that I think is becoming more and more available to women certainly more now than it was when I first started. >> Yeah, It's interesting cause I think a lot of people think of women naturally in human resources roles. My wife was a head of human resources back in the day, or a lot of marketing, but not necessarily on the finance side. And then Kate go over to you. You're one of the rare birds you've been at Accenture  for over 20 years. So you must like airplanes and travel to stay there that long. But doing a little homework for this, I saw a really interesting piece of you talking about your boss challenging you to ask for more work, to ask for a new opportunity. And I thought that was really insightful that you, you picked up on that like Oh, I guess it's incumbent on me to ask for more, not necessarily wait for that to be given to me, it sounds like a really seminal moment in your career. >> It was important but before I tell you that story, because it was an important moment of my career and probably something that a lot of the women here on the panel here can relate to as well. You mentioned airplanes and it made me think of my dad. My father was in the air force and I remember him telling stories when I was little about his career change from the air force into a career in telecommunications. So technology for me growing up Jeff was, it was kind of part of the dinner table. I mean it was just a conversation that was constantly ongoing in our house. And I also, as a young girl, I loved playing video games. We had a Tandy computer down in the basement and I remember spending too many hours playing video games down there. And so for me my history and my really at a young age, my experience and curiosity around tech was there. And so maybe that's, what's fueling my inspiration to stay at Accenture for as long as I have. And you're right It's been two decades, which feels tremendous, but I've had the chance to work across a bunch of different industries, but you're right. I mean, during that time and I relate with what Jennifer said in terms of self editing, right? Women do this and I'm no exception, I did this. And I do remember I'm a mentor and a sponsor of mine who called me up when I'm kind of I was at a pivotal moment in my career and he said you know Kate, I've been waiting for you to call me and tell me you want this job. And I never even thought about it. I mean I just never thought that I'd be a candidate for the job and let alone somebody waiting for me to kind of make the phone call. I haven't made that mistake again, (laughing) but I like to believe I learned from it, but it was an important lesson. >> It's such a great lesson and women are often accused of being a little bit too passive and not necessarily looking out for in salary negotiations or looking for that promotion or kind of stepping up to take the crappy job because that's another thing we hear over and over from successful people is that some point in their career, they took that job that nobody else wanted. They took that challenge that really enabled them to take a different path and really a different Ascension. And I'm just curious if there's any stories on that or in terms of a leader or a mentor, whether it was in the career, somebody that you either knew or didn't know that was someone that you got kind of strength from kind of climbing through your own, kind of career progression. Will go to you first Annabel. >> I actually would love to talk about the salary negotiations piece because I have a group of friends about that we've been to meeting together once a month for the last six years now. And one of the things that we committed to being very transparent with each other about was salary negotiations and signing bonuses and all of the hard topics that you kind of don't want to talk about as a manager and the women that I'm in this group with span all types of different industries. And I've learned so much from them, from my different job transitions about understanding the signing bonus, understanding equity, which is totally foreign to me coming from law and politics. And that was one of the most impactful tools that I've ever had was a group of people that I could be open with talking about salary negotiations and talking about how to really manage equity. Those are totally foreign to me up until this group of women really connected me to these topics and gave me some of that expertise. So that is something I strongly encourage is that if you haven't openly talked about salary negotiations before you should begin to do so. >> It begs the question, how was the sensitivity between the person that was making a lot of money and the person that wasn't? And how did you kind of work through that as a group for the greater good of everyone? >> Yeah, I think what's really eye opening is that for example, We had friends who were friends who were on tech, we had friends who were actually the entrepreneurs starting their own businesses or law firm, associates, law firm partners, people in PR, so we understood that there was going to be differences within industry and frankly in scale, but it was understanding even the tools, whether I think the most interesting one would be signing bonus, right? Because up until a few years ago, recruiters could ask you what you made and how do you avoid that question? How do you anchor yourself to a lower salary range or avoid that happening? I didn't know this, I didn't know how to do that. And a couple of women that had been in more senior negotiations shared ways to make sure that I was pinning myself to a higher salary range that I wanted to be in. >> That's great. That's a great story and really important to like say pin. it's a lot of logistical details, right? You just need to learn the techniques like any other skill. Inamarie, I wonder if you've got a story to share here. >> Sure. I just want to say, I love the example that you just gave because it's something I'm super passionate about, which is transparency and trust. Then I think that we're building that every day into all of our people processes. So sure, talk about sign on bonuses, talk about pay parody because that is the landscape. But a quick story for me, I would say is all about stepping into uncertainty. And when I coach younger professionals of course women, I often talk about, don't be afraid to step into the role where all of the answers are not vetted down because at the end of the day, you can influence what those answers are. I still remember when Honeywell asked me to leave the comfort of California and to come to the East coast to New Jersey and bring my family. And I was doing well in my career. I didn't feel like I needed to do that, but I was willing after some coaching to step into that uncertainty. And it was one of the best pivotal moment in my career. I didn't always know who I was going to work with. I didn't know the challenges and scope I would take on, but those were some of the biggest learning experiences and opportunities and it made me a better executive. So that's always my coaching, like go where the answers aren't quite vetted down because you can influence that as a leader. >> That's great, I mean, Beth Comstock former vice chair at GE, one of her keynotes I saw had a great line, get comfortable with being uncomfortable. And I think that its a really good kind of message, especially in the time we're living in with accelerated change. But I'm curious, Inamarie was the person that got you to take that commitment. Would you consider that a sponsor, a mentor, was it a boss? Was it maybe somebody not at work, your spouse or a friend that said go for it. What kind of pushed you over the edge to take that? >> It's a great question. It was actually the boss I was going to work for. He was the CHRO, and he said something that was so important to me that I've often said it to others. And he said trust me, he's like I know you don't have all the answers, I know we don't have this role all figured out, I know you're going to move your family, but if you trust me, there is a ton of learning on the other side of this. And sometimes that's the best thing a boss can do is say we will go on this journey together. I will help you figure it out. So it was a boss, but I think it was that trust and that willingness for him to stand and go alongside of me that made me pick up my family and be willing to move across the country. And we stayed five years and really, I am not the same executive because of that experience. >> Right, that's a great story, Jennifer, I want to go to you, you work for two owners that are so progressive and I remember when Joe Lacob came on the floor a few years back and was booed aggressively coming into a franchise that hadn't seen success in a very long time, making really aggressive moves in terms of personnel, both at the coaches and the players level, the GM level. But he had a vision and he stuck to it. And the net net was tremendous success. I wonder if you can share any of the stories, for you coming into that organization and being able to feel kind of that level of potential success and really kind of the vision and also really a focus on execution to make the vision real cause vision without execution doesn't really mean much. If you could share some stories of working for somebody like Joe Lacob, who's so visionary but also executes so very, very effectively. >> Yeah, Joe is, well I have the honor of working for Joe, for Rick Welts to who's our president. Who's living legend with the NBA with Peter Guber. Our leadership at the Warriors are truly visionary and they set audacious targets. And I would say from a story the most recent is, right now what we're living through today. And I will say Joe will not accept that we are not having games with fans. I agree he is so committed to trying to solve for this and he has really put the organization sort of on his back cause we're all like well, what do we do? And he has just refused to settle and is looking down every path as to how do we ensure the safety of our fans, the safety of our players, but how do we get back to live entertainment? And this is like a daily mantra and now the entire organization is so focused on this and it is because of his vision. And I think you need leaders like that who can set audacious goals, who can think beyond what's happening today and really energize the entire organization. And that's really what he's done. And when I talked to my peers and other teams in there they're talking about trying to close out their season or do these things. And they're like well, we're talking about, how do we open the building? And we're going to have fans, we're going to do this. And they look at me and they're like, what are you talking about? And I said, well we are so fortunate. We have leadership that just is not going to settle. Like they are just always looking to get out of whatever it is that's happening and fix it. So Joe is so committed His background, he's an epidemiologist major I think. Can you imagine how unique a background that is and how timely. And so his knowledge of just around the pandemic and how the virus is spread. And I mean it's phenomenal to watch him work and leverage sort of his business acumen, his science acumen and really think through how do we solve this. Its amazing. >> The other thing thing that you had said before is that you basically intentionally told people that they need to rethink their jobs, right? You didn't necessarily want to give them permission to get you told them we need to rethink their jobs. And it's a really interesting approach when the main business is just not happening, right? There's just no people coming through the door and paying for tickets and buying beers and hotdogs. It's a really interesting talk. And I'm curious, kind of what was the reception from the people like hey, you're the boss, you just figure it out or were they like hey, this is terrific that he pressed me to come up with some good ideas. >> Yeah, I think when all of this happened, we were resolved to make sure that our workforce is safe and that they had the tools that they needed to get through their day. But then we really challenged them with re imagining what the next normal is. Because when we come out of this, we want to be ahead of everybody else. And that comes again from the vision that Joe set, that we're going to use this time to make ourselves better internally because we have the time. I mean, we had been racing towards opening Chase Center and not having time to pause. Now let's use this time to really rethink how we're doing business. What can we do better? And I think it's really reinvigorated teams to really think and innovate in their own areas because you can innovate anything, right?. We're innovating how you pay payables, we're all innovating, we're rethinking the fan experience and queuing and lines and all of these things because now we have the time that it's really something that top down we want to come out of this stronger. >> Right, that's great. Kate I'll go to you, Julie Sweet, I'm a big fan of Julie Sweet. we went to the same school so go go Claremont. But she's been super aggressive lately on a lot of these things, there was a get to... I think it's called Getting to 50 50 by 25 initiative, a formal initiative with very specific goals and objectives. And then there was a recent thing in terms of doing some stuff in New York with retraining. And then as you said, military being close to your heart, a real specific military recruiting process, that's formal and in place. And when you see that type of leadership and formal programs put in place not just words, really encouraging, really inspirational, and that's how you actually get stuff done as you get even the consulting businesses, if you can't measure it, you can't improve it. >> Yeah Jeff, you're exactly right. And as Jennifer was talking, Julie is exactly who I was thinking about in my mind as well, because I think it takes strong leadership and courage to set bold bold goals, right? And you talked about a few of those bold goals and Julie has certainly been at the forefront of that. One of the goals we set in 2018 actually was as you said to achieve essentially a gender balance workforce. So 50% men, 50% women by 2025, I mean, that's ambitious for any company, but for us at the time we were 400,000 people. They were 500, 6,000 globally. So when you set a goal like that, it's a bold goal and it's a bold vision. And we have over 40% today, We're well on our path to get to 50%, I think by 2025. And I was really proud to share that goal in front of a group of 200 clients the day that it came out, it's a proud moment. And I think it takes leaders like Julie and many others by the way that are also setting bold goals, not just in my company to turn the dial here on gender equality in the workforce, but it's not just about gender equality. You mentioned something I think it's probably at as, or more important right now. And that's the fact that at least our leadership has taken a Stand, a pretty bold stand against social injustice and racism, >> Right which is... >> And so through that we've made some very transparent goals in North America in terms of the recruitment and retention of our black African American, Hispanic American, Latinex communities. We've set a goal to increase those populations in our workforce by 60% by 2025. And we're requiring mandatory training for all of our people to be able to identify and speak up against racism. Again, it takes courage and it takes a voice. And I think it takes setting bold goals to make a change and these are changes we're committed to. >> Right, that's terrific. I mean, we started the conversation with Grace Hopper, they put out an index for companies that don't have their own kind of internal measure to do surveys again so you can get kind of longitudinal studies over time and see how you're improving Inamarie, I want to go to you on the social justice thing. I mean, you've talked a lot about values and culture. It's a huge part of what you say. And I think that the quote that you use, if I can steal it is " no culture eats strategy for breakfast" and with the social injustice. I mean, you came out with special values just about what Zendesk is doing on social injustice. And I thought I was actually looking up just your regular core mission and value statement. And this is what came up on my Google search. So I wanted to A, you published this in a blog in June, taking a really proactive stand. And I think you mentioned something before that, but then you're kind of stuck in this role as a mind reader. I wonder if you can share a little bit of your thoughts of taking a proactive stand and what Zendesk is doing both you personally, as well as a company in supporting this. And then what did you say as a binder Cause I think these are difficult kind of uncharted waters on one hand, on the other hand, a lot of people say, hello, this has been going on forever. You guys are just now seeing cellphone footage of madness. >> Yeah Wow, there's a lot in there. Let me go to the mind reader comments, cause people are probably like, what is that about? My point was last December, November timing. I've been the Chief People Officer for about two years And I decided that it really was time with support from my CEO that Zendesk have a Chief Diversity Officer sitting in at the top of the company, really putting a face to a lot of the efforts we were doing. And so the mind reader part comes in little did I know how important that stance would become, in the may June Timing? So I joked that, it almost felt like I could have been a mind reader, but as to what have we done, a couple of things I would call out that I think are really aligned with who we are as a company because our culture is highly threaded with the concept of empathy it's been there from our beginning. We have always tried to be a company that walks in the shoes of our customers. So in may with the death of George Floyd and the world kind of snapping and all of the racial injustice, what we said is we wanted to not stay silent. And so most of my postings and points of view were that as a company, we would take a stand both internally and externally and we would also partner with other companies and organizations that are doing the big work. And I think that is the humble part of it, we can't do it all at Zendesk, we can't write all the wrongs, but we can be in partnership and service with other organizations. So we used funding and we supported those organizations and partnerships. The other thing that I would say we did that was super important along that empathy is that we posted space for our employees to come together and talk about the hurt and the pain and the experiences that were going on during those times and we called those empathy circles. And what I loved is initially, it was through our mosaic community, which is what we call our Brown and black and persons of color employee resource group. But it grew into something bigger. We ended up doing five of these empathy circles around the globe and as leadership, what we were there to do is to listen and stand as an ally and support. And the stories were life changing. And the stories really talked about a number of injustice and racism aspects that are happening around the world. And so we are committed to that journey, we will continue to support our employees, we will continue to partner and we're doing a number of the things that have been mentioned. But those empathy circles, I think were definitely a turning point for us as an organization. >> That's great, and people need it right? They need a place to talk and they also need a place to listen if it's not their experience and to be empathetic, if you just have no data