(upbeat music) >> Welcome back. We're here at Supercomputing 22 in Dallas. My name's Paul Gill, I'm your host. With me, Dave Nicholson, my co-host. And one thing that struck me about this conference arriving here, was the number of universities that are exhibiting here. I mean, big, big exhibits from universities. Never seen that at a conference before. And one of those universities is Indiana University. Our two guests, Dave Jent, who's the AVP of Networks at Indiana University, Aaron Neal, Deputy CIO at Indiana University. Welcome, thanks for joining us. >> Thank you for having us. >> Thank you. >> I've always thought that the CIO job at a university has got to be the toughest CIO job there is, because you're managing this sprawling network, people are doing all kinds of different things on it. You've got to secure it. You've got to make it performant. And it just seems to be a big challenge. Talk about the network at Indiana University and what you have done particularly since the pandemic, how that has affected the architecture of your network. And what you do to maintain the levels of performance and security that you need. >> On the network side one of the things we've done is, kept in close contact with what the incoming students are looking for. It's a different environment than it was then 10 years ago when a student would come, maybe they had a phone, maybe they had one laptop. Today they're coming with multiple phones, multiple laptops, gaming devices. And the expectation that they have to come on a campus and plug all that stuff in causes lots of problems for us, in managing just the security aspect of it, the capacity, the IP space required to manage six, seven devices per student when you have 35,000 students on campus, has always been a challenge. And keeping ahead of that knowing what students are going to come in with, has been interesting. During the pandemic the campus was closed for a bit of time. What we found was our biggest challenge was keeping up with the number of people who wanted to VPN to campus. We had to buy additional VPN licenses so they could do their work, authenticate to the network. We doubled, maybe even tripled our our VPN license count. And that has settled down now that we're back on campus. But again, they came back with a vengeance. More gaming devices, more things to be connected, and into an environment that was a couple years old, that we hadn't done much with. We had gone through a pretty good size network deployment of new hardware to try to get ready for them. And it's worked well, but it's always challenging to keep up with students. >> Aaron, I want to ask you about security because that really is one of your key areas of focus. And you're collaborating with counties, local municipalities, as well as other educational institutions. How's your security strategy evolving in light of some of the vulnerabilities of VPNs that became obvious during the pandemic, and this kind of perfusion of new devices that that Dave was talking about? >> Yeah, so one of the things that we we did several years ago was establish what we call OmniSOC, which is a shared security operations center in collaboration with other institutions as well as research centers across the United States and in Indiana. And really what that is, is we took the lessons that we've learned and the capabilities that we've had within the institution and looked to partner with those key institutions to bring that data in-house, utilize our staff such that we can look for security threats and share that information across the the other institutions so that we can give each of those areas a heads up and work with those institutions to address any kind of vulnerabilities that might be out there. One of the other things that you mentioned is, we're partnering with Purdue in the Indiana Office of Technology on a grant to actually work with municipalities, county governments, to really assess their posture as it relates to security in those areas. It's a great opportunity for us to work together as institutions as well as work with the state in general to increase our posture as it relates to security. >> Dave, what brings IU to Supercomputing 2022? >> We've been here for a long time. And I think one of the things that we're always interested in is, what's next? What's new? There's so many, there's network vendors, software vendors, hardware vendors, high performance computing suppliers. What is out there that we're interested in? IU runs a large Cray system in Indiana called Big Red 200. And with any system you procure it, you get it running, you operate it, and your next goal is to upgrade it. And what's out there that we might be interested? That I think why we come to IU. We also like to showcase what we do at IU. If you come by the booth you'll see the OmniSOC, there's some video on that. The GlobalNOC, which I manage, which supports a lot of the RNE institutions in the country. We talk about that. Being able to have a place for people to come and see us. If you stand by the booth long enough people come and find you, and want to talk about a project they have, or a collaboration they'd like to partner with. We had a guy come by a while ago wanting a job. Those are all good things having a big booth can do for you. >> Well, so on that subject, in each of your areas of expertise and your purview are you kind of interleaved with the academic side of things on campus? Do you include students? I mean, I would think it would be a great source of cheap labor for you at least. Or is there kind of a wall between what you guys are responsible for and what students? >> Absolutely we try to support faculty and students as much as we can. And just to go back a little bit on the OmniSOC discussion. One of the things that we provide is internships for each of the universities that we work with. They have to sponsor at least three students every year and make that financial commitment. We bring them on site for three weeks. They learn us alongside the other analysts, information security analysts and work in a real world environment and gain those skills to be able to go back to their institutions and do an additional work there. So it's a great program for us to work with students. I think the other thing that we do is we provide obviously the infrastructure that enable our faculty members to do the research that they need to do. Whether that's through Big Red 200, our Supercomputer or just kind of the everyday infrastructure that allows them to do what they need to do. We have an environment on premise called our Intelligent Infrastructure, that we provide managed access to hardware and storage resources in a way that we know it's secure and they can utilize that environment to do virtually anything that they need in a server environment. >> Dave, I want to get back to the GigaPOP, which you mentioned earlier you're the managing director of the Indiana GigaPOP. What exactly is it? >> Well, the GigaPOP and there are a number of GigaPOP around the country. It was really the aggregation facility for Indiana and all of the universities in Indiana to connect to outside resources. GigaPOP has connections to internet too, the commodity internet, Esnet, the Big Ten or the BTAA a network in Chicago. It's a way for all universities in Indiana to connect to a single source to allow them to connect nationally to research organizations. >> And what are the benefits of having this collaboration of university. >> If you could think of a researcher at Indiana wants to do something with a researcher in Wisconsin, they both connect to their research networks in Wisconsin and Indiana, and they have essentially direct connection. There's no commodity internet, there's no throttling of of capacity. Both networks and the interconnects because we use internet too, are essentially UNT throttled access for the researchers to do anything they need to do. It's secure, it's fast, easy to use, in fact, so easy they don't even know that they're using it. It just we manage the networks and organize the networks in a way configure them that's the path of least resistance and that's the path traffic will take. And that's nationally. There are lots of these that are interconnected in various ways. I do want to get back to the labor point, just for a moment. (laughs) Because... >> You're here to claim you're not violating any labor laws. Is that what you're going to be? >> I'm here to hopefully hire, get more people to be interested to coming to IU. >> Stop by the booth. >> It's a great place to work. >> Exactly. >> We hire lots of interns and in the network space hiring really experienced network engineers, really hard to do, hard to attract people. And these days when you can work from anywhere, you don't have to be any place to work for anybody. We try to attract as many students as we can. And really we're exposing 'em to an environment that exists in very few places. Tens of thousands of wireless access points, big fast networks, interconnections and national international networks. We support the Noah network which supports satellite systems and secure traffic. It really is a very unique experience and you can come to IU, spend lots of years there and never see the same thing twice. We think we have an environment that's really a good way for people to come out of college, graduate school, work for some number of years and hopefully stay at IU, but if not, leave and get a good job and talk well about IU. In fact, the wireless network today here at SC was installed and is managed by a person who manages our campus network wireless, James Dickerson. That's the kind of opportunity we can provide people at IU. >> Aaron, I'd like to ask, you hear a lot about everything moving to the cloud these days, but in the HPC world I don't think that move is happening as quickly as it is in some areas. In fact, there's a good argument some workloads should never move to the cloud. You're having to balance these decisions. Where are you on the thinking of what belongs in the data center and what belongs in the cloud? >> I think our approach has really been specific to what the needs are. As an institution, we've not pushed all our chips in on the cloud, whether it be for high performance computing or otherwise. It's really looking at what the specific need is and addressing it with the proper solution. We made an investment several years ago in a data center internally, and we're leveraging that through the intelligent infrastructure that I spoke about. But really it's addressing what the specific need is and finding the specific solution, rather than going all in in one direction or another. I dunno if Jet Stream is something that you would like to bring up as well. >> By having our own data center and having our own facilities we're able to compete for NSF grants and work on projects that provide shared resources for the research community. Just dream is a project that does that. Without a data center and without the ability to work on large projects, we don't have any of that. If you don't have that then you're dependent on someone else. We like to say that, what we are proud of is the people come to IU and ask us if they can partner on our projects. Without a data center and those resources we are the ones who have to go out and say can we partner on your project? We'd like to be the leaders of that in that space. >> I wanted to kind of double click on something you mentioned. Couple of things. Historically IU has been I'm sure closely associated with Chicago. You think of what are students thinking of doing when they graduate? Maybe they're going to go home, but the sort of center of gravity it's like Chicago. You mentioned talking about, especially post pandemic, the idea that you can live anywhere. Not everybody wants to live in Manhattan or Santa Clara. And of course, technology over decades has given us the ability to do things remotely and IU is plugged into the globe, doesn't matter where you are. But have you seen either during or post pandemic 'cause we're really in the early stages of this. Are you seeing that? Are you seeing people say, Hey, thinking about their family, where do I want to live? Where do I want to raise my family? I'm in academia and no, I don't want to live in Manhattan. Hey, we can go to IU and we're plugged into the globe. And then students in California we see this, there's some schools on the central coast where people loved living there when they were in college but there was no economic opportunity there. Are you seeing a shift, are basically houses in Bloomington becoming unaffordable because people are saying, you know what, I'm going to stay here. What does that look like? >> I mean, for our group there are a lot of people who do work from home, have chosen to stay in Bloomington. We have had some people who for various reasons want to leave. We want to retain them, so we allow them to work remotely. And that has turned into a tool for recruiting. The kid that graduates from Caltech. Doesn't want to stay in Caltech in California, we have an opportunity now he can move to wherever between here and there and we can hire him do work. We love to have people come to Indiana. We think it is a unique experience, Bloomington, Indianapolis are great places. But I think the reality is, we're not going to get everybody to come live, be a Hoosier, how do we get them to come and work at IU? In some ways disappointing when we don't have buildings full of people, but 40 paying Zoom or teams window, not kind the same thing. But I think this is what we're going to have to figure out, how do we make this kind of environment work. >> Last question here, give you a chance to put in a plug for Indiana University. For those those data scientists those researchers who may be open to working somewhere else, why would they come to Indiana University? What's different about what you do from what every other academic institution does, Aaron? >> Yeah, I think a lot of what we just talked about today in terms of from a network's perspective, that were plugged in globally. I think if you look beyond the networks I think there are tremendous opportunities for folks to come to Bloomington and experience some bleeding edge technology and to work with some very talented people. I've been amazed, I've been at IU for 20 years and as I look at our peers across higher ed, well, I don't want to say they're not doing as well I do want brag at how well we're doing in terms of organizationally addressing things like security in a centralized way that really puts us in a better position. We're just doing a lot of things that I think some of our peers are catching up to and have been catching up to over the last 10, 12 years. >> And I think to sure scale of IU goes unnoticed at times. IU has the largest medical school in the country. One of the largest nursing schools in the country. And people just kind of overlook some of that. Maybe we need to do a better job of talking about it. But for those who are aware there are a lot of opportunities in life sciences, healthcare, the social sciences. IU has the largest logistics program in the world. We teach more languages than anybody else in the world. The varying kinds of things you can get involved with at IU including networks, I think pretty unparalleled. >> Well, making the case for high performance computing in the Hoosier State. Aaron, Dave, thanks very much for joining you making a great case. >> Thank you. >> Thank you. >> We'll be back right after this short message. This is theCUBE. (upbeat music)