or no knowledge of something, you need to be educated So that is phenomenal. I want to go to you Jennifer. Cause obviously the NBA has been very, very progressive on this topic both as a league, and then of course the Warriors. We were joking before. I mean, I don't think Steph Curry has ever had a verbal misstep in the history of his time in the NBA, the guy so eloquent and so well-spoken, but I wonder if you can share kind of inside the inner circle in terms of the conversations, that the NBA enabled right. For everything from the jerseys and going out on marches and then also from the team level, how did that kind of come down and what's of the perception inside the building? >> Sure, obviously I'm so proud to be part of a league that is as progressive and has given voice and loud, all the teams, all the athletes to express how they feel, The Warriors have always been committed to creating a diverse and equitable workplace and being part of a diverse and equitable community. I mean that's something that we've always said, but I think the situation really allowed us, over the summer to come up with a real formal response, aligning ourselves with the Black Lives Matter movement in a really meaningful way, but also in a way that allows us to iterate because as you say, it's evolving and we're learning. So we created or discussed four pillars that we wanted to work around. And that was really around wallet, heart, beat, and then tongue or voice. And Wallet is really around putting our money where our mouth is, right? And supporting organizations and groups that aligned with the values that we were trying to move forward. Heart is around engaging our employees and our fan base really, right? And so during this time we actually launched our employee resource groups for the first time and really excited and energized about what that's doing for our workforce. This is about promoting real action, civic engagement, advocacy work in the community and what we've always been really focused in a community, but this really hones it around areas that we can all rally around, right? So registration and we're really focused on supporting the election day results in terms of like having our facilities open to all the electorate. So we're going to have our San Francisco arena be a ballot drop off, our Oakland facilities is a polling site, Santa Cruz site is also a polling location, So really promoting sort of that civic engagement and causing people to really take action. heart is all around being inclusive and developing that culture that we think is really reflective of the community. And voice is really amplifying and celebrating one, the ideas, the (indistinct) want to put forth in the community, but really understanding everybody's culture and really just providing and using the platform really to provide a basis in which as our players, like Steph Curry and the rest want to share their own experiences. we have a platform that can't be matched by any pedigree, right? I mean, it's the Warriors. So I think really getting focused and rallying around these pillars, and then we can iterate and continue to grow as we define the things that we want to get involved in. >> That's terrific. So I have like pages and pages and pages of notes and could probably do this for hours and hours, but unfortunately we don't have that much time we have to wrap. So what I want to do is give you each of you the last word again as we know from this problem, right? It's not necessarily a pipeline problem, it's really a retention problem. We hear that all the time from Girls in Code and Girls in Tech. So what I'd like you to do just to wrap is just a couple of two or three sentences to a 25 year old, a young woman sitting across from you having coffee socially distanced about what you would tell her early in the career, not in college but kind of early on, what would the be the two or three sentences that you would share with that person across the table and Annabel, we'll start with you. >> Yeah, I will have to make a pitch for transportation. So in transportation only 15% of the workforce is made up of women. And so my advice would be that there are these fields, there are these opportunities where you can make a massive impact on the future of how people move or how they consume things or how they interact with the world around them. And my hope is that being at Waymo, with our self driving car technology, that we are going to change the world. And I am one of the initial people in this group to help make that happen. And one thing that I would add is women spend almost an hour a day, shuttling their kids around, and we will give you back that time one day with our self driving cars so that I'm a mom. And I know that that is going to be incredibly powerful on our daily lives. >> Jeff: That's great. Kate, I think I might know what you're already going to say, but well maybe you have something else you wanted to say too. >> I don't know, It'll be interesting. Like if I was sitting across the table from a 25 year old right now I would say a couple of things first I'd say look intentionally for a company that has an inclusive culture. Intentionally seek out the company that has an inclusive culture, because we know that companies that have inclusive cultures retain women in tech longer. And the companies that can build inclusive cultures will retain women in tech, double, double the amount that they are today in the next 10 years. That means we could put another 1.4 million women in tech and keep them in tech by 2030. So I'd really encourage them to look for that. I'd encouraged them to look for companies that have support network and reinforcements for their success, and to obviously find a Waymo car so that they can not have to worry where kids are on for an hour when you're parenting in a few years. >> Jeff: I love the intentional, it's such a great word. Inamarie, >> I'd like to imagine that I'm sitting across from a 25 year old woman of color. And what I would say is be authentically you and know that you belong in the organization that you are seeking and you were there because you have a unique perspective and a voice that needs to be heard. And don't try to be anything that you're not, be who you are and bring that voice and that perspective, because the company will be a better company, the management team will be a better management team, the workforce will be a better workforce when you belong, thrive and share that voice. >> I love that, I love that. That's why you're the Chief People Officer and not Human Resources Officer, cause people are not resources like steel and cars and this and that. All right, Jennifer, will go to you for the wrap. >> Oh my gosh, I can't follow that. But yes, I would say advocate for yourself and know your value. I think really understanding what you're worth and being willing to fight for that is critical. And I think it's something that women need to do more. >> Awesome, well again, I wish we could go all day, but I will let you get back to your very, very busy day jobs. Thank you for participating and sharing your insight. I think it's super helpful. And there and as we said at the beginning, there's no better example for young girls and young women than to see people like you in leadership roles and to hear your voices. So thank you for sharing. >> Thank you. >> All right. >> Thank you. >> Okay thank you. >> Thank you >> All right, so that was our diversity panel. I hope you enjoyed it, I sure did. I'm looking forward to chapter two. We'll get it scheduled as soon as we can. Thanks for watching. We'll see you next time. (upbeat music)

>> Announcer: From Miami Beach, Florida, it's theCUBE, covering Acronis Global Cyber Summit 2019. Brought to you by Acronis. >> Hey, welcome back everyone. It's theCUBE's coverage here in Miami Beach, Florida at the Fontainebleau Hotel for Acronis' Global Cyber Summit 2019's inaugural event with cyber protection, the new category that's emerging. It's been really exciting, it's a platform to really protect the data, protect cyber. Data protection's evolving to cyber protection. This is part of the Cloud 2.0 coverage that we've been covering on SiliconANGLE and theCUBE. Over the past year we're seeing more and more modernization of IT and systems. We're here with Ryan Davis, director of enterprise sales for Acronis. He's out on the front lines. This company has a great platform and a great field team out pushing the envelope, educating customers, having great success. I thought it would be great to have you on. Ryan, welcome to theCUBE. >> Ryan: Thank you for having me. >> So one of the things that I've observed and noticed with you guys is that you have a very strong field customer presence, you guys do a great job across the board on a direct touch basis, but also a huge channel operation, so you guys sell a lot through the channel, which is all good stuff, but you still got to talk to the big companies, still got to go to the large enterprises where you're having success. So you're doing that. What are some of the things that you're seeing when you're out pitching clients on Acronis, what are some of the concerns that you're hearing, what are the patterns, what's going on in the general broader market that's teasing out the Acronis value proposition? >> Sure, absolutely. So really where a lot of the focus and a lot of the attention is is on the edge. Five years ago, all the data was generated, produced, and analyzed in the core, in the data centers, whereas now, with the IoT devices, the proliferation of smart devices generating the data, they can't send it all to one central location. So networks are springing up out there in a distributed manner, and they have to be able to secure those smart devices and those edge networks. And that's where Acronis has a really compelling story, especially for enterprise. Because while they have a lot of consistency in the core, there's a lot of diversity on the edge. So it creates challenges for their IT teams to be able to manage it. So we can work with their field teams to provide a platform that can actually secure the devices in place and then protect them as well. >> So what's the pitch? Give us the pitch on that problem that you've just addressed, because that is legit. The edge is springing up, you're seen more and more edge cases and there's the outer edges, wearables, right? But the industrial edge, the company's edge, where you guys have a solution, that's challenging. The surface area for attacks are high, you have data as a challenge, you move compute to the data, you move data across the network, these are all costs, so costs are going up too. So with that problem, what is the pitch? >> Sure, well it really depends on who you're talking to, but there's two levels to it, right? So when you're talking industrial networks, the cost of downtime is huge, you know? You have 1,200 employees, at an automotive plant and you have a key industrial controller goes down, and that plant stops production, the cost is enormous. So at the plant level, they feel that pain, so they recognize the need for disaster recovery and business continuity capabilities. But when you start moving up a level at the executive level, it's what's really compelling and what's sexy for them. And that's really enabling digital transformation. And so I mentioned the concept of diversity a little bit earlier today. It's really hard for IT teams to do things on the edge when they may have 20,000, 40,000 edge devices that are going to run from NT, XP up to the most modern operating systems. It's difficult to implement a solution that's going to touch all of those devices. And backup and disaster recovery is critical for that, because if you're going to touch that many devices, you need the rollback capability. So being able to communicate a path forward to digital transformation on the edge is what is really exciting a lot of our executive customers. >> All right, so pretend I'm a customer for a minute, I'm like, hey Ryan, so hey, love the pitch, but I had XYZ data recovery company just came in earlier, they said they got an amazing platform. Why are you different, why should I not go with them? Why should I go with you? >> Sure, absolutely. Well all the competing vendors, all they know is the data center, right? So Acronis, part of our unique value proposition is not just the technology, it's really people, processes, and technology. So our experience working with industrial companies, pharmaceutical companies, working in compliant GXP, NERC CIP, this allowed us to develop expertise to come in not just with our product and the tech, but with people that know their environments and processes for successful implementation that other vendors can't bring. And our relationship with key automation vendors, we have our partners Honeywell, Emerson that embed our product, these are leading automation vendors that touch thousands of enterprises, and again, those experiences give us an understanding of these environments that other companies don't have. >> All right, so now I can come back and say, okay, well Ryan, you know, I like what you're saying, but I don't want to boil the ocean over. I don't see a path from what you're saying to execution. How can you help me figure this out? What do you offer me, as a client, if I'm the client, how do I get started? Is there a methodology, land, adopt, expand, how do you guys do that? >> Absolutely. Well, again, every customer's going to be different, right? But we don't like to boil the ocean either. What we're talking about is a path to digital transformation. We're not talking about the end result, right? So the first piece, the land, is always backup, right? When you backup the system, that provides a rollback mechanism so that provides an opportunity for you to do a lot more things with the computer. But the first piece is always just an assessment. You have to do an assessment, take stock of what you have, and Acronis is building technologies around discovery to help customers wrap their arms around these environments to make decisions on what they should do. >> So what's in it for me when I hear a platform, I hear about maybe complexity, is the platform really going to be the silver bullet? How do you manage that concern? >> Sure, sure. Well, most enterprises have at least five to seven different data protection solutions out there. So when you start talking about platform, you start talking kind of jargon words like unifying, consolidating their data protection suite. And that's really what Acronis is trying to do but not just in backup, but also offering more services through a single platform, so reducing the overall stack of tools that they're using to manage these environments. And again, going back to the edge, they don't have their big IT team that is versed in managing complex applications, right? You have controls engineers, plant engineers, scientists, that are interacting with these devices just enough to be dangerous. Think of it like a mechanic, so he's been working on cars his whole life, is very familiar with carburetors and brakes but now he gets a Tesla that's got sensors all over the place, and infotainment systems that run diagnostics, that doesn't make him an expert in that computer. So what Acronis is trying to do is provide you an easy-to-use platform that can solve multiple problems so that way a non-IT expert can service their compute infrastructure on the edge. >> So you guys have a good story for the edge. Also one story that's coming up here is ransomware. >> Correct. >> Ransomware is one of those disruptions that wasn't factored into the design of, you know, old-school legacy data protection and recovery systems. Those disruptions were hurricane, floods, some sort of mechanical failure, not a logical vector, in this case, security, which is going up high frequency. More and more every day, ransomware, malware, ZeroDay, others, incidents are on the rise. So more disruption. >> Correct. >> You guys are coming from that angle. >> Well, we're building security first into the platform. And that's a pivot that we made over the last 12 to 24 months. The first piece of that has already been released, which is called Active Protection, which is a module that actually monitors for changes and can prevent unauthorized changes to the file system like encryption. And so we're the only backup application that creates that proactive layer of protection. Everybody else is only going to be able to recover and be reactive. So we're trying to create a layered approach there and improve our customer security posture through an agent that's-- They would need to do the backup anyways. >> All right, so final track I want to chat with you about is take us through the real-life use case of an ideal sales process motion that encapsulates this modern era challenges and opportunities. You don't have to name the customer's name, you can use an anonymized case, but take use through what is a typical motion for you guys where you're successful, and what does it look like? >> Sure, absolutely. So it's pretty consistent, and I would say a pretty simple sales motion. The first piece is you have to do an assessment and a basic inventory in terms of what platforms are you going to have out there, and then, you're going to assess the sites that you have 'cause you need to create a deployment plan. And edge environments, it's not like the data center where you're just going to login to SCCM and push this out to your thousands of devices. They got to go to 40, 60 different plants. So you have to build, typically, a 12-month deployment plan where you're going to hit all of these different sites, build change windows, build maintenance windows. But before you can get to that, we do a POC on-site, where you touch, make sure that you have compatibility with the automation vendors, make sure you have compatibility with these networks, which are, again, very diverse and customized at each plant. Once you have a validated deployment process, you build out a timeline where you go site to site to site to deploy it. >> Take us through a POC. What does that look like, what's a typical POC for you guys? >> Sure, it's very simple based on what the ultimate objectives are. Most of our customers on the edge are primarily interested in business continuity, which would be backup, system recovery, application restore, right? On the edge it's not as much about the data, it's about securing the application that's performing the work, and so we protect the system, allow them to roll it back, once you validate that on the different platforms that they have, they're ready to move forward. >> And workloads are key criteria in all of this, that's a key factor. >> Absolutely, distributed control systems, R and D systems, lab systems, they have a lot of different types of applications you're not going to see in the data center, and we just want to get validated. >> John: So you hit your number? >> Absolutely, every year! (laughs) >> Over quota? >> Every year! >> All right. Ryan, thanks for coming on and sharing stories from the field, really appreciate it. >> Appreciate it, have a great one. >> CUBE Coverage here in Miami Beach, not a bad venue for a conference. This is the first conference that Acronis is putting on around cyber protection, Acronis' Global Cyber Summit 2019. Cyber protection new category emerging from the data protection world, this is the big story here. TheCUBE's covering two days, we'll be back with more after this short break. (electronic music)