(upbeat music) >> We're back. This is Dave Vellante of theCUBE, and we're here with Ray Wang, who just wrote a book reminiscent of the famous Tears for Fears song, Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants. Ray, great to see again, man. >> What's going on, man, how are you? >> Oh great, thanks for coming on. You know, it was crazy, been crazy, but it's good to see you face-to-face. >> Ray: This is, we're in the flesh, it's live, we're having conversations, and the information that we're getting is cut right. >> Dave: Yeah, so why did you write this book and how did you find the time? >> Hey, we're in the middle of pandemic. No, I wrote the book because what was happening was digital transformation efforts, they're starting to pop up, but companies weren't always succeeding. And something was happening with digital giants that was very different. They were winning in the marketplace. And never in the form of, if you think about extreme capitalism, if we think about capitalism in general, never in the history of capitalism have we seen growth of large companies. They get large, they fall apart, they don't have anything to build, they can't scale. Their organizations are in shambles. But what happened? If you look at 2017, the combined market cap of the FAANGs and Microsoft was 2 trillion. Today, it is almost 10.2 trillion. It's quintupled. That's never happened. And there's something behind that business model that they put into place that others have copied, from the Airbnbs to the Robloxes to what's going to happen with like a Starlink, and of course, the Robinhoods and you know, Robinhoods and Coinbases of the world. >> And the fundamental premise is all around data, right? Putting data at the core, if you don't do that, you're going to fly blind. >> It is and the secret behind that is the long-term platforms called data-driven digital networks. These platforms take the ability, large memberships, our large devices, they look at that effect. Then they look at figuring out how to actually win on data supremacy. And then of course, they monetize off that data. And that's really the secret behind that is you've got to build that capability and what they do really well is they dis-intermediate customer account control. They take the relationships, aggregate them together. So food delivery app companies are great example of that. You know, small businesses are out there that hundreds and thousands of customers. Today, what happens? Well, they've been aggregated. Millions of customers together into food delivery app. >> Well, I think, you know, this is really interesting what you're saying, because if you think about how we deal with Netflix, we don't call the Netflix sales department or the marketing department of the service, just one interface, the Netflix. So they've been able to put data at their core. Can incumbents do that? How can they do that? >> Incumbents can definitely do that. And it's really about figuring out how to automate that capture. What you really want to do is you start in the cloud, you bring the data together, and you start putting the three A's, analytics, automation, and AI are what you have to be able to put into place. And when you do do that, you now have the ability to go out and figure out how to create that flywheel effect inside those data-driven digital networks. These DDDNS are important. So in Netflix, what are they capturing? They're looking at sentiment, they're looking at context. Like why did you interact with, you know, one title versus another? Did you watch Ted Lasso? Did you switch out of Apple TV to Netflix? Well, I want to know why, right? Did you actually jump into another category? You switched into genres. After 10:00 p.m., what are you watching? Maybe something very different than what you're watching at 2:00 p.m.. How many members are in the home, right? All these questions are being answered and that's the business graph behind all this. >> How much of this is kind of related to the way organizations or companies are organized? In other words, you think about, historically, they would maybe put the process at the core or the, in a bottling plant, the manufacturing facility at the core and the data's all dispersed. Everybody talks about silos. So will AI be the answer to that? Will some new database, Snowflake? Is that the answer? What's the answer to sort of bringing that data together and how do you deal with the organizational inertia? >> Well, the trick to it is really to have a single plane to be able to access that data. I don't care where the data sits, whether it's on premise, whether it's in the cloud, whether it's in the edge, it makes no difference. That's really what you want to be able to do is bring that information together. But the glue is the context. What time was it? What's the weather outside? What location are you in? What's your heart rate? Are you smiling, right? All of those factors come into play. And what we're trying to do is take a user, right? So it could be a customer, a supplier, a partner, or an employee. And how do they interact with an order doc, an invoice, an incident, and then apply the context. And what we're doing is mining that context and information. Now, the more, back to your other point on self service and automation, the more you can actually collect those data points, the more you can capture that context, the more you're able to get to refine that information. >> Context, that's interesting, because if you think about our operational systems, we've contextualized most of them, whether it's sales, marketing, logistics, but we haven't really contextualized our data systems, our data architecture. It's generally run by a technical group. They don't necessarily have the line of business context. You see what HPE is doing today is trying to be inclusive of data on prem. I mentioned Snowflake, they're saying no way. Frank Slootman says we're not going on prem. So that's kind of interesting. So how do you see sort of context evolving with the actually the business line? Not only who has the context actually can, I hate to use the word, but I'm going to, own the data. >> You have to have a data to decisions pathway. That data decisions pathway is you start with all types of data, structured, unstructured, semi-structured, you align it to a business process as an issue, issue to resolution, order to cash, procure to pay, hire to retire. You bring that together, and then you start mining and figuring out what patterns exist. Once you have the patterns, you can then figure out the next best action. And when you get the next best action, you can compete on decisions. And that becomes a very important part. That decision piece, that's going to be automated. And when we think about that, you and I make a decision one per second, how long does it get out of management committee? Could be a week, two weeks, a quarter, a year. It takes forever to get anything out of management committee. But these new systems, if you think about machines, can make decisions a hundred times per second, a thousand times per second. And that's what we're competing against. That asymmetry is the decision velocity. How quickly you can make decisions will be a competitive weapon. >> Is there a dissonance between the fact that you just mentioned, speed, compressing, that sort of time to decision, and the flip side of that coin, quality, security, governance. How do you see squaring that circle? >> Well, that's really why we're going to have to make that, that's the automated, that's the AI piece. Just like we have all types of data, we got to spew up automated ontologies, we got to spit them up, we got to be using, we've got to put them back into play, and then we got to be able to take back into action. And so you want enterprise class capabilities. That's your data quality. That's your security. That's the data governance. That's the ability to actually take that data and understand time series, and actually make sure that the integrity of that data is there. >> What do you think about this sort of notion that increasingly, people are going to be building data products and services that can be monetized? And that's kind of goes back to context, the business lines kind of being responsible for their own data, not having to get permission to add another data source. Do you see that trend? Do you see that decentralization trend? Two-part question. And where do you see HPE fitting into that? >> I see, one, that that trend is definitely going to exist. I'll give you an example. I can actually destroy the top two television manufacturers in the world in less than five years. I could take them out of the business and I'll show you how to do it. So I'm going to make you an offer. $15 per month for the next five years. I'm going to give you a 72 inch, is it 74? 75 inch, 75 inch smart TV, 4k, big TV, right? And it comes with a warranty. And if anything breaks, I'm going to return it to you in 48 hours or less with a brand new one. I don't want your personal information. I'm only going to monitor performance data. I want to know the operations. I want to know which supplier lied to me, which components are working, what features you use. I don't need to know your personal viewing habits, okay? Would you take that deal? >> TV is a service, sure, of course I would. >> 15 bucks and I'm going to make you a better deal. For $25 a month, you get to make an upgrade anytime during that five-year period. What would happen to the two largest TV manufacturers if I did that? >> Yeah, they'd be disrupted. Now, you obviously have a pile of VC money that you're going to do that. Will you ever make money at that model? >> Well, here's why I'll get there and I'll explain. What's going to happen is I lock them out of the market for four to five years. I'm going to take 50 to 60% of the market. Yes, I got to raise $10 billion to figure out how to do that. But that's not really what happens at the end. I become a data company because I have warranty data. I'm going to buy a company that does, you know, insurance like in Asurion. I'm going to get break/fix data from like a Best Buy or a company like that. I'm going to get at safety data from an underwriter's lab. It's a competition for data. And suddenly, I know those habits better than anyone else. I'm going to go do other things more than the TV. I'm not done with the TV. I'm going to do your entire kitchen. For $100 a month, I'll do a mid range. For like $500 a month, I'm going to take your dish washer, your washer, your dryer, your refrigerator, your range. And I'll do like Miele, Gaggenau, right? If you want to go down Viking, Wolf, I'll do it for $450 a month for the next 10 years. By year five, I have better insurance information than the insurance companies from warranty. And I can even make that deal portable. You see where we're going? >> Yeah so each of those are, I see them as data products. So you've got your TV service products, you've got your kitchen products, you've got your maintenance, you know, data products. All those can be monetized. >> And I went from TV manufacturer to underwriter overnight. I'm competing on data, on insurance, and underwriting. And more importantly, here's the green initiative. Here's why someone would give me $10 billion to do it. I now control 50% of all power consumption in North America because I'm also going to do HVAC units, right? And I can actually engineer the green capabilities in there to actually do better power purchase consumption, better monitoring, and of course, smart capabilities in those, in those appliances. And that's how you actually build a model like that. And that's how you can win on a data model. Now, where does HPE fit into that? Their job is to bring that data together at the edge. They bring that together in the middle. Then they have the ability to manage that on a remote basis and actually deliver those services in the cloud so that someone else can consume it. >> All right, so if you, you're hitting on something that some people have have talked about, but it's, I don't think it's widely sort of discussed. And that is, historically, if you're in an industry, you're in that industry's vertical stack, the sales, the marketing, the manufacturing, the R&D. You become an expert in insurance or financial services or whatever, you know, automobile manufacturing or radio and television, et cetera. Obviously, you're seeing the big internet giants, those 10 trillion, you know, some of the market caps, they're using data to traverse industries. We've never seen this before. Amazon in content, you're seeing Apple in finance, others going into the healthcare. So they're technology companies that are able to traverse industries. Never seen this before, and it's because of data. >> And it's the collapsing value chains. Their data value chains are collapsing. Comms, media, entertainment, tech, same business. Whether you sell me a live stream TV, a book, a video game, or some enterprise software, it's the same data value stream on multi-sided networks. And once you understand that, you can see retail, right? Distribution, manufacturing collapsed in the same kind of way. >> So Silicon Valley broadly defined, if I can include, you know, Microsoft and Amazon in there, they seem to have a dual disruption agenda, right? One is on the technology front, disrupting, you know, the traditional enterprise business. The other is they're disrupting industries. How do you see that playing out? >> Well the problem is, they're never going to be able to get into new industries going forward because of the monopoly power that people believe they have, and that's what's going on, but they're going to invest in creating joint venture startups in other industries, as they power the tools to enable other industries to jump and leap frog from where they are. So healthcare, for example, we're going to have AI in monitoring in ways that we never seen before. You can see devices enter healthcare, but you see joint venture partnerships between a big hyperscaler and some of the healthcare providers. >> So HPE transforming into a cloud company as a service, do you see them getting into insurance as you just described in your little digital example? >> No, but I see them powering the folks that are in insurance, right? >> They're not going to compete with their customers maybe the way that Amazon did. >> No, that's actually why you would go to them as opposed to a hyperscale that might compete with you, right? So is Google going to get into the insurance business? Probably not. Would Amazon? Maybe. Is Tesla in the business? Yeah, they're definitely in insurance. >> Yeah, big time, right. So, okay. So tell me more about your book. How's it being received? What's the reaction? What's your next book? >> So the book is doing well. We're really excited. We did a 20 city book tour. We had chances to meet everybody across the board. Clients we couldn't see in a while, partners we didn't see in a while. And that was fun. The reaction is, if you read the book carefully, there are $3 trillion market cap opportunities, $1000 billion unicorns that can be built right there. >> Is, do you have a copy for me that's signed? (audience laughing) >> Ray: Sorry (coughs) I'm choking on my makeup. I can get one actually, do you want one? >> Dave: I do, I want, I want one. >> Can someone bring my book bag? I actually have one, I can sign it right here. >> Dave: Yeah, you know what? If we have a book, I'd love to hold it. >> Ray: Do you have any here as well? >> So it's obviously you know, Everybody Wants to Rule the World: Surviving and Thriving in a world of Digital Giants, available, you know, wherever you buy books. >> Yeah, so, oh, are we still going? >> Dave: Yeah, yeah, we're going. >> Okay. >> Dave: What's the next book? >> Next book? Well, it's about disrupting those digital giants and it's going to happen in the metaverse economy. If we think about where the metaverse is, not just the hardware platforms, not just the engines, not just what's going on with the platforms around defy decentralization and the content producers, we see those as four different parts today. What we're going to actually see is a whole comp, it's a confluence of events that's going to happen where we actually bring in the metaverse economy and the stuff that Neal Stephenson was writing about ages ago in Snow Crash is going to come out real. >> So, okay. So you're laying out a scenario that the big guys, the disruptors, could get disrupted. It sounds like crypto is possibly a force in that disruption. >> Ray: Decentralized currencies, crypto plays a role, but it's the value exchange mechanisms in an Algorand, in an Ether, right, in a Cardano, that actually enables that to happen because the value exchange in the smart contracts power that capability, and what we're actually seeing is the reinvention of the internet. So you think, see things like SIOM pop-up, which actually is creating the new set of the internet standards, and when those things come together, what we're actually going to move from is the seller is completely transparent, the buyer's completely anonymous and it's in a trust framework that actually allows you to do that. >> Well, you think about those protocols, the internet protocols that were invented whenever, 30 years ago, maybe more, TCP/IP, wow. I mean, okay. And they've been co-opted by the internet giants. It's the crypto guys, some of the guys you've mentioned that are actually innovating and putting, putting down new innovation really and have been well-funded to do so. >> I mean, I'll give you another example of how this could happen. About four years ago, five years ago, I wanted to buy Air Canada's mileage program, $400 million, 10 million users, 40 bucks a user. What do I want them in a mileage program? Well think about it. It's funded, a penny per mile. It's redeemed at 1.6 cents a mile. It's 2 cents if you buy magazines, 2 1/2 cents if you want, you know, electronics, jewelry, or sporting equipment. You don't lose money on these. CFOs hate them, they're just like (groans) liability on the books, but they mortgage the crap out of them in the middle of an ish problem and banks pay millions of dollars a year pour those mileage points. But I don't want it for the 10 million flyers in Canada. What I really want is the access to 762 million people in Star Alliance. What would happen if I turned that airline mileage program into cryptocurrency? One, I would be the world's largest cryptocurrency on day one. What would happen on day two? I'd be the world's largest ad network. Cookie apocalypse, go away. We don't need that anymore. And more importantly, on day three, what would I do? My ESG here? 2.2 billion people are unbanked in the world. All you need is a mobile device and a connection, now you have a currency without any government regulation around, you know, crayon banking, intermediaries, a whole bunch of people like taking cuts, loansharking, that all goes away. You suddenly have people that are now banked and you've unbanked, you've banked the unbanked. And that creates a whole very different environment. >> Not a lot of people thinking about how the big giants get disintermediated. Get the book, look into it, big ideas. Ray Wang, great to see you, man. >> Ray: Hey man, thanks a lot. >> Hey, thank you. All right and thank you for watching. Keep it right there for more great content from HPE's big GreenLake announcements. Be right back. (bright music)

>>Okay, >>welcome to Accelerating next. Thank you so much for joining us today. We have a great program. We're gonna talk tech with experts, will be diving into the changing economics of our industry and how to think about the next phase of your digital transformation. Now. Very importantly, we're also going to talk about how to optimize workloads from edge to excess scale with full security and automation all coming to you as a service. And with me to kick things off as Neil Mcdonald, who's the GM of compute at HP NEAL. Always a pleasure. Great to have you on. >>It's great to see you dad >>now, of course, when we spoke a year ago, we had hoped by this time we'd be face to face. But here we are again, you know, this pandemic, It's obviously affected businesses and people in so many ways that we could never have imagined. But the reality is in reality, tech companies have literally saved the day. Let's start off, how is HPV contributing to helping your customers navigate through things that are so rapidly shifting in the marketplace, >>although it's nice to be speaking to you again and I look forward to being able to do this in person. At some >>point. The >>pandemic has really accelerated the need for transformation and businesses of all sizes. More than three quarters of C. I. O. S. Report that the crisis has forced them to accelerate their strategic agendas, organizations that were ready transforming or having to transform faster and organizations that weren't on that journey yet are having to rapidly develop and execute a plan to adapt to this new reality. Our customers are on this journey and they need a partner for not just the computer technology but also the expertise and economics that they need for that digital transformation. And for us this is all about unmatched optimization for workloads from the edge to the enterprise to extra scale With 360° security and the intelligent automation all available in that as a service experience. >>Well, you know, as you well know, it's a challenge to manage through any transformation, let alone having to set up remote workers overnight, securing them, re setting budget priorities. What are some of the barriers that you see customers are working hard to overcome? >>Simply put the organizations that we talk with our challenged in three areas. They need the financial capacity to actually execute a transformation. They need the access to the resource and the expertise needed to successfully deliver on a transformation. And they have to find the way to match their investments with the revenues for the new services that they're putting in place to service their customers in this environment. >>You know, we have a data partner E. T. R. Enterprise Technology Research and the spending data that we see from them is it's quite dramatic. I mean last year we saw a contraction of roughly 5% of in terms of I. T. Spending budgets etcetera. And this year we're seeing a pretty significant rebound. Maybe a 67% growth ranges is the prediction. The challenge we see his organizations have to they got to iterate on that. I call it the forced march to digital transformation and yet they also have to balance their investments. For example that the corporate headquarters which have kind of been neglected. Is there any help in sight for the customers that are trying to reduce their spending and also take advantage of their investment capacity? >>I think you're right. Many businesses are understandably reluctant to loosen the purse strings right now given all of the uncertainty. And often a digital transformation is viewed as a massive upfront investment that will pay off in the long term, and that can be a real challenge in an environment like this, but it doesn't need to be uh, we work through HP financial services to help our customers create the investment capacity to accelerate the transformation, often by leveraging assets they already have and helping them monetize them in order to free up the capacity to accelerate what's next for their infrastructure and for the business. >>So can we drill into that? I would wonder if you could add some specifics. I mean, how do you ensure a successful outcome? What are you really paying attention to as those sort of markers for success? >>Well, when you think about the journey that an organization is going through, it's tough to be able to run the business and transform at the same time and one of the constraints is having the people with enough bandwidth and enough expertise to be able to do both. So we're addressing that in two ways for our customers. One is by helping them confidently deploy new solutions which we have engineered, leveraging decades of expertise and experience in engineering to deliver those workload optimized portfolios that take the risk and the complexity out of assembling some of these solutions and give them a prepackaged validated supported solution intact that simplifies that work for them. But in other cases we can enhance our customers bandwidth by bringing them HP point Next experts with all of the capabilities we have to help them plan, deliver and support these I. T. Projects and transformations. Organizations can get on a faster track of modernization, getting greater insight and control as they do it. We're a trusted partner to get the most for a business that's on this journey in making these critical computer investments to underpin the transformations and whether that's planning to optimizing to save for retirement at the end of life. We can bring that expertise to bear to help amplify what our customers already have in house and help them accelerate and succeed in executing these transformations. >>Thank you for that. Let's let's talk about some of the other changes that customers see him in the cloud is obviously forced customers and their suppliers to really rethink how technology is packaged, how it's consumed, how it's priced. I mean there's no doubt in that. So take Green Lake, it's obviously leading example of a pay as you scale infrastructure model and it could be applied on prem or hybrid. Can you maybe give us a sense as to where you are today with Green Lake? >>Well, it's really exciting now from our first pay, as you go offering back in 2006, 15 years ago to the introduction of Green Lake. HBs really been paving the way on consumption-based services through innovation and partnership to help meet the exact needs of our customers. Hp Green Lake provides an experience, is the best of both worlds. A simple paper use technology model with the risk management of data that's under our customers direct control and it lets customers shift to everything as a service in order to free up capital and avoid that upfront expense that we talked about. They can do this anywhere at any scale or any size and really HP Greenlee because the cloud that comes to you >>like that. So we've touched a little bit on how customers can maybe overcome some of the barriers to transformation. What about the nature of transformations themselves? I mean historically there was a lot of lip service paid to digital and and there's a lot of complacency, frankly, but you know that covid wrecking ball meme that so well describes that if you're not a digital business, essentially you're gonna be out of business. So, you know, those things have evolved, how is HPV addressed the new requirements? >>Well, the new requirements are really about what customers are trying to achieve. And four very common themes that we see are enabling the productivity of remote workforce. That was never really part of the plan for many organizations being able to develop and deliver new apps and services in order to service customers in a different way or drive new revenue streams, being able to get insights from data so that in these tough times they can optimize their business more thoroughly. And then finally think about the efficiency of an agile hybrid private cloud infrastructure. Especially one that now has to integrate the edge. And we're really thrilled to be helping our customers accelerate all of these and more with HP computer. >>I want to double click on that remote workforce productivity. I mean again the surveys that we see, 46 of the ceo say that productivity improved with the whole work from home remote work trend. And on average those improvements were in the four range which is absolutely enormous. I mean when you think about that how does HP specifically help here? What do you guys do? >>Well every organization in the world has had to adapt to a different style of working and with more remote workers than they had before. And for many organizations that's going to become the new normal. Even post pandemic, many I. T. Shops are not well equipped for the infrastructure to provide that experience because if all your workers are remote the resiliency of that infrastructure, the latency is of that infrastructure, the reliability of are all incredibly important. So we provide comprehensive solutions expertise and as a service options that support that remote work through virtual desktop infrastructure or V. D. I. So that our customers can support that new normal of virtual engagements online everything across industries wherever they are. And that's just one example of many of the workload optimized solutions that we're providing for our customers is about taking out the guesswork and the uncertainty in delivering on these changes that they have to deploy as part of their transformation. And we can deliver that range of workload optimized solutions across all of these different use cases. Because of our broad range of innovation in compute platforms that span from the ruggedized edge to the data center all the way up to exa scale in HPC. >>I mean that's key if you're trying to affect the digital transformation and you don't have to fine tune, you know, basically build your own optimized solutions if I can buy that rather than having to build it and rely on your R and D. You know, that's key. What else is HP doing? You know, to deliver new apps, new services, you your microservices, containers, the whole developer trend, what's going on there? >>Well, that's really key because organizations are all seeking to evolve their mix of business and bring new services and new capabilities, new ways to reach their customers, new way to reach their employees, new ways to interact in their ecosystem all digitally. And that means that development and many organizations of course are embracing container technology to do that today. So with the HP container platform, our customers can realize that agility and efficiency that comes with container ization and use it to provide insight to their data more and more on that data of course is being machine generated or generated the edge or the near edge. And it can be a real challenge to manage that data holistically and not of silos and islands at H. P. S. Moral data fabric speeds the agility and access to data with a unified platform that can span across the data centers, multiple clouds and even the edge. And that enables data analytics that can create insights powering a data driven production oriented cloud enabled analytics and AI available anytime anywhere and at any scale. And it's really exciting to see the kind of impact that that can have in helping businesses optimize their operations in these challenging times. >>You gotta go where the data is and the data is distributed. It's decentralized. I I like the liberal vision and execution there so that all sounds good. But with digital transformation you're gonna see more compute in hybrid deployments. You mentioned edge. So the surface area, it's like the universe its its ever expanding. You mentioned, you know, remote work and work from home before. So I'm curious where are you investing your resources from a cyber security perspective? What can we count on from H P. E there >>Or you can count on continued leadership from hp as the world's most secure industry standard server portfolio. We provide an enhanced and holistic 360° view to security that begins in the manufacturing supply chain and concludes with a safeguarded end of life Decommissioning. And of course we've long set the bar for security with our work on silicon root of trust and we're extending that to the application tier. But in addition to the security customers that are building this modern Khyber or private cloud, including the integration of the Edge need other elements to they need an intelligent software defined control plane so that they can automate their compute fleets from all the way at the edge to the core. And while scale and automation enable efficiency, all private cloud infrastructures are competing with Web scale economics and that's why we're democratizing web scale technologies like Pensando to bring web scale economics and web scale architecture to the private cloud. Our partners are so important in helping us serve our customers needs. >>Yeah. I mean H. P. Is really up to its ecosystem game since the middle of last decade when when you guys reorganized and it became even more partner friendly. So maybe give us a preview of what's coming next in that regard from today's event. >>Well, they were really excited to have HP. Ceo, Antonio Neri speaking with Pat Gelsinger's from Intel and later lisa su from A. M. D. And later I'll have the chance to catch up with john Chambers, the founder and Ceo of J. C. Two ventures to discuss the state of the market today. >>Yeah, I'm jealous. You got, yeah, that's a good interviews coming up, NEal, thanks so much for joining us today on the virtual cube. You've really shared a lot of great insight how HP is is partner with customers. It's, it's always great to catch up with you. Hopefully we can do so face to face, you know, sooner rather than later. >>I look forward to that. And you know, no doubt our world has changed and we're here to help our customers and partners with the technology, the expertise and the economics they need For these digital transformations. And we're going to bring them unmatched workload optimization from the edge to exa scale with that 360° security with the intelligent automation. And we're gonna deliver it all as an as a service experience. We're really excited to be helping our customers accelerate what's next for their businesses. And it's been really great talking with you today about that day. Thanks for having me >>very welcome. It's been super Neil and I actually, you know, I had the opportunity to speak with some of your customers about their digital transformation and the role of that HPV plays there. So let's dive right in. >>Yeah. Mm.