>>From Miami beach, Florida. It's the cube covering a Cronus global cyber summit 2019. Brought to you by Acronis. >>So Ron, welcome back to the keeps coverage of kronas cyber global cyber summit 2019. I'm John furrier here in Miami beach. Our next guest is Pat Hurley, vice president, general manager of the Americas in sales and customer relationships. Get Debbie Juan. Hey, thanks for having me. Welcome to Miami beach. Lovely place to have an event. So I hear ya. You got a lot of competition going on between the U S America's in the AMIA teens and it's very competitive group. >> The European team is very confident. I think we'll show them tomorrow what we're made of. We've been recruited very hard for some players that are Latin American. I think we'll show them a finger too. You've got a big soccer story there. We do. Yeah. We've, uh, we've got a few sports partnerships that we have across the globe. Uh, some of the first partnerships we had were actually within formula one. >>And we really try to correlate the story of the importance of, uh, data protection and cyber protection in the sporting industry because a lot of people don't think about the amount of data that's actually being generated in the space. A formula one car generates between, you know, two and three terabyte through three gigabytes of data on every lap, tons of telemetry devices that are kicked, collecting information from the car, from the road service, from the, the general environment. They're taking that data and then sending it back to the headquarter, analyzing it and making very small improvements to the car to make sure that they can qualify faster, run a faster lap, make the right type of angle into a turn, uh, which can really differentiate them from being, you know, first, second, third, 10th in a qualifying session. On the soccer side. We do have some partnerships with uh, arsenal, Manchester city, inter Milan, and we just signed a partnership as well with Liverpool. >>So we are very popping in that space here in the U S we have some other sports that we're big fans of. I'm personally a big Boston red Sox fan, being a Boston native and we do have a sports partnership with the red Sox, which has been an unbelievable partnership with them. And learning more about the use cases that they solve and using our technology has been really cool. >> You know, Patty, you bring up the sports thing and we were kidding before we on camera around the trading, you know how people do sports deals and they trade, you know, merchandise for consumer benefit or customer benefits. But really what is happening is sports teams encapsulate really the digital transformation in a nutshell because most sports franchises are, have been traditionally behind. But now with the consumerization of it and digital can go back to 2007 since the mobile phone. >>Really, I mean it's iPhone. Yeah. Since that time, sports and capsulates every aspect of it, consumer business fan experience. And it really has every, every, almost every element of what we see now as a global IOT problem opportunity. So it really encapsulates the use case of an integrated and and needed solution. Oh yeah, absolutely. I mean, if you think about the amount of data that's, that's out there today and the fast way that it's growing, you know, the explosion of, uh, of data in the, in the world today, sports have different unique challenges. So obviously they have large fan bases that need to be able to access the data and understand what's going on with their favorite sports teams. Um, for us it's really, you know, these technology partnerships that we have with these guys, it runs through all these different areas of, you know, in many cases we didn't really understand that they were using it for. >>So, you know, the red Sox for example, they've got Fenway park and iconic stadium, you know, the Mecca of baseball. If you haven't been there yet, I suggest all your viewers that they go and check it out, give me a call, we'll try and get you set up there. But, um, you know, the, the, the experience that the fans have there is all around their data experienced there. Right? And it's not just baseball games. It could be hockey games that Fenway park, it could be a concert that they're having. A phone buys a lot of different events. These stadiums are open year round and the ability to move, share access, protect the data in that stadium is really important to how they're functioning as an organization. We talked to their I-Team quite regularly about how they're using our solutions. They're talking about uh, different aspects of artificial intelligence, different ways they can use our products and machine learning. >>Obviously with the new solutions that we have in the market today around cybersecurity or helping them to address other challenges that they face. Um, as an organization, these are realtime challenges in their physical locations, national security issues, terrorist attacks could happen. There are venues, there are public gathering places too. Absolutely. We announced our partnership with them back in may and I was shocked to hear them on the main stage announcing that they had this great partnership with the Kronos was talking about their unique cyber security needs. They started talking about drone technology and I'm thinking, all right, a drone flies in the stadium. Maybe at breaks and it falls on a player and we're paying $20 million for one of these pitchers to be out there on the Hill or an interest, a fan or maybe they're collecting some video data to then share it out. >>And that's red Sox IP. No, they're talking about cybersecurity threats in the sense that a drone, a remotely controlled device could come in and lightened incendiary device in the, in the stadium and that to them as a real security server. And that's frontline for the it guys. That's what keeps them up at night. Yeah. And that's really an attack take time. Oh yeah, absolutely. What are the use cases that are coming out of some of your customers, cause you guys have a unique integrated solution with a platform as an end to end component too. You have a holistic view on data, which is interesting and unique. People are kind of figuring this out, but you guys are ahead of the game. What are some of the use cases that you've seen in the field with customers that highlight the benefits of taking a holistic view of the data? >>Yeah, absolutely. So we look at it as kind of backups dead, right? We have, we've combined the old world of backup and disaster recovery with the new world of cybersecurity and we combine that to a term we're calling cyber protection because it really requires an end to end solution and a lot of different things need to be working properly to prevent these attacks from happening. Uh, you need to be very proactive in how you're going about that. We address it with what we call 'em, the Kronos cyber platform. And what this is, is a unique, multi-tiered multi-tenant offering that's designed specifically for service providers. We have just under 6,000 servers, providers actively selling our cyber protection solutions today and they use this for are for a multiple different aspects. And usually the beachhead has something like backup. Every company needs backup. It's more of a commodity type solutions, a lot of different players in the game out there, but they take it a step further, use that same backup technology to then do disaster recovery. >>They can do files, they can share, they can do monitoring. We have notary solutions based on blockchain technologies. Now, this whole suite of cybersecurity solutions, all of this is with a single pane of glass, one platform that of a service provider can go in and work with their customers and make sure that their data is protected, make sure that their physical machines are virtual machines, they're PCs, their Macs are all protected, that data's protected, it's secure, but it's also accessible, which is an important part of you can take your data wrapping a nice bow buried a hundred feet underground, but then you can't use it, right? So you want to be able to make sure that you can actually, uh, leverage the technology there. Um, we've seen explosive growth, especially in, in my market. I think the numbers are pretty crazy. It's something like 90% of the market today in the U S has served in some capacity by a service provider. >>And this could be a small to medium size business that's served by local service fire to those really big guys that are out there. Let's on with how large your target audience, you mentioned search probably multiple times when you're out selling your target persona, your target audience, and you're trying to reach into, so we touch, everybody know, you equate it to kind of what we do with the red Sox, right? You walk into that city and the 38,000 people that, well, some of those people are just, you know, regular Joe's, right? They, they go to work every day. They have a computer at home, they have a mobile device. They probably have multiple mobile devices. We protect that for them. We call them a consumer. Slash. Prosumers. We work at a lot of very large retail organizations. If you walk into some of those shops today, you'll be able to see our software on a shelf there. >>You work with one of those tech squads where they're starting to attach services to it and you get more of a complete offering there. We then scale up a little bit further to some OEM providers. You work with companies like Honeywell and Emerson that are manufacturing devices that embed our software on there. They white label it and deliver it out. These are connected devices. You think about the, you know the, the explosion of IOT devices in the market today. We're protecting that stuff as well. We work with very large enterprises, so some of the, the major players that you see in the manufacturing space are standing up standardizing on Acronis process control process automation vendors are using our Chronis and we can deliver the solution because of the way it's so flexible in a very consumable way for them. Those enterprises can actually act as a service provider for their employees so we can actually take our technology, deploy the layer in their infrastructure where they have complete control. >>They might not want to be in an Uber cloud, they might not want to work with Chrome OS data center. They want to have and hold that data. They want to make sure it's on site. We enable that type of functionality and then the fastest growing area for us is what I hit on earlier within the service provider community. We're recruiting hundreds of service providers every quarter. We've got some great partners here. Give you an example of a service provider. You mentioned the red size, I'm assuming is that a vendor that might be working within that organization, but still it sounds like that's a supplier to the red Sox. How, how broad is that definition? It gives us many points. Yeah, it's a really good point. So we work with hosting providers. Look, can be regional hosting providers to multinational hosting providers. Some of the very big names that you've, you're probably familiar with. >>We work with, uh, we work with, uh, telco providers who work with ISV providers or sorry, ISP providers, um, kind of regional telco providers that provide a myriad of different services all the way down to your kind of local mom and pop type service providers where you've got a small business, maybe they've got 30 to 50 employees, they're servicing probably 200 to 300 customers and they want to provide a very secure, safe, easy to use complete solution to their customers. Uh, those could be focused on certain verticals so they could be focused on healthcare, financial services, construction, et cetera. Um, we have some that are very niche within like dental services or chiropractice offices, small regional doctor's offices. Uh, and the, the beauty of that, and I was getting the partners earlier, is we have partnerships with companies like ConnectWise where those are tools that service providers are using on a very daily basis. >>So essentially the platform gives you that range and that's the typical typical platform. So you have that broad horizontally scalable capability and the domain expertise either be what solution from you guys or can ISV or someone within your ecosystem is that they get that. Right? Absolutely. And that's what really differentiates us is our ability to integrate into that plat, into our platform, into their platform and make those connections. So you don't need to learn 12, 14, 15 different technologies. You've got a small suite of offerings in a single pane of glass, very easy to use, very intuitive. Um, the integrations that we have with these partners like ConnectWise, like Ingram micro, really differentiate us because what they do is they provide open API capabilities. They provide software development kits where these partners can go ahead and build it the way they want to sell it. >>You know, it's interesting when the cloud came out and as on premise has changed to a much more agile dev ops kind of mindset that forced it to think like a service provider. I think like an operating system, it's an operating environment basically. So that service provides an interesting angle and I want to get your thoughts on this because I think this is where you guys have such a unique opportunity to just integrate solution because you could get into anything and you got ISV to back that up. So I guess the question I would have is for that enterprise that's out there that's looking to refactor and replatform their entire operation, or it could be a large enterprise, it has a huge IOT opportunity or challenge or a service provider is looking at having a solution. What's the pitch that you would give me if I'm the one of those customers? >>Say, Hey Pat, what's the pitch? So you need a, you need a trusted provider that's been in the business for a number of years that understands the data protection and security markets that Kronos has that brand. We've been doing this for about 16 years. We were founded in Singapore, we're headquartered out of Switzerland and we've got a lot of really smart guys in the back room. Was building good technologies that our partners were able to use. Um, we look at it a lot of different ways. I mentioned our go to market across a lot of different verticals and a lot of different um, kind of routes for those. The way we deliver our solution. It provides the flexibility for an enterprise to a classic reseller to um, you know, a VAR or a service, right? It's delivering services. It can be delivered to those guys how they want to consume it. >>So as an example, we may work with a smaller service provider that doesn't have any colo capabilities. We provide data centers so they could have a very quick turnkey solution, allows them to get up and running with their business, selling backup within minutes to their customers. We can also work with very large enterprises where we can deliver the complete platform to them and then they have complete control over it. We sprinkle in some professional services to make sure that we're giving them the support that they need and then they're running the service for themselves. What we've really seen in terms of a trend is that a lot of these VARs, we have about 4,500 of them in North America and they're starting to look at their businesses differently. Say, I gotta adapt or die here. I gotta figure out what my next business model is. >>How am I going to be the next one that's in the news flash that says, Hey, they've been acquired, or Hey Thoma Bravo made a big investment in me. Right? They need to convert to this services business or Kronos enables that transformation to happen. I mean, I can see you guys really making money for channel partners because they want solutions. They want to touch the customer, they want to maybe add something they could bring into it or have high service gross profits around services. Absolutely. So, yeah, our solution is unique in the sense that allows partners to sell multiple offerings to, you're getting an additional layer of stickiness providing multiple solutions to a customer. You're using the same technology, so your it team is very familiar with what they're using on a daily basis. Um, you're reducing the amount of churn for your customers because you're selling so much additional there that they're really stuck with you. >>That's a good thing. Uh, and beyond that, your increasing ARPU, average revenue per user is a key metric that all of our partners are looking at. And these guys are owner operators, right? They're business owners. They're looking at the bottom line. I mean, it's interesting the operating leverage around the consistent platform just lowers, it gives them software economic model. They can get more profit over time as they make that investment look at at the end of the day, channel partners care about a couple things, money, profit and customer happiness. Absolutely. And it helps to have them want to have a lot of one offs and a lot of, you know, training, you know, anything complicated, anything confusing, anything that requires a lot of resources, they're not going to like a, it's also great to have events like this where you're able to, to press the flesh with these guys and, and being face to face and understand their real world challenges that they're dealing with on a daily basis. >>How has the sport's a solution set that you've been involved in? How has that changed the culture of Acronis? Is that, has that, has that changed as, you know, sports is fun. People love sports, they have real problems. It's a really great use case as well. How's that change the culture? It's been amazing. I, so one from a branding perspective, we are a lot more recognized, right? Um, the most important thing about these partnerships for us is that they're actually using the technology. So, you know, we've got the red Sox here with us today. We've got arsenal represented, we've got Williams, we've got Roush racing, we've got a NASCAR car back here. Um, they use our technology on a daily basis and for each one of them we solve different types of use cases. Whether it's sending them large amount of video data from an essence studio over to Fenway park, or if it's a scout out in the field that needs to send information back and their laptop crashes, how do they recover? >>A lot of these different use cases, you can call them right back to a small business owner. You don't have to be a multibillion dollar sports organization with the same challenge. Well, I'm smiling because we've been called the ESPN of tech to they bring our set. We do let the game day thing. We certainly could love to come join you in all these marquee events that you have. We'd love to have it. Yeah, so if you follow us on social, we're out there and that, that's a big part of it. You mentioned one of ours looking for what our partners looking for. They want a personal relationship too. A lot of that goes away with technology nowadays and being able to really generate that type of a, of a personal relationship. These partnerships enable that to happen and they're very anything, I don't know anything about cars. >>We started partnering with formula one. All of a sudden I know everything about 41 I go to these races. I tell everybody I don't know anything about cars and I ended up being the, the subject matter export for him over over the weekend. So we'd love to have you guys join us. We'd love all of our partners. They get more engaged in the sports aspect of it because for us, it really is something that, um, again, they're using us in real life scenarios. We're not paying to put a sticker on a car that's going 300 miles. It's not traveling as a real partnership. Exactly. Pat, congratulations on your success and good luck on people owning away the numbers. Congratulations. Thank you very much. Just the cube coverage here at the Chronis global cyber summit 2019 I'm John furry. More coverage after this short break.