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hello, everyone, welcome back. This is theCUBE's exclusive coverage of Informatica World 2018. It's our fourth year covering Informatica on the front lines. Every year it gets bigger and bigger. I'm John Furrier, the host of theCUBE, with Peter Burris, my co-host, with some, chief analyst at Wikibon and SiliconANGLE on theCUBE. Our next guest is theCUBE alumni Amit Walia, who's been on many times, even before he was president. Now he's the president of products and strategic ecosystems for Informatica. Great to see you, great to have you on. Congratulations on your keynote. Thanks for stopping by. >> Thanks, John, glad to be here. Always good to be back. >> You're super, well, I love talking with you because one, you know, the business is growing. You've been in the product side, you guys are all great product folks. And this, they're shipping products. It's not like it's, like, vaporware. It's, like, great stuff. Now Azure deal was announced. But now the timing of the data play with Switzerland, we talked about this fabric, better time than ever. This year, you got data lakes turned into data swamps last year. This year it's about governance and catalog. Good timing. What's your assessment? Give us your point of view from the keynote, timing, product. >> Well, I mean, I think you're exactly right. We see that it's a unique time, and it was building over the last couple of years. So, you know, we have this phrase that this is a data 3.0 world where data has become its own thing. It's no more captive to an application or a database. Those days are gone. And I think in data 3.0 world, I think we talked about it this morning in my keynote, that, you know, customers have to step back and think differently. You can't just do the same old things and expect to be different, and especially as they're driving digital transformations. So we introduced this concept of system thinking 3.0, where as you're thinking about a data, you have to think about it as a platform. A nimble platform, not a ERP-ish platform. Think of it at scale. It's doubling every year. >> Yeah. >> Think of it metadata in, metadata out. Let AI assist you. You know, you've got to have, we as humans are just going to be swamped with so much data we can't process it. And last, very important thesis, as you all know, is that governance, security, and privacy have to be design principles. They cannot be an afterthought. >> Last year you announced CLAIRE AI component of the system. >> Amit: Yeah. >> How has that evolved this year? I mean, I know it was a strategic centerpiece for you guys. Obviously the catalog is looking really strong right now, a lot of buzzing to show around the enterprise catalog. Where is the AI, the CLAIRE piece fitting in? Can you just give us the update on CLAIRE? >> Well, CLAIRE's come a long way. Basically part of every product we have. So it manifests itself probably most holistically in the catalog, but whether in the data lake, it's in the context of surfacing data, discovering data, giving recommendations of data to an analyst in a very business user context, all in the context of an MBM, giving you relationship discovery of, let's say, John, who you are, into who you are. So it is in Secure@Source helping anomaly detection happen. So CLAIRE has now made its way into every product. But as you said, the one product where it basically surfaces itself in its full bloom is the catalog, which, by the way, has been the fastest growing product in Informatica's history. One year since launch, it has just gone, taken off. >> Well, presumably there's a relationship. Sorry, John. Presumably there's relationship there. Catalogs have been around for years, but they've been very, very difficult to build and sustain and maintain. CLAIRE presumably is providing a capability that removes a lot of the drudgery associated with catalogs, and that's one of the things that's making it possible. Have I got that right? >> No, yeah, absolutely. And actually, building the new catalog also has been a hard thing. So in some ways building it for scale has been a massive common sense problem that we've been solving for the last three, four years. You know, collecting metadata across the full enterprise is a non-trivial activity, so it was never done across the enterprise ever. If you remember when I was here last time, our vision for the catalog was very simple. We want to be the Google for enterprise data... >> Peter: Yeah. >> ...through metadata. >> And that's what we were able to do through the catalog. But as you rightfully said, it's very hard to consume it if you don't write AI to help it. That's where CLAIRE made a very big road. So the UI's very straightforward. It's a Google UI, and any business user can, with the help of CLAIRE, start using it. >> But it persists. >> Yes. >> So unlike just putting a search term in and getting a page of stuff back, a catalog has to persist. >> Has a persistence, exactly. >> And so describe, now that you have that in place with CLAIRE, as John asked, where does it go? >> Solving use cases. Actually, I'll give you a little preview. Tomorrow I do the closing keynote, and usually what I do, the closing keynote is all about features. So actually, it's a whole demo on CLAIRE where we're taking CLAIRE to the whole next level. As a great example, you know, building data supply chains, you know, it's a manual activity that you have to do. With the help of the catalog, we actually understand the system architecture. So if you want to add new sources of data or change anything you want to do, you don't have to go through those steps again. We will service it to you and we'll tell you what to do. In fact, tomorrow I'll show what we call a self-integrating system. It'll happen by itself. You have to just go and say whether I agree or not agree and the machine learns. Next time it gets smarter and smarter. Or in the context of governance. If a new policy comes up in an enterprise, the biggest challenge is how do I even know what the impact of the new policy is? Look at GDPR right now. So with the help of CLAIRE, we can understand across the entire enterprise what would be the impact of that policy across different functions and what the gaps are. Those are the kind of places we are taking CLAIRE towards more bigger business-driven initiatives. In fact, tomorrow there'll be a whole demo on that one. >> I mean, GDPR is interesting because it really exposes who's ready. >> Yeah. >> Who has had invested their, the engineering in data, understands the data. So that's clear. We're seeing some, and it's also a shot across the bow of companies saying, look, you got to think strategically around your data. We talk about this all the time with you guys, so it's not new to us, but it is new to the fact that some people are right now sitting there going, oh no, I need to do something. >> Amit: Yeah. >> How is Informatica going to help me if I have a GDPR awakening of, oh man, I got to do something. >> You know, GDPR... >> Do I just call you up and do the, roll in the catalog? Do I... >> That's a great place to begin, by the way. So GDPR, by the way, is a data problem. So GDPR is not necessarily a compliance/security problem, because you want to understand which data pass through boundaries, who's accessing it. It's a true data problem. So today, I mean, in fact, at Informatica World, we have customers like PayPal talking about their journey with us on GDPR. And so you begin with the catalog, and then we have three products that help in the GDPR journey, the catalog, Secure@Source, and the Data Governance Axon product. And again, each company's GDPR implications are slightly different, and companies, as I said, like MasterCard, like PayPal, that are using our products to run their GDPR activity right now, it's a... So we are seeing that going through the roof. And in fact, one of the big use cases for catalog has been in the context of governance and GDPR. >> I want to talk about the trends on, that are impacting you guys. Again, I was saying earlier that it's a tailwind for you guys. The timing's perfect. Multi-cloud, hybrid cloud. I'd say hybrid cloud's probably in its second year, maybe third year hype, but now multi-cloud is real. You have announced a Azure relationship. You guys have a growing ecosystem opportunity. >> Amit: Yeah. >> How are you guys looking at it? 'Cause it's really emergent. It's happening right now. How are you guys targeting the ecosystem, whether it's business development partnerships, joint product development go to market, and/or on the business side? What's the orientation, what's the posture? Are you guy taking a certain approach, expecting certain growth? What's the update on the ecosystem, the global partner landscape? >> You know, the way we think about ourselves is that we've been the Switzerland of data always. And customers, actually, I always say it's always customer-backed. >> John: Yeah. >> If you solve for the customer, everything goes good. Customers expect us to do that. And customers are going to be in a heterogeneous world. Nobody's going to ever pick one stack. You know, you all know, right, there are customers who are still, larger devices still running mainframe for some processing, and they are already using new platforms for IoT, so they have to somehow manage this entire transition, and there will be multi-cloud, cloud hybrid world. So they naturally expect us to be a Switzerland of data across the board, and that's our overall strategy. We will always be there for them. In that context, we work with, we have learned the art of working with their ecosystem. >> John: Yeah. >> So you saw Azure today, and we are very close partners of hundreds of customers. Amazon, hundreds of customers. Google's coming up. So those are common. So we, Adobe, tomorrow you'll have Adobe. >> John: So you're cool with all the cloud players. >> And, you know, I always look at it this way. If you solve for the customer, everybody will work with you, and I think we're doing meaningful work. So that's helping our strategy. But what we have done two very different things with that. We've gone deep in terms of product integration. I mean, you saw today. We are making it easy from a customer experience point of view to get these jobs done, right? If you are spinning up a data warehouse in the cloud, you don't want to repeat the mistakes of the last 20 years. So now it's five clicks, you should be good to go. >> John: Yeah. >> That's an area we've invested a lot to make sure that those experiences are a lot simpler and easier and very native. >> We had Bruce Chizen on earlier. He was implying that you guys have significant R&D, and he was trying to get me to get you the number. I think it was on Twitter. I think I'll ask Neal. I think he's out there already. But it's not so much the numbers. It's about the investment and the mindset you guys have for R&D. I know you had, went with a private equity company. >> Amit: Yes. >> We talked about that. >> You guy are growing. >> So this is a growth company. >> Amit: Yeah. >> You need R&D. >> Absolutely. >> What is the priority? How are you looking at that? How would you talk to the industry and customers about your R&D priorities? >> Well, I think we've been very blessed, and I think our investors, and I think Bruce, when we sit in a board meeting, you know, we always joke around. They have never skimped on investing in products. And I think that we've been, our belief is that we are the innovation leader in our markets. There is a massive opportunity in front of us to obviously capitalize on, and the only way you do it where you innovate, and innovate means we invest. And I tell you we've been very fortunate that the investment in products has continuously increased every year. I mean, this year, forget just the products and technologies. We made, John, double digit million dollar investments in building a brand-new hosting architecture across the world, in Americas, NMEI and APJ, and we benchmarked ourselves against the Amazons and the Azures of the world, not our competitors. So not just products, but taking the cloud infrastructure across the globe, most secure, most... >> So your own infrastructure. >> Absolutely. >> Well, I mean, we run our own stuff. >> Yeah. >> But we leverage both AWS and Azure in that context. But our goal is that we can be in the countries because data should not leave some of those countries. We comply to the biggest regulations. So we've made lots of investment, and hence we can also innovate and get into new product categories. I mean, you see we have a whole new cloud architecture out there. Catalog, security, these are all brand new markets that actually, some of them have all come out since we went private. Actually more innovation has come out of Informatica since we went private than in the three years previous to going private. >> So, you know, let's play a game. Let's say that the catalog, doing very well. Let's say that you, working with Microsoft, working with AWS, you're actually successful at establishing a standard... >> Amit: Yeah. >> ...for how we think about data catalogs in a hybrid, multi-cloud world. Combine that with R&D and products. If you have, in a data-first world, where the next generation of applications are going to be data-first, that catalog gives you an inside edge to an enormous number of new application forms. >> Amit: Yeah. >> How far does Informatica go? >> Well, that's a great question. I mean, I think, I generally believe that in some ways, we are barely scratching the opportunity in front of us. I mean, none of us have seen where this world will go. I mean, who would have imagined, think of all the trends that have happened. Look at the world of social, where it has brought us to bear. I generally think that, look, each company that I talk to, each customer I talk to, and I talk to hundreds of customers across the earth, they all want to become a tech company. They all want to be an Amazon or a Google. And they realize that they will not become an Amazon Google by replicating them. The best way they can become an Amazon Google is to figure out all of the data they have and start using it, right? >> Institutionalizing their work around their data. >> Exactly. So that's where the catalog becomes very handy. It's a great first step to begin that. And in that context, there are Fortune 5000, there's Fortune 10,000, there are mid-market customers. I think we have just literally scratched the surface of that. >> Do you envision catalog-driven applications... >> Amit: Oh, absolutely. >> ...that get into, with the Informatica brand on them? >> Oh, so we actually have, so a great point. We actually made the catalog rest API-driven. So there are customers who are building their applications on the catalog. In fact, I'll give you a preview of that tomorrow. I'll show a demo where Cognizant took our catalog, took CLAIRE within the catalog, used Microsoft's chatbot to create a complete third-party custom application called the Data Concierge, where you can go ask for data. So it's Microsoft chatbot, our CLAIRE engine, and a custom app written by... So the world where I see is that it will be, that is a central nervous system of the platform, and enough custom apps will be written in time. >> It's a real enabler. So I got to ask, and I know we got not a lot of time left, I mean, but I want to get thoughts on cloud native. >> Amit: Yeah. >> 'Cause you have, with containers, you don't have to kill the old to bring in the new. And what you guys are doing is with on-prem and some of the coolness, ease of use around getting the data kind of cataloged in with the metadata, you're enabling potentially developers. Where does this lead us with containers, microservices, service meshes, 'cause that's right around the corner. >> It's happening as we speak. I mean, so we rewrote the cloud platform as I just talked about. It's completely microservices-based, completely. We had to, we had a whole cloud platform. We basically said we're going to rewrite the whole thing. Microservices-based. And it's containerized. So the idea is that A, microservices give you agility, as we all very well know. We can innovate a lot faster. And with the help of containers, you can just rapidly scale, I mean, rapidly deploy. You can test. Dev becomes a whole lot easy. The, I mean, today's cycle is so short. Customers want to do things rapidly. So we are just really helping them be able to do that. >> So you see the data actually being an input into the development process... >> Oh, absolutely. >> ...via microservices and your service mesh. >> I mean, if you don't do that, you don't know what you're building. >> It's going to be a data-first world. My, going back to my point, I think there's an opportunity for you guys to then go to the marketplace with some thought leadership about what does it mean to build data-first applications. Historically we start with a process and we imagine what the data structure's going to look like, we put it in the database, and then there's all the plumbing about interaction and integration. You guys are saying get your data assets, get your data objects rendered inside the catalog and think about the new ways you can put them to work, and you think of your code... >> Amit: Yeah. >> ...as the mechanism by which that happens. >> Flips everything on its ear. >> Amit: Yeah. >> It's a data-first world, and a data-first approach to building applications seems like it's an appropriate next conversation. >> That, I agree with that, and that's a big opportunity, and obviously there's a task at hand to make sure we can help educate everyone to get there. And I think, you know, it'll take some time, but of course that's the, anything which is easy is not interesting. It's a hard problem that where you basically, you solve and you kind of make it a big industry. >> I mean, it's great to see you. We feel like we've been following the journey of the success of you guys. We've been talking, go back four years. >> Amit: Yeah. >> You can go back to thecube.net, look at the tape. You can see the conversations. You guys stayed on task. Great product team, very, you guys are kicking some butt out there. Congratulations. Final question for you. Put you on the spot. Biggest surprise this year for you. What's, obviously the catalog, you mentioned it's been taking off. What surprised you? Anything jump out in terms of successes, speed bumps in the road, architecture trends? What's the big surprise? >> You know, I think I'm actually very warmed up by seeing, I talked about the day zero. You know, it is a data-driven world where we see so many customers looking to come here. We've become the biggest data conference of the industry. In fact, we were reflecting, Informatica World has become the biggest accumulation of people who think data-first. And I think that has been more than any technology. To me, at the end of the day, look, as much technology will come and stay, I'm a big believer it's people that make the difference. >> John: Yeah. >> And I've been seeing all of those people here, seeing them make contributions, learn, and drive change has been my biggest, not only a positive surprise, but biggest, you know, gratification that I've seen at Informatica World. >> And the emphasis of not having such a big hype. I mean, getting excited about new technology is one thing, but the rubber's got to hit the road. You've got to have real performance, real software... >> Yeah. >> ...real results. >> 'Cause the pressure of scale fast, time to market... >> ...all that stuff. >> Right. >> Congratulations, great to see you. Amit Walia, president here at Informatica on products and strategic ecosystems. I'm sure he's going to continue to be busy over the next year when we see him certainly at our next theCUBE event. Amit, great to see you. I'm John Furrier, Peter Burris, live here at Informatica World 2018. It's the largest data-first conference on the planet We'll be right back with more after this short break. (musical sting)

hi I'm Peter Burris and welcome to this week's action item once again we're broadcasting from our beautiful the cube Studios in Palo Alto California and the wiki bond team is a little bit smaller this week for variety of reasons I'm being joined remotely by Neil Raiden and Jim Kabila's how you doing guys we're doing great Peter I'd be good thank you alright and it's actually a good team what we're gonna talk about we're gonna be specifically talking about some interesting developments and 14 days or so gdpr is gonna kick in and people who are behind will find themselves potentially subject to significant fines we actually were talking to a chief privacy officer here in the US who told us that had the Equinix breach occurred in Europe after May 25 2008 eeen it would have cost or Equifax the Equifax breach it would have cost Equifax over 160 billion dollars so these are very very real types of money that we're talking about but as we started thinking about some of the implications of gdpr and when it's going to happen and the circumstances of its of its success or failure and what its gonna mean commercially to businesses we also started trying to fold in a second trend and that second trend is the role of bitcoins going to play Bitcoin has a number of different benefits we'll get into some of that in a bit but one of them is that the data is immutable and gdpr has certain expectations regarding a firm's flexibility and how it can manage and handle data and blockchain may not line up with some of those issues as well as a lot of the Braque blockchain advocates might think Jim what are some of the specifics well Peter yeah blockchain is the underlying distributed hyper ledger or trusted database underlying Bitcoin and many other things blockchain yeah you know the one of the core things about blockchain that makes it distinctive is that you can create records and append them to block change you can read from them but can't delete them or update them it's not a crud database it's essentially for you to be able to go in and you know and erase a personally identifiable information record on an EU subject is you EU citizen in a blockchain it's not possible if you stored it there in other words blockchain then at the very start because it's an immutable database would not allow you to comply with the GDP ours were quite that people have been given a right to be forgotten as what what it's called that is a huge issue that might put the big kibosh on implementation of blockchain not just for PII in the EU but really for multinational businesses anybody who does business in Europe and the core you know coordination is like you know we're disregard brexit for now like Germany and France and Italy you got to be conformant completely worldwide essentially with your in your your PII management capabilities in order to pass muster with the regulators in the EU and avoid these massive fines blockchain seems like it would be incompatible with that compliance so where does the blockchain industry go or does it go anywhere or will it shrink well the mania died because of the GDP our slap in the face probably not there is a second issue as well Jim Lise I think there is and that is blockchain is allows for anonymity which means that everybody effectively has a copy of the ledger anywhere in the world so if you've got personally identifiable information coming out of the EU and you're a member or you're a part of that blockchain Network living in California you get a copy of the ledger now you may not be able to read the details and maybe that protects folks who might implement applications in blockchain but it's a combination of both the fact that the ledger is fully distributed and that you can't go in and make adjustments so that people can be forgotten based on EU laws if I got that right that's right and then there's a gray area you can't encrypt any and every record in a blockchain and conceal it from the prying eyes of people in California or in Thailand or wherever in the EU but that doesn't delete it that's not the same as erasing or deleting so there's a gray issue and there's no clarity from the EU regulators on this what if you use secret keys to encrypt individual records PII on a blockchain and then lost the keys or deleted the keys is that effectively would that be the same as he racing the record even though those bits still be there to be unreadable none of this has really been addressed in practice and so it's all a gray area it's a huge risk factor for companies that are considering exploring uses of blockchain for managing identity and you know security and all that other good stuff related to the records of people living in EU member countries so it seems as though we have two things they're gonna have that are that are likely to happen first off it's very clear that a lot of the GDP are related regulations were written in advance of comprehending what blockchain might be and so it doesn't and GDP are typically doesn't dictate implementation styles so it may have to be amended to accommodate some of the blocks a blockchain implementation style but it also suggests that increasingly we're going to hear from a design standpoint the breaking up of data associated with a transaction so that some of the metadata associated with that transaction may end up in the blockchain but some of the actual PII related data that is more sensitive from a GDP or other standpoint might remain outside of the blockchain so the blockchain effectively becomes a distributed secure network for managing metadata in certain types of complex transactions this is is that is that in scope of what we're talking about Jim yeah I bet you've raised and alluded to a big issue for implementers there will be on chain implementations of particular data data applications and off chain implementations off chain off blockchain will probably be all the PII you know in databases relational and so forth that allow you to do deletes and updates and so forth in you know to comply with you know gdpr and so forth and similar mandates elsewhere gdpr is not the only privacy mandate on earth and then there's on chain applications that you'll word the data what data sets will you store in blockchain you mentioned metadata now metadata I'm not sure because metadata quite often is is updated for lots of reasons for lots of operational patience but really fundamentally if we look at what a blockchain is it's a audit log it's an archive potentially of a just industry fashioned historical data that never changes and you don't want it to change ideally I mean I get an audit log you know let's say in the Internet of Things autonomous vehicles crashed and so forth and the data on how they operate should be stored you know either in a black box on the devices on the cars themself and also possibly backed up to a distributed blockchain where there is a transact or there's a there they a trusted persistent resilient record of what went on that would be a perfect idea for using block chains for storing perhaps trusted timestamp maybe encrypted records on things like that because ultimately the regulators and the courts and the lawyers and everybody else will want to come back and subpoena and use those records to and analyze what went on I mean for example that's an idea where something like a block shape and simile might be employed that doesn't necessarily have to involve PII unless of course it's an individual persons car and so there's all those great areas for those kinds of applications so right now it's kind of looking fuzzy for blockchain in lots of applications where identity can be either you know where you can infer easily the infer the identity of individuals from data that may not on the face of it look like it's PII so Neal I want to come back to you because it's this notion of being able to infer one of the things that's been going on in the industry for the past well 60 years is the dream of being able to create a transaction and persist that data but then generate derivative you out of that data through things like analytics data sharing etc blockchain because it is but you know it basically locks that data away from prying eyes it kind of suggests that we want to be careful about utilizing blockchain for applications where the data could have significant or could generate significant derivative use what do you think well we've known for a long long time that if you have anonymized data in the data set that it can merge that data with data from another data set relatively easy to find out who the individuals are right you add you add DNA stuff to that eh our records surveys things from social media you know everything about people and that's dangerous because we used to think that while losing are losing our privacy means that are going to keep giving us recommendations to buy these hands and shoes it's much more sinister than that you can be discriminated against in employment in insurance in your credit rating and all sorts of things so it's it's I think a really burning issue but what does it have to do with blockchain and G GD R that's an important question I think that blockchain is a really emerge short technology right now and like all image search technologies it's either going to evolve very quickly or it's gonna wither and die I'm not going to speculate which one it's going to be but this issue of how you can use it and how you can monetize data and things that are immutable I think they're all unanswered questions for the wider role of applications but to me it seems like you can get away from the immutable part by taking previous information and simply locking it away with encryption or something else and adding new information the problem becomes I think what happens to that data once someone uses it for other purpose than putting it in a ledger and the other question I have about GD d are in blockchain is who's enforcing this one army of people are sifting through all the stated at the side use and violation does it take a breach before they have it or is there something else going on the act of participating in a blockchain equivalent to owning or or having some visibility or something into a system so I am gdpr again hasn't doesn't seem to have answers to that question Jim what were you gonna say yeah the EU and its member nations have not worked out have not worked out those issues in terms of how will you know they monitor enforcement and enforce GDP are in practical terms I mean clearly it's gonna require on the parts of Germany and France and the others and maybe you know out of Brussels there might be some major Directorate for GDP our monitoring and oversight in terms of you know both companies operating in those nations as well as overseas with European Berger's none of that's been worked out by those nations clearly that's like you know it's just like the implementation issues like blockchain are not blockchain it's we're moving it toward the end of the month with you know not only those issues networked out many companies many enterprises both in Europe and elsewhere are not GDP are ready there may be some of them I'm not gonna name names may make a good boast that they are but know nobody really knows what it needs to be ready at this point I just this came to me very clearly when I asked Bernard Marr well-known author and you know influencer and the big data space at UM in Berlin a few weeks ago at at the data works and I said Bernard you know you consult all over with big companies what percentage of your clients and without giving names do you think are really truly GDP are already perm age when he said very few because they're not sure what it means either everybody's groping their way towards some kind of a hopefully risk mitigations threatened risk mitigation strategy for you know addressing this issue well the technology certainly is moving faster than the law and I'd say an argue even faster than the ethics it's going to be very interesting to see how things play out so we're just for anybody that's interested we are actually in the midst right now of doing right now doing some a nice piece of research on blockchain patterns for applications and what we're talking about essentially here is the idea that blockchain will be applicable to certain classes of applications but a whole bunch of other applications it will not be applicable to so it's another example of a technology that initially people go oh wow that's the technology it's going to solve all problems all date is going to move into the cloud Jim you like to point out Hadoop all data and all applications are going to migrate to the doop and clearly it's not going to happen Neil the way I would answer the question is it blockchain reduces the opportunity for multiple parties to enter into opportunism so that you can use a blockchain as a basis for assuring certain classes of behaviors as a group as a community and and and B and had that be relatively audible and understandable so it can reduce the opportunity for opportunism so you know companies like IBM probably you're right that the idea of a supply chain oriented blockchain that's capable of of assuring that all parties when they are working together are not exploiting holes in the contracts that they're actually complying in getting equal value out of whatever that blockchain system is and they're not gaining it while they can go off and use their own data to do other things if they want that's kind of the in chain and out of chain notion so it's going to be very interesting