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

>> Announcer: Live, from Stanford University in Palo Alto, California, it's theCUBE covering Women in Data Science Conference 2018, brought to you by Stanford. (light techno music) >> Welcome back to theCUBE's continuing coverage of the Women in Data Science event, WiDS 2018. We are live at Stanford University. You can hear some great buzz around us. A lot of these exciting ladies in data science are here around us. I'm pleased to be joined by my next guest, Bhavani Thuraisingham, who is one of the speakers this afternoon, as well as a distinguished professor of computer science and the executive director of Cyber Security Institute at the University of Texas at Dallas. Bhavani, thank you so much for joining us. >> Thank you very much for having me in your program. >> You have an incredible career, but before we get into that I'd love to understand your thoughts on WiDS. In it's third year alone, they're expecting to reach over 100,000 people today, both here at Stanford, as well as more than 150 regional events in over 50 countries. When you were early in your career you didn't have a mentor. What does an event like WiDS mean to you? What are some of the things that excite you about giving your time to this exciting event? >> This is such an amazing event and just in three years it has just grown and I'm just so motivated myself and it's just, words cannot express to see so many women working in data science or wanting to work in data science, and not just in U.S. and in Stanford, it's around the world. I was reading some information about WiDS and I'm finding that there are WiDS ambassadors in Africa, South America, Asia, Australia, Europe, of course U.S., Central America, all over the world. And data science is exploding so rapidly because data is everywhere, right? And so you really need to collect the data, stow the data, analyze the data, disseminate the data, and for that you need data scientists. And what I'm so encouraged is that when I started getting into this field back in 1985, and that was 32 plus years ago in the fall, I worked 50% in cyber security, what used to be called computer security, and 50% in data science, what used to be called data management at the time. And there were so few women and we did not have, as I said, women role models, and so I had to sort of work really hard, the commercial industry and then the MITRE Corporation and the U.S. Government, but slowly I started building a network and my strongest supporters have been women. And so that was sort of in the early 90's when I really got started to build this network and today I have a strong support group of women and we support each other and we also mentor so many of the junior women and so that, you know, they don't go through, have to learn the hard way like I have and so I'm very encouraged to see the enthusiasm, the motivation, both the part of the mentors as well as the mentees, so that's very encouraging but we really have to do so much more. >> We do, you're right. It's really kind of the tip of the iceberg, but I think this scale at which WiDS has grown so quickly shines a massive spotlight on there's clearly such a demand for it. I'd love to get a feel now for the female undergrads in the courses that you teach at UT Dallas. What are some of the things that you are seeing in terms of their beliefs in themselves, their interests in data science, computer science, cyber security. Tell me about that dynamic. >> Right, so I have been teaching for 13 plus years full-time now, after a career in industry and federal research lab and government and I find that we have women, but still not enough. But just over the last 13 years I'm seeing so much more women getting so involved and wanting to further their careers, coming and talking to me. When I first joined in 2004 fall, there weren't many women, but now with programs like WiDS and I also belong to another conference and actually I shared that in 2016, called WiCyS, Women in Cyber Security. So, through these programs, we've been able to recruit more women, but I would still have to say that most of the women, especially in our graduate programs are from South Asia and East Asia. We hardly find women from the U.S., right, U.S. born women pursuing careers in areas like cyber security and to some extent I would also say data science. And so we really need to do a lot more and events like WiDS and WiCys, and we've also started a Grace Lecture Series. >> Grace Hopper. >> We call it Grace Lecture at our university. Of course there's Grace Hopper, we go to Grace Hopper as well. So through these events I think that, you know women are getting more encouraged and taking leadership roles so that's very encouraging. But I still think that we are really behind, right, when you compare men and women. >> Yes and if you look at the statistics. So you have a speaking session this afternoon. Share with our audience some of the things that you're going to be sharing with the audience and some of the things that you think you'll be able to impart, in terms of wisdom, on the women here today. >> Okay, so, what I'm going to do is that, first start off with some general background, how I got here so I've already mentioned some of it to you, because it's not just going to be a U.S. event, you know, it's going to be in Forbes reports that around 100,000 people are going to watch this event from all over the world so I'm going to sort of speak to this global audience as to how I got here, to motivate these women from India, from Nigeria, from New Zealand, right? And then I'm going to talk about the work I've done. So over the last 32 years I've said about 50% of my time has been in cyber security, 50% in data science, roughly. Sometimes it's more in cyber, sometimes more in data. So my work has been integrating the two areas, okay? So my talk, first I'm going to wear my data science hat, and as a data scientist I'm developing data science techniques, which is integration of statistical reasoning, machine learning, and data management. So applying data science techniques for cyber security applications. What are these applications? Intrusion detection, insider threat detection, email spam filtering, website fingerprinting, malware analysis, so that's going to be my first part of the talk, a couple of charts. But then I'm going to wear my cyber security hat. What does that mean? These data science techniques could be hacked. That's happening now, there are some attacks that have been published where the data science, the models are being thwarted by the attackers. So you can do all the wonderful data science in the world but if your models are thwarted and they go and do something completely different, it's going to be of no use. So I'm going to wear my cyber security hat and I'm going to talk about how we are taking the attackers into consideration in designing our data science models. It's not easy, it's extremely challenging. We are getting some encouraging results but it doesn't mean that we have solved the problem. Maybe we will never solve the problem but we want to get close to it. So this area called Adversarial Machine Learning, it started probably around five years ago, in fact our team has been doing some really good work for the Army, Army research office, on Adversarial Machine Learning. And when we started, I believe it was in 2012, almost six years ago, there weren't many people doing this work, but now, there are more and more. So practically every cyber security conference has got tracks in data science machine learning. And so their point of view, I mean, their focus is not, sort of, designing machine learning techniques. That's the area of data scientists. Their focus is going to be coming up with appropriate models that are going to take the attackers into consideration. Because remember, attackers are always trying to thwart your learning process. >> Right, we were just at Fortinet Accelerate last week, theCUBE was, and cyber security and data science are such interesting and pervasive topics, right, cyber security things when Equifax happened, right, it suddenly translates to everyone, male, female, et cetera. And the same thing with data science in terms of the social impact. I'd love your thoughts on how cyber security and data science, how you can educate the next generation and maybe even reinvigorate the women that are currently in STEM fields to go look at how much more open and many more opportunities there are for women to make massive impact socially. >> There are, I would say at this time, unlimited opportunities in both areas. Now, in data science it's really exploding because every company wants to do data science because data gives them the edge. But what's the point in having raw data when you cannot analyze? That's why data science is just exploding. And in fact, most of our graduate students, especially international students, want to focus in data science. So that's one thing. Cyber security is also exploding because every technology that is being developed, anything that has a microprocessor could be hacked. So, we can do all the great data science in the world but an attacker can thwart everything, right? And so cyber security is really crucial because you have to try and stop the attacker, or at least detect what the attacker is doing. So every step that you move forward you're going to be attacked. That doesn't mean you want to give up technology. One could say, okay, let's just forget about Facebook, and Google, and Amazon, and the whole lot and let's just focus on cyber security but we cannot. I mean we have to make progress in technology. Whenever we make for progress in technology, driver-less cars or pacemakers, these technologies could be attacked. And with cyber security there is such a shortage with the U.S. Government. And so we have substantial funding from the National Science Foundation to educate U.S. citizen students in cyber security. And especially recruit more women in cyber security. So that's why we're also focusing, we are a permanent coach here for the women in cyber security event. >> What have some of the things along that front, and I love that, that you think are key to successfully recruiting U.S. females into cyber security? What do you think speaks to them? >> So, I think what speaks to them, and we have been successful in recent years, this program started in 2010 for us, so it's about eight years. The first phase we did not have women, so 2000 to 2014, because we were trying to get this education program going, giving out the scholarships, then we got our second round of funding, but our program director said, look, you guys have done a phenomenal job in having students, educating them, and placing them with U.S. Government, but you have not recruited female students. So what we did then is to get some of our senior lecturers, a superb lady called Dr. Janelle Stratch, she can really speak to these women, so we started the Grace Lecture. And so with those events, and we started the women in cyber security center as part of my cyber security institute. Through these events we were able to recruit more women. We are, women are still under-represented in our cyber security program but still, instead of zero women, I believe now we have about five women, and that's, five, by the time we will have finished a second phase we will have total graduated about 50 plus students, 52 to 55 students, out of which, I would say about eight would be female. So from zero to go to eight is a good thing, but it's not great. >> We want to keep going, keep growing that. >> We want out of 50 we should get at least 25. But at least it's a start for us. But data science we don't have as much of a problem because we have lots of international students, remember you don't need U.S. citizenship to get jobs at Facebook or, but you need U.S. citizenships to get jobs as NSA or CIA. So we get many international students and we have more women and I would say we have, I don't have the exact numbers, but in my classes I would say about 30%, maybe just under 30%, female, which is encouraging but still it's not good. >> 30% now, right, you're right, it's encouraging. What was that 13 years ago when you started? >> When I started, before data science and everything it was more men, very few women. I would say maybe about 10%. >> So even getting to 30% now is a pretty big accomplishment. >> Exactly, in data science, but we need to get our cyber security numbers up. >> So last question for you as we have about a minute left, what are some of the things that excite you about having the opportunity, to not just mentor your students, but to reach such a massive audience as you're going to be able to reach through WiDS? >> I, it's as I said, words cannot express my honor and how pleased and touched, these are the words, touched I am to be able to talk to so many women, and I want to say why, because I'm of, I'm a tamil of Sri Lanka origin and so I had to make a journey, I got married and I'm going to talk about, at 20, in 1975 and my husband was finishing, I was just finishing my undergraduate in mathematics and physics, my husband was finishing his Ph.D. at University of Cambridge, England, and so soon after marriage, at 20 I moved to England, did my master's and Ph.D., so I joined University of Bristol and then we came here in 1980, and my husband got a position at New Mexico Petroleum Recovery Center and so New Mexico Tech offered me a tenure-track position but my son was a baby and so I turned it down. Once you do that, it's sort of hard to, so I took visiting faculty positions for three years in New Mexico then in Minneapolis, then I was a senior software developer at Control Data Corporation it was one of the big companies. Then I had a lucky break in 1985. So I wanted to get back into research because I liked development but I wanted to get back into research. '85 I became, I was becoming in the fall, a U.S. citizen. Honeywell got a contract to design and develop a research contract from United States Air Force, one of the early secure database systems and Honeywell had to interview me and they had to like me, hire me. All three things came together. That was a lucky break and since then my career has been just so thankful, so grateful. >> And you've turned that lucky break by a lot of hard work into what you're doing now. We thank you so much for stopping. >> Thank you so much for having me, yes. >> And sharing your story and we're excited to hear some of the things you're going to speak about later on. So have a wonderful rest of the conference. >> Thank you very much. >> We wanted to thank you for watching theCUBE. Again, we are live at Stanford University at the third annual Women in Data Science Conference, #WiDs2018, I am Lisa Martin. After this short break I'll be back with my next guest. Stick around. (light techno music)

>> Hi, I'm Peter Burris, and welcome to Wikibon's Action Item. (funky electronic music) We're broadcasting, once again, from theCUBE studios in lovely Palo Alto. And I've got the Wikibon research team assembled here with me. I want to introduce each of them. David Floyer. >> Hi. >> George Gilbert are here in the studio with me. Remote we have Jim Kobielus, Stu Miniman, and Neil Raden. Thanks everybody for joining. Now, we're going to talk about something that is increasingly overlooked, that we still think has enormous importance in the industry. And that is, does hardware matter? For 50 years, in many respects, the rate of change in industry has been strongly influenced, if not determined by the rate of change in the underlying hardware technologies. As hardware technologies improved, the result was that software developers would create software that would fill up that capacity. But we're experiencing a period where some of the traditional approaches to improving hardware performance are going down. We're also seeing that there is an enormous, obviously, move to the cloud. And the cloud is promising different ways of procuring the infrastructure capacity that businesses need. So that raises the question with potential technologies constraints on the horizon, and an increasing emphasis on utilization of the cloud, is systems integration and hardware going to continue to be a viable business option? And something that users are going to have to consider as they think about how to source their infrastructure? Now there are a couple of considerations today that are making this important right now. Jim Kobielus, what are some of those considerations that increase the likelihood that we'll see some degree of specialization that's likely to turn into different hardware options? >> Yeah Peter, hi everybody. I think one of the core considerations is that edge computing has become the new approach to architecting enterprise and consumer grade applications everywhere. And edge computing is nothing without hardware on the edge, devices as well as hubs and gateways and so forth, to offload and the handle much of the processing needed. And increasingly, it's AI, artificial intelligence. deep learning, machine learning. So going forward now, looking at how it's shaping up, hardware's critically important. Burning AI, putting AI onto chipsets, low power, low cost chips that can do deep learning, machine learning, natural language processing, fast, cheaply, in an embedded form factor, critically important for the development of edge computing as a truly end-to-end distributed fabric for the next generation of application. >> So Jim, are we likely to see greater specialization of some of those AI algorithms and data structures and what not, drive specialization and the characteristics of the chips that support it, or is it all going to be just default down to tensor flow or GPUs? >> It has been GPUs for AI. Much of AI, in terms of training and inferencing, has been in the cloud, and much of it has been based historically, heretofore, on GPUs, and video being the predominant provider. However, GPUs historically have not been optimized for AI, because they've been built for gaming and consumer applications. However, the next generation, the current generation, from Nvidia and others, are chipsets in the cloud and other form factors for AI, incorporates what's called tensor core processing, really a highly densely packed tensor core processing components to be able to handle deep learning neural networks, very fast, very efficiently for inferencing and training. So Nvidia and everybody else now is making a big bet on tensor core processing architecture. Of course Google's got one of the more famous ones, their TPU architecture, but they're not the only ones. So going forward, we're looking at, in the AI ecosystem, especially for edge computing, there increasingly will be a blend of GPUs like for cloud based core processing, TPUs or similar architecture, or device-level processing. But also, FPGAs, A6, and CPUs are not out of the running because for example, CPUs are critically important for systems on the chip, which are quite fundamentally important for unattended operation as well as attended operation in terms of edge devices to handle things like natural language processing for conversational UIs. >> So that suggests that we're going to see a lot of new architecture thinking introduced as a consequence of trying to increase the parallelism through a system by incorporating more processing at the edge. >> Jim: Right. >> That's going to have an impact on volume economics and where the industry goes from an architecture standpoint. David Floyer, does that ultimately diminish the importance of systems integration as we move from the edge back towards the core and towards cloud in whatever architectural form it takes? >> I think the opposite, it actually is, systems integration becomes more important. And the key question has been can software do everything? Do we need specialized hardware for anything? And the answer is yes, because the standard x86 systems are just not improving in speed at all. >> Why not? >> That's a long answer to that. But it's to do with the amount of heat that's produced, and the degree of density that you can achieve. Even the chip itself-- >> So the ability to control bits flying around the chip-- >> Correct. >> Is going down-- >> Right. >> As a consequence of dispersion of energy and heat into the chip. >> Right, There are a lot of other factors as well. >> Other reasons as well, sure. >> But the important thing is, how do you increase the speed? And a standard x86 cycle time with it's instruction set, that's now fixed. So what can you do? Well, you can obviously, reduce the number of instructions and then parallelize those instructions within that same space. And that's going to give you a very significant improvement. And that's the basis of GPUs and FPGAs. So GPUs for example, you could have floating point arithmetic, or standard numbers or extended floating point arithmetic. All of those help in calculations, large scale calculations. The FPGAs are much more flexible. They can be programmed in very good ways, so they're useful for smaller volume things. A6 are important, but what we're seeing is a movement to specialized hardware to process AI in particular. And one area is very interesting to me is, to take the devices at the edge, what we call the level one systems. Those devices need to be programmed very, very intently for what is happening there. They are bringing all the data in, they're making that first line reduction of data, they're making the inferences, they're taking the decisions based on that information coming in and then sending much less data up to the level twos above it. So what are examples of this type of system that exist now? Because in hardware, volume matters. The amount of stuff you produce, the costs go down dramatically. >> And software too, in the computing industry, volume matters. >> Absolutely, absolutely. >> I think it's pretty safe to say that. >> Yeah, absolutely. So volume matters, so it's interesting to look at one of the first real volume AI applications, which is in the iPhone X. And Apple have introduced the latest chipset. It has neural networks within it. It has GPUs built in, and it's being used for simple things like face recognition and other areas of AI. And the interesting thing is the cost of this. The cost of that whole set, the chip itself, is $27. The total cost with all the senors and everything, to do that sort of AI work is $100. And that's a very low bar, and very, very difficult to introduce in other ways. So this level of integration for the consumer business in my opinion, is going to have a very significant effect on the choices that are made by manufacturers of devices going into industry and other things. They're going to take advantage of this in a big way. >> So Neil Raden, we've heard, or we've been down the FPGA road for example, in the past, data warehousing introduced, or it was thought that data warehouse workloads which did not necessarily lend themselves to a lot of the prevailing architectures in the early 90s, could get this enormous acceleration by giving users greater programmable control over the hardware. How'd that work out? >> Well, for Intersil for example, what actually worked out pretty well for awhile. But what they did is they used that PGA to handle the low-level data stuff and maybe reducing the complexity of the query before it was passed on to the CPUs where things ran in parallel. But that was before Intel introduced multi-core chips. And it kind of killed the effectiveness. And the other thing was, it was highly proprietary which made it impossible to take up to the cloud. And there was no programming. I always laugh when people say FPGA because it should have been called FGA. Because there was no end user computing of an FPGA. >> So that means that, although we still think we're going to see some benefit from this. But it kind of brings us back to the cloud, because if hardware economics are improved to scale, then that says that there are a few companies that are likely to drive a lot of the integration issues. If things like FPGAs don't get broadly diffused and programmed by large numbers of people, but we can see how they could, in fact, dramatically improve the performance, and quality of workloads, then it suggests that some of these hyperscalers are going to have an enormous impact ultimately on defining what constitutes systems integration. Stu, take us through some of the challenges that we've heard recently on the cloud, or on theCUBE at reinvent and other places, about how we start seeing some of the hyperscalers make commitments about specialized hardware, the role that systems integration's going to play, and then we'll talk about whether that could be replicated across more on-premise types of systems. >> Sure Peter, and to go back to your opening remarks for this segment, does hardware matter? When we first saw cloud computing roll out, many people thought that this was just undifferentiated commodity equipment. But if you really dig in and understand what the hyperscalers, the public cloud companies are doing, they really do what I've called hyperoptimize the solution. So when James Hamilton and AWS talks about their infrastructure, they don't just take components and throw a bunch of stuff from off the shelf out there. They build for every application, a configuration, and they just scale that to tens of thousands of nodes. So like what we had done in the enterprise before, which was build a stack for an application, now the public cloud does that for services and for applications that they're building up the stack. So hardware absolutely matters. And if we look not only at the public cloud, but you mentioned on the enterprise side, it's where do I need to think about hardware? Where do I need to put time and effort? What David Floyer's talked about is that integration is still critically important. But the enterprise should not be worrying about taking all of the pieces and putting them together. They should be able to buy solutions, leverage platforms that take care of that environment. Very timely discussion about all of the Intel issues that are happening. If I'm using a public cloud, well I don't have to necessarily worry about, I need to worry about that there was an issue, but I need to go to my supplier (chuckles) and make sure that they are handling that. And if I'm using serverless technology, obviously I'm a little bit detached from what that, whether or not I have that issue, and how that gets resolved. So absolutely, hardware is important. It's just, who manages that hardware, what pieces I need to think about, and where that happens. And the fascinating stuff happening in the AI pieces that Jim's been talking about, where you're really seeing some of the differentiation and innovation happening at the hardware level, to make sure that it can react for those applications that need it. >> So we've got this tension in the model right now. We've got this tension in the marketplace, where a lot of the new design decisions are going to be driven by what's happening at the edge. As we try to put more software out to where more human activity or system activity's actually taking place. And at the same time, a lot of the new design and architecture decisions being, first identified and encountered by some of the hyperscalers. The workloads are at the edge, the new design decisions are at the hyperscaler, latency is going to ensure that there is a fair amount of, a lot of workload that remains at the edge, as well as cost. So what does that mean for that central class of system? Are we going to see, as we talk about, TPC, true private cloud, becoming a focal point for new classes of designs, new classes of engineering? Are we going to see a Dell-EMC box that says, "designed in Texas," or "designed in Hopkinton," and is that going to matter to users? David Floyer, what do we think? >> So it's really important from the consumer point, from the customer's point of view, that they can deal with a total system. So if they want a system at the very edge, the level one we want, to do something in the manufacturing, they may go to Dell, but they may also go to Sony or they may go to Honeywell or NCL-- >> Rahway, or who knows. >> Rahway, yes, Alibaba. There are a whole number of probably new people that are going to be in that space. When you're talking about systems on site for the high level systems, level two and above, then they are going to be very, it will be very important to them that the service level that comes from the manufacturer, the integration of all the different components, both software and hardware, come from that manufacturer. He is organizing it from a service perspective. All of those things become actually more important in this environment. It's more complex, there are more components. There are more FPGAs and GPUs and all sorts of other things, connected together, it'll be their responsibility as the deliverer of a solution, to put that together and to make sure it works, and that it can be serviced. >> And very importantly to make sure, as you said, that it works and it can be serviced. >> Yeah. >> So that's going to be there. So the differentiation will be, does the design and engineering lead to simpler configuration, simpler change. >> Absolutely. >> Accommodate the programming requirements, accommodate the application requirements, all that are-- >> All in there, yes. >> Approximate to the realities of where data needs to be. George, you had a comment? >> Yeah, I got to say, having gone to IBM's IOT event a year ago in Munich, it was pretty clear that, when you're selling these new types of systems that we're alluding to here, it's like a turnkey appliance. It's not just bringing the Intel chip down. It's as David and Jim pointed out, it's a system on a chip that's got transistor real estate for specialized functions. And because it's not running the same scalable clustered software that you'd find in the cloud, you have small footprint software that's highly verticalized or specialized. So we're looking at lower volume, specialized turnkey appliances, that don't really share the architectural and compatibility traits of the enterprise and true private cloud cousins. And we're selling it, for the most part, to new customers, the operations technology folks, not IT, and often, you're selling it in conjunction with the supply chain master. In other words, auto OEM might go to their suppliers in conjunction with another vendor and sell these edge devices or edge gateways. >> And so that raises another very important question. Stu, I'm going to ask this of you. We're not going to be able to answer this question today. It's a topic for another conversation. But one of the things that the industry's not spending enough time talking about is that we are in the midst of a pretty consequential shift from a product orientation in business models to a service orientation in business models. We talk about APIs, we talk about renting, we talk about pay-as-you-go. And there is still an open question about how well those models are going to are going to end up on premise in a lot of circumstances. But Stu, when we think about this notion of the cloud experience, providing a common way of thinking about a cloud operating model, clearly the design decisions that are going to have to be made by the traditional providers of integrated systems are going to have to start factoring that question of how do we move from a product to a service orientation along with their business models, their way of financing, et cetera. What do you think is happening? Where's the state of the art in that today? >> Yeah, and Peter, it actually goes back to when we at Wikibon launched the true private cloud research a little bit over two years ago. It was not just saying, "How do we do something "better than virtualization?" It was really looking at, as you said, that cloud operating model. And what we're hearing very loud from customers today is, it's not that they have a public cloud strategy and an private cloud strategy. They have a cloud strategy (chuckles). And one of the challenges that they're really having is, how do they get their arms around that? Because today their private cloud and their public cloud a lot of times it's different suppliers, it's different operating environments as you said. We could spend a whole nother call on just discussing some of the nuance and pieces here. But the real trend we've been seeing, and kind of the second half of last year, and big thing we'll see, I'm sure, through this year, is what are the solutions? And how can customers manage this much simpler? And what are the technology pieces? And operational paradigms that are going to help them through this environment? And yeah, it's a little bit detached from some of the hardware discussion we're having here. Because of course, at the end of the day, it shouldn't matter what hardware or what locale I'm in, it's how I manage the entire environment. >> But it does (laughs). >> Yeah. >> It shouldn't matter, but the reality is, I think we're concluding that it does. >> Right, we think back to, oh back in the early days, "Oh, virtualization, great. "I can take any x86. "Oh wait, but I had a BIOS problem, "and that broke things." So when containers rolled out, we had the same kind of discussion, this, "Oh wait." There was something down at the storage or networking layer that broke. So it's always, where is the proper