to see what happens over the course of next few years but clearly even in the example that I described the whole question of gdb our compliance doesn't go away all right so let's get to some action items here Nia what's your action item I suppose but when it comes to gdpr and blockchain I just have a huge number of questions about how they're actually going to be able to enforce it and when it comes to a personal information you know back in the Middle Ages when we went to the market to buy a baby pig they put it in a bag and tied it because they wouldn't want the piglet to run away because it'd take too much trouble to find it but when you got at home sometimes they actually didn't give you a pig they gave you a cat and when you opened up bag the cat was out of the bag that's where the phrase comes from so I'm just waiting for the cat to come out of the bag I I think this sounds like a real fad that was built around Bitcoin and we're trying to find some way to use it in some other way but I'm I just don't know what it is I'm not convinced Jim oxidiser my yeah my advice for Dana managers is to start to segment your data sets into those that are forgettable under gdpr and those that are unforgettable but forgettable ones is anything that has publicly identifiable information or that can be easily aggregated into identifying specific attributes and specific people whether they're in Europe or elsewhere is a secondary issue The Unforgettable is a stuff that it has to remain inviolate and persistent and can that be deleted and so forth the stuff all the unforgettables are suited to writing to one or more locked chains but they are not kosher with gdpr and other privacy mandates and focusing on the unforgettable data whatever that might be then conceivably investigate using blockchain for distributed you know you know access and so forth but they're mine the blockchain just one database technology among many in a very hybrid data architecture you got the Whitman way to skin the cat in terms of HDFS versus blockchain versus you know you know no first no sequel variants don't imagine because blockchain is the flavor of mania of the day that you got to go there there's lots and lots of alternatives all right so here's our action item overall this week we discussed on action item the coming confrontation between gdpr which is has been in effect for a while but actually fines will start being levied after May 25th and blockchain GPR has relatively or prescribed relatively script strict rules regarding a firm's control over personally identifiable in from you have to have it stored within the bounds of the EU if it's derives from an EU source and also it has to be forgettable that source if they choose to be forgotten the firm that owns that data or administers and stewards that data has to be able to get rid of it this is in conflict with blockchain which says that the Ledger's associated with a blockchain will be first of all fully distributed and second of all immutable and that provides some very powerful application opportunities but it's not gdpr compliant on the face of it over the course of the next few years no doubt we will see the EU and other bodies try to bring blockchain and block thing related technologies into a regulatory regime that actually is administrable as as well as auditable and enforceable but it's not there yet does that mean that folks in the EU should not be thinking about blockchains we don't know it means it introduces a risk that has to be accommodated but we at least think that the that what has to happen is data managers on a global basis need to start adding to it this concept of forgettable data and unforgettable data to ensure the cake can remain in compliance the final thing will say is that ultimately blockchain is another one of those technologies that has great science-fiction qualities to it but when you actually start thinking about how you're going to deploy it there are very practical realities associated with what it means to build an application on top of a blockchain datastore ultimately our expectation is that blockchain will be an important technology but it's going to take a number of years for knowledge to diffuse about what blockchain actually is suitable for and what it's not suitable for and this question of gdpr and blockchain interactions is going to be a important catalyst to having some of those conversations once again Neil Jim thank you very much for participating in action today my pleasure I'm Peter burger I'm Peter bursts and you've been once again listening to a wiki bond action item until we talk again

hi I'm Peter Burroughs welcome to a wiki bond action item quick take Neal Raiden Terry data announced earnings this week what does it tell us about Terry data and the overall market for analytics well tear date announced their first quarter earnings and they beat estimates for both earnings than revenues but they but lo they announced lower guidance for the fiscal year which I guess you know failed to impress Wall Street but recurring quarter one revenue was up 11% nearly a year to three hundred and two million dollars but perpetual revenue was down 23% from quarter one seventeen consulting was up to 135 million for the quarter you know not not altogether shabby for a company in transition but I think what it shows is that Teradata is executing this transitional program and there are some pluses and minuses but they're making progress jury's out but I think overall I'd consider it a good quarter what does it tell us about the market anything we can glean from their daters results about the market overall Neal it's hard to say there's a lot of you know at the ATW conference last week I listened to the keynote from Mike Ferguson I've known Mike for years and I think I always think that Mike's the real deal because he spends all of his time doing consulting and when he speaks he's there to tell us what's happening it he gave a great presentation about datawarehouse versus data Lake and if if he's correct there is still a market for a company like Terra data so you know we'll just have to see excellent Neil Raiden thanks very much this has been a wiki bond critique or actually it's been a wiki bond action item quick-take talk to you again

>> [Peter] Hi, this is Peter Burris. Welcome to Wikibon Research's Friday research meeting on theCUBE. (tech music) Today, we're going to talk about something that's especially important, given the events of this week. As many of you know, Apple announced a new iOS 11, and a whole bunch of new devices. Now we're not going to talk about the devices so much, but rather some of the function that's being introduced in iOS 11. Specifically, things like facial recognition. An enormous amount of processing is going to go into providing that type of service on devices like this, and that processing capability, those systems capabilities, are going to be provided by some new technologies that are related to artificial intelligence, big data, and something called deep learning. And the challenge that the industry's going to face is, where will this processing take place? Where will the data be captured? Where will the data be stored? How will the data be moved? What types of devices will actually handle this processing? Is this going to all end up in the cloud, or is it going to happen on increasingly intelligent smart devices? What about some of the different platform? And, ultimately, one of the biggest questions of all is, and how are we going to bring some degree of consistency and control to all of these potentially distributed architectures, platforms, and even industries, as we try to weave all of this into something that serves all of us and not just a few problems. Now to kick this off, Jim Kobielus, why don't you start by making a quick observation in what we mean by deep learning. >> [Jim] Yeah, thank you, Peter. Deep learning. The term has been around for a number of years. Essentially, it's machine learning, but with more layers of neuron, and able to do higher level abstractions from the data. Abstractions, such as face recognition, natural language processing, and speech recognition, and so forth, so when we talk about deep learning now, as a client, to what extent can more of these function? Face recognition as in iOS 11, or iPhone 10. What then will this technology, with capability, be baked into all edge endpoints now? >> [Peter] Jim, I'm having a little bit of a problem hearing you, so maybe we can make sure that we can hear that a little bit better. But, very quickly, and very importantly, it suggests that, the term deep learning suggests something a little different than I think we're actually going to see. Deep learning suggests that there's going to be a centralization, a function for some process. It's going to be the ultimate source of value. And I don't think we mean that. When we talk about deep learning, let's draw a distinction between deep learning, as a process, and deep learning as a set of systems and designs and investment that's going to be made to deliver on this type of business function. Does deep learning fully capture what's going to happen here? >> [James] Is this for me, Peter? Can you hear me, Peter? >> [Peter] I can hear you better now, a little bit saturated. >> [James] Okay, I got my earbuds in. Yeah, essentially the term deep learning is a broad paradigm that describes both the development pipeline function that quite often will, more often than not, will be handled in the cloud among distributed teams, and those function of deep learning that can be brought to the edge, to the end devices, the mobile devices, the smart sensors. When we talk about deep learning at the edge, as enabled through chip sets, we're talking about functions such as local sensing, local inference, from the data that's being acquired there, local actuation as it were taking action, like an autonomous vehicle steering right or left, based on whether there is an obstacle in their path. So really, in the broadest sense, you need that full infrastructure to do all the building and the tuning and the training of deep learning models, and, of course, you need the enabling chip sets and tools to build those devices, those functions, deep learning functions, that need to be pushed for local, often autonomous execution at endpoints. >> [Peter] So, David Floyer, that strongly suggest that, in fact, deep learning is suggestive of a new system architecture model that is not going to be large and centralized, but rather is going to be dependent upon where data actually exists and how proximate it is to the set of events that we're both monitoring, and ultimately trying to guide, as we think about new automation, new types of behavior. Take us through our thinking on some of these questions of where the data's going to reside, where the function's going to reside, and ultimately, how the architecture's going to evolve. >> [James] I think you're on mute, David. >> [David] Yes, I would put forward the premise that the majority of the processing of this data and the majority of the spend on equipment for this data will exist at the edge. Neal brought forward a very good differentiation between second-hand data, which is where bit data is today, and primary data, which is what we're going to be analyzing and taking as decisions on at the edge. As senses increase the amount of data and smart senses come, so we're going to see more and more of a processing shift from the traditional centralized, to the edge. And taking Apple as another example, they're doing locally, all of this processing of data. Siri, itself, is becoming more and more local, as opposed to centralized, and we're seeing the shift of computing down to the edge. And if we look at the amount of computing we're talking about, we're talking, with the Apple 10, it's six hundred billion operations a second. That's a lot of computing power. We see the same thing in other industries. There's the self-driving car. If you take the Nvidia Drive-2 it has a huge amount of computing power within that to process all of the different sources of data in a device which is costing less than $1,000, $600, $700. So much lower pricing of processing, et cetera. Now the challenge of data, the traditional model, is that all of the data goes to the center, is the cost of all this data, moving it from the edge to the center is just astronomical. It would never happen. So only a sum set of that data will be able to be moved. And people who develop systems, AI systems, for example, at the edge, will have to have simulation factories very local to them to do it, so car manufacturers, for example, having a small city, if you like, where they have very, very fast communication devices. And the amount of data that can be stored, as well, from this new primary source of data is going to be very, very small, so most of that data either is processed immediately, or it disappears. And after it's processed, in our opinion, most of that will disappear, 99% of that class will disappear completely. So the traditional model of big data is being turned upside down by these new and prolific sources of data, and the value will be generated at the edge. That's where the value is in recognizing a bad person coming into a building, or recognizing your friends, or recognizing that something is going wrong with a smart sensor locally. The vibrations are too high, or whatever the particular example is. That value will be generated at the edge by new classes of people and new classes of actors is this space. >> [Peter] So, Neil Raden, one of the interesting things that we're talking about here, is that we're talking about here is that we're talking about some pretty consequential changes in the nature of the applications, and the nature of the architectures and infrastructures that we're going to build to support these applications. But those kinds of changes don't take place without serious consideration of the business impacts. Is this something that companies are going to do, kind of willy-nilly? How deeply are companies going to have to think about how deeply are users going to have to think about deploying these within their business? Because it seems like it's going to have a pretty consequential impact on how businesses behave. >> [Neil] Well, they're going to need some guidance, because there just aren't enough people out there with the skill to implement this sort of thing for all the companies that may want to do it. But more importantly than that, I think that our canonical models, right now, for deep learning and intelligence at the edge are pretty thin. We talk about autonomous cars or facial recognition, something like that, there's probably a lot more things we need to think about. And from that we can derive some conclusions about how to do all this. But when it comes to the persistence of data, there's a difference between a B to C application, where we're watching people click, and deciding next best offer, and anything that happened a few months ago was irrelevant, so maybe we can throw that data away. But when you're talking about monitoring the performance of an aircraft in flight or a nuclear power plant, or something like that, you really need to keep that data. Not just for analytical purposes, but probably for regulatory purposes. In addition to that, if you get sued, you want to have some record of actually what happened. So I think we're going to have to look at this whole business, and all of its different components, before we can categorically say, yes we saved this data, here's the best application. Everything should be done in the cloud. I don't think we really know that yet. >> [Peter] But the issue that's going to determine that decision is going to be a combination of costs today, although we know that those costs are going to change over time, and knowledge of where people are and the degree to which people really understand some of these questions. And, ultimately, what folks are trying to achieve as they invest to get to some sort of objective. So there's probably going to be a difference in the next few years between, in which we do a lot of learning about deep learning systems, and some steady state that we get to. And my guess is that the ecosystem is going to change pretty dramatically between now and then. So it may be the telcos think that they're going to enjoy a bonanza on communications costs over the next few years, as people think about moving all this data. If they try to do that, that's going to have an impact on how Amazon and Google, and some of the big cloud suppliers invest to try to facilitate the movement of the data. But there's a lot of uncertainty here. Jim, why don't you take us through some of the ecosystem questions. What role will developers