layer? How do we manage that? >> Right, I for one just continue to hope that we're going to see the Harry Potter computing model show up at some point in time. But until then, magic is not going to run software. It's going to have to run on hardware, and that has physical and other realities. All right, thanks guys. Let's wrap this one up. Let me give some, what the action item is. So this week, we've talked about the importance of hardware in the marketplace going forward. And partly, it's catalyzed by an event that occurred this week. A security firm discovered a couple of flaws in some of the predominant, common, standard volume CPUs, including Intel's, that have long term ramifications. And while one of the fixes is not going to be easy, the other one can be fixed by software. But the suggestion is that the fix, that software fix would take out 30% of the computing power of the chip. And we were thinking to ourselves, what would happen if the world suddenly lost 30% of their computing power overnight? And the reality is, a lot of bad things would happen. And it's very clear that hardware still matters. And we have this tension between what's happening at the edge, where we're starting to see a need for greater distribution of function that's performing increasingly specialized workloads, utilizing increasingly new technology, that's not, that the prevailing stack is not necessarily built for. So the edge is driving new opportunities for design that's going to turn into new requirements for hardware that will only be possible if there's new volume markets capable of supporting it, and new suppliers bringing it to market. That doesn't however mean that the whole concept of systems integration goes away. On the contrary, even though we're going to see this enormous amount of change at the edge, there's an enormous net new invention in what does it mean to do systems integration? We're seeing a lot of that happen in the hyperscalers first, in companies like Amazon, and Google, and elsewhere. But don't be fooled. The HPE's the IBM's, the Dell-EMC's are all very cognizant of these approaches and these changes, and these challenges. And in many respects, a lot of the original work, a lot of the original invention is still being performed in their labs. So the expectation is the new design model is being driven by the edge. Plus the new engineering model's being driven by the hyperscalers, will not mean that it all ends up in two tiers. But we will see a need for modern systems integration happening in the true private cloud, on the premise, where a lot of the data and a lot of the workloads and a lot of the intellectual property is still going to reside. That however, does not mean that the model going forward is the same. Some of the new engineering dynamics, or some of the new design dynamics will have to start factoring in how the hardware simplifies configuration. For example, FPGAs have been around for a long time. But end users don't program FPGAs. So what good does it do to reflect the FPGA capability inside a box, inside a true private cloud box, if the user doesn't have any simple, straightforward, meaningful way to make use of it? So a lot of new emphasis on improve manageability, AI for ITOM, ways of providing application developers access to accelerated devices. This is where the new systems and design issues are going to manifest themselves in the marketplace. Underneath this, when we talk about unigrid, we're talking about some pretty consequential changes ultimately in how design and engineering of some of these big systems works. So our conclusion is, lots that the hardware still matters, but that the industry continued to move and drive in a direction that reduces the complexity of the underlying hardware. But that doesn't mean that users aren't going to have to, aren't going to encounter serious, serious decisions and serious issues regarding which supplier they should work with. So the action item is this. As we move from a product to a service orientation in the marketplace, hardware is still going to matter. That creates a significant challenge for a lot of users, because now we're talking about how that hardware is rendered as platforms that will have long-term consequences inside a business. So CIOs, start thinking about 2018 as the year in which you start to consider the new classes of platforms that you're going to move to. Because those platforms will be the basis for simplifying a lot of underlying decisions regarding where is the best design and engineering of infrastructure going forward. Once again, I want to thank my Wikibon teammates. George Gilbert, David Floyer, Stu Miniman, Neil Raden, Jim Kobielus, for a great Action Item. From theCUBE studios in Palo Alto, this has been Action Item. Talk to you soon. (funky electronic music)

>> Hi I'm Peter Burris. Welcome to Action Item. (upbeat electronic music) Every week I bring the Wikibon research team together to talk about some of the issues that are most important in the computing industry and this week is no different. This week I'm joined by four esteemed Wikibon analysts, David Floyer, Neil Radon, Jim Kobielus, Ralph Finos, and what we're going to do is we're going to talk a few minutes about some of the predictions that we did not get into, our recent predictions webinar. So, I'd like to start off with Jim Kobielus. Jim, one of the things that we didn't get a chance to talk about yesterday in the overall predictions webinar was some of the new AI frameworks that are on the horizon for developers. So, let's take a look at it. What's the prediction? >> Prediction for 2018, Peter, is that the AI community will converge on an open framework. An open framework for developing, training and deploying deep learning and machine learning applications. In fact, in 2017, we've seen the momentum in this direction, strong momentum. If you were at AWS re:Invent just a few weeks ago, you'll notice that on the main stage, they discuss what they're doing in terms of catalyzing an open API, per building AI, an open model interchange format, and an open model compilation framework, and they're not the only vendor who's behind this. Microsoft has been working with AWS, as well as independently and with other partners to catalyze various aspects of this open framework. We also see Intel and Google and IBM and others marching behind a variety of specifications such as Gluon (mumbles) NNVM and so forth, so we expect continued progress along these lines in 2018, and that we expect that other AI solution provider, as well as users and developers will increasingly converge on this, basically, the abstraction framework that will make it irrelevant whether you build your model in TensorFlow or MXNet or whatever, you'd be able to compile it and run it in anybody else's back end. >> So Jim, one question then we'll move on to Neil really quickly but one question that i have is the relationship between tool choice and role in the organization has always been pretty tight. Roles have changed as a consequence of the availability of tools. Now we talked about some of the other predictions. How the data scientist role is going to change. As we think about some of these open AI development frameworks, how are they going to accommodate the different people that are going to be responsible for building and creating business value out of AI and data? >> Pete, hit it on another level that i didn't raise in my recent predictions document, but i'll just quickly touch on it. We're also seeing the development of open devops environments within which teams of collaborators, data scientists, subject matter experts, data engineers and so forth will be able to build and model and train and deploy deep learning and so forth within a standard workflow where each one of them has task-oriented tools to enable their piece but they all share a common governance around the models, the data and so forth. In fact, we published a report several months ago, Wikibon, talking about devops for data science, and this is a huge research focus for us going forward, and really, for the industry as a whole. It's productionizing of AI in terms of building and deploying the most critical applications, the most innovative applications now in business. >> Great, Jim, thanks very much for that. So Neil, I want to turn to you now. One of the challenges that the big data and the computing industry faces overall is that how much longer are we going to be able to utilize the technologies that have taken us through the first 50 years at the hardware level, and there is some promise in some new approaches to thinking about computing. What's your prediction? >> Well in 2018, you're going to see a demonstration of an actual quantum computer chip that's built on top of existing silicone technology and fabrication. This is a real big deal because what this group in the University of New South Wales came up with was a way to layer traditional transistors and silicon on top of those wacky quantum bits to control them, and to deal with, I don't want to get too technical about that, but the point is that quantum computing has the promise of moving computing light years ahead of where we are now. We've managed to build lots of great software on things that go on or off, and quantum computing is much more than that. I think what you're going to see in 2018 is a demonstration of actual quantum computing chips built on this, and the big deal in that is that we can take these existing machines and factories and capital equipment designed for silicone, and start to produce quantum chips without basically developing a whole new industry. Now why is this important? It's only the first step because these things are not going to be based on the existing Intel i86 instruction set, so all new software will have to be developed, software engineers are going to have to learn a whole new way of doing things, but the possibilities are endless. If you can think about a drug discovery, or curing disease, or dealing with the climate, or new forms of energy to propel us into space, that's where quantum computing is likely to take this. >> Yeah, quantum computing, just to bring a, kind of a fine point on it, allows, at any given time, the machine to be in multiple different states, and it's that fact that allows, in many respects, a problem to be attacked from a large number of directions at the same time, and then test each of them out, so it has a natural affinity with some of the things that we think about in AI, so it's going to have an enormous impact over the course of the next few years and it's going to be interesting to see how this plays out. So David Floyer, I now want to turn to you. We're not likely to see quantum computing at the edge anytime soon, by virtue of some of the technologies we face. More likely it'll be specialized processors up in the cloud service provider in the near term. But what are you going to talk about when we think about the role that the edge is going to play in the industry, and the impacts it's going to have on, quite frankly, the evolution of de facto standards? >> Well, I'd like to focus on the economics of edge devices. And my prediction is that the economics of consumer-led volume will dominate the design of IoT devices at the edge. If you take an IoT device, it's made up of sensors and advanced analytics and AI, and specifically designed compute elements, and together with the physical setup of fitting it into wherever you're going to put it, that is the overall device that will be put into the edge, and that's where all of the data is going to be generated, and obviously, if you generate data somewhere, the most efficient way of processing that data is actually at the edge itself, so you don't have to transport huge amounts of data. So the prediction is that new vendors with deep knowledge of the technology itself, using all the tools that Jim was talking about, and deep knowledge of the end user environments and the specific solutions that they're going to offer, they will come out with much lower cost solutions than traditional vendors. So to put a little bit of color around it, let's take a couple of real-world examples where this is already in place in the consumer world, and will be the basis of solutions in the enterprise. If we take the Apple iPhone X, it has facial recognition built-in, and it has facial recognition built-in on their A11 chips, but they're bionic chips. They've got GPUs, they've got neural networks all in the chip itself, and the total cost of that solution is around a hundred dollars in terms of these parts, and that includes the software. So if we take that hundred dollars and put it into what it would actually be priced at, that's around $300. So that's a much, much lower cost than a traditional IT vendor could ever do, and a much, at least an order of magnitude, and probably two orders of magnitude cheaper than an IT department could produce for its own use. So that leaves (mumbles) inclusions, going to be a lot of new vendors. People like Sony, for example, Hitachi, Fujitsu, Honeywell. Possibly people like Apple and Microsoft. Nvidia, Samsung, and many companies that we'll predict are going to come out of India, China and Russia who have strong mathematical educational programs. So the action item is for CIOs, is to really look carefully at the projects that you are looking at, and determine, do I really have the volume to be unique in this area? If that volume, if it's a problem which is going to be industry-wide, the advice we would give is wait for that device to come out from a specialized vendor rather than develop it yourself. And focus investment on areas where you have both the volume of devices and the volume of data that will allow you to be successful. >> All right, David, thank you very much. So let me wrap this week's Action Item, which has been kind of a bridge, but we've looked specifically at some of the predictions that didn't make it into our recent predictions webinar, and if I want to try to summarize or try to bring all these things together, here's what I think what we'd say. Number one, we'd say that the development community has to prepare itself for some pretty significant changes as a consequence of having an application development environment that's more probabilistic, driven by data and driven by AI and related technologies, and we think that there will be new frameworks that are deployed in 2018, and that's just where it's going to start, and will mature over the next few years as we heard from Jim Kobielus. We've also heard that there is going to be a new computing architecture that's going to drive change, perhaps for the next 50 years, and the whole concept of quantum computing is very, very real, and it's going to have significant implications. Now it will take some time to roll out, but again, software developers have to think about the implications of some these new architectures on their work because not only are they going to have to deal with technology approaches that are driven by data, but they're also going to have to look at entirely new ways of framing problems because it used to be about something different than it is today. The next thing that we need to think about is that there still is going to be the economics of computing that are going to ultimately shape how all of this plays out. David Floyer talked about, specifically at the edge, where Wikibon believes it's going to have an enormous implication on the true cost of computing and how well some of these complex problems actually find their way into commercial and other domains. So with a background of those threee things, we think, ultimately, that's an addendum to the predictions that we have and once again, i'm Peter Burris. Thank you very much for joining us for Action Item, and we look forward to working with you more closely over the course of the next year, 2018, as we envision the new changes and the practice of how to make those changes a reality. From our Palo Alto theCUBE studios, this has been Action Item. (bright 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)

>> Narrator: Live from Las Vegas, it's the Cube covering VMworld 2017. Brought to you by VMware and it's ecosystem partners. >> Hey. Welcome back everyone. Live here in Las Vegas, this is the Cube's exclusive coverage of VMworld 2017. Now I'm John Furrier, the co-host of the Cube with my partner Dave Vellante co-host. Eighth year of Cube coverage at VMworld's since 2010, we've been documenting the evolution of VMware. Next guest is Bask Iyer, who's the CIO of Vmware and Dell. Big time CIO, been in the field. Been in working, real practitioner. Now at the company. Going to the cloud. Hybrid cloud. Bask great to see you. >> Good to see you. Yeah. >> So, Pat Kelson's keynote really relevant. I just want to say, you know our conversation last year and even the year before, you're like Nostradamus. You're like predicting the future. We talk about IoT's and now IoT edge. Are you helping messaging it with Vmware, I mean? >> Well my background having working in Honeywell and so on is that and I saw IoT as a big opportunity. So, it was easy for me to see that it was going to be big but I didn't see, think it was this big. But I'm messaging more with CIOs, more than Vmware to say you're going to miss this wave. It looks like a lot of CIOs are so focused on business IT, they're missing IoT. So, my message is here's a great opportunity for you to get ahead of don't miss it. >> I want to talk about waves cause last year we really made and then you look at what Pat Kelson did last year. We were commenting that he gave the speech of his life. Was that two years ago I can't remember. He really was like under a lot of pressure. His toggle was like a 42, very low. Is he the right guy? He made some bets. Pat's a wave guy. He's all about the waves cause he said, "If you're not out for that next wave, you become drift wood." So, I got to ask you the question. By the way, he's got the great wave slide here. From a customer perspective, they're watching here, Gelsinger lay out a great vision, the stock price is booming, strategy is clear. Andy Jassy from Amazon comes on stage. There is clarity in this direction and the waves that you are on. Now customers have to make the choice of bets, they're looking at the waves and saying what are my bets; The question I have for you what bets are customers making now as CIO and what should they look at, in what sequence, how do they attack those bets and which are the right bets? >> So I think the cloud is a big bet. People don't want to talk about cloud because they think we have been talking about it for a long time but enterprise hasn't really gone much in the journey. There is still a lot of data centers running virtual machines which is great but you really don't have a private cloud set up and then this burst capacity do go to public cloud and so very few people have examples of that. There are some people but not the large majority. What happens in IT is, when you get spooked when you see a public cloud and a private cloud and your not sure which way it's going. So the nice thing about this announcement is that thing's mystery is out right. So you want to go to public cloud here's the way to get it. You want to stay in your private cloud here's the way you can stay in your private cloud. Plus moving legacy applications people never talk about legacy. They always talk about you know if you and I are building a new company to go to a public cloud, do cloud ready, pretty easy. But I have some old applications even in a technology company, how do I move it? So I think that as a customer when I look at past message they said that make sense to me, I can choose to run it on my data center, go to a private cloud and go into Amazon. >> I got to ask you, I know Dave was jumping he's got some good private cloud data to talk about. About a true private cloud data. You mentioned how hard it is to move legacy apps, can you give some illustration and some color to how hard it is. Because a lot of people in the press analyst even startups, It's so easy, I want to just win the enterprise. If it's a clean sheet of paper I get that but there is a lot of important things. How hard is it to really deal with this legacy data center environment in the path to hybrid and public cloud? >> Well there is still, you know people think of, if you think of an ERP, people have four or five ERPs still. You know you were just imagining everybody is just on one nice SAP or one nice Oracle. There are several instances and the reason we haven' migrating to one is not because is not because we don't know how to do it, there is an ROI you know, do I invest the money, do I do this right now, do I get the people, another $400 million to invest in an ERP system. >> Risky. >> Very risky. So you got a lot of these. You've got PeopleSoft which has different versions. You got HR systems, sale systems. So that's what in a lot of data centers believe it or not. How do you move it? Then when you go to a public cloud the guy says are you cloud ready? No you're not. You got a legacy system. >> What's that? >> You just don't want to run this. You want to run it in the most efficient way within a container. So I think people don't see that. The other thing they don't see is at scale it is expensive sometimes to go to public cloud. If you and I are starting a company, we won't build a data center we can probably go to the public cloud. But if I have scale, I already have data centers that I am running at scale and not everything is unpredictable. A lot of business IT is very predictable workloads, right. I know what I need to buy next year generally. So what burst capacity am I looking for? Not everybody requires that, so that's another reason. Security both ways right, people say that public cloud is more secure but there is a lot of regulatory bodies who want you to show and there is a lot of work that I have to certify to show that. So what [CIS 00:05:30] is trying to do is to say we will try to go cloud where we can but there is still 80% 90% of your stuff running in a data center. Help me bridge that. >> Well we talk about cloud, private cloud, we coin this term true private cloud and the basic concept is bring the cloud model to your data. >> Right. >> We tell our CIOs, look don't try to form your business and fit it into the cloud. Fit the cloud into your business wherever the data lives. >> Yeah. >> Is that a reasonable way to look at it and is that what you're doing with your business? >> Yeah, so I'd define it a even more simply. I'd kind of say if you have a lot of people running your data center, you don't have a cloud. I mean the whole point of cloud is automation. The reason public clouds are cheaper or better is because it is highly automated. So that's the trick. If you have people in the data center then it's not a cloud. So get your data center modernized. I define it as private cloud you can call it whatever you want, you can call it automation. But get it automated. Then the scale comes up and your cost comes down. But then when you want burst capacity you don't have to build servers for that you can go to the public cloud for burst capacity. But the big point for me is, people ought to sit down and figure out a strategy. Few years ago people said don't go into infrastructure just outsource it. So we all outsource it and that became a mess. Sooner or later you got to figure out what you need to do. You can't just outsource it, put it in the cloud, not think about it, make it go away. So you see a lot of CIOs coming back and saying I want that but I also want to fix this, how do I automate? I want to get the cost down. That's how I define a private cloud don't have too much cost. >> So are you running a private cloud or? >> Not only am I, I should be modest but I'm not going to be. I think we run one of the best private clouds there is for VMware. Everything that you see in Vmware, the hands on labs you see there is all running on a private cloud at scale. We are extending the cloud to now Dell Technologies. We are taking the same model and cut and paste it. Imagine how much leverage you get from EMC and Dell Data Centers when you extend the private cloud. So for a company like us it's a sure bet. >> So what is it look like underneath? I mean you got vSan running. >> Yeah. You have vSan, NSX everything we talk about. >> Have you thrown out all your arrays? >> No, you don't throw out all our arrays but vSan is. What you see in the market is happening in my data center. So vSan is, there is more and more vSan nodes now but your mission critical SAP and Oracle stuff that I don't want to necessarily save dollars I would want something that is mission critical, proven, ready, certified, etc. So the other things don't go away but your storage is growing. As the storage grows you see a lot more of the vSan growing with that. I use to have a lot of vSan, a lot of NSX. >> You know how many clusters you have now? Probably a zillion. I mean a pretty large number. >> It's a large number of clusters. >> It's just a, the reason I don't know is every month they just amazingly growing. Last year when we talked about it when you asked the question about vSan there was only a few left in my data center. So I deliberately dint talk a whole lot about it. Now it's taking on like fire. >> Yeah >> Right, as the reliability increases, the cost value proposition is taking off. >> Your talking about tens of thousands of vms and petabytes of data. >> Yeah, multiple petabytes of data. Over 60% of that is growing. The growth mark is really large in the vSan as well. >> I got to ask you the journey for the CIO and the CXOs out there, cause there's multiple CXOs. You've got chief security officers, some say chief economic officer because of crypto currency block chains coming around the corner. We got to talk about block chains because next year it's going to be in the wave slide. Cause decentralization is all about block chain. There should be a computing areas there. They all want to get in, they don't want to screw up. I need the head room but I don't want to make any move too early to get over my skis or foreclose an opportunity. So what's the path, Are they getting there house in order with the private cloud as a stepping stone to hybrid cloud. What are some of the day in life of the CIO right now because what we're seeing with the data is true private cloud on premise is growing really well. It's not declining in any capacity. That where the action is right now more than hybrid clouds. >> Yeah >> What's the CIO doing, is that the trend that you see, what's going on in their world? >> Well there are three or four things going. Then their SAS application that compute is going with that SAS vendor. So that is happening a bit. But I see the private cloud growing. Right, you know, I don't see it disappearing anywhere and I talked to my other CIOs and say should I be saying this or is it true or not? And everybody say yes it is growing and so is SAS and so is public cloud. But you know, a big majority of Vmware Compute is run on a private cloud and so I see it grow. So what the CIO's would look for is I want to run my private cloud efficiently but I also want to I don't want to have this large boxes for burst capacity. Say I have a Thanksgiving sale or a Christmas sale I don't want to have boxes sitting doing nothing. Can I take advantage of the public cloud for that and then cloud ready when I want to do some experiments on the newer development, let me try it on the public cloud. My feeling is my stats tells me and you guys are the experts on it is. If you have a scale at some scale, if your on a good private cloud the costs are going to be better for you. That's what my experience tells me. >> Because some of the things are predictable like hey retail seasons here, I can go burst in the cloud for that. >> Right. >> Then everything else kind of overflow to the cloud auto scaling. >> The key is labor. >> Yeah >> You could take labor out. So I just want to share some numbers with you guys. >> Sure. >> We so, what we call the true private cloud you're calling private cloud. >> Yeah. >> Growing at 33% vs the infrastructure as a service for the public cloud going at 15%. >> Wow. >> It's a 10 year forecast. We have true private cloud at 230 billion. The infrastructure as a service public cloud at 150 billion. So the biggest market growing the fastest to your point, SAS is bigger than both. >> Right. >> That's growing really really fast but it's the IT labor piece $150 billion coming out of labor going into, then their R&D and shifting to analytics and >> Value. >> Transformations, value producing things. >> I think that is the transformation. The transformation is labor is going out, automation is coming in. So I can put that on DevOps or the business kind of transformation projects. That's good to see. That's where intuitively as a practitioner I say, but it's good to have the data. I'm going to go read it up and see. That makes a lot of sense to me. >> Pat Gelsinger actually made a quote on the keynote I thought this is why I was honed in on that is that. He actually said shifting to value activities. That's analytics, you called vendor R&D which is basically a way to fund some of the new project where the hybrid and public are being operationalize to be predictable to some level. >> Sure exactly. >> But I totally see that the hybrid cloud is stalled in my opinion you guys can comment on it but based on my anecdotal hundreds of shows we go to it's hyped up beyond all recognition. >> Yeah. >> But it's happening after private cloud is set up because the operating model of the clouds got to get set up and it's just a law for the enterprises. >> Good points, maybe bursting, maybe some DR, but it's not a federated, set a federated apps or is it. >> At least I don't see it that way. I mean so things should be simple but not simpler is what they say. You got to get your house in order. I mean you can't, I mean I made the mistake of saying let's just outsource it because I don't want to think about it. This is the same thing that we are talking about let's just put it all in the cloud. What do you mean, I mean there are legacy apps. You still have them running at a good cost. You still have to know it. So I'm little old fashioned that way to say your house in order and have the options open for burst and other kind of things that you want to do. >> Well digital transformation also has a lot of pressure on top line revenues. So now >> Yeah. >> You can't just put the paint a side and not look at it. >> Sure. >> Put it in the corner. >> But look, IoT, we talked about this. You're going to have to set your whole business being censored. That needs a lot of late latency and other kind of issues lot of data. You need to be better have a good private cloud story for the IoT. Not everything can be put on the public cloud to make it happen. They just don't have the latency. There's law of physics still. So like a car is going to be a data center more less right, you need to make a response in a very short time. Factories have to have responsive systems and robotics. You can't go traverse the internet, go get a data from a public cloud, come back to make a decision on robot. So don't ignore as all I'm saying. Do everything but don't ignore it. >> The future, let's talk about future. AI is here, that's all also hyped up beyond all recognition but I love AI because it's got a software aspect to it. Machine learning super relevant. Block chain, Pat Gelsinger in his keynote really address and I thought a really clever way to weave this in, decentralization. >> Yeah. >> I see we all know it distributed computing is. >> Sure. >> Centralized database can be hacked. Distribution and decentralization around blockchain is interesting. So if we're putting our futuristic hats on. >> Yeah. >> What is IT look like in a totally non controllable, fully instrumented, blockchain crypto currency market? Is there going to be IT coins? I want some IT. >> I think so, I mean it's exciting, the only the thing with blockchain in enterprise is not the technology, it's our ability to think creatively on it. Right we are not able to envision these kind of things yet. It'll come in a year, I think it's our. We have to sit down and think about how to take advantage of that it's pretty exciting and you know we still have simple issues on you know. We know we can't centralize everything. That we've tried for years and years and years it's gone already. Now I want to decentralized, perhaps use technology like this to make sure I can still control what I want to control right. So the thing with block chain internally when I talk to people is, don't show me a proof of concept of technology I get the tech. What is the use case? >> Yeah. >> So we have to use our brains and I think in 6 months we will have it. We're just not there yet completed . >> That's where the destruction vector will be. >> Right. >> If anyone is doing in IT coin token you can say I'm interested. >> IoT we talked last time it looked like vaporware and now we have examples and every bodies doing it. I think block chain is definitely there. >> I mean supply chain could be applied to network with packets as we would say at the edge. Bask thanks for coming on the Cube. >> Sure, thank you. >> Great stuff good to see you. Cube coverage live here in Las Vegas with Vmworld 2017. We'll be right back with more coverage after this break. Thank you.