play? Where's the software going to end up? And to what degree is this going to end up in hardware and is going to lead to or catalyze kind of a Renaissance in the notion of specialized hardware? >> [James] Yeah, those are great questions. I think most of the functionality, meaning the local sensing and inference, and actuation, is inevitably going to end up in hardware, in highly specialized and optimized hardware for particular use cases. In other words, smart everything. Smart appliances, smart clothing, smart lamps, smart... You know, what's going to happen is that more and more of what we now call deep learning will just be built-in by designers and engineers of all sorts, regardless of whether they have a science or a computer background. And so it's, I think going to be part of the material fabric of reality, the bringing intelligence that, with that said then, if you look at the chip set architectures, and if we can use the term chip set here, that will enable this vast palette of embedding of this intelligence in physical reality. The jury is really out about whether it will be GPUs, like in video, of course, it's power out behind GPUs, versus CPUs, versus FPGAs, A6, there's various neuromorphic chip sets from IBM and others. It'll be, it's clearly going to be a fairly very innovative period of great ferment in innovation in the underlying hardware substrate, the chip sets, to enable all these different use cases in embedding of all this. In terms of developer, take the software developers. Definitely, they're still very much at the core of this phenomenon, when I say they, data scientists, as the core developers of this new era who are the ones who are building these convolutional neural networks and recurrent neural networks, and long, short-term, and so forth. All these DL algorithms very much are the province of data scientists, for the new generation of data scientists who specialize in those areas and that who work hand-in-hand with traditional programmers and so forth, to put all of this intelligence into a shape that can then be embedded and might, containerized, whatever, and brought into some degree of harmonization with the physical hardware layer into which hardware could be used for terms like, clothing, smart clothing. What gave us that, now we have a new era where the collaborations are going to be diverse among nontraditional job, or skills categories, who are focused on bringing AI into everything that touches our lives. It's wide open now. >> [Peter] so David Floyer, let me throw it over to you, because Jim's raised some interesting points about where the various propositions, the value propositions, and how the ecosystem is going to emerge. This sounds like a, once again, going back to the role that consumer markets are going to play from a volume, cost, and driving innovation standpoint. Are we seeing kind of a repeat of that, are the economics going to, of volume going to also play a role here? Muted? >> [David] Yes, I believe so, very strongly. If you look at technologies and how they evolve. If you look for example at Intel, and how they became so successful in the chip market. They developed the chips with Microsoft for the PC. That was very, very successful, and from that they then created the chip set for the data senses, themselves. When we look at the consumer volumes, we see a very different marketplace. For example, GPUs are completely winning in the consumer market. So Apple introduced GPUs into their ARM processes this time around. Nvidia has been very, very successful, together with ARM, in producing systems for self-driving cars. Very, very powerful systems. So we're looking at new architectures. We're looking at consumer architectures, that in Nvidia's case came from game playing, and in ARM, has come all of the distributed ecosystems, the clients, et cetera, all ARM-based. We're seeing that it's likely that consumer technologies will be utilized in these ecosystems because volume wins. Volume means reduction in price. And when you look at, for example, the cost of an ARM processor within an Apple iPhone, it's $26.90. That's pretty low compared with the thousands of dollars you're talking about for a processor going into a PC. And when you look at the processing power of these things, in terms of operation, they're actually greater power. And same with Nvidia with the GPUs. So yes, I think there is a potential for a big, big change. And a challenge to the existing vendors that they have to change and go for volume and pricing for volume in a different way than they do at the moment. >> [Peter] So that's going to have an enormous impact, ultimately, on the types of hardware designs that we see emerge over the course of the next few years. And the nature of the applications that the ecosystem is willing to undertake. I want to pivot and bring it back to the notion of deep learning as we think about the client. Because it ultimately describes a new role for analytics and how analytics are going to impact the value propositions, the behaviors, and ultimately, the experience of consumers, and everybody, has with some of these new technologies. So Neil, what's the difference between deep learning-related analytics on the client, and a traditional way of thinking about analytics? Take us through that a little bit. >> [Neil] Deep learning on the client? You mean at the edge? >> [Peter] Well deep learning on a client, deep learning on the edge, yeah. Deep learning out away from the center. When we start talking about some of this edge work, what's the difference between that work and the traditional approach for data analytics, data warehousing, et cetera? >> [Neil] Well, my naive point of view is deep learning involves crunching through tons of data in training models to come up with something you can deploy. So I don't really see deep learning happening at the edge very much. I think David said this earlier, that the deep learning is happening in the big data world when they have trillions of observations to use. Am I missing your point? >> [Peter] No, no. We talked earlier about the difference between deep learning as a process and deep learning as a metaphor for a new class of systems. So when we think about utilizing these technologies, whether it's deep learning, or AI, whatever we call it, and we imagine deploying more complex models close to the edge, what's that mean from the standpoint of the nature of the data that we're going to use, the approach, the tooling that we're going to use, the approach we're going to take organizationally, institutionally, to try to ensure that that work happens. Is there a difference between that and doing data warehousing with financial systems? >> [Neil] Well, there's a difference in terms of the technology. I think that 10 years ago, we were talking about complex event processing. The data wasn't really flowing from centers, it was scraping Web screens and that sort of thing, but it was using decision-making technology to look for patterns and pass things along. But you have to look at the whole process of decision making. If you're talking about commercial organizations, it's not really that much in commercial organizations that requires complex, real-time, yeah, making decisions about supply chain or shop floor automation, or that sort of thing. But from a management point of view, it's not really something that you do. The other part of decision making that troubles me is, I wrote about this 10 years ago, and that was we shouldn't be using any kind of computer-generated decision making that affects human lives. And I think you could even expand that to living things, or harming the environment and so forth. So I'm a little bit negative about things like autonomous cars. It's one thing to generate a decision-making thing that issues credit cards, and maybe it's acceptable to have 5% or 3% of decision just completely wrong. But it's that many wrong in autonomous driving, especially trucks, the consequences are disastrous. So we have to be really careful about this whole thing with IoT, we've got to be a lot more specific about what we mean, what kinds of architectures, and what kind of decisions we're trying on. >> [Peter] I think that's a great point, Neil. There's a lot that can be done, and then the question is that we have to make sure that it's done well. We understand some of the implications, and again, I think there's a difference between a transition period and a steady state. We're going to see a lot of change over the next few years. The technology's making it possible to do so, but there's going to be a lot of social impacts that ultimately have to be worked out. And I'll get, we'll get to some of those in a second. But George, George Gilbert, I wanted to give you an opportunity to talk a little bit about the way that we're going to get this done. Talk about how we're, where's this training going to take place, per what Neil said? Is the training going to take place at the edge? Is the training going to take place in the cloud? Institutionally, what is the CIO and the IT organization have to do to prepare for this? >> [George] So I think the sort of widespread consensus is that the inferencing and sort of predicting for the low latency actions will be at the edge, and some smaller amount of data goes up into the cloud training, but the class of training that we will do over time changes. And we've been very fixated on sort of the data centricity, like most of the data's at the edge a little bit in the center. And Neil has talked about sort of secondary, or reference data, to help build the model from the center. But the models themselves that we build in the center and then push out, will change in the sense that we look at the compute intensity. The compute intensity of the cloud will be, will evolve, so that it's more advantageous there to build models that become rich enough to be like simulation. So in other words, it's not do I, if I see myself drifting over the lane marker on the right, do I correct left? But you have a whole bunch of different, different knobs that get tuned, in that it happens over time. So that the idea of the model is almost like a digital twin, but not of, let's say, just an asset or physical device, but almost like a domain, in that that model, it's very compute intensive, it generates a lot of data sets, but then the model itself can be distilled down and pushed out to the edge. Or, essentially, guiding or informing decisions, or even making decisions with a lot more knobs than you would have with a more simplistic model. >> [Peter] So, Ralph, I know that we've spent some time looking at some of the market questions of this. Based on this conversation, can you kind of give a summary of how much data volume we think is happening, data movement's happening? What's the big, broad impact on some of the segments and opportunities over the course of the next couple of years? >> [Ralph] Yeah, I think the, think back maybe 10 years, the amount of unstructured data that was out there was not all that great. Obviously, in the last 10 years of war, there's a lot more of it. So the growth of data is dramatically increasing. Most of it is going to be in the mobile area. So there's just a lot of it out there. And this, I think fishing for where you derive value from that data is really critical for moving optimization of processes forward. But I think I agree with Neil that there's a lot of work to be done yet about how that actually unfolds. >> [Peter] And there's also a lot of work to be done in areas like, what will the role of, who's going to help define how a lot of these platforms are going to be integrated together. What's the role of standards? What role will government play? There's an enormous number of questions here. But one thing we all agree on ultimately, is that this is an emerging source of, or this technology is an emerging source of dramatic new types of business value taking on problems that we've never thought about taking on before. And it's going to have an enormous impact on how IT organizations work with business, how they work with each other, how businesses work together. This is the centerpiece of the new digital business transformation. Alright, so let me summarize this week's findings. The first observation we make is that this week, Apple introduced facial recognition directly in iOS 11, and it wowed much of the industry, and didn't get a lot of people excited for a variety of reasons, but it does point to the idea that increasingly we're going to see new classes of deep learning, AI, machine learning, and other big data-type technologies, being embedded more deeply in systems as a way of improving the quality of the customer experience, improving operational effectiveness and efficiency, and ultimately, even dramatically improving the ratio between product and service revenue in virtually everything that we can think about. Now, that has led folks to presume that there's, again, going to be this massive migration of workload back into the cloud, both from a data standpoint, as well as from a workload standpoint. But when we stop and think about what it really means to provide this value, it's pretty clear that for a number of reasons, including real-time processing to provide these services, the cost of moving data from one point to another, and that the characteristics of the intellectual property controls, et cetera, restricts the pressure to try to move all this data from the edge, client, and device back into the cloud. And that the new architectures, increasingly, are going to feature a utilization of dramatic new levels of processing on devices. We observe, for example, that the new iPhone is capable of performing 600 billion instructions per second. That's an unbelievable amount of processing power. And we're going to find ways to use that up, to provide services closer to end users without forcing a connection. This is going to have enormous implications, overall, in the industry. Questions, for example, like how are we going to institutionally set up the development flow? We think we're going to see more model building at the center, with a constrained amount of the data, and more execution of these models at the edge. But we note that there's going to be a transition period here. There's going to be a process by which we're learning what data's important, what services are important, et cetera. We also think it's going to have an enormous impact, for example, on even describing the value proposition. If everything is sold as a product, that means the cost of moving the data, the cost of liability, et cetera, on these devices is going to be extreme. It's going to have an enormous impact on the architectures and infrastructures we use. If we think in terms of services, that might have a different, or lead to a different set of ecosystem structures being put in place, because it will change the transaction costs. The service provider, perhaps, is going to be more willing to move the data, because they'll price it into their service. Ultimately, it's going to have a dramatic impact on the organization of the technology industry. The past 25, 30, 40 years have been defined, for the first time, by the role that volume plays within the ecosystem. Where Microsoft and Intel were the primary beneficiaries, or were primary beneficiaries of that change. As we move to this notion of deep learning and related technologies at the edge, providing new classes of behavior, it opens up the opportunity to envision a transitioning of where the value is up and down the stack. And we expect that we're going to see more of that value be put directly into hardware that's capable of running these models with enormous speed and certainty in execution. So a lot of new hardware gets deployed, and then the software ecosystem is going to have to rely on that hardware to provide the data and build the systems that are very data rich to utilize and execute on a lot of these, mainly ARM processors that are likely to end up in a lot of different devices, in a lot of different locations, in its highly distributed world. The action item for CIOs is this. This is an area that's going to ensure that a role for IT within the business, as we think about what it means for a business to exploit some of these new technologies, in a purposeful and planful and architected way. But it also is going to mean that more of the value moves away from the traditional way of thinking about business systems with highly stylized data to a more clear focus on how consumers are going to be supported, devices are going to be supported, and how we're going to improve and enhance the security and the utilization of more distributed, high quality processing at the edge, utilizing a new array of hardware and software within the ecosystem. Alright, so I'm going to close out this week's Wikibon Friday Research Meeting on theCUBE, and invite you back next week where we'll be talking about new things that are happening in the industry that impact your lives and the industry. Thank you very much for attending. (tech music)