(upbeat electronic music) >> Everyone, welcome to our special Mobile World Congress 2017 coverage. I'm John Furrier here in theCUBE for two days of wall-to-wall coverage. Monday and Tuesday, February 27th and 28th, and we have on the phone right now, Lynn Comp, who's the Senior Director of the Network Platforms Group within Intel, part of the team doing the whole network transformation. The big announcements that went out prior to Mobile World Congress and hitting the ground on Monday and Tuesday of all next week in Barcelona. Lynn, great to have you on the phone. Thanks for taking the time to walk through some of the big announcements. >> Lynn: Thanks, John, for having us. It's a really exciting Mobile World Congress. We're seeing more and more of the promise of the next generation networks starting to take solution form from ingredient form a couple years ago, so it's a great, great time to be in this business. >> So 5G is happening now. You're seeing it in the network and the cloud and at the client, that you guys use the word "client" but essentially, it's the people with their smartphones and devices, wearables, AIs, and now the client is now cars, and flying drones and potentially, whatever else is connected to the Internet as an Internet of things. This has been a really big moment and I think I want to take some time to kind of unpack with you some of the complexities and kind of what's going on under the hood because 4G to 5G is a huge step up in the announcement and capabilities, and it's not just another device. There's really unique intellectual property involved, there's more power, there's a market leadership in the ecosystem, and really is a new way for service providers to achieve profitability, and get those products that are trying to connect, that need more power, more bandwidth, more capabilities. Can you take a minute just to talk about the key announcements impacting Mobile World Congress from Intel's perspective this week in your area? >> Lynn: Yeah, so we had a group of announcements that came out. Everything from solutions labs where operators are invited in to work with Nokia and Intel starting out to start working through what does it mean to try and manage a network that includes unlicensed and licensed spectrum and all these different usage models, very different model for them, to Ericsson, an initiative with GE and Honeywell and Intel, that is in Innovator's Initiative, where companies are invited to come in in the ecosystem. An early start working through what does it mean to have this kind of network capability? If you think what happened, 2G, 3G, to 4G, you start looking at the iPhone, been around for 10 years, and you've seen how the uses have changed, and how application developers have come up with completely new ways of doing things, like, who would have thought about crowdsourcing traffic patterns for driving directions? We all wanted it years ago, but it was just recently that we were able to have that on a smartphone. They're trying to unleash that with pretty unique companies. I mean, GE and Honeywell, UC Berkeley, you wouldn't necessarily think of them as being first on innovating new usage models for a wireless network, but with something like 5G, with all of these diverse use cases, you end up with a completely different ecosystem, really wanting to come in early and take advantage of the potential that's there. >> Lynn, talk about this end-to-end store because one of the things that got hidden in all the news, and certainly SiliconANGLE covered it, as well as, there was a great article in Fortune about it, but kind of talk about more of the 5G versus Qualcomm, that was kind of the big story that, the battle of the chips, if you will, and the big 5G angle there, but there's more to it and one thing that caught my attention was this end-to-end architecture, and it wasn't just Intel. You guys are a big part of that as an ingredient, but it's not just Intel, and what does that mean, end-to-end, 'cause I can see the wireless pieces and overlaying connecting devices, but where's the end-to-end fit in? Can you give some color on that? >> Lynn: Absolutely. You know what's really fascinating is you've got Intel and we've been in the cloud and heard of the genesis of what would become the consumer and the enterprise cloud from the very start, and so what we've been doing in working in that end-to-end arena is taking things like virtualization, which has allowed these service providers and enterprises to slice up compute resources and instead of having something that's completely locked and dedicated on one workload, they can create slices of different applications that all sit on the same hardware and share it, and so if you look, years ago, many of the service providers, cloud and enterprise, they were looking at utilization rights as maybe 15% of the compute power of a server, and now, a lot of them are aiming for 75 to 85% utilization, and that's just a crazy amount of (mumbles) so bringing that to this market that in traditional, we had single purpose boxes, there's various detections for one thing, but that creates a business challenge if you need to do more than one thing, so really what we're showing, for example, at Mobile World Congress, it's something that we call FlexRAN, and it's an example of how to run a radio area network on a standard server on the technology, and it does implement that network slicing. Its's very similar to the virtualization and the compute slicing, but taking advantage of it to use different bandwidths and different rates for different scenarios, whether IoT or smartphones, or even connected cars. >> So I got to ask you about, the big question I get is, first of all, thanks for that, but the big question I get is, this isn't turning into an app show, we're Mobile World Congress, and apps are everything from cars to just phone apps to network apps, et cetera, and the question that everyone's asking is, we need more bandwidth, and certainly, 5G addresses that, but the service providers are saying, "Do we really need all that power? And "When is it coming?" "What's the timing of all this?" So, specific question to you is, Lynn, is what is Intel doing to accelerate the network transformation for the service providers to get 5G ready, 'cause that seems to be the main theme as the orientation of where the progress bar is relative to is it ready for primetime, is it here and now, is it out in the future, is this kind of a pre-announcement, so there's kind of some confusion. Clarify that up. Where's the progress bar and how is Intel accelerating network transformation for folks in the service provider vis-a-vis 5G-ready? >> Lynn: So there's a couple things. So let me start with the accelerating piece because it also relates to the end-to-end piece. When you look at the way that networks have been constructed all the way, end-to-end, it has traditionally been a very, very limited set of solution providers, and they tend to survive pretty granular, pretty high-granular functions, so the appliance, the full appliance, software, hardware, everything, and I would look at some of the smartphones up until you could put new applications on it, as appliances, it did voice, and so, we have this service provider begging us for many years, "Give us an ecosystem that looks like server and PC. "I want a building block ecosystem. "I want to be able to take advantage of fast and free wires "in software and hardware. "I need people to come innovate, "like they go innovate on Amazon," and so building an ecosystem, so Intel Network Builders is something that was started about three years ago, and we had, oh, half dozen to maybe 12 different vendors who were part of it, mostly software vendors. Since then, we have 250-plus number and they range from service providers like GT and Telefonica all the way to the hardware vendors like Cisco and Ericsson, and then the software vendors that you would expect. So that's one thing that we've been really working, for a few years now, on giving these operators building block approaches, supporting them in open source. We had a big announcement from AT&T, talking about how they're putting about seven millions lines of code into the Linux Foundation, and its code has been deployed in their network already, so pretty big departure from normal practice, and then today, we had an announcement that came out, where not only did AT&T and Bell Canada and Orange in that community. Now we've got China Mobile, China Telecom, and a project called Open-O, also joining forces. If you were to map out the topics for these operators, we've got almost all of the top ten. They are joining this project to completely change the way that they run their networks, and that translates into the kind of innovation, the kind of applications that consumers love, that they're already getting out of the cloud, now they can begin to get that piece of innovation and creativity in the network as well. So the building block approach seems to be your strategy for the ecosystem. What's the challenge to keep that rolling and cohesive? How are you guys going to foster that growth on the ecosystem? You guys going to be doing a lot of joint marketing, funding, projects, and (chuckles) how are you going to foster that continuing growth? >> Lynn: Well there's a couple, it's such an opportunity-rich environment right now. Even things that you would assume would be normal and kind of standard practice, like standardized benchmarking, because you want apples-to-apples performance comparison. Well that's something that this industry really hasn't had. We've done very conceptualized testing, so we're working with the operators in a project called OPNSG to make sure that the operators have a uniform way, even if it's synthetic benchmark, but they at least understand this synthetic benchmark has this kind of performance, so they start really being able to translate and have the vendors do comparisons on paper, and they can actually do better comparisons without having to do six months of testing, so that's a really big deal. The other thing that I do want to also say about 5G is we're in a pre-standards world right now. ITU and 3GPP will have standards dropped in 2018 and 2020 is when it will be final, but every time that you're looking at a new wireless standard, there's a lot of pretrials that are happening, and that's because you want to test before you state everything has to work a specific way, so there was a trial just announced in December, with Erisson, AT&T in Austin, Texas in the Intel offices, and so if you happen to be in that office, you're starting to be able to experiment with what you could possibly get out of 5G. You'll see more of that with the Olympics in 2018 and 2020, where you've got, Japan and Korea have said we're going to have 5G at those Olympics. >> So I got to ask you some of the questions that we are going to have some guests on here in theCUBE in the Palo Alto coverage around NFV, network function virtualization, plays right into the software-defined networking virtualization world, so why is NFV and SDN so vital to the network transformation? Why now and what's happening in those two areas, and what's the enabler? >> Lynn: The enabler really started about 10 years ago, the real inspiration for it, when we were all in a world of packet processing engines and network processors, and we had some people in our research labs that realized that a lot of the efficiency in doing packet processing quickly came from parallelism, and we knew there were about two or three years to wait, but that was when multi-core came out, and so this thing called data plane development kit was born. We've referred to it as DPDK. It's now an industry organization, not an Intel invention anymore. The industry's starting to foster it. Now is really when the operators realized, "I can run a network on a general purpose processor." (coughs) Excuse me, so they can use cores for running operating systems and applications, of course, they always do that for compute cores, but they can also use the compute cores for passing packets back and forth. The line rates that we're getting are astonishing. 160 gigabits per second, which at the time, we were getting six million packets per second. Very unimpressive 10 years ago, but now, for many of those applications, we're at line rate, so that allows you to then separate the hardware and the software, which is where virtualization comes in, and when you do that, you aren't actually embedding software and hardware together in creating an appliance that, if you needed to do a software update, you might as well update the hardware, too, 'cause there's absolutely no new software load that can happen unless you're in an environment with virtualization or something like containers. So that's why NFV, network function virtualization is important. Gives the operator the ability to use general purpose processors for more than one thing, and have the ability to have future proofing of workloads where a new application or a new use becomes really popular, you don't have to issue new hardware, they just need to spin up the new virtual machine and be able to put function in it. >> So that, I got-- >> Lynn: If you went back and, we were talking about 5G and all of this new way of managing the network, now management in orchestration, it's really important but SDN is also really critical, both for cloud and for comm, because it gives you one map of the connections on the network, so you know what is connected where, and it gives you the ability to remotely change how the servers or how the hardware is connected together. If you were going to ask the CIO, "What's your biggest problem today?" they would tell you that it's almost impossible for them to be able to spin up a fully functional, new application that meets all the security protocols because they don't have a network map of everything that's connected to everything. They don't really have an easy way to be able to issue a command and then have all of the reconfigurations happen. A lot of the information's embedded in router tables. >> Yeah. >> Lynn: So it makes it very, very hard to take advantage of a really complicated network connection map, and be agile. That's where SDN comes in. It just kind of like a command control center, whereas NFV gives them the ability to have agility and spin up new functions very quickly. >> Yeah, and certainly that's where the good security part of the action is. Lynn, I want to get your final thoughts on the final question is this Mobile World Congress, it really encapsulates years and years in the industry of kind of a tipping point, and this is kind of my observation, and I want to get your thoughts on this and reaction to it, is the telcos and the service providers are finally at a moment where there's been so much pressure on the business model. We heard this, you can go on back many, many years ago, "Oh, over the top, " and you're starting to see more and more pressure. This seems to be the year that people have a focus on seeing a straight and narrow set of solutions, building blocks and a ecosystem that poised to go to the next level, where there can be a business model that actually can scale, whether it's scaling the edge, or having the core of the network work well, and up and down the stack. Can you talk about the key challenges that these service providers have to do to address that key profitability equation that being a sustainable entity rather than being the pipes? >> Lynn: Well it comes down to being able to respond to the needs of the user. I will refer to a couple demos that we have in the data center section of our booth, and one of them is so impressive to China Telecom that have put together on complete commercial off-the-shelf hardware that a cloud vendor might use. A demo that shows 4K video running from a virtualized, fixed wireline connection, so one of the cable kind of usage. Now 4K video goes over a virtualized environment from a cable-like environment, to what we call virtual INF, and that's the way that you get different messages passed between different kinds of systems. So INF is wireless, so they've got 4K video from cable out to a wireless capability, running in a virtualized environment at performance in hardware that can be used in the cloud, it could be used in communication service providers 'cause it's general purpose. That kind of capability gives a company like China Telecom the flexibility they need, so with 5G, it's the usage model for 5G that's most important. Turns out to be fixed wireless, because it's so expensive for them to deploy in fiber, well, they have the ability to do it and they can spin it up, maybe not in real time, but certainly, it's not going to take a three-month rollout. >> Yes, and-- >> Lynn: So hopefully, that gives you one example. >> Well that's great enablement 'cause in a lot of execution, well, I thought it gave me one more idea for a question, so since I have my final, final question for you is, what are you most excited about 'cause you sounded super excited with that demo. What other exciting things are happening in the Intel demo area from Intel that's exciting for you, that you could share with the folks listening and watching? >> Lynn: So, I used to never be a believer in augmented reality. (John chuckling) I thought, who's going to walk around with goggles, it's just silly, (coughs) it seemed to me like a toy and maybe I shouldn't admit that on a radio show but I became a believer, and I started to really understand how powerful it could be when Pokemon Go took over all the world in over the summer, to this, an immersive experience, and it's sort of reality, but you're interacting with a brand, or in the booth, we have a really cool virtual reality demo and it was with Nokia next and it's showing 5G network transformation. The thing about virtual reality, we have to really have low latency for it to feel real, quote-unquote, and so, it harnesses the power that we can see just emerging with 5G, and then we get this really great immersive experience, so that, I think, is one that innovate how popular brands like Disney or Disney World or Disneyland, that immersive experience, so I think we're just starting to scratch the surface on the opportunities there. >> Lynn, thanks so much for spending the time. Know you got to go and run. Thanks so much for the commentary. We are low latency here inside theCUBE, bringing you all the action. It's a good title for a show, low latency. Really fast, bringing all the action. Lynn, thanks so much for sharing the color and congratulations on your success at Mobile World Congress and looking forward to getting more post-show, post-mortem after the event's over. Thanks for taking the time. We'll be back with more coverage of Mobile World Congress for a special CUBE live in studio in Palo Alto, covering all the action in Barcelona on Monday and Tuesday, 27th and 28th. I'm John Furrier. Wrap it with more after this short break, thanks for watching. (upbeat electronic music) (bright electronic music)