live from Orlando Florida it's the kue covering sapphire now headline sponsored by ASAP Hana cloud the leader in platform-as-a-service with support from console Inc the cloud internet company now here's your host John furrier hey welcome back and we are here live in sapphire now in orlando florida this is the cube silicon angles flagship program we go out to the events and extract the signal noise want to thank our sponsors SI p HANA cloud platform and console inc at consoled cloud our next guest is an eel cebu vp of business development at fred balboni who is the GM of IBM here on the cube together SI p time you book them back of the cube good to see you guys like when is down so microsoft's up on stage ibm's here with SI p this is the old sav no real change of the game in terms of you guys have been multi-vendor very partnering very eco system driven but yet the game is changing very rapidly in this ecosystem of multi partnering with joint solutions i mean even apple your announcement earlier so is this kind of like a bunch of Barney deals as we used to say in the old days or what is the new relationship dynamic because data is the new currency it's the new oil it's the digital capital data is capital data is a digital asset partnerships are critical talk about this dynamic partnerships are critical and I think what we're doing is we are going deeper than we've ever gone with these partnerships with IBM we announced last month we announced the joint ASAP IBM partnership for digital transformation what does this do so what we've been doing traditionally with IBM we've had siloed partnerships with different IBM brands right we had a partnership with a power brand we had a partnership with the cloud team we are a partnership with GBS what we've done now with the digital transformation is bringing it all together so we have a CEO level discussion that's driven this partnership and I think that's really the differentiation so we have moved away from the so-called Barney deals because our customers expect bill talked about it in the keynote today he says when it's a multi partner situation customers expect that you're going to have one voice you're going to be a line you're going to provide value to those customers that's what we're trying to do and that's what this partnership is all right I want to get your thoughts on this I mean I'm Barnum's reference to the character you know I love you you love me kind of like a statement of mission but really not walking the talk so to speak but but I want to get your thoughts because you have a look at the analytics background at IBM when you built that business up there's a conflict in a way but it's also a great thing in the market apps are changing in very workload specific at the edge with its IOT or a mobile or whatever digital app they have to be unique they have to have data they got to be they have to be somewhat siloed but yet the trend is to break down the silos for the customer so how do you guys is it the data that does that because you guys doing a lot of work in this year you want to build great apps and be highly differentiated yet no silos how do you make that ok so it is its first of all it's very exciting and a confronting but also exciting for not only our companies but also for our customers it's all enabled really simply because of a couple of major technology shifts that have happened number one technology shift is the cloud the cloud without question is driving driving all of this in addition to your notion about data readily available data and the algorithms and software that can you know make cognitive sense of that is both driving of this whole change last but not least and I think Hana really enables this you know embodies this is the architectural change so you put those three things together availability of data cloud which means the capital investment required to build the infrastructure is inexpensive and then finally Hana which is the technology platform that rapidly allows you to take using you know a generic term api's and wire them to different sources allow you to dynamically reconfigure businesses now there's one last thing I think is really important here that we don't want to underplay and this is the social phenomena of the consumerization of IT and this has been going on for many many years but we've really seen it accelerate in the last 3 to 4 100 ala dated yeah absolutely and when you see a device like this becomes the system of engagement and oh by the way if you don't like if you don't like dark skies weather app well then go to the weather channel's weather app and if you don't like their weather I've go to one of 40 other weather apps so therefore this consumerization of IT is bombarding our CIOs what's exciting is that cloud cognitive insight a flexible core with great social engagement allows a CIO to really rapidly reconfigure so that's why these partnerships are rising that's very important you just said to about this relationship now about consumerization of IT is a complete game changer on the enterprise software business because now the relationship to the suppliers I'm the CXO or CIO I had a traditional siloed as you use that word earlier relationship with my my vendors one pane of glass like that IT Service Management down here I got the operations I up changed my appt every six months or six years the cadence of interaction was very inside the firewall absolutely so the relationship has changed with the suppliers expand on that because that really hits a whole nother thread I'm the buyer i don't want complexity you don't and what you do want is time to value so combining that with the beautiful user experience that you know thanks to devices like the one that Fred showed you know are an absolute necessity they it's it's understood now it's an expectation that customers have and customers of customers also have so i think that is impacted us in multiple ways what you heard and build scheme out you heard that with our supplier Network you heard our president for ASAP Arriba Alex talk about it he is that the change within that organization itself with our different vendors with the fact that we have to provide choice to our customers i think that is that has changed the way we do business and it's interesting to just I mean this is right now a moment in history as a flashpoint not that's a big of event but it's been seeing this trend happening over the hundreds of cube events that we've been to over the past few years is that now in just today highlights it the Giants of tech are here ASAP IBM or I mean Microsoft Office state's atty Nutella the apple announcement you guys have a similar deal with Apple these are the Giants okay working together now iBM has bluemix you have HANA cloud platform you have on a cloud everyone's got cloud so this kind of highlights that it's not a one cloud world absolutely and so this really kind of changes the game so I got to ask you given all that how do you guys talk to the ecosystem because they're our total transistors going on at capgemini Accenture pwc CSC it's an outside-in dynamic now how is that change for you guys as you guys go to market together in a variety of things in a coop efficient some faces how does that dynamic change with it for the partners that have to implement this stuff so co-op edition is is a reality i think we've asap we've learnt this probably from a partner that does the best which is IBM they probably they practically invented cooperation in the enterprise software space so i think here's how here's the way we look at it right so so we are looking at with with hana with HANA cloud platform we're really morphing into a platform and applications company and and we have the strategy of essentially later thousand apps blue so what are we doing on HANA cloud platform in such a short time so we have two about 2600 plus customers we have I think the more important part is that our ecosystem around HANA cloud platform is 400 + partners so that's an advantage visa V say Oracle for instance which is waves to have an ecosystem they lot of people there too I think I think the DNA of SI p isn't being an open company we've had that for ages so we work closely with Barton's and by the way I used to be at Oracle I was there for seven years and I know the difference its it's stuck Oracle's got a different strategy we've got a very very different very open strategy so I think what we're doing is we coalescing around these key assets right our digital Korres for Hana Hana cloud platform as the key platform for our customers okay so a nice watching out there and looking out over the next year so what execution successes do you put out there that's a to prove that you guys are are open and you guys are doing good deals what success kpi's key indicators would you say look for the following things to happen so number one available availability of AP is I think if you look at the different api's they access to the variety of SI p systems what you did see is that there's a digital core there's all of the different assets we've got in the cloud easy access to those I think customers can look for that right how can they rapidly develop an essay p successfactors extension or how can they extend ASAP arriba very quickly integrating that with the s100 digital core I think that's number one number two is the HCP App Center so we have probably about a thousand plus apps out there and by the way I do need to give a shout out here because we've got three apps that three iOS apps that IBM pour it onto HANA cloud platform in the last six weeks was it Fred six weeks we're talking about you know an incredibly short amount of time that are now highlighted on HANA cloud platform app center Fred talk about IBM right now because this isn't a game finished shift I've noticed more aggressively the three years ago I saw the wave coming at IBM and now remote past two years it's just been constant battering on the beachhead iBM has been donating a ton of IP with open sores everyone's behind blue bluemix has gone from you know a fork of cloud foundry to a now really fast they're moving very very quickly yes sir writing apps you're partnering is this part of the strategy just to kind of keep humbling the Markowitz assets like this is that's open the more open IBM and how is open mean to for you guys today well because I think at the end of the day we got to realize that I mean us to question a couple couple questions ago and I Neal answered it quite well which is customers are going to make the choice customers want to be flexible in their choice so understand I want to first of all shout outs IV to Apple excuse me to sav a shadow tennis AP here which is s ap has always been about partnering an ecosystem and so that's a court that's a core belief of theirs so when you look at what they've technically done here with the HANA cloud platform you know one of the many strategists can put this on a board enjoys well this is what this is what they should be doing but the reality of it is is the reason companies stay with existing service providers the reason companies say with existing technologies is because they've already got it it's what they know how to do and so and what they want to do is very hard so the Hana architecture in the hunting club platform was probably drawn on a board ten years ago the fact that it's real and here now now mace clients the ability to actually make these kind of ships IBM's move to the cloud moving assets to the cloud because we recognize clients are actually going to want to pick and choose and build these things in a dynamic fashion and we want our workloads to be on the IBM cloud every single show I go to down basically feels like a cloud in a data show even amplify which is kind of a commerce show sure it's all about data and the cloud so I we got to get we got to get wrapped up I want to get one final thread in with you guys and that is unpardonable Apple just spent the billion dollars with the uber clone and China so you see their partner strategy they did partner with you guys and now SI p this is a really interesting strategy for Apple to go into the enterprise they don't have to get over their skis and over-rotate on this market that can come in pre existing players and extend out versus trying to just have a strategy of rolling products out so it seems that Apple is partnering creating alliances as their way into the enterprise similar to what they're doing in in China with who were just a random example but which is impressed this week is that the Apple strategy I mean you guys both talk to Apple I mean you guys have both of deals share some color on Apple's partnering and alliances their joint venture not your invention for joint development seems to be very cool so I it's not I I I want you know when I look at what we're doing with that you know we have a goal and our goal is we believe that we can transform the enterprise you know we I BM we IBM and SI p we IBM and our partners including Apple we want to transform enterprise Apple signed on to that because Apple realized that they were changing consumers lives and and then they woke up and they said well actually but many people spend a large part of their waking day at work so if I can change a consumers life I can also change an enterprise employees life and that is the work that we are setting about doing and so therefore the partnership IBM understands enterprise really well SI p was Bill statistic today seventy-three percent of the world's transactions run through an essay peak or so yeah Apple's very obviously very delivered in picking their partners we're thrilled with the mobile first for iOS worked in Swiss great programming language has great legs is so elegant and sweet it's like see but more elegant absolutely I think again when you look at what Apple's mission has been and you look at sa peace mission right we talked about helping companies run better and transforming lives so i think i think the missions actually do intersect here and and I think SI p is a very different company than we were you know 20 years ago so for us now that user experience and product while agent by the way absence proc solid quality absolutely so I think I i think you know we converge on those areas so I would say that it's a it's a very natural farming from Apple's a brilliant strategy because it's interbred and it prizes hard you guys to live that every day it's not easy and we see venture-backed startups try to get into the enterprise and the barriers just go up every day with dev ops and you know integration now is mrs. Ann we could talk about another segment with a break but we haven't gone to the whole what does it mean to integrate that's a whole nother complex world that requires orchestration really really interesting and you just write that over the weekend and a hackathon absolutely and I think now with the tools that we're making available on our cloud platform as part of a platform as a service I think again that's the way where we can get the user interface the experience that apple provides combined with the enterprise solid stuff that we do that's awesome I'll give you guys both the final word on the segment and a bumper sticker what is this show about this year what is s AP sapphire 2016 about what's the the bumper sticker what's the theme I you know what I love builds words today I think it's about empathy it's about making it real for customers I think you'll see you know our demos are joined demos as well both in an essay p IBM Joint Center here as well as in the IBM boat you see real life solutions that are real that customers can touch that they can use so I'd like to go with that predicate real hey listen to me it's a really simple to two simple words digital reinvention every single company in the world is trying to become a digital company I think about my Hilton app when I checked into my hotel yesterday and I opened my door with my iPhone my hotel my room door you know it is every company is endeavoring to become a digital company and what what sapphire is about this year is everyone realizes at the core of every company is that platform that s AP gahanna or ECC platform and every major enterprise that's waking up to that suddenly realizes we've got to do something an essay p nibm our partner here to help thanks guys so much for sharing your insight digital reinvention going on for real here at sapphire this is the cube you're watching the cube live at sapphire now we'll be right